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Training analysis for marathon mirabelle 2017
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04-Multiple_Trainning.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import folium\n",
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"from geopy.geocoders import Nominatim\n",
"import geocoder\n",
"import matplotlib.pyplot as plt\n",
"import json\n",
"from folium import plugins\n",
"import branca.colormap as cm\n",
"from tqdm import tqdm_notebook\n",
"plt.style.use('ggplot')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_elevation(lat,lon):\n",
" try:\n",
" return geocoder.google([lat, lon], method='elevation', key=os.environ['GOOGLE_API_KEY']).meters\n",
" except:\n",
" return np.NaN"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def read_csv(filename):\n",
" df = pd.read_csv(filename)\n",
" NewLap_idx = df[df.Time == 'New Lap'].index.values\n",
" df['Time'] = pd.to_datetime(df['Time'], errors='coerce')\n",
" df.dropna(how='any',axis=0, subset=['Time'], inplace=True)\n",
" for lab in ['LongitudeDegrees', 'LatitudeDegrees', 'AltitudeMeters', 'heartratebpm/value']:\n",
" df[lab] = df[lab].interpolate()\n",
" df['dist'] = \\\n",
" haversine_np(df.LongitudeDegrees.shift(), df.LatitudeDegrees.shift(),\n",
" df.loc[1:, 'LongitudeDegrees'], df.loc[1:, 'LatitudeDegrees'])\n",
" df['cum_dis'] = df.dist.cumsum()\n",
" df['DeltaTime'] = (df.Time - df.Time.shift()).dt.total_seconds()\n",
" df['Speed'] = df.apply(lambda x: x['dist']/float(x['DeltaTime'])*3600.0 if x['DeltaTime'] > 0.5 else np.NaN, axis=1)\n",
" df['Speed'] = df.apply(lambda x: x['Speed'] if x['Speed'] < 20 else np.NaN, axis=1)\n",
" df['DeltaAltitude'] = df.AltitudeMeters - df.AltitudeMeters.shift()\n",
" df.to_csv('output/csv/' + os.path.basename(filename))\n",
" return df, NewLap_idx"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def haversine_np(lon1, lat1, lon2, lat2):\n",
" \"\"\"\n",
" Calculate the great circle distance between two points\n",
" on the earth (specified in decimal degrees)\n",
"\n",
" All args must be of equal length. \n",
"\n",
" \"\"\"\n",
" lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n",
"\n",
" dlon = lon2 - lon1\n",
" dlat = lat2 - lat1\n",
"\n",
" a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2\n",
"\n",
" c = 2 * np.arcsin(np.sqrt(a))\n",
" km = 6367 * c\n",
" return km"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "526a4989a6324149aa2fa6a41085b8f8"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"#path = 'data/csv/'\n",
"path = 'output/csv'\n",
"geolocator = Nominatim()\n",
"allFiles = os.listdir(path)\n",
"frame = pd.DataFrame(columns=('Date', 'Filename', 'Kilometers', 'Speed', 'Time', 'Climb', 'Lat', 'Long', 'Adress'))\n",
"dict_df = dict()\n",
"#dict_newlap = dict()\n",
"for i, file in tqdm_notebook(enumerate(allFiles)):\n",
" df = pd.read_csv('output/csv/' + file, index_col=0, parse_dates=['Time'])\n",
" #df, newlap = read_csv(path + file)\n",
" dict_df[df['Time'][0].strftime(\"%Y-%m-%d\")] = df\n",
" location = geolocator.reverse(str(df.LatitudeDegrees.mean()) + \",\" + str(df.LongitudeDegrees.mean()))[0]\n",
" frame.loc[i] = [df['Time'][0], file, df.cum_dis.max(), df.Speed.mean(), df.DeltaTime.sum()/60,\n",
" df['DeltaAltitude'][df['DeltaAltitude'] > 0].sum(),\n",
" df.LatitudeDegrees.mean(), df.LongitudeDegrees.mean(), location]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Session manual add"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"frame.loc[frame[frame['Filename'] == '2017-09-01T05_07_50Z_PT50M7S_Running.csv'].index,'Speed'] = 10.9\n",
"frame.loc[frame[frame['Filename'] == '2017-09-01T05_07_50Z_PT50M7S_Running.csv'].index,'Kilometers'] = 8.09\n",
"frame.loc[frame[frame['Filename'] == '2017-09-01T05_07_50Z_PT50M7S_Running.csv'].index,'Time'] = 44.5\n",
"frame.loc[frame[frame['Filename'] == '2017-09-01T05_07_50Z_PT50M7S_Running.csv'].index,'Climb'] = 130"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Filename</th>\n",
" <th>Kilometers</th>\n",
" <th>Speed</th>\n",
" <th>Time</th>\n",
" <th>Climb</th>\n",
" <th>Lat</th>\n",
" <th>Long</th>\n",
" <th>Adress</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>2017-09-28 17:31:41.836</td>\n",
" <td>2017-09-28T17_32_18Z_PT40M33S_Running.csv</td>\n",
" <td>7.643801</td>\n",
" <td>11.602275</td>\n",
" <td>41.166867</td>\n",
" <td>742.000</td>\n",
" <td>48.822395</td>\n",
" <td>2.338011</td>\n",
" <td>Le Petit Train, Allée du Bardo, Montsouris, Pa...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-09-26 17:29:38.784</td>\n",
" <td>2017-09-26T17_29_38Z_PT42M55S_Running.csv</td>\n",
" <td>8.761183</td>\n",
" <td>11.979408</td>\n",
" <td>42.932483</td>\n",
" <td>890.000</td>\n",
" <td>48.822418</td>\n",
" <td>2.337851</td>\n",
" <td>Le Petit Train, Allée du Bardo, Montsouris, Pa...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>2017-09-23 09:23:27.402</td>\n",
" <td>2017-09-23T09_23_58Z_PT1H59M11S_Running.csv</td>\n",
" <td>21.848359</td>\n",
" <td>11.175643</td>\n",
" <td>119.718800</td>\n",
" <td>2166.000</td>\n",
" <td>48.780568</td>\n",
" <td>2.292178</td>\n",
" <td>Avenue Charles Péguy, Sceaux, Antony, Hauts-de...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>2017-09-21 16:34:23.641</td>\n",
" <td>2017-09-21T16_34_23Z_PT45M38S_Running.csv</td>\n",
" <td>10.592581</td>\n",
" <td>11.908545</td>\n",
" <td>45.635200</td>\n",
" <td>1048.000</td>\n",
" <td>48.846442</td>\n",
" <td>2.335671</td>\n",
" <td>Jardin du Luxembourg, Cour de Jonction, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>2017-09-19 16:36:08.981</td>\n",
" <td>2017-09-19T16_36_08Z_PT44M32S_Running.csv</td>\n",
" <td>9.041560</td>\n",
" <td>12.643044</td>\n",
" <td>44.544367</td>\n",
" <td>897.000</td>\n",
" <td>48.846427</td>\n",
" <td>2.335698</td>\n",
" <td>Jardin du Luxembourg, Cour de Jonction, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2017-09-16 07:17:00.070</td>\n",
" <td>2017-09-16T07_17_00Z_PT1H30M8S_Running.csv</td>\n",
" <td>17.096634</td>\n",
" <td>11.390383</td>\n",
" <td>90.146483</td>\n",
" <td>1854.000</td>\n",
" <td>48.784790</td>\n",
" <td>2.291165</td>\n",
