Created on Cognitive Class Labs

Capstone Project_Silicon Beach.ipynb

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   "source": [
    "import pandas as pd # library for data analsysis\n",
    "import numpy as np # library to handle data in a vectorized manner"
   ]
  },
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   "source": [
    "import numpy as np # library to handle data in a vectorized manner\n",
    "\n",
    "import pandas as pd # library for data analsysis\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "import json # library to handle JSON files\n",
    "\n",
    "#!conda install -c conda-forge geopy --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
    "\n",
    "import requests # library to handle requests\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
    "\n",
    "# Matplotlib and associated plotting modules\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "# import k-means from clustering stage\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#!conda install -c conda-forge folium=0.5.0 --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "import folium # map rendering library\n",
    "\n",
    "\n",
    "from geopy.geocoders import Nominatim\n",
    "from geopy.distance import vincenty\n",
    "import datetime as DT\n",
    "import hmac\n",
    "import pandas as pd\n",
    "import io\n",
    "import requests\n"
   ]
  },
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   "execution_count": 33,
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/lib/python3.6/site-packages/pandas/util/_decorators.py:188: FutureWarning: The `sheetname` keyword is deprecated, use `sheet_name` instead\n",
      "  return func(*args, **kwargs)\n"
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       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Population</th>\n",
       "      <th>Median Age</th>\n",
       "      <th>Average Income</th>\n",
       "      <th>Venue Number</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>39283</td>\n",
       "      <td>40.9</td>\n",
       "      <td>86997</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>El Segundo</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>16853</td>\n",
       "      <td>38.7</td>\n",
       "      <td>92942</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>87854</td>\n",
       "      <td>33.0</td>\n",
       "      <td>47636</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>19708</td>\n",
       "      <td>39.5</td>\n",
       "      <td>124849</td>\n",
       "      <td>66</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Inglewood</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>110598</td>\n",
       "      <td>34.5</td>\n",
       "      <td>46389</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>35924</td>\n",
       "      <td>43.7</td>\n",
       "      <td>148899</td>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>8866</td>\n",
       "      <td>39.0</td>\n",
       "      <td>101860</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>67908</td>\n",
       "      <td>40.2</td>\n",
       "      <td>104548</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>92306</td>\n",
       "      <td>40.5</td>\n",
       "      <td>86084</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Torrance</td>\n",
       "      <td>33.834966</td>\n",
       "      <td>-118.341431</td>\n",
       "      <td>146758</td>\n",
       "      <td>41.7</td>\n",
       "      <td>85070</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>40885</td>\n",
       "      <td>35.0</td>\n",
       "      <td>67647</td>\n",
       "      <td>29</td>\n",
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       "               City   Latitude   Longitude  Population  Median Age  \\\n",
       "0       Culver City  34.005820 -118.396781       39283        40.9   \n",
       "1        El Segundo  33.917145 -118.401554       16853        38.7   \n",
       "2         Hawthorne  33.914775 -118.348083       87854        33.0   \n",
       "3     Hermosa Beach  33.865268 -118.396297       19708        39.5   \n",
       "4         Inglewood  33.956068 -118.344274      110598        34.5   \n",
       "5   Manhattan Beach  33.889632 -118.397370       35924        43.7   \n",
       "6    Marina del Rey  33.981510 -118.453229        8866        39.0   \n",
       "7     Redondo Beach  33.856817 -118.377137       67908        40.2   \n",
       "8      Santa Monica  34.023413 -118.481666       92306        40.5   \n",
       "9          Torrance  33.834966 -118.341431      146758        41.7   \n",
       "10     Venice Beach  33.985000 -118.469500       40885        35.0   \n",
       "\n",
       "    Average Income  Venue Number  \n",
       "0            86997             9  \n",
       "1            92942            11  \n",
       "2            47636            23  \n",
       "3           124849            66  \n",
       "4            46389             8  \n",
       "5           148899            26  \n",
       "6           101860            20  \n",
       "7           104548             8  \n",
       "8            86084            21  \n",
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   "source": [
    "df4 = pd.read_excel('LAcoor2.xlsx', sheetname='Sheet1')\n",
    "df4"
   ]
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   "execution_count": 9,
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     "text": [
      "The geograpical coordinate of Los Angeles are 34.0536909, -118.2427666.\n"
     ]
    }
   ],
   "source": [
    "address = 'Los Angeles, CA'\n",
    "\n",
    "geolocator = Nominatim(user_agent=\"la_explorer\")\n",
    "location = geolocator.geocode(address)\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "print('The geograpical coordinate of Los Angeles are {}, {}.'.format(latitude, longitude))"
   ]
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  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_c07d6e7dd8f042adb0c391d4ddaf5d58);
            
    
            var popup_28bfcf346e5a40a7b4a552fc3b803383 = L.popup({maxWidth: '300'});

            
                var html_9d83ee55691048aeaa9c92e35aecac5d = $('<div id="html_9d83ee55691048aeaa9c92e35aecac5d" style="width: 100.0%; height: 100.0%;">Redondo Beach</div>')[0];
                popup_28bfcf346e5a40a7b4a552fc3b803383.setContent(html_9d83ee55691048aeaa9c92e35aecac5d);
            

            circle_marker_c9c24abf2a42420490a55a6f061ca5df.bindPopup(popup_28bfcf346e5a40a7b4a552fc3b803383);

            
        
    
            var circle_marker_b8737f9166154154b2f8b52f862f2ebb = L.circleMarker(
                [34.023413,-118.481666],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_c07d6e7dd8f042adb0c391d4ddaf5d58);
            
    
            var popup_b9de38418a3a47989861b15eebc4523d = L.popup({maxWidth: '300'});

            
                var html_768d3c971f674ce9abc01c8d54ce2cdf = $('<div id="html_768d3c971f674ce9abc01c8d54ce2cdf" style="width: 100.0%; height: 100.0%;">Santa Monica</div>')[0];
                popup_b9de38418a3a47989861b15eebc4523d.setContent(html_768d3c971f674ce9abc01c8d54ce2cdf);
            

            circle_marker_b8737f9166154154b2f8b52f862f2ebb.bindPopup(popup_b9de38418a3a47989861b15eebc4523d);

            
        
    
            var circle_marker_7ebd6be3466c4a4985ebb8e14947acc6 = L.circleMarker(
                [33.834966,-118.341431],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_c07d6e7dd8f042adb0c391d4ddaf5d58);
            
    
            var popup_0abdd5cf41c5478d9a697db8ac3a3c21 = L.popup({maxWidth: '300'});

            
                var html_94885c9cc81148a58bda158b8b7b7a87 = $('<div id="html_94885c9cc81148a58bda158b8b7b7a87" style="width: 100.0%; height: 100.0%;">Torrance</div>')[0];
                popup_0abdd5cf41c5478d9a697db8ac3a3c21.setContent(html_94885c9cc81148a58bda158b8b7b7a87);
            

            circle_marker_7ebd6be3466c4a4985ebb8e14947acc6.bindPopup(popup_0abdd5cf41c5478d9a697db8ac3a3c21);

            
        
    
            var circle_marker_e083539dccd749e28c9de30300356063 = L.circleMarker(
                [33.985,-118.4695],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_c07d6e7dd8f042adb0c391d4ddaf5d58);
            
    
            var popup_8ac7f35be40548fdb8227e516437e82d = L.popup({maxWidth: '300'});

            
                var html_01b7f350a5034a2eab4e4b943f32942b = $('<div id="html_01b7f350a5034a2eab4e4b943f32942b" style="width: 100.0%; height: 100.0%;">Venice Beach</div>')[0];
                popup_8ac7f35be40548fdb8227e516437e82d.setContent(html_01b7f350a5034a2eab4e4b943f32942b);
            

            circle_marker_e083539dccd749e28c9de30300356063.bindPopup(popup_8ac7f35be40548fdb8227e516437e82d);

