strava_part1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e4f3243f-ed2f-4e0b-87b5-22db9477c6b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pandas will be the backbone of our data manipulation.\n",
    "import pandas as pd\n",
    "from pandas.io.json import json_normalize\n",
    "#Seaborn is a data visualization library.\n",
    "import seaborn as sns\n",
    "#Matplotlib is a data visualization library. \n",
    "#Seaborn is actually built on top of Matplotlib. \n",
    "import matplotlib.pyplot as plt\n",
    "#Numpy will help us handle some work with arrays.\n",
    "import numpy as np\n",
    "#Datetime will allow Python to recognize dates as dates, not strings.\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "09ded35e-121d-48ce-9409-6281f1866844",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('activities.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e80fd2c-bfac-44a6-b3e9-7e12d9a9f8a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1308 entries, 0 to 1307\n",
      "Data columns (total 80 columns):\n",
      " #   Column                                                                                                                                                                    Non-Null Count  Dtype  \n",
      "---  ------                                                                                                                                                                    --------------  -----  \n",
      " 0   Activity ID                                                                                                                                                               1308 non-null   int64  \n",
      " 1   Activity Date                                                                                                                                                             1308 non-null   object \n",
      " 2   Activity Name                                                                                                                                                             1308 non-null   object \n",
      " 3   Activity Type                                                                                                                                                             1308 non-null   object \n",
      " 4   Activity Description                                                                                                                                                      558 non-null    object \n",
      " 5   Elapsed Time                                                                                                                                                              1308 non-null   int64  \n",
      " 6   Distance                                                                                                                                                                  1308 non-null   float64\n",
      " 7   Relative Effort                                                                                                                                                           1142 non-null   float64\n",
      " 8   Commute                                                                                                                                                                   1308 non-null   bool   \n",
      " 9   Activity Gear                                                                                                                                                             1096 non-null   object \n",
      " 10  Filename                                                                                                                                                                  1302 non-null   object \n",
      " 11  Athlete Weight                                                                                                                                                            872 non-null    float64\n",
      " 12  Bike Weight                                                                                                                                                               1095 non-null   float64\n",
      " 13  Elapsed Time.1                                                                                                                                                            1272 non-null   float64\n",
      " 14  Moving Time                                                                                                                                                               1308 non-null   float64\n",
      " 15  Distance.1                                                                                                                                                                1308 non-null   float64\n",
      " 16  Max Speed                                                                                                                                                                 1302 non-null   float64\n",
      " 17  Average Speed                                                                                                                                                             575 non-null    float64\n",
      " 18  Elevation Gain                                                                                                                                                            1214 non-null   float64\n",
      " 19  Elevation Loss                                                                                                                                                            656 non-null    float64\n",
      " 20  Elevation Low                                                                                                                                                             1081 non-null   float64\n",
      " 21  Elevation High                                                                                                                                                            1081 non-null   float64\n",
      " 22  Max Grade                                                                                                                                                                 1302 non-null   float64\n",
      " 23  Average Grade                                                                                                                                                             1308 non-null   float64\n",
      " 24  Average Positive Grade                                                                                                                                                    0 non-null      float64\n",
      " 25  Average Negative Grade                                                                                                                                                    0 non-null      float64\n",
      " 26  Max Cadence                                                                                                                                                               1125 non-null   float64\n",
      " 27  Average Cadence                                                                                                                                                           1134 non-null   float64\n",
      " 28  Max Heart Rate                                                                                                                                                            719 non-null    float64\n",
      " 29  Average Heart Rate                                                                                                                                                        1140 non-null   float64\n",
      " 30  Max Watts                                                                                                                                                                 0 non-null      float64\n",
      " 31  Average Watts                                                                                                                                                             1155 non-null   float64\n",
      " 32  Calories                                                                                                                                                                  1274 non-null   float64\n",
      " 33  Max Temperature                                                                                                                                                           0 non-null      float64\n",
      " 34  Average Temperature                                                                                                                                                       777 non-null    float64\n",
      " 35  Relative Effort.1                                                                                                                                                         1142 non-null   float64\n",
      " 36  Total Work                                                                                                                                                                651 non-null    float64\n",
      " 37  Number of Runs                                                                                                                                                            0 non-null      float64\n",
      " 38  Uphill Time                                                                                                                                                               0 non-null      float64\n",
      " 39  Downhill Time                                                                                                                                                             0 non-null      float64\n",
      " 40  Other Time                                                                                                                                                                0 non-null      float64\n",
      " 41  Perceived Exertion                                                                                                                                                        0 non-null      float64\n",