" <td>Rue François Moreau, Fontenay-aux-Roses, Anton...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2017-09-14 17:23:15.896</td>\n",
" <td>2017-09-14T16_20_43Z_PT1H42M47S_Biking,Running...</td>\n",
" <td>8.048995</td>\n",
" <td>11.910404</td>\n",
" <td>36.919733</td>\n",
" <td>848.000</td>\n",
" <td>48.822385</td>\n",
" <td>2.338098</td>\n",
" <td>Le Petit Train, Allée du Bardo, Montsouris, Pa...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>2017-09-12 16:59:14.957</td>\n",
" <td>2017-09-12T16_59_14Z_PT51M33S_Running.csv</td>\n",
" <td>9.248593</td>\n",
" <td>12.426092</td>\n",
" <td>51.559717</td>\n",
" <td>909.000</td>\n",
" <td>48.846306</td>\n",
" <td>2.335477</td>\n",
" <td>Anne de Beaujeu, Cour de la Présidence, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>2017-09-09 08:27:03.960</td>\n",
" <td>2017-09-09T08_27_03Z_PT1H55M7S_Running.csv</td>\n",
" <td>21.728122</td>\n",
" <td>11.392283</td>\n",
" <td>115.132050</td>\n",
" <td>2317.000</td>\n",
" <td>48.779607</td>\n",
" <td>2.291531</td>\n",
" <td>Fontenay-Houdan, Rue de Fontenay, Sceaux, Anto...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2017-09-07 17:01:55.000</td>\n",
" <td>RunnerUp_0058.csv</td>\n",
" <td>8.679763</td>\n",
" <td>12.729851</td>\n",
" <td>41.800000</td>\n",
" <td>853.000</td>\n",
" <td>48.846475</td>\n",
" <td>2.335826</td>\n",
" <td>Jardin du Luxembourg, Cour de Jonction, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>2017-09-05 17:06:22.000</td>\n",
" <td>RunnerUp_0057.csv</td>\n",
" <td>8.958269</td>\n",
" <td>12.844046</td>\n",
" <td>41.433333</td>\n",
" <td>822.000</td>\n",
" <td>48.846517</td>\n",
" <td>2.335812</td>\n",
" <td>Jardin du Luxembourg, Cour de Jonction, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2017-09-04 17:38:54.000</td>\n",
" <td>RunnerUp_0056.csv</td>\n",
" <td>3.398560</td>\n",
" <td>11.900422</td>\n",
" <td>17.116667</td>\n",
" <td>301.000</td>\n",
" <td>48.844327</td>\n",
" <td>2.335079</td>\n",
" <td>15, Rue Auguste Comte, Odéon, 6e, Paris, Île-d...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>2017-09-02 08:52:00.450</td>\n",
" <td>2017-09-02T08_52_00Z_PT1H58M34S_Running.csv</td>\n",
" <td>21.516255</td>\n",
" <td>8.859990</td>\n",
" <td>121.576833</td>\n",
" <td>1717.000</td>\n",
" <td>48.777818</td>\n",
" <td>2.290877</td>\n",
" <td>EHPAD Résidence Renaudin, Passage Marguerite R...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2017-09-01 05:07:50.590</td>\n",
" <td>2017-09-01T05_07_50Z_PT50M7S_Running.csv</td>\n",
" <td>8.090000</td>\n",
" <td>10.900000</td>\n",
" <td>44.500000</td>\n",
" <td>130.000</td>\n",
" <td>48.842785</td>\n",
" <td>2.329997</td>\n",
" <td>Starbucks Coffee, Rue Jules Chaplain, ND-des-C...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2017-08-30 17:20:06.174</td>\n",
" <td>Cristian_Perez_2017-08-30_19-20-05.csv</td>\n",
" <td>8.146645</td>\n",
" <td>11.694941</td>\n",
" <td>41.716667</td>\n",
" <td>121.772</td>\n",
" <td>48.846472</td>\n",
" <td>2.335846</td>\n",
" <td>Jardin du Luxembourg, Cour de Jonction, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>2017-08-29 17:15:33.209</td>\n",
" <td>Cristian_Perez_2017-08-29_19-15-32.csv</td>\n",
" <td>8.462380</td>\n",
" <td>11.721614</td>\n",
" <td>42.616667</td>\n",
" <td>164.441</td>\n",
" <td>48.846504</td>\n",
" <td>2.335784</td>\n",
" <td>Jardin du Luxembourg, Cour de Jonction, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>2017-08-26 08:07:54.000</td>\n",
" <td>RunnerUp_0051.csv</td>\n",
" <td>23.109283</td>\n",
" <td>10.833283</td>\n",
" <td>122.233333</td>\n",
" <td>2713.000</td>\n",
" <td>48.778201</td>\n",
" <td>2.291200</td>\n",
" <td>EHPAD Résidence Renaudin, Passage Marguerite R...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>2017-08-23 17:28:48.000</td>\n",
" <td>RunnerUp_0050.csv</td>\n",
" <td>5.046360</td>\n",
" <td>12.262504</td>\n",
" <td>24.100000</td>\n",
" <td>515.000</td>\n",
" <td>48.846125</td>\n",
" <td>2.335200</td>\n",
" <td>Théâtre du Luxembourg, Rue Auguste Comte, Odéo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2017-08-22 17:48:37.000</td>\n",
" <td>RunnerUp_0049.csv</td>\n",
" <td>7.042143</td>\n",
" <td>11.952149</td>\n",
" <td>33.416667</td>\n",
" <td>825.000</td>\n",
" <td>48.846353</td>\n",
" <td>2.335627</td>\n",
" <td>Anne de Beaujeu, Cour de la Présidence, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>2017-08-21 17:50:10.000</td>\n",
" <td>RunnerUp_0048.csv</td>\n",
" <td>7.073655</td>\n",
" <td>12.216654</td>\n",
" <td>32.850000</td>\n",
" <td>801.000</td>\n",
" <td>48.846266</td>\n",
" <td>2.335489</td>\n",
" <td>Anne de Beaujeu, Cour de la Présidence, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2017-08-19 08:43:04.000</td>\n",
" <td>RunnerUp_0047.csv</td>\n",
" <td>17.425455</td>\n",
" <td>11.227346</td>\n",
" <td>89.066667</td>\n",
" <td>1879.000</td>\n",
" <td>48.785283</td>\n",
" <td>2.290892</td>\n",
" <td>Rue François Moreau, Fontenay-aux-Roses, Anton...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2017-08-17 18:03:58.000</td>\n",
" <td>RunnerUp_0046.csv</td>\n",
" <td>5.329528</td>\n",
" <td>12.031507</td>\n",
" <td>25.083333</td>\n",
" <td>581.000</td>\n",
" <td>48.846002</td>\n",
" <td>2.335284</td>\n",
" <td>Théâtre du Luxembourg, Rue Auguste Comte, Odéo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2017-08-16 17:46:09.000</td>\n",
" <td>RunnerUp_0044.csv</td>\n",
" <td>7.007194</td>\n",
" <td>12.314706</td>\n",
" <td>32.766667</td>\n",
" <td>774.000</td>\n",
" <td>48.846335</td>\n",
" <td>2.335589</td>\n",
" <td>Anne de Beaujeu, Cour de la Présidence, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>2017-08-14 10:08:22.000</td>\n",
" <td>RunnerUp_43.csv</td>\n",
" <td>6.288584</td>\n",
" <td>11.746766</td>\n",
" <td>30.150000</td>\n",
" <td>654.000</td>\n",
" <td>48.822337</td>\n",
" <td>2.338085</td>\n",
" <td>Le Petit Train, Allée du Bardo, Montsouris, Pa...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-08-12 08:00:36.000</td>\n",
" <td>RunnerUp_0042.csv</td>\n",
" <td>20.083537</td>\n",
" <td>10.992925</td>\n",
" <td>97.766667</td>\n",
" <td>2349.000</td>\n",
" <td>48.782023</td>\n",
" <td>2.290309</td>\n",