            
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f0c74c79710>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map of New York using latitude and longitude values\n",
    "map_losangeles = folium.Map(location=[latitude, longitude], zoom_start=10)\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, City in zip(df['Latitude'], df['Longitude'], df['City']):\n",
    "    label = '{}'.format(City)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_losangeles)  \n",
    "    \n",
    "map_losangeles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'East Los Angeles'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your credentails:\n",
      "CLIENT_ID: PJS5VPSQJK5GSZVFE2QVMOMLPYLINYY1XLXGOU31EELHT1MJ\n",
      "CLIENT_SECRET:GPZVYLDEHPZPGQ0RPXZHNSQU2T3TDWNY2DGDP5H1H4EM5GRR\n"
     ]
    }
   ],
   "source": [
    "CLIENT_ID = 'PJS5VPSQJK5GSZVFE2QVMOMLPYLINYY1XLXGOU31EELHT1MJ' # your Foursquare ID\n",
    "CLIENT_SECRET = 'GPZVYLDEHPZPGQ0RPXZHNSQU2T3TDWNY2DGDP5H1H4EM5GRR' # your Foursquare Secret\n",
    "VERSION = '20180605' # Foursquare API version\n",
    "\n",
    "print('Your credentails:')\n",
    "print('CLIENT_ID: ' + CLIENT_ID)\n",
    "print('CLIENT_SECRET:' + CLIENT_SECRET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Redondo Beach'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[7, 'City']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latitude and longitude values of Manhattan Beach are 33.856817, -118.377137.\n"
     ]
    }
   ],
   "source": [
    "neighborhood_latitude = df.loc[7, 'Latitude'] # neighborhood latitude value\n",
    "neighborhood_longitude = df.loc[7, 'Longitude'] # neighborhood longitude value\n",
    "\n",
    "neighborhood_name = df.loc[5, 'City'] # neighborhood name\n",
    "\n",
    "print('Latitude and longitude values of {} are {}, {}.'.format(neighborhood_name, \n",
    "                                                               neighborhood_latitude, \n",
    "                                                               neighborhood_longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://api.foursquare.com/v2/venues/explore?&client_id=PJS5VPSQJK5GSZVFE2QVMOMLPYLINYY1XLXGOU31EELHT1MJ&client_secret=GPZVYLDEHPZPGQ0RPXZHNSQU2T3TDWNY2DGDP5H1H4EM5GRR&v=20180605&ll=33.856817,-118.377137&radius=4828&limit=100'"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LIMIT = 100 # limit of number of venues returned by Foursquare API\n",
    "radius = 4828 #4828 define radius\n",
    "# create URL\n",
    "url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "    CLIENT_ID, \n",
    "    CLIENT_SECRET, \n",
    "    VERSION, \n",
    "    neighborhood_latitude, \n",
    "    neighborhood_longitude, \n",
    "    radius, \n",
    "    LIMIT)\n",
    "url # display URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = requests.get(url).json()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_category_type(row):\n",
    "    try:\n",
    "        categories_list = row['categories']\n",
    "    except:\n",
    "        categories_list = row['venue.categories']\n",
    "        \n",
    "    if len(categories_list) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        return categories_list[0]['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>iLoveKickboxing - Redondo Beach, CA</td>\n",
       "      <td>Gym</td>\n",
       "      <td>33.854484</td>\n",
       "      <td>-118.379196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Redondo Beach Dog Park</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>33.857577</td>\n",
       "      <td>-118.377995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bistro Miyoda</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>33.854176</td>\n",
       "      <td>-118.379762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dollar Tree</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>33.853951</td>\n",
       "      <td>-118.380787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VONS</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>33.853943</td>\n",
       "      <td>-118.380257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  name           categories        lat  \\\n",
       "0  iLoveKickboxing - Redondo Beach, CA                  Gym  33.854484   \n",
       "1               Redondo Beach Dog Park              Dog Run  33.857577   \n",
       "2                        Bistro Miyoda  Japanese Restaurant  33.854176   \n",
       "3                          Dollar Tree       Discount Store  33.853951   \n",
       "4                                 VONS        Grocery Store  33.853943   \n",
       "\n",
       "          lng  \n",
       "0 -118.379196  \n",
       "1 -118.377995  \n",
       "2 -118.379762  \n",
       "3 -118.380787  \n",
       "4 -118.380257  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues = results['response']['groups'][0]['items']\n",
    "    \n",
    "nearby_venues = json_normalize(venues) # flatten JSON\n",
    "\n",
    "# filter columns\n",
    "filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']\n",
    "nearby_venues =nearby_venues.loc[:, filtered_columns]\n",
    "\n",
    "# filter the category for each row\n",
    "nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)\n",
    "\n",
    "# clean columns\n",
    "nearby_venues.columns = [col.split(\".\")[-1] for col in nearby_venues.columns]\n",
    "\n",
    "nearby_venues.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 venues were returned by Foursquare.\n"
     ]
    }
   ],
   "source": [
    "print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "            \n",
    "        # create the API request URL\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            LIMIT)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "\n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['City', \n",
    "                  'City Latitude', \n",
    "                  'City Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Culver City\n",
      "El Segundo\n",
      "Hawthorne\n",
      "Hermosa Beach\n",
      "Inglewood\n",
      "Manhattan Beach\n",
      "Marina del Rey\n",
      "Redondo Beach\n",
      "Santa Monica\n",
      "Torrance\n",
      "Venice Beach\n"
     ]
    }
   ],
   "source": [
    "manhattan_venues = getNearbyVenues(names=df['City'],\n",
    "                                   latitudes=df['Latitude'],\n",
    "                                   longitudes=df['Longitude']\n",
    "                                  )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(208, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>City Latitude</th>\n",
       "      <th>City Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>Lindberg Park</td>\n",
       "      <td>34.003238</td>\n",
       "      <td>-118.398011</td>\n",
       "      <td>Playground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>Ralphs</td>\n",
       "      <td>34.002368</td>\n",
       "      <td>-118.393548</td>\n",
       "      <td>Supermarket</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>Pizza Hut</td>\n",
       "      <td>34.002809</td>\n",
       "      <td>-118.393751</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>Redbox</td>\n",
       "      <td>34.002558</td>\n",
       "      <td>-118.393167</td>\n",
       "      <td>Video Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>The Spot Cafe &amp; Lounge</td>\n",
       "      <td>34.009331</td>\n",
       "      <td>-118.398429</td>\n",
       "      <td>Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>Denny's</td>\n",
       "      <td>34.003629</td>\n",
       "      <td>-118.393315</td>\n",
       "      <td>Diner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>Coombs Park</td>\n",
       "      <td>34.008639</td>\n",
       "      <td>-118.400031</td>\n",
       "      <td>Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>All That &amp; MORE Boutique</td>\n",
       "      <td>34.002879</td>\n",
       "      <td>-118.393184</td>\n",
       "      <td>Boutique</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>The No. 1 Currywurst Truck of Los Angeles</td>\n",
       "      <td>34.007730</td>\n",
       "      <td>-118.391969</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>El Segundo</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>Yellow Brick Road Doggie Playcare &amp; Gym</td>\n",
       "      <td>33.916564</td>\n",
       "      <td>-118.400132</td>\n",
       "      <td>Pet Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>El Segundo</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>Grateful Dogs Clubhouse</td>\n",
       "      <td>33.918022</td>\n",
       "      <td>-118.398334</td>\n",
       "      <td>Pet Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>El Segundo</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>R6 Distillery</td>\n",
       "      <td>33.916528</td>\n",
       "      <td>-118.405822</td>\n",
       "      <td>Distillery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>El Segundo</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>El Segundo Animal Hospital</td>\n",
       "      <td>33.919209</td>\n",
       "      <td>-118.404653</td>\n",
       "      <td>Pet Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>El Segundo</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>33.915868</td>\n",
       "      <td>-118.396384</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>El Segundo</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>ONEHOPE Wine</td>\n",
       "      <td>33.918588</td>\n",
       "      <td>-118.401736</td>\n",
       "      <td>Office</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>El Segundo</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>El Segundo DinDinAGoGo</td>\n",
       "      <td>33.918328</td>\n",
       "      <td>-118.403435</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>El Segundo</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>Chevron Park</td>\n",
       "      <td>33.915467</td>\n",
       "      <td>-118.399067</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>El Segundo</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>International Garden And Floral Design</td>\n",
       "      <td>33.917639</td>\n",
       "      <td>-118.396242</td>\n",
       "      <td>Garden Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>Tacos Mexico</td>\n",
       "      <td>33.916662</td>\n",
       "      <td>-118.349048</td>\n",
       "      <td>Taco Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>Systems Training Center</td>\n",
       "      <td>33.913871</td>\n",
       "      <td>-118.352290</td>\n",
       "      <td>Martial Arts Dojo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>Continental Gourmet Market - Hawthorne</td>\n",
       "      <td>33.914933</td>\n",
       "      <td>-118.344192</td>\n",
       "      <td>Latin American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>Bangkok Grill</td>\n",
       "      <td>33.914941</td>\n",
       "      <td>-118.352006</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>Superior Grocers</td>\n",
       "      <td>33.917602</td>\n",
       "      <td>-118.351620</td>\n",
       "      <td>Market</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>Dollar Tree</td>\n",
       "      <td>33.913837</td>\n",
       "      <td>-118.353326</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>Domino's Pizza</td>\n",
       "      <td>33.916737</td>\n",
       "      <td>-118.349853</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>Starbucks</td>\n",
       "      <td>33.916610</td>\n",
       "      <td>-118.352196</td>\n",
       "      <td>Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>GNC</td>\n",
       "      <td>33.917102</td>\n",
       "      <td>-118.351676</td>\n",
       "      <td>Supplement Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>99 Cents Only Stores</td>\n",
       "      <td>33.914137</td>\n",
       "      <td>-118.352905</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>T-Mobile</td>\n",
       "      <td>33.916596</td>\n",
       "      <td>-118.353020</td>\n",
       "      <td>Mobile Phone Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>Rally's Hamburgers</td>\n",
       "      <td>33.915843</td>\n",
       "      <td>-118.352072</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>ampm</td>\n",
       "      <td>33.916703</td>\n",
       "      <td>-118.344206</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>SUBWAY</td>\n",
       "      <td>33.916816</td>\n",
       "      <td>-118.351883</td>\n",
       "      <td>Sandwich Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>33.916158</td>\n",
       "      <td>-118.347386</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>AT&amp;T</td>\n",
       "      <td>33.916687</td>\n",
       "      <td>-118.351987</td>\n",
       "      <td>Mobile Phone Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>Taco Bell</td>\n",
       "      <td>33.916664</td>\n",
       "      <td>-118.350514</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>Liquor Deli</td>\n",
       "      <td>33.913970</td>\n",
       "      <td>-118.344680</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>Costa Del Sol</td>\n",
       "      <td>33.915834</td>\n",
       "      <td>-118.344343</td>\n",
       "      <td>Latin American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>Sprint Store</td>\n",
       "      <td>33.916787</td>\n",
       "      <td>-118.351198</td>\n",
       "      <td>Mobile Phone Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>Vicky's Donuts</td>\n",
       "      <td>33.916194</td>\n",
       "      <td>-118.344065</td>\n",
       "      <td>Donut Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>Fj Catering Service</td>\n",
       "      <td>33.918807</td>\n",
       "      <td>-118.349160</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Creme de la Crepe</td>\n",
       "      <td>33.864180</td>\n",
       "      <td>-118.397104</td>\n",
       "      <td>French Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>The Strand</td>\n",
       "      <td>33.867030</td>\n",
       "      <td>-118.394904</td>\n",
       "      <td>Trail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Hermosa Beach Fish Shop</td>\n",
       "      <td>33.865028</td>\n",
       "      <td>-118.394182</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>The Source Cafe</td>\n",
       "      <td>33.864499</td>\n",
       "      <td>-118.396651</td>\n",
       "      <td>Vegetarian / Vegan Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Fritto Misto Italian Cafe</td>\n",
       "      <td>33.863774</td>\n",
       "      <td>-118.398149</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Bikram Yoga</td>\n",
       "      <td>33.864195</td>\n",
       "      <td>-118.396400</td>\n",
       "      <td>Yoga Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>The Gum Tree Cafe &amp; Boutique</td>\n",
       "      <td>33.863280</td>\n",
       "      <td>-118.398680</td>\n",
       "      <td>Australian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Hermosa Beach Community Center</td>\n",
       "      <td>33.867028</td>\n",
       "      <td>-118.394938</td>\n",
       "      <td>Government Building</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Chipotle Mexican Grill</td>\n",
       "      <td>33.865331</td>\n",
       "      <td>-118.393487</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>The Rockefeller</td>\n",
       "      <td>33.864037</td>\n",
       "      <td>-118.397268</td>\n",
       "      <td>American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Abigaile</td>\n",
       "      <td>33.863159</td>\n",
       "      <td>-118.399601</td>\n",
       "      <td>New American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Massage Spot</td>\n",
       "      <td>33.864534</td>\n",
       "      <td>-118.396695</td>\n",
       "      <td>Massage Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Día de Campo</td>\n",
       "      <td>33.862863</td>\n",
       "      <td>-118.400010</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Planet Earth Eco Cafe</td>\n",
       "      <td>33.864537</td>\n",
       "      <td>-118.396682</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Deep Pocket Jean Company</td>\n",
       "      <td>33.863115</td>\n",
       "      <td>-118.399008</td>\n",
       "      <td>Men's Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>The Standing Room</td>\n",
       "      <td>33.863269</td>\n",
       "      <td>-118.400137</td>\n",
       "      <td>Burger Joint</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>El Pollo Inka</td>\n",
       "      <td>33.863498</td>\n",
       "      <td>-118.391998</td>\n",
       "      <td>Peruvian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Starbucks</td>\n",
       "      <td>33.865963</td>\n",
       "      <td>-118.394487</td>\n",
       "      <td>Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>CVS pharmacy</td>\n",
       "      <td>33.865660</td>\n",
       "      <td>-118.394869</td>\n",
       "      <td>Pharmacy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Java Man</td>\n",
       "      <td>33.863001</td>\n",
       "      <td>-118.399254</td>\n",
       "      <td>Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Fusion Sushi</td>\n",
       "      <td>33.864155</td>\n",
       "      <td>-118.392755</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Uncorked The Wine Shop</td>\n",
       "      <td>33.863649</td>\n",
       "      <td>-118.398262</td>\n",
       "      <td>Wine Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>The Grindz at 1601</td>\n",
       "      <td>33.866915</td>\n",
       "      <td>-118.393830</td>\n",
       "      <td>Hawaiian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Laurel Tavern</td>\n",
       "      <td>33.862620</td>\n",
       "      <td>-118.400027</td>\n",
       "      <td>American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Starbucks</td>\n",
       "      <td>33.863130</td>\n",
       "      <td>-118.400336</td>\n",
       "      <td>Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Chelsea Pub and Lounge</td>\n",
       "      <td>33.863646</td>\n",
       "      <td>-118.400162</td>\n",
       "      <td>Lounge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Underground Pub and Grill</td>\n",
       "      <td>33.863540</td>\n",
       "      <td>-118.399961</td>\n",
       "      <td>Sports Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Chef Melba's Bistro</td>\n",
       "      <td>33.864508</td>\n",
       "      <td>-118.400723</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Paisanos Pizza &amp; Pasta</td>\n",
       "      <td>33.861845</td>\n",
       "      <td>-118.399776</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Zane's</td>\n",
       "      <td>33.862155</td>\n",
       "      <td>-118.399883</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Curious</td>\n",
       "      <td>33.862359</td>\n",
       "      <td>-118.399397</td>\n",
       "      <td>Boutique</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>New Orleans Cajun Cuisine</td>\n",
       "      <td>33.862473</td>\n",
       "      <td>-118.399285</td>\n",
       "      <td>Cajun / Creole Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>LYFE Yoga Center</td>\n",
       "      <td>33.864673</td>\n",
       "      <td>-118.392629</td>\n",
       "      <td>Yoga Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>VONS</td>\n",
       "      <td>33.865946</td>\n",
       "      <td>-118.394449</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Paradise Bowls</td>\n",
       "      <td>33.863048</td>\n",
       "      <td>-118.400156</td>\n",
       "      <td>Juice Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Star Antiques Market</td>\n",
       "      <td>33.864281</td>\n",
       "      <td>-118.396288</td>\n",
       "      <td>Antique Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Vans</td>\n",
       "      <td>33.864700</td>\n",
       "      <td>-118.392784</td>\n",
       "      <td>Shoe Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Steak &amp; Whisky</td>\n",
       "      <td>33.862550</td>\n",
       "      <td>-118.399697</td>\n",
       "      <td>Steakhouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Rok Sushi Kitchen</td>\n",
       "      <td>33.862373</td>\n",
       "      <td>-118.400060</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Massage Envy - Hermosa Beach</td>\n",
       "      <td>33.863417</td>\n",
       "      <td>-118.399516</td>\n",
       "      <td>Spa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Clark Field</td>\n",
       "      <td>33.862316</td>\n",
       "      <td>-118.394941</td>\n",
       "      <td>Baseball Field</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>The Bar Method</td>\n",
       "      <td>33.862832</td>\n",
       "      <td>-118.400504</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Wells Fargo</td>\n",
       "      <td>33.865734</td>\n",
       "      <td>-118.393582</td>\n",
       "      <td>Bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>The UPS Store</td>\n",
       "      <td>33.864931</td>\n",
       "      <td>-118.394961</td>\n",
       "      <td>Shipping Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>24 Hour Fitness</td>\n",
       "      <td>33.867043</td>\n",
       "      <td>-118.393986</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Hermosa Philly Pub</td>\n",
       "      <td>33.863195</td>\n",
       "      <td>-118.400130</td>\n",
       "      <td>Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Trader Joe's</td>\n",
       "      <td>33.863890</td>\n",
       "      <td>-118.392037</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>ARCO</td>\n",
       "      <td>33.863644</td>\n",
       "      <td>-118.393016</td>\n",
       "      <td>Gas Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Pick Up Stix</td>\n",
       "      <td>33.864943</td>\n",
       "      <td>-118.395070</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>the Hook &amp; Plow</td>\n",
       "      <td>33.864433</td>\n",
       "      <td>-118.397138</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Hermosa Beach Farmers Market</td>\n",
       "      <td>33.862447</td>\n",
       "      <td>-118.395657</td>\n",
       "      <td>Farmers Market</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>33.863234</td>\n",
       "      <td>-118.392910</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Hampton Inn &amp; Suites Hermosa Beach</td>\n",
       "      <td>33.866246</td>\n",
       "      <td>-118.392961</td>\n",
       "      <td>Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Hermosa Valley Greenbelt</td>\n",
       "      <td>33.867047</td>\n",
       "      <td>-118.394963</td>\n",
       "      <td>Trail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Nutrakick</td>\n",
       "      <td>33.867034</td>\n",
       "      <td>-118.393698</td>\n",
       "      <td>Juice Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Oasis Thai Massage &amp; Spa</td>\n",
       "      <td>33.864193</td>\n",
       "      <td>-118.392392</td>\n",
       "      <td>Massage Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Tennis Courts On Valley</td>\n",
       "      <td>33.861761</td>\n",
       "      <td>-118.394805</td>\n",
       "      <td>Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Paradis</td>\n",
       "      <td>33.865390</td>\n",
       "      <td>-118.400940</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Inglewood</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>The Forum</td>\n",
       "      <td>33.958209</td>\n",
       "      <td>-118.341878</td>\n",
       "      <td>Stadium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Inglewood</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>7-Eleven</td>\n",
       "      <td>33.952217</td>\n",
       "      <td>-118.344243</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Inglewood</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>M&amp;M Soul Food Restaurant</td>\n",
       "      <td>33.959956</td>\n",
       "      <td>-118.345284</td>\n",
       "      <td>Southern / Soul Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Inglewood</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>Sizzler</td>\n",
       "      <td>33.960016</td>\n",
       "      <td>-118.344099</td>\n",
       "      <td>Steakhouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Inglewood</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>Bourbon Street Fish</td>\n",
       "      <td>33.956555</td>\n",
       "      <td>-118.344286</td>\n",
       "      <td>Cajun / Creole Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Inglewood</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>Chase Lounge at The Forum</td>\n",
       "      <td>33.958165</td>\n",
       "      <td>-118.342369</td>\n",
       "      <td>Rock Club</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Inglewood</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>Bacon Hot Dog @ Forum Parking Lot</td>\n",
       "      <td>33.957977</td>\n",
       "      <td>-118.341951</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Inglewood</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>Martino's Liquor</td>\n",
       "      <td>33.959682</td>\n",
       "      <td>-118.346327</td>\n",
       "      <td>Smoke Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Manhattan House</td>\n",
       "      <td>33.887478</td>\n",
       "      <td>-118.397039</td>\n",
       "      <td>Gastropub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Target</td>\n",
       "      <td>33.888286</td>\n",
       "      <td>-118.394624</td>\n",
       "      <td>Big Box Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Grow - The Produce Shop</td>\n",
       "      <td>33.892042</td>\n",
       "      <td>-118.395740</td>\n",
       "      <td>Gourmet Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Manhattan Bread &amp; Bagel</td>\n",
       "      <td>33.891883</td>\n",
       "      <td>-118.395812</td>\n",
       "      <td>Sandwich Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Rubio's</td>\n",
       "      <td>33.892651</td>\n",
       "      <td>-118.395783</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Barsha Wines &amp; Spirits</td>\n",
       "      <td>33.885295</td>\n",
       "      <td>-118.396195</td>\n",
       "      <td>Wine Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Thai Dishes</td>\n",
       "      <td>33.886200</td>\n",
       "      <td>-118.396231</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Starbucks</td>\n",
       "      <td>33.888348</td>\n",
       "      <td>-118.395120</td>\n",
       "      <td>Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Baskin-Robbins</td>\n",
       "      <td>33.885459</td>\n",
       "      <td>-118.396353</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>AT&amp;T</td>\n",
       "      <td>33.887633</td>\n",
       "      <td>-118.395298</td>\n",
       "      <td>Mobile Phone Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Grunions Sports Bar &amp; Grill</td>\n",
       "      <td>33.889647</td>\n",
       "      <td>-118.396293</td>\n",
       "      <td>Sports Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>GameStop</td>\n",
       "      <td>33.891968</td>\n",
       "      <td>-118.395556</td>\n",
       "      <td>Video Game Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>RockIt Body Pilates</td>\n",
       "      <td>33.892106</td>\n",
       "      <td>-118.395768</td>\n",
       "      <td>Pilates Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Fresh Brothers - Manhattan Beach</td>\n",
       "      <td>33.892605</td>\n",
       "      <td>-118.395548</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>El Gringo Mexican Restaurant</td>\n",
       "      <td>33.885342</td>\n",
       "      <td>-118.396363</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>The UPS Store</td>\n",
       "      <td>33.889971</td>\n",
       "      <td>-118.396257</td>\n",
       "      <td>Shipping Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Enterprise Rent-A-Car</td>\n",
       "      <td>33.893550</td>\n",
       "      <td>-118.396340</td>\n",
       "      <td>Rental Car Location</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Chase Bank</td>\n",
       "      <td>33.887643</td>\n",
       "      <td>-118.395614</td>\n",
       "      <td>Bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Wells Fargo</td>\n",
       "      <td>33.887601</td>\n",
       "      <td>-118.396320</td>\n",
       "      <td>Bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Firestone Complete Auto Care</td>\n",
       "      <td>33.890330</td>\n",
       "      <td>-118.396442</td>\n",
       "      <td>Automotive Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Castle</td>\n",
       "      <td>33.894041</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>Sports Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Residence Inn Manhattan Beach</td>\n",
       "      <td>33.890584</td>\n",
       "      <td>-118.395601</td>\n",
       "      <td>Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Hawthorne Suites by Wyndham Manhattan Beach</td>\n",
       "      <td>33.891774</td>\n",
       "      <td>-118.396174</td>\n",
       "      <td>Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Kah Asian Restaurant and Lounge</td>\n",
       "      <td>33.887541</td>\n",
       "      <td>-118.397104</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>Sepulveda Wine Co.</td>\n",
       "      <td>33.885264</td>\n",
       "      <td>-118.396330</td>\n",
       "      <td>Wine Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>Del Rey Yacht Club</td>\n",
       "      <td>33.981711</td>\n",
       "      <td>-118.450213</td>\n",
       "      <td>Boat or Ferry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>Chart House Restaurant</td>\n",
       "      <td>33.978692</td>\n",
       "      <td>-118.453332</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>Killer Shrimp</td>\n",
       "      <td>33.983164</td>\n",
       "      <td>-118.456707</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>California Pizza Kitchen</td>\n",
       "      <td>33.979026</td>\n",
       "      <td>-118.453839</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>The Ritz-Carlton, Marina del Rey</td>\n",
       "      <td>33.984155</td>\n",
       "      <td>-118.450223</td>\n",
       "      <td>Resort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>Mother's Beach</td>\n",
       "      <td>33.981535</td>\n",
       "      <td>-118.458084</td>\n",
       "      <td>Beach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>Marina del Rey Beach</td>\n",
       "      <td>33.979891</td>\n",
       "      <td>-118.458216</td>\n",
       "      <td>Beach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>Killer Cafe</td>\n",
       "      <td>33.983571</td>\n",
       "      <td>-118.456435</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>Jamaica Bay Inn</td>\n",
       "      <td>33.982687</td>\n",
       "      <td>-118.457709</td>\n",
       "      <td>Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>Beachside Restaurant and Bar</td>\n",
       "      <td>33.982455</td>\n",
       "      <td>-118.457597</td>\n",
       "      <td>American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>Cast &amp; Plow</td>\n",
       "      <td>33.984350</td>\n",
       "      <td>-118.450400</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>Hilton Garden Inn</td>\n",
       "      <td>33.983874</td>\n",
       "      <td>-118.457787</td>\n",
       "      <td>Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>Marina Del Rey pier</td>\n",
       "      <td>33.978651</td>\n",
       "      <td>-118.452251</td>\n",
       "      <td>Beach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>Marina Grill &amp; Bar</td>\n",
       "      <td>33.983710</td>\n",
       "      <td>-118.457370</td>\n",
       "      <td>American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>Whitehall Spirit Rowing Club Marina Del Rey</td>\n",
       "      <td>33.979537</td>\n",
       "      <td>-118.456442</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>Waterfront Walk</td>\n",
       "      <td>33.984174</td>\n",
       "      <td>-118.450755</td>\n",
       "      <td>Harbor / Marina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>Wave Poolside Bar &amp; Grill</td>\n",
       "      <td>33.984408</td>\n",
       "      <td>-118.450300</td>\n",
       "      <td>New American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>iLoveKickboxing - Redondo Beach, CA</td>\n",
       "      <td>33.854484</td>\n",
       "      <td>-118.379196</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>Redondo Beach Dog Park</td>\n",
       "      <td>33.857577</td>\n",
       "      <td>-118.377995</td>\n",
       "      <td>Dog Run</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>Bistro Miyoda</td>\n",
       "      <td>33.854176</td>\n",
       "      <td>-118.379762</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>Dollar Tree</td>\n",
       "      <td>33.853951</td>\n",
       "      <td>-118.380787</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>VONS</td>\n",
       "      <td>33.853943</td>\n",
       "      <td>-118.380257</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>Papa John's Pizza</td>\n",
       "      <td>33.853732</td>\n",
       "      <td>-118.380786</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>Sweet Creations Bakery Shop</td>\n",
       "      <td>33.854085</td>\n",
       "      <td>-118.379961</td>\n",
       "      <td>Bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>Co-Opportunity</td>\n",
       "      <td>34.024289</td>\n",
       "      <td>-118.482684</td>\n",
       "      <td>Supermarket</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>Memorial Park</td>\n",
       "      <td>34.021249</td>\n",
       "      <td>-118.480600</td>\n",
       "      <td>Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>DK's Donuts and Bakery</td>\n",
       "      <td>34.025783</td>\n",
       "      <td>-118.483409</td>\n",
       "      <td>Donut Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>The Cove Skatepark</td>\n",
       "      <td>34.020673</td>\n",
       "      <td>-118.480594</td>\n",
       "      <td>Skate Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>Tacos Por Favor</td>\n",
       "      <td>34.019741</td>\n",
       "      <td>-118.480247</td>\n",
       "      <td>Taco Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>The Chestnut Club</td>\n",
       "      <td>34.024216</td>\n",
       "      <td>-118.485936</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>Memorial Park Tennis Courts</td>\n",
       "      <td>34.021049</td>\n",
       "      <td>-118.481452</td>\n",
       "      <td>Tennis Court</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>IRON Fitness</td>\n",
       "      <td>34.026909</td>\n",
       "      <td>-118.479188</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>18th Street Art Center</td>\n",
       "      <td>34.023753</td>\n",
       "      <td>-118.477566</td>\n",
       "      <td>Art Gallery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>18th Street Coffee House</td>\n",
       "      <td>34.025625</td>\n",
       "      <td>-118.480777</td>\n",
       "      <td>Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>King Baby Studio - Santa Monica</td>\n",
       "      <td>34.019423</td>\n",
       "      <td>-118.483585</td>\n",
       "      <td>Accessories Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>Truxton's American Bistro</td>\n",
       "      <td>34.024099</td>\n",
       "      <td>-118.485922</td>\n",
       "      <td>American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>Chomp Eatery &amp; Juice Station</td>\n",
       "      <td>34.025754</td>\n",
       "      <td>-118.483389</td>\n",
       "      <td>Juice Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>Enterprise Rent-A-Car</td>\n",
       "      <td>34.026461</td>\n",
       "      <td>-118.482439</td>\n",
       "      <td>Rental Car Location</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>FIAT Auto Gallery Santa Monica</td>\n",
       "      <td>34.027620</td>\n",
       "      <td>-118.481330</td>\n",
       "      <td>Auto Dealership</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>Buffalo Club</td>\n",
       "      <td>34.020868</td>\n",
       "      <td>-118.478760</td>\n",
       "      <td>New American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>7-Eleven</td>\n",
       "      <td>34.025449</td>\n",
       "      <td>-118.483298</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>Taco Bell</td>\n",
       "      <td>34.026098</td>\n",
       "      <td>-118.483078</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>The Room</td>\n",
       "      <td>34.024126</td>\n",
       "      <td>-118.486086</td>\n",
       "      <td>Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>Hertz</td>\n",
       "      <td>34.024545</td>\n",
       "      <td>-118.484930</td>\n",
       "      <td>Rental Car Location</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>Highways Performance Space</td>\n",
       "      <td>34.023552</td>\n",
       "      <td>-118.477277</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Torrance</td>\n",
       "      <td>33.834966</td>\n",
       "      <td>-118.341431</td>\n",
       "      <td>VICTOR E. BENSTEAD PLUNGE</td>\n",
       "      <td>33.837976</td>\n",
       "      <td>-118.343823</td>\n",
       "      <td>Pool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Torrance</td>\n",
       "      <td>33.834966</td>\n",
       "      <td>-118.341431</td>\n",
       "      <td>7-Eleven</td>\n",
       "      <td>33.837863</td>\n",
       "      <td>-118.345517</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Torrance</td>\n",
       "      <td>33.834966</td>\n",
       "      <td>-118.341431</td>\n",
       "      <td>Metropark</td>\n",
       "      <td>33.833820</td>\n",
       "      <td>-118.345391</td>\n",
       "      <td>Clothing Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Torrance</td>\n",
       "      <td>33.834966</td>\n",
       "      <td>-118.341431</td>\n",
       "      <td>Ken Miller Recreation Center</td>\n",
       "      <td>33.837735</td>\n",
       "      <td>-118.345399</td>\n",
       "      <td>Recreation Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Ocean Front Walk</td>\n",
       "      <td>33.984314</td>\n",
       "      <td>-118.471504</td>\n",
       "      <td>Pedestrian Plaza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Poke-Poke</td>\n",
       "      <td>33.984498</td>\n",
       "      <td>-118.471577</td>\n",
       "      <td>Poke Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Tocaya Organica</td>\n",
       "      <td>33.986651</td>\n",
       "      <td>-118.471597</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>James' Beach</td>\n",
       "      <td>33.984866</td>\n",
       "      <td>-118.470275</td>\n",
       "      <td>American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Venice Beach Paddle Tennis Courts</td>\n",
       "      <td>33.984954</td>\n",
       "      <td>-118.472070</td>\n",
       "      <td>Tennis Court</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Venice Canals</td>\n",
       "      <td>33.983440</td>\n",
       "      <td>-118.466314</td>\n",
       "      <td>Canal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Green Goddess Collective</td>\n",
       "      <td>33.987603</td>\n",
       "      <td>-118.470676</td>\n",
       "      <td>Marijuana Dispensary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Venice Beach Basketball Courts</td>\n",
       "      <td>33.985959</td>\n",
       "      <td>-118.472995</td>\n",
       "      <td>Basketball Court</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Jody Maroni's Sausage Kingdom</td>\n",
       "      <td>33.984526</td>\n",
       "      <td>-118.471617</td>\n",
       "      <td>Hot Dog Joint</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Great White</td>\n",
       "      <td>33.987638</td>\n",
       "      <td>-118.472002</td>\n",
       "      <td>Australian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>High Rooftop Bar at Hotel Erwin</td>\n",
       "      <td>33.986816</td>\n",
       "      <td>-118.472705</td>\n",
       "      <td>Hotel Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Hotel Erwin</td>\n",
       "      <td>33.986950</td>\n",
       "      <td>-118.472450</td>\n",
       "      <td>Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Venice Farmers Market</td>\n",
       "      <td>33.986961</td>\n",
       "      <td>-118.466477</td>\n",
       "      <td>Farmers Market</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Barlo Kitchen and Cocktails</td>\n",
       "      <td>33.987231</td>\n",
       "      <td>-118.472011</td>\n",
       "      <td>American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Menotti's Coffee Stop</td>\n",
       "      <td>33.987271</td>\n",
       "      <td>-118.472610</td>\n",
       "      <td>Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Eggslut</td>\n",
       "      <td>33.987405</td>\n",
       "      <td>-118.472084</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Venice Beach Playground</td>\n",
       "      <td>33.985768</td>\n",
       "      <td>-118.473935</td>\n",
       "      <td>Playground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>The Poké Shack</td>\n",
       "      <td>33.987621</td>\n",
       "      <td>-118.472355</td>\n",
       "      <td>Poke Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Mao's Kitchen</td>\n",
       "      <td>33.987983</td>\n",
       "      <td>-118.472487</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Mollusk Surf Shop</td>\n",
       "      <td>33.987776</td>\n",
       "      <td>-118.471980</td>\n",
       "      <td>Board Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Larry's</td>\n",
       "      <td>33.987014</td>\n",
       "      <td>-118.473064</td>\n",
       "      <td>Gastropub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>33.987335</td>\n",
       "      <td>-118.472662</td>\n",
       "      <td>Dive Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Hama Sushi</td>\n",
       "      <td>33.988448</td>\n",
       "      <td>-118.470796</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Amazebowls</td>\n",
       "      <td>33.987566</td>\n",
       "      <td>-118.473892</td>\n",
       "      <td>Dessert Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Windward Farms</td>\n",
       "      <td>33.987987</td>\n",
       "      <td>-118.472062</td>\n",
       "      <td>Sandwich Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Windward Circle</td>\n",
       "      <td>33.988045</td>\n",
       "      <td>-118.471863</td>\n",
       "      <td>Plaza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Studio Surya Yoga</td>\n",
       "      <td>33.988550</td>\n",
       "      <td>-118.471777</td>\n",
       "      <td>Yoga Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>Del Monte Speakeasy</td>\n",
       "      <td>33.987321</td>\n",
       "      <td>-118.472694</td>\n",
       "      <td>Speakeasy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                City  City Latitude  City Longitude  \\\n",
       "0        Culver City      34.005820     -118.396781   \n",
       "1        Culver City      34.005820     -118.396781   \n",
       "2        Culver City      34.005820     -118.396781   \n",
       "3        Culver City      34.005820     -118.396781   \n",
       "4        Culver City      34.005820     -118.396781   \n",
       "5        Culver City      34.005820     -118.396781   \n",
       "6        Culver City      34.005820     -118.396781   \n",
       "7        Culver City      34.005820     -118.396781   \n",
       "8        Culver City      34.005820     -118.396781   \n",
       "9         El Segundo      33.917145     -118.401554   \n",
       "10        El Segundo      33.917145     -118.401554   \n",
       "11        El Segundo      33.917145     -118.401554   \n",
       "12        El Segundo      33.917145     -118.401554   \n",
       "13        El Segundo      33.917145     -118.401554   \n",
