TreefortBnB.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['plt']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "%pylab inline --no-import-all\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "\n",
    "from matplotlib.colors import LogNorm\n",
    "plt.rc('font', family='serif', size=16)\n",
    "pd.options.display.mpl_style = 'default'\n",
    "matplotlib.style.use('ggplot')\n",
    "new_style = {'grid': False}\n",
    "matplotlib.rc('axes', **new_style)\n",
    "figsize(10, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://priceonomics.com/the-priceonomics-data-puzzle-treefortbnb/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 1030k  100 1030k    0     0   332k      0  0:00:03  0:00:03 --:--:--  332k\n"
     ]
    }
   ],
   "source": [
    "!curl https://s3.amazonaws.com/pix-media/Data+for+TreefortBnB+Puzzle.csv > Puzzle.csv\n",
    "df = pd.read_csv(\"./Puzzle.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    median price ($)\n",
      "City         State                  \n",
      "Indianapolis IN                  650\n",
      "Malibu       CA                  304\n",
      "Park City    UT                  299\n",
      "Truckee      NV                  275\n",
      "Healdsburg   CA                  275\n"
     ]
    }
   ],
   "source": [
    "basicsummary = df.assign(City = df.City.str.title()) \\\n",
    "                 .groupby([\"City\",\"State\"])[\"$ Price\"] \\\n",
    "                 .describe().unstack().sort_values(\"count\").tail(100)[\"50%\"] \\\n",
    "                 .sort_values(ascending=False).to_frame() \\\n",
    "                 .rename(columns = {\"50%\":\"median price ($)\"})\n",
    "print basicsummary.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$ Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>42802.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>187.117074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>263.002109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>130.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            $ Price\n",
       "count  42802.000000\n",
       "mean     187.117074\n",
       "std      263.002109\n",
       "min       10.000000\n",
       "25%       90.000000\n",
       "50%      130.000000\n",
       "75%      200.000000\n",
       "max    10000.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"$ Price\"].describe().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x106b5ad50>]], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UiC45jG3atCk2b94cPT09sXfv3li5cmVUVVVFc3NzrF+/fuRuSgAASldyGGtqaoqmpqZD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103d8f5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column = \"$ Price\",bins = 64,log=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unique id</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>$ Price</th>\n",
       "      <th># of Reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>2820</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>10000</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5135</th>\n",
       "      <td>5136</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>6000</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8328</th>\n",
       "      <td>8329</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>6500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8638</th>\n",
       "      <td>8639</td>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>IN</td>\n",
       "      <td>5500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8867</th>\n",
       "      <td>8868</td>\n",
       "      <td>Park City</td>\n",
       "      <td>UT</td>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10332</th>\n",
       "      <td>10333</td>\n",
       "      <td>Park City</td>\n",
       "      <td>UT</td>\n",
       "      <td>6500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13431</th>\n",
       "      <td>13432</td>\n",
       "      <td>Queens</td>\n",
       "      <td>NY</td>\n",
       "      <td>6500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17195</th>\n",
       "      <td>17196</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>5225</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20279</th>\n",
       "      <td>20280</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>5600</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23817</th>\n",
       "      <td>23818</td>\n",
       "      <td>Park City</td>\n",
       "      <td>UT</td>\n",
       "      <td>5500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26232</th>\n",
       "      <td>26233</td>\n",
       "      <td>Park City</td>\n",
       "      <td>UT</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28626</th>\n",
       "      <td>28627</td>\n",
       "      <td>Miami Beach</td>\n",
       "      <td>FL</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35136</th>\n",
       "      <td>35137</td>\n",
       "      <td>Miami Beach</td>\n",
       "      <td>FL</td>\n",
       "      <td>6000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38261</th>\n",
       "      <td>38262</td>\n",
       "      <td>Miami Beach</td>\n",
       "      <td>FL</td>\n",
       "      <td>5750</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unique id           City State  $ Price  # of Reviews\n",
       "2819        2820  San Francisco    CA    10000            31\n",
       "5135        5136  San Francisco    CA     6000            11\n",
       "8328        8329        Chicago    IL     6500             1\n",
       "8638        8639   Indianapolis    IN     5500             0\n",
       "8867        8868      Park City    UT    10000             1\n",
       "10332      10333      Park City    UT     6500             0\n",
       "13431      13432         Queens    NY     6500             0\n",
       "17195      17196         Boston    MA     5225             0\n",
       "20279      20280       New York    NY     5600             0\n",
       "23817      23818      Park City    UT     5500             0\n",
       "26232      26233      Park City    UT    10000             0\n",
       "28626      28627    Miami Beach    FL    10000             0\n",
       "35136      35137    Miami Beach    FL     6000             0\n",
       "38261      38262    Miami Beach    FL     5750             0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"$ Price\"] > 5000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x103d8f1d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I5GeDi7edoWNkfXpshSNMyuKeedmwmzQQ0ssEqQo5H7auypXc/JST4giVhBjo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XTzztE+ovE4l83M54ltLVS4zUzOM4rPmIDHHuoN8/NNjVQWW1lYZJXEkCXquHVJEW6TimmO/5+9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AyAPxOKtXICaVRwv4QoBBDQRjbNtmHVsmgn3PoQV6pXbQqFj983VAn5QUleNZ/bUqIBJnbclz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\nQgghhJAsowEZIYQQQkiW/X9LKsSdE6kf/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103b4c410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter',x=\"$ Price\",y =\"# of Reviews\",\n",
    "        logx=True,xlim = [9,11000],ylim = [-10,110])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108498a10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x+mSf4x32d0kf+4vmd2ayizSpf0h1yLKzs+X3+3XvvfdKktatWxfxBQDDzegR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107633bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.City == \"Indianapolis\"].plot(kind='scatter',x=\"$ Price\",y =\"# of Reviews\",\n",