" <td>16, Boulevard Desgranges, Sceaux, Antony, Haut...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2017-08-10 18:25:48.000</td>\n",
" <td>RunnerUp_0040.csv</td>\n",
" <td>5.048189</td>\n",
" <td>11.765151</td>\n",
" <td>25.500000</td>\n",
" <td>470.000</td>\n",
" <td>48.846094</td>\n",
" <td>2.335153</td>\n",
" <td>Théâtre du Luxembourg, Rue Auguste Comte, Odéo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-08-08 18:01:53.000</td>\n",
" <td>RunnerUp_0038.csv</td>\n",
" <td>5.364445</td>\n",
" <td>11.557867</td>\n",
" <td>26.633333</td>\n",
" <td>609.000</td>\n",
" <td>48.846072</td>\n",
" <td>2.335317</td>\n",
" <td>Théâtre du Luxembourg, Rue Auguste Comte, Odéo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>2017-08-06 10:10:35.000</td>\n",
" <td>RunnerUp_0037.csv</td>\n",
" <td>7.722241</td>\n",
" <td>11.964914</td>\n",
" <td>36.833333</td>\n",
" <td>801.000</td>\n",
" <td>48.846405</td>\n",
" <td>2.335812</td>\n",
" <td>Jardin du Luxembourg, Cour de Jonction, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>2017-08-03 17:59:08.000</td>\n",
" <td>RunnerUp_0036.csv</td>\n",
" <td>5.534886</td>\n",
" <td>11.573736</td>\n",
" <td>27.000000</td>\n",
" <td>639.000</td>\n",
" <td>48.846058</td>\n",
" <td>2.335346</td>\n",
" <td>Poste des surveillants du jardin, Place André ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2017-07-30 09:42:55.000</td>\n",
" <td>RunnerUp_0035.csv</td>\n",
" <td>7.526653</td>\n",
" <td>11.697312</td>\n",
" <td>36.283333</td>\n",
" <td>894.000</td>\n",
" <td>48.846395</td>\n",
" <td>2.335657</td>\n",
" <td>Anne de Beaujeu, Cour de la Présidence, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>2017-07-27 18:20:01.000</td>\n",
" <td>RunnerUp_0034.csv</td>\n",
" <td>5.841380</td>\n",
" <td>11.599974</td>\n",
" <td>29.850000</td>\n",
" <td>628.000</td>\n",
" <td>48.845605</td>\n",
" <td>2.335139</td>\n",
" <td>Comtesse de Ségur, Rue Auguste Comte, Odéon, 6...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>2017-07-25 19:50:41.000</td>\n",
" <td>RunnerUp_0033.csv</td>\n",
" <td>5.027124</td>\n",
" <td>11.628791</td>\n",
" <td>22.233333</td>\n",
" <td>633.000</td>\n",
" <td>48.846215</td>\n",
" <td>2.335679</td>\n",
" <td>Anne de Beaujeu, Cour de la Présidence, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2017-07-24 20:54:20.000</td>\n",
" <td>RunnerUp_0032.csv</td>\n",
" <td>4.530297</td>\n",
" <td>11.905375</td>\n",
" <td>21.183333</td>\n",
" <td>443.000</td>\n",
" <td>48.846382</td>\n",
" <td>2.335684</td>\n",
" <td>Anne de Beaujeu, Cour de la Présidence, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2017-07-19 19:55:02.000</td>\n",
" <td>RunnerUp_31.csv</td>\n",
" <td>4.006633</td>\n",
" <td>11.753229</td>\n",
" <td>19.050000</td>\n",
" <td>537.000</td>\n",
" <td>48.849479</td>\n",
" <td>2.332601</td>\n",
" <td>Sœur, Rue Honoré Chevalier, Odéon, 6e, Paris, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>2017-07-18 20:14:04.000</td>\n",
" <td>RunnerUp_0030.csv</td>\n",
" <td>4.014472</td>\n",
" <td>11.086862</td>\n",
" <td>26.450000</td>\n",
" <td>486.000</td>\n",
" <td>48.844326</td>\n",
" <td>2.334989</td>\n",
" <td>15, Rue Auguste Comte, Odéon, 6e, Paris, Île-d...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>2017-07-11 18:09:12.000</td>\n",
" <td>RunnerUp_0029.csv</td>\n",
" <td>5.035661</td>\n",
" <td>11.444715</td>\n",
" <td>25.900000</td>\n",
" <td>495.000</td>\n",
" <td>48.846123</td>\n",
" <td>2.335252</td>\n",
" <td>Théâtre du Luxembourg, Rue Auguste Comte, Odéo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-07-10 18:30:43.000</td>\n",
" <td>RunnerUp_0028.csv</td>\n",
" <td>5.086652</td>\n",
" <td>11.832552</td>\n",
" <td>24.866667</td>\n",
" <td>528.000</td>\n",
" <td>48.846127</td>\n",
" <td>2.335270</td>\n",
" <td>Théâtre du Luxembourg, Rue Auguste Comte, Odéo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2017-07-09 09:53:50.000</td>\n",
" <td>RunnerUp_0027.csv</td>\n",
" <td>8.109093</td>\n",
" <td>11.397367</td>\n",
" <td>37.083333</td>\n",
" <td>817.000</td>\n",
" <td>48.846400</td>\n",
" <td>2.335600</td>\n",
" <td>Anne de Beaujeu, Cour de la Présidence, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>2017-07-06 18:12:50.000</td>\n",
" <td>RunnerUp_0026.csv</td>\n",
" <td>4.665151</td>\n",
" <td>9.431150</td>\n",
" <td>30.233333</td>\n",
" <td>631.000</td>\n",
" <td>48.843965</td>\n",
" <td>2.333494</td>\n",
" <td>57b, Rue d'Assas, Odéon, 6e, Paris, Île-de-Fra...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2017-07-05 18:35:48.000</td>\n",
" <td>RunnerUp_0025.csv</td>\n",
" <td>5.030024</td>\n",
" <td>11.446046</td>\n",
" <td>24.616667</td>\n",
" <td>521.000</td>\n",
" <td>48.846461</td>\n",
" <td>2.335721</td>\n",
" <td>Jardin du Luxembourg, Cour de Jonction, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2017-07-04 17:42:55.000</td>\n",
" <td>RunnerUp_24.csv</td>\n",
" <td>5.209022</td>\n",
" <td>11.345380</td>\n",
" <td>27.383333</td>\n",
" <td>513.000</td>\n",
" <td>48.846047</td>\n",
" <td>2.335238</td>\n",
" <td>Théâtre du Luxembourg, Rue Auguste Comte, Odéo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2017-06-18 09:39:15.000</td>\n",
" <td>RunnerUp_0023.csv</td>\n",
" <td>5.019963</td>\n",
" <td>10.748088</td>\n",
" <td>27.833333</td>\n",
" <td>547.000</td>\n",
" <td>48.846146</td>\n",
" <td>2.335195</td>\n",
" <td>Théâtre du Luxembourg, Rue Auguste Comte, Odéo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>2017-05-28 10:27:13.000</td>\n",
" <td>RunnerUp_22.csv</td>\n",
" <td>5.021169</td>\n",
" <td>11.038293</td>\n",
" <td>26.450000</td>\n",
" <td>571.000</td>\n",
" <td>48.846203</td>\n",
" <td>2.335309</td>\n",
" <td>Théâtre du Luxembourg, Rue Auguste Comte, Odéo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>2017-05-21 09:57:30.000</td>\n",
" <td>RunnerUp_0021.csv</td>\n",
" <td>5.031971</td>\n",
" <td>11.359628</td>\n",
" <td>25.750000</td>\n",
" <td>527.000</td>\n",
" <td>48.846216</td>\n",
" <td>2.335359</td>\n",
" <td>Blanche de Castille, Cour de la Présidence, Od...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2017-04-30 09:52:48.000</td>\n",
" <td>RunnerUp_0020.csv</td>\n",
" <td>5.017558</td>\n",
" <td>11.314596</td>\n",
" <td>25.783333</td>\n",