       "14        El Segundo      33.917145     -118.401554   \n",
       "15        El Segundo      33.917145     -118.401554   \n",
       "16        El Segundo      33.917145     -118.401554   \n",
       "17        El Segundo      33.917145     -118.401554   \n",
       "18         Hawthorne      33.914775     -118.348083   \n",
       "19         Hawthorne      33.914775     -118.348083   \n",
       "20         Hawthorne      33.914775     -118.348083   \n",
       "21         Hawthorne      33.914775     -118.348083   \n",
       "22         Hawthorne      33.914775     -118.348083   \n",
       "23         Hawthorne      33.914775     -118.348083   \n",
       "24         Hawthorne      33.914775     -118.348083   \n",
       "25         Hawthorne      33.914775     -118.348083   \n",
       "26         Hawthorne      33.914775     -118.348083   \n",
       "27         Hawthorne      33.914775     -118.348083   \n",
       "28         Hawthorne      33.914775     -118.348083   \n",
       "29         Hawthorne      33.914775     -118.348083   \n",
       "30         Hawthorne      33.914775     -118.348083   \n",
       "31         Hawthorne      33.914775     -118.348083   \n",
       "32         Hawthorne      33.914775     -118.348083   \n",
       "33         Hawthorne      33.914775     -118.348083   \n",
       "34         Hawthorne      33.914775     -118.348083   \n",
       "35         Hawthorne      33.914775     -118.348083   \n",
       "36         Hawthorne      33.914775     -118.348083   \n",
       "37         Hawthorne      33.914775     -118.348083   \n",
       "38         Hawthorne      33.914775     -118.348083   \n",
       "39         Hawthorne      33.914775     -118.348083   \n",
       "40     Hermosa Beach      33.865268     -118.396297   \n",
       "41     Hermosa Beach      33.865268     -118.396297   \n",
       "42     Hermosa Beach      33.865268     -118.396297   \n",
       "43     Hermosa Beach      33.865268     -118.396297   \n",
       "44     Hermosa Beach      33.865268     -118.396297   \n",
       "45     Hermosa Beach      33.865268     -118.396297   \n",
       "46     Hermosa Beach      33.865268     -118.396297   \n",
       "47     Hermosa Beach      33.865268     -118.396297   \n",
       "48     Hermosa Beach      33.865268     -118.396297   \n",
       "49     Hermosa Beach      33.865268     -118.396297   \n",
       "50     Hermosa Beach      33.865268     -118.396297   \n",
       "51     Hermosa Beach      33.865268     -118.396297   \n",
       "52     Hermosa Beach      33.865268     -118.396297   \n",
       "53     Hermosa Beach      33.865268     -118.396297   \n",
       "54     Hermosa Beach      33.865268     -118.396297   \n",
       "55     Hermosa Beach      33.865268     -118.396297   \n",
       "56     Hermosa Beach      33.865268     -118.396297   \n",
       "57     Hermosa Beach      33.865268     -118.396297   \n",
       "58     Hermosa Beach      33.865268     -118.396297   \n",
       "59     Hermosa Beach      33.865268     -118.396297   \n",
       "60     Hermosa Beach      33.865268     -118.396297   \n",
       "61     Hermosa Beach      33.865268     -118.396297   \n",
       "62     Hermosa Beach      33.865268     -118.396297   \n",
       "63     Hermosa Beach      33.865268     -118.396297   \n",
       "64     Hermosa Beach      33.865268     -118.396297   \n",
       "65     Hermosa Beach      33.865268     -118.396297   \n",
       "66     Hermosa Beach      33.865268     -118.396297   \n",
       "67     Hermosa Beach      33.865268     -118.396297   \n",
       "68     Hermosa Beach      33.865268     -118.396297   \n",
       "69     Hermosa Beach      33.865268     -118.396297   \n",
       "70     Hermosa Beach      33.865268     -118.396297   \n",
       "71     Hermosa Beach      33.865268     -118.396297   \n",
       "72     Hermosa Beach      33.865268     -118.396297   \n",
       "73     Hermosa Beach      33.865268     -118.396297   \n",
       "74     Hermosa Beach      33.865268     -118.396297   \n",
       "75     Hermosa Beach      33.865268     -118.396297   \n",
       "76     Hermosa Beach      33.865268     -118.396297   \n",
       "77     Hermosa Beach      33.865268     -118.396297   \n",
       "78     Hermosa Beach      33.865268     -118.396297   \n",
       "79     Hermosa Beach      33.865268     -118.396297   \n",
       "80     Hermosa Beach      33.865268     -118.396297   \n",
       "81     Hermosa Beach      33.865268     -118.396297   \n",
       "82     Hermosa Beach      33.865268     -118.396297   \n",
       "83     Hermosa Beach      33.865268     -118.396297   \n",
       "84     Hermosa Beach      33.865268     -118.396297   \n",
       "85     Hermosa Beach      33.865268     -118.396297   \n",
       "86     Hermosa Beach      33.865268     -118.396297   \n",
       "87     Hermosa Beach      33.865268     -118.396297   \n",
       "88     Hermosa Beach      33.865268     -118.396297   \n",
       "89     Hermosa Beach      33.865268     -118.396297   \n",
       "90     Hermosa Beach      33.865268     -118.396297   \n",
       "91     Hermosa Beach      33.865268     -118.396297   \n",
       "92     Hermosa Beach      33.865268     -118.396297   \n",
       "93     Hermosa Beach      33.865268     -118.396297   \n",
       "94     Hermosa Beach      33.865268     -118.396297   \n",
       "95     Hermosa Beach      33.865268     -118.396297   \n",
       "96     Hermosa Beach      33.865268     -118.396297   \n",
       "97     Hermosa Beach      33.865268     -118.396297   \n",
       "98         Inglewood      33.956068     -118.344274   \n",
       "99         Inglewood      33.956068     -118.344274   \n",
       "100        Inglewood      33.956068     -118.344274   \n",
       "101        Inglewood      33.956068     -118.344274   \n",
       "102        Inglewood      33.956068     -118.344274   \n",
       "103        Inglewood      33.956068     -118.344274   \n",
       "104        Inglewood      33.956068     -118.344274   \n",
       "105        Inglewood      33.956068     -118.344274   \n",
       "106  Manhattan Beach      33.889632     -118.397370   \n",
       "107  Manhattan Beach      33.889632     -118.397370   \n",
       "108  Manhattan Beach      33.889632     -118.397370   \n",
       "109  Manhattan Beach      33.889632     -118.397370   \n",
       "110  Manhattan Beach      33.889632     -118.397370   \n",
       "111  Manhattan Beach      33.889632     -118.397370   \n",
       "112  Manhattan Beach      33.889632     -118.397370   \n",
       "113  Manhattan Beach      33.889632     -118.397370   \n",
       "114  Manhattan Beach      33.889632     -118.397370   \n",
       "115  Manhattan Beach      33.889632     -118.397370   \n",
       "116  Manhattan Beach      33.889632     -118.397370   \n",
       "117  Manhattan Beach      33.889632     -118.397370   \n",
       "118  Manhattan Beach      33.889632     -118.397370   \n",
       "119  Manhattan Beach      33.889632     -118.397370   \n",
       "120  Manhattan Beach      33.889632     -118.397370   \n",
       "121  Manhattan Beach      33.889632     -118.397370   \n",
       "122  Manhattan Beach      33.889632     -118.397370   \n",
       "123  Manhattan Beach      33.889632     -118.397370   \n",
       "124  Manhattan Beach      33.889632     -118.397370   \n",
       "125  Manhattan Beach      33.889632     -118.397370   \n",
       "126  Manhattan Beach      33.889632     -118.397370   \n",
       "127  Manhattan Beach      33.889632     -118.397370   \n",
       "128  Manhattan Beach      33.889632     -118.397370   \n",
       "129  Manhattan Beach      33.889632     -118.397370   \n",
       "130  Manhattan Beach      33.889632     -118.397370   \n",
       "131   Marina del Rey      33.981510     -118.453229   \n",
       "132   Marina del Rey      33.981510     -118.453229   \n",
       "133   Marina del Rey      33.981510     -118.453229   \n",
       "134   Marina del Rey      33.981510     -118.453229   \n",
       "135   Marina del Rey      33.981510     -118.453229   \n",
       "136   Marina del Rey      33.981510     -118.453229   \n",
       "137   Marina del Rey      33.981510     -118.453229   \n",
       "138   Marina del Rey      33.981510     -118.453229   \n",
       "139   Marina del Rey      33.981510     -118.453229   \n",
       "140   Marina del Rey      33.981510     -118.453229   \n",
       "141   Marina del Rey      33.981510     -118.453229   \n",
       "142   Marina del Rey      33.981510     -118.453229   \n",
       "143   Marina del Rey      33.981510     -118.453229   \n",
       "144   Marina del Rey      33.981510     -118.453229   \n",
       "145   Marina del Rey      33.981510     -118.453229   \n",
       "146   Marina del Rey      33.981510     -118.453229   \n",
       "147   Marina del Rey      33.981510     -118.453229   \n",
       "148    Redondo Beach      33.856817     -118.377137   \n",
       "149    Redondo Beach      33.856817     -118.377137   \n",
       "150    Redondo Beach      33.856817     -118.377137   \n",
       "151    Redondo Beach      33.856817     -118.377137   \n",
       "152    Redondo Beach      33.856817     -118.377137   \n",
       "153    Redondo Beach      33.856817     -118.377137   \n",
       "154    Redondo Beach      33.856817     -118.377137   \n",
       "155     Santa Monica      34.023413     -118.481666   \n",
       "156     Santa Monica      34.023413     -118.481666   \n",
       "157     Santa Monica      34.023413     -118.481666   \n",
       "158     Santa Monica      34.023413     -118.481666   \n",
       "159     Santa Monica      34.023413     -118.481666   \n",
       "160     Santa Monica      34.023413     -118.481666   \n",
       "161     Santa Monica      34.023413     -118.481666   \n",
       "162     Santa Monica      34.023413     -118.481666   \n",
       "163     Santa Monica      34.023413     -118.481666   \n",
       "164     Santa Monica      34.023413     -118.481666   \n",
       "165     Santa Monica      34.023413     -118.481666   \n",
       "166     Santa Monica      34.023413     -118.481666   \n",
       "167     Santa Monica      34.023413     -118.481666   \n",
       "168     Santa Monica      34.023413     -118.481666   \n",
       "169     Santa Monica      34.023413     -118.481666   \n",
       "170     Santa Monica      34.023413     -118.481666   \n",
       "171     Santa Monica      34.023413     -118.481666   \n",
       "172     Santa Monica      34.023413     -118.481666   \n",
       "173     Santa Monica      34.023413     -118.481666   \n",
       "174     Santa Monica      34.023413     -118.481666   \n",
       "175     Santa Monica      34.023413     -118.481666   \n",
       "176         Torrance      33.834966     -118.341431   \n",
       "177         Torrance      33.834966     -118.341431   \n",
       "178         Torrance      33.834966     -118.341431   \n",
       "179         Torrance      33.834966     -118.341431   \n",
       "180     Venice Beach      33.985000     -118.469500   \n",
       "181     Venice Beach      33.985000     -118.469500   \n",
       "182     Venice Beach      33.985000     -118.469500   \n",
       "183     Venice Beach      33.985000     -118.469500   \n",
       "184     Venice Beach      33.985000     -118.469500   \n",
       "185     Venice Beach      33.985000     -118.469500   \n",
       "186     Venice Beach      33.985000     -118.469500   \n",
       "187     Venice Beach      33.985000     -118.469500   \n",
       "188     Venice Beach      33.985000     -118.469500   \n",
       "189     Venice Beach      33.985000     -118.469500   \n",
       "190     Venice Beach      33.985000     -118.469500   \n",
       "191     Venice Beach      33.985000     -118.469500   \n",
       "192     Venice Beach      33.985000     -118.469500   \n",
       "193     Venice Beach      33.985000     -118.469500   \n",
       "194     Venice Beach      33.985000     -118.469500   \n",
       "195     Venice Beach      33.985000     -118.469500   \n",
       "196     Venice Beach      33.985000     -118.469500   \n",
       "197     Venice Beach      33.985000     -118.469500   \n",
       "198     Venice Beach      33.985000     -118.469500   \n",
       "199     Venice Beach      33.985000     -118.469500   \n",
       "200     Venice Beach      33.985000     -118.469500   \n",
       "201     Venice Beach      33.985000     -118.469500   \n",
       "202     Venice Beach      33.985000     -118.469500   \n",
       "203     Venice Beach      33.985000     -118.469500   \n",
       "204     Venice Beach      33.985000     -118.469500   \n",
       "205     Venice Beach      33.985000     -118.469500   \n",
       "206     Venice Beach      33.985000     -118.469500   \n",
       "207     Venice Beach      33.985000     -118.469500   \n",
       "\n",
       "                                           Venue  Venue Latitude  \\\n",
       "0                                  Lindberg Park       34.003238   \n",
       "1                                         Ralphs       34.002368   \n",
       "2                                      Pizza Hut       34.002809   \n",
       "3                                         Redbox       34.002558   \n",
       "4                         The Spot Cafe & Lounge       34.009331   \n",
       "5                                        Denny's       34.003629   \n",
       "6                                    Coombs Park       34.008639   \n",
       "7                       All That & MORE Boutique       34.002879   \n",
       "8      The No. 1 Currywurst Truck of Los Angeles       34.007730   \n",
       "9        Yellow Brick Road Doggie Playcare & Gym       33.916564   \n",
       "10                       Grateful Dogs Clubhouse       33.918022   \n",
       "11                                 R6 Distillery       33.916528   \n",
       "12                    El Segundo Animal Hospital       33.919209   \n",
       "13                                    McDonald's       33.915868   \n",
       "14                                  ONEHOPE Wine       33.918588   \n",
       "15                        El Segundo DinDinAGoGo       33.918328   \n",
       "16                                  Chevron Park       33.915467   \n",
       "17        International Garden And Floral Design       33.917639   \n",
       "18                                  Tacos Mexico       33.916662   \n",
       "19                       Systems Training Center       33.913871   \n",
       "20        Continental Gourmet Market - Hawthorne       33.914933   \n",
       "21                                 Bangkok Grill       33.914941   \n",
       "22                              Superior Grocers       33.917602   \n",
       "23                                   Dollar Tree       33.913837   \n",
       "24                                Domino's Pizza       33.916737   \n",
       "25                                     Starbucks       33.916610   \n",
       "26                                           GNC       33.917102   \n",
       "27                          99 Cents Only Stores       33.914137   \n",
       "28                                      T-Mobile       33.916596   \n",
       "29                            Rally's Hamburgers       33.915843   \n",
       "30                                          ampm       33.916703   \n",
       "31                                        SUBWAY       33.916816   \n",
       "32                                    McDonald's       33.916158   \n",
       "33                                          AT&T       33.916687   \n",
       "34                                     Taco Bell       33.916664   \n",
       "35                                   Liquor Deli       33.913970   \n",
       "36                                 Costa Del Sol       33.915834   \n",
       "37                                  Sprint Store       33.916787   \n",
       "38                                Vicky's Donuts       33.916194   \n",
       "39                           Fj Catering Service       33.918807   \n",
       "40                             Creme de la Crepe       33.864180   \n",
       "41                                    The Strand       33.867030   \n",
       "42                       Hermosa Beach Fish Shop       33.865028   \n",
       "43                               The Source Cafe       33.864499   \n",
       "44                     Fritto Misto Italian Cafe       33.863774   \n",
       "45                                   Bikram Yoga       33.864195   \n",
       "46                  The Gum Tree Cafe & Boutique       33.863280   \n",
       "47                Hermosa Beach Community Center       33.867028   \n",
       "48                        Chipotle Mexican Grill       33.865331   \n",
       "49                               The Rockefeller       33.864037   \n",
       "50                                      Abigaile       33.863159   \n",
       "51                                  Massage Spot       33.864534   \n",
       "52                                  Día de Campo       33.862863   \n",
       "53                         Planet Earth Eco Cafe       33.864537   \n",
       "54                      Deep Pocket Jean Company       33.863115   \n",
       "55                             The Standing Room       33.863269   \n",
       "56                                 El Pollo Inka       33.863498   \n",
       "57                                     Starbucks       33.865963   \n",
       "58                                  CVS pharmacy       33.865660   \n",
       "59                                      Java Man       33.863001   \n",
       "60                                  Fusion Sushi       33.864155   \n",
       "61                        Uncorked The Wine Shop       33.863649   \n",
       "62                            The Grindz at 1601       33.866915   \n",
       "63                                 Laurel Tavern       33.862620   \n",
       "64                                     Starbucks       33.863130   \n",
       "65                        Chelsea Pub and Lounge       33.863646   \n",
       "66                     Underground Pub and Grill       33.863540   \n",
       "67                           Chef Melba's Bistro       33.864508   \n",
       "68                        Paisanos Pizza & Pasta       33.861845   \n",
       "69                                        Zane's       33.862155   \n",
       "70                                       Curious       33.862359   \n",
       "71                     New Orleans Cajun Cuisine       33.862473   \n",
       "72                              LYFE Yoga Center       33.864673   \n",
       "73                                          VONS       33.865946   \n",
       "74                                Paradise Bowls       33.863048   \n",
       "75                          Star Antiques Market       33.864281   \n",
       "76                                          Vans       33.864700   \n",
       "77                                Steak & Whisky       33.862550   \n",
       "78                             Rok Sushi Kitchen       33.862373   \n",
       "79                  Massage Envy - Hermosa Beach       33.863417   \n",
       "80                                   Clark Field       33.862316   \n",
       "81                                The Bar Method       33.862832   \n",
       "82                                   Wells Fargo       33.865734   \n",
       "83                                 The UPS Store       33.864931   \n",
       "84                               24 Hour Fitness       33.867043   \n",
       "85                            Hermosa Philly Pub       33.863195   \n",
       "86                                  Trader Joe's       33.863890   \n",
       "87                                          ARCO       33.863644   \n",
       "88                                  Pick Up Stix       33.864943   \n",
       "89                               the Hook & Plow       33.864433   \n",
       "90                  Hermosa Beach Farmers Market       33.862447   \n",
       "91                                    McDonald's       33.863234   \n",
       "92            Hampton Inn & Suites Hermosa Beach       33.866246   \n",
       "93                      Hermosa Valley Greenbelt       33.867047   \n",
       "94                                     Nutrakick       33.867034   \n",
       "95                      Oasis Thai Massage & Spa       33.864193   \n",
       "96                       Tennis Courts On Valley       33.861761   \n",
       "97                                       Paradis       33.865390   \n",
       "98                                     The Forum       33.958209   \n",
       "99                                      7-Eleven       33.952217   \n",
       "100                     M&M Soul Food Restaurant       33.959956   \n",
       "101                                      Sizzler       33.960016   \n",
       "102                          Bourbon Street Fish       33.956555   \n",
       "103                    Chase Lounge at The Forum       33.958165   \n",
       "104            Bacon Hot Dog @ Forum Parking Lot       33.957977   \n",
       "105                             Martino's Liquor       33.959682   \n",
       "106                              Manhattan House       33.887478   \n",
       "107                                       Target       33.888286   \n",
       "108                      Grow - The Produce Shop       33.892042   \n",
       "109                      Manhattan Bread & Bagel       33.891883   \n",
       "110                                      Rubio's       33.892651   \n",
       "111                       Barsha Wines & Spirits       33.885295   \n",
       "112                                  Thai Dishes       33.886200   \n",
       "113                                    Starbucks       33.888348   \n",
       "114                               Baskin-Robbins       33.885459   \n",
       "115                                         AT&T       33.887633   \n",
       "116                  Grunions Sports Bar & Grill       33.889647   \n",
       "117                                     GameStop       33.891968   \n",
       "118                          RockIt Body Pilates       33.892106   \n",
       "119             Fresh Brothers - Manhattan Beach       33.892605   \n",
       "120                 El Gringo Mexican Restaurant       33.885342   \n",
       "121                                The UPS Store       33.889971   \n",
       "122                        Enterprise Rent-A-Car       33.893550   \n",
       "123                                   Chase Bank       33.887643   \n",
       "124                                  Wells Fargo       33.887601   \n",
       "125                 Firestone Complete Auto Care       33.890330   \n",
       "126                                       Castle       33.894041   \n",
       "127                Residence Inn Manhattan Beach       33.890584   \n",
       "128  Hawthorne Suites by Wyndham Manhattan Beach       33.891774   \n",
       "129              Kah Asian Restaurant and Lounge       33.887541   \n",
       "130                           Sepulveda Wine Co.       33.885264   \n",
       "131                           Del Rey Yacht Club       33.981711   \n",
       "132                       Chart House Restaurant       33.978692   \n",
       "133                                Killer Shrimp       33.983164   \n",
       "134                     California Pizza Kitchen       33.979026   \n",
       "135             The Ritz-Carlton, Marina del Rey       33.984155   \n",
       "136                               Mother's Beach       33.981535   \n",
       "137                         Marina del Rey Beach       33.979891   \n",
       "138                                  Killer Cafe       33.983571   \n",
       "139                              Jamaica Bay Inn       33.982687   \n",
       "140                 Beachside Restaurant and Bar       33.982455   \n",
       "141                                  Cast & Plow       33.984350   \n",
       "142                            Hilton Garden Inn       33.983874   \n",
       "143                          Marina Del Rey pier       33.978651   \n",
       "144                           Marina Grill & Bar       33.983710   \n",
       "145  Whitehall Spirit Rowing Club Marina Del Rey       33.979537   \n",
       "146                              Waterfront Walk       33.984174   \n",
       "147                    Wave Poolside Bar & Grill       33.984408   \n",
       "148          iLoveKickboxing - Redondo Beach, CA       33.854484   \n",
       "149                       Redondo Beach Dog Park       33.857577   \n",
       "150                                Bistro Miyoda       33.854176   \n",
       "151                                  Dollar Tree       33.853951   \n",
       "152                                         VONS       33.853943   \n",
       "153                            Papa John's Pizza       33.853732   \n",
       "154                  Sweet Creations Bakery Shop       33.854085   \n",
       "155                               Co-Opportunity       34.024289   \n",
       "156                                Memorial Park       34.021249   \n",
       "157                       DK's Donuts and Bakery       34.025783   \n",
       "158                           The Cove Skatepark       34.020673   \n",
       "159                              Tacos Por Favor       34.019741   \n",
       "160                            The Chestnut Club       34.024216   \n",
       "161                  Memorial Park Tennis Courts       34.021049   \n",
       "162                                 IRON Fitness       34.026909   \n",
       "163                       18th Street Art Center       34.023753   \n",
       "164                     18th Street Coffee House       34.025625   \n",
       "165              King Baby Studio - Santa Monica       34.019423   \n",
       "166                    Truxton's American Bistro       34.024099   \n",
       "167                 Chomp Eatery & Juice Station       34.025754   \n",
       "168                        Enterprise Rent-A-Car       34.026461   \n",
       "169               FIAT Auto Gallery Santa Monica       34.027620   \n",
       "170                                 Buffalo Club       34.020868   \n",
       "171                                     7-Eleven       34.025449   \n",
       "172                                    Taco Bell       34.026098   \n",
       "173                                     The Room       34.024126   \n",
       "174                                        Hertz       34.024545   \n",
       "175                   Highways Performance Space       34.023552   \n",
       "176                    VICTOR E. BENSTEAD PLUNGE       33.837976   \n",
       "177                                     7-Eleven       33.837863   \n",
       "178                                    Metropark       33.833820   \n",
       "179                 Ken Miller Recreation Center       33.837735   \n",
       "180                             Ocean Front Walk       33.984314   \n",
       "181                                    Poke-Poke       33.984498   \n",
       "182                              Tocaya Organica       33.986651   \n",
       "183                                 James' Beach       33.984866   \n",
       "184            Venice Beach Paddle Tennis Courts       33.984954   \n",
       "185                                Venice Canals       33.983440   \n",
       "186                     Green Goddess Collective       33.987603   \n",
       "187               Venice Beach Basketball Courts       33.985959   \n",
       "188                Jody Maroni's Sausage Kingdom       33.984526   \n",
       "189                                  Great White       33.987638   \n",
       "190              High Rooftop Bar at Hotel Erwin       33.986816   \n",
       "191                                  Hotel Erwin       33.986950   \n",
       "192                        Venice Farmers Market       33.986961   \n",
       "193                  Barlo Kitchen and Cocktails       33.987231   \n",
       "194                        Menotti's Coffee Stop       33.987271   \n",
       "195                                      Eggslut       33.987405   \n",
       "196                      Venice Beach Playground       33.985768   \n",
       "197                               The Poké Shack       33.987621   \n",
       "198                                Mao's Kitchen       33.987983   \n",
       "199                            Mollusk Surf Shop       33.987776   \n",
       "200                                      Larry's       33.987014   \n",
       "201                                    Townhouse       33.987335   \n",
       "202                                   Hama Sushi       33.988448   \n",
       "203                                   Amazebowls       33.987566   \n",
       "204                               Windward Farms       33.987987   \n",
       "205                              Windward Circle       33.988045   \n",
       "206                            Studio Surya Yoga       33.988550   \n",
       "207                          Del Monte Speakeasy       33.987321   \n",
       "\n",
       "     Venue Longitude                   Venue Category  \n",
       "0        -118.398011                       Playground  \n",
       "1        -118.393548                      Supermarket  \n",
       "2        -118.393751                      Pizza Place  \n",
       "3        -118.393167                      Video Store  \n",
       "4        -118.398429                      Coffee Shop  \n",
       "5        -118.393315                            Diner  \n",
       "6        -118.400031                             Park  \n",
       "7        -118.393184                         Boutique  \n",
       "8        -118.391969                       Food Truck  \n",
       "9        -118.400132                        Pet Store  \n",
       "10       -118.398334                        Pet Store  \n",
       "11       -118.405822                       Distillery  \n",
       "12       -118.404653                        Pet Store  \n",
       "13       -118.396384             Fast Food Restaurant  \n",
       "14       -118.401736                           Office  \n",
       "15       -118.403435                       Food Truck  \n",
       "16       -118.399067               Athletics & Sports  \n",
       "17       -118.396242                    Garden Center  \n",
       "18       -118.349048                       Taco Place  \n",
       "19       -118.352290                Martial Arts Dojo  \n",
       "20       -118.344192        Latin American Restaurant  \n",
       "21       -118.352006                  Thai Restaurant  \n",
       "22       -118.351620                           Market  \n",
       "23       -118.353326                   Discount Store  \n",
       "24       -118.349853                      Pizza Place  \n",
       "25       -118.352196                      Coffee Shop  \n",
       "26       -118.351676                  Supplement Shop  \n",
       "27       -118.352905                   Discount Store  \n",
       "28       -118.353020                Mobile Phone Shop  \n",
       "29       -118.352072             Fast Food Restaurant  \n",
       "30       -118.344206                Convenience Store  \n",
       "31       -118.351883                   Sandwich Place  \n",
       "32       -118.347386             Fast Food Restaurant  \n",
       "33       -118.351987                Mobile Phone Shop  \n",
       "34       -118.350514             Fast Food Restaurant  \n",
       "35       -118.344680                Convenience Store  \n",
       "36       -118.344343        Latin American Restaurant  \n",
       "37       -118.351198                Mobile Phone Shop  \n",
       "38       -118.344065                       Donut Shop  \n",
       "39       -118.349160                             Food  \n",
       "40       -118.397104                French Restaurant  \n",
       "41       -118.394904                            Trail  \n",
       "42       -118.394182               Seafood Restaurant  \n",
       "43       -118.396651    Vegetarian / Vegan Restaurant  \n",
       "44       -118.398149               Italian Restaurant  \n",
       "45       -118.396400                      Yoga Studio  \n",
       "46       -118.398680            Australian Restaurant  \n",
       "47       -118.394938              Government Building  \n",
       "48       -118.393487               Mexican Restaurant  \n",
       "49       -118.397268              American Restaurant  \n",
       "50       -118.399601          New American Restaurant  \n",
       "51       -118.396695                   Massage Studio  \n",
       "52       -118.400010               Mexican Restaurant  \n",
       "53       -118.396682                             Café  \n",
       "54       -118.399008                      Men's Store  \n",
       "55       -118.400137                     Burger Joint  \n",
       "56       -118.391998              Peruvian Restaurant  \n",
       "57       -118.394487                      Coffee Shop  \n",
       "58       -118.394869                         Pharmacy  \n",
       "59       -118.399254                      Coffee Shop  \n",
       "60       -118.392755                 Sushi Restaurant  \n",
       "61       -118.398262                        Wine Shop  \n",
       "62       -118.393830              Hawaiian Restaurant  \n",
       "63       -118.400027              American Restaurant  \n",
       "64       -118.400336                      Coffee Shop  \n",
       "65       -118.400162                           Lounge  \n",
       "66       -118.399961                       Sports Bar  \n",
       "67       -118.400723               Italian Restaurant  \n",
       "68       -118.399776                      Pizza Place  \n",
       "69       -118.399883                       Restaurant  \n",
       "70       -118.399397                         Boutique  \n",
       "71       -118.399285        Cajun / Creole Restaurant  \n",
       "72       -118.392629                      Yoga Studio  \n",
       "73       -118.394449                    Grocery Store  \n",
       "74       -118.400156                        Juice Bar  \n",
       "75       -118.396288                     Antique Shop  \n",
       "76       -118.392784                       Shoe Store  \n",
       "77       -118.399697                       Steakhouse  \n",
       "78       -118.400060                 Sushi Restaurant  \n",
       "79       -118.399516                              Spa  \n",
       "80       -118.394941                   Baseball Field  \n",
       "81       -118.400504             Gym / Fitness Center  \n",
       "82       -118.393582                             Bank  \n",
       "83       -118.394961                   Shipping Store  \n",
       "84       -118.393986             Gym / Fitness Center  \n",
       "85       -118.400130                              Bar  \n",
       "86       -118.392037                    Grocery Store  \n",
       "87       -118.393016                      Gas Station  \n",
       "88       -118.395070               Chinese Restaurant  \n",
       "89       -118.397138                       Restaurant  \n",
       "90       -118.395657                   Farmers Market  \n",
       "91       -118.392910             Fast Food Restaurant  \n",
       "92       -118.392961                            Hotel  \n",
       "93       -118.394963                            Trail  \n",
       "94       -118.393698                        Juice Bar  \n",
       "95       -118.392392                   Massage Studio  \n",
       "96       -118.394805                             Park  \n",
       "97       -118.400940                   Ice Cream Shop  \n",
       "98       -118.341878                          Stadium  \n",
       "99       -118.344243                Convenience Store  \n",
       "100      -118.345284  Southern / Soul Food Restaurant  \n",
       "101      -118.344099                       Steakhouse  \n",
       "102      -118.344286        Cajun / Creole Restaurant  \n",
       "103      -118.342369                        Rock Club  \n",
       "104      -118.341951                       Food Truck  \n",
       "105      -118.346327                       Smoke Shop  \n",
       "106      -118.397039                        Gastropub  \n",
       "107      -118.394624                    Big Box Store  \n",
       "108      -118.395740                     Gourmet Shop  \n",
       "109      -118.395812                   Sandwich Place  \n",
       "110      -118.395783               Seafood Restaurant  \n",
       "111      -118.396195                         Wine Bar  \n",
       "112      -118.396231                  Thai Restaurant  \n",
       "113      -118.395120                      Coffee Shop  \n",
       "114      -118.396353                   Ice Cream Shop  \n",
       "115      -118.395298                Mobile Phone Shop  \n",
       "116      -118.396293                       Sports Bar  \n",
       "117      -118.395556                 Video Game Store  \n",
       "118      -118.395768                   Pilates Studio  \n",
       "119      -118.395548                      Pizza Place  \n",
       "120      -118.396363               Mexican Restaurant  \n",
       "121      -118.396257                   Shipping Store  \n",
       "122      -118.396340              Rental Car Location  \n",
       "123      -118.395614                             Bank  \n",
       "124      -118.396320                             Bank  \n",
       "125      -118.396442                  Automotive Shop  \n",
       "126      -118.396297                       Sports Bar  \n",
       "127      -118.395601                            Hotel  \n",
       "128      -118.396174                            Hotel  \n",
       "129      -118.397104                 Asian Restaurant  \n",
       "130      -118.396330                        Wine Shop  \n",
       "131      -118.450213                    Boat or Ferry  \n",
       "132      -118.453332               Seafood Restaurant  \n",
       "133      -118.456707               Seafood Restaurant  \n",
       "134      -118.453839                      Pizza Place  \n",
       "135      -118.450223                           Resort  \n",
       "136      -118.458084                            Beach  \n",
       "137      -118.458216                            Beach  \n",
       "138      -118.456435                   Breakfast Spot  \n",
       "139      -118.457709                            Hotel  \n",
       "140      -118.457597              American Restaurant  \n",
       "141      -118.450400                       Restaurant  \n",
       "142      -118.457787                            Hotel  \n",
       "143      -118.452251                            Beach  \n",
       "144      -118.457370              American Restaurant  \n",
       "145      -118.456442             Gym / Fitness Center  \n",
       "146      -118.450755                  Harbor / Marina  \n",
       "147      -118.450300          New American Restaurant  \n",
       "148      -118.379196                              Gym  \n",
       "149      -118.377995                          Dog Run  \n",
       "150      -118.379762              Japanese Restaurant  \n",
       "151      -118.380787                   Discount Store  \n",
       "152      -118.380257                    Grocery Store  \n",
       "153      -118.380786                      Pizza Place  \n",
       "154      -118.379961                           Bakery  \n",
       "155      -118.482684                      Supermarket  \n",
       "156      -118.480600                             Park  \n",
       "157      -118.483409                       Donut Shop  \n",
       "158      -118.480594                       Skate Park  \n",
       "159      -118.480247                       Taco Place  \n",
       "160      -118.485936                     Cocktail Bar  \n",
       "161      -118.481452                     Tennis Court  \n",
       "162      -118.479188                              Gym  \n",
       "163      -118.477566                      Art Gallery  \n",
       "164      -118.480777                      Coffee Shop  \n",
       "165      -118.483585                Accessories Store  \n",
       "166      -118.485922              American Restaurant  \n",
       "167      -118.483389                        Juice Bar  \n",
       "168      -118.482439              Rental Car Location  \n",
       "169      -118.481330                  Auto Dealership  \n",
       "170      -118.478760          New American Restaurant  \n",
       "171      -118.483298                Convenience Store  \n",
       "172      -118.483078             Fast Food Restaurant  \n",
       "173      -118.486086                              Bar  \n",
       "174      -118.484930              Rental Car Location  \n",
       "175      -118.477277            Performing Arts Venue  \n",
       "176      -118.343823                             Pool  \n",
       "177      -118.345517                Convenience Store  \n",
       "178      -118.345391                   Clothing Store  \n",
       "179      -118.345399                Recreation Center  \n",
       "180      -118.471504                 Pedestrian Plaza  \n",
       "181      -118.471577                       Poke Place  \n",
       "182      -118.471597               Mexican Restaurant  \n",
       "183      -118.470275              American Restaurant  \n",
       "184      -118.472070                     Tennis Court  \n",
       "185      -118.466314                            Canal  \n",
       "186      -118.470676             Marijuana Dispensary  \n",
       "187      -118.472995                 Basketball Court  \n",
       "188      -118.471617                    Hot Dog Joint  \n",
       "189      -118.472002            Australian Restaurant  \n",
       "190      -118.472705                        Hotel Bar  \n",
       "191      -118.472450                            Hotel  \n",
       "192      -118.466477                   Farmers Market  \n",
       "193      -118.472011              American Restaurant  \n",
       "194      -118.472610                      Coffee Shop  \n",
       "195      -118.472084                   Breakfast Spot  \n",
       "196      -118.473935                       Playground  \n",
       "197      -118.472355                       Poke Place  \n",
       "198      -118.472487               Chinese Restaurant  \n",
       "199      -118.471980                       Board Shop  \n",
       "200      -118.473064                        Gastropub  \n",
       "201      -118.472662                         Dive Bar  \n",
       "202      -118.470796                 Sushi Restaurant  \n",
       "203      -118.473892                     Dessert Shop  \n",
       "204      -118.472062                   Sandwich Place  \n",
       "205      -118.471863                            Plaza  \n",
       "206      -118.471777                      Yoga Studio  \n",
       "207      -118.472694                        Speakeasy  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(manhattan_venues.shape)\n",
    "manhattan_venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City Latitude</th>\n",
       "      <th>City Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Culver City</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>El Segundo</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawthorne</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hermosa Beach</th>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inglewood</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan Beach</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marina del Rey</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Redondo Beach</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santa Monica</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Torrance</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venice Beach</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 City Latitude  City Longitude  Venue  Venue Latitude  \\\n",
