Strava_inital_data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0f011a3e-4885-4d41-802a-6d2a329e2830",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e2db6e7-64ce-41f6-ae02-4298f62595cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'id': 3506416, 'username': 'rhish_hatt', 'resource_state': 2, 'firstname': 'Rhish', 'lastname': 'Hatt', 'bio': 'ASU Grad Student 🔱 🚴\\u200d♂️', 'city': 'Tempe', 'state': 'Arizona', 'country': 'United States', 'sex': 'M', 'premium': True, 'summit': True, 'created_at': '2013-12-17T14:40:20Z', 'updated_at': '2023-09-18T01:41:06Z', 'badge_type_id': 1, 'weight': 79.0, 'profile_medium': 'https://dgalywyr863hv.cloudfront.net/pictures/athletes/3506416/1139281/30/medium.jpg', 'profile': 'https://dgalywyr863hv.cloudfront.net/pictures/athletes/3506416/1139281/30/large.jpg', 'friend': None, 'follower': None}\n"
     ]
    }
   ],
   "source": [
    "basic_athlete_info = json.loads('''{\n",
    "    \"id\": 3506416,\n",
    "    \"username\": \"rhish_hatt\",\n",
    "    \"resource_state\": 2,\n",
    "    \"firstname\": \"Rhish\",\n",
    "    \"lastname\": \"Hatt\",\n",
    "    \"bio\": \"ASU Grad Student 🔱 🚴‍♂️\",\n",
    "    \"city\": \"Tempe\",\n",
    "    \"state\": \"Arizona\",\n",
    "    \"country\": \"United States\",\n",
    "    \"sex\": \"M\",\n",
    "    \"premium\": true,\n",
    "    \"summit\": true,\n",
    "    \"created_at\": \"2013-12-17T14:40:20Z\",\n",
    "    \"updated_at\": \"2023-09-18T01:41:06Z\",\n",
    "    \"badge_type_id\": 1,\n",
    "    \"weight\": 79.0,\n",
    "    \"profile_medium\": \"https://dgalywyr863hv.cloudfront.net/pictures/athletes/3506416/1139281/30/medium.jpg\",\n",
    "    \"profile\": \"https://dgalywyr863hv.cloudfront.net/pictures/athletes/3506416/1139281/30/large.jpg\",\n",
    "    \"friend\": null,\n",
    "    \"follower\": null\n",
    "}''')\n",
    "\n",
    "print(basic_athlete_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1c7655da-ad7d-4e9a-b527-7ebf27405ce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('strava_data_python_dict.pickle', 'rb') as data_pickle:\n",
    "    full_dataset = pickle.load(data_pickle)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75dbcce7-5137-4f58-a346-189630a1ebd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"resource_state\": 2, \"athlete\": {\"id\": 3506416, \"resource_state\": 1}, \"name\": \"AZ State ITT - 7th/11 in Men Merckx - destroyed by the headwind\", \"distance\": 39964.1, \"moving_time\": 4020, \"elapsed_time\": 4020, \"total_elevation_gain\": 42.0, \"type\": \"Ride\", \"sport_type\": \"Ride\", \"workout_type\": 11, \"id\": 9912737242, \"start_date\": \"2023-09-24T14:45:58Z\", \"start_date_local\": \"2023-09-24T07:45:58Z\", \"timezone\": \"(GMT-07:00) America/Phoenix\", \"utc_offset\": -25200.0, \"location_city\": null, \"location_state\": null, \"location_country\": \"United States\", \"achievement_count\": 0, \"kudos_count\": 25, \"comment_count\": 6, \"athlete_count\": 3, \"photo_count\": 0, \"map\": {\"id\": \"a9912737242\", \"summary_polyline\": \"ykgfEhyzfTbUw\\\\\\\\xAsB~AqB~`@se@j_@qc@pCwCpDeDdiAc}@~L}Jpi@ob@rO_MbHsFrIaHxk@od@tDuCfLgJxMgKpJyHbKaIz[gWnAaAdAs@lAo@jCoAdAq@|I_HlQqNbA}@fBkB~BuCd@g@|A{AnDqCbEwCrPuMnWwShM{J~c@a^rXiTrIaHhY}T|VeS|KuIvl@me@vEoDrQuNzDyC`r@wi@vFqEb^}XzEyDlVmRdBgAbFkCtCmBbZoUJOAECAEB_\\\\\\\\dWwChBmDhBkAp@gAx@cDfCql@de@o`@|ZeVpRaLvIaw@~m@yAfAkM`K{RrO_F~DyB`BwYjUufAd{@{TdQiB|A}GjFyYpU{EtDoBpAmCpBkB~Ao@l@o@t@sBjCsCrC}StPaHlF{@h@qEzBwBvAgCnB{n@dg@}XrTkH~FuIxGqgBxvAgOnLiMdK}SjP}l@te@oDdDuCzCsSdVol@pr@aC~CmBnCkSnZ\", \"resource_state\": 2}, \"trainer\": false, \"commute\": false, \"manual\": false, \"private\": false, \"visibility\": \"everyone\", \"flagged\": false, \"gear_id\": \"b12532853\", \"start_latlng\": [32.64717631973326, -111.38980829156935], \"end_latlng\": [32.647236585617065, -111.38979689218104], \"average_speed\": 9.941, \"max_speed\": 15.096, \"average_cadence\": 86.7, \"average_temp\": 22, \"average_watts\": 249.9, \"max_watts\": 731, \"weighted_average_watts\": 249, \"kilojoules\": 1004.5, \"device_watts\": true, \"has_heartrate\": true, \"average_heartrate\": 175.6, \"max_heartrate\": 185.0, \"heartrate_opt_out\": false, \"display_hide_heartrate_option\": true, \"elev_high\": 583.6, \"elev_low\": 536.4, \"upload_id\": 10624071939, \"upload_id_str\": \"10624071939\", \"external_id\": \"garmin_ping_296207949248\", \"from_accepted_tag\": false, \"pr_count\": 0, \"total_photo_count\": 6, \"has_kudoed\": false, \"suffer_score\": 211.0}'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json.dumps(full_dataset[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f9c6795f-9ce2-454b-b9e5-ad62e27ca41f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(full_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c9315ac7-4153-48b2-83c0-2660b438e9cf",
   "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>resource_state</th>\n",
       "      <th>athlete</th>\n",
       "      <th>name</th>\n",
       "      <th>distance</th>\n",
       "      <th>moving_time</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>total_elevation_gain</th>\n",
       "      <th>type</th>\n",
       "      <th>sport_type</th>\n",
       "      <th>workout_type</th>\n",
       "      <th>id</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_date_local</th>\n",
       "      <th>timezone</th>\n",
       "      <th>utc_offset</th>\n",
       "      <th>location_city</th>\n",
       "      <th>location_state</th>\n",
       "      <th>location_country</th>\n",
       "      <th>achievement_count</th>\n",
       "      <th>kudos_count</th>\n",
       "      <th>comment_count</th>\n",
       "      <th>athlete_count</th>\n",
       "      <th>photo_count</th>\n",
       "      <th>map</th>\n",
       "      <th>trainer</th>\n",
       "      <th>commute</th>\n",
       "      <th>manual</th>\n",
       "      <th>private</th>\n",
       "      <th>visibility</th>\n",
       "      <th>flagged</th>\n",
       "      <th>gear_id</th>\n",
       "      <th>start_latlng</th>\n",
       "      <th>end_latlng</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>max_speed</th>\n",
       "      <th>average_cadence</th>\n",
       "      <th>average_temp</th>\n",
       "      <th>average_watts</th>\n",
       "      <th>max_watts</th>\n",
       "      <th>weighted_average_watts</th>\n",
       "      <th>kilojoules</th>\n",
       "      <th>device_watts</th>\n",
       "      <th>has_heartrate</th>\n",
       "      <th>average_heartrate</th>\n",
       "      <th>max_heartrate</th>\n",
       "      <th>heartrate_opt_out</th>\n",
       "      <th>display_hide_heartrate_option</th>\n",
       "      <th>elev_high</th>\n",
       "      <th>elev_low</th>\n",
       "      <th>upload_id</th>\n",
       "      <th>upload_id_str</th>\n",
       "      <th>external_id</th>\n",
       "      <th>from_accepted_tag</th>\n",
       "      <th>pr_count</th>\n",
       "      <th>total_photo_count</th>\n",
       "      <th>has_kudoed</th>\n",
       "      <th>suffer_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>{'id': 3506416, 'resource_state': 1}</td>\n",
       "      <td>Lunch Ride</td>\n",
       "      <td>3564.2</td>\n",
       "      <td>723</td>\n",
       "      <td>830</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9913883505</td>\n",
       "      <td>2023-09-24T18:41:03Z</td>\n",
       "      <td>2023-09-24T11:41:03Z</td>\n",
       "      <td>(GMT-07:00) America/Phoenix</td>\n",
       "      <td>-25200.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 'a9913883505', 'summary_polyline': 'al_...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>b12532853</td>\n",
       "      <td>[33.425444243475795, -111.94648995995522]</td>\n",
       "      <td>[33.419371051713824, -111.92317754030228]</td>\n",
       "      <td>4.930</td>\n",
       "      <td>9.588</td>\n",
       "      <td>84.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>81.3</td>\n",
       "      <td>807.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>58.8</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>127.9</td>\n",
       "      <td>147.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>355.6</td>\n",
       "      <td>348.4</td>\n",
       "      <td>1.062527e+10</td>\n",
       "      <td>10625272364</td>\n",
       "      <td>garmin_ping_296232055012</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>{'id': 3506416, 'resource_state': 1}</td>\n",
       "      <td>Morning Ride</td>\n",
       "      <td>4904.7</td>\n",
       "      <td>1355</td>\n",
       "      <td>1355</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9912921254</td>\n",
       "      <td>2023-09-24T15:54:28Z</td>\n",
       "      <td>2023-09-24T08:54:28Z</td>\n",
       "      <td>(GMT-07:00) America/Phoenix</td>\n",
       "      <td>-25200.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 'a9912921254', 'summary_polyline': 'woh...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>b12532853</td>\n",
       "      <td>[32.6519475504756, -111.39609991572797]</td>\n",
       "      <td>[32.650608625262976, -111.39441523700953]</td>\n",
       "      <td>3.620</td>\n",
       "      <td>6.050</td>\n",
       "      <td>71.2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.7</td>\n",
       "      <td>202.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>41.6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>126.4</td>\n",
       "      <td>153.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>545.0</td>\n",
       "      <td>534.4</td>\n",
       "      <td>1.062426e+10</td>\n",
       "      <td>10624263906</td>\n",
       "      <td>garmin_ping_296211250274</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>{'id': 3506416, 'resource_state': 1}</td>\n",
       "      <td>AZ State ITT - 7th/11 in Men Merckx - destroye...</td>\n",
       "      <td>39964.1</td>\n",
       "      <td>4020</td>\n",
       "      <td>4020</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9912737242</td>\n",
       "      <td>2023-09-24T14:45:58Z</td>\n",
       "      <td>2023-09-24T07:45:58Z</td>\n",
       "      <td>(GMT-07:00) America/Phoenix</td>\n",
       "      <td>-25200.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 'a9912737242', 'summary_polyline': 'ykg...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>b12532853</td>\n",
       "      <td>[32.64717631973326, -111.38980829156935]</td>\n",
       "      <td>[32.647236585617065, -111.38979689218104]</td>\n",
       "      <td>9.941</td>\n",
       "      <td>15.096</td>\n",
       "      <td>86.7</td>\n",
       "      <td>22.0</td>\n",
       "      <td>249.9</td>\n",
       "      <td>731.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>1004.5</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>175.6</td>\n",
       "      <td>185.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>583.6</td>\n",
       "      <td>536.4</td>\n",
       "      <td>1.062407e+10</td>\n",
       "      <td>10624071939</td>\n",
       "      <td>garmin_ping_296207949248</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>{'id': 3506416, 'resource_state': 1}</td>\n",
       "      <td>Morning Ride</td>\n",
       "      <td>2744.7</td>\n",
       "      <td>427</td>\n",
       "      <td>427</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9912187409</td>\n",
       "      <td>2023-09-24T14:36:32Z</td>\n",
       "      <td>2023-09-24T07:36:32Z</td>\n",
       "      <td>(GMT-07:00) America/Phoenix</td>\n",
       "      <td>-25200.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 'a9912187409', 'summary_polyline': 'wvg...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>b12532853</td>\n",
       "      <td>[32.65025801025331, -111.39397384598851]</td>\n",
       "      <td>[32.647967990487814, -111.39085778966546]</td>\n",
       "      <td>6.428</td>\n",
       "      <td>11.564</td>\n",
       "      <td>81.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>131.5</td>\n",
       "      <td>487.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>56.1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>132.1</td>\n",
       "      <td>154.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>522.0</td>\n",
       "      <td>519.4</td>\n",
       "      <td>1.062350e+10</td>\n",
       "      <td>10623497643</td>\n",
       "      <td>garmin_ping_296198158652</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>{'id': 3506416, 'resource_state': 1}</td>\n",
       "      <td>New tyre testing</td>\n",
       "      <td>29299.1</td>\n",
       "      <td>4662</td>\n",
       "      <td>5821</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9901444795</td>\n",
       "      <td>2023-09-22T22:39:32Z</td>\n",
       "      <td>2023-09-22T15:39:32Z</td>\n",
       "      <td>(GMT-07:00) America/Phoenix</td>\n",
       "      <td>-25200.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 'a9901444795', 'summary_polyline': '{f~...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>b12532853</td>\n",
       "      <td>[33.41989994980395, -111.92341181449592]</td>\n",
       "      <td>[33.41937582939863, -111.92321039736271]</td>\n",
       "      <td>6.285</td>\n",
       "      <td>12.682</td>\n",
       "      <td>83.1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>125.4</td>\n",
       "      <td>954.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>584.7</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>135.3</td>\n",