" <td>554.000</td>\n",
" <td>48.846171</td>\n",
" <td>2.335251</td>\n",
" <td>Théâtre du Luxembourg, Rue Auguste Comte, Odéo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>2017-04-17 11:01:08.000</td>\n",
" <td>RunnerUp_0019.csv</td>\n",
" <td>5.012422</td>\n",
" <td>11.671251</td>\n",
" <td>24.950000</td>\n",
" <td>510.000</td>\n",
" <td>48.846221</td>\n",
" <td>2.335243</td>\n",
" <td>Théâtre du Luxembourg, Rue Auguste Comte, Odéo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2017-04-16 10:38:27.000</td>\n",
" <td>RunnerUp_0018.csv</td>\n",
" <td>4.602083</td>\n",
" <td>11.295570</td>\n",
" <td>23.600000</td>\n",
" <td>470.000</td>\n",
" <td>48.846213</td>\n",
" <td>2.335456</td>\n",
" <td>Anne de Beaujeu, Cour de la Présidence, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>2017-03-26 11:59:32.000</td>\n",
" <td>RunnerUp_0017.csv</td>\n",
" <td>5.688288</td>\n",
" <td>11.581280</td>\n",
" <td>34.716667</td>\n",
" <td>577.000</td>\n",
" <td>48.845600</td>\n",
" <td>2.335287</td>\n",
" <td>Pierre Mendès France, Rue Auguste Comte, Odéon...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2017-03-25 10:01:18.000</td>\n",
" <td>RunnerUp_0016.csv</td>\n",
" <td>4.629997</td>\n",
" <td>11.634636</td>\n",
" <td>23.100000</td>\n",
" <td>459.000</td>\n",
" <td>48.846481</td>\n",
" <td>2.335620</td>\n",
" <td>Blanche de Castille, Cour de la Présidence, Od...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2016-10-08 05:20:17.000</td>\n",
" <td>RunnerUp_10.csv</td>\n",
" <td>15.028409</td>\n",
" <td>8.991190</td>\n",
" <td>100.200000</td>\n",
" <td>1486.000</td>\n",
" <td>36.450690</td>\n",
" <td>25.415905</td>\n",
" <td>Θήρα - Οία, Φοινικιά, Δήμος Θήρας, Περιφερειακ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2016-10-04 09:45:14.000</td>\n",
" <td>RunnerUp_0007.csv</td>\n",
" <td>0.005205</td>\n",
" <td>6.246022</td>\n",
" <td>0.050000</td>\n",
" <td>4.000</td>\n",
" <td>48.725136</td>\n",
" <td>2.145281</td>\n",
" <td>458, Avenue des Accélérateurs, CEA Saclay, Sai...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>2016-10-02 09:31:51.000</td>\n",
" <td>RunnerUp_0006.csv</td>\n",
" <td>7.001115</td>\n",
" <td>11.774183</td>\n",
" <td>34.916667</td>\n",
" <td>655.000</td>\n",
" <td>48.846376</td>\n",
" <td>2.335594</td>\n",
" <td>Anne de Beaujeu, Cour de la Présidence, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2016-10-01 08:02:18.000</td>\n",
" <td>RunnerUp_0005.csv</td>\n",
" <td>4.008485</td>\n",
" <td>11.110023</td>\n",
" <td>20.733333</td>\n",
" <td>481.000</td>\n",
" <td>48.846831</td>\n",
" <td>2.336178</td>\n",
" <td>Jardin du Luxembourg, Cour de Jonction, Odéon,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2016-09-13 19:59:11.000</td>\n",
" <td>RunnerUp_0004.csv</td>\n",
" <td>2.004824</td>\n",
" <td>10.999175</td>\n",
" <td>10.866667</td>\n",
" <td>153.000</td>\n",
" <td>43.601009</td>\n",
" <td>3.897712</td>\n",
" <td>Banque Populaire, Rue de Syracuse, Port Marian...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>2016-09-13 19:49:33.000</td>\n",
" <td>RunnerUp_0002.csv</td>\n",
" <td>1.037511</td>\n",
" <td>11.742230</td>\n",
" <td>5.150000</td>\n",
" <td>111.000</td>\n",
" <td>43.607851</td>\n",
" <td>3.889808</td>\n",
" <td>Hortus, 38, Place du Millénaire, Antigone, Cen...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2016-09-10 06:16:10.000</td>\n",
" <td>RunnerUp_0001.csv</td>\n",
" <td>4.040153</td>\n",
" <td>10.275758</td>\n",
" <td>23.133333</td>\n",
" <td>409.000</td>\n",
" <td>48.845935</td>\n",
" <td>2.335594</td>\n",
" <td>Poste des surveillants du jardin, Place André ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Filename \\\n",
"31 2017-09-28 17:31:41.836 2017-09-28T17_32_18Z_PT40M33S_Running.csv \n",
"1 2017-09-26 17:29:38.784 2017-09-26T17_29_38Z_PT42M55S_Running.csv \n",
"53 2017-09-23 09:23:27.402 2017-09-23T09_23_58Z_PT1H59M11S_Running.csv \n",
"45 2017-09-21 16:34:23.641 2017-09-21T16_34_23Z_PT45M38S_Running.csv \n",
"41 2017-09-19 16:36:08.981 2017-09-19T16_36_08Z_PT44M32S_Running.csv \n",
"5 2017-09-16 07:17:00.070 2017-09-16T07_17_00Z_PT1H30M8S_Running.csv \n",
"6 2017-09-14 17:23:15.896 2017-09-14T16_20_43Z_PT1H42M47S_Biking,Running... \n",
"37 2017-09-12 16:59:14.957 2017-09-12T16_59_14Z_PT51M33S_Running.csv \n",
"50 2017-09-09 08:27:03.960 2017-09-09T08_27_03Z_PT1H55M7S_Running.csv \n",
"24 2017-09-07 17:01:55.000 RunnerUp_0058.csv \n",
"47 2017-09-05 17:06:22.000 RunnerUp_0057.csv \n",
"21 2017-09-04 17:38:54.000 RunnerUp_0056.csv \n",
"38 2017-09-02 08:52:00.450 2017-09-02T08_52_00Z_PT1H58M34S_Running.csv \n",
"27 2017-09-01 05:07:50.590 2017-09-01T05_07_50Z_PT50M7S_Running.csv \n",
"15 2017-08-30 17:20:06.174 Cristian_Perez_2017-08-30_19-20-05.csv \n",
"43 2017-08-29 17:15:33.209 Cristian_Perez_2017-08-29_19-15-32.csv \n",
"32 2017-08-26 08:07:54.000 RunnerUp_0051.csv \n",
"54 2017-08-23 17:28:48.000 RunnerUp_0050.csv \n",
"28 2017-08-22 17:48:37.000 RunnerUp_0049.csv \n",
"39 2017-08-21 17:50:10.000 RunnerUp_0048.csv \n",
"11 2017-08-19 08:43:04.000 RunnerUp_0047.csv \n",
"22 2017-08-17 18:03:58.000 RunnerUp_0046.csv \n",
"25 2017-08-16 17:46:09.000 RunnerUp_0044.csv \n",
"55 2017-08-14 10:08:22.000 RunnerUp_43.csv \n",
"4 2017-08-12 08:00:36.000 RunnerUp_0042.csv \n",
"13 2017-08-10 18:25:48.000 RunnerUp_0040.csv \n",
"0 2017-08-08 18:01:53.000 RunnerUp_0038.csv \n",
"34 2017-08-06 10:10:35.000 RunnerUp_0037.csv \n",
"35 2017-08-03 17:59:08.000 RunnerUp_0036.csv \n",
"14 2017-07-30 09:42:55.000 RunnerUp_0035.csv \n",
"30 2017-07-27 18:20:01.000 RunnerUp_0034.csv \n",
"46 2017-07-25 19:50:41.000 RunnerUp_0033.csv \n",
"23 2017-07-24 20:54:20.000 RunnerUp_0032.csv \n",
"12 2017-07-19 19:55:02.000 RunnerUp_31.csv \n",
"48 2017-07-18 20:14:04.000 RunnerUp_0030.csv \n",
"42 2017-07-11 18:09:12.000 RunnerUp_0029.csv \n",
"2 2017-07-10 18:30:43.000 RunnerUp_0028.csv \n",
"16 2017-07-09 09:53:50.000 RunnerUp_0027.csv \n",
"52 2017-07-06 18:12:50.000 RunnerUp_0026.csv \n",
"29 2017-07-05 18:35:48.000 RunnerUp_0025.csv \n",
"17 2017-07-04 17:42:55.000 RunnerUp_24.csv \n",
"7 2017-06-18 09:39:15.000 RunnerUp_0023.csv \n",
"49 2017-05-28 10:27:13.000 RunnerUp_22.csv \n",