       "City                                                                    \n",
       "Culver City                  9               9      9               9   \n",
       "El Segundo                   9               9      9               9   \n",
       "Hawthorne                   22              22     22              22   \n",
       "Hermosa Beach               58              58     58              58   \n",
       "Inglewood                    8               8      8               8   \n",
       "Manhattan Beach             25              25     25              25   \n",
       "Marina del Rey              17              17     17              17   \n",
       "Redondo Beach                7               7      7               7   \n",
       "Santa Monica                21              21     21              21   \n",
       "Torrance                     4               4      4               4   \n",
       "Venice Beach                28              28     28              28   \n",
       "\n",
       "                 Venue Longitude  Venue Category  \n",
       "City                                              \n",
       "Culver City                    9               9  \n",
       "El Segundo                     9               9  \n",
       "Hawthorne                     22              22  \n",
       "Hermosa Beach                 58              58  \n",
       "Inglewood                      8               8  \n",
       "Manhattan Beach               25              25  \n",
       "Marina del Rey                17              17  \n",
       "Redondo Beach                  7               7  \n",
       "Santa Monica                  21              21  \n",
       "Torrance                       4               4  \n",
       "Venice Beach                  28              28  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = manhattan_venues.groupby('City').count() \n",
    "manhattan_venues.groupby('City').count() \n",
    "df3 = df3.drop(columns=['City Latitude', 'City Longitude', 'Venue Latitude', 'Venue Longitude', 'Venue Category'])\n",
    "df3 = df3.sort_values(by=['Venue'])\n",
    "manhattan_venues.groupby('City').count() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 111 uniques categories.\n"
     ]
    }
   ],
   "source": [
    "print('There are {} uniques categories.'.format(len(manhattan_venues['Venue Category'].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Accessories Store</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Antique Shop</th>\n",
       "      <th>Art Gallery</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>Athletics &amp; Sports</th>\n",
       "      <th>Australian Restaurant</th>\n",
       "      <th>Auto Dealership</th>\n",
       "      <th>Automotive Shop</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Baseball Field</th>\n",
       "      <th>Basketball Court</th>\n",
       "      <th>Beach</th>\n",
       "      <th>Big Box Store</th>\n",
       "      <th>Board Shop</th>\n",
       "      <th>Boat or Ferry</th>\n",
       "      <th>Boutique</th>\n",
       "      <th>Breakfast Spot</th>\n",
       "      <th>Burger Joint</th>\n",
       "      <th>Café</th>\n",
       "      <th>Cajun / Creole Restaurant</th>\n",
       "      <th>Canal</th>\n",
       "      <th>Chinese Restaurant</th>\n",
       "      <th>Clothing Store</th>\n",
       "      <th>Cocktail Bar</th>\n",
       "      <th>Coffee Shop</th>\n",
       "      <th>Convenience Store</th>\n",
       "      <th>Dessert Shop</th>\n",
       "      <th>Diner</th>\n",
       "      <th>Discount Store</th>\n",
       "      <th>Distillery</th>\n",
       "      <th>Dive Bar</th>\n",
       "      <th>Dog Run</th>\n",
       "      <th>Donut Shop</th>\n",
       "      <th>Farmers Market</th>\n",
       "      <th>Fast Food Restaurant</th>\n",
       "      <th>Food</th>\n",
       "      <th>Food Truck</th>\n",
       "      <th>French Restaurant</th>\n",
       "      <th>Garden Center</th>\n",
       "      <th>Gas Station</th>\n",
       "      <th>Gastropub</th>\n",
       "      <th>Gourmet Shop</th>\n",
       "      <th>Government Building</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Gym / Fitness Center</th>\n",
       "      <th>Harbor / Marina</th>\n",
       "      <th>Hawaiian Restaurant</th>\n",
       "      <th>Hot Dog Joint</th>\n",
       "      <th>Hotel</th>\n",
       "      <th>Hotel Bar</th>\n",
       "      <th>Ice Cream Shop</th>\n",
       "      <th>Italian Restaurant</th>\n",
       "      <th>Japanese Restaurant</th>\n",
       "      <th>Juice Bar</th>\n",
       "      <th>Latin American Restaurant</th>\n",
       "      <th>Lounge</th>\n",
       "      <th>Marijuana Dispensary</th>\n",
       "      <th>Market</th>\n",
       "      <th>Martial Arts Dojo</th>\n",
       "      <th>Massage Studio</th>\n",
       "      <th>Men's Store</th>\n",
       "      <th>Mexican Restaurant</th>\n",
       "      <th>Mobile Phone Shop</th>\n",
       "      <th>New American Restaurant</th>\n",
       "      <th>Office</th>\n",
       "      <th>Park</th>\n",
       "      <th>Pedestrian Plaza</th>\n",
       "      <th>Performing Arts Venue</th>\n",
       "      <th>Peruvian Restaurant</th>\n",
       "      <th>Pet Store</th>\n",
       "      <th>Pharmacy</th>\n",
       "      <th>Pilates Studio</th>\n",
       "      <th>Pizza Place</th>\n",
       "      <th>Playground</th>\n",
       "      <th>Plaza</th>\n",
       "      <th>Poke Place</th>\n",
       "      <th>Pool</th>\n",
       "      <th>Recreation Center</th>\n",
       "      <th>Rental Car Location</th>\n",
       "      <th>Resort</th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>Rock Club</th>\n",
       "      <th>Sandwich Place</th>\n",
       "      <th>Seafood Restaurant</th>\n",
       "      <th>Shipping Store</th>\n",
       "      <th>Shoe Store</th>\n",
       "      <th>Skate Park</th>\n",
       "      <th>Smoke Shop</th>\n",
       "      <th>Southern / Soul Food Restaurant</th>\n",
       "      <th>Spa</th>\n",
       "      <th>Speakeasy</th>\n",
       "      <th>Sports Bar</th>\n",
       "      <th>Stadium</th>\n",
       "      <th>Steakhouse</th>\n",
       "      <th>Supermarket</th>\n",
       "      <th>Supplement Shop</th>\n",
       "      <th>Sushi Restaurant</th>\n",
       "      <th>Taco Place</th>\n",
       "      <th>Tennis Court</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Trail</th>\n",
       "      <th>Vegetarian / Vegan Restaurant</th>\n",
       "      <th>Video Game Store</th>\n",
       "      <th>Video Store</th>\n",
       "      <th>Wine Bar</th>\n",
       "      <th>Wine Shop</th>\n",
       "      <th>Yoga Studio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Culver City</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          City  Accessories Store  American Restaurant  Antique Shop  \\\n",
       "0  Culver City                  0                    0             0   \n",
       "1  Culver City                  0                    0             0   \n",
       "2  Culver City                  0                    0             0   \n",
       "3  Culver City                  0                    0             0   \n",
       "4  Culver City                  0                    0             0   \n",
       "\n",
       "   Art Gallery  Asian Restaurant  Athletics & Sports  Australian Restaurant  \\\n",
       "0            0                 0                   0                      0   \n",
       "1            0                 0                   0                      0   \n",
       "2            0                 0                   0                      0   \n",
       "3            0                 0                   0                      0   \n",
       "4            0                 0                   0                      0   \n",
       "\n",
       "   Auto Dealership  Automotive Shop  Bakery  Bank  Bar  Baseball Field  \\\n",
       "0                0                0       0     0    0               0   \n",
       "1                0                0       0     0    0               0   \n",
       "2                0                0       0     0    0               0   \n",
       "3                0                0       0     0    0               0   \n",
       "4                0                0       0     0    0               0   \n",
       "\n",
       "   Basketball Court  Beach  Big Box Store  Board Shop  Boat or Ferry  \\\n",
       "0                 0      0              0           0              0   \n",
       "1                 0      0              0           0              0   \n",
       "2                 0      0              0           0              0   \n",
       "3                 0      0              0           0              0   \n",
       "4                 0      0              0           0              0   \n",
       "\n",
       "   Boutique  Breakfast Spot  Burger Joint  Café  Cajun / Creole Restaurant  \\\n",
       "0         0               0             0     0                          0   \n",
       "1         0               0             0     0                          0   \n",
       "2         0               0             0     0                          0   \n",
       "3         0               0             0     0                          0   \n",
       "4         0               0             0     0                          0   \n",
       "\n",
       "   Canal  Chinese Restaurant  Clothing Store  Cocktail Bar  Coffee Shop  \\\n",
       "0      0                   0               0             0            0   \n",
       "1      0                   0               0             0            0   \n",
       "2      0                   0               0             0            0   \n",
       "3      0                   0               0             0            0   \n",
       "4      0                   0               0             0            1   \n",
       "\n",
       "   Convenience Store  Dessert Shop  Diner  Discount Store  Distillery  \\\n",
       "0                  0             0      0               0           0   \n",
       "1                  0             0      0               0           0   \n",
       "2                  0             0      0               0           0   \n",
       "3                  0             0      0               0           0   \n",
       "4                  0             0      0               0           0   \n",
       "\n",
       "   Dive Bar  Dog Run  Donut Shop  Farmers Market  Fast Food Restaurant  Food  \\\n",
       "0         0        0           0               0                     0     0   \n",
       "1         0        0           0               0                     0     0   \n",
       "2         0        0           0               0                     0     0   \n",
       "3         0        0           0               0                     0     0   \n",
       "4         0        0           0               0                     0     0   \n",
       "\n",
       "   Food Truck  French Restaurant  Garden Center  Gas Station  Gastropub  \\\n",
       "0           0                  0              0            0          0   \n",
       "1           0                  0              0            0          0   \n",
       "2           0                  0              0            0          0   \n",
       "3           0                  0              0            0          0   \n",
       "4           0                  0              0            0          0   \n",
       "\n",
       "   Gourmet Shop  Government Building  Grocery Store  Gym  \\\n",
       "0             0                    0              0    0   \n",
       "1             0                    0              0    0   \n",
       "2             0                    0              0    0   \n",
       "3             0                    0              0    0   \n",
       "4             0                    0              0    0   \n",
       "\n",
       "   Gym / Fitness Center  Harbor / Marina  Hawaiian Restaurant  Hot Dog Joint  \\\n",
       "0                     0                0                    0              0   \n",
       "1                     0                0                    0              0   \n",
       "2                     0                0                    0              0   \n",
       "3                     0                0                    0              0   \n",
       "4                     0                0                    0              0   \n",
       "\n",
       "   Hotel  Hotel Bar  Ice Cream Shop  Italian Restaurant  Japanese Restaurant  \\\n",
       "0      0          0               0                   0                    0   \n",
       "1      0          0               0                   0                    0   \n",
       "2      0          0               0                   0                    0   \n",
       "3      0          0               0                   0                    0   \n",
       "4      0          0               0                   0                    0   \n",
       "\n",
       "   Juice Bar  Latin American Restaurant  Lounge  Marijuana Dispensary  Market  \\\n",
       "0          0                          0       0                     0       0   \n",
       "1          0                          0       0                     0       0   \n",
       "2          0                          0       0                     0       0   \n",
       "3          0                          0       0                     0       0   \n",
       "4          0                          0       0                     0       0   \n",
       "\n",
       "   Martial Arts Dojo  Massage Studio  Men's Store  Mexican Restaurant  \\\n",
       "0                  0               0            0                   0   \n",
       "1                  0               0            0                   0   \n",
       "2                  0               0            0                   0   \n",
       "3                  0               0            0                   0   \n",
       "4                  0               0            0                   0   \n",
       "\n",
       "   Mobile Phone Shop  New American Restaurant  Office  Park  Pedestrian Plaza  \\\n",
       "0                  0                        0       0     0                 0   \n",
       "1                  0                        0       0     0                 0   \n",
       "2                  0                        0       0     0                 0   \n",
       "3                  0                        0       0     0                 0   \n",
       "4                  0                        0       0     0                 0   \n",
       "\n",
       "   Performing Arts Venue  Peruvian Restaurant  Pet Store  Pharmacy  \\\n",
       "0                      0                    0          0         0   \n",
       "1                      0                    0          0         0   \n",
       "2                      0                    0          0         0   \n",
       "3                      0                    0          0         0   \n",
       "4                      0                    0          0         0   \n",
       "\n",
       "   Pilates Studio  Pizza Place  Playground  Plaza  Poke Place  Pool  \\\n",
       "0               0            0           1      0           0     0   \n",
       "1               0            0           0      0           0     0   \n",
       "2               0            1           0      0           0     0   \n",
       "3               0            0           0      0           0     0   \n",
       "4               0            0           0      0           0     0   \n",
       "\n",
       "   Recreation Center  Rental Car Location  Resort  Restaurant  Rock Club  \\\n",
       "0                  0                    0       0           0          0   \n",
       "1                  0                    0       0           0          0   \n",
       "2                  0                    0       0           0          0   \n",
       "3                  0                    0       0           0          0   \n",
       "4                  0                    0       0           0          0   \n",
       "\n",
       "   Sandwich Place  Seafood Restaurant  Shipping Store  Shoe Store  Skate Park  \\\n",
       "0               0                   0               0           0           0   \n",
       "1               0                   0               0           0           0   \n",
       "2               0                   0               0           0           0   \n",
       "3               0                   0               0           0           0   \n",
       "4               0                   0               0           0           0   \n",
       "\n",
       "   Smoke Shop  Southern / Soul Food Restaurant  Spa  Speakeasy  Sports Bar  \\\n",
       "0           0                                0    0          0           0   \n",
       "1           0                                0    0          0           0   \n",
       "2           0                                0    0          0           0   \n",
       "3           0                                0    0          0           0   \n",
       "4           0                                0    0          0           0   \n",
       "\n",
       "   Stadium  Steakhouse  Supermarket  Supplement Shop  Sushi Restaurant  \\\n",
       "0        0           0            0                0                 0   \n",
       "1        0           0            1                0                 0   \n",
       "2        0           0            0                0                 0   \n",
       "3        0           0            0                0                 0   \n",
       "4        0           0            0                0                 0   \n",
       "\n",
       "   Taco Place  Tennis Court  Thai Restaurant  Trail  \\\n",
       "0           0             0                0      0   \n",
       "1           0             0                0      0   \n",
       "2           0             0                0      0   \n",
       "3           0             0                0      0   \n",
       "4           0             0                0      0   \n",
       "\n",
       "   Vegetarian / Vegan Restaurant  Video Game Store  Video Store  Wine Bar  \\\n",
       "0                              0                 0            0         0   \n",
       "1                              0                 0            0         0   \n",
       "2                              0                 0            0         0   \n",
       "3                              0                 0            1         0   \n",
       "4                              0                 0            0         0   \n",
       "\n",
       "   Wine Shop  Yoga Studio  \n",
       "0          0            0  \n",
       "1          0            0  \n",
       "2          0            0  \n",
       "3          0            0  \n",
       "4          0            0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding\n",
    "manhattan_onehot = pd.get_dummies(manhattan_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "manhattan_onehot['City'] = manhattan_venues['City'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [manhattan_onehot.columns[-1]] + list(manhattan_onehot.columns[:-1])\n",
    "manhattan_onehot = manhattan_onehot[fixed_columns]\n",
    "\n",
    "manhattan_onehot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(208, 112)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manhattan_onehot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Accessories Store</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Antique Shop</th>\n",
       "      <th>Art Gallery</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>Athletics &amp; Sports</th>\n",
       "      <th>Australian Restaurant</th>\n",
       "      <th>Auto Dealership</th>\n",
       "      <th>Automotive Shop</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Baseball Field</th>\n",
       "      <th>Basketball Court</th>\n",
       "      <th>Beach</th>\n",
       "      <th>Big Box Store</th>\n",
       "      <th>Board Shop</th>\n",
       "      <th>Boat or Ferry</th>\n",
       "      <th>Boutique</th>\n",
       "      <th>Breakfast Spot</th>\n",
       "      <th>Burger Joint</th>\n",
       "      <th>Café</th>\n",
       "      <th>Cajun / Creole Restaurant</th>\n",
       "      <th>Canal</th>\n",
       "      <th>Chinese Restaurant</th>\n",
       "      <th>Clothing Store</th>\n",
       "      <th>Cocktail Bar</th>\n",
       "      <th>Coffee Shop</th>\n",
       "      <th>Convenience Store</th>\n",
       "      <th>Dessert Shop</th>\n",
       "      <th>Diner</th>\n",
       "      <th>Discount Store</th>\n",
       "      <th>Distillery</th>\n",
       "      <th>Dive Bar</th>\n",
       "      <th>Dog Run</th>\n",
       "      <th>Donut Shop</th>\n",
       "      <th>Farmers Market</th>\n",
       "      <th>Fast Food Restaurant</th>\n",
       "      <th>Food</th>\n",
       "      <th>Food Truck</th>\n",
       "      <th>French Restaurant</th>\n",
       "      <th>Garden Center</th>\n",
       "      <th>Gas Station</th>\n",
       "      <th>Gastropub</th>\n",
       "      <th>Gourmet Shop</th>\n",
       "      <th>Government Building</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Gym / Fitness Center</th>\n",
       "      <th>Harbor / Marina</th>\n",
       "      <th>Hawaiian Restaurant</th>\n",
       "      <th>Hot Dog Joint</th>\n",
       "      <th>Hotel</th>\n",
       "      <th>Hotel Bar</th>\n",
       "      <th>Ice Cream Shop</th>\n",
       "      <th>Italian Restaurant</th>\n",
       "      <th>Japanese Restaurant</th>\n",
       "      <th>Juice Bar</th>\n",
       "      <th>Latin American Restaurant</th>\n",
       "      <th>Lounge</th>\n",
       "      <th>Marijuana Dispensary</th>\n",
       "      <th>Market</th>\n",
       "      <th>Martial Arts Dojo</th>\n",
       "      <th>Massage Studio</th>\n",
       "      <th>Men's Store</th>\n",
       "      <th>Mexican Restaurant</th>\n",
       "      <th>Mobile Phone Shop</th>\n",
       "      <th>New American Restaurant</th>\n",
       "      <th>Office</th>\n",
       "      <th>Park</th>\n",
       "      <th>Pedestrian Plaza</th>\n",
       "      <th>Performing Arts Venue</th>\n",
       "      <th>Peruvian Restaurant</th>\n",
       "      <th>Pet Store</th>\n",
       "      <th>Pharmacy</th>\n",
       "      <th>Pilates Studio</th>\n",
       "      <th>Pizza Place</th>\n",
       "      <th>Playground</th>\n",
       "      <th>Plaza</th>\n",
       "      <th>Poke Place</th>\n",
       "      <th>Pool</th>\n",
       "      <th>Recreation Center</th>\n",
       "      <th>Rental Car Location</th>\n",
       "      <th>Resort</th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>Rock Club</th>\n",
       "      <th>Sandwich Place</th>\n",
       "      <th>Seafood Restaurant</th>\n",
       "      <th>Shipping Store</th>\n",
       "      <th>Shoe Store</th>\n",
       "      <th>Skate Park</th>\n",
       "      <th>Smoke Shop</th>\n",
       "      <th>Southern / Soul Food Restaurant</th>\n",
       "      <th>Spa</th>\n",
       "      <th>Speakeasy</th>\n",
       "      <th>Sports Bar</th>\n",
       "      <th>Stadium</th>\n",
       "      <th>Steakhouse</th>\n",
       "      <th>Supermarket</th>\n",
       "      <th>Supplement Shop</th>\n",
       "      <th>Sushi Restaurant</th>\n",
       "      <th>Taco Place</th>\n",
       "      <th>Tennis Court</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Trail</th>\n",
       "      <th>Vegetarian / Vegan Restaurant</th>\n",
       "      <th>Video Game Store</th>\n",
       "      <th>Video Store</th>\n",
       "      <th>Wine Bar</th>\n",
       "      <th>Wine Shop</th>\n",
       "      <th>Yoga Studio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051724</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.034483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Inglewood</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Torrance</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               City  Accessories Store  American Restaurant  Antique Shop  \\\n",
       "0       Culver City           0.000000             0.000000      0.000000   \n",
       "1        El Segundo           0.000000             0.000000      0.000000   \n",
       "2         Hawthorne           0.000000             0.000000      0.000000   \n",
       "3     Hermosa Beach           0.000000             0.034483      0.017241   \n",
       "4         Inglewood           0.000000             0.000000      0.000000   \n",
       "5   Manhattan Beach           0.000000             0.000000      0.000000   \n",
       "6    Marina del Rey           0.000000             0.117647      0.000000   \n",
       "7     Redondo Beach           0.000000             0.000000      0.000000   \n",
       "8      Santa Monica           0.047619             0.047619      0.000000   \n",
       "9          Torrance           0.000000             0.000000      0.000000   \n",
       "10     Venice Beach           0.000000             0.071429      0.000000   \n",
       "\n",
       "    Art Gallery  Asian Restaurant  Athletics & Sports  Australian Restaurant  \\\n",
       "0      0.000000              0.00            0.000000               0.000000   \n",
       "1      0.000000              0.00            0.111111               0.000000   \n",
       "2      0.000000              0.00            0.000000               0.000000   \n",
       "3      0.000000              0.00            0.000000               0.017241   \n",
       "4      0.000000              0.00            0.000000               0.000000   \n",
       "5      0.000000              0.04            0.000000               0.000000   \n",
       "6      0.000000              0.00            0.000000               0.000000   \n",
       "7      0.000000              0.00            0.000000               0.000000   \n",
       "8      0.047619              0.00            0.000000               0.000000   \n",
       "9      0.000000              0.00            0.000000               0.000000   \n",
       "10     0.000000              0.00            0.000000               0.035714   \n",
       "\n",
       "    Auto Dealership  Automotive Shop    Bakery      Bank       Bar  \\\n",
       "0          0.000000             0.00  0.000000  0.000000  0.000000   \n",
       "1          0.000000             0.00  0.000000  0.000000  0.000000   \n",
       "2          0.000000             0.00  0.000000  0.000000  0.000000   \n",
       "3          0.000000             0.00  0.000000  0.017241  0.017241   \n",
       "4          0.000000             0.00  0.000000  0.000000  0.000000   \n",
       "5          0.000000             0.04  0.000000  0.080000  0.000000   \n",
       "6          0.000000             0.00  0.000000  0.000000  0.000000   \n",
       "7          0.000000             0.00  0.142857  0.000000  0.000000   \n",
       "8          0.047619             0.00  0.000000  0.000000  0.047619   \n",
       "9          0.000000             0.00  0.000000  0.000000  0.000000   \n",
       "10         0.000000             0.00  0.000000  0.000000  0.000000   \n",
       "\n",
       "    Baseball Field  Basketball Court     Beach  Big Box Store  Board Shop  \\\n",
       "0         0.000000          0.000000  0.000000           0.00    0.000000   \n",
       "1         0.000000          0.000000  0.000000           0.00    0.000000   \n",
       "2         0.000000          0.000000  0.000000           0.00    0.000000   \n",
       "3         0.017241          0.000000  0.000000           0.00    0.000000   \n",
       "4         0.000000          0.000000  0.000000           0.00    0.000000   \n",
       "5         0.000000          0.000000  0.000000           0.04    0.000000   \n",
       "6         0.000000          0.000000  0.176471           0.00    0.000000   \n",
       "7         0.000000          0.000000  0.000000           0.00    0.000000   \n",
       "8         0.000000          0.000000  0.000000           0.00    0.000000   \n",
       "9         0.000000          0.000000  0.000000           0.00    0.000000   \n",
       "10        0.000000          0.035714  0.000000           0.00    0.035714   \n",
       "\n",
       "    Boat or Ferry  Boutique  Breakfast Spot  Burger Joint      Café  \\\n",
       "0        0.000000  0.111111        0.000000      0.000000  0.000000   \n",
       "1        0.000000  0.000000        0.000000      0.000000  0.000000   \n",
       "2        0.000000  0.000000        0.000000      0.000000  0.000000   \n",
       "3        0.000000  0.017241        0.000000      0.017241  0.017241   \n",
       "4        0.000000  0.000000        0.000000      0.000000  0.000000   \n",
       "5        0.000000  0.000000        0.000000      0.000000  0.000000   \n",
       "6        0.058824  0.000000        0.058824      0.000000  0.000000   \n",
       "7        0.000000  0.000000        0.000000      0.000000  0.000000   \n",
       "8        0.000000  0.000000        0.000000      0.000000  0.000000   \n",
       "9        0.000000  0.000000        0.000000      0.000000  0.000000   \n",
       "10       0.000000  0.000000        0.035714      0.000000  0.000000   \n",
       "\n",
       "    Cajun / Creole Restaurant     Canal  Chinese Restaurant  Clothing Store  \\\n",
       "0                    0.000000  0.000000            0.000000            0.00   \n",
       "1                    0.000000  0.000000            0.000000            0.00   \n",
       "2                    0.000000  0.000000            0.000000            0.00   \n",
       "3                    0.017241  0.000000            0.017241            0.00   \n",
       "4                    0.125000  0.000000            0.000000            0.00   \n",
       "5                    0.000000  0.000000            0.000000            0.00   \n",
       "6                    0.000000  0.000000            0.000000            0.00   \n",
       "7                    0.000000  0.000000            0.000000            0.00   \n",
       "8                    0.000000  0.000000            0.000000            0.00   \n",
       "9                    0.000000  0.000000            0.000000            0.25   \n",
       "10                   0.000000  0.035714            0.035714            0.00   \n",
       "\n",
       "    Cocktail Bar  Coffee Shop  Convenience Store  Dessert Shop     Diner  \\\n",
       "0       0.000000     0.111111           0.000000      0.000000  0.111111   \n",
       "1       0.000000     0.000000           0.000000      0.000000  0.000000   \n",
       "2       0.000000     0.045455           0.090909      0.000000  0.000000   \n",
       "3       0.000000     0.051724           0.000000      0.000000  0.000000   \n",
       "4       0.000000     0.000000           0.125000      0.000000  0.000000   \n",
       "5       0.000000     0.040000           0.000000      0.000000  0.000000   \n",
       "6       0.000000     0.000000           0.000000      0.000000  0.000000   \n",
       "7       0.000000     0.000000           0.000000      0.000000  0.000000   \n",
       "8       0.047619     0.047619           0.047619      0.000000  0.000000   \n",
       "9       0.000000     0.000000           0.250000      0.000000  0.000000   \n",
       "10      0.000000     0.035714           0.000000      0.035714  0.000000   \n",
       "\n",
       "    Discount Store  Distillery  Dive Bar   Dog Run  Donut Shop  \\\n",
       "0         0.000000    0.000000  0.000000  0.000000    0.000000   \n",
       "1         0.000000    0.111111  0.000000  0.000000    0.000000   \n",
       "2         0.090909    0.000000  0.000000  0.000000    0.045455   \n",
       "3         0.000000    0.000000  0.000000  0.000000    0.000000   \n",
       "4         0.000000    0.000000  0.000000  0.000000    0.000000   \n",
       "5         0.000000    0.000000  0.000000  0.000000    0.000000   \n",
       "6         0.000000    0.000000  0.000000  0.000000    0.000000   \n",
       "7         0.142857    0.000000  0.000000  0.142857    0.000000   \n",
       "8         0.000000    0.000000  0.000000  0.000000    0.047619   \n",
       "9         0.000000    0.000000  0.000000  0.000000    0.000000   \n",
       "10        0.000000    0.000000  0.035714  0.000000    0.000000   \n",
       "\n",
       "    Farmers Market  Fast Food Restaurant      Food  Food Truck  \\\n",
       "0         0.000000              0.000000  0.000000    0.111111   \n",
       "1         0.000000              0.111111  0.000000    0.111111   \n",
       "2         0.000000              0.136364  0.045455    0.000000   \n",
       "3         0.017241              0.017241  0.000000    0.000000   \n",
       "4         0.000000              0.000000  0.000000    0.125000   \n",
       "5         0.000000              0.000000  0.000000    0.000000   \n",
       "6         0.000000              0.000000  0.000000    0.000000   \n",
       "7         0.000000              0.000000  0.000000    0.000000   \n",
       "8         0.000000              0.047619  0.000000    0.000000   \n",
       "9         0.000000              0.000000  0.000000    0.000000   \n",
       "10        0.035714              0.000000  0.000000    0.000000   \n",
       "\n",
       "    French Restaurant  Garden Center  Gas Station  Gastropub  Gourmet Shop  \\\n",
       "0            0.000000       0.000000     0.000000   0.000000          0.00   \n",
       "1            0.000000       0.111111     0.000000   0.000000          0.00   \n",
       "2            0.000000       0.000000     0.000000   0.000000          0.00   \n",
       "3            0.017241       0.000000     0.017241   0.000000          0.00   \n",
       "4            0.000000       0.000000     0.000000   0.000000          0.00   \n",
       "5            0.000000       0.000000     0.000000   0.040000          0.04   \n",
       "6            0.000000       0.000000     0.000000   0.000000          0.00   \n",
       "7            0.000000       0.000000     0.000000   0.000000          0.00   \n",
       "8            0.000000       0.000000     0.000000   0.000000          0.00   \n",
       "9            0.000000       0.000000     0.000000   0.000000          0.00   \n",
       "10           0.000000       0.000000     0.000000   0.035714          0.00   \n",
       "\n",
       "    Government Building  Grocery Store       Gym  Gym / Fitness Center  \\\n",
       "0              0.000000       0.000000  0.000000              0.000000   \n",
       "1              0.000000       0.000000  0.000000              0.000000   \n",
       "2              0.000000       0.000000  0.000000              0.000000   \n",
       "3              0.017241       0.034483  0.000000              0.034483   \n",
       "4              0.000000       0.000000  0.000000              0.000000   \n",
       "5              0.000000       0.000000  0.000000              0.000000   \n",
       "6              0.000000       0.000000  0.000000              0.058824   \n",
       "7              0.000000       0.142857  0.142857              0.000000   \n",
       "8              0.000000       0.000000  0.047619              0.000000   \n",
       "9              0.000000       0.000000  0.000000              0.000000   \n",
       "10             0.000000       0.000000  0.000000              0.000000   \n",
       "\n",
       "    Harbor / Marina  Hawaiian Restaurant  Hot Dog Joint     Hotel  Hotel Bar  \\\n",
       "0          0.000000             0.000000       0.000000  0.000000   0.000000   \n",
       "1          0.000000             0.000000       0.000000  0.000000   0.000000   \n",
       "2          0.000000             0.000000       0.000000  0.000000   0.000000   \n",
       "3          0.000000             0.017241       0.000000  0.017241   0.000000   \n",
       "4          0.000000             0.000000       0.000000  0.000000   0.000000   \n",
       "5          0.000000             0.000000       0.000000  0.080000   0.000000   \n",
       "6          0.058824             0.000000       0.000000  0.117647   0.000000   \n",
       "7          0.000000             0.000000       0.000000  0.000000   0.000000   \n",
       "8          0.000000             0.000000       0.000000  0.000000   0.000000   \n",
       "9          0.000000             0.000000       0.000000  0.000000   0.000000   \n",
       "10         0.000000             0.000000       0.035714  0.035714   0.035714   \n",
       "\n",
       "    Ice Cream Shop  Italian Restaurant  Japanese Restaurant  Juice Bar  \\\n",
       "0         0.000000            0.000000             0.000000   0.000000   \n",
       "1         0.000000            0.000000             0.000000   0.000000   \n",
       "2         0.000000            0.000000             0.000000   0.000000   \n",
       "3         0.017241            0.034483             0.000000   0.034483   \n",
       "4         0.000000            0.000000             0.000000   0.000000   \n",
       "5         0.040000            0.000000             0.000000   0.000000   \n",
       "6         0.000000            0.000000             0.000000   0.000000   \n",
       "7         0.000000            0.000000             0.142857   0.000000   \n",
       "8         0.000000            0.000000             0.000000   0.047619   \n",
       "9         0.000000            0.000000             0.000000   0.000000   \n",
       "10        0.000000            0.000000             0.000000   0.000000   \n",
       "\n",
       "    Latin American Restaurant    Lounge  Marijuana Dispensary    Market  \\\n",
       "0                    0.000000  0.000000              0.000000  0.000000   \n",
       "1                    0.000000  0.000000              0.000000  0.000000   \n",
       "2                    0.090909  0.000000              0.000000  0.045455   \n",
       "3                    0.000000  0.017241              0.000000  0.000000   \n",
       "4                    0.000000  0.000000              0.000000  0.000000   \n",
       "5                    0.000000  0.000000              0.000000  0.000000   \n",
       "6                    0.000000  0.000000              0.000000  0.000000   \n",
       "7                    0.000000  0.000000              0.000000  0.000000   \n",
       "8                    0.000000  0.000000              0.000000  0.000000   \n",
       "9                    0.000000  0.000000              0.000000  0.000000   \n",
       "10                   0.000000  0.000000              0.035714  0.000000   \n",
       "\n",
       "    Martial Arts Dojo  Massage Studio  Men's Store  Mexican Restaurant  \\\n",
       "0            0.000000        0.000000     0.000000            0.000000   \n",
       "1            0.000000        0.000000     0.000000            0.000000   \n",
       "2            0.045455        0.000000     0.000000            0.000000   \n",
       "3            0.000000        0.034483     0.017241            0.034483   \n",
       "4            0.000000        0.000000     0.000000            0.000000   \n",
       "5            0.000000        0.000000     0.000000            0.040000   \n",
       "6            0.000000        0.000000     0.000000            0.000000   \n",
       "7            0.000000        0.000000     0.000000            0.000000   \n",
       "8            0.000000        0.000000     0.000000            0.000000   \n",
       "9            0.000000        0.000000     0.000000            0.000000   \n",
       "10           0.000000        0.000000     0.000000            0.035714   \n",
       "\n",
       "    Mobile Phone Shop  New American Restaurant    Office      Park  \\\n",
       "0            0.000000                 0.000000  0.000000  0.111111   \n",
       "1            0.000000                 0.000000  0.111111  0.000000   \n",
       "2            0.136364                 0.000000  0.000000  0.000000   \n",
       "3            0.000000                 0.017241  0.000000  0.017241   \n",
       "4            0.000000                 0.000000  0.000000  0.000000   \n",
       "5            0.040000                 0.000000  0.000000  0.000000   \n",
       "6            0.000000                 0.058824  0.000000  0.000000   \n",
       "7            0.000000                 0.000000  0.000000  0.000000   \n",
       "8            0.000000                 0.047619  0.000000  0.047619   \n",
       "9            0.000000                 0.000000  0.000000  0.000000   \n",
       "10           0.000000                 0.000000  0.000000  0.000000   \n",
       "\n",
       "    Pedestrian Plaza  Performing Arts Venue  Peruvian Restaurant  Pet Store  \\\n",
       "0           0.000000               0.000000             0.000000   0.000000   \n",
       "1           0.000000               0.000000             0.000000   0.333333   \n",
       "2           0.000000               0.000000             0.000000   0.000000   \n",
       "3           0.000000               0.000000             0.017241   0.000000   \n",
       "4           0.000000               0.000000             0.000000   0.000000   \n",
       "5           0.000000               0.000000             0.000000   0.000000   \n",
       "6           0.000000               0.000000             0.000000   0.000000   \n",
       "7           0.000000               0.000000             0.000000   0.000000   \n",
       "8           0.000000               0.047619             0.000000   0.000000   \n",
       "9           0.000000               0.000000             0.000000   0.000000   \n",
       "10          0.035714               0.000000             0.000000   0.000000   \n",
       "\n",
       "    Pharmacy  Pilates Studio  Pizza Place  Playground     Plaza  Poke Place  \\\n",
       "0   0.000000            0.00     0.111111    0.111111  0.000000    0.000000   \n",
       "1   0.000000            0.00     0.000000    0.000000  0.000000    0.000000   \n",
       "2   0.000000            0.00     0.045455    0.000000  0.000000    0.000000   \n",
       "3   0.017241            0.00     0.017241    0.000000  0.000000    0.000000   \n",
       "4   0.000000            0.00     0.000000    0.000000  0.000000    0.000000   \n",
       "5   0.000000            0.04     0.040000    0.000000  0.000000    0.000000   \n",
       "6   0.000000            0.00     0.058824    0.000000  0.000000    0.000000   \n",
       "7   0.000000            0.00     0.142857    0.000000  0.000000    0.000000   \n",
       "8   0.000000            0.00     0.000000    0.000000  0.000000    0.000000   \n",
       "9   0.000000            0.00     0.000000    0.000000  0.000000    0.000000   \n",
       "10  0.000000            0.00     0.000000    0.035714  0.035714    0.071429   \n",
       "\n",
       "    Pool  Recreation Center  Rental Car Location    Resort  Restaurant  \\\n",
       "0   0.00               0.00             0.000000  0.000000    0.000000   \n",
       "1   0.00               0.00             0.000000  0.000000    0.000000   \n",
       "2   0.00               0.00             0.000000  0.000000    0.000000   \n",
       "3   0.00               0.00             0.000000  0.000000    0.034483   \n",
       "4   0.00               0.00             0.000000  0.000000    0.000000   \n",
       "5   0.00               0.00             0.040000  0.000000    0.000000   \n",
       "6   0.00               0.00             0.000000  0.058824    0.058824   \n",
       "7   0.00               0.00             0.000000  0.000000    0.000000   \n",
       "8   0.00               0.00             0.095238  0.000000    0.000000   \n",
       "9   0.25               0.25             0.000000  0.000000    0.000000   \n",
       "10  0.00               0.00             0.000000  0.000000    0.000000   \n",
       "\n",
       "    Rock Club  Sandwich Place  Seafood Restaurant  Shipping Store  Shoe Store  \\\n",
       "0       0.000        0.000000            0.000000        0.000000    0.000000   \n",
       "1       0.000        0.000000            0.000000        0.000000    0.000000   \n",
       "2       0.000        0.045455            0.000000        0.000000    0.000000   \n",
       "3       0.000        0.000000            0.017241        0.017241    0.017241   \n",