       "      <td>190.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>421.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>1.061218e+10</td>\n",
       "      <td>10612179069</td>\n",
       "      <td>garmin_ping_295937594218</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   resource_state                               athlete  \\\n",
       "0               2  {'id': 3506416, 'resource_state': 1}   \n",
       "1               2  {'id': 3506416, 'resource_state': 1}   \n",
       "2               2  {'id': 3506416, 'resource_state': 1}   \n",
       "3               2  {'id': 3506416, 'resource_state': 1}   \n",
       "4               2  {'id': 3506416, 'resource_state': 1}   \n",
       "\n",
       "                                                name  distance  moving_time  \\\n",
       "0                                         Lunch Ride    3564.2          723   \n",
       "1                                       Morning Ride    4904.7         1355   \n",
       "2  AZ State ITT - 7th/11 in Men Merckx - destroye...   39964.1         4020   \n",
       "3                                       Morning Ride    2744.7          427   \n",
       "4                                   New tyre testing   29299.1         4662   \n",
       "\n",
       "   elapsed_time  total_elevation_gain  type sport_type  workout_type  \\\n",
       "0           830                   3.0  Ride       Ride           NaN   \n",
       "1          1355                   3.0  Ride       Ride           NaN   \n",
       "2          4020                  42.0  Ride       Ride          11.0   \n",
       "3           427                   1.0  Ride       Ride           NaN   \n",
       "4          5821                  52.0  Ride       Ride          10.0   \n",
       "\n",
       "           id            start_date      start_date_local  \\\n",
       "0  9913883505  2023-09-24T18:41:03Z  2023-09-24T11:41:03Z   \n",
       "1  9912921254  2023-09-24T15:54:28Z  2023-09-24T08:54:28Z   \n",
       "2  9912737242  2023-09-24T14:45:58Z  2023-09-24T07:45:58Z   \n",
       "3  9912187409  2023-09-24T14:36:32Z  2023-09-24T07:36:32Z   \n",
       "4  9901444795  2023-09-22T22:39:32Z  2023-09-22T15:39:32Z   \n",
       "\n",
       "                      timezone  utc_offset location_city location_state  \\\n",
       "0  (GMT-07:00) America/Phoenix    -25200.0          None           None   \n",
       "1  (GMT-07:00) America/Phoenix    -25200.0          None           None   \n",
       "2  (GMT-07:00) America/Phoenix    -25200.0          None           None   \n",
       "3  (GMT-07:00) America/Phoenix    -25200.0          None           None   \n",
       "4  (GMT-07:00) America/Phoenix    -25200.0          None           None   \n",
       "\n",
       "  location_country  achievement_count  kudos_count  comment_count  \\\n",
       "0    United States                  0           11              0   \n",
       "1    United States                  0            8              0   \n",
       "2    United States                  0           25              6   \n",
       "3    United States                  0            6              0   \n",
       "4    United States                  5           23              0   \n",
       "\n",
       "   athlete_count  photo_count  \\\n",
       "0              1            0   \n",
       "1              1            0   \n",
       "2              3            0   \n",
       "3              3            0   \n",
       "4              1            0   \n",
       "\n",
       "                                                 map  trainer  commute  \\\n",
       "0  {'id': 'a9913883505', 'summary_polyline': 'al_...    False    False   \n",
       "1  {'id': 'a9912921254', 'summary_polyline': 'woh...    False    False   \n",
       "2  {'id': 'a9912737242', 'summary_polyline': 'ykg...    False    False   \n",
       "3  {'id': 'a9912187409', 'summary_polyline': 'wvg...    False    False   \n",
       "4  {'id': 'a9901444795', 'summary_polyline': '{f~...    False    False   \n",
       "\n",
       "   manual  private visibility  flagged    gear_id  \\\n",
       "0   False    False   everyone    False  b12532853   \n",
       "1   False    False   everyone    False  b12532853   \n",
       "2   False    False   everyone    False  b12532853   \n",
       "3   False    False   everyone    False  b12532853   \n",
       "4   False    False   everyone    False  b12532853   \n",
       "\n",
       "                                start_latlng  \\\n",
       "0  [33.425444243475795, -111.94648995995522]   \n",
       "1    [32.6519475504756, -111.39609991572797]   \n",
       "2   [32.64717631973326, -111.38980829156935]   \n",
       "3   [32.65025801025331, -111.39397384598851]   \n",
       "4   [33.41989994980395, -111.92341181449592]   \n",
       "\n",
       "                                  end_latlng  average_speed  max_speed  \\\n",
       "0  [33.419371051713824, -111.92317754030228]          4.930      9.588   \n",
       "1  [32.650608625262976, -111.39441523700953]          3.620      6.050   \n",
       "2  [32.647236585617065, -111.38979689218104]          9.941     15.096   \n",
       "3  [32.647967990487814, -111.39085778966546]          6.428     11.564   \n",
       "4   [33.41937582939863, -111.92321039736271]          6.285     12.682   \n",
       "\n",
       "   average_cadence  average_temp  average_watts  max_watts  \\\n",
       "0             84.5          33.0           81.3      807.0   \n",
       "1             71.2          27.0           30.7      202.0   \n",
       "2             86.7          22.0          249.9      731.0   \n",
       "3             81.0          19.0          131.5      487.0   \n",
       "4             83.1          31.0          125.4      954.0   \n",
       "\n",
       "   weighted_average_watts  kilojoules device_watts  has_heartrate  \\\n",
       "0                    93.0        58.8         True           True   \n",
       "1                    45.0        41.6         True           True   \n",
       "2                   249.0      1004.5         True           True   \n",
       "3                   154.0        56.1         True           True   \n",
       "4                   167.0       584.7         True           True   \n",
       "\n",
       "   average_heartrate  max_heartrate  heartrate_opt_out  \\\n",
       "0              127.9          147.0              False   \n",
       "1              126.4          153.0              False   \n",
       "2              175.6          185.0              False   \n",
       "3              132.1          154.0              False   \n",
       "4              135.3          190.0              False   \n",
       "\n",
       "   display_hide_heartrate_option  elev_high  elev_low     upload_id  \\\n",
       "0                           True      355.6     348.4  1.062527e+10   \n",
       "1                           True      545.0     534.4  1.062426e+10   \n",
       "2                           True      583.6     536.4  1.062407e+10   \n",
       "3                           True      522.0     519.4  1.062350e+10   \n",
       "4                           True      421.0     384.0  1.061218e+10   \n",
       "\n",
       "  upload_id_str               external_id  from_accepted_tag  pr_count  \\\n",
       "0   10625272364  garmin_ping_296232055012              False         0   \n",
       "1   10624263906  garmin_ping_296211250274              False         0   \n",
       "2   10624071939  garmin_ping_296207949248              False         0   \n",
       "3   10623497643  garmin_ping_296198158652              False         0   \n",
       "4   10612179069  garmin_ping_295937594218              False         2   \n",
       "\n",
       "   total_photo_count  has_kudoed  suffer_score  \n",
       "0                  0       False           3.0  \n",
       "1                  0       False           5.0  \n",
       "2                  6       False         211.0  \n",
       "3                  0       False           2.0  \n",
       "4                  1       False          42.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the display options to show all columns\n",
    "pd.set_option('display.max_columns', None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b062c044-c504-4244-9319-b460ba7b73fa",
   "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>resource_state</th>\n",
       "      <th>athlete</th>\n",
       "      <th>name</th>\n",
       "      <th>distance</th>\n",
       "      <th>moving_time</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>total_elevation_gain</th>\n",
       "      <th>type</th>\n",
       "      <th>sport_type</th>\n",
       "      <th>workout_type</th>\n",
       "      <th>id</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_date_local</th>\n",
       "      <th>timezone</th>\n",
       "      <th>utc_offset</th>\n",
       "      <th>location_city</th>\n",
       "      <th>location_state</th>\n",
       "      <th>location_country</th>\n",
       "      <th>achievement_count</th>\n",
       "      <th>kudos_count</th>\n",
       "      <th>comment_count</th>\n",
       "      <th>athlete_count</th>\n",
       "      <th>photo_count</th>\n",
       "      <th>map</th>\n",
       "      <th>trainer</th>\n",
       "      <th>commute</th>\n",
       "      <th>manual</th>\n",
       "      <th>private</th>\n",
       "      <th>visibility</th>\n",
       "      <th>flagged</th>\n",
       "      <th>gear_id</th>\n",
       "      <th>start_latlng</th>\n",
       "      <th>end_latlng</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>max_speed</th>\n",
       "      <th>average_cadence</th>\n",
       "      <th>average_temp</th>\n",
       "      <th>average_watts</th>\n",
       "      <th>max_watts</th>\n",
       "      <th>weighted_average_watts</th>\n",
       "      <th>kilojoules</th>\n",
       "      <th>device_watts</th>\n",
       "      <th>has_heartrate</th>\n",
       "      <th>average_heartrate</th>\n",
       "      <th>max_heartrate</th>\n",
       "      <th>heartrate_opt_out</th>\n",
       "      <th>display_hide_heartrate_option</th>\n",
       "      <th>elev_high</th>\n",
       "      <th>elev_low</th>\n",
       "      <th>upload_id</th>\n",
       "      <th>upload_id_str</th>\n",
       "      <th>external_id</th>\n",
       "      <th>from_accepted_tag</th>\n",
       "      <th>pr_count</th>\n",
       "      <th>total_photo_count</th>\n",
       "      <th>has_kudoed</th>\n",
       "      <th>suffer_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1581</th>\n",
       "      <td>2</td>\n",
       "      <td>{'id': 3506416, 'resource_state': 1}</td>\n",
       "      <td>Morning quickie</td>\n",
       "      <td>21360.6</td>\n",
       "      <td>3429</td>\n",
       "      <td>4037</td>\n",
       "      <td>193.1</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101915468</td>\n",
       "      <td>2013-12-26T00:41:59Z</td>\n",
       "      <td>2013-12-26T06:11:59Z</td>\n",
       "      <td>(GMT+05:30) Asia/Kolkata</td>\n",
       "      <td>19800.0</td>\n",
       "      <td>Pune</td>\n",
       "      <td>MH</td>\n",
       "      <td>India</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 'a101915468', 'summary_polyline': 'g|}o...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>b1081294</td>\n",
       "      <td>[18.508452, 73.790259]</td>\n",
       "      <td>[18.509716, 73.788072]</td>\n",
       "      <td>6.229</td>\n",
       "      <td>13.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>392.1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>663.2</td>\n",
       "      <td>551.4</td>\n",
       "      <td>111431783.0</td>\n",
       "      <td>111431783</td>\n",
       "      <td>12448D74-ED27-4042-9DE9-37F3088E19DC</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1582</th>\n",
       "      <td>2</td>\n",
       "      <td>{'id': 3506416, 'resource_state': 1}</td>\n",
       "      <td>Necklace loop with arai</td>\n",
       "      <td>24106.3</td>\n",
       "      <td>3704</td>\n",
       "      <td>4795</td>\n",
       "      <td>297.9</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101640999</td>\n",
       "      <td>2013-12-24T00:43:50Z</td>\n",
       "      <td>2013-12-24T06:13:50Z</td>\n",
       "      <td>(GMT+05:30) Asia/Kolkata</td>\n",
       "      <td>19800.0</td>\n",
       "      <td>Pune</td>\n",
       "      <td>MH</td>\n",
       "      <td>India</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 'a101640999', 'summary_polyline': 'o|}o...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>b1081294</td>\n",
       "      <td>[18.50829, 73.790242]</td>\n",
       "      <td>[18.509713, 73.788052]</td>\n",
       "      <td>6.508</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>130.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>484.6</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>670.1</td>\n",
       "      <td>567.9</td>\n",
       "      <td>111131538.0</td>\n",
       "      <td>111131538</td>\n",
       "      <td>33E0CC5C-C67F-4F32-ACFF-210DED039CE5</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1583</th>\n",
       "      <td>2</td>\n",
       "      <td>{'id': 3506416, 'resource_state': 1}</td>\n",