"51 2017-05-21 09:57:30.000 RunnerUp_0021.csv \n",
"10 2017-04-30 09:52:48.000 RunnerUp_0020.csv \n",
"44 2017-04-17 11:01:08.000 RunnerUp_0019.csv \n",
"8 2017-04-16 10:38:27.000 RunnerUp_0018.csv \n",
"40 2017-03-26 11:59:32.000 RunnerUp_0017.csv \n",
"20 2017-03-25 10:01:18.000 RunnerUp_0016.csv \n",
"26 2016-10-08 05:20:17.000 RunnerUp_10.csv \n",
"18 2016-10-04 09:45:14.000 RunnerUp_0007.csv \n",
"36 2016-10-02 09:31:51.000 RunnerUp_0006.csv \n",
"9 2016-10-01 08:02:18.000 RunnerUp_0005.csv \n",
"19 2016-09-13 19:59:11.000 RunnerUp_0004.csv \n",
"33 2016-09-13 19:49:33.000 RunnerUp_0002.csv \n",
"3 2016-09-10 06:16:10.000 RunnerUp_0001.csv \n",
"\n",
" Kilometers Speed Time Climb Lat Long \\\n",
"31 7.643801 11.602275 41.166867 742.000 48.822395 2.338011 \n",
"1 8.761183 11.979408 42.932483 890.000 48.822418 2.337851 \n",
"53 21.848359 11.175643 119.718800 2166.000 48.780568 2.292178 \n",
"45 10.592581 11.908545 45.635200 1048.000 48.846442 2.335671 \n",
"41 9.041560 12.643044 44.544367 897.000 48.846427 2.335698 \n",
"5 17.096634 11.390383 90.146483 1854.000 48.784790 2.291165 \n",
"6 8.048995 11.910404 36.919733 848.000 48.822385 2.338098 \n",
"37 9.248593 12.426092 51.559717 909.000 48.846306 2.335477 \n",
"50 21.728122 11.392283 115.132050 2317.000 48.779607 2.291531 \n",
"24 8.679763 12.729851 41.800000 853.000 48.846475 2.335826 \n",
"47 8.958269 12.844046 41.433333 822.000 48.846517 2.335812 \n",
"21 3.398560 11.900422 17.116667 301.000 48.844327 2.335079 \n",
"38 21.516255 8.859990 121.576833 1717.000 48.777818 2.290877 \n",
"27 8.090000 10.900000 44.500000 130.000 48.842785 2.329997 \n",
"15 8.146645 11.694941 41.716667 121.772 48.846472 2.335846 \n",
"43 8.462380 11.721614 42.616667 164.441 48.846504 2.335784 \n",
"32 23.109283 10.833283 122.233333 2713.000 48.778201 2.291200 \n",
"54 5.046360 12.262504 24.100000 515.000 48.846125 2.335200 \n",
"28 7.042143 11.952149 33.416667 825.000 48.846353 2.335627 \n",
"39 7.073655 12.216654 32.850000 801.000 48.846266 2.335489 \n",
"11 17.425455 11.227346 89.066667 1879.000 48.785283 2.290892 \n",
"22 5.329528 12.031507 25.083333 581.000 48.846002 2.335284 \n",
"25 7.007194 12.314706 32.766667 774.000 48.846335 2.335589 \n",
"55 6.288584 11.746766 30.150000 654.000 48.822337 2.338085 \n",
"4 20.083537 10.992925 97.766667 2349.000 48.782023 2.290309 \n",
"13 5.048189 11.765151 25.500000 470.000 48.846094 2.335153 \n",
"0 5.364445 11.557867 26.633333 609.000 48.846072 2.335317 \n",
"34 7.722241 11.964914 36.833333 801.000 48.846405 2.335812 \n",
"35 5.534886 11.573736 27.000000 639.000 48.846058 2.335346 \n",
"14 7.526653 11.697312 36.283333 894.000 48.846395 2.335657 \n",
"30 5.841380 11.599974 29.850000 628.000 48.845605 2.335139 \n",
"46 5.027124 11.628791 22.233333 633.000 48.846215 2.335679 \n",
"23 4.530297 11.905375 21.183333 443.000 48.846382 2.335684 \n",
"12 4.006633 11.753229 19.050000 537.000 48.849479 2.332601 \n",
"48 4.014472 11.086862 26.450000 486.000 48.844326 2.334989 \n",
"42 5.035661 11.444715 25.900000 495.000 48.846123 2.335252 \n",
"2 5.086652 11.832552 24.866667 528.000 48.846127 2.335270 \n",
"16 8.109093 11.397367 37.083333 817.000 48.846400 2.335600 \n",
"52 4.665151 9.431150 30.233333 631.000 48.843965 2.333494 \n",
"29 5.030024 11.446046 24.616667 521.000 48.846461 2.335721 \n",
"17 5.209022 11.345380 27.383333 513.000 48.846047 2.335238 \n",
"7 5.019963 10.748088 27.833333 547.000 48.846146 2.335195 \n",
"49 5.021169 11.038293 26.450000 571.000 48.846203 2.335309 \n",
"51 5.031971 11.359628 25.750000 527.000 48.846216 2.335359 \n",
"10 5.017558 11.314596 25.783333 554.000 48.846171 2.335251 \n",
"44 5.012422 11.671251 24.950000 510.000 48.846221 2.335243 \n",
"8 4.602083 11.295570 23.600000 470.000 48.846213 2.335456 \n",
"40 5.688288 11.581280 34.716667 577.000 48.845600 2.335287 \n",
"20 4.629997 11.634636 23.100000 459.000 48.846481 2.335620 \n",
"26 15.028409 8.991190 100.200000 1486.000 36.450690 25.415905 \n",
"18 0.005205 6.246022 0.050000 4.000 48.725136 2.145281 \n",
"36 7.001115 11.774183 34.916667 655.000 48.846376 2.335594 \n",
"9 4.008485 11.110023 20.733333 481.000 48.846831 2.336178 \n",
"19 2.004824 10.999175 10.866667 153.000 43.601009 3.897712 \n",
"33 1.037511 11.742230 5.150000 111.000 43.607851 3.889808 \n",
"3 4.040153 10.275758 23.133333 409.000 48.845935 2.335594 \n",
"\n",
" Adress \n",
"31 Le Petit Train, Allée du Bardo, Montsouris, Pa... \n",
"1 Le Petit Train, Allée du Bardo, Montsouris, Pa... \n",
"53 Avenue Charles Péguy, Sceaux, Antony, Hauts-de... \n",
"45 Jardin du Luxembourg, Cour de Jonction, Odéon,... \n",
"41 Jardin du Luxembourg, Cour de Jonction, Odéon,... \n",
"5 Rue François Moreau, Fontenay-aux-Roses, Anton... \n",
"6 Le Petit Train, Allée du Bardo, Montsouris, Pa... \n",
"37 Anne de Beaujeu, Cour de la Présidence, Odéon,... \n",
"50 Fontenay-Houdan, Rue de Fontenay, Sceaux, Anto... \n",
"24 Jardin du Luxembourg, Cour de Jonction, Odéon,... \n",
"47 Jardin du Luxembourg, Cour de Jonction, Odéon,... \n",
"21 15, Rue Auguste Comte, Odéon, 6e, Paris, Île-d... \n",
"38 EHPAD Résidence Renaudin, Passage Marguerite R... \n",
"27 Starbucks Coffee, Rue Jules Chaplain, ND-des-C... \n",
"15 Jardin du Luxembourg, Cour de Jonction, Odéon,... \n",
"43 Jardin du Luxembourg, Cour de Jonction, Odéon,... \n",
"32 EHPAD Résidence Renaudin, Passage Marguerite R... \n",
"54 Théâtre du Luxembourg, Rue Auguste Comte, Odéo... \n",
"28 Anne de Beaujeu, Cour de la Présidence, Odéon,... \n",
"39 Anne de Beaujeu, Cour de la Présidence, Odéon,... \n",
"11 Rue François Moreau, Fontenay-aux-Roses, Anton... \n",
"22 Théâtre du Luxembourg, Rue Auguste Comte, Odéo... \n",
"25 Anne de Beaujeu, Cour de la Présidence, Odéon,... \n",
"55 Le Petit Train, Allée du Bardo, Montsouris, Pa... \n",
"4 16, Boulevard Desgranges, Sceaux, Antony, Haut... \n",
"13 Théâtre du Luxembourg, Rue Auguste Comte, Odéo... \n",
"0 Théâtre du Luxembourg, Rue Auguste Comte, Odéo... \n",
"34 Jardin du Luxembourg, Cour de Jonction, Odéon,... \n",
"35 Poste des surveillants du jardin, Place André ... \n",
"14 Anne de Beaujeu, Cour de la Présidence, Odéon,... \n",
"30 Comtesse de Ségur, Rue Auguste Comte, Odéon, 6... \n",