       "4       0.125        0.000000            0.000000        0.000000    0.000000   \n",
       "5       0.000        0.040000            0.040000        0.040000    0.000000   \n",
       "6       0.000        0.000000            0.117647        0.000000    0.000000   \n",
       "7       0.000        0.000000            0.000000        0.000000    0.000000   \n",
       "8       0.000        0.000000            0.000000        0.000000    0.000000   \n",
       "9       0.000        0.000000            0.000000        0.000000    0.000000   \n",
       "10      0.000        0.035714            0.000000        0.000000    0.000000   \n",
       "\n",
       "    Skate Park  Smoke Shop  Southern / Soul Food Restaurant       Spa  \\\n",
       "0     0.000000       0.000                            0.000  0.000000   \n",
       "1     0.000000       0.000                            0.000  0.000000   \n",
       "2     0.000000       0.000                            0.000  0.000000   \n",
       "3     0.000000       0.000                            0.000  0.017241   \n",
       "4     0.000000       0.125                            0.125  0.000000   \n",
       "5     0.000000       0.000                            0.000  0.000000   \n",
       "6     0.000000       0.000                            0.000  0.000000   \n",
       "7     0.000000       0.000                            0.000  0.000000   \n",
       "8     0.047619       0.000                            0.000  0.000000   \n",
       "9     0.000000       0.000                            0.000  0.000000   \n",
       "10    0.000000       0.000                            0.000  0.000000   \n",
       "\n",
       "    Speakeasy  Sports Bar  Stadium  Steakhouse  Supermarket  Supplement Shop  \\\n",
       "0    0.000000    0.000000    0.000    0.000000     0.111111         0.000000   \n",
       "1    0.000000    0.000000    0.000    0.000000     0.000000         0.000000   \n",
       "2    0.000000    0.000000    0.000    0.000000     0.000000         0.045455   \n",
       "3    0.000000    0.017241    0.000    0.017241     0.000000         0.000000   \n",
       "4    0.000000    0.000000    0.125    0.125000     0.000000         0.000000   \n",
       "5    0.000000    0.080000    0.000    0.000000     0.000000         0.000000   \n",
       "6    0.000000    0.000000    0.000    0.000000     0.000000         0.000000   \n",
       "7    0.000000    0.000000    0.000    0.000000     0.000000         0.000000   \n",
       "8    0.000000    0.000000    0.000    0.000000     0.047619         0.000000   \n",
       "9    0.000000    0.000000    0.000    0.000000     0.000000         0.000000   \n",
       "10   0.035714    0.000000    0.000    0.000000     0.000000         0.000000   \n",
       "\n",
       "    Sushi Restaurant  Taco Place  Tennis Court  Thai Restaurant     Trail  \\\n",
       "0           0.000000    0.000000      0.000000         0.000000  0.000000   \n",
       "1           0.000000    0.000000      0.000000         0.000000  0.000000   \n",
       "2           0.000000    0.045455      0.000000         0.045455  0.000000   \n",
       "3           0.034483    0.000000      0.000000         0.000000  0.034483   \n",
       "4           0.000000    0.000000      0.000000         0.000000  0.000000   \n",
       "5           0.000000    0.000000      0.000000         0.040000  0.000000   \n",
       "6           0.000000    0.000000      0.000000         0.000000  0.000000   \n",
       "7           0.000000    0.000000      0.000000         0.000000  0.000000   \n",
       "8           0.000000    0.047619      0.047619         0.000000  0.000000   \n",
       "9           0.000000    0.000000      0.000000         0.000000  0.000000   \n",
       "10          0.035714    0.000000      0.035714         0.000000  0.000000   \n",
       "\n",
       "    Vegetarian / Vegan Restaurant  Video Game Store  Video Store  Wine Bar  \\\n",
       "0                        0.000000              0.00     0.111111      0.00   \n",
       "1                        0.000000              0.00     0.000000      0.00   \n",
       "2                        0.000000              0.00     0.000000      0.00   \n",
       "3                        0.017241              0.00     0.000000      0.00   \n",
       "4                        0.000000              0.00     0.000000      0.00   \n",
       "5                        0.000000              0.04     0.000000      0.04   \n",
       "6                        0.000000              0.00     0.000000      0.00   \n",
       "7                        0.000000              0.00     0.000000      0.00   \n",
       "8                        0.000000              0.00     0.000000      0.00   \n",
       "9                        0.000000              0.00     0.000000      0.00   \n",
       "10                       0.000000              0.00     0.000000      0.00   \n",
       "\n",
       "    Wine Shop  Yoga Studio  \n",
       "0    0.000000     0.000000  \n",
       "1    0.000000     0.000000  \n",
       "2    0.000000     0.000000  \n",
       "3    0.017241     0.034483  \n",
       "4    0.000000     0.000000  \n",
       "5    0.040000     0.000000  \n",
       "6    0.000000     0.000000  \n",
       "7    0.000000     0.000000  \n",
       "8    0.000000     0.000000  \n",
       "9    0.000000     0.000000  \n",
       "10   0.000000     0.035714  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manhattan_grouped = manhattan_onehot.groupby('City').mean().reset_index()\n",
    "manhattan_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 112)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manhattan_grouped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Culver City----\n",
      "         venue  freq\n",
      "0        Diner  0.11\n",
      "1   Playground  0.11\n",
      "2  Supermarket  0.11\n",
      "3         Park  0.11\n",
      "4   Food Truck  0.11\n",
      "\n",
      "\n",
      "----El Segundo----\n",
      "                  venue  freq\n",
      "0             Pet Store  0.33\n",
      "1            Distillery  0.11\n",
      "2    Athletics & Sports  0.11\n",
      "3  Fast Food Restaurant  0.11\n",
      "4            Food Truck  0.11\n",
      "\n",
      "\n",
      "----Hawthorne----\n",
      "                       venue  freq\n",
      "0          Mobile Phone Shop  0.14\n",
      "1       Fast Food Restaurant  0.14\n",
      "2          Convenience Store  0.09\n",
      "3  Latin American Restaurant  0.09\n",
      "4             Discount Store  0.09\n",
      "\n",
      "\n",
      "----Hermosa Beach----\n",
      "                venue  freq\n",
      "0         Coffee Shop  0.05\n",
      "1  Italian Restaurant  0.03\n",
      "2               Trail  0.03\n",
      "3    Sushi Restaurant  0.03\n",
      "4          Restaurant  0.03\n",
      "\n",
      "\n",
      "----Inglewood----\n",
      "                       venue  freq\n",
      "0          Convenience Store  0.12\n",
      "1                 Steakhouse  0.12\n",
      "2                  Rock Club  0.12\n",
      "3                 Food Truck  0.12\n",
      "4  Cajun / Creole Restaurant  0.12\n",
      "\n",
      "\n",
      "----Manhattan Beach----\n",
      "            venue  freq\n",
      "0      Sports Bar  0.08\n",
      "1           Hotel  0.08\n",
      "2            Bank  0.08\n",
      "3  Sandwich Place  0.04\n",
      "4    Gourmet Shop  0.04\n",
      "\n",
      "\n",
      "----Marina del Rey----\n",
      "                 venue  freq\n",
      "0                Beach  0.18\n",
      "1  American Restaurant  0.12\n",
      "2   Seafood Restaurant  0.12\n",
      "3                Hotel  0.12\n",
      "4               Resort  0.06\n",
      "\n",
      "\n",
      "----Redondo Beach----\n",
      "                 venue  freq\n",
      "0       Discount Store  0.14\n",
      "1  Japanese Restaurant  0.14\n",
      "2              Dog Run  0.14\n",
      "3               Bakery  0.14\n",
      "4          Pizza Place  0.14\n",
      "\n",
      "\n",
      "----Santa Monica----\n",
      "                  venue  freq\n",
      "0   Rental Car Location  0.10\n",
      "1     Accessories Store  0.05\n",
      "2          Tennis Court  0.05\n",
      "3  Fast Food Restaurant  0.05\n",
      "4     Convenience Store  0.05\n",
      "\n",
      "\n",
      "----Torrance----\n",
      "               venue  freq\n",
      "0  Convenience Store  0.25\n",
      "1     Clothing Store  0.25\n",
      "2  Recreation Center  0.25\n",
      "3               Pool  0.25\n",
      "4         Restaurant  0.00\n",
      "\n",
      "\n",
      "----Venice Beach----\n",
      "                 venue  freq\n",
      "0  American Restaurant  0.07\n",
      "1           Poke Place  0.07\n",
      "2          Yoga Studio  0.04\n",
      "3            Gastropub  0.04\n",
      "4   Chinese Restaurant  0.04\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_top_venues = 5\n",
    "for hood in manhattan_grouped['City']:\n",
    "    print(\"----\"+hood+\"----\")\n",
    "    temp = manhattan_grouped[manhattan_grouped['City'] == hood].T.reset_index()\n",
    "    temp.columns = ['venue','freq']\n",
    "    temp = temp.iloc[1:]\n",
    "    temp['freq'] = temp['freq'].astype(float)\n",
    "    temp = temp.round({'freq': 2})\n",
    "    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_most_common_venues(row, num_top_venues):\n",
    "    row_categories = row.iloc[1:]\n",
    "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
    "    \n",
    "    return row_categories_sorted.index.values[0:num_top_venues]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Boutique</td>\n",
       "      <td>Park</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Video Store</td>\n",
       "      <td>Government Building</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>El Segundo</td>\n",
       "      <td>Pet Store</td>\n",
       "      <td>Office</td>\n",
       "      <td>Distillery</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Garden Center</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Mobile Phone Shop</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Latin American Restaurant</td>\n",
       "      <td>Market</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Food</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Juice Bar</td>\n",
       "      <td>Massage Studio</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Inglewood</td>\n",
       "      <td>Smoke Shop</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Cajun / Creole Restaurant</td>\n",
       "      <td>Stadium</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Rock Club</td>\n",
       "      <td>Southern / Soul Food Restaurant</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>Farmers Market</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Sports Bar</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Shipping Store</td>\n",
       "      <td>Mobile Phone Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>Beach</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>New American Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Resort</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Diner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Accessories Store</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>New American Restaurant</td>\n",
       "      <td>Skate Park</td>\n",
       "      <td>Park</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "      <td>Juice Bar</td>\n",
       "      <td>Supermarket</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Torrance</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Recreation Center</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Distillery</td>\n",
       "      <td>Dive Bar</td>\n",
       "      <td>Dog Run</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>Poke Place</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Pedestrian Plaza</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Canal</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Dessert Shop</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               City 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0       Culver City            Food Truck           Supermarket   \n",
       "1        El Segundo             Pet Store                Office   \n",
       "2         Hawthorne  Fast Food Restaurant     Mobile Phone Shop   \n",
       "3     Hermosa Beach           Coffee Shop           Yoga Studio   \n",
       "4         Inglewood            Smoke Shop     Convenience Store   \n",
       "5   Manhattan Beach                 Hotel            Sports Bar   \n",
       "6    Marina del Rey                 Beach   American Restaurant   \n",
       "7     Redondo Beach   Japanese Restaurant        Discount Store   \n",
       "8      Santa Monica   Rental Car Location     Accessories Store   \n",
       "9          Torrance                  Pool     Recreation Center   \n",
       "10     Venice Beach            Poke Place   American Restaurant   \n",
       "\n",
       "   3rd Most Common Venue      4th Most Common Venue  \\\n",
       "0             Playground                Pizza Place   \n",
       "1             Distillery         Athletics & Sports   \n",
       "2         Discount Store          Convenience Store   \n",
       "3       Sushi Restaurant        American Restaurant   \n",
       "4             Food Truck  Cajun / Creole Restaurant   \n",
       "5                   Bank                Coffee Shop   \n",
       "6     Seafood Restaurant                      Hotel   \n",
       "7            Pizza Place                        Gym   \n",
       "8             Donut Shop    New American Restaurant   \n",
       "9         Clothing Store          Convenience Store   \n",
       "10      Pedestrian Plaza             Sandwich Place   \n",
       "\n",
       "        5th Most Common Venue 6th Most Common Venue    7th Most Common Venue  \\\n",
       "0                       Diner              Boutique                     Park   \n",
       "1        Fast Food Restaurant         Garden Center               Food Truck   \n",
       "2   Latin American Restaurant                Market              Pizza Place   \n",
       "3               Grocery Store  Gym / Fitness Center                Juice Bar   \n",
       "4                     Stadium            Steakhouse                Rock Club   \n",
       "5         Rental Car Location    Mexican Restaurant           Sandwich Place   \n",
       "6                 Pizza Place        Breakfast Spot  New American Restaurant   \n",
       "7               Grocery Store                Bakery                  Dog Run   \n",
       "8                  Skate Park                  Park                      Bar   \n",
       "9                Dessert Shop                 Diner           Discount Store   \n",
       "10                      Canal    Chinese Restaurant                    Plaza   \n",
       "\n",
       "              8th Most Common Venue 9th Most Common Venue  \\\n",
       "0                       Coffee Shop           Video Store   \n",
       "1                       Yoga Studio                 Diner   \n",
       "2                              Food            Donut Shop   \n",
       "3                    Massage Studio    Mexican Restaurant   \n",
       "4   Southern / Soul Food Restaurant            Donut Shop   \n",
       "5                Seafood Restaurant        Shipping Store   \n",
       "6                        Restaurant                Resort   \n",
       "7                       Yoga Studio            Food Truck   \n",
       "8             Performing Arts Venue             Juice Bar   \n",
       "9                        Distillery              Dive Bar   \n",
       "10                       Playground           Coffee Shop   \n",
       "\n",
       "   10th Most Common Venue  \n",
       "0     Government Building  \n",
       "1          Discount Store  \n",
       "2             Coffee Shop  \n",
       "3              Restaurant  \n",
       "4          Farmers Market  \n",
       "5       Mobile Phone Shop  \n",
       "6    Gym / Fitness Center  \n",
       "7                   Diner  \n",
       "8             Supermarket  \n",
       "9                 Dog Run  \n",
       "10           Dessert Shop  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['City']\n",
    "for ind in np.arange(num_top_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "# create a new dataframe\n",
    "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
    "neighborhoods_venues_sorted['City'] = manhattan_grouped['City']\n",
    "\n",
    "for ind in np.arange(manhattan_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(manhattan_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "City               object\n",
       "Latitude          float64\n",
       "Longitude         float64\n",
       "Population          int64\n",
       "Median Age          int64\n",
       "Average Income      int64\n",
       "Venue Number        int64\n",
       "cluster             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kclusters = 3\n",
    "\n",
    "#manhattan_grouped_clustering = manhattan_grouped.drop('Neighborhood', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "#kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(manhattan_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "#kmeans.labels_[0:10] \n",
    "\n",
    "\n",
    "km = KMeans(n_clusters=3)\n",
    "km\n",
    "y_predicted = km.fit_predict(df4[['Average Income', 'Venue Number']])\n",
    "y_predicted\n",
    "\n",
    "\n",
    "df4['cluster'] = y_predicted\n",
    "\n",
    "df4['Median Age'] = df4['Median Age'].astype(int)\n",
    "\n",
    "df4['Population'] = df4['Population'].astype(float)\n",
    "df4['Median Age'] = df4['Median Age'].astype(float)\n",
    "df4['Average Income'] = df4['Average Income'].astype(float)\n",
    "df4['Venue Number'] = df4['Venue Number'].astype(float)\n",
    "df4['cluster'] = df4['cluster'].astype(float)\n",
    "df4.dtypes\n",
    "\n",
    "df41 = df4[df4.cluster==0]\n",
    "df42 = df4[df4.cluster==1]\n",
    "df43 = df4[df4.cluster==2]\n",
    "\n",
    "plt.scatter(df41['Average Income'],df41['Venue Number'],color='green')\n",
    "plt.scatter(df42['Average Income'],df42['Venue Number'],color='red')\n",
    "plt.scatter(df43['Average Income'],df43['Venue Number'],color='black')\n",
    "\n",
    "plt.xlabel('average income')\n",
    "plt.ylabel('venue number')\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "df4['Median Age'] = df4['Median Age'].astype(int)\n",
    "\n",
    "#df4['Population'] = df4['Population'].astype(int)\n",
    "#df4['Median Age'] = df4['Median Age'].astype(int)\n",
    "#df4['Average Income'] = df4['Average Income'].astype(int)\n",
    "#df4['Venue Number'] = df4['Venue Number'].astype(int)\n",
    "#df4['cluster'] = df4['cluster'].astype(int)\n",
    "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='*',label='centroid')\n",
    "\n",
    "df4['Median Age'] = df4['Median Age'].astype(int)\n",
    "df4['Population'] = df4['Population'].astype(int)\n",
    "df4['Median Age'] = df4['Median Age'].astype(int)\n",
    "df4['Average Income'] = df4['Average Income'].astype(int)\n",
    "df4['Venue Number'] = df4['Venue Number'].astype(int)\n",
    "df4['cluster'] = df4['cluster'].astype(int)\n",
    "\n",
    "df4.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'kmeans' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-b3b869721a93>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mneighborhoods_venues_sorted\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Cluster_Labels2'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkmeans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmanhattan_merged\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'kmeans' is not defined"
     ]
    }
   ],
   "source": [
    "neighborhoods_venues_sorted.insert(0, 'Cluster_Labels2', kmeans.labels_)\n",
    "\n",
    "manhattan_merged = df\n",
    "\n",
    "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
    "manhattan_merged = manhattan_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='City')\n",
    "\n",
    "manhattan_merged.head() #\n",
    "manhattan_merged[['Cluster_Labels2']] = manhattan_merged[['Cluster_Labels2']].fillna(value=0)\n",
    "manhattan_merged.fillna({'Cluster_Labels2':0}, inplace=True)\n",
    "manhattan_merged.Cluster_Labels2 = manhattan_merged.Cluster_Labels2.astype(int)\n",
    "manhattan_merged.dtypes\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f0c6d8fa2b0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
    "\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(kclusters)\n",
    "ys = [i + x + (i*x)**2 for i in range(kclusters)]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "# add markers to the map\n",
    "markers_colors = []\n",
    "for lat, lon, poi, cluster in zip(df4['Latitude'], df4['Longitude'], df4['City'], df4['cluster']):\n",
    "    label = folium.Popup(str(poi) + ' cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color=rainbow[cluster-3],\n",
    "        fill=True,\n",
    "        fill_color=rainbow[cluster],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Cluster_Labels5</th>\n",
       "      <th>Cluster_Labels4</th>\n",
       "      <th>Cluster_Labels3</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Culver City</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Film Studio</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Video Store</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Park</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Mobile Phone Shop</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Taco Place</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Martial Arts Dojo</td>\n",
       "      <td>Market</td>\n",
       "      <td>Supplement Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Cajun / Creole Restaurant</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Juice Bar</td>\n",
       "      <td>Massage Studio</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Inglewood</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cajun / Creole Restaurant</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Smoke Shop</td>\n",
       "      <td>Southern / Soul Food Restaurant</td>\n",
       "      <td>Food Court</td>\n",
       "      <td>Stadium</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Music Venue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Sports Bar</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Shipping Store</td>\n",
       "      <td>Mobile Phone Shop</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Big Box Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Beach</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Boat or Ferry</td>\n",
       "      <td>Bike Rental / Bike Share</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Resort</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>New American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Shop &amp; Service</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Park</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Accessories Store</td>\n",
       "      <td>Auto Dealership</td>\n",
       "      <td>Skate Park</td>\n",
       "      <td>New American Restaurant</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>Park</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Supermarket</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Poke Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Canal</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>Playground</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               City  Cluster_Labels5  Cluster_Labels4  Cluster_Labels3  \\\n",
       "0       Culver City                0                0                0   \n",
       "2         Hawthorne                0                0                0   \n",
       "3     Hermosa Beach                0                0                0   \n",
       "4         Inglewood                0                0                0   \n",
       "5   Manhattan Beach                0                0                0   \n",
       "6    Marina del Rey                0                0                0   \n",
       "7     Redondo Beach                0                0                0   \n",
       "8      Santa Monica                0                0                0   \n",
       "10     Venice Beach                0                0                0   \n",
       "\n",
       "        1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue  \\\n",
       "0                 Film Studio           Coffee Shop           Video Store   \n",
       "2        Fast Food Restaurant     Mobile Phone Shop     Convenience Store   \n",
       "3                 Coffee Shop         Grocery Store           Yoga Studio   \n",
       "4   Cajun / Creole Restaurant            Food Truck     Convenience Store   \n",
       "5                       Hotel            Sports Bar                  Bank   \n",
       "6                       Beach   American Restaurant                 Hotel   \n",
       "7               Grocery Store        Shop & Service        Discount Store   \n",
       "8         Rental Car Location     Accessories Store       Auto Dealership   \n",
       "10        American Restaurant            Poke Place           Coffee Shop   \n",
       "\n",
       "   4th Most Common Venue    5th Most Common Venue  \\\n",
       "0                  Diner               Playground   \n",
       "2         Discount Store              Coffee Shop   \n",
       "3       Sushi Restaurant      American Restaurant   \n",
       "4             Restaurant               Smoke Shop   \n",
       "5            Coffee Shop                Gastropub   \n",
       "6     Seafood Restaurant            Boat or Ferry   \n",
       "7    Japanese Restaurant       Chinese Restaurant   \n",
       "8             Skate Park  New American Restaurant   \n",
       "10           Yoga Studio             Dessert Shop   \n",
       "\n",
       "              6th Most Common Venue 7th Most Common Venue  \\\n",
       "0                       Pizza Place            Food Truck   \n",
       "2                        Taco Place    Mexican Restaurant   \n",
       "3         Cajun / Creole Restaurant  Gym / Fitness Center   \n",
       "4   Southern / Soul Food Restaurant            Food Court   \n",
       "5                Seafood Restaurant        Shipping Store   \n",
       "6          Bike Rental / Bike Share                  Pool   \n",
       "7                       Pizza Place                   Gym   \n",
       "8                        Donut Shop                  Park   \n",
       "10                   Sandwich Place                 Canal   \n",
       "\n",
       "    8th Most Common Venue 9th Most Common Venue   10th Most Common Venue  \n",
       "0                    Park           Supermarket              Dry Cleaner  \n",
       "2       Martial Arts Dojo                Market          Supplement Shop  \n",
       "3               Juice Bar        Massage Studio       Mexican Restaurant  \n",
       "4                 Stadium            Steakhouse              Music Venue  \n",
       "5       Mobile Phone Shop   Rental Car Location            Big Box Store  \n",
       "6                  Resort            Restaurant  New American Restaurant  \n",
       "7                    Park               Dog Run                   Bakery  \n",
       "8   Performing Arts Venue                   Bar              Supermarket  \n",
       "10     Chinese Restaurant                 Plaza               Playground  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manhattan_merged.loc[manhattan_merged['Cluster_Labels2'] == 0, manhattan_merged.columns[[1] + list(range(5, manhattan_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Cluster_Labels5</th>\n",
       "      <th>Cluster_Labels4</th>\n",
       "      <th>Cluster_Labels3</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Torrance</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Recreation Center</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Ski Area</td>\n",
       "      <td>Film Studio</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Distillery</td>\n",
       "      <td>Dive Bar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City  Cluster_Labels5  Cluster_Labels4  Cluster_Labels3  \\\n",
       "9  Torrance                1                1                1   \n",
       "\n",
       "  1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue  \\\n",
       "9     Recreation Center                  Pool     Convenience Store   \n",
       "\n",
       "  4th Most Common Venue 5th Most Common Venue 6th Most Common Venue  \\\n",
       "9              Ski Area           Film Studio          Dessert Shop   \n",
       "\n",
       "  7th Most Common Venue 8th Most Common Venue 9th Most Common Venue  \\\n",
       "9                 Diner        Discount Store            Distillery   \n",
       "\n",
       "  10th Most Common Venue  \n",
       "9               Dive Bar  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manhattan_merged.loc[manhattan_merged['Cluster_Labels2'] == 1, manhattan_merged.columns[[1] + list(range(5, manhattan_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Cluster_Labels5</th>\n",
       "      <th>Cluster_Labels4</th>\n",
       "      <th>Cluster_Labels3</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>El Segundo</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Pet Store</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Office</td>\n",
       "      <td>Distillery</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Dive Bar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         City  Cluster_Labels5  Cluster_Labels4  Cluster_Labels3  \\\n",
       "1  El Segundo                2                2                2   \n",
       "\n",
       "  1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue  \\\n",
       "1             Pet Store                   Gym    Athletics & Sports   \n",
       "\n",
       "  4th Most Common Venue 5th Most Common Venue 6th Most Common Venue  \\\n",
       "1            Food Truck  Fast Food Restaurant  Gym / Fitness Center   \n",
       "\n",
       "  7th Most Common Venue 8th Most Common Venue 9th Most Common Venue  \\\n",
       "1                Office            Distillery               Dog Run   \n",
       "\n",
       "  10th Most Common Venue  \n",
       "1               Dive Bar  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manhattan_merged.loc[manhattan_merged['Cluster_Labels2'] == 2, manhattan_merged.columns[[1] + list(range(5, manhattan_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Cluster_Labels5</th>\n",
       "      <th>Cluster_Labels4</th>\n",
       "      <th>Cluster_Labels3</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [City, Cluster_Labels5, Cluster_Labels4, Cluster_Labels3, 1st Most Common Venue, 2nd Most Common Venue, 3rd Most Common Venue, 4th Most Common Venue, 5th Most Common Venue, 6th Most Common Venue, 7th Most Common Venue, 8th Most Common Venue, 9th Most Common Venue, 10th Most Common Venue]\n",
       "Index: []"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manhattan_merged.loc[manhattan_merged['Cluster_Labels2'] == 3, manhattan_merged.columns[[1] + list(range(5, manhattan_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Cluster_Labels5</th>\n",
       "      <th>Cluster_Labels4</th>\n",
       "      <th>Cluster_Labels3</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [City, Cluster_Labels5, Cluster_Labels4, Cluster_Labels3, 1st Most Common Venue, 2nd Most Common Venue, 3rd Most Common Venue, 4th Most Common Venue, 5th Most Common Venue, 6th Most Common Venue, 7th Most Common Venue, 8th Most Common Venue, 9th Most Common Venue, 10th Most Common Venue]\n",
       "Index: []"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manhattan_merged.loc[manhattan_merged['Cluster_Labels2'] == 4, manhattan_merged.columns[[1] + list(range(5, manhattan_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.1.0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.style.use('ggplot') # optional: for ggplot-like style\n",
    "\n",
    "# check for latest version of Matplotlib\n",
    "print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df3.plot(kind='bar', figsize=(10, 6))\n",
    "\n",
    "plt.xlabel('City') # add to x-label to the plot\n",
    "plt.ylabel('Number of Venues') # add y-label to the plot\n",
    "plt.title('Venues per City') # add title to the plot\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics\n",
    "from scipy.spatial.distance import cdist\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/lib/python3.6/site-packages/pandas/util/_decorators.py:188: FutureWarning: The `sheetname` keyword is deprecated, use `sheet_name` instead\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>City</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Population</th>\n",
       "      <th>Median Age</th>\n",
       "      <th>Average Income</th>\n",
       "      <th>Venue Number</th>\n",
       "      <th>Latitude.1</th>\n",
       "      <th>Longitude.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Culver City</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>39283</td>\n",
       "      <td>40.9</td>\n",
       "      <td>86997</td>\n",
       "      <td>9</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>El Segundo</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>16853</td>\n",
       "      <td>38.7</td>\n",
       "      <td>92942</td>\n",
       "      <td>11</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>87854</td>\n",
       "      <td>33.0</td>\n",
       "      <td>47636</td>\n",
       "      <td>23</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>19708</td>\n",
       "      <td>39.5</td>\n",
       "      <td>124849</td>\n",
       "      <td>66</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Inglewood</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>110598</td>\n",
       "      <td>34.5</td>\n",
       "      <td>46389</td>\n",
       "      <td>8</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>35924</td>\n",
       "      <td>43.7</td>\n",
       "      <td>148899</td>\n",
       "      <td>26</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>8866</td>\n",
       "      <td>39.0</td>\n",
       "      <td>101860</td>\n",
       "      <td>20</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>67908</td>\n",
       "      <td>40.2</td>\n",
       "      <td>104548</td>\n",
       "      <td>8</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>92306</td>\n",
       "      <td>40.5</td>\n",
       "      <td>86084</td>\n",
       "      <td>21</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Torrance</td>\n",
       "      <td>33.834966</td>\n",
       "      <td>-118.341431</td>\n",
       "      <td>146758</td>\n",
       "      <td>41.7</td>\n",
       "      <td>85070</td>\n",
       "      <td>4</td>\n",
       "      <td>33.834966</td>\n",
       "      <td>-118.341431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40885</td>\n",
       "      <td>35.0</td>\n",
       "      <td>67647</td>\n",
       "      <td>29</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0             City   Latitude   Longitude  Population  \\\n",
       "0            1      Culver City  34.005820 -118.396781       39283   \n",
       "1            2       El Segundo  33.917145 -118.401554       16853   \n",
       "2            3        Hawthorne  33.914775 -118.348083       87854   \n",
       "3            4    Hermosa Beach  33.865268 -118.396297       19708   \n",
       "4            5        Inglewood  33.956068 -118.344274      110598   \n",
       "5            6  Manhattan Beach  33.889632 -118.397370       35924   \n",
       "6            7   Marina del Rey  33.981510 -118.453229        8866   \n",
       "7            8    Redondo Beach  33.856817 -118.377137       67908   \n",
       "8            9     Santa Monica  34.023413 -118.481666       92306   \n",
       "9           10         Torrance  33.834966 -118.341431      146758   \n",
       "10          11     Venice Beach        NaN         NaN       40885   \n",
       "\n",
       "    Median Age  Average Income  Venue Number  Latitude.1  Longitude.1  \n",
       "0         40.9           86997             9   34.005820  -118.396781  \n",
       "1         38.7           92942            11   33.917145  -118.401554  \n",
       "2         33.0           47636            23   33.914775  -118.348083  \n",
       "3         39.5          124849            66   33.865268  -118.396297  \n",
       "4         34.5           46389             8   33.956068  -118.344274  \n",
       "5         43.7          148899            26   33.889632  -118.397370  \n",
       "6         39.0          101860            20   33.981510  -118.453229  \n",
       "7         40.2          104548             8   33.856817  -118.377137  \n",
       "8         40.5           86084            21   34.023413  -118.481666  \n",
       "9         41.7           85070             4   33.834966  -118.341431  \n",
       "10        35.0           67647            29   33.985000  -118.469500  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df4 = pd.read_excel('LAcoor2.xlsx', sheetname='Sheet3')\n",
    "df4\n",
    "df4  #df4.drop(columns=['Unnamed: 0', 'City', 'Latitude','Longitude'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0c6d6c9358>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df4['Average Income'],df4['Venue Number'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters=3)\n",
    "km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 2, 1, 2, 1, 0, 0, 0, 0, 2], dtype=int32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = km.fit_predict(df4[['Average Income', 'Venue Number']])\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0          int64\n",
       "City               object\n",
       "Latitude          float64\n",
       "Longitude         float64\n",
       "Population        float64\n",
       "Median Age        float64\n",
       "Average Income    float64\n",
       "Venue Number      float64\n",
       "Latitude.1        float64\n",
       "Longitude.1       float64\n",
       "cluster           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['cluster'] = y_predicted\n",
    "\n",
    "df4['Median Age'] = df4['Median Age'].astype(int)\n",
    "\n",
    "df4['Population'] = df4['Population'].astype(float)\n",
    "df4['Median Age'] = df4['Median Age'].astype(float)\n",
    "df4['Average Income'] = df4['Average Income'].astype(float)\n",
    "df4['Venue Number'] = df4['Venue Number'].astype(float)\n",
    "df4['cluster'] = df4['cluster'].astype(float)\n",
    "df4.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0c6d6a81d0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+B7jN3R8D6mJNQi25KCISvzmoAbjbzF4Hbgdud/fnyhdW+WnJRRGRmB3D7n4hcDDwBeAQ4Jdm9riZXVTO4Mopm83S3t4+okxLLopI2sReWczddwIPAw+b2eXAzcC3gG+XKbay0pKL9SG/JU9udY7ebb20zGghuzBL2yxdQ5G4YicBM9sPOI0wdcTHgEeAReUJKxlacrG25bfk6bi/g54tBSO8Xu2m6+QuJQKRmOJ2DP8IOBnoBu4AFrl7fzkDE9mb3OrciAQA0LOlh9zqHEuPV3IXiSPuncBq4MvuPqGnqczsEMJzBi3ATmCZu19nZnOAO4F5wBrA3H3TRM4h6dO7bYwRXtuqf4SXmrGkWsTtGL56ogkgMkBIIu8mLFDzRTM7HLgEWOnuC4CV0bZILC0zxhjhNaO6R3gNN2OteGEFq9avYsULK+i4v4P8Fj2xLsmLO23EpLj7enfvjr7fCjwLHAScCgyvVdBJ6HMQiSW7MEv7rFEjvGa1k11Y3SO89tSMJZK02B3DpWJm84D3A48Cze6+HkKiMLMDxzhmMbA42o9MJpNQtJXR1NRU93UcNpm6ZjIZHjzzQZY8soT1r61n7n5zWfLRJcyfPb/EUZbGcF03vrmx6Osb39xYN9ddf8O1I9EkEI0w+jHw1+6+xcxiHefuy4Bl0eZQf39990lnMhnqvY7DJlvXmczkmuOueatggKr93Q3Xdc7UOUVfnzN1TtXGPl76G64+ra2tRcsTaQ4CMLOphARwm7v/Y1TcZ2Zzo9fnAhuSikekUmq1GUvqUyJJIJp6ejnwrLsXPlx2D289a7AIuDuJeEQqqW1WG10nd/GpQz/Fh+Z+iE8d+ik92yAVk1Rz0HHAWcDTZvZEVHYZcBXgZnYekAc+nVA8IhXVNqtNzzJIVUgkCbj7LwiT0BVzQhIxiIjI7hLrExARkeqjJCAikmJKAiIiKaYkICKSYkoCIiIppiQgIpJiSgIiIimmJCAikmJKAiIiKaYkICKSYkoCIiIppiQgIpJiSgIiIimmJCAikmJKAiIiKaYkICKSYkoCIiIppiQgIpJiSgIiIimmJCAikmKJLDQvUivyW/LkVufo3dZLy4wWsguztM1qq3RYImWjJCASyW/J03F/Bz1benaVdb/aTdfJXUoEUrfUHCQSya3OjUgAAD1besitzlUoIpHyS+ROwMxuAk4BNrj7EVHZHOBOYB6wBjB335REPCLF9G7rLVret60v4UhEkpPUncAtwEmjyi4BVrr7AmBltC1SMS0zWoqWN89oTjgSkeQkkgTc/efAxlHFpwKd0fedwGlJxCIyluzCLO2z2keUtc9qJ7swW6GIRMqvkh3Dze6+HsDd15vZgWPtaGaLgcXRvmQymYRCrIympqa6r+OwUtb1pc0vseSRJax/bT1z95vLko8uYf7s+bGPz2QyPHjmg5P6GXui61qfar2uNTE6yN2XAcuizaH+/v5KhlN2mUyGeq/jsFLVtdjInlVrV417ZM9MZnLNcde8VTBAya6Frmt9qpW6tra2Fi2v5OigPjObCxB93VDBWKTGaWSPyMRU8k7gHmARcFX09e5ynzCfz5PL5ejt7aWlpYVsNktbm8Z/14N6G9mjh9YkKUkNEb0D+BiQMbO1wJWEN383s/OAPPDpcsaQz+fp6Oigp6fgQaDubrq6upQI6kA9jezRQ2uSpESSgLufMcZLJyRxfoBcLjciAQD09PSQy+VYunRpUmFImWQXZul+tXvEG2etjuzZU9PW0uP1tyqlVRMdw6XQ2ztGc0FfbTYXyEhts9roOrmL3Oocfdv6aJ7RXLNNKPXWtCXVLTVJoKVljOaC5tprLpDi2ma11cUn5Xpq2pLql5q5g7LZLO3tox4Eam8nm6295gKpb3poTZKUmjuBtrY2urq6yOVy9PX10dzcrNFBUpXqqWlLql/D0NBQpWMYr6F169ZVOoayqpWHT0pBda1Pqmv1iR4WaxhdnprmIBER2Z2SgIhIiikJiIikmJKAiEiKKQmIiKRYaoaISv3Q5GoipaMkIDVFk6uJlJaag6SmaN0AkdJSEpCaosnVREpLSUBqiiZXEyktJQGpKZpcTaS01DEsNUWTq4mUlpKA1Jx6WTdApBqoOUhEJMWUBEREUkxJQEQkxZQERERSTElARCTFanJ5yUoHICJSo+piecmGev9nZo9XOgbVVXVVXeuyrrupxSQgIiIloiQgIpJiSgLVaVmlA0iQ6lqfVNcaUYsdwyIiUiK6ExARSTElARGRFNMsomVkZrOBG4EjCM83fBZ4DrgTmAesAczdN5lZA3Ad8AlgG3COu3dHP2cR8DfRj/1bd++Myo8GbgGmAz8BLnT3irTvmdl/Az5HqOfTwLnAXKALmAN0A2e5+w4z2we4FTga+C3w5+6+Jvo5lwLnAYPABe7+YFR+EuH30wjc6O5XJVi3m4BTgA3ufkRUNocyX8exzlGBun4L+M/ADuAF4Fx33xy9Nq7rZWbzGeffRJJ1LXjtK8C3gLe7e3+tX9c90Z1AeV0HPODu7wKOBJ4FLgFWuvsCYGW0DXAysCD6txi4Hna92VwJfBA4BrjSzPaPjrk+2nf4uJMSqNNuzOwg4AJgYfSfqRHoAK4Gro3quonwZkH0dZO7vxO4NtoPMzs8Ou49hLr8LzNrNLNG4HuE39HhwBnRvkm5hd1/t0lcx7HOUU63sHtdHwaOcPf3Af8BXAoTvl7j+psos1so8n/GzA4BTgTyBcW1fl3HpCRQJmY2C/gIsBzA3XdEn55OBTqj3TqB06LvTwVudfchd/8lMNvM5gJ/Ajzs7hujTwsPAydFr81y91XRp/9bC35WJTQB082sCZgBrAeOB+6KXh9d1+HfwV3ACdEnrVOBLnd/w91fAp4n/Mc6Bnje3V909x2ET5KnJlAnANz958DGUcVJXMexzlE2xerq7g+5+0C0+Uvg4IL4Yl+v6BqP92+ibMa4rhCSUJaRsxPU9HXdEyWB8nkH8Cpws5n9u5ndaGZvA5rdfT1A9PXAaP+DgJcLjl8ble2pfG2R8sS5+yvA/yR8cloP/A54HNhc8OZRGN+uOkWv/w44gPH/Diopies41jkq6bPA/dH3463rAYz/byJRZvZJ4BV3f3LUS3V7XZUEyqcJ+ABwvbu/H3idPd/2FfvUMzSB8sRFt7+nAvOBVuBthNvn0Ybjq9m6xlC3dTOzrwIDwG1RUSnrWvHfg5nNAL4KXFHk5bq9rkoC5bMWWOvuj0bbdxGSQl90q0j0dUPB/ocUHH8wsG4v5QcXKa+EPwZecvdX3f1N4B+BDxFumYcHHxTGt6tO0et/QLgtH+/voJKSuI5jnSNxUefnKcCZBYMPxlvXfsb/N5GkQwkfZJ40szVRfN1m1kKdXldQEigbd+8FXjazw6KiE4BngHuARVHZIuDu6Pt7gLPNrMHMjgV+F90qPgh83Mz2jz5xfxx4MHptq5kdG7Wdnl3ws5KWB441sxlRLMN1/RlwerTP6LoO/w5OB34avbHcA3SY2T7RKJIFwL8BjwELzGy+mU0jdEbek0C99iSJ6zjWORIVjfS5GPiku28reGlc1yu6xuP9m0iMuz/t7ge6+zx3n0d4I/9A9H+57q7rMCWB8voScJuZPQUcBfwdcBVwopn9hjACYXio40+AFwmdaz8AvgDg7huBbxD+Yz0GfD0qA/grwhDU5wlD94bbahMV3e3cRRjy9zTh72oZ4Y3jIjN7ntC+uzw6ZDlwQFR+EVEzmbv/GnBCAnkA+KK7D0ZtxOcT/sM9G3b1XydUPczsDmAVcJiZrTWz80jmOo51jrIZo65LgZnAw2b2hJl9P6rTRK7XuP4mKlDXsdT0dd0TTRshIpJiuhMQEUkxJQERkRRTEhARSTElARGRFFMSEBFJMSUBkTIzs8vM7MZKxyFSjIaIioikmO4EJHUKpi0QST3dCUhVMbNLgM8TZlZ8Gfiqu6+IFh3pAz7s7r+K9n07YcqKdnffYGanAH9LWKzjGeAv3f2paN81hPndzwQOI0xy95Vi54r2bwRyhMf6twLXAN8Fprr7gJn9AfBtwiIjO4GbgSvdfbBInZYA73T3/2pm84CXgHMIT5rOIMyv/82C815MmF//QML8/ae5+8tm9iHCGhV/GJVf6O7/NzruX4BfEKZqfh9heoZzgL8nLAjzHPBpf2vxnndF9TmaMNvt5e7uca6R1BfdCUi1eQH4T4QJxL4G/IOZzXX3NwgT051RsK8Bj0QJ4APATcBfEKYjuAG4J0oew84A/hSYHU1tUPRc0b6fJ8yEehRh4r/Rc753EmbUfCfwfsKcMZ8bRz0/TEhGJwBXmNm7o/KLojg/AcwiTN28LVq85D7Cm/oBhAR0n5kVTrfcAZxFmLL4UMKUCDcTVvF6lrD4CdGU5g8DtxMSzRmEBWHeM474pU7otliqirv/qGDzTgvLFx5DmGTrdsKcRF+NXv8M4c0ewpv2DQWztnaa2WXAscAjUdnfu/uuud/3ci4DrnP3tQBmdhXhDRszayYkiNnu/nvgdTO7lrCK1A3E87Xo2CfN7EneWnnuc0DW3Z+L9nsyOudZwG/c/YdR+R1mdgHhU/4tUdnN7v5CtP/9wOHu/s/R9o8Idx4QZgNd4+43R9vdZvZjwsRtic3JJNVBSUCqipmdTfg0PC8q2g/IRN//lLB62QeBXsKn9BXRa+3AIjP7UsGPm0ZY32BY4eIfeztX66j9C79vB6YC681suGzK6J+/F70F32+Lzg1hWuIXiuzfCvSMKuth5OI6fQXf/77I9vA52oEPmtnmgtebgB8iqaMkIFXDzNoJMzSeAKxy90Eze4JogQ5332lmTmi+6APudfet0eEvA98cblsfw64OsL2di7BCWuF88IVzxr8MvAFkClbJKpWXCU05vxpVvo7w5l2ojTB750TO8Yi7nziBY6XOKAlINXkb4Y36VQAzOxc4YtQ+twP/G/gtbzULQXhDX2Fm/0yY034G8DHg5wWJYjzncuBCM7uPsCrcxbtecF9vZg8B15jZ5cBrhMVIDnb3R5icG4FvmNkzhCmI3wu8QpjK+Ltm9pkotv9CWMT93gmc417gqqiJqSsqOwp4zd2fnWT8UmPUMSxVw92fIYzCWUX4pP9e4P+M2udRwptyKwXrJ7j7akK/wFJgE+EN9JxJnOsHwEPAU8C/E96EB4Dh0T9nE5qbnonOdxcwl8n7NuFN/iFgC2Ge/enu/ltCW/6XCQkwC5zi7v3jPUGUFD9O6EheR2iauhrYZ0/HSX3SEFGRGMzsZOD77j66SUakpqk5SKQIM5sO/BHhE3kzYXjlij0eJFKD1BwkUlwD4dmBTYTmoGeBKyoakUgZqDlIRCTFdCcgIpJiSgIiIimmJCAikmJKAiIiKaYkICKSYv8fykZ5zIOh+20AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df41 = df4[df4.cluster==0]\n",
    "df42 = df4[df4.cluster==1]\n",
    "df43 = df4[df4.cluster==2]\n",
    "\n",