       "      <td>Morning Ride</td>\n",
       "      <td>22477.9</td>\n",
       "      <td>3159</td>\n",
       "      <td>3159</td>\n",
       "      <td>265.0</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101505856</td>\n",
       "      <td>2013-12-23T01:01:14Z</td>\n",
       "      <td>2013-12-23T06:31:14Z</td>\n",
       "      <td>(GMT+05:30) Asia/Kolkata</td>\n",
       "      <td>19800.0</td>\n",
       "      <td>Pune</td>\n",
       "      <td>MH</td>\n",
       "      <td>India</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 'a101505856', 'summary_polyline': 'i|}o...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>b1081294</td>\n",
       "      <td>[18.509687, 73.788011]</td>\n",
       "      <td>[18.509672, 73.788027]</td>\n",
       "      <td>7.116</td>\n",
       "      <td>17.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>138.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>437.5</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>663.8</td>\n",
       "      <td>584.3</td>\n",
       "      <td>110982840.0</td>\n",
       "      <td>110982840</td>\n",
       "      <td>D8D93A54-F378-49D9-BA2C-9FD6C4879555</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1584</th>\n",
       "      <td>2</td>\n",
       "      <td>{'id': 3506416, 'resource_state': 1}</td>\n",
       "      <td>Morning Ride</td>\n",
       "      <td>18113.8</td>\n",
       "      <td>2695</td>\n",
       "      <td>2695</td>\n",
       "      <td>183.0</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101017658</td>\n",
       "      <td>2013-12-20T00:52:37Z</td>\n",
       "      <td>2013-12-20T06:22:37Z</td>\n",
       "      <td>(GMT+05:30) Asia/Kolkata</td>\n",
       "      <td>19800.0</td>\n",
       "      <td>Pune</td>\n",
       "      <td>MH</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 'a101017658', 'summary_polyline': 'i|}o...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>b1081294</td>\n",
       "      <td>[18.509514, 73.788157]</td>\n",
       "      <td>[18.50948, 73.788237]</td>\n",
       "      <td>6.721</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347.3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>663.3</td>\n",
       "      <td>584.1</td>\n",
       "      <td>110454042.0</td>\n",
       "      <td>110454042</td>\n",
       "      <td>EEA2B8D9-1CBC-44E0-8C0B-E665DECC38EC</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1585</th>\n",
       "      <td>2</td>\n",
       "      <td>{'id': 3506416, 'resource_state': 1}</td>\n",
       "      <td>Morning Ride</td>\n",
       "      <td>18797.8</td>\n",
       "      <td>2510</td>\n",
       "      <td>2510</td>\n",
       "      <td>174.5</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100725977</td>\n",
       "      <td>2013-12-18T00:42:13Z</td>\n",
       "      <td>2013-12-18T06:12:13Z</td>\n",
       "      <td>(GMT+05:30) Asia/Kolkata</td>\n",
       "      <td>19800.0</td>\n",
       "      <td>Pune</td>\n",
       "      <td>MH</td>\n",
       "      <td>India</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'id': 'a100725977', 'summary_polyline': '{{}o...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>[18.509647, 73.788261]</td>\n",
       "      <td>[18.509685, 73.788085]</td>\n",
       "      <td>7.489</td>\n",
       "      <td>14.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>664.2</td>\n",
       "      <td>567.8</td>\n",
       "      <td>110156907.0</td>\n",
       "      <td>110156907</td>\n",
       "      <td>9E9074B0-AC14-4332-90BB-9262CDF8971F</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      resource_state                               athlete  \\\n",
       "1581               2  {'id': 3506416, 'resource_state': 1}   \n",
       "1582               2  {'id': 3506416, 'resource_state': 1}   \n",
       "1583               2  {'id': 3506416, 'resource_state': 1}   \n",
       "1584               2  {'id': 3506416, 'resource_state': 1}   \n",
       "1585               2  {'id': 3506416, 'resource_state': 1}   \n",
       "\n",
       "                         name  distance  moving_time  elapsed_time  \\\n",
       "1581          Morning quickie   21360.6         3429          4037   \n",
       "1582  Necklace loop with arai   24106.3         3704          4795   \n",
       "1583             Morning Ride   22477.9         3159          3159   \n",
       "1584             Morning Ride   18113.8         2695          2695   \n",
       "1585             Morning Ride   18797.8         2510          2510   \n",
       "\n",
       "      total_elevation_gain  type sport_type  workout_type         id  \\\n",
       "1581                 193.1  Ride       Ride           NaN  101915468   \n",
       "1582                 297.9  Ride       Ride           NaN  101640999   \n",
       "1583                 265.0  Ride       Ride           NaN  101505856   \n",
       "1584                 183.0  Ride       Ride           NaN  101017658   \n",
       "1585                 174.5  Ride       Ride           NaN  100725977   \n",
       "\n",
       "                start_date      start_date_local                  timezone  \\\n",
       "1581  2013-12-26T00:41:59Z  2013-12-26T06:11:59Z  (GMT+05:30) Asia/Kolkata   \n",
       "1582  2013-12-24T00:43:50Z  2013-12-24T06:13:50Z  (GMT+05:30) Asia/Kolkata   \n",
       "1583  2013-12-23T01:01:14Z  2013-12-23T06:31:14Z  (GMT+05:30) Asia/Kolkata   \n",
       "1584  2013-12-20T00:52:37Z  2013-12-20T06:22:37Z  (GMT+05:30) Asia/Kolkata   \n",
       "1585  2013-12-18T00:42:13Z  2013-12-18T06:12:13Z  (GMT+05:30) Asia/Kolkata   \n",
       "\n",
       "      utc_offset location_city location_state location_country  \\\n",
       "1581     19800.0          Pune             MH            India   \n",
       "1582     19800.0          Pune             MH            India   \n",
       "1583     19800.0          Pune             MH            India   \n",
       "1584     19800.0          Pune             MH            India   \n",
       "1585     19800.0          Pune             MH            India   \n",
       "\n",
       "      achievement_count  kudos_count  comment_count  athlete_count  \\\n",
       "1581                  5            0              0              1   \n",
       "1582                  5            0              0              1   \n",
       "1583                  3            1              0              1   \n",
       "1584                  1            0              0              1   \n",
       "1585                  2            0              0              1   \n",
       "\n",
       "      photo_count                                                map  trainer  \\\n",
       "1581            0  {'id': 'a101915468', 'summary_polyline': 'g|}o...    False   \n",
       "1582            0  {'id': 'a101640999', 'summary_polyline': 'o|}o...    False   \n",
       "1583            0  {'id': 'a101505856', 'summary_polyline': 'i|}o...    False   \n",
       "1584            0  {'id': 'a101017658', 'summary_polyline': 'i|}o...    False   \n",
       "1585            0  {'id': 'a100725977', 'summary_polyline': '{{}o...    False   \n",
       "\n",
       "      commute  manual  private visibility  flagged   gear_id  \\\n",
       "1581    False   False    False   everyone    False  b1081294   \n",
       "1582    False   False    False   everyone    False  b1081294   \n",
       "1583    False   False    False   everyone    False  b1081294   \n",
       "1584    False   False    False   everyone    False  b1081294   \n",
       "1585    False   False    False   everyone    False      None   \n",
       "\n",
       "                start_latlng              end_latlng  average_speed  \\\n",
       "1581  [18.508452, 73.790259]  [18.509716, 73.788072]          6.229   \n",
       "1582   [18.50829, 73.790242]  [18.509713, 73.788052]          6.508   \n",
       "1583  [18.509687, 73.788011]  [18.509672, 73.788027]          7.116   \n",
       "1584  [18.509514, 73.788157]   [18.50948, 73.788237]          6.721   \n",
       "1585  [18.509647, 73.788261]  [18.509685, 73.788085]          7.489   \n",
       "\n",
       "      max_speed  average_cadence  average_temp  average_watts  max_watts  \\\n",
       "1581       13.9              NaN           NaN          114.3        NaN   \n",
       "1582       13.0              NaN           NaN          130.8        NaN   \n",
       "1583       17.2              NaN           NaN          138.5        NaN   \n",
       "1584       15.0              NaN           NaN          128.9        NaN   \n",
       "1585       14.1              NaN           NaN            NaN        NaN   \n",
       "\n",
       "      weighted_average_watts  kilojoules device_watts  has_heartrate  \\\n",
       "1581                     NaN       392.1        False          False   \n",
       "1582                     NaN       484.6        False          False   \n",
       "1583                     NaN       437.5        False          False   \n",
       "1584                     NaN       347.3        False          False   \n",
       "1585                     NaN         NaN        False          False   \n",
       "\n",
       "      average_heartrate  max_heartrate  heartrate_opt_out  \\\n",
       "1581                NaN            NaN              False   \n",
       "1582                NaN            NaN              False   \n",
       "1583                NaN            NaN              False   \n",
       "1584                NaN            NaN              False   \n",
       "1585                NaN            NaN              False   \n",
       "\n",
       "      display_hide_heartrate_option  elev_high  elev_low    upload_id  \\\n",
       "1581                          False      663.2     551.4  111431783.0   \n",
       "1582                          False      670.1     567.9  111131538.0   \n",
       "1583                          False      663.8     584.3  110982840.0   \n",
       "1584                          False      663.3     584.1  110454042.0   \n",
       "1585                          False      664.2     567.8  110156907.0   \n",
       "\n",
       "     upload_id_str                           external_id  from_accepted_tag  \\\n",
       "1581     111431783  12448D74-ED27-4042-9DE9-37F3088E19DC              False   \n",
       "1582     111131538  33E0CC5C-C67F-4F32-ACFF-210DED039CE5              False   \n",
       "1583     110982840  D8D93A54-F378-49D9-BA2C-9FD6C4879555              False   \n",
       "1584     110454042  EEA2B8D9-1CBC-44E0-8C0B-E665DECC38EC              False   \n",
       "1585     110156907  9E9074B0-AC14-4332-90BB-9262CDF8971F              False   \n",
       "\n",
       "      pr_count  total_photo_count  has_kudoed  suffer_score  \n",
       "1581         4                  0       False           NaN  \n",
       "1582         5                  0       False           NaN  \n",
       "1583         1                  0       False           NaN  \n",
       "1584         0                  0       False           NaN  \n",
       "1585         0                  0       False           NaN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ab062741-bb65-4934-9162-dcf7c5931961",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1586 entries, 0 to 1585\n",
      "Data columns (total 57 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   resource_state                 1586 non-null   int64  \n",
      " 1   athlete                        1586 non-null   object \n",
      " 2   name                           1586 non-null   object \n",
      " 3   distance                       1586 non-null   float64\n",
      " 4   moving_time                    1586 non-null   int64  \n",
      " 5   elapsed_time                   1586 non-null   int64  \n",
      " 6   total_elevation_gain           1586 non-null   float64\n",
      " 7   type                           1586 non-null   object \n",
      " 8   sport_type                     1586 non-null   object \n",
      " 9   workout_type                   788 non-null    float64\n",
      " 10  id                             1586 non-null   int64  \n",
      " 11  start_date                     1586 non-null   object \n",
      " 12  start_date_local               1586 non-null   object \n",
      " 13  timezone                       1586 non-null   object \n",
      " 14  utc_offset                     1586 non-null   float64\n",
      " 15  location_city                  707 non-null    object \n",
      " 16  location_state                 710 non-null    object \n",
      " 17  location_country               1586 non-null   object \n",
      " 18  achievement_count              1586 non-null   int64  \n",
      " 19  kudos_count                    1586 non-null   int64  \n",
      " 20  comment_count                  1586 non-null   int64  \n",
      " 21  athlete_count                  1586 non-null   int64  \n",
      " 22  photo_count                    1586 non-null   int64  \n",
      " 23  map                            1586 non-null   object \n",
      " 24  trainer                        1586 non-null   bool   \n",
      " 25  commute                        1586 non-null   bool   \n",
      " 26  manual                         1586 non-null   bool   \n",
      " 27  private                        1586 non-null   bool   \n",