"46 Anne de Beaujeu, Cour de la Présidence, Odéon,... \n",
"23 Anne de Beaujeu, Cour de la Présidence, Odéon,... \n",
"12 Sœur, Rue Honoré Chevalier, Odéon, 6e, Paris, ... \n",
"48 15, Rue Auguste Comte, Odéon, 6e, Paris, Île-d... \n",
"42 Théâtre du Luxembourg, Rue Auguste Comte, Odéo... \n",
"2 Théâtre du Luxembourg, Rue Auguste Comte, Odéo... \n",
"16 Anne de Beaujeu, Cour de la Présidence, Odéon,... \n",
"52 57b, Rue d'Assas, Odéon, 6e, Paris, Île-de-Fra... \n",
"29 Jardin du Luxembourg, Cour de Jonction, Odéon,... \n",
"17 Théâtre du Luxembourg, Rue Auguste Comte, Odéo... \n",
"7 Théâtre du Luxembourg, Rue Auguste Comte, Odéo... \n",
"49 Théâtre du Luxembourg, Rue Auguste Comte, Odéo... \n",
"51 Blanche de Castille, Cour de la Présidence, Od... \n",
"10 Théâtre du Luxembourg, Rue Auguste Comte, Odéo... \n",
"44 Théâtre du Luxembourg, Rue Auguste Comte, Odéo... \n",
"8 Anne de Beaujeu, Cour de la Présidence, Odéon,... \n",
"40 Pierre Mendès France, Rue Auguste Comte, Odéon... \n",
"20 Blanche de Castille, Cour de la Présidence, Od... \n",
"26 Θήρα - Οία, Φοινικιά, Δήμος Θήρας, Περιφερειακ... \n",
"18 458, Avenue des Accélérateurs, CEA Saclay, Sai... \n",
"36 Anne de Beaujeu, Cour de la Présidence, Odéon,... \n",
"9 Jardin du Luxembourg, Cour de Jonction, Odéon,... \n",
"19 Banque Populaire, Rue de Syracuse, Port Marian... \n",
"33 Hortus, 38, Place du Millénaire, Antigone, Cen... \n",
"3 Poste des surveillants du jardin, Place André ... "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame.sort_values('Date', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"recent_frame = frame[frame['Date'] > pd.datetime(2017, 6, 1)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Kilometers by date"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"recent_frame = recent_frame.assign(Month=recent_frame['Date'].dt.month, Week=recent_frame['Date'].dt.week, \n",
" Weekday=recent_frame['Date'].dt.weekday)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
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j4sWLFQ++Gvn5+eH48eNaZfqWx7ezs8Nrr72GVatW4dSpU0hPT6/VrxaqTrwz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\nly5dJEnHjh1jNkUAALxl3LhxOn36tMLCwlRVVSXp/HiQAQMGaMWKFXr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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe169518748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"months = ['June', 'July', 'August', 'Sept']\n",
"fig, axr = plt.subplots(1,3, figsize=(14,5))\n",
"axr[0].bar(recent_frame.groupby(recent_frame.Date.dt.month).agg({'Kilometers': 'sum'}).index, \n",
" recent_frame.groupby(recent_frame.Date.dt.month).agg({'Kilometers': 'sum'}).values,\n",
" yerr=recent_frame.groupby(recent_frame.Date.dt.month).agg({'Kilometers': 'std'}).values,\n",
" tick_label=months, label='Number of kilometers')\n",
"axr[0].set_title('Total number of kilometers')\n",
"axr[0].set_ylabel('Km')\n",
"recent_frame.boxplot(['Speed'], by='Month', ax=axr[1])\n",
"axr[1].set_xticklabels(months)\n",
"axr[1].set_ylabel('Km/h')\n",
"recent_frame.groupby('Month')['Speed'].count().plot.bar(ax=axr[2], color='C1')\n",
"axr[2].set_xticklabels(months)\n",
"axr[2].set_title('Number of trainings')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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qSZMmGjFihPz9eXUYAAAAAN9hSoVTuXJlPfnkkwoMDFReXp4mTZqktm3b6tNP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\nFVdcoZkzZ+qKK65QSkqKGaEAAAAAgKl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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe16898c550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"months = ['June', 'July', 'August', 'Sept']\n",
"fig, axr = plt.subplots(3, figsize=(14,8), sharex=True)\n",
"recent_frame.groupby('Week')['Kilometers'].sum().plot.bar(ax=axr[0])\n",
"axr[0].set_title('Total number of kilometers')\n",
"axr[0].set_ylabel('Km')\n",
"recent_frame.boxplot(['Speed'], by='Week', ax=axr[1])\n",
"axr[1].set_ylabel('Km/h')\n",
"recent_frame.groupby('Week')['Speed'].count().plot.bar(ax=axr[2], color='C1')\n",
"axr[2].set_title('Number of trainings')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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tSGw01A9GA1cTAA8DcD2liI0EMJsgCz+C+cuPIVkZ5R8oVSpM5IiIiIiIbkLd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\nnzaiiAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe16977c940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"weekdays = [ 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
"fig, axr = plt.subplots(3, figsize=(14,8))\n",
"recent_frame.groupby('Weekday')['Kilometers'].sum().plot.area(ax=axr[0])\n",
"axr[0].set_title('Total number of kilometers')\n",
"axr[0].set_ylabel('Km')\n",
"axr[0].set_xticklabels(weekdays)\n",
"recent_frame.boxplot(['Speed'], by='Weekday', ax=axr[1])\n",
"axr[1].set_ylabel('Km/h')\n",
"axr[1].set_xticklabels(weekdays)\n",
"recent_frame.groupby('Weekday')['Speed'].count().plot.area(ax=axr[2], color='C1')\n",
"axr[2].set_title('Number of trainings')\n",
"axr[2].set_xticklabels(weekdays)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Marathon prediction"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn import datasets, linear_model\n",
"from sklearn.metrics import mean_squared_error, r2_score"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(42, 2)"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"recent_frame[['Kilometers', 'Climb']].values.shape"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(42, 3)"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.stack([recent_frame.index.values,recent_frame['Time'].values, recent_frame['Climb'].values], axis=1).shape"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#X_train = np.stack([recent_frame.index.values, recent_frame['Kilometers'].values, recent_frame['Climb'].values ], axis=1)\n",
"X_train = recent_frame[['Kilometers','Climb']].values\n",
"y_train = recent_frame['Time']\n",
"X_test = np.array([42.195, 400]).reshape(1,-1)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Distance: 42.195 Km, Speed: 10.563563454086022 Km/h, Time: 3.99439073598586 hours \n",
"\n"
]
}
],
"source": [
"regr = linear_model.LinearRegression()\n",
"regr.fit(X_train, y_train)\n",