    "plt.scatter(df41['Average Income'],df41['Venue Number'],color='green')\n",
    "plt.scatter(df42['Average Income'],df42['Venue Number'],color='red')\n",
    "plt.scatter(df43['Average Income'],df43['Venue Number'],color='black')\n",
    "\n",
    "plt.xlabel('average income')\n",
    "plt.ylabel('venue number')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>City</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Population</th>\n",
       "      <th>Median Age</th>\n",
       "      <th>Average Income</th>\n",
       "      <th>Venue Number</th>\n",
       "      <th>Latitude.1</th>\n",
       "      <th>Longitude.1</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Culver City</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>39283.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.396137</td>\n",
       "      <td>0.080645</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>El Segundo</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>16853.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.454131</td>\n",
       "      <td>0.112903</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>87854.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.012165</td>\n",
       "      <td>0.306452</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>19708.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.765389</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Inglewood</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>110598.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>35924.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>8866.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.541128</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>67908.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.567350</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>92306.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.387231</td>\n",
       "      <td>0.274194</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Torrance</td>\n",
       "      <td>33.834966</td>\n",
       "      <td>-118.341431</td>\n",
       "      <td>146758.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.377339</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.834966</td>\n",
       "      <td>-118.341431</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40885.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.207375</td>\n",
       "      <td>0.403226</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0             City   Latitude   Longitude  Population  \\\n",
       "0            1      Culver City  34.005820 -118.396781     39283.0   \n",
       "1            2       El Segundo  33.917145 -118.401554     16853.0   \n",
       "2            3        Hawthorne  33.914775 -118.348083     87854.0   \n",
       "3            4    Hermosa Beach  33.865268 -118.396297     19708.0   \n",
       "4            5        Inglewood  33.956068 -118.344274    110598.0   \n",
       "5            6  Manhattan Beach  33.889632 -118.397370     35924.0   \n",
       "6            7   Marina del Rey  33.981510 -118.453229      8866.0   \n",
       "7            8    Redondo Beach  33.856817 -118.377137     67908.0   \n",
       "8            9     Santa Monica  34.023413 -118.481666     92306.0   \n",
       "9           10         Torrance  33.834966 -118.341431    146758.0   \n",
       "10          11     Venice Beach        NaN         NaN     40885.0   \n",
       "\n",
       "    Median Age  Average Income  Venue Number  Latitude.1  Longitude.1  cluster  \n",
       "0         40.0        0.396137      0.080645   34.005820  -118.396781      0.0  \n",
       "1         38.0        0.454131      0.112903   33.917145  -118.401554      0.0  \n",
       "2         33.0        0.012165      0.306452   33.914775  -118.348083      2.0  \n",
       "3         39.0        0.765389      1.000000   33.865268  -118.396297      1.0  \n",
       "4         34.0        0.000000      0.064516   33.956068  -118.344274      2.0  \n",
       "5         43.0        1.000000      0.354839   33.889632  -118.397370      1.0  \n",
       "6         39.0        0.541128      0.258065   33.981510  -118.453229      0.0  \n",
       "7         40.0        0.567350      0.064516   33.856817  -118.377137      0.0  \n",
       "8         40.0        0.387231      0.274194   34.023413  -118.481666      0.0  \n",
       "9         41.0        0.377339      0.000000   33.834966  -118.341431      0.0  \n",
       "10        35.0        0.207375      0.403226   33.985000  -118.469500      2.0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaler.fit(df4[['Average Income']])\n",
    "df4['Average Income'] = scaler.transform(df4[['Average Income']])\n",
    "\n",
    "scaler.fit(df4[['Venue Number']])\n",
    "df4['Venue Number'] = scaler.transform(df4[['Venue Number']])\n",
    "\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Unnamed: 0</th>\n",
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       "      <th>Median Age</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Culver City</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>39283.0</td>\n",
       "      <td>40.0</td>\n",
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       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>16853.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.454131</td>\n",
       "      <td>0.112903</td>\n",
       "      <td>33.917145</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>87854.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.012165</td>\n",
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       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>19708.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.765389</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Inglewood</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>110598.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>35924.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>8866.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.541128</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>67908.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.567350</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>92306.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.387231</td>\n",
       "      <td>0.274194</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Torrance</td>\n",
       "      <td>33.834966</td>\n",
       "      <td>-118.341431</td>\n",
       "      <td>146758.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.377339</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>-118.341431</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40885.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.207375</td>\n",
       "      <td>0.403226</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    Unnamed: 0             City   Latitude   Longitude  Population  \\\n",
       "0            1      Culver City  34.005820 -118.396781     39283.0   \n",
       "1            2       El Segundo  33.917145 -118.401554     16853.0   \n",
       "2            3        Hawthorne  33.914775 -118.348083     87854.0   \n",
       "3            4    Hermosa Beach  33.865268 -118.396297     19708.0   \n",
       "4            5        Inglewood  33.956068 -118.344274    110598.0   \n",
       "5            6  Manhattan Beach  33.889632 -118.397370     35924.0   \n",
       "6            7   Marina del Rey  33.981510 -118.453229      8866.0   \n",
       "7            8    Redondo Beach  33.856817 -118.377137     67908.0   \n",
       "8            9     Santa Monica  34.023413 -118.481666     92306.0   \n",
       "9           10         Torrance  33.834966 -118.341431    146758.0   \n",
       "10          11     Venice Beach        NaN         NaN     40885.0   \n",
       "\n",
       "    Median Age  Average Income  Venue Number  Latitude.1  Longitude.1  cluster  \n",
       "0         40.0        0.396137      0.080645   34.005820  -118.396781      0.0  \n",
       "1         38.0        0.454131      0.112903   33.917145  -118.401554      0.0  \n",
       "2         33.0        0.012165      0.306452   33.914775  -118.348083      1.0  \n",
       "3         39.0        0.765389      1.000000   33.865268  -118.396297      2.0  \n",
       "4         34.0        0.000000      0.064516   33.956068  -118.344274      1.0  \n",
       "5         43.0        1.000000      0.354839   33.889632  -118.397370      2.0  \n",
       "6         39.0        0.541128      0.258065   33.981510  -118.453229      0.0  \n",
       "7         40.0        0.567350      0.064516   33.856817  -118.377137      0.0  \n",
       "8         40.0        0.387231      0.274194   34.023413  -118.481666      0.0  \n",
       "9         41.0        0.377339      0.000000   33.834966  -118.341431      0.0  \n",
       "10        35.0        0.207375      0.403226   33.985000  -118.469500      1.0  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 1, 0, 1, 0, 2, 2, 2, 2, 1], dtype=int32)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters=3)\n",
    "y_predicted = km.fit_predict(df4[['Average Income', 'Venue Number']])\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Culver City</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>39283.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.396137</td>\n",
       "      <td>0.080645</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>El Segundo</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>16853.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.454131</td>\n",
       "      <td>0.112903</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>87854.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.012165</td>\n",
       "      <td>0.306452</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>19708.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.765389</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Inglewood</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>110598.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>35924.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>8866.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.541128</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>67908.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.567350</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>92306.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.387231</td>\n",
       "      <td>0.274194</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Torrance</td>\n",
       "      <td>33.834966</td>\n",
       "      <td>-118.341431</td>\n",
       "      <td>146758.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.377339</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.834966</td>\n",
       "      <td>-118.341431</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40885.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.207375</td>\n",
       "      <td>0.403226</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0             City   Latitude   Longitude  Population  \\\n",
       "0            1      Culver City  34.005820 -118.396781     39283.0   \n",
       "1            2       El Segundo  33.917145 -118.401554     16853.0   \n",
       "2            3        Hawthorne  33.914775 -118.348083     87854.0   \n",
       "3            4    Hermosa Beach  33.865268 -118.396297     19708.0   \n",
       "4            5        Inglewood  33.956068 -118.344274    110598.0   \n",
       "5            6  Manhattan Beach  33.889632 -118.397370     35924.0   \n",
       "6            7   Marina del Rey  33.981510 -118.453229      8866.0   \n",
       "7            8    Redondo Beach  33.856817 -118.377137     67908.0   \n",
       "8            9     Santa Monica  34.023413 -118.481666     92306.0   \n",
       "9           10         Torrance  33.834966 -118.341431    146758.0   \n",
       "10          11     Venice Beach        NaN         NaN     40885.0   \n",
       "\n",
       "    Median Age  Average Income  Venue Number  Latitude.1  Longitude.1  cluster  \n",
       "0         40.0        0.396137      0.080645   34.005820  -118.396781        2  \n",
       "1         38.0        0.454131      0.112903   33.917145  -118.401554        2  \n",
       "2         33.0        0.012165      0.306452   33.914775  -118.348083        1  \n",
       "3         39.0        0.765389      1.000000   33.865268  -118.396297        0  \n",
       "4         34.0        0.000000      0.064516   33.956068  -118.344274        1  \n",
       "5         43.0        1.000000      0.354839   33.889632  -118.397370        0  \n",
       "6         39.0        0.541128      0.258065   33.981510  -118.453229        2  \n",
       "7         40.0        0.567350      0.064516   33.856817  -118.377137        2  \n",
       "8         40.0        0.387231      0.274194   34.023413  -118.481666        2  \n",
       "9         41.0        0.377339      0.000000   33.834966  -118.341431        2  \n",
       "10        35.0        0.207375      0.403226   33.985000  -118.469500        1  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['cluster'] = y_predicted\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fae096a8d68>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df41 = df4[df4.cluster==0]\n",
    "df42 = df4[df4.cluster==1]\n",
    "df43 = df4[df4.cluster==2]\n",
    "\n",
    "plt.scatter(df41['Average Income'],df41['Venue Number'],color='green')\n",
    "plt.scatter(df42['Average Income'],df42['Venue Number'],color='red')\n",
    "plt.scatter(df43['Average Income'],df43['Venue Number'],color='black')\n",
    "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='*',label='centroid')\n",
    "plt.xlabel('average income')\n",
    "plt.ylabel('venue number')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.07317985, 0.25806452],\n",
       "       [0.4538858 , 0.13172043],\n",
       "       [0.88269437, 0.67741935]])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_rng = range(1,10)\n",
    "sse = []\n",
    "for k in k_rng:\n",
    "    km = KMeans(n_clusters=k)\n",
    "    km.fit(df4[['Average Income','Venue Number']])\n",
    "    sse.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.660920796405157,\n",
       " 0.7406114031140982,\n",
       " 0.4814789322211169,\n",
       " 0.18317394389309843,\n",
       " 0.118949540481645,\n",
       " 0.06443466711939291,\n",
       " 0.04069852919238138,\n",
       " 0.02194063303338859,\n",
       " 0.009968389069220717]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (repodata.json): done\n",
      "Solving environment: \\ \n",
      "The environment is inconsistent, please check the package plan carefully\n",
      "The following packages are causing the inconsistency:\n",
      "\n",
      "  - anaconda/linux-64::grpcio==1.16.1=py36hf8bcb03_1\n",
      "  - anaconda/linux-64::keras==2.1.5=py36_0\n",
      "  - anaconda/linux-64::tensorboard==1.8.0=py36hf484d3e_0\n",
      "  - anaconda/linux-64::tensorflow==1.8.0=h57681fa_0\n",
      "  - anaconda/linux-64::tensorflow-base==1.8.0=py36h5f64886_0\n",
      "  - defaults/linux-64::anaconda==5.3.1=py37_0\n",
      "  - defaults/linux-64::astropy==3.0.4=py37h14c3975_0\n",
      "  - defaults/linux-64::bkcharts==0.2=py37_0\n",
      "  - defaults/linux-64::blaze==0.11.3=py37_0\n",
      "  - defaults/linux-64::bokeh==0.13.0=py37_0\n",
      "  - defaults/linux-64::bottleneck==1.2.1=py37h035aef0_1\n",
      "  - defaults/linux-64::dask==0.19.1=py37_0\n",
      "  - defaults/linux-64::datashape==0.5.4=py37_1\n",
      "  - defaults/linux-64::mkl-service==1.1.2=py37h90e4bf4_5\n",
      "  - defaults/linux-64::numba==0.39.0=py37h04863e7_0\n",
      "  - defaults/linux-64::numexpr==2.6.8=py37hd89afb7_0\n",
      "  - defaults/linux-64::odo==0.5.1=py37_0\n",
      "  - defaults/linux-64::pytables==3.4.4=py37ha205bf6_0\n",
      "  - defaults/linux-64::pytest-arraydiff==0.2=py37h39e3cac_0\n",
      "  - defaults/linux-64::pytest-astropy==0.4.0=py37_0\n",
      "  - defaults/linux-64::pytest-doctestplus==0.1.3=py37_0\n",
      "  - defaults/linux-64::pywavelets==1.0.0=py37hdd07704_0\n",
      "  - defaults/linux-64::scikit-image==0.14.0=py37hf484d3e_1\n",
      "failed\n",
      "Initial quick solve with frozen env failed.  Unfreezing env and trying again.\n",
      "Solving environment: failed\n",
      "\n",
      "UnsatisfiableError: The following specifications were found\n",
      "to be incompatible with the existing python installation in your environment:\n",
      "\n",
      "  - _ipyw_jlab_nb_ext_conf -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - absl-py -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - alabaster -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0']\n",
      "  - altair -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - anaconda -> python[version='2.7.*,>=2.7,<2.8.0a0']\n",
      "  - anaconda-client -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - anaconda-navigator -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - anaconda-project -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - appdirs -> python[version='2.7.*,3.5.*,3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - asn1crypto -> python[version='3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - astor -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - astroid -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - astropy -> python[version='2.7.*,>=2.7,<2.8.0a0']\n",
      "  - atomicwrites -> python[version='3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - attrs -> python[version='2.7.*,>=2.7,<2.8.0a0']\n",
      "  - automat -> python[version='2.7.*,>=2.7,<2.8.0a0']\n",
      "  - babel -> python[version='2.7.*,3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - backcall -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - backports.os -> python[version='3.6.*,>=3.6,<3.7.0a0']\n",
      "  - backports.shutil_get_terminal_size -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - basemap==1.2.0 -> python[version='2.7.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - bcrypt -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - beautifulsoup4 -> python[version='2.7.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - bitarray -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - bkcharts -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - blaze -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - bleach -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - bokeh -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - boto -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - boto3==1.7.62 -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - botocore -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - bottleneck -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - branca -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - certifi -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - cffi -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - chardet -> python[version='2.7.*,3.5.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - click -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - cloudpickle -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - clyent -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - colorama -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - conda-build -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - conda-env -> python[version='2.7.*,3.4.*,3.5.*']\n",
      "  - conda-package-handling -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - conda[version='>=4.7.10'] -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - constantly -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - contextlib2 -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - cryptography -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - cycler -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - cython -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - cytoolz -> python[version='2.7.*,3.4.*,>=3.6,<3.7.0a0']\n",
      "  - dask -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - dask-core -> python[version='>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - datashape -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - decorator -> python[version='3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0']\n",
      "  - defusedxml -> python[version='3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - distributed -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - docutils -> python[version='2.7.*,3.5.*,3.6.*,>=3.7,<3.8.0a0']\n",
      "  - entrypoints -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - et_xmlfile -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - fastcache -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - filelock -> python[version='2.7.*,3.4.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - flask -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - flask-cors -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - folium=0.5.0 -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - gast -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - geopy==1.11.0 -> python[version='2.7.*,3.4.*,3.5.*,3.6.*']\n",
      "  - get_terminal_size -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - gevent -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - glob2 -> python[version='3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - gmpy2 -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - greenlet -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - grpcio -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - h5py==2.8.0 -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - heapdict -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - html5lib -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0']\n",
      "  - hyperlink -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - idna -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - imageio==2.4.1 -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - imagesize -> python[version='3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - importlib_metadata -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - incremental -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - ipykernel -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - ipython -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - ipython-sql==0.3.9 -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - ipython_genutils -> python[version='2.7.*,3.4.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - ipywidgets==7.4.2 -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - isort -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - itsdangerous -> python[version='3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - jdcal -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - jedi -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - jeepney -> python[version='>=3.5,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - jinja2 -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - jmespath -> python[version='2.7.*,3.5.*,3.6.*']\n",
      "  - jsonschema -> python[version='2.7.*,>=2.7,<2.8.0a0']\n",
      "  - jupyter -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - jupyter_client -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - jupyter_console -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - jupyter_core -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - jupyterlab==1.0.1 -> python[version='3.6.*,>=3.6,<3.7.0a0']\n",
      "  - jupyterlab_launcher -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - jupyterlab_server -> python[version='3.5.*,3.6.*,>=3.6,<3.7.0a0']\n",
      "  - keras -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - keyring -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - kiwisolver -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - lazy-object-proxy -> python[version='3.5.*,3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - libarchive -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - llvmlite -> python[version='2.7.*,>=2.7,<2.8.0a0']\n",
      "  - locket -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - lxml -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - markdown -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - markupsafe -> python[version='3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - matplotlib -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - mccabe -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - mistune -> python[version='3.5.*,3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - mkl-service -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - mkl_fft -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - mkl_random -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - more-itertools -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - mpmath -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - msgpack-python -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - multipledispatch -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - navigator-updater -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - nbconvert -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - nbformat -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - networkx -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - ninja -> python[version='>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - nltk -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - nose -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - notebook -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - numba -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - numexpr -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - numpy -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - numpy-base -> python[version='>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - numpydoc -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - odo -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - olefile -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - opencv==3.4.2 -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - openpyxl -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - packaging -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - pandas -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - pandocfilters -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0']\n",
      "  - parso -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - partd -> python[version='2.7.*,3.4.*,>=3.6,<3.7.0a0']\n",
      "  - path.py -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - pathlib2 -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - patsy -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - pep8 -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - pexpect -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - pickleshare -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - pillow -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - pip -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - pkginfo -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - pluggy -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - ply -> python[version='2.7.*,3.5.*,3.6.*,>=3.5,<3.6.0a0']\n",
      "  - prettytable -> python[version='2.7.*,3.4.*,3.5.*,3.6.*']\n",
      "  - prometheus_client -> python[version='2.7.*,>=2.7,<2.8.0a0']\n",
      "  - prompt_toolkit -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - protobuf -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - psutil -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - ptyprocess -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - py -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - py-lief -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - py-opencv -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - pyasn1 -> python[version='3.6.*,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - pyasn1-modules -> python[version='3.6.*,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - pycodestyle -> python[version='2.7.*,3.4.*,3.5.*,>=3.5,<3.6.0a0']\n",
      "  - pycosat -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - pycrypto -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - pycurl -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - pydotplus==2.0.2 -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - pyflakes -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - pygments -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - pyhamcrest -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - pylint -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - pyodbc -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - pyopenssl -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - pyparsing -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - pyproj -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - pyqt -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - pyrsistent -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - pyshp -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - pysocks -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - pytables -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - pytest -> python[version='2.7.*,>=2.7,<2.8.0a0']\n",
      "  - pytest-arraydiff -> python[version='2.7.*,>=2.7,<2.8.0a0']\n",
      "  - pytest-astropy -> python[version='2.7.*,>=2.7,<2.8.0a0']\n",
      "  - pytest-doctestplus -> python[version='2.7.*,>=2.7,<2.8.0a0']\n",
      "  - pytest-openfiles -> python[version='2.7.*,>=2.7,<2.8.0a0']\n",
      "  - pytest-remotedata -> python[version='2.7.*,>=2.7,<2.8.0a0']\n",
      "  - python-dateutil -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - python-libarchive-c==2.8 -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - pytorch==0.4.1 -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - pytz -> python[version='2.7.*,3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - pywavelets -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - pyyaml -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - pyzmq -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - qt -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - qtawesome -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - qtconsole -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - qtpy -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - requests -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - rope -> python[version='3.5.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - ruamel_yaml -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - s3transfer -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - scikit-image -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - scikit-learn==0.20.1 -> python[version='2.7.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - scipy -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - seaborn==0.9.0 -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - secretstorage -> python[version='3.5.*,3.6.*,>=3.7,<3.8.0a0']\n",
      "  - send2trash -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - service_identity -> python[version='2.7.*,>=2.7,<2.8.0a0']\n",
      "  - setuptools -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - simplegeneric -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - singledispatch -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - sip -> python[version='3.4.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - snowballstemmer -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - sortedcollections -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - sortedcontainers -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - soupsieve -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - sphinx -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - sphinxcontrib -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - sphinxcontrib-websupport -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - spyder -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - spyder-kernels -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - sqlalchemy -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - sqlparse -> python[version='2.7.*,3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0']\n",
      "  - statsmodels -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - sympy -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - tblib -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - tensorboard -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - tensorflow -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - tensorflow-base -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - termcolor -> python[version='2.7.*,3.4.*,3.5.*,>=3.6,<3.7.0a0']\n",
      "  - terminado -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - testpath -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - toolz -> python[version='2.7.*,3.4.*,>=3.6,<3.7.0a0']\n",
      "  - torchvision==0.2.1 -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - tornado -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - tqdm -> python[version='3.4.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - traitlets -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - twisted -> python[version='2.7.*,>=2.7,<2.8.0a0']\n",
      "  - typed-ast -> python[version='3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - unicodecsv -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - urllib3 -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0']\n",
      "  - vincent -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - wcwidth -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - webencodings -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0']\n",
      "  - werkzeug -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0']\n",
      "  - wheel -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - widgetsnbextension -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - wrapt -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - wurlitzer -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - xlrd -> python[version='2.7.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0']\n",
      "  - xlsxwriter -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0']\n",
      "  - xlwt -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - zict -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0']\n",
      "  - zipp -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - zope -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0']\n",
      "  - zope.interface -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "  - zstd -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0']\n",
      "\n",
      "If python is on the left-most side of the chain, that's the version you've asked for.\n",
      "When python appears to the right, that indicates that the thing on the left is somehow\n",
      "not available for the python version you are constrained to.  Your current python version\n",
      "is (python=3.7).  Note that conda will not change your python version to a different minor version\n",
      "unless you explicitly specify that.\n",
      "\n",
      "The following specifications were found to be incompatible with each other:\n",
      "\n",
      "\n",
      "\n",
      "Package wheel conflicts for:\n",
      "ninja -> python[version='>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "altair -> pandas -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "anaconda-client -> requests[version='>=2.0,>=2.9.1'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "markdown -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "cloudpickle -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py35he6673a0_0 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "anaconda -> _anaconda_depends -> prometheus_client -> twisted -> service_identity[version='>=18.1.0'] -> attrs[version='>=16.0.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "jsonschema -> attrs[version='>=17.4.0'] -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "appdirs -> python[version='2.7.*,3.5.*,3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "qt -> gtk2 -> gdk-pixbuf -> gobject-introspection -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "mistune -> python[version='3.5.*,3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "alabaster -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip -> wheel\n",
      "zict -> heapdict -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "scikit-learn==0.20.1 -> scipy -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "flask-cors -> flask[version='>=0.9'] -> jinja2[version='>=2.10,>=2.4'] -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "ptyprocess -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "folium=0.5.0 -> vincent -> pandas -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "requests -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "xlsxwriter -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip -> wheel\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py37he6673a0_0 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "zipp -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "toolz -> python[version='2.7.*,3.4.*,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "idna -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "tensorflow -> tensorflow-estimator[version='>=1.14.0,<1.15.0'] -> tensorflow-base[version='>=1.14.0,<1.15.0a0'] -> keras-preprocessing[version='>=1.0.5'] -> keras[version='>=2.1.6'] -> keras-base=2.2.0 -> keras-applications[version='1.0.2.*,1.0.4.*,>=1.0.6'] -> numpy[version='>=1.9.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "msgpack-python -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py36hb342d67_1 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "statsmodels -> patsy[version='>=0.4.0'] -> scipy -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "llvmlite -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "torchvision==0.2.1 -> pytorch[version='>=0.3,>=0.4'] -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "blaze -> odo[version='>=0.5.0'] -> dask[version='>=0.11.1'] -> distributed[version='>=1.16.0,>=1.21.0,>=1.23.2,>=1.23.3,>=1.25.3,>=1.26.0'] -> bokeh[version='>=0.12.1,>=0.12.3'] -> numpy[version='>=1.7.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "gast -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "protobuf -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "ruamel_yaml -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "entrypoints -> configparser[version='>=3.5'] -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "bokeh -> numpy[version='>=1.7.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "chardet -> python[version='2.7.*,3.5.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "pyrsistent -> six -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py35h8fa1ad8_0 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "matplotlib -> numpy[version='1.10.*,1.11.*'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "xlwt -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "branca -> jinja2 -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "flask -> jinja2[version='>=2.10,>=2.4'] -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "pyyaml -> cython -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "pytest-remotedata -> pytest[version='>=3.1'] -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "keras -> keras-base=2.2.0 -> keras-preprocessing[version='1.0.1.*,1.0.2.*,>=1.0.5'] -> scipy[version='>=0.14'] -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "sphinxcontrib-serializinghtml -> python[version='>=3.5'] -> pip -> wheel\n",
      "glob2 -> python[version='3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "kiwisolver -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "packaging -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pyopenssl -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "geopy==1.11.0 -> python[version='2.7.*,3.4.*,3.5.*,3.6.*'] -> pip -> wheel\n",
      "soupsieve -> backports.functools_lru_cache -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "certifi -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "attrs -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "jupyterlab_launcher -> notebook -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "sortedcontainers -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "tensorboard -> numpy[version='>=1.12,>=1.12.0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "ipywidgets==7.4.2 -> widgetsnbextension[version='>=1.2.3,>=3.0.0,<4.0.0,>=3.1.0,<4.0,>=3.1.0,<4.0.0'] -> notebook[version='>=4.2.0,>=4.4.1'] -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "mpmath -> gmpy2 -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pytest-arraydiff -> pytest -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "imageio==2.4.1 -> numpy -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "beautifulsoup4 -> python[version='2.7.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "jedi -> parso[version='>=0.1.0,<0.2,>=0.2.0,>=0.3.0'] -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "get_terminal_size -> backports.shutil_get_terminal_size -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "docutils -> python[version='2.7.*,3.5.*,3.6.*,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "scikit-image -> dask[version='>=0.5'] -> distributed[version='>=1.16.0,>=1.21.0,>=1.23.2,>=1.23.3,>=1.25.3,>=1.26.0'] -> bokeh[version='>=0.12.1,>=0.12.3'] -> numpy[version='>=1.7.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "terminado -> tornado[version='>=4'] -> ssl_match_hostname -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "bleach -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "pluggy -> importlib_metadata[version='>=0.12'] -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "mkl_random -> numpy[version='>=1.11,>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_fft -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "h5py==2.8.0 -> numpy[version='1.12.*,>=1.8,>=1.8,<1.14,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "grpcio -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "locket -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "html5lib -> webencodings -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip -> wheel\n",
      "asn1crypto -> python[version='3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "singledispatch -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "hyperlink -> idna[version='>=2.5'] -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "sip -> python[version='3.4.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "jupyterlab_server -> notebook -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='3.5.*,3.6.*,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "importlib_metadata -> pathlib2 -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "testpath -> pathlib2 -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "jinja2 -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "sphinxcontrib-qthelp -> python[version='>=3.5'] -> pip -> wheel\n",
      "nltk -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pydotplus==2.0.2 -> pyparsing[version='>=2.0.1'] -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "sphinxcontrib-htmlhelp -> python[version='>=3.5'] -> pip -> wheel\n",
      "defusedxml -> python[version='3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "odo -> dask[version='>=0.11.1'] -> distributed[version='>=1.16.0,>=1.21.0,>=1.23.2,>=1.23.3,>=1.25.3,>=1.26.0'] -> bokeh[version='>=0.12.1,>=0.12.3'] -> numpy[version='>=1.7.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "colorama -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "traitlets -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "partd -> toolz -> python[version='2.7.*,3.4.*,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "boto -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "ipython_genutils -> python[version='2.7.*,3.4.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "olefile -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "fastcache -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "conda-build -> conda-verify -> conda-package-handling[version='>=1.0.4'] -> python-libarchive-c -> libarchive -> zstd[version='>=1.3.3,<1.3.4.0a0,>=1.3.7,<1.3.8.0a0'] -> lz4 -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "spyder-kernels -> ipykernel[version='>4.9.0'] -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "nbconvert -> nbformat[version='>=4.4'] -> jupyter_core -> traitlets -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "itsdangerous -> python[version='3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "sphinx -> requests[version='>=2.0.0,>=2.5.0'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pip -> wheel\n",