      " 28  visibility                     1586 non-null   object \n",
      " 29  flagged                        1586 non-null   bool   \n",
      " 30  gear_id                        1432 non-null   object \n",
      " 31  start_latlng                   1586 non-null   object \n",
      " 32  end_latlng                     1586 non-null   object \n",
      " 33  average_speed                  1586 non-null   float64\n",
      " 34  max_speed                      1586 non-null   float64\n",
      " 35  average_cadence                694 non-null    float64\n",
      " 36  average_temp                   815 non-null    float64\n",
      " 37  average_watts                  1372 non-null   float64\n",
      " 38  max_watts                      610 non-null    float64\n",
      " 39  weighted_average_watts         610 non-null    float64\n",
      " 40  kilojoules                     1372 non-null   float64\n",
      " 41  device_watts                   1373 non-null   object \n",
      " 42  has_heartrate                  1586 non-null   bool   \n",
      " 43  average_heartrate              901 non-null    float64\n",
      " 44  max_heartrate                  901 non-null    float64\n",
      " 45  heartrate_opt_out              1586 non-null   bool   \n",
      " 46  display_hide_heartrate_option  1586 non-null   bool   \n",
      " 47  elev_high                      1529 non-null   float64\n",
      " 48  elev_low                       1529 non-null   float64\n",
      " 49  upload_id                      1584 non-null   float64\n",
      " 50  upload_id_str                  1584 non-null   object \n",
      " 51  external_id                    1584 non-null   object \n",
      " 52  from_accepted_tag              1586 non-null   bool   \n",
      " 53  pr_count                       1586 non-null   int64  \n",
      " 54  total_photo_count              1586 non-null   int64  \n",
      " 55  has_kudoed                     1586 non-null   bool   \n",
      " 56  suffer_score                   904 non-null    float64\n",
      "dtypes: bool(10), float64(18), int64(11), object(18)\n",
      "memory usage: 598.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bfb747df-0147-465a-b95c-970eed77dccd",
   "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>resource_state</th>\n",
       "      <th>distance</th>\n",
       "      <th>moving_time</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>total_elevation_gain</th>\n",
       "      <th>workout_type</th>\n",
       "      <th>id</th>\n",
       "      <th>utc_offset</th>\n",
       "      <th>achievement_count</th>\n",
       "      <th>kudos_count</th>\n",
       "      <th>comment_count</th>\n",
       "      <th>athlete_count</th>\n",
       "      <th>photo_count</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>max_speed</th>\n",
       "      <th>average_cadence</th>\n",
       "      <th>average_temp</th>\n",
       "      <th>average_watts</th>\n",
       "      <th>max_watts</th>\n",
       "      <th>weighted_average_watts</th>\n",
       "      <th>kilojoules</th>\n",
       "      <th>average_heartrate</th>\n",
       "      <th>max_heartrate</th>\n",
       "      <th>elev_high</th>\n",
       "      <th>elev_low</th>\n",
       "      <th>upload_id</th>\n",
       "      <th>pr_count</th>\n",
       "      <th>total_photo_count</th>\n",
       "      <th>suffer_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1586.0</td>\n",
       "      <td>1586.000000</td>\n",
       "      <td>1586.000000</td>\n",
       "      <td>1.586000e+03</td>\n",
       "      <td>1586.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>1.586000e+03</td>\n",
       "      <td>1586.000000</td>\n",
       "      <td>1586.000000</td>\n",
       "      <td>1586.000000</td>\n",
       "      <td>1586.000000</td>\n",
       "      <td>1586.000000</td>\n",
       "      <td>1586.0</td>\n",
       "      <td>1586.000000</td>\n",
       "      <td>1586.000000</td>\n",
       "      <td>694.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>1372.000000</td>\n",
       "      <td>610.000000</td>\n",
       "      <td>610.000000</td>\n",
       "      <td>1372.000000</td>\n",
       "      <td>901.000000</td>\n",
       "      <td>901.000000</td>\n",
       "      <td>1529.000000</td>\n",
       "      <td>1529.000000</td>\n",
       "      <td>1.584000e+03</td>\n",
       "      <td>1586.000000</td>\n",
       "      <td>1586.000000</td>\n",
       "      <td>904.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.0</td>\n",
       "      <td>33294.370240</td>\n",
       "      <td>5079.875158</td>\n",
       "      <td>2.756729e+05</td>\n",
       "      <td>311.814124</td>\n",
       "      <td>8.470812</td>\n",
       "      <td>2.866887e+09</td>\n",
       "      <td>12593.190416</td>\n",
       "      <td>4.312736</td>\n",
       "      <td>16.772383</td>\n",
       "      <td>0.937579</td>\n",
       "      <td>1.745271</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.192291</td>\n",
       "      <td>12.504759</td>\n",
       "      <td>82.437896</td>\n",
       "      <td>21.312883</td>\n",
       "      <td>135.218440</td>\n",
       "      <td>632.595082</td>\n",
       "      <td>169.419672</td>\n",
       "      <td>777.597668</td>\n",
       "      <td>137.349057</td>\n",
       "      <td>167.831299</td>\n",
       "      <td>651.753564</td>\n",
       "      <td>554.846239</td>\n",
       "      <td>3.074681e+09</td>\n",
       "      <td>1.540984</td>\n",
       "      <td>0.125473</td>\n",
       "      <td>64.651549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>29819.385303</td>\n",
       "      <td>4123.521390</td>\n",
       "      <td>1.073685e+07</td>\n",
       "      <td>477.720807</td>\n",
       "      <td>3.649569</td>\n",
       "      <td>3.214000e+09</td>\n",
       "      <td>16508.790631</td>\n",
       "      <td>11.448055</td>\n",
       "      <td>12.075703</td>\n",
       "      <td>2.525432</td>\n",
       "      <td>3.172744</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.210344</td>\n",
       "      <td>4.461734</td>\n",
       "      <td>9.639675</td>\n",
       "      <td>5.578039</td>\n",
       "      <td>44.324677</td>\n",
       "      <td>357.837161</td>\n",
       "      <td>43.773236</td>\n",
       "      <td>680.335920</td>\n",
       "      <td>19.450446</td>\n",
       "      <td>21.135393</td>\n",
       "      <td>184.524553</td>\n",
       "      <td>137.118737</td>\n",
       "      <td>3.434546e+09</td>\n",
       "      <td>4.747760</td>\n",
       "      <td>0.577185</td>\n",
       "      <td>61.627783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>8.900000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.007260e+08</td>\n",
       "      <td>-25200.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>36.700000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>44.700000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-53.000000</td>\n",
       "      <td>1.101569e+08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8614.250000</td>\n",
       "      <td>2102.500000</td>\n",
       "      <td>2.401000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.151952e+08</td>\n",
       "      <td>19800.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.392250</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>78.900000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>103.475000</td>\n",
       "      <td>378.000000</td>\n",
       "      <td>144.250000</td>\n",
       "      <td>279.150000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>548.600000</td>\n",
       "      <td>4.649201e+08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>25625.600000</td>\n",
       "      <td>3891.500000</td>\n",
       "      <td>4.500000e+03</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.315509e+09</td>\n",
       "      <td>19800.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.376000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>83.900000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>134.950000</td>\n",
       "      <td>522.500000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>608.600000</td>\n",
       "      <td>140.300000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>662.100000</td>\n",
       "      <td>570.900000</td>\n",
       "      <td>1.422842e+09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>49391.575000</td>\n",
       "      <td>7240.500000</td>\n",
       "      <td>8.351250e+03</td>\n",
       "      <td>448.925000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.718611e+09</td>\n",
       "      <td>19800.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.646000</td>\n",
       "      <td>15.800000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>164.700000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>1137.750000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>699.600000</td>\n",
       "      <td>623.400000</td>\n",
       "      <td>5.034710e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.0</td>\n",
       "      <td>162929.000000</td>\n",
       "      <td>51131.000000</td>\n",
       "      <td>4.275968e+08</td>\n",
       "      <td>4011.100000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>9.913884e+09</td>\n",
       "      <td>19800.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.414000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>379.400000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>3862.500000</td>\n",
       "      <td>186.700000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>1426.600000</td>\n",
       "      <td>1359.100000</td>\n",
       "      <td>1.062527e+10</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>401.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       resource_state       distance   moving_time  elapsed_time  \\\n",
       "count          1586.0    1586.000000   1586.000000  1.586000e+03   \n",
       "mean              2.0   33294.370240   5079.875158  2.756729e+05   \n",
       "std               0.0   29819.385303   4123.521390  1.073685e+07   \n",
       "min               2.0       0.000000     89.000000  8.900000e+01   \n",
       "25%               2.0    8614.250000   2102.500000  2.401000e+03   \n",
       "50%               2.0   25625.600000   3891.500000  4.500000e+03   \n",
       "75%               2.0   49391.575000   7240.500000  8.351250e+03   \n",
       "max               2.0  162929.000000  51131.000000  4.275968e+08   \n",
       "\n",
       "       total_elevation_gain  workout_type            id    utc_offset  \\\n",
       "count           1586.000000    788.000000  1.586000e+03   1586.000000   \n",
       "mean             311.814124      8.470812  2.866887e+09  12593.190416   \n",
       "std              477.720807      3.649569  3.214000e+09  16508.790631   \n",
       "min                0.000000      0.000000  1.007260e+08 -25200.000000   \n",
       "25%                0.000000     10.000000  4.151952e+08  19800.000000   \n",
       "50%              133.000000     10.000000  1.315509e+09  19800.000000   \n",
       "75%              448.925000     10.000000  4.718611e+09  19800.000000   \n",
       "max             4011.100000     11.000000  9.913884e+09  19800.000000   \n",
       "\n",
       "       achievement_count  kudos_count  comment_count  athlete_count  \\\n",
       "count        1586.000000  1586.000000    1586.000000    1586.000000   \n",
       "mean            4.312736    16.772383       0.937579       1.745271   \n",
       "std            11.448055    12.075703       2.525432       3.172744   \n",
       "min             0.000000     0.000000       0.000000       1.000000   \n",
       "25%             0.000000     6.000000       0.000000       1.000000   \n",
       "50%             0.000000    17.000000       0.000000       1.000000   \n",
       "75%             4.000000    25.000000       0.000000       1.000000   \n",
       "max           153.000000    56.000000      23.000000      48.000000   \n",
       "\n",
       "       photo_count  average_speed    max_speed  average_cadence  average_temp  \\\n",
       "count       1586.0    1586.000000  1586.000000       694.000000    815.000000   \n",
       "mean           0.0       6.192291    12.504759        82.437896     21.312883   \n",
       "std            0.0       2.210344     4.461734         9.639675      5.578039   \n",
       "min            0.0       0.000000     0.000000        36.700000      7.000000   \n",
       "25%            0.0       5.392250    10.100000        78.900000     18.000000   \n",
       "50%            0.0       6.376000    13.400000        83.900000     22.000000   \n",
       "75%            0.0       7.646000    15.800000        89.000000     25.000000   \n",
       "max            0.0      12.414000    23.400000       106.000000     40.000000   \n",
       "\n",
       "       average_watts    max_watts  weighted_average_watts   kilojoules  \\\n",
       "count    1372.000000   610.000000              610.000000  1372.000000   \n",
       "mean      135.218440   632.595082              169.419672   777.597668   \n",
       "std        44.324677   357.837161               43.773236   680.335920   \n",
       "min         0.400000    66.000000                3.000000     0.200000   \n",
       "25%       103.475000   378.000000              144.250000   279.150000   \n",