"y_pred = regr.predict(X_test)\n",
"print('Distance: {} Km, Speed: {} Km/h, Time: {} hours \\n'.format(X_test[0][0], X_test[0][0]/float(y_pred[0]/60), y_pred[0]/60))"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 11.210528]\n"
]
}
],
"source": [
"Dis = 10\n",
"Climb = 400\n",
"val = np.array([Dis, Climb]).reshape(1,-1)\n",
"print(Dis/(regr.predict(val)/60))\n",
"#print('Distance: {} Km, Speed: {} Km/h, Time: {} hours \\n'.format(X_test[0][1], X_test[0][1]/float(y_pred[0]/60), y_pred[0]/60))"
]
},
{
"cell_type": "code",
"execution_count": 221,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Lmn4+SynRep5+Ew5+Tz/99LjHuVyOF154gRUrVkzo/bZts337dq666iouv/zy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\nOIIfAAAAABgcwQ8AAAAADO4vHQ0/FaKsDiYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe1541edcc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15,4))\n",
"plt.scatter(X_train[:,0], regr.predict(X_train)/60)\n",
"plt.scatter(X_test[:,0], y_pred/60)\n",
"plt.plot([0, X_test[:,0]], [0, y_pred/60], '--', color='C2')\n",
"plt.ylabel('Hours')\n",
"plt.xlabel('Kilometers')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Speed by training"
]
},
{
"cell_type": "code",
"execution_count": 223,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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t4IX3N3DBuD7cfOlIkTr6DONQzt7PrW4dzCSEB/HczAye+7GQMpN70xj6xoXw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\nLL9qDE9+9A27T2VT7qz9QqsQGgM7/sTYbrtIjM4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bcesILv/dYKLjG5emUDUjY7MYTi9T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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe153ff4d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15,4))\n",
"ax = plt.axes()\n",
"ax.set_xlim([pd.datetime(2017,6,15),pd.datetime(2017,10,10)])\n",
"\n",
"legend_sizes = np.sort(recent_frame.Climb)[::len(recent_frame.Climb)//4][-3:]\n",
"# get the indices for each of the legend sizes\n",
"indices = [np.where(recent_frame.Climb==v)[0][0] for v in legend_sizes]\n",
"# plot each point again, and its value as a label\n",
"for i in indices:\n",
" plt.scatter(x=recent_frame.Date.iloc[i], y=recent_frame.Speed.iloc[i], c=recent_frame.Kilometers.iloc[i], s=recent_frame.Climb.iloc[i]/2,\n",
" vmin=0,vmax=1,edgecolors='none',\n",
" label='{:.2f}'.format(recent_frame.Climb.iloc[i]))\n",
"# add the legend\n",
"plt.scatter(x=recent_frame.Date.values, y=recent_frame.Speed, c=recent_frame.Kilometers, s=recent_frame.Climb, label=None)\n",
"#plt.scatter(x=pd.to_datetime('2017-10-09', format='%Y-%m-%d'), y=X_test[0][0]/float(y_pred[0]/60), c=X_test[0][0], s=X_test[0][1], linewidth=3)\n",
"plt.plot(X_test[0][0]/float(y_pred[0]/60))\n",
"plt.colorbar(label='Distance')\n",
"plt.xlabel('Date')\n",
"plt.ylabel('Average Speed')\n",
"plt.legend(scatterpoints=1, title='Climb', labelspacing=1.2)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 261,
"metadata": {},
"outputs": [],
"source": [
"list_speed_bubble = list()\n",
"list_speed_prediction = list()\n",
"list_linear_regression = list()\n",
"for k,val in recent_frame.iterrows():\n",
" list_speed_bubble.append({'x': val['Date'].strftime(\"%Y-%m-%d\"), 'y': np.round(val['Speed'],2), 'r': np.round(val['Kilometers'],2)})\n",
" to_predict = val[['Kilometers', 'Climb']].values.reshape(1,-1)\n",
" list_linear_regression.append({'x': np.round(val['Kilometers'],2), 'y': np.round(regr.predict(to_predict)[0]/60,2)})\n",
" #list_speed_prediction.append({'x': val['Date'].strftime(\"%Y-%m-%d\"), 'y': np.round(val['Kilometers']/(regr.predict(to_predict)[0]/60),2), 'r': np.round(val['Kilometers'],2)})\n",
"\n",
"list_speed_bubble.append({'x': '2017-10-08', 'y': np.round(X_test[0][0]/float(y_pred[0]/60),2), 'r': np.round(X_test[0][0],2)})\n",
"list_speed_prediction.append({'x': '2017-10-08', 'y': np.round(X_test[0][0]/float(y_pred[0]/60),2), 'r': np.round(X_test[0][0],2)})"
]
},
{
"cell_type": "code",
"execution_count": 262,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-0.048497971407600253"
]
},
"execution_count": 262,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"to_predict = np.reshape([0,40], (1,-1))\n",
"regr.predict(to_predict)[0]/60\n",
"#list_speed_predi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Export json"
]
},
{
"cell_type": "code",
"execution_count": 263,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_steps = pd.concat([recent_frame.Date.dt.strftime(\"%s\"), recent_frame.Kilometers.round(1)], axis=1)\n",
"\n",
"dic_steps = dict()\n",
"for k,x in df_steps.iterrows():\n",
" dic_steps[x['Date']] = x['Kilometers']\n"
]
},
{
"cell_type": "code",
"execution_count": 264,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open('output/marathon.json', 'w') as outfile:\n",
" json.dump({'weekday': {'Kilometers': [i[0] for i in recent_frame.groupby(recent_frame.Date.dt.weekday).agg({'Kilometers': 'mean'}).round(1).values.tolist()],\n",
" 'Speed': [i[0] for i in recent_frame.groupby(recent_frame.Date.dt.weekday).agg({'Speed': 'mean'}).round(1).values.tolist()],\n",
" 'Trains': [i[0] for i in recent_frame.groupby(recent_frame.Date.dt.weekday).agg({'Kilometers': 'count'}).round(1).values.tolist()],\n",
" 'index' : weekdays\n",
" },\n",
" 'week': {'index' : [float(i) for i in recent_frame.groupby(recent_frame.Date.dt.week).agg({'Kilometers': 'count'}).index.tolist()],\n",
" 'Kilometers': [float(i[0]) for i in recent_frame.groupby(recent_frame.Date.dt.week).agg({'Kilometers': 'sum'}).round(1).values],\n",
" 'Speed': [float(i[0]) for i in recent_frame.groupby(recent_frame.Date.dt.week).agg({'Speed': 'mean'}).round(1).values],\n",
" 'Trains': [float(i[0]) for i in recent_frame.groupby(recent_frame.Date.dt.week).agg({'Kilometers': 'count'}).round(1).values]\n",
" },\n",
" 'month': {'Kilometers' : [float(i) for i in recent_frame.groupby(recent_frame.Date.dt.month).agg({'Kilometers': 'sum'}).round(1).values],\n",
" 'Speed' : [float(i) for i in recent_frame.groupby(recent_frame.Date.dt.month).agg({'Speed': 'mean'}).round(1).values],\n",
" 'Trains' : [float(i) for i in recent_frame.groupby(recent_frame.Date.dt.month).agg({'Kilometers': 'count'}).round(1).values],\n",
" 'index' : months\n",