      "python-libarchive-c==2.8 -> libarchive -> zstd[version='>=1.3.3,<1.3.4.0a0,>=1.3.7,<1.3.8.0a0'] -> lz4 -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "sphinxcontrib-jsmath -> python[version='>=3.5'] -> pip -> wheel\n",
      "tblib -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "bkcharts -> pandas -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pytest -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "pickleshare -> pathlib2 -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "openpyxl -> jdcal -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "automat -> attrs -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "pycodestyle -> python[version='2.7.*,3.4.*,3.5.*,>=3.5,<3.6.0a0'] -> pip -> wheel\n",
      "pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "parso -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "boto3==1.7.62 -> s3transfer[version='>=0.1.10,<0.2.0'] -> botocore[version='>=1.3.0,<2.0.0'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "networkx -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "mccabe -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "anaconda-navigator -> anaconda-project[version='>=0.4'] -> anaconda-client -> requests[version='>=2.0,>=2.9.1'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "cython -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "pysocks -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "lazy-object-proxy -> python[version='3.5.*,3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pycrypto -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "qtawesome -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "webencodings -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip -> wheel\n",
      "backports.os -> future -> python[version='3.6.*,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "ply -> python[version='2.7.*,3.5.*,3.6.*,>=3.5,<3.6.0a0'] -> pip -> wheel\n",
      "prettytable -> python[version='2.7.*,3.4.*,3.5.*,3.6.*'] -> pip -> wheel\n",
      "cryptography -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "wurlitzer -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pytest-doctestplus -> pytest[version='>=2.8'] -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "sphinxcontrib-applehelp -> python[version='>=3.5'] -> pip -> wheel\n",
      "py-lief -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "sortedcollections -> sortedcontainers[version='>=2.0'] -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "zstd -> lz4 -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "numpy -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "py -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "xlrd -> python[version='2.7.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "cytoolz -> toolz[version='>=0.10.0,>=0.8.0'] -> python[version='2.7.*,3.4.*,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "qtpy -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "astor -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "qtconsole -> pyqt[version='4.11.*,>=5.9.2,<5.10.0a0'] -> qt[version='4.8.*,5.6.*,5.9.*,>=4.8.6,<5.0,>=5.6.2,<5.7.0a0,>=5.9.4,<5.10.0a0,>=5.9.6,<5.10.0a0'] -> gtk2 -> gdk-pixbuf -> gobject-introspection -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "keyring -> secretstorage -> cryptography -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "ipython -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "sphinxcontrib-websupport -> sphinxcontrib -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "libarchive -> zstd[version='>=1.3.3,<1.3.4.0a0,>=1.3.7,<1.3.8.0a0'] -> lz4 -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "_ipyw_jlab_nb_ext_conf -> jupyterlab -> jupyterlab_server[version='>=0.2.0,<0.3.0'] -> notebook -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "tornado -> ssl_match_hostname -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "termcolor -> python[version='2.7.*,3.4.*,3.5.*,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "bcrypt -> cffi[version='>=1.1'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "anaconda-project -> anaconda-client -> requests[version='>=2.0,>=2.9.1'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "tensorflow-base -> keras-preprocessing[version='>=1.0.5'] -> keras[version='>=2.1.6'] -> keras-base=2.2.0 -> keras-applications[version='1.0.2.*,1.0.4.*,>=1.0.6'] -> numpy[version='>=1.9.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "mkl_fft -> numpy[version='>=1.11,>=1.11.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pandas -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pyshp -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "cffi -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "numpy-base -> python[version='>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "botocore -> urllib3[version='>=1.20,<1.24,>=1.20,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "contextlib2 -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "lxml -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "isort -> backports.functools_lru_cache -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "numpydoc -> sphinx -> requests[version='>=2.0.0,>=2.5.0'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "s3transfer -> botocore[version='>=1.12.36,<2.0.0,>=1.3.0,<2.0.0'] -> urllib3[version='>=1.20,<1.24,>=1.20,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pathlib2 -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "conda[version='>=4.7.10'] -> requests[version='>=2.12.4,>=2.12.4,<3,>=2.18.4,<3,>=2.5.3'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "basemap==1.2.0 -> pyproj[version='>=1.9.3,>=1.9.3,<2'] -> numpy -> mkl_random -> python[version='2.7.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pep8 -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "bottleneck -> numpy[version='1.10.*,1.11.*,1.12.*,>=1.11.3,<2.0a0,>=1.8,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "imagesize -> python[version='3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "nose -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "jupyter_console -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "prompt_toolkit -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "rope -> python[version='3.5.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "typed-ast -> python[version='3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "jmespath -> python[version='2.7.*,3.5.*,3.6.*'] -> pip -> wheel\n",
      "pexpect -> ptyprocess[version='>=0.5'] -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pyqt -> qt[version='4.8.*,5.6.*,5.9.*,>=4.8.6,<5.0,>=5.6.2,<5.7.0a0,>=5.9.4,<5.10.0a0,>=5.9.6,<5.10.0a0'] -> gtk2 -> gdk-pixbuf -> gobject-introspection -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "nbformat -> jupyter_core -> traitlets -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "patsy -> scipy -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "incremental -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "pytorch==0.4.1 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "constantly -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "seaborn==0.9.0 -> statsmodels[version='>=0.5.0'] -> patsy[version='>=0.4.0'] -> scipy -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pillow -> olefile -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py37h8fa1ad8_0 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "jupyter_core -> traitlets -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "jupyter_client -> tornado[version='>=4.1'] -> ssl_match_hostname -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "gmpy2 -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "mkl-service -> numpy[version='>=1.11.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "backcall -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "et_xmlfile -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "sphinxcontrib-devhelp -> python[version='>=3.5'] -> pip -> wheel\n",
      "ipython-sql==0.3.9 -> ipython[version='>=1.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "pytest-openfiles -> pytest[version='>=2.8.0,>=3.1'] -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "sphinxcontrib -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "click -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "numba -> numpy[version='1.15.*,>=1.11,<1.12.0a0,>=1.13,<1.14.0a0,>=1.14,<1.15.0a0,>=1.15.4,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "snowballstemmer -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "py-opencv -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "backports.shutil_get_terminal_size -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "psutil -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "jeepney -> python[version='>=3.5,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pyproj -> numpy -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "datashape -> numpy[version='>=1.7'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "json5 -> python -> pip -> wheel\n",
      "numexpr -> numpy[version='1.11.*,1.12.*,>=1.11.3,>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py35hb342d67_1 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "spyder -> numpydoc -> sphinx -> requests[version='>=2.0.0,>=2.5.0'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pyodbc -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "clyent -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "astropy -> pytest[version='<3.7'] -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "tqdm -> python[version='3.4.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "werkzeug -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "vincent -> pandas -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "path.py -> importlib_metadata[version='>=0.5'] -> pathlib2 -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "secretstorage -> cryptography -> cffi[version='>=1.7'] -> pycparser -> python[version='3.5.*,3.6.*,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pyzmq -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "dask -> distributed[version='>=1.16.0,>=1.21.0,>=1.23.2,>=1.23.3,>=1.25.3,>=1.26.0'] -> bokeh[version='>=0.12.1,>=0.12.3'] -> numpy[version='>=1.7.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "wcwidth -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "filelock -> python[version='2.7.*,3.4.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "urllib3 -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "python=3.7 -> pip -> wheel\n",
      "conda-env -> python[version='2.7.*,3.4.*,3.5.*'] -> pip -> wheel\n",
      "distributed -> dask[version='>=0.11.0,>=0.12.0,>=0.13.0'] -> pandas[version='>=0.18.0,>=0.19.0'] -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "service_identity -> attrs[version='>=16.0.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "twisted -> service_identity[version='>=18.1.0'] -> attrs[version='>=16.0.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "atomicwrites -> python[version='3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "navigator-updater -> pyqt[version='>=5.6'] -> qt[version='4.8.*,5.6.*,5.9.*,>=4.8.6,<5.0,>=5.6.2,<5.7.0a0,>=5.9.4,<5.10.0a0,>=5.9.6,<5.10.0a0'] -> gtk2 -> gdk-pixbuf -> gobject-introspection -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "pytz -> python[version='2.7.*,3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "more-itertools -> six[version='>=1.0.0,<2.0.0'] -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "gevent -> cffi[version='>=1.11.5,>=1.3.0'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "markupsafe -> python[version='3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "sympy -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "greenlet -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "absl-py -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "zope.interface -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "sqlalchemy -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "sqlparse -> python[version='2.7.*,3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip -> wheel\n",
      "multipledispatch -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pyhamcrest -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pycosat -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "zope -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "send2trash -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "heapdict -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pytables -> numexpr -> numpy[version='1.11.*,1.12.*,>=1.11.3,>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "notebook -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "cycler -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pycurl -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "wrapt -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "python-dateutil -> six[version='>=1.5'] -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pyasn1 -> python[version='3.6.*,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "jupyterlab==1.0.1 -> notebook -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='3.6.*,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "pywavelets -> numpy[version='1.11.*,>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "astroid -> backports.functools_lru_cache -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "pkginfo -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "prometheus_client -> twisted -> service_identity[version='>=18.1.0'] -> attrs[version='>=16.0.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "pytest-astropy -> pytest[version='>=3.1,>=3.1.0'] -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel\n",
      "conda-package-handling -> python-libarchive-c -> libarchive -> zstd[version='>=1.3.3,<1.3.4.0a0,>=1.3.7,<1.3.8.0a0'] -> lz4 -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "pyasn1-modules -> pyasn1[version='>=0.1.8,>=0.4.1,<0.5.0'] -> python[version='3.6.*,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pyflakes -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "pyparsing -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "decorator -> python[version='3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip -> wheel\n",
      "pandocfilters -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip -> wheel\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py36h765d7f9_1 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "pylint -> isort[version='>=4.2.5'] -> backports.functools_lru_cache -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "scipy -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "unicodecsv -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "jupyter -> ipywidgets -> widgetsnbextension[version='>=1.2.3,>=2.0.0,<3.0.0,>=3.0.0,<4.0.0,>=3.1.0,<4.0,>=3.1.0,<4.0.0,>=3.3.0,<3.4.0'] -> notebook[version='>=4.2.0,>=4.4.1'] -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "dask-core -> python[version='>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "simplegeneric -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "widgetsnbextension -> notebook[version='>=4.2.0,>=4.4.1'] -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "babel -> pytz -> python[version='2.7.*,3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel\n",
      "bitarray -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "jdcal -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel\n",
      "Package setuptools conflicts for:\n",
      "pytest-arraydiff -> pytest -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "ipython -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools\n",
      "vincent -> pandas -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "scikit-learn==0.20.1 -> scipy -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "sortedcontainers -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "et_xmlfile -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "colorama -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pycosat -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "glob2 -> python[version='3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "cytoolz -> toolz[version='>=0.10.0,>=0.8.0'] -> python[version='2.7.*,3.4.*,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "simplegeneric -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "itsdangerous -> python[version='3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "pyflakes -> setuptools\n",
      "tensorflow -> tensorflow-estimator[version='>=1.14.0,<1.15.0'] -> tensorflow-base[version='>=1.14.0,<1.15.0a0'] -> keras-preprocessing[version='>=1.0.5'] -> keras[version='>=2.1.6'] -> keras-base=2.2.0 -> keras-applications[version='1.0.2.*,1.0.4.*,>=1.0.6'] -> numpy[version='>=1.9.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "send2trash -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "spyder -> numpydoc -> sphinx -> requests[version='>=2.0.0,>=2.5.0'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "sqlparse -> python[version='2.7.*,3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip -> wheel -> setuptools\n",
      "backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "pycrypto -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "qt -> gtk2 -> gdk-pixbuf -> gobject-introspection -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "python=3.7 -> pip -> wheel -> setuptools\n",
      "fastcache -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "ply -> python[version='2.7.*,3.5.*,3.6.*,>=3.5,<3.6.0a0'] -> pip -> wheel -> setuptools\n",
      "importlib_metadata -> pathlib2 -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pydotplus==2.0.2 -> pyparsing[version='>=2.0.1'] -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pycodestyle -> python[version='2.7.*,3.4.*,3.5.*,>=3.5,<3.6.0a0'] -> pip -> wheel -> setuptools\n",
      "sip -> python[version='3.4.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pyasn1-modules -> pyasn1[version='>=0.1.8,>=0.4.1,<0.5.0'] -> python[version='3.6.*,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "numpy -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "attrs -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "jedi -> parso[version='>=0.1.0,<0.2,>=0.2.0,>=0.3.0'] -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "soupsieve -> backports.functools_lru_cache -> setuptools\n",
      "matplotlib -> numpy[version='1.10.*,1.11.*'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "cython -> setuptools\n",
      "zstd -> lz4 -> setuptools\n",
      "nltk -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "py-lief -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "numba -> numpy[version='1.15.*,>=1.11,<1.12.0a0,>=1.13,<1.14.0a0,>=1.14,<1.15.0a0,>=1.15.4,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "atomicwrites -> python[version='3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "statsmodels -> patsy[version='>=0.4.0'] -> scipy -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "sphinxcontrib-applehelp -> python[version='>=3.5'] -> pip -> wheel -> setuptools\n",
      "bokeh -> numpy[version='>=1.7.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "bleach -> setuptools\n",
      "wrapt -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pytest -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "tblib -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "hyperlink -> idna[version='>=2.5'] -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "incremental -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py35hb342d67_1 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "rope -> python[version='3.5.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "beautifulsoup4 -> python[version='2.7.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "conda-env -> python[version='2.7.*,3.4.*,3.5.*'] -> pip -> wheel -> setuptools\n",
      "grpcio -> setuptools\n",
      "singledispatch -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "backports.os -> future -> python[version='3.6.*,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "s3transfer -> botocore[version='>=1.12.36,<2.0.0,>=1.3.0,<2.0.0'] -> urllib3[version='>=1.20,<1.24,>=1.20,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "typed-ast -> python[version='3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pysocks -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "heapdict -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "spyder-kernels -> ipykernel[version='>4.9.0'] -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools\n",
      "sphinxcontrib-htmlhelp -> python[version='>=3.5'] -> pip -> wheel -> setuptools\n",
      "pyparsing -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "lazy-object-proxy -> python[version='3.5.*,3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "wurlitzer -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py37he6673a0_0 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "nose -> setuptools\n",
      "pyyaml -> cython -> setuptools\n",
      "conda-package-handling -> python-libarchive-c -> libarchive -> zstd[version='>=1.3.3,<1.3.4.0a0,>=1.3.7,<1.3.8.0a0'] -> lz4 -> setuptools\n",
      "botocore -> urllib3[version='>=1.20,<1.24,>=1.20,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "conda[version='>=4.7.10'] -> requests[version='>=2.12.4,>=2.12.4,<3,>=2.18.4,<3,>=2.5.3'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "backcall -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "networkx -> setuptools\n",
      "mccabe -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "ipython_genutils -> python[version='2.7.*,3.4.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "bottleneck -> numpy[version='1.10.*,1.11.*,1.12.*,>=1.11.3,<2.0a0,>=1.8,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "requests -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "jupyter -> ipywidgets -> widgetsnbextension[version='>=1.2.3,>=2.0.0,<3.0.0,>=3.0.0,<4.0.0,>=3.1.0,<4.0,>=3.1.0,<4.0.0,>=3.3.0,<3.4.0'] -> notebook[version='>=4.2.0,>=4.4.1'] -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools\n",
      "absl-py -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py35h8fa1ad8_0 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "alabaster -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip -> wheel -> setuptools\n",
      "jeepney -> python[version='>=3.5,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "sortedcollections -> sortedcontainers[version='>=2.0'] -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "packaging -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pyasn1 -> python[version='3.6.*,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pip -> wheel -> setuptools\n",
      "pytest-openfiles -> pytest[version='>=2.8.0,>=3.1'] -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "mkl-service -> numpy[version='>=1.11.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "webencodings -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip -> wheel -> setuptools\n",
      "jupyterlab_server -> notebook -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools\n",
      "xlrd -> python[version='2.7.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "protobuf -> setuptools\n",
      "bkcharts -> pandas -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "psutil -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "geopy==1.11.0 -> python[version='2.7.*,3.4.*,3.5.*,3.6.*'] -> pip -> wheel -> setuptools\n",
      "libarchive -> zstd[version='>=1.3.3,<1.3.4.0a0,>=1.3.7,<1.3.8.0a0'] -> lz4 -> setuptools\n",
      "pkginfo -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "folium=0.5.0 -> vincent -> pandas -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "traitlets -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "lxml -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "sphinxcontrib -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "anaconda-project -> anaconda-client -> requests[version='>=2.0,>=2.9.1'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "cffi -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pexpect -> ptyprocess[version='>=0.5'] -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "automat -> attrs -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pathlib2 -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pywavelets -> numpy[version='1.11.*,>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pyshp -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "entrypoints -> configparser[version='>=3.5'] -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "mpmath -> gmpy2 -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py36h765d7f9_1 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "python-dateutil -> six[version='>=1.5'] -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "sqlalchemy -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "click -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "boto3==1.7.62 -> s3transfer[version='>=0.1.10,<0.2.0'] -> botocore[version='>=1.3.0,<2.0.0'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "jdcal -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "kiwisolver -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "python-libarchive-c==2.8 -> libarchive -> zstd[version='>=1.3.3,<1.3.4.0a0,>=1.3.7,<1.3.8.0a0'] -> lz4 -> setuptools\n",
      "boto -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "olefile -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "pyqt -> qt[version='4.8.*,5.6.*,5.9.*,>=4.8.6,<5.0,>=5.6.2,<5.7.0a0,>=5.9.4,<5.10.0a0,>=5.9.6,<5.10.0a0'] -> gtk2 -> gdk-pixbuf -> gobject-introspection -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "chardet -> python[version='2.7.*,3.5.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "xlsxwriter -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip -> wheel -> setuptools\n",
      "pytest-remotedata -> pytest[version='>=3.1'] -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "sphinxcontrib-jsmath -> python[version='>=3.5'] -> pip -> wheel -> setuptools\n",
      "bitarray -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "h5py==2.8.0 -> numpy[version='1.12.*,>=1.8,>=1.8,<1.14,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "ninja -> python[version='>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pyhamcrest -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "wheel -> setuptools\n",
      "pycurl -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "sphinxcontrib-serializinghtml -> python[version='>=3.5'] -> pip -> wheel -> setuptools\n",
      "altair -> pandas -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "filelock -> python[version='2.7.*,3.4.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "torchvision==0.2.1 -> pytorch[version='>=0.3,>=0.4'] -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "babel -> pytz -> python[version='2.7.*,3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "prompt_toolkit -> pygments -> setuptools\n",
      "jupyterlab==1.0.1 -> notebook -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools\n",
      "mistune -> python[version='3.5.*,3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "cycler -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "snowballstemmer -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pylint -> isort[version='>=4.2.5'] -> backports.functools_lru_cache -> setuptools\n",
      "branca -> jinja2 -> setuptools\n",
      "flask-cors -> flask[version='>=0.9'] -> jinja2[version='>=2.10,>=2.4'] -> setuptools\n",
      "msgpack-python -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "prometheus_client -> twisted -> service_identity[version='>=18.1.0'] -> attrs[version='>=16.0.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pep8 -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pyopenssl -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "docutils -> python[version='2.7.*,3.5.*,3.6.*,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pluggy -> importlib_metadata[version='>=0.12'] -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools\n",
      "keyring -> secretstorage -> cryptography -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "flask -> jinja2[version='>=2.10,>=2.4'] -> setuptools\n",
      "pyproj -> numpy -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "unicodecsv -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "jmespath -> python[version='2.7.*,3.5.*,3.6.*'] -> pip -> wheel -> setuptools\n",
      "patsy -> scipy -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pillow -> olefile -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "widgetsnbextension -> notebook[version='>=4.2.0,>=4.4.1'] -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools\n",
      "zipp -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "llvmlite -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "jsonschema -> attrs[version='>=17.4.0'] -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pytz -> python[version='2.7.*,3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "jupyter_client -> tornado[version='>=4.1'] -> ssl_match_hostname -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "distributed -> dask[version='>=0.11.0,>=0.12.0,>=0.13.0'] -> pandas[version='>=0.18.0,>=0.19.0'] -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "tensorboard -> numpy[version='>=1.12,>=1.12.0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "qtconsole -> pyqt[version='4.11.*,>=5.9.2,<5.10.0a0'] -> qt[version='4.8.*,5.6.*,5.9.*,>=4.8.6,<5.0,>=5.6.2,<5.7.0a0,>=5.9.4,<5.10.0a0,>=5.9.6,<5.10.0a0'] -> gtk2 -> gdk-pixbuf -> gobject-introspection -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "appdirs -> python[version='2.7.*,3.5.*,3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "keras -> keras-base=2.2.0 -> keras-preprocessing[version='1.0.1.*,1.0.2.*,>=1.0.5'] -> scipy[version='>=0.14'] -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "anaconda-client -> requests[version='>=2.0,>=2.9.1'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "partd -> toolz -> python[version='2.7.*,3.4.*,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "imageio==2.4.1 -> numpy -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pandas -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "mkl_random -> numpy[version='>=1.11,>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_fft -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "gmpy2 -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py37h8fa1ad8_0 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pytables -> numexpr -> numpy[version='1.11.*,1.12.*,>=1.11.3,>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "idna -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pytest-doctestplus -> pytest[version='>=2.8'] -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "gast -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "scikit-image -> dask[version='>=0.5'] -> distributed[version='>=1.16.0,>=1.21.0,>=1.23.2,>=1.23.3,>=1.25.3,>=1.26.0'] -> bokeh[version='>=0.12.1,>=0.12.3'] -> numpy[version='>=1.7.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "scipy -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "defusedxml -> python[version='3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "pyzmq -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "seaborn==0.9.0 -> statsmodels[version='>=0.5.0'] -> patsy[version='>=0.4.0'] -> scipy -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "tornado -> ssl_match_hostname -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "qtawesome -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "nbformat -> jupyter_core -> traitlets -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "numexpr -> numpy[version='1.11.*,1.12.*,>=1.11.3,>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "ptyprocess -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "parso -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "openpyxl -> jdcal -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "get_terminal_size -> backports.shutil_get_terminal_size -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "imagesize -> python[version='3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "urllib3 -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "twisted -> service_identity[version='>=18.1.0'] -> attrs[version='>=16.0.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "numpydoc -> sphinx -> requests[version='>=2.0.0,>=2.5.0'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "anaconda-navigator -> anaconda-project[version='>=0.4'] -> anaconda-client -> requests[version='>=2.0,>=2.9.1'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "zope.interface -> setuptools\n",
      "xlwt -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "sympy -> setuptools\n",
      "toolz -> python[version='2.7.*,3.4.*,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "termcolor -> python[version='2.7.*,3.4.*,3.5.*,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "gevent -> cffi[version='>=1.11.5,>=1.3.0'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "sphinxcontrib-websupport -> sphinxcontrib -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pytorch==0.4.1 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "dask-core -> python[version='>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "markdown -> setuptools\n",
      "testpath -> pathlib2 -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "service_identity -> attrs[version='>=16.0.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "astroid -> backports.functools_lru_cache -> setuptools\n",
      "pyodbc -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "ipython-sql==0.3.9 -> ipython[version='>=1.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools\n",
      "certifi -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "cryptography -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "notebook -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools\n",
      "tqdm -> python[version='3.4.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "bcrypt -> cffi[version='>=1.1'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "py-opencv -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "multipledispatch -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "werkzeug -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "astor -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "sphinx -> requests[version='>=2.0.0,>=2.5.0'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "dask -> distributed[version='>=1.16.0,>=1.21.0,>=1.23.2,>=1.23.3,>=1.25.3,>=1.26.0'] -> bokeh[version='>=0.12.1,>=0.12.3'] -> numpy[version='>=1.7.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "numpy-base -> python[version='>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pandocfilters -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip -> wheel -> setuptools\n",
      "terminado -> tornado[version='>=4'] -> ssl_match_hostname -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "pickleshare -> pathlib2 -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "path.py -> importlib_metadata[version='>=0.5'] -> pathlib2 -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pyrsistent -> six -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "datashape -> numpy[version='>=1.7'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "qtpy -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "json5 -> python -> pip -> wheel -> setuptools\n",
      "ruamel_yaml -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "locket -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "jupyter_console -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools\n",
      "jupyterlab_launcher -> notebook -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools\n",
      "prettytable -> python[version='2.7.*,3.4.*,3.5.*,3.6.*'] -> pip -> wheel -> setuptools\n",
      "pytest-astropy -> pytest[version='>=3.1,>=3.1.0'] -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "mkl_fft -> numpy[version='>=1.11,>=1.11.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "zope -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "anaconda -> _anaconda_depends -> prometheus_client -> twisted -> service_identity[version='>=18.1.0'] -> attrs[version='>=16.0.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "isort -> backports.functools_lru_cache -> setuptools\n",
      "blaze -> odo[version='>=0.5.0'] -> dask[version='>=0.11.1'] -> distributed[version='>=1.16.0,>=1.21.0,>=1.23.2,>=1.23.3,>=1.25.3,>=1.26.0'] -> bokeh[version='>=0.12.1,>=0.12.3'] -> numpy[version='>=1.7.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "conda-build -> conda-verify -> conda-package-handling[version='>=1.0.4'] -> python-libarchive-c -> libarchive -> zstd[version='>=1.3.3,<1.3.4.0a0,>=1.3.7,<1.3.8.0a0'] -> lz4 -> setuptools\n",
      "odo -> dask[version='>=0.11.1'] -> distributed[version='>=1.16.0,>=1.21.0,>=1.23.2,>=1.23.3,>=1.25.3,>=1.26.0'] -> bokeh[version='>=0.12.1,>=0.12.3'] -> numpy[version='>=1.7.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "secretstorage -> cryptography -> cffi[version='>=1.7'] -> pycparser -> python[version='3.5.*,3.6.*,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "markupsafe -> python[version='3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "jupyter_core -> traitlets -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "decorator -> python[version='3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip -> wheel -> setuptools\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py35he6673a0_0 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "contextlib2 -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "_ipyw_jlab_nb_ext_conf -> jupyterlab -> jupyterlab_server[version='>=0.2.0,<0.3.0'] -> notebook -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools\n",
      "jinja2 -> setuptools\n",
      "backports.shutil_get_terminal_size -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "constantly -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "astropy -> pytest[version='<3.7'] -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip -> wheel -> setuptools\n",
      "pygments -> setuptools\n",
      "more-itertools -> six[version='>=1.0.0,<2.0.0'] -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "wcwidth -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "greenlet -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "asn1crypto -> python[version='3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py36hb342d67_1 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "py -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "html5lib -> webencodings -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip -> wheel -> setuptools\n",
      "sphinxcontrib-devhelp -> python[version='>=3.5'] -> pip -> wheel -> setuptools\n",
      "nbconvert -> nbformat[version='>=4.4'] -> jupyter_core -> traitlets -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "zict -> heapdict -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "tensorflow-base -> keras-preprocessing[version='>=1.0.5'] -> keras[version='>=2.1.6'] -> keras-base=2.2.0 -> keras-applications[version='1.0.2.*,1.0.4.*,>=1.0.6'] -> numpy[version='>=1.9.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "navigator-updater -> pyqt[version='>=5.6'] -> qt[version='4.8.*,5.6.*,5.9.*,>=4.8.6,<5.0,>=5.6.2,<5.7.0a0,>=5.9.4,<5.10.0a0,>=5.9.6,<5.10.0a0'] -> gtk2 -> gdk-pixbuf -> gobject-introspection -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip -> wheel -> setuptools\n",
      "cloudpickle -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "clyent -> setuptools\n",
      "sphinxcontrib-qthelp -> python[version='>=3.5'] -> pip -> wheel -> setuptools\n",
      "ipywidgets==7.4.2 -> widgetsnbextension[version='>=1.2.3,>=3.0.0,<4.0.0,>=3.1.0,<4.0,>=3.1.0,<4.0.0'] -> notebook[version='>=4.2.0,>=4.4.1'] -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools\n",
      "basemap==1.2.0 -> pyproj[version='>=1.9.3,>=1.9.3,<2'] -> numpy -> mkl_random -> python[version='2.7.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip -> wheel -> setuptools\n",
      "Package pip conflicts for:\n",
      "odo -> dask[version='>=0.11.1'] -> distributed[version='>=1.16.0,>=1.21.0,>=1.23.2,>=1.23.3,>=1.25.3,>=1.26.0'] -> bokeh[version='>=0.12.1,>=0.12.3'] -> numpy[version='>=1.7.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "anaconda -> _anaconda_depends -> prometheus_client -> twisted -> service_identity[version='>=18.1.0'] -> attrs[version='>=16.0.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip\n",
      "jupyterlab_server -> notebook -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='3.5.*,3.6.*,>=3.6,<3.7.0a0'] -> pip\n",
      "python-dateutil -> six[version='>=1.5'] -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "wcwidth -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pep8 -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pywavelets -> numpy[version='1.11.*,>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "typed-ast -> python[version='3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "mpmath -> gmpy2 -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "decorator -> python[version='3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip\n",
      "astroid -> backports.functools_lru_cache -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "conda-package-handling -> python-libarchive-c -> libarchive -> zstd[version='>=1.3.3,<1.3.4.0a0,>=1.3.7,<1.3.8.0a0'] -> lz4 -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "tensorflow-base -> keras-preprocessing[version='>=1.0.5'] -> keras[version='>=2.1.6'] -> keras-base=2.2.0 -> keras-applications[version='1.0.2.*,1.0.4.*,>=1.0.6'] -> numpy[version='>=1.9.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "hyperlink -> idna[version='>=2.5'] -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pycrypto -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "zope -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "py -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "openpyxl -> jdcal -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "networkx -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "html5lib -> webencodings -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip\n",
      "pyhamcrest -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "protobuf -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "nbformat -> jupyter_core -> traitlets -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "send2trash -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "isort -> backports.functools_lru_cache -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "pyopenssl -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "partd -> toolz -> python[version='2.7.*,3.4.*,>=3.6,<3.7.0a0'] -> pip\n",
      "patsy -> scipy -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "colorama -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "get_terminal_size -> backports.shutil_get_terminal_size -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "tornado -> ssl_match_hostname -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "pylint -> isort[version='>=4.2.5'] -> backports.functools_lru_cache -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "datashape -> numpy[version='>=1.7'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "terminado -> tornado[version='>=4'] -> ssl_match_hostname -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "jupyter -> ipywidgets -> widgetsnbextension[version='>=1.2.3,>=2.0.0,<3.0.0,>=3.0.0,<4.0.0,>=3.1.0,<4.0,>=3.1.0,<4.0.0,>=3.3.0,<3.4.0'] -> notebook[version='>=4.2.0,>=4.4.1'] -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "jeepney -> python[version='>=3.5,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "jupyter_console -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "jinja2 -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "secretstorage -> cryptography -> cffi[version='>=1.7'] -> pycparser -> python[version='3.5.*,3.6.*,>=3.7,<3.8.0a0'] -> pip\n",
      "spyder-kernels -> ipykernel[version='>4.9.0'] -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "sympy -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "requests -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "service_identity -> attrs[version='>=16.0.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip\n",
      "sip -> python[version='3.4.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "sqlparse -> python[version='2.7.*,3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip\n",
      "pyodbc -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "gevent -> cffi[version='>=1.11.5,>=1.3.0'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "spyder -> numpydoc -> sphinx -> requests[version='>=2.0.0,>=2.5.0'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "testpath -> pathlib2 -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "python=3.7 -> pip\n",