       "50%       134.950000   522.500000              177.000000   608.600000   \n",
       "75%       164.700000   816.000000              199.000000  1137.750000   \n",
       "max       379.400000  2000.000000              269.000000  3862.500000   \n",
       "\n",
       "       average_heartrate  max_heartrate    elev_high     elev_low  \\\n",
       "count         901.000000     901.000000  1529.000000  1529.000000   \n",
       "mean          137.349057     167.831299   651.753564   554.846239   \n",
       "std            19.450446      21.135393   184.524553   137.118737   \n",
       "min            44.700000      70.000000     0.000000   -53.000000   \n",
       "25%           125.000000     155.000000   631.000000   548.600000   \n",
       "50%           140.300000     176.000000   662.100000   570.900000   \n",
       "75%           150.000000     184.000000   699.600000   623.400000   \n",
       "max           186.700000     201.000000  1426.600000  1359.100000   \n",
       "\n",
       "          upload_id     pr_count  total_photo_count  suffer_score  \n",
       "count  1.584000e+03  1586.000000        1586.000000    904.000000  \n",
       "mean   3.074681e+09     1.540984           0.125473     64.651549  \n",
       "std    3.434546e+09     4.747760           0.577185     61.627783  \n",
       "min    1.101569e+08     0.000000           0.000000      0.000000  \n",
       "25%    4.649201e+08     0.000000           0.000000     15.000000  \n",
       "50%    1.422842e+09     0.000000           0.000000     54.000000  \n",
       "75%    5.034710e+09     1.000000           0.000000     92.000000  \n",
       "max    1.062527e+10    54.000000          10.000000    401.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9378015b-17a8-454e-911a-98eeb839a09a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>athlete</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>sport_type</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_date_local</th>\n",
       "      <th>timezone</th>\n",
       "      <th>location_city</th>\n",
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       "      <th>map</th>\n",
       "      <th>visibility</th>\n",
       "      <th>gear_id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>1586</td>\n",
       "      <td>1586</td>\n",
       "      <td>1586</td>\n",
       "      <td>1586</td>\n",
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       "      <td>1586</td>\n",
       "      <td>1586</td>\n",
       "      <td>1432</td>\n",
       "      <td>1586</td>\n",
       "      <td>1586</td>\n",
       "      <td>1373</td>\n",
       "      <td>1584</td>\n",
       "      <td>1584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1</td>\n",
       "      <td>856</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1586</td>\n",
       "      <td>1586</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1586</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1223</td>\n",
       "      <td>1217</td>\n",
       "      <td>2</td>\n",
       "      <td>1584</td>\n",
       "      <td>1583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>{'id': 3506416, 'resource_state': 1}</td>\n",
       "      <td>Morning Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>2023-09-24T18:41:03Z</td>\n",
       "      <td>2023-09-24T11:41:03Z</td>\n",
       "      <td>(GMT+05:30) Asia/Kolkata</td>\n",
       "      <td>Pune</td>\n",
       "      <td>MH</td>\n",
       "      <td>India</td>\n",
       "      <td>{'id': 'a9913883505', 'summary_polyline': 'al_...</td>\n",
       "      <td>everyone</td>\n",
       "      <td>b2517905</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>10625272364</td>\n",
       "      <td>77d628a2-7471-4e6c-b1a1-7103fa3a29d4-activity.fit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1586</td>\n",
       "      <td>263</td>\n",
       "      <td>1422</td>\n",
       "      <td>1422</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1332</td>\n",
       "      <td>682</td>\n",
       "      <td>676</td>\n",
       "      <td>1350</td>\n",
       "      <td>1</td>\n",
       "      <td>1432</td>\n",
       "      <td>753</td>\n",
       "      <td>362</td>\n",
       "      <td>362</td>\n",
       "      <td>763</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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      ],
      "text/plain": [
       "                                     athlete          name  type sport_type  \\\n",
       "count                                   1586          1586  1586       1586   \n",
       "unique                                     1           856     9          9   \n",
       "top     {'id': 3506416, 'resource_state': 1}  Morning Ride  Ride       Ride   \n",
       "freq                                    1586           263  1422       1422   \n",
       "\n",
       "                  start_date      start_date_local                  timezone  \\\n",
       "count                   1586                  1586                      1586   \n",
       "unique                  1586                  1586                         3   \n",
       "top     2023-09-24T18:41:03Z  2023-09-24T11:41:03Z  (GMT+05:30) Asia/Kolkata   \n",
       "freq                       1                     1                      1332   \n",
       "\n",
       "       location_city location_state location_country  \\\n",
       "count            707            710             1586   \n",
       "unique            18              4                2   \n",
       "top             Pune             MH            India   \n",
       "freq             682            676             1350   \n",
       "\n",
       "                                                      map visibility  \\\n",
       "count                                                1586       1586   \n",
       "unique                                               1586          3   \n",
       "top     {'id': 'a9913883505', 'summary_polyline': 'al_...   everyone   \n",
       "freq                                                    1       1432   \n",
       "\n",
       "         gear_id start_latlng end_latlng device_watts upload_id_str  \\\n",
       "count       1432         1586       1586         1373          1584   \n",
       "unique         6         1223       1217            2          1584   \n",
       "top     b2517905           []         []        False   10625272364   \n",
       "freq         753          362        362          763             1   \n",
       "\n",
       "                                              external_id  \n",
       "count                                                1584  \n",
       "unique                                               1583  \n",
       "top     77d628a2-7471-4e6c-b1a1-7103fa3a29d4-activity.fit  \n",
       "freq                                                    2  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c037ac22-6360-4554-be87-4ef6e3491933",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>has_heartrate</th>\n",
       "      <th>average_heartrate</th>\n",
       "      <th>max_heartrate</th>\n",
       "      <th>heartrate_opt_out</th>\n",
       "      <th>display_hide_heartrate_option</th>\n",
       "      <th>elev_high</th>\n",
       "      <th>elev_low</th>\n",
       "      <th>upload_id</th>\n",
       "      <th>upload_id_str</th>\n",
       "      <th>external_id</th>\n",
       "      <th>from_accepted_tag</th>\n",
       "      <th>pr_count</th>\n",
       "      <th>has_kudoed</th>\n",
       "      <th>suffer_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lunch Ride</td>\n",
       "      <td>3564.2</td>\n",
       "      <td>723</td>\n",
       "      <td>830</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-24T18:41:03Z</td>\n",
       "      <td>2023-09-24T11:41:03Z</td>\n",
       "      <td>(GMT-07:00) America/Phoenix</td>\n",
       "      <td>-25200.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'id': 'a9913883505', 'summary_polyline': 'al_...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>b12532853</td>\n",
       "      <td>[33.425444243475795, -111.94648995995522]</td>\n",
       "      <td>[33.419371051713824, -111.92317754030228]</td>\n",
       "      <td>4.930</td>\n",
       "      <td>9.588</td>\n",
       "      <td>84.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>81.3</td>\n",
       "      <td>807.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>58.8</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>127.9</td>\n",
       "      <td>147.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>355.6</td>\n",
       "      <td>348.4</td>\n",
       "      <td>1.062527e+10</td>\n",
       "      <td>10625272364</td>\n",
       "      <td>garmin_ping_296232055012</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Morning Ride</td>\n",
       "      <td>4904.7</td>\n",
       "      <td>1355</td>\n",
       "      <td>1355</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-24T15:54:28Z</td>\n",
       "      <td>2023-09-24T08:54:28Z</td>\n",
       "      <td>(GMT-07:00) America/Phoenix</td>\n",
       "      <td>-25200.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'id': 'a9912921254', 'summary_polyline': 'woh...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>b12532853</td>\n",
       "      <td>[32.6519475504756, -111.39609991572797]</td>\n",
       "      <td>[32.650608625262976, -111.39441523700953]</td>\n",
       "      <td>3.620</td>\n",
       "      <td>6.050</td>\n",
       "      <td>71.2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.7</td>\n",
       "      <td>202.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>41.6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>126.4</td>\n",
       "      <td>153.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>545.0</td>\n",
       "      <td>534.4</td>\n",
       "      <td>1.062426e+10</td>\n",
       "      <td>10624263906</td>\n",
       "      <td>garmin_ping_296211250274</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ State ITT - 7th/11 in Men Merckx - destroye...</td>\n",
       "      <td>39964.1</td>\n",
       "      <td>4020</td>\n",
       "      <td>4020</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2023-09-24T14:45:58Z</td>\n",
       "      <td>2023-09-24T07:45:58Z</td>\n",
       "      <td>(GMT-07:00) America/Phoenix</td>\n",
       "      <td>-25200.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>{'id': 'a9912737242', 'summary_polyline': 'ykg...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>b12532853</td>\n",
       "      <td>[32.64717631973326, -111.38980829156935]</td>\n",
       "      <td>[32.647236585617065, -111.38979689218104]</td>\n",
       "      <td>9.941</td>\n",
       "      <td>15.096</td>\n",
       "      <td>86.7</td>\n",
       "      <td>22.0</td>\n",
       "      <td>249.9</td>\n",
       "      <td>731.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>1004.5</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>175.6</td>\n",
       "      <td>185.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>583.6</td>\n",
       "      <td>536.4</td>\n",
       "      <td>1.062407e+10</td>\n",
       "      <td>10624071939</td>\n",
       "      <td>garmin_ping_296207949248</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Morning Ride</td>\n",
       "      <td>2744.7</td>\n",
       "      <td>427</td>\n",
       "      <td>427</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-09-24T14:36:32Z</td>\n",
       "      <td>2023-09-24T07:36:32Z</td>\n",
       "      <td>(GMT-07:00) America/Phoenix</td>\n",
       "      <td>-25200.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>{'id': 'a9912187409', 'summary_polyline': 'wvg...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>b12532853</td>\n",
       "      <td>[32.65025801025331, -111.39397384598851]</td>\n",
       "      <td>[32.647967990487814, -111.39085778966546]</td>\n",
       "      <td>6.428</td>\n",
       "      <td>11.564</td>\n",
       "      <td>81.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>131.5</td>\n",
       "      <td>487.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>56.1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>132.1</td>\n",
       "      <td>154.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>522.0</td>\n",
       "      <td>519.4</td>\n",
       "      <td>1.062350e+10</td>\n",
       "      <td>10623497643</td>\n",
       "      <td>garmin_ping_296198158652</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New tyre testing</td>\n",
       "      <td>29299.1</td>\n",
       "      <td>4662</td>\n",
       "      <td>5821</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Ride</td>\n",
       "      <td>Ride</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2023-09-22T22:39:32Z</td>\n",
       "      <td>2023-09-22T15:39:32Z</td>\n",
       "      <td>(GMT-07:00) America/Phoenix</td>\n",
       "      <td>-25200.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'id': 'a9901444795', 'summary_polyline': '{f~...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>b12532853</td>\n",
       "      <td>[33.41989994980395, -111.92341181449592]</td>\n",
       "      <td>[33.41937582939863, -111.92321039736271]</td>\n",
       "      <td>6.285</td>\n",