    "                                   logx=True,xlim = [9,11000],ylim = [-10,110])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th># of Reviews</th>\n",
       "      <th></th>\n",
       "      <th></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>False</th>\n",
       "      <td>16000</td>\n",
       "      <td>238.025500</td>\n",
       "      <td>365.393895</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "      <td>250</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>26802</td>\n",
       "      <td>156.726252</td>\n",
       "      <td>168.202838</td>\n",
       "      <td>10</td>\n",
       "      <td>85</td>\n",
       "      <td>125</td>\n",
       "      <td>185</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0                                                  \n",
       "              count        mean         std min  25%  50%  75%    max\n",
       "# of Reviews                                                         \n",
       "False         16000  238.025500  365.393895  10  100  150  250  10000\n",
       "True          26802  156.726252  168.202838  10   85  125  185  10000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_reviewed = df[\"# of Reviews\"] > 0\n",
    "df.groupby(is_reviewed)[\"$ Price\"].describe().to_frame().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>median price ($)</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300.0</td>\n",
       "      <td>Carmel (CA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>225.0</td>\n",
       "      <td>Healdsburg (CA) / Malibu (CA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200.0</td>\n",
       "      <td>Incline Village (NV) / Laguna Beach (CA) / Tru...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>199.0</td>\n",
       "      <td>Hermosa Beach (CA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>184.5</td>\n",
       "      <td>Napa (CA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>179.0</td>\n",
       "      <td>Park City (UT)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>177.5</td>\n",
       "      <td>Sunny Isles Beach (FL)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>175.0</td>\n",
       "      <td>Sonoma (CA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>165.0</td>\n",
       "      <td>New York (NY)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>162.0</td>\n",
       "      <td>Manhattan Beach (CA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>157.5</td>\n",
       "      <td>La Jolla (CA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>150.0</td>\n",
       "      <td>Sausalito (CA) / Beverly Hills (CA) / Newport ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>149.0</td>\n",
       "      <td>Marina Del Rey (CA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>148.0</td>\n",
       "      <td>Mill Valley (CA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>130.0</td>\n",
       "      <td>Santa Monica (CA) / Miami Beach (FL)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      median price ($)                                           Location\n",
       "rank                                                                     \n",
       "1                300.0                                        Carmel (CA)\n",
       "2                225.0                      Healdsburg (CA) / Malibu (CA)\n",
       "3                200.0  Incline Village (NV) / Laguna Beach (CA) / Tru...\n",
       "4                199.0                                 Hermosa Beach (CA)\n",
       "5                184.5                                          Napa (CA)\n",
       "6                179.0                                     Park City (UT)\n",
       "7                177.5                             Sunny Isles Beach (FL)\n",
       "8                175.0                                        Sonoma (CA)\n",
       "9                165.0                                      New York (NY)\n",
       "10               162.0                               Manhattan Beach (CA)\n",
       "11               157.5                                      La Jolla (CA)\n",
       "12               150.0  Sausalito (CA) / Beverly Hills (CA) / Newport ...\n",
       "13               149.0                                Marina Del Rey (CA)\n",
       "14               148.0                                   Mill Valley (CA)\n",
       "15               130.0               Santa Monica (CA) / Miami Beach (FL)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refinedsummary = df.get(df[\"# of Reviews\"] > 0) \\\n",
    "                   .assign(Location = df.City.str.title()+df.State.apply(lambda s: ' ('+s+')')) \\\n",
    "                   .groupby(\"Location\")[\"$ Price\"].describe().unstack() \\\n",
    "                   .sort_values(\"count\").tail(100) \\\n",
    "                   .reset_index().groupby(\"50%\")[\"Location\"].apply(lambda x: \"%s\" % ' / '.join(x)) \\\n",
    "                   .sort_index(ascending=False).to_frame() \\\n",
    "                   .reset_index().rename(columns = {\"50%\":\"median price ($)\"})\n",
    "refinedsummary.set_index(refinedsummary.index+1,inplace = True)\n",
    "refinedsummary.index.rename(\"rank\",inplace = True)\n",
    "refinedsummary.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>total listings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <th>NY</th>\n",
       "      <td>5597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn</th>\n",
       "      <th>NY</th>\n",
       "      <td>3017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco</th>\n",
       "      <th>CA</th>\n",
       "      <td>2455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles</th>\n",
       "      <th>CA</th>\n",
       "      <td>1941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin</th>\n",
       "      <th>TX</th>\n",
       "      <td>1495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago</th>\n",
       "      <th>IL</th>\n",
       "      <td>875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <th>DC</th>\n",
       "      <td>836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miami Beach</th>\n",
       "      <th>FL</th>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portland</th>\n",
       "      <th>OR</th>\n",
       "      <td>586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seattle</th>\n",
       "      <th>WA</th>\n",
       "      <td>573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     total listings\n",
       "City          State                \n",
       "New York      NY               5597\n",
       "Brooklyn      NY               3017\n",
       "San Francisco CA               2455\n",
       "Los Angeles   CA               1941\n",
       "Austin        TX               1495\n",
       "Chicago       IL                875\n",
       "Washington    DC                836\n",
       "Miami Beach   FL                790\n",
       "Portland      OR                586\n",
       "Seattle       WA                573"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlistings = df.get(df[\"# of Reviews\"] > 0) \\\n",
    "         .groupby([\"City\",\"State\"]).count()[\"$ Price\"] \\\n",
    "         .sort_values(ascending = False).to_frame() \\\n",
    "         .rename(columns = {\"$ Price\":\"total listings\"})\n",
    "nlistings.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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