      "defusedxml -> python[version='3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "soupsieve -> backports.functools_lru_cache -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "ruamel_yaml -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "click -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pyasn1-modules -> pyasn1[version='>=0.1.8,>=0.4.1,<0.5.0'] -> python[version='3.6.*,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "absl-py -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "pysocks -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "cytoolz -> toolz[version='>=0.10.0,>=0.8.0'] -> python[version='2.7.*,3.4.*,>=3.6,<3.7.0a0'] -> pip\n",
      "incremental -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "tblib -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "matplotlib -> numpy[version='1.10.*,1.11.*'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "scipy -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "flask-cors -> flask[version='>=0.9'] -> jinja2[version='>=2.10,>=2.4'] -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "rope -> python[version='3.5.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "jupyter_client -> tornado[version='>=4.1'] -> ssl_match_hostname -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "markupsafe -> python[version='3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "kiwisolver -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "jupyterlab==1.0.1 -> notebook -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='3.6.*,>=3.6,<3.7.0a0'] -> pip\n",
      "tensorflow -> tensorflow-estimator[version='>=1.14.0,<1.15.0'] -> tensorflow-base[version='>=1.14.0,<1.15.0a0'] -> keras-preprocessing[version='>=1.0.5'] -> keras[version='>=2.1.6'] -> keras-base=2.2.0 -> keras-applications[version='1.0.2.*,1.0.4.*,>=1.0.6'] -> numpy[version='>=1.9.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "jmespath -> python[version='2.7.*,3.5.*,3.6.*'] -> pip\n",
      "prometheus_client -> twisted -> service_identity[version='>=18.1.0'] -> attrs[version='>=16.0.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip\n",
      "mistune -> python[version='3.5.*,3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pexpect -> ptyprocess[version='>=0.5'] -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "certifi -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "snowballstemmer -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "_ipyw_jlab_nb_ext_conf -> jupyterlab -> jupyterlab_server[version='>=0.2.0,<0.3.0'] -> notebook -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "urllib3 -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "lxml -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "json5 -> python -> pip\n",
      "ipywidgets==7.4.2 -> widgetsnbextension[version='>=1.2.3,>=3.0.0,<4.0.0,>=3.1.0,<4.0,>=3.1.0,<4.0.0'] -> notebook[version='>=4.2.0,>=4.4.1'] -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "py-lief -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "sortedcollections -> sortedcontainers[version='>=2.0'] -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "py-opencv -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "zict -> heapdict -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "sqlalchemy -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "simplegeneric -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "python-libarchive-c==2.8 -> libarchive -> zstd[version='>=1.3.3,<1.3.4.0a0,>=1.3.7,<1.3.8.0a0'] -> lz4 -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "bkcharts -> pandas -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pytz -> python[version='2.7.*,3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "folium=0.5.0 -> vincent -> pandas -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "sphinxcontrib-htmlhelp -> python[version='>=3.5'] -> pip\n",
      "sortedcontainers -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "ply -> python[version='2.7.*,3.5.*,3.6.*,>=3.5,<3.6.0a0'] -> pip\n",
      "lazy-object-proxy -> python[version='3.5.*,3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "ipython_genutils -> python[version='2.7.*,3.4.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "path.py -> importlib_metadata[version='>=0.5'] -> pathlib2 -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pluggy -> importlib_metadata[version='>=0.12'] -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "scikit-learn==0.20.1 -> scipy -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "termcolor -> python[version='2.7.*,3.4.*,3.5.*,>=3.6,<3.7.0a0'] -> pip\n",
      "pycosat -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "prompt_toolkit -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "contextlib2 -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "unicodecsv -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py37he6673a0_0 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "cycler -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "sphinxcontrib -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "glob2 -> python[version='3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "backports.os -> future -> python[version='3.6.*,>=3.6,<3.7.0a0'] -> pip\n",
      "sphinxcontrib-serializinghtml -> python[version='>=3.5'] -> pip\n",
      "pathlib2 -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "sphinxcontrib-qthelp -> python[version='>=3.5'] -> pip\n",
      "fastcache -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "jupyter_core -> traitlets -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py35hb342d67_1 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "grpcio -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "mkl_fft -> numpy[version='>=1.11,>=1.11.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "prettytable -> python[version='2.7.*,3.4.*,3.5.*,3.6.*'] -> pip\n",
      "pyproj -> numpy -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "qtpy -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "gmpy2 -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pyasn1 -> python[version='3.6.*,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "xlrd -> python[version='2.7.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "backcall -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "boto3==1.7.62 -> s3transfer[version='>=0.1.10,<0.2.0'] -> botocore[version='>=1.3.0,<2.0.0'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pytorch==0.4.1 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "zope.interface -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "greenlet -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "cloudpickle -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "altair -> pandas -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "libarchive -> zstd[version='>=1.3.3,<1.3.4.0a0,>=1.3.7,<1.3.8.0a0'] -> lz4 -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "werkzeug -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0'] -> pip\n",
      "toolz -> python[version='2.7.*,3.4.*,>=3.6,<3.7.0a0'] -> pip\n",
      "parso -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pillow -> olefile -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "s3transfer -> botocore[version='>=1.12.36,<2.0.0,>=1.3.0,<2.0.0'] -> urllib3[version='>=1.20,<1.24,>=1.20,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pyshp -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "imagesize -> python[version='3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pytest-arraydiff -> pytest -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip\n",
      "clyent -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "bleach -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "numpy-base -> python[version='>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "automat -> attrs -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip\n",
      "bokeh -> numpy[version='>=1.7.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pyyaml -> cython -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "pkginfo -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "scikit-image -> dask[version='>=0.5'] -> distributed[version='>=1.16.0,>=1.21.0,>=1.23.2,>=1.23.3,>=1.25.3,>=1.26.0'] -> bokeh[version='>=0.12.1,>=0.12.3'] -> numpy[version='>=1.7.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "bitarray -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "branca -> jinja2 -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py37h8fa1ad8_0 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "psutil -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pandocfilters -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip\n",
      "cffi -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "bottleneck -> numpy[version='1.10.*,1.11.*,1.12.*,>=1.11.3,<2.0a0,>=1.8,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "boto -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "msgpack-python -> python[version='2.7.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "sphinxcontrib-applehelp -> python[version='>=3.5'] -> pip\n",
      "tensorboard -> numpy[version='>=1.12,>=1.12.0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py35h8fa1ad8_0 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "numpy -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "zstd -> lz4 -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "jsonschema -> attrs[version='>=17.4.0'] -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip\n",
      "qtawesome -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "llvmlite -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip\n",
      "pytables -> numexpr -> numpy[version='1.11.*,1.12.*,>=1.11.3,>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "botocore -> urllib3[version='>=1.20,<1.24,>=1.20,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "dask-core -> python[version='>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "widgetsnbextension -> notebook[version='>=4.2.0,>=4.4.1'] -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "locket -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "pydotplus==2.0.2 -> pyparsing[version='>=2.0.1'] -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "twisted -> service_identity[version='>=18.1.0'] -> attrs[version='>=16.0.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip\n",
      "backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "entrypoints -> configparser[version='>=3.5'] -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py36hb342d67_1 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "xlwt -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "tqdm -> python[version='3.4.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "ipython -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py35he6673a0_0 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "xlsxwriter -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip\n",
      "nltk -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "astropy -> pytest[version='<3.7'] -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip\n",
      "keyring -> secretstorage -> cryptography -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pyzmq -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "h5py==2.8.0 -> numpy[version='1.12.*,>=1.8,>=1.8,<1.14,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "markdown -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "conda-build -> conda-verify -> conda-package-handling[version='>=1.0.4'] -> python-libarchive-c -> libarchive -> zstd[version='>=1.3.3,<1.3.4.0a0,>=1.3.7,<1.3.8.0a0'] -> lz4 -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "attrs -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip\n",
      "filelock -> python[version='2.7.*,3.4.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "chardet -> python[version='2.7.*,3.5.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "anaconda-navigator -> anaconda-project[version='>=0.4'] -> anaconda-client -> requests[version='>=2.0,>=2.9.1'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "ninja -> python[version='>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "distributed -> dask[version='>=0.11.0,>=0.12.0,>=0.13.0'] -> pandas[version='>=0.18.0,>=0.19.0'] -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "singledispatch -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "asn1crypto -> python[version='3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "atomicwrites -> python[version='3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "statsmodels -> patsy[version='>=0.4.0'] -> scipy -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "sphinxcontrib-devhelp -> python[version='>=3.5'] -> pip\n",
      "sphinxcontrib-websupport -> sphinxcontrib -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "packaging -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "torchvision==0.2.1 -> pytorch[version='>=0.3,>=0.4'] -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pytest-doctestplus -> pytest[version='>=2.8'] -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip\n",
      "anaconda-client -> requests[version='>=2.0,>=2.9.1'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "opencv==3.4.2 -> py-opencv==3.4.2=py36h765d7f9_1 -> numpy[version='>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "geopy==1.11.0 -> python[version='2.7.*,3.4.*,3.5.*,3.6.*'] -> pip\n",
      "backports.shutil_get_terminal_size -> backports -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "mkl_random -> numpy[version='>=1.11,>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_fft -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "astor -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "heapdict -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "numexpr -> numpy[version='1.11.*,1.12.*,>=1.11.3,>=1.11.3,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "anaconda-project -> anaconda-client -> requests[version='>=2.0,>=2.9.1'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "traitlets -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "alabaster -> python[version='2.7.*,3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip\n",
      "wheel -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "pytest-remotedata -> pytest[version='>=3.1'] -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip\n",
      "cryptography -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pyparsing -> python[version='2.7.*,3.4.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "appdirs -> python[version='2.7.*,3.5.*,3.6.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "idna -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "dask -> distributed[version='>=1.16.0,>=1.21.0,>=1.23.2,>=1.23.3,>=1.25.3,>=1.26.0'] -> bokeh[version='>=0.12.1,>=0.12.3'] -> numpy[version='>=1.7.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "jedi -> parso[version='>=0.1.0,<0.2,>=0.2.0,>=0.3.0'] -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "constantly -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "webencodings -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0'] -> pip\n",
      "imageio==2.4.1 -> numpy -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "notebook -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "keras -> keras-base=2.2.0 -> keras-preprocessing[version='1.0.1.*,1.0.2.*,>=1.0.5'] -> scipy[version='>=0.14'] -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "seaborn==0.9.0 -> statsmodels[version='>=0.5.0'] -> patsy[version='>=0.4.0'] -> scipy -> numpy[version='1.10.*,>=1.11.3,<2.0a0,>=1.9,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "olefile -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "pytest-openfiles -> pytest[version='>=2.8.0,>=3.1'] -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip\n",
      "six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "jdcal -> python[version='2.7.*,3.5.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "docutils -> python[version='2.7.*,3.5.*,3.6.*,>=3.7,<3.8.0a0'] -> pip\n",
      "mccabe -> python[version='2.7.*,3.4.*,3.6.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "pyrsistent -> six -> python[version='3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pycurl -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pytest -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip\n",
      "pyflakes -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "wurlitzer -> python[version='>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "qtconsole -> pyqt[version='4.11.*,>=5.9.2,<5.10.0a0'] -> qt[version='4.8.*,5.6.*,5.9.*,>=4.8.6,<5.0,>=5.6.2,<5.7.0a0,>=5.9.4,<5.10.0a0,>=5.9.6,<5.10.0a0'] -> gtk2 -> gdk-pixbuf -> gobject-introspection -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "pycodestyle -> python[version='2.7.*,3.4.*,3.5.*,>=3.5,<3.6.0a0'] -> pip\n",
      "cython -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "gast -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "conda[version='>=4.7.10'] -> requests[version='>=2.12.4,>=2.12.4,<3,>=2.18.4,<3,>=2.5.3'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "conda-env -> python[version='2.7.*,3.4.*,3.5.*'] -> pip\n",
      "mkl-service -> numpy[version='>=1.11.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "beautifulsoup4 -> python[version='2.7.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pytest-astropy -> pytest[version='>=3.1,>=3.1.0'] -> attrs[version='>=17.2.0,>=17.4.0'] -> hypothesis -> enum34 -> python[version='2.7.*,>=2.7,<2.8.0a0'] -> pip\n",
      "more-itertools -> six[version='>=1.0.0,<2.0.0'] -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pandas -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "basemap==1.2.0 -> pyproj[version='>=1.9.3,>=1.9.3,<2'] -> numpy -> mkl_random -> python[version='2.7.*,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "flask -> jinja2[version='>=2.10,>=2.4'] -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "multipledispatch -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "sphinxcontrib-jsmath -> python[version='>=3.5'] -> pip\n",
      "zipp -> python[version='>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "bcrypt -> cffi[version='>=1.1'] -> pycparser -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "numpydoc -> sphinx -> requests[version='>=2.0.0,>=2.5.0'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "navigator-updater -> pyqt[version='>=5.6'] -> qt[version='4.8.*,5.6.*,5.9.*,>=4.8.6,<5.0,>=5.6.2,<5.7.0a0,>=5.9.4,<5.10.0a0,>=5.9.6,<5.10.0a0'] -> gtk2 -> gdk-pixbuf -> gobject-introspection -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "nose -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "ptyprocess -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "pyqt -> qt[version='4.8.*,5.6.*,5.9.*,>=4.8.6,<5.0,>=5.6.2,<5.7.0a0,>=5.9.4,<5.10.0a0,>=5.9.6,<5.10.0a0'] -> gtk2 -> gdk-pixbuf -> gobject-introspection -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "pickleshare -> pathlib2 -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "babel -> pytz -> python[version='2.7.*,3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "nbconvert -> nbformat[version='>=4.4'] -> jupyter_core -> traitlets -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "qt -> gtk2 -> gdk-pixbuf -> gobject-introspection -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "importlib_metadata -> pathlib2 -> six -> python[version='3.4.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "vincent -> pandas -> numpy[version='1.10.*,1.11.*,>=1.9,>=1.9.*,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "sphinx -> requests[version='>=2.0.0,>=2.5.0'] -> urllib3[version='>=1.21.1,<1.22,>=1.21.1,<1.23,>=1.21.1,<1.25'] -> pyopenssl[version='>=0.14'] -> cryptography[version='>=1.9,>=2.1.4,>=2.2.1'] -> cffi[version='>=1.7'] -> pycparser -> python[version='2.7.*,3.4.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "numba -> numpy[version='1.15.*,>=1.11,<1.12.0a0,>=1.13,<1.14.0a0,>=1.14,<1.15.0a0,>=1.15.4,<2.0a0,>=1.9.3,<2.0a0'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "itsdangerous -> python[version='3.4.*,3.5.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "blaze -> odo[version='>=0.5.0'] -> dask[version='>=0.11.1'] -> distributed[version='>=1.16.0,>=1.21.0,>=1.23.2,>=1.23.3,>=1.25.3,>=1.26.0'] -> bokeh[version='>=0.12.1,>=0.12.3'] -> numpy[version='>=1.7.1'] -> mkl_random -> python[version='2.7.*,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "ipython-sql==0.3.9 -> ipython[version='>=1.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "wrapt -> python[version='2.7.*,3.5.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "jupyterlab_launcher -> notebook -> ipykernel -> ipython[version='>=4.0,>=4.0.0,>=5.0'] -> prompt_toolkit[version='>=1.0.4,<2.0.0,>=2.0.0'] -> pygments -> setuptools -> certifi[version='>=2016.09'] -> python[version='2.7.*,3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.6,<3.7.0a0'] -> pip\n",
      "et_xmlfile -> python[version='3.5.*,3.6.*,>=2.7,<2.8.0a0,>=3.5,<3.6.0a0,>=3.6,<3.7.0a0,>=3.7,<3.8.0a0'] -> pip\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!conda install -c conda-forge folium=0.5.0 --yes\n",
    "import folium\n",
    "from folium import plugins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PdId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12005827212120</th>\n",
       "      <td>120058272</td>\n",
       "      <td>SOUTHERN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12005827212168</th>\n",
       "      <td>120058272</td>\n",
       "      <td>SOUTHERN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14105926363010</th>\n",
       "      <td>141059263</td>\n",
       "      <td>BAYVIEW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16001366271000</th>\n",
       "      <td>160013662</td>\n",
       "      <td>TENDERLOIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16000274071000</th>\n",
       "      <td>160002740</td>\n",
       "      <td>MISSION</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Count Neighborhood\n",
       "PdId                                  \n",
       "12005827212120  120058272     SOUTHERN\n",
       "12005827212168  120058272     SOUTHERN\n",
       "14105926363010  141059263      BAYVIEW\n",
       "16001366271000  160013662   TENDERLOIN\n",
       "16000274071000  160002740      MISSION"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6=pd.read_csv(\"https://cocl.us/sanfran_crime_dataset\")\n",
    "df6.set_index('PdId', inplace=True)\n",
    "df6[\"PdDistrict\"].value_counts()\n",
    "df6['IncidntNum'].value_counts()\n",
    "\n",
    "\n",
    "df6.drop(['Category','Descript','DayOfWeek','Date','Time','Resolution','Address','X','Y','Location'], axis = 1, inplace=True)\n",
    "\n",
    "df6.rename(columns={'IncidntNum':'Count'}, inplace=True)\n",
    "df6.rename(columns={'PdDistrict':'Neighborhood'}, inplace=True)\n",
    "df6.groupby('Neighborhood').count()\n",
    "df6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IncidntNum</th>\n",
       "      <th>Category</th>\n",
       "      <th>Descript</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>PdDistrict</th>\n",
       "      <th>Resolution</th>\n",
       "      <th>Address</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Location</th>\n",
       "      <th>PdId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>120058272</td>\n",
       "      <td>WEAPON LAWS</td>\n",
       "      <td>POSS OF PROHIBITED WEAPON</td>\n",
       "      <td>Friday</td>\n",
       "      <td>01/29/2016 12:00:00 AM</td>\n",
       "      <td>11:00</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>ARREST, BOOKED</td>\n",
       "      <td>800 Block of BRYANT ST</td>\n",
       "      <td>-122.403405</td>\n",
       "      <td>37.775421</td>\n",
       "      <td>(37.775420706711, -122.403404791479)</td>\n",
       "      <td>12005827212120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120058272</td>\n",
       "      <td>WEAPON LAWS</td>\n",
       "      <td>FIREARM, LOADED, IN VEHICLE, POSSESSION OR USE</td>\n",
       "      <td>Friday</td>\n",
       "      <td>01/29/2016 12:00:00 AM</td>\n",
       "      <td>11:00</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>ARREST, BOOKED</td>\n",
       "      <td>800 Block of BRYANT ST</td>\n",
       "      <td>-122.403405</td>\n",
       "      <td>37.775421</td>\n",
       "      <td>(37.775420706711, -122.403404791479)</td>\n",
       "      <td>12005827212168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141059263</td>\n",
       "      <td>WARRANTS</td>\n",
       "      <td>WARRANT ARREST</td>\n",
       "      <td>Monday</td>\n",
       "      <td>04/25/2016 12:00:00 AM</td>\n",
       "      <td>14:59</td>\n",
       "      <td>BAYVIEW</td>\n",
       "      <td>ARREST, BOOKED</td>\n",
       "      <td>KEITH ST / SHAFTER AV</td>\n",
       "      <td>-122.388856</td>\n",
       "      <td>37.729981</td>\n",
       "      <td>(37.7299809672996, -122.388856204292)</td>\n",
       "      <td>14105926363010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160013662</td>\n",
       "      <td>NON-CRIMINAL</td>\n",
       "      <td>LOST PROPERTY</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>01/05/2016 12:00:00 AM</td>\n",
       "      <td>23:50</td>\n",
       "      <td>TENDERLOIN</td>\n",
       "      <td>NONE</td>\n",
       "      <td>JONES ST / OFARRELL ST</td>\n",
       "      <td>-122.412971</td>\n",
       "      <td>37.785788</td>\n",
       "      <td>(37.7857883766888, -122.412970537591)</td>\n",
       "      <td>16001366271000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>160002740</td>\n",
       "      <td>NON-CRIMINAL</td>\n",
       "      <td>LOST PROPERTY</td>\n",
       "      <td>Friday</td>\n",
       "      <td>01/01/2016 12:00:00 AM</td>\n",
       "      <td>00:30</td>\n",
       "      <td>MISSION</td>\n",
       "      <td>NONE</td>\n",
       "      <td>16TH ST / MISSION ST</td>\n",
       "      <td>-122.419672</td>\n",
       "      <td>37.765050</td>\n",
       "      <td>(37.7650501214668, -122.419671780296)</td>\n",
       "      <td>16000274071000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IncidntNum      Category                                        Descript  \\\n",
       "0   120058272   WEAPON LAWS                       POSS OF PROHIBITED WEAPON   \n",
       "1   120058272   WEAPON LAWS  FIREARM, LOADED, IN VEHICLE, POSSESSION OR USE   \n",
       "2   141059263      WARRANTS                                  WARRANT ARREST   \n",
       "3   160013662  NON-CRIMINAL                                   LOST PROPERTY   \n",
       "4   160002740  NON-CRIMINAL                                   LOST PROPERTY   \n",
       "\n",
       "  DayOfWeek                    Date   Time  PdDistrict      Resolution  \\\n",
       "0    Friday  01/29/2016 12:00:00 AM  11:00    SOUTHERN  ARREST, BOOKED   \n",
       "1    Friday  01/29/2016 12:00:00 AM  11:00    SOUTHERN  ARREST, BOOKED   \n",
       "2    Monday  04/25/2016 12:00:00 AM  14:59     BAYVIEW  ARREST, BOOKED   \n",
       "3   Tuesday  01/05/2016 12:00:00 AM  23:50  TENDERLOIN            NONE   \n",
       "4    Friday  01/01/2016 12:00:00 AM  00:30     MISSION            NONE   \n",
       "\n",
       "                  Address           X          Y  \\\n",
       "0  800 Block of BRYANT ST -122.403405  37.775421   \n",
       "1  800 Block of BRYANT ST -122.403405  37.775421   \n",
       "2   KEITH ST / SHAFTER AV -122.388856  37.729981   \n",
       "3  JONES ST / OFARRELL ST -122.412971  37.785788   \n",
       "4    16TH ST / MISSION ST -122.419672  37.765050   \n",
       "\n",
       "                                Location            PdId  \n",
       "0   (37.775420706711, -122.403404791479)  12005827212120  \n",
       "1   (37.775420706711, -122.403404791479)  12005827212168  \n",
       "2  (37.7299809672996, -122.388856204292)  14105926363010  \n",
       "3  (37.7857883766888, -122.412970537591)  16001366271000  \n",
       "4  (37.7650501214668, -122.419671780296)  16000274071000  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff58a22c5f8>]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('K')\n",
    "plt.ylabel('Sum of squared error')\n",
    "plt.plot(k_rng,sse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GeoJSON file downloaded!\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'Neighborhood'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2656\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2657\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Neighborhood'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-78-b8fb76ea41d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0mfill_opacity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0mline_opacity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m     \u001b[0mlegend_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Crime Rate in San Francisco'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m )\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/lib/python3.6/site-packages/folium/folium.py\u001b[0m in \u001b[0;36mchoropleth\u001b[0;34m(self, geo_data, data, columns, key_on, threshold_scale, fill_color, fill_opacity, line_color, line_weight, line_opacity, name, legend_name, topojson, reset, smooth_factor, highlight)\u001b[0m\n\u001b[1;32m    250\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'set_index'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m             \u001b[0;31m# This is a pd.DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m             \u001b[0mcolor_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    253\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'to_dict'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m             \u001b[0;31m# This is a pd.Series\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mset_index\u001b[0;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[1;32m   4176\u001b[0m                 \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4177\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4178\u001b[0;31m                 \u001b[0mlevel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4179\u001b[0m                 \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4180\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2925\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2926\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2927\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2928\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2929\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2657\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2659\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Neighborhood'"
     ]
    }
   ],
   "source": [
    "SF_map = folium.Map(location=[33.865268, -118.396297], zoom_start=12)\n",
    "\n",
    "df6=pd.read_csv(\"https://cocl.us/sanfran_crime_dataset\")\n",
    "\n",
    "SF_map = folium.Map(location=[37.77, -122.42], zoom_start=12)\n",
    "\n",
    "# display SF map\n",
    "SF_map\n",
    "# download SF geojson file\n",
    "# !wget --quiet https://ibm.box.com/shared/static/cto2qv7nx6yq19logfcissyy4euo8lho.json -O world_countries.json\n",
    "!wget --quiet https://cocl.us/sanfran_geojson    \n",
    "print('GeoJSON file downloaded!')\n",
    "\n",
    "SF_geo = 'san-francisco.geojson' # geojson file\n",
    "\n",
    "# create a plain world map\n",
    "SF_map = folium.Map(location=[37.77, -122.42], zoom_start=11.5)\n",
    "# generate choropleth map using the total crime numbers per district for SF\n",
    "SF_map.choropleth(\n",
    "    geo_data=SF_geo,\n",
    "    data=df6,\n",
    "    columns=['Neighborhood', 'Count'],\n",
    "    key_on='feature.properties.DISTRICT',\n",
    "    fill_color='YlOrRd', \n",
    "    fill_opacity=0.7, \n",
    "    line_opacity=0.2,\n",
    "    legend_name='Crime Rate in San Francisco'\n",
    ")\n",
    "\n",
    "# display map\n",
    "SF_map\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>City</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Population</th>\n",
       "      <th>Median Age</th>\n",
       "      <th>Average Income</th>\n",
       "      <th>Venue Number</th>\n",
       "      <th>Latitude.1</th>\n",
       "      <th>Longitude.1</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Culver City</td>\n",
       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
       "      <td>39283.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0.396137</td>\n",
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       "      <td>34.005820</td>\n",
       "      <td>-118.396781</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>El Segundo</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>16853.0</td>\n",
       "      <td>38</td>\n",
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       "      <td>0.112903</td>\n",
       "      <td>33.917145</td>\n",
       "      <td>-118.401554</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>87854.0</td>\n",
       "      <td>33</td>\n",
       "      <td>0.012165</td>\n",
       "      <td>0.306452</td>\n",
       "      <td>33.914775</td>\n",
       "      <td>-118.348083</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Hermosa Beach</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>19708.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0.765389</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.865268</td>\n",
       "      <td>-118.396297</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Inglewood</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>110598.0</td>\n",
       "      <td>34</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>33.956068</td>\n",
       "      <td>-118.344274</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Manhattan Beach</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>35924.0</td>\n",
       "      <td>43</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>33.889632</td>\n",
       "      <td>-118.397370</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Marina del Rey</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>8866.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0.541128</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>33.981510</td>\n",
       "      <td>-118.453229</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Redondo Beach</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>67908.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0.567350</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>33.856817</td>\n",
       "      <td>-118.377137</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Santa Monica</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>92306.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0.387231</td>\n",
       "      <td>0.274194</td>\n",
       "      <td>34.023413</td>\n",
       "      <td>-118.481666</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Torrance</td>\n",
       "      <td>33.834966</td>\n",
       "      <td>-118.341431</td>\n",
       "      <td>146758.0</td>\n",
       "      <td>41</td>\n",
       "      <td>0.377339</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.834966</td>\n",
       "      <td>-118.341431</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Venice Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40885.0</td>\n",
       "      <td>35</td>\n",
       "      <td>0.207375</td>\n",
       "      <td>0.403226</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>-118.469500</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0             City   Latitude   Longitude  Population  \\\n",
       "0            1      Culver City  34.005820 -118.396781     39283.0   \n",
       "1            2       El Segundo  33.917145 -118.401554     16853.0   \n",
       "2            3        Hawthorne  33.914775 -118.348083     87854.0   \n",
       "3            4    Hermosa Beach  33.865268 -118.396297     19708.0   \n",
       "4            5        Inglewood  33.956068 -118.344274    110598.0   \n",
       "5            6  Manhattan Beach  33.889632 -118.397370     35924.0   \n",
       "6            7   Marina del Rey  33.981510 -118.453229      8866.0   \n",
       "7            8    Redondo Beach  33.856817 -118.377137     67908.0   \n",
       "8            9     Santa Monica  34.023413 -118.481666     92306.0   \n",
       "9           10         Torrance  33.834966 -118.341431    146758.0   \n",
       "10          11     Venice Beach        NaN         NaN     40885.0   \n",
       "\n",
       "    Median Age  Average Income  Venue Number  Latitude.1  Longitude.1  cluster  \n",
       "0           40        0.396137      0.080645   34.005820  -118.396781      1.0  \n",
       "1           38        0.454131      0.112903   33.917145  -118.401554      1.0  \n",
       "2           33        0.012165      0.306452   33.914775  -118.348083      2.0  \n",
       "3           39        0.765389      1.000000   33.865268  -118.396297      0.0  \n",
       "4           34        0.000000      0.064516   33.956068  -118.344274      2.0  \n",
       "5           43        1.000000      0.354839   33.889632  -118.397370      0.0  \n",
       "6           39        0.541128      0.258065   33.981510  -118.453229      1.0  \n",
       "7           40        0.567350      0.064516   33.856817  -118.377137      1.0  \n",
       "8           40        0.387231      0.274194   34.023413  -118.481666      1.0  \n",
       "9           41        0.377339      0.000000   33.834966  -118.341431      1.0  \n",
       "10          35        0.207375      0.403226   33.985000  -118.469500      2.0  "
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df4.drop(columns=['0'])\n",
    "df4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "'return' outside function (<ipython-input-177-5ef9d22531fd>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-177-5ef9d22531fd>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    return requests.get(url).json()\u001b[0m\n\u001b[0m                                   ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m 'return' outside function\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "return requests.get(url).json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "argument of type 'NoneType' is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-175-3ec67ad3bf3c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;34m'json'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'https://github.com/codeforamerica/click_that_hood/blob/master/public/data/los-angeles-county.geojson'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mjs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: argument of type 'NoneType' is not iterable"
     ]
    }
   ],
   "source": [
    "url = 'http://httpbin.org/status/200'\n",
    "r = requests.get(url)\n",
    "\n",
    "if 'json' in r.headers.get('https://github.com/codeforamerica/click_that_hood/blob/master/public/data/los-angeles-county.geojson'):\n",
    "    js = r.json()\n",
    "else:\n",
    "    print('Response content is not in JSON format.')\n",
    "    js = 'spam'\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import requests\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-180-a0d5fe9924c5>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-180-a0d5fe9924c5>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    return requests.get(https://github.com/codeforamerica/click_that_hood/blob/master/public/data/los-angeles-county.geojson).json()\u001b[0m\n\u001b[0m                             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "return requests.get(https://github.com/codeforamerica/click_that_hood/blob/master/public/data/los-angeles-county.geojson).json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata: done\n",
      "Solving environment: | \n",
      "The environment is inconsistent, please check the package plan carefully\n",
      "The following packages are causing the inconsistency:\n",
      "\n",
      "  - anaconda/linux-64::conda-build==3.17.8=py36_0\n",
      "  - anaconda/linux-64::grpcio==1.16.1=py36hf8bcb03_1\n",
      "  - anaconda/linux-64::keras==2.1.5=py36_0\n",
      "  - anaconda/linux-64::libarchive==3.3.3=h5d8350f_5\n",
      "  - anaconda/linux-64::python-libarchive-c==2.8=py36_6\n",
      "  - anaconda/linux-64::tensorboard==1.8.0=py36hf484d3e_0\n",
      "  - anaconda/linux-64::tensorflow==1.8.0=h57681fa_0\n",
      "  - anaconda/linux-64::tensorflow-base==1.8.0=py36h5f64886_0\n",
      "  - defaults/linux-64::anaconda==5.3.1=py37_0\n",
      "  - defaults/linux-64::astropy==3.0.4=py37h14c3975_0\n",
      "  - defaults/linux-64::bkcharts==0.2=py37_0\n",
      "  - defaults/linux-64::blaze==0.11.3=py37_0\n",
      "  - defaults/linux-64::bokeh==0.13.0=py37_0\n",
      "  - defaults/linux-64::bottleneck==1.2.1=py37h035aef0_1\n",
      "  - defaults/linux-64::dask==0.19.1=py37_0\n",
      "  - defaults/linux-64::datashape==0.5.4=py37_1\n",
      "  - defaults/linux-64::mkl-service==1.1.2=py37h90e4bf4_5\n",
      "  - defaults/linux-64::numba==0.39.0=py37h04863e7_0\n",
      "  - defaults/linux-64::numexpr==2.6.8=py37hd89afb7_0\n",
      "  - defaults/linux-64::odo==0.5.1=py37_0\n",
      "  - defaults/linux-64::pytables==3.4.4=py37ha205bf6_0\n",
      "  - defaults/linux-64::pytest-arraydiff==0.2=py37h39e3cac_0\n",
      "  - defaults/linux-64::pytest-astropy==0.4.0=py37_0\n",
      "  - defaults/linux-64::pytest-doctestplus==0.1.3=py37_0\n",
      "  - defaults/linux-64::pywavelets==1.0.0=py37hdd07704_0\n",
      "  - defaults/linux-64::scikit-image==0.14.0=py37hf484d3e_1\n",
      "done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /home/jupyterlab/conda\n",
      "\n",
      "  added / updated specs:\n",
      "    - folium=0.5.0\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    certifi-2019.6.16          |           py36_1         149 KB  conda-forge\n",
      "    conda-4.7.10               |           py36_0         3.0 MB  conda-forge\n",
      "    conda-package-handling-1.3.11|           py36_0         257 KB  conda-forge\n",
      "    libarchive-3.3.3           |    hb44662c_1005         1.4 MB  conda-forge\n",
      "    libiconv-1.15              |    h516909a_1005         2.0 MB  conda-forge\n",
      "    libxml2-2.9.9              |       h13577e0_2         1.3 MB  conda-forge\n",
      "    openssl-1.1.1c             |       h516909a_0         2.1 MB  conda-forge\n",
      "    python-libarchive-c-2.8    |        py36_1004          21 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:        10.3 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  conda-package-han~ conda-forge/linux-64::conda-package-handling-1.3.11-py36_0\n",
      "  libiconv           conda-forge/linux-64::libiconv-1.15-h516909a_1005\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  certifi                                  2019.6.16-py36_0 --> 2019.6.16-py36_1\n",
      "  conda                       anaconda::conda-4.6.14-py36_0 --> conda-forge::conda-4.7.10-py36_0\n",
      "  libarchive          anaconda::libarchive-3.3.3-h5d8350f_5 --> conda-forge::libarchive-3.3.3-hb44662c_1005\n",
      "  libxml2               pkgs/main::libxml2-2.9.8-h26e45fe_1 --> conda-forge::libxml2-2.9.9-h13577e0_2\n",
      "  openssl                                 1.1.1b-h14c3975_1 --> 1.1.1c-h516909a_0\n",
      "  python-libarchive~ anaconda::python-libarchive-c-2.8-py3~ --> conda-forge::python-libarchive-c-2.8-py36_1004\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "certifi-2019.6.16    | 149 KB    | ##################################### | 100% \n",
      "openssl-1.1.1c       | 2.1 MB    | ##################################### | 100% \n",
      "python-libarchive-c- | 21 KB     | ##################################### | 100% \n",
      "libiconv-1.15        | 2.0 MB    | ##################################### | 100% \n",
      "libxml2-2.9.9        | 1.3 MB    | ##################################### | 100% \n",
      "libarchive-3.3.3     | 1.4 MB    | ##################################### | 100% \n",
      "conda-4.7.10         | 3.0 MB    | ##################################### | 100% \n",
      "conda-package-handli | 257 KB    | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "Folium installed and imported!\n",
      "GeoJSON file downloaded!\n"
     ]
    }
   ],
   "source": [
    "!conda install -c conda-forge folium=0.5.0 --yes\n",
    "import folium\n",
    "from folium import plugins\n",
    "print('Folium installed and imported!')\n",
    "print('GeoJSON file downloaded!')\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "file_extension": ".py",
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