       "      <td>12.682</td>\n",
       "      <td>83.1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>125.4</td>\n",
       "      <td>954.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>584.7</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>135.3</td>\n",
       "      <td>190.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>421.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>1.061218e+10</td>\n",
       "      <td>10612179069</td>\n",
       "      <td>garmin_ping_295937594218</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                name  distance  moving_time  \\\n",
       "0                                         Lunch Ride    3564.2          723   \n",
       "1                                       Morning Ride    4904.7         1355   \n",
       "2  AZ State ITT - 7th/11 in Men Merckx - destroye...   39964.1         4020   \n",
       "3                                       Morning Ride    2744.7          427   \n",
       "4                                   New tyre testing   29299.1         4662   \n",
       "\n",
       "   elapsed_time  total_elevation_gain  type sport_type  workout_type  \\\n",
       "0           830                   3.0  Ride       Ride           NaN   \n",
       "1          1355                   3.0  Ride       Ride           NaN   \n",
       "2          4020                  42.0  Ride       Ride          11.0   \n",
       "3           427                   1.0  Ride       Ride           NaN   \n",
       "4          5821                  52.0  Ride       Ride          10.0   \n",
       "\n",
       "             start_date      start_date_local                     timezone  \\\n",
       "0  2023-09-24T18:41:03Z  2023-09-24T11:41:03Z  (GMT-07:00) America/Phoenix   \n",
       "1  2023-09-24T15:54:28Z  2023-09-24T08:54:28Z  (GMT-07:00) America/Phoenix   \n",
       "2  2023-09-24T14:45:58Z  2023-09-24T07:45:58Z  (GMT-07:00) America/Phoenix   \n",
       "3  2023-09-24T14:36:32Z  2023-09-24T07:36:32Z  (GMT-07:00) America/Phoenix   \n",
       "4  2023-09-22T22:39:32Z  2023-09-22T15:39:32Z  (GMT-07:00) America/Phoenix   \n",
       "\n",
       "   utc_offset location_country  achievement_count  kudos_count  comment_count  \\\n",
       "0    -25200.0    United States                  0           11              0   \n",
       "1    -25200.0    United States                  0            8              0   \n",
       "2    -25200.0    United States                  0           25              6   \n",
       "3    -25200.0    United States                  0            6              0   \n",
       "4    -25200.0    United States                  5           23              0   \n",
       "\n",
       "   athlete_count                                                map  trainer  \\\n",
       "0              1  {'id': 'a9913883505', 'summary_polyline': 'al_...    False   \n",
       "1              1  {'id': 'a9912921254', 'summary_polyline': 'woh...    False   \n",
       "2              3  {'id': 'a9912737242', 'summary_polyline': 'ykg...    False   \n",
       "3              3  {'id': 'a9912187409', 'summary_polyline': 'wvg...    False   \n",
       "4              1  {'id': 'a9901444795', 'summary_polyline': '{f~...    False   \n",
       "\n",
       "   commute  manual  flagged    gear_id  \\\n",
       "0    False   False    False  b12532853   \n",
       "1    False   False    False  b12532853   \n",
       "2    False   False    False  b12532853   \n",
       "3    False   False    False  b12532853   \n",
       "4    False   False    False  b12532853   \n",
       "\n",
       "                                start_latlng  \\\n",
       "0  [33.425444243475795, -111.94648995995522]   \n",
       "1    [32.6519475504756, -111.39609991572797]   \n",
       "2   [32.64717631973326, -111.38980829156935]   \n",
       "3   [32.65025801025331, -111.39397384598851]   \n",
       "4   [33.41989994980395, -111.92341181449592]   \n",
       "\n",
       "                                  end_latlng  average_speed  max_speed  \\\n",
       "0  [33.419371051713824, -111.92317754030228]          4.930      9.588   \n",
       "1  [32.650608625262976, -111.39441523700953]          3.620      6.050   \n",
       "2  [32.647236585617065, -111.38979689218104]          9.941     15.096   \n",
       "3  [32.647967990487814, -111.39085778966546]          6.428     11.564   \n",
       "4   [33.41937582939863, -111.92321039736271]          6.285     12.682   \n",
       "\n",
       "   average_cadence  average_temp  average_watts  max_watts  \\\n",
       "0             84.5          33.0           81.3      807.0   \n",
       "1             71.2          27.0           30.7      202.0   \n",
       "2             86.7          22.0          249.9      731.0   \n",
       "3             81.0          19.0          131.5      487.0   \n",
       "4             83.1          31.0          125.4      954.0   \n",
       "\n",
       "   weighted_average_watts  kilojoules device_watts  has_heartrate  \\\n",
       "0                    93.0        58.8         True           True   \n",
       "1                    45.0        41.6         True           True   \n",
       "2                   249.0      1004.5         True           True   \n",
       "3                   154.0        56.1         True           True   \n",
       "4                   167.0       584.7         True           True   \n",
       "\n",
       "   average_heartrate  max_heartrate  heartrate_opt_out  \\\n",
       "0              127.9          147.0              False   \n",
       "1              126.4          153.0              False   \n",
       "2              175.6          185.0              False   \n",
       "3              132.1          154.0              False   \n",
       "4              135.3          190.0              False   \n",
       "\n",
       "   display_hide_heartrate_option  elev_high  elev_low     upload_id  \\\n",
       "0                           True      355.6     348.4  1.062527e+10   \n",
       "1                           True      545.0     534.4  1.062426e+10   \n",
       "2                           True      583.6     536.4  1.062407e+10   \n",
       "3                           True      522.0     519.4  1.062350e+10   \n",
       "4                           True      421.0     384.0  1.061218e+10   \n",
       "\n",
       "  upload_id_str               external_id  from_accepted_tag  pr_count  \\\n",
       "0   10625272364  garmin_ping_296232055012              False         0   \n",
       "1   10624263906  garmin_ping_296211250274              False         0   \n",
       "2   10624071939  garmin_ping_296207949248              False         0   \n",
       "3   10623497643  garmin_ping_296198158652              False         0   \n",
       "4   10612179069  garmin_ping_295937594218              False         2   \n",
       "\n",
       "   has_kudoed  suffer_score  \n",
       "0       False           3.0  \n",
       "1       False           5.0  \n",
       "2       False         211.0  \n",
       "3       False           2.0  \n",
       "4       False          42.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dropped = df.drop(columns=['resource_state',\t'athlete', 'id', 'location_city', 'location_state', 'photo_count',\t'private', 'visibility', 'total_photo_count'])\n",
    "\n",
    "df_dropped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a973014d-c278-4686-b7af-8e0d400cccef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1422 entries, 0 to 1585\n",
      "Data columns (total 48 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   name                           1422 non-null   object \n",
      " 1   distance                       1422 non-null   float64\n",
      " 2   moving_time                    1422 non-null   int64  \n",
      " 3   elapsed_time                   1422 non-null   int64  \n",
      " 4   total_elevation_gain           1422 non-null   float64\n",
      " 5   type                           1422 non-null   object \n",
      " 6   sport_type                     1422 non-null   object \n",
      " 7   workout_type                   715 non-null    float64\n",
      " 8   start_date                     1422 non-null   object \n",
      " 9   start_date_local               1422 non-null   object \n",
      " 10  timezone                       1422 non-null   object \n",
      " 11  utc_offset                     1422 non-null   float64\n",
      " 12  location_country               1422 non-null   object \n",
      " 13  achievement_count              1422 non-null   int64  \n",
      " 14  kudos_count                    1422 non-null   int64  \n",
      " 15  comment_count                  1422 non-null   int64  \n",
      " 16  athlete_count                  1422 non-null   int64  \n",
      " 17  map                            1422 non-null   object \n",
      " 18  trainer                        1422 non-null   bool   \n",
      " 19  commute                        1422 non-null   bool   \n",
      " 20  manual                         1422 non-null   bool   \n",
      " 21  flagged                        1422 non-null   bool   \n",
      " 22  gear_id                        1421 non-null   object \n",
      " 23  start_latlng                   1422 non-null   object \n",
      " 24  end_latlng                     1422 non-null   object \n",
      " 25  average_speed                  1422 non-null   float64\n",
      " 26  max_speed                      1422 non-null   float64\n",
      " 27  average_cadence                663 non-null    float64\n",
      " 28  average_temp                   773 non-null    float64\n",
      " 29  average_watts                  1360 non-null   float64\n",
      " 30  max_watts                      600 non-null    float64\n",
      " 31  weighted_average_watts         600 non-null    float64\n",
      " 32  kilojoules                     1360 non-null   float64\n",
      " 33  device_watts                   1361 non-null   object \n",
      " 34  has_heartrate                  1422 non-null   bool   \n",
      " 35  average_heartrate              781 non-null    float64\n",
      " 36  max_heartrate                  781 non-null    float64\n",
      " 37  heartrate_opt_out              1422 non-null   bool   \n",
      " 38  display_hide_heartrate_option  1422 non-null   bool   \n",
      " 39  elev_high                      1415 non-null   float64\n",
      " 40  elev_low                       1415 non-null   float64\n",
      " 41  upload_id                      1421 non-null   float64\n",
      " 42  upload_id_str                  1421 non-null   object \n",
      " 43  external_id                    1421 non-null   object \n",
      " 44  from_accepted_tag              1422 non-null   bool   \n",
      " 45  pr_count                       1422 non-null   int64  \n",
      " 46  has_kudoed                     1422 non-null   bool   \n",
      " 47  suffer_score                   781 non-null    float64\n",
      "dtypes: bool(9), float64(18), int64(7), object(14)\n",
      "memory usage: 456.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_rides = df_dropped.loc[df_dropped['type'] == 'Ride']\n",
    "\n",
    "df_rides.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e2f4421b-2e1d-4af1-8d7a-37350672c0be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1420 entries, 0 to 1585\n",
      "Data columns (total 48 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   name                           1420 non-null   object \n",
      " 1   distance                       1420 non-null   float64\n",
      " 2   moving_time                    1420 non-null   int64  \n",
      " 3   elapsed_time                   1420 non-null   int64  \n",
      " 4   total_elevation_gain           1420 non-null   float64\n",
      " 5   type                           1420 non-null   object \n",
      " 6   sport_type                     1420 non-null   object \n",
      " 7   workout_type                   714 non-null    float64\n",
      " 8   start_date                     1420 non-null   object \n",
      " 9   start_date_local               1420 non-null   object \n",
      " 10  timezone                       1420 non-null   object \n",
      " 11  utc_offset                     1420 non-null   float64\n",
      " 12  location_country               1420 non-null   object \n",
      " 13  achievement_count              1420 non-null   int64  \n",
      " 14  kudos_count                    1420 non-null   int64  \n",
      " 15  comment_count                  1420 non-null   int64  \n",
      " 16  athlete_count                  1420 non-null   int64  \n",
      " 17  map                            1420 non-null   object \n",
      " 18  trainer                        1420 non-null   bool   \n",
      " 19  commute                        1420 non-null   bool   \n",
      " 20  manual                         1420 non-null   bool   \n",
      " 21  flagged                        1420 non-null   bool   \n",
      " 22  gear_id                        1419 non-null   object \n",
      " 23  start_latlng                   1420 non-null   object \n",
      " 24  end_latlng                     1420 non-null   object \n",
      " 25  average_speed                  1420 non-null   float64\n",
      " 26  max_speed                      1420 non-null   float64\n",