" },\n",
" 'heatmap_km': dic_steps,\n",
" 'bubble_speed': {'real': list_speed_bubble, 'predict': sorted(list_speed_prediction, key=lambda x : x['x'], reverse=False)},\n",
" 'linear_reg': {'points': list_linear_regression, 'line': [{'x': 0, 'y': -0.04}, {'x': X_test[:,0][0], 'y' : np.round((y_pred/60)[0], 2)}]}\n",
" #'leaflet': [[i[0], i[1], 1] for i in [[float(v.Lat), float(v.Long)] for k,v in recent_frame.iterrows()]],\n",
" }, outfile, indent=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Heatmap folium"
]
},
{
"cell_type": "code",
"execution_count": 254,
"metadata": {},
"outputs": [
{
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'#fff000', '#ffef00', '#ffee00', '#ffee00', '#ffed00', '#ffec00', '#ffec00', '#ffeb00', '#ffea00', '#ffe900', '#ffe900', '#ffe800', '#ffe700', '#ffe700', '#ffe600', '#ffe500', '#ffe400', '#ffe400', '#ffe300', '#ffe200', '#ffe100', '#ffe100', '#ffe000', '#ffdf00', '#ffdf00', '#ffde00', '#ffdd00', '#ffdc00', '#ffdc00', '#ffdb00', '#ffda00', '#ffd900', '#ffd900', '#ffd800', '#ffd700', '#ffd700', '#ffd600', '#ffd500', '#ffd400', '#ffd400', '#ffd300', '#ffd200', '#ffd200', '#ffd100', '#ffd000', '#ffcf00', '#ffcf00', '#ffce00', '#ffcd00', '#ffcc00', '#ffcc00', '#ffcb00', '#ffca00', '#ffca00', '#ffc900', '#ffc800', '#ffc700', '#ffc700', '#ffc600', '#ffc500', '#ffc400', '#ffc400', '#ffc300', '#ffc200', '#ffc200', '#ffc100', '#ffc000', '#ffbf00', '#ffbf00', '#ffbe00', '#ffbd00', '#ffbd00', '#ffbc00', '#ffbb00', '#ffba00', '#ffba00', '#ffb900', '#ffb800', '#ffb700', '#ffb700', '#ffb600', '#ffb500', '#ffb500', '#ffb400', '#ffb300', '#ffb200', '#ffb200', '#ffb100', '#ffb000', '#ffaf00', '#ffaf00', '#ffae00', '#ffad00', '#ffad00', '#ffac00', '#ffab00', '#ffaa00', '#ffaa00', '#ffa900', '#ffa800', '#ffa800', '#ffa700', '#ffa600', '#ffa500', '#ffa400', '#ffa300', '#ffa100', '#ffa000', '#ff9f00', '#ff9e00', '#ff9c00', '#ff9b00', '#ff9a00', '#ff9800', '#ff9700', '#ff9600', '#ff9400', '#ff9300', '#ff9200', '#ff9000', '#ff8f00', '#ff8e00', '#ff8c00', '#ff8b00', '#ff8a00', '#ff8800', '#ff8700', '#ff8600', '#ff8400', '#ff8300', '#ff8200', '#ff8000', '#ff7f00', '#ff7e00', '#ff7c00', '#ff7b00', '#ff7a00', '#ff7800', '#ff7700', '#ff7600', '#ff7400', '#ff7300', '#ff7200', '#ff7000', '#ff6f00', '#ff6e00', '#ff6c00', '#ff6b00', '#ff6a00', '#ff6800', '#ff6700', '#ff6600', '#ff6400', '#ff6300', '#ff6200', '#ff6000', '#ff5f00', '#ff5e00', '#ff5c00', '#ff5b00', '#ff5a00', '#ff5800', '#ff5700', '#ff5600', '#ff5400', '#ff5300', '#ff5200', '#ff5000', '#ff4f00', '#ff4e00', '#ff4d00', '#ff4b00', '#ff4a00', '#ff4900', '#ff4700', '#ff4600', '#ff4500', '#ff4300', '#ff4200', '#ff4100', '#ff3f00', '#ff3e00', '#ff3d00', '#ff3b00', '#ff3a00', '#ff3900', '#ff3700', '#ff3600', '#ff3500', '#ff3300', '#ff3200', '#ff3100', '#ff2f00', '#ff2e00', '#ff2d00', '#ff2b00', '#ff2a00', '#ff2900', '#ff2700', '#ff2600', '#ff2500', '#ff2300', '#ff2200', '#ff2100', '#ff1f00', '#ff1e00', '#ff1d00', '#ff1b00', '#ff1a00', '#ff1900', '#ff1700', '#ff1600', '#ff1500', '#ff1300', '#ff1200', '#ff1100', '#ff0f00', '#ff0e00', '#ff0d00', '#ff0b00', '#ff0a00', '#ff0900', '#ff0700', '#ff0600', '#ff0500', '#ff0300', '#ff0200', '#ff0100', '#ff0000']);
    

    color_map_47f509d3746544e5aae4a3dc8777b828.x = d3.scale.linear()
              .domain([0.0, 20.0])
              .range([0, 400]);

    color_map_47f509d3746544e5aae4a3dc8777b828.legend = L.control({position: 'topright'});
    color_map_47f509d3746544e5aae4a3dc8777b828.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
    color_map_47f509d3746544e5aae4a3dc8777b828.legend.addTo(map_06ed8a3aebee4421bd58f074ba8a8410);

    color_map_47f509d3746544e5aae4a3dc8777b828.xAxis = d3.svg.axis()
        .scale(color_map_47f509d3746544e5aae4a3dc8777b828.x)
        .orient("top")
        .tickSize(1)
        .tickValues([0.0, 5.0, 10.0, 15.0, 20.0]);

    color_map_47f509d3746544e5aae4a3dc8777b828.svg = d3.select(".legend.leaflet-control").append("svg")
        .attr("id", 'legend')
        .attr("width", 450)
        .attr("height", 40);

    color_map_47f509d3746544e5aae4a3dc8777b828.g = color_map_47f509d3746544e5aae4a3dc8777b828.svg.append("g")
        .attr("class", "key")
        .attr("transform", "translate(25,16)");

    color_map_47f509d3746544e5aae4a3dc8777b828.g.selectAll("rect")
        .data(color_map_47f509d3746544e5aae4a3dc8777b828.color.range().map(function(d, i) {
          return {
            x0: i ? color_map_47f509d3746544e5aae4a3dc8777b828.x(color_map_47f509d3746544e5aae4a3dc8777b828.color.domain()[i - 1]) : color_map_47f509d3746544e5aae4a3dc8777b828.x.range()[0],
            x1: i < color_map_47f509d3746544e5aae4a3dc8777b828.color.domain().length ? color_map_47f509d3746544e5aae4a3dc8777b828.x(color_map_47f509d3746544e5aae4a3dc8777b828.color.domain()[i]) : color_map_47f509d3746544e5aae4a3dc8777b828.x.range()[1],
            z: d
          };
        }))
      .enter().append("rect")
        .attr("height", 10)
        .attr("x", function(d) { return d.x0; })
        .attr("width", function(d) { return d.x1 - d.x0; })
        .style("fill", function(d) { return d.z; });

    color_map_47f509d3746544e5aae4a3dc8777b828.g.call(color_map_47f509d3746544e5aae4a3dc8777b828.xAxis).append("text")
        .attr("class", "caption")
        .attr("y", 21)
        .text('Number of trainings');
</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 0x7fe153de8208>"
]
},
"execution_count": 254,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_lat = np.mean([i[0] for i in [[float(v.Lat), float(v.Long)] for k,v in recent_frame.iterrows()]])\n",
"mean_long = np.mean([i[1] for i in [[float(v.Lat), float(v.Long)] for k,v in recent_frame.iterrows()]])\n",
"m = folium.Map(location=[mean_lat, mean_long ], zoom_start=11, tiles='OpenStreetMap')\n",
"m.add_child(plugins.HeatMap(recent_frame[['Lat','Long']].values, radius = 20), name='Training location')\n",
"colormap = cm.LinearColormap(['blue', 'green', 'yellow', 'orange', 'red'], index=[0, 0.25, 0.5, 0.75, 1.0]).scale(0,20)\n",
"colormap.caption = 'Number of trainings'\n",
"m.add_child(colormap)\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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