      " 27  average_cadence                663 non-null    float64\n",
      " 28  average_temp                   772 non-null    float64\n",
      " 29  average_watts                  1359 non-null   float64\n",
      " 30  max_watts                      600 non-null    float64\n",
      " 31  weighted_average_watts         600 non-null    float64\n",
      " 32  kilojoules                     1359 non-null   float64\n",
      " 33  device_watts                   1360 non-null   object \n",
      " 34  has_heartrate                  1420 non-null   bool   \n",
      " 35  average_heartrate              780 non-null    float64\n",
      " 36  max_heartrate                  780 non-null    float64\n",
      " 37  heartrate_opt_out              1420 non-null   bool   \n",
      " 38  display_hide_heartrate_option  1420 non-null   bool   \n",
      " 39  elev_high                      1414 non-null   float64\n",
      " 40  elev_low                       1414 non-null   float64\n",
      " 41  upload_id                      1420 non-null   float64\n",
      " 42  upload_id_str                  1420 non-null   object \n",
      " 43  external_id                    1420 non-null   object \n",
      " 44  from_accepted_tag              1420 non-null   bool   \n",
      " 45  pr_count                       1420 non-null   int64  \n",
      " 46  has_kudoed                     1420 non-null   bool   \n",
      " 47  suffer_score                   780 non-null    float64\n",
      "dtypes: bool(9), float64(18), int64(7), object(14)\n",
      "memory usage: 456.2+ KB\n"
     ]
    },
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       "                name  type sport_type            start_date  \\\n",
       "count           1420  1420       1420                  1420   \n",
       "unique           779     1          1                  1420   \n",
       "top     Morning Ride  Ride       Ride  2023-09-24T18:41:03Z   \n",
       "freq             263  1420       1420                     1   \n",
       "\n",
       "            start_date_local                  timezone location_country  \\\n",
       "count                   1420                      1420             1420   \n",
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       "freq                       1                      1186             1204   \n",
       "\n",
       "                                                      map   gear_id  \\\n",
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       "unique                                               1420         6   \n",
       "top     {'id': 'a9913883505', 'summary_polyline': 'al_...  b2517905   \n",
       "freq                                                    1       741   \n",
       "\n",
       "       start_latlng end_latlng device_watts upload_id_str  \\\n",
       "count          1420       1420         1360          1420   \n",
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       "\n",
       "                     external_id  \n",
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       "top     garmin_ping_296232055012  \n",
       "freq                           1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_non_manual_or_flagged = df_rides.loc[(df_rides['manual'] == False) & (df_rides['flagged'] == False)]\n",
    "\n",
    "df_non_manual_or_flagged.info()\n",
    "df_non_manual_or_flagged.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1abeef65-2a70-4c4a-a00e-bd280a7349f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       manual  flagged  commute  trainer  heartrate_opt_out  \\\n",
      "False    1420     1420     1398     1143               1420   \n",
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      "\n",
      "       display_hide_heartrate_option  from_accepted_tag  \n",
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      "<class 'pandas.core.frame.DataFrame'>\n",
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      " 31  suffer_score            780 non-null    float64\n",
      "dtypes: bool(2), float64(16), int64(7), object(7)\n",
      "memory usage: 346.7+ KB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ph/n1bcmp_s2rlgrd9vg896nks80000gn/T/ipykernel_35254/3276792800.py:2: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  'display_hide_heartrate_option', 'from_accepted_tag']].applymap(lambda x: x if isinstance(x, bool) else None).apply(pd.value_counts)\n",
      "/var/folders/ph/n1bcmp_s2rlgrd9vg896nks80000gn/T/ipykernel_35254/3276792800.py:2: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
      "  'display_hide_heartrate_option', 'from_accepted_tag']].applymap(lambda x: x if isinstance(x, bool) else None).apply(pd.value_counts)\n"
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       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New tyre testing</td>\n",
       "      <td>29299.1</td>\n",
       "      <td>4662</td>\n",
       "      <td>5821</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2023-09-22T22:39:32Z</td>\n",
       "      <td>United States</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>b12532853</td>\n",
       "      <td>6.285</td>\n",
       "      <td>12.682</td>\n",
       "      <td>83.1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>125.4</td>\n",
       "      <td>954.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>584.7</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>135.3</td>\n",
       "      <td>190.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>1.061218e+10</td>\n",
       "      <td>10612179069</td>\n",
       "      <td>garmin_ping_295937594218</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                name  distance  moving_time  \\\n",
       "0                                         Lunch Ride    3564.2          723   \n",
       "1                                       Morning Ride    4904.7         1355   \n",
       "2  AZ State ITT - 7th/11 in Men Merckx - destroye...   39964.1         4020   \n",
       "3                                       Morning Ride    2744.7          427   \n",
       "4                                   New tyre testing   29299.1         4662   \n",
       "\n",
       "   elapsed_time  total_elevation_gain            start_date location_country  \\\n",
       "0           830                   3.0  2023-09-24T18:41:03Z    United States   \n",
       "1          1355                   3.0  2023-09-24T15:54:28Z    United States   \n",
       "2          4020                  42.0  2023-09-24T14:45:58Z    United States   \n",
       "3           427                   1.0  2023-09-24T14:36:32Z    United States   \n",
       "4          5821                  52.0  2023-09-22T22:39:32Z    United States   \n",
       "\n",
       "   achievement_count  kudos_count  comment_count  athlete_count    gear_id  \\\n",
       "0                  0           11              0              1  b12532853   \n",
       "1                  0            8              0              1  b12532853   \n",
       "2                  0           25              6              3  b12532853   \n",
       "3                  0            6              0              3  b12532853   \n",
       "4                  5           23              0              1  b12532853   \n",
       "\n",
       "   average_speed  max_speed  average_cadence  average_temp  average_watts  \\\n",
       "0          4.930      9.588             84.5          33.0           81.3   \n",
       "1          3.620      6.050             71.2          27.0           30.7   \n",
       "2          9.941     15.096             86.7          22.0          249.9   \n",
       "3          6.428     11.564             81.0          19.0          131.5   \n",
       "4          6.285     12.682             83.1          31.0          125.4   \n",
       "\n",
       "   max_watts  weighted_average_watts  kilojoules device_watts  has_heartrate  \\\n",
       "0      807.0                    93.0        58.8         True           True   \n",
       "1      202.0                    45.0        41.6         True           True   \n",
       "2      731.0                   249.0      1004.5         True           True   \n",
       "3      487.0                   154.0        56.1         True           True   \n",
       "4      954.0                   167.0       584.7         True           True   \n",
       "\n",
       "   average_heartrate  max_heartrate  elev_high  elev_low     upload_id  \\\n",
       "0              127.9          147.0      355.6     348.4  1.062527e+10   \n",
       "1              126.4          153.0      545.0     534.4  1.062426e+10   \n",
       "2              175.6          185.0      583.6     536.4  1.062407e+10   \n",
       "3              132.1          154.0      522.0     519.4  1.062350e+10   \n",
       "4              135.3          190.0      421.0     384.0  1.061218e+10   \n",
       "\n",
       "  upload_id_str               external_id  pr_count  has_kudoed  suffer_score  \n",
       "0   10625272364  garmin_ping_296232055012         0       False           3.0  \n",
       "1   10624263906  garmin_ping_296211250274         0       False           5.0  \n",
       "2   10624071939  garmin_ping_296207949248         0       False         211.0  \n",
       "3   10623497643  garmin_ping_296198158652         0       False           2.0  \n",
       "4   10612179069  garmin_ping_295937594218         2       False          42.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_counts = df_non_manual_or_flagged[['manual', 'flagged', 'commute', 'trainer', 'heartrate_opt_out', \n",
    "                                         'display_hide_heartrate_option', 'from_accepted_tag']].applymap(lambda x: x if isinstance(x, bool) else None).apply(pd.value_counts)\n",
    "value_counts = value_counts.fillna(0).astype(int)  # Fill NaNs with 0 and convert to integer\n",
    "\n",
    "# Display the value counts for all columns\n",
    "print(value_counts)\n",
    "\n",
    "df_all_rides = df_non_manual_or_flagged.drop(columns=['type', 'sport_type', 'workout_type', 'manual', 'flagged', 'commute', 'trainer', \n",
    "                                                      'heartrate_opt_out', 'display_hide_heartrate_option', 'from_accepted_tag', \n",
    "                                                      'map', 'start_latlng', 'end_latlng', 'start_date_local', 'timezone', 'utc_offset'])\n",
    "\n",
    "\n",
    "df_all_rides.info()\n",
    "df_all_rides.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "60cf5b3f-2796-4627-86e7-3c750d75170b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9114f719-a2af-4d25-929e-bd5fd6fe0a83",
   "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>name</th>\n",
       "      <th>distance</th>\n",
       "      <th>moving_time</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>total_elevation_gain</th>\n",
       "      <th>start_date</th>\n",
       "      <th>location_country</th>\n",
       "      <th>achievement_count</th>\n",
       "      <th>kudos_count</th>\n",
       "      <th>comment_count</th>\n",
       "      <th>athlete_count</th>\n",
       "      <th>gear_id</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>max_speed</th>\n",
       "      <th>average_cadence</th>\n",
       "      <th>average_temp</th>\n",
       "      <th>average_watts</th>\n",
       "      <th>max_watts</th>\n",
       "      <th>weighted_average_watts</th>\n",
       "      <th>kilojoules</th>\n",
       "      <th>device_watts</th>\n",
       "      <th>has_heartrate</th>\n",
       "      <th>average_heartrate</th>\n",
       "      <th>max_heartrate</th>\n",
       "      <th>elev_high</th>\n",
       "      <th>elev_low</th>\n",
       "      <th>upload_id</th>\n",
       "      <th>upload_id_str</th>\n",
       "      <th>external_id</th>\n",
       "      <th>pr_count</th>\n",
       "      <th>has_kudoed</th>\n",
       "      <th>suffer_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lunch Ride</td>\n",
       "      <td>3564.2</td>\n",
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       "      <td>81.3</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>Morning Ride</td>\n",
       "      <td>4904.7</td>\n",
       "      <td>1355</td>\n",
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       "      <td>b12532853</td>\n",
       "      <td>13.0320</td>\n",
       "      <td>6.050</td>\n",
       "      <td>71.2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.7</td>\n",
       "      <td>202.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>41.6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>126.4</td>\n",
       "      <td>153.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>534.4</td>\n",
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       "      <td>garmin_ping_296211250274</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ State ITT - 7th/11 in Men Merckx - destroye...</td>\n",
       "      <td>39964.1</td>\n",
       "      <td>4020</td>\n",
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       "      <td>2023-09-24</td>\n",
       "      <td>United States</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
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       "      <td>b12532853</td>\n",
       "      <td>35.7876</td>\n",
       "      <td>15.096</td>\n",
       "      <td>86.7</td>\n",
       "      <td>22.0</td>\n",
       "      <td>249.9</td>\n",
       "      <td>731.0</td>\n",
       "      <td>249.0</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>175.6</td>\n",
       "      <td>185.0</td>\n",
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       "      <td>536.4</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2744.7</td>\n",
       "      <td>427</td>\n",
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       "      <td>2023-09-24</td>\n",
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       "      <td>19.0</td>\n",
       "      <td>131.5</td>\n",
       "      <td>487.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>56.1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>132.1</td>\n",
       "      <td>154.0</td>\n",
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       "      <td>519.4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New tyre testing</td>\n",
       "      <td>29299.1</td>\n",
       "      <td>4662</td>\n",
       "      <td>5821</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2023-09-22</td>\n",
       "      <td>United States</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>b12532853</td>\n",
       "      <td>22.6260</td>\n",
       "      <td>12.682</td>\n",
       "      <td>83.1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>125.4</td>\n",
       "      <td>954.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>584.7</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>135.3</td>\n",
       "      <td>190.0</td>\n",
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       "      <td>384.0</td>\n",
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       "      <td>10612179069</td>\n",
       "      <td>garmin_ping_295937594218</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                name  distance  moving_time  \\\n",
       "0                                         Lunch Ride    3564.2          723   \n",
       "1                                       Morning Ride    4904.7         1355   \n",
       "2  AZ State ITT - 7th/11 in Men Merckx - destroye...   39964.1         4020   \n",
       "3                                       Morning Ride    2744.7          427   \n",
       "4                                   New tyre testing   29299.1         4662   \n",
       "\n",
       "   elapsed_time  total_elevation_gain  start_date location_country  \\\n",
       "0           830                   3.0  2023-09-24    United States   \n",
       "1          1355                   3.0  2023-09-24    United States   \n",
       "2          4020                  42.0  2023-09-24    United States   \n",
       "3           427                   1.0  2023-09-24    United States   \n",
       "4          5821                  52.0  2023-09-22    United States   \n",
       "\n",
       "   achievement_count  kudos_count  comment_count  athlete_count    gear_id  \\\n",
       "0                  0           11              0              1  b12532853   \n",
       "1                  0            8              0              1  b12532853   \n",
       "2                  0           25              6              3  b12532853   \n",
       "3                  0            6              0              3  b12532853   \n",
       "4                  5           23              0              1  b12532853   \n",
       "\n",
       "   average_speed  max_speed  average_cadence  average_temp  average_watts  \\\n",
       "0        17.7480      9.588             84.5          33.0           81.3   \n",
       "1        13.0320      6.050             71.2          27.0           30.7   \n",
       "2        35.7876     15.096             86.7          22.0          249.9   \n",
       "3        23.1408     11.564             81.0          19.0          131.5   \n",
       "4        22.6260     12.682             83.1          31.0          125.4   \n",
       "\n",
       "   max_watts  weighted_average_watts  kilojoules device_watts  has_heartrate  \\\n",
       "0      807.0                    93.0        58.8         True           True   \n",
       "1      202.0                    45.0        41.6         True           True   \n",
       "2      731.0                   249.0      1004.5         True           True   \n",
       "3      487.0                   154.0        56.1         True           True   \n",
       "4      954.0                   167.0       584.7         True           True   \n",
       "\n",
       "   average_heartrate  max_heartrate  elev_high  elev_low     upload_id  \\\n",
       "0              127.9          147.0      355.6     348.4  1.062527e+10   \n",
       "1              126.4          153.0      545.0     534.4  1.062426e+10   \n",
       "2              175.6          185.0      583.6     536.4  1.062407e+10   \n",
       "3              132.1          154.0      522.0     519.4  1.062350e+10   \n",
       "4              135.3          190.0      421.0     384.0  1.061218e+10   \n",
       "\n",
       "  upload_id_str               external_id  pr_count  has_kudoed  suffer_score  \n",
       "0   10625272364  garmin_ping_296232055012         0       False           3.0  \n",
       "1   10624263906  garmin_ping_296211250274         0       False           5.0  \n",
       "2   10624071939  garmin_ping_296207949248         0       False         211.0  \n",
       "3   10623497643  garmin_ping_296198158652         0       False           2.0  \n",
       "4   10612179069  garmin_ping_295937594218         2       False          42.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_rides['start_date'] = pd.to_datetime(df_all_rides['start_date']) \n",
    "df_all_rides['start_date'] = df_all_rides['start_date'].dt.date \n",
    "df_all_rides['average_speed'] = df_all_rides['average_speed'] * 3.6 # converting from meters/sec to km/hr\n",
    "df_all_rides.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2cbed328-d298-43e7-84fc-b5b83ed738d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19358\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ph/n1bcmp_s2rlgrd9vg896nks80000gn/T/ipykernel_35254/2478237745.py:38: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "import datetime\n",
    "\n",
    "fig = plt.figure() #create overall container\n",
    "ax1 = fig.add_subplot(111) #add a 1 by 1 plot to the figure\n",
    "x = np.asarray(df_all_rides.start_date)  #convert data to numpy array\n",
    "y = np.asarray(df_all_rides.average_speed)\n",
    "ax1.plot_date(x, y) #plot data points in scatter plot on ax1\n",
    "ax1.set_title('Average Speed over Time')\n",
    "\n",
    "# Convert datetime.date to Unix timestamp (float)\n",
    "reference_date = datetime.date(1970, 1, 1)\n",
    "print((datetime.date(2023, 1, 1) - reference_date).days)\n",
    "df_all_rides['start_date_unix'] = df_all_rides['start_date'].apply(lambda x : (x - reference_date).days)\n",
    "\n",
    "# add trend line\n",
    "def quadratic_func(x, a, b, c):\n",
    "    return a * x**2 + b * x + c\n",
    "\n",
    "# Fit the curve to the data\n",
    "params, covariance = curve_fit(quadratic_func, df_all_rides['start_date_unix'], y)\n",
    "\n",
    "# Create an array of x values for the curve\n",
    "x_fit = np.linspace((min(x) - reference_date).days, (max(x) - reference_date).days, 100)\n",
    "\n",
    "# Calculate the corresponding y values using the fitted parameters\n",
    "y_fit = quadratic_func(x_fit, *params)\n",
    "\n",
    "# Plot the fitted curve\n",
    "ax1.plot(x_fit, y_fit, 'r-', label='Fitted Curve')\n",
    "\n",
    "#format the figure and display\n",
    "fig.autofmt_xdate(rotation=45)\n",
    "fig.tight_layout()\n",
    "# Add a legend\n",
    "ax1.legend()\n",
    "fig.savefig('visuals/average_speed_over_time.png')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6d5df668-96aa-4440-a94a-616dffc15ec2",
   "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>distance</th>\n",
       "      <th>moving_time</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>total_elevation_gain</th>\n",
       "      <th>start_date</th>\n",
       "      <th>location_country</th>\n",
       "      <th>achievement_count</th>\n",
       "      <th>kudos_count</th>\n",
       "      <th>comment_count</th>\n",
       "      <th>athlete_count</th>\n",
       "      <th>gear_id</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>max_speed</th>\n",
       "      <th>average_cadence</th>\n",
       "      <th>average_temp</th>\n",
       "      <th>average_watts</th>\n",
       "      <th>max_watts</th>\n",
       "      <th>weighted_average_watts</th>\n",
       "      <th>kilojoules</th>\n",
       "      <th>device_watts</th>\n",
       "      <th>has_heartrate</th>\n",
       "      <th>average_heartrate</th>\n",
       "      <th>max_heartrate</th>\n",
       "      <th>elev_high</th>\n",
       "      <th>elev_low</th>\n",
       "      <th>upload_id</th>\n",
       "      <th>upload_id_str</th>\n",
       "      <th>external_id</th>\n",
       "      <th>pr_count</th>\n",
       "      <th>has_kudoed</th>\n",
       "      <th>suffer_score</th>\n",
       "      <th>start_date_unix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>Evening Ride</td>\n",
       "      <td>8378.2</td>\n",
       "      <td>51131</td>\n",
       "      <td>51131</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-09-28</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>b2517905</td>\n",
       "      <td>0.5904</td>\n",
       "      <td>7.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>88.3</td>\n",
       "      <td>160.0</td>\n",
       "      <td>641.2</td>\n",
       "      <td>618.2</td>\n",
       "      <td>4.415218e+09</td>\n",
       "      <td>4415218135</td>\n",
       "      <td>garmin_push_5605842604</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>98.0</td>\n",
       "      <td>18533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name  distance  moving_time  elapsed_time  total_elevation_gain  \\\n",
       "450  Evening Ride    8378.2        51131         51131                   0.0   \n",
       "\n",
       "     start_date location_country  achievement_count  kudos_count  \\\n",
       "450  2020-09-28            India                  0           19   \n",
       "\n",
       "     comment_count  athlete_count   gear_id  average_speed  max_speed  \\\n",
       "450              0              1  b2517905         0.5904        7.9   \n",
       "\n",
       "     average_cadence  average_temp  average_watts  max_watts  \\\n",
       "450              NaN          25.0            NaN        NaN   \n",
       "\n",
       "     weighted_average_watts  kilojoules device_watts  has_heartrate  \\\n",
       "450                     NaN         NaN          NaN           True   \n",
       "\n",
       "     average_heartrate  max_heartrate  elev_high  elev_low     upload_id  \\\n",
       "450               88.3          160.0      641.2     618.2  4.415218e+09   \n",
       "\n",
       "    upload_id_str             external_id  pr_count  has_kudoed  suffer_score  \\\n",
       "450    4415218135  garmin_push_5605842604         0       False          98.0   \n",
       "\n",
       "     start_date_unix  \n",
       "450            18533  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_rides['moving_time'].describe()\n",
    "\n",
    "df_all_rides.loc[df_all_rides['moving_time'] == 51131]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "79e5f415-297c-48a9-aab4-d4707f3069ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1420"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_all_rides)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "df557418-95bc-494d-98ba-e1e6b92c0568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1419"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_rides = df_all_rides[df_all_rides['moving_time'] != 51131]\n",
    "len(df_all_rides)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "364bbc11-8569-4ba6-a85e-72cde6dbb5dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ph/n1bcmp_s2rlgrd9vg896nks80000gn/T/ipykernel_35254/1523899791.py:26: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() #create overall container\n",
    "ax1 = fig.add_subplot(111) #add a 1 by 1 plot to the figure\n",
    "x = np.asarray(df_all_rides.start_date)  #convert data to numpy array\n",
    "df_all_rides['moving_time'] = df_all_rides['moving_time'].apply(lambda x : x / 60)\n",
    "y = np.asarray(df_all_rides.moving_time)\n",
    "ax1.plot_date(x, y) #plot data points in scatter plot on ax1\n",
    "ax1.set_title('Moving time/Ride duration over Time')\n",
    "\n",
    "# Fit the curve to the data\n",
    "params, covariance = curve_fit(quadratic_func, df_all_rides['start_date_unix'], y)\n",
    "\n",
    "# Create an array of x values for the curve\n",
    "x_fit = np.linspace((min(x) - reference_date).days, (max(x) - reference_date).days, 100)\n",
    "\n",
    "# Calculate the corresponding y values using the fitted parameters\n",
    "y_fit = quadratic_func(x_fit, *params)\n",
    "\n",
    "# Plot the fitted curve\n",
    "ax1.plot(x_fit, y_fit, 'r-', label='Fitted Curve')\n",
    "\n",
    "#format the figure and display\n",
    "fig.autofmt_xdate(rotation=45)\n",
    "fig.tight_layout()\n",
    "# Add a legend\n",
    "ax1.legend()\n",
    "fig.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}