      " 42  <span class=\"translation_missing\" title=\"translation missing: en-US.lib.export.portability_exporter.activities.horton_values.type\">Type</span>                            0 non-null      float64\n",
      " 43  <span class=\"translation_missing\" title=\"translation missing: en-US.lib.export.portability_exporter.activities.horton_values.start_time\">Start Time</span>                0 non-null      float64\n",
      " 44  Weighted Average Power                                                                                                                                                    761 non-null    float64\n",
      " 45  Power Count                                                                                                                                                               761 non-null    float64\n",
      " 46  Prefer Perceived Exertion                                                                                                                                                 458 non-null    float64\n",
      " 47  Perceived Relative Effort                                                                                                                                                 0 non-null      float64\n",
      " 48  Commute.1                                                                                                                                                                 1216 non-null   float64\n",
      " 49  Total Weight Lifted                                                                                                                                                       0 non-null      float64\n",
      " 50  From Upload                                                                                                                                                               555 non-null    float64\n",
      " 51  Grade Adjusted Distance                                                                                                                                                   27 non-null     float64\n",
      " 52  Weather Observation Time                                                                                                                                                  215 non-null    float64\n",
      " 53  Weather Condition                                                                                                                                                         215 non-null    float64\n",
      " 54  Weather Temperature                                                                                                                                                       215 non-null    float64\n",
      " 55  Apparent Temperature                                                                                                                                                      215 non-null    float64\n",
      " 56  Dewpoint                                                                                                                                                                  215 non-null    float64\n",
      " 57  Humidity                                                                                                                                                                  215 non-null    float64\n",
      " 58  Weather Pressure                                                                                                                                                          215 non-null    float64\n",
      " 59  Wind Speed                                                                                                                                                                215 non-null    float64\n",
      " 60  Wind Gust                                                                                                                                                                 215 non-null    float64\n",
      " 61  Wind Bearing                                                                                                                                                              215 non-null    float64\n",
      " 62  Precipitation Intensity                                                                                                                                                   215 non-null    float64\n",
      " 63  Sunrise Time                                                                                                                                                              215 non-null    float64\n",
      " 64  Sunset Time                                                                                                                                                               215 non-null    float64\n",
      " 65  Moon Phase                                                                                                                                                                215 non-null    float64\n",
      " 66  Bike                                                                                                                                                                      1096 non-null   float64\n",
      " 67  Gear                                                                                                                                                                      136 non-null    float64\n",
      " 68  Precipitation Probability                                                                                                                                                 210 non-null    float64\n",
      " 69  Precipitation Type                                                                                                                                                        93 non-null     float64\n",
      " 70  Cloud Cover                                                                                                                                                               210 non-null    float64\n",
      " 71  Weather Visibility                                                                                                                                                        210 non-null    float64\n",
      " 72  UV Index                                                                                                                                                                  210 non-null    float64\n",
      " 73  Weather Ozone                                                                                                                                                             210 non-null    float64\n",
      " 74  <span class=\"translation_missing\" title=\"translation missing: en-US.lib.export.portability_exporter.activities.horton_values.jump_count\">Jump Count</span>                0 non-null      float64\n",
      " 75  <span class=\"translation_missing\" title=\"translation missing: en-US.lib.export.portability_exporter.activities.horton_values.total_grit\">Total Grit</span>                0 non-null      float64\n",
      " 76  <span class=\"translation_missing\" title=\"translation missing: en-US.lib.export.portability_exporter.activities.horton_values.avg_flow\">Avg Flow</span>                    0 non-null      float64\n",
      " 77  <span class=\"translation_missing\" title=\"translation missing: en-US.lib.export.portability_exporter.activities.horton_values.flagged\">Flagged</span>                      16 non-null     float64\n",
      " 78  <span class=\"translation_missing\" title=\"translation missing: en-US.lib.export.portability_exporter.activities.horton_values.avg_elapsed_speed\">Avg Elapsed Speed</span>  59 non-null     float64\n",
      " 79  <span class=\"translation_missing\" title=\"translation missing: en-US.lib.export.portability_exporter.activities.horton_values.dirt_distance\">Dirt Distance</span>          0 non-null      float64\n",
      "dtypes: bool(1), float64(71), int64(2), object(6)\n",
      "memory usage: 808.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9be71e3b-fba0-48f7-8c48-f4041fe7e35b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ride               911\n",
       "Virtual Ride       245\n",
       "Run                140\n",
       "Yoga                 5\n",
       "Weight Training      4\n",
       "Walk                 2\n",
       "Hike                 1\n",
       "Name: Activity Type, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Activity Type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4cf354c-cd7c-4165-aa7b-9b8adb973803",
   "metadata": {},
   "source": [
    "Selecting only \"Ride\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "375c4af4-258a-4a05-b311-195dde7da4a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Activity Type'] == 'Ride']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788511a5-a8ed-424a-acc7-ce9eb71eab4a",
   "metadata": {},
   "source": [
    "Selecting the features that are relevant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a16c14da-4286-44c2-9f11-bd62cd6aae93",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Activity Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Athlete Weight</th>\n",
       "      <th>Bike Weight</th>\n",
       "      <th>Moving Time</th>\n",
       "      <th>Max Speed</th>\n",
       "      <th>Average Speed</th>\n",
       "      <th>Elevation Gain</th>\n",
       "      <th>Elevation Loss</th>\n",
       "      <th>Elevation Low</th>\n",
       "      <th>Elevation High</th>\n",
       "      <th>Average Cadence</th>\n",
       "      <th>Average Heart Rate</th>\n",
       "      <th>Average Watts</th>\n",
       "      <th>Weighted Average Power</th>\n",
       "      <th>Wind Speed</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>May 9, 2015, 7:07:25 PM</td>\n",
       "      <td>13.39</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2927.0</td>\n",
       "      <td>12.7</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May 23, 2015, 7:29:40 PM</td>\n",
       "      <td>13.74</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3843.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>49.228699</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jan 20, 2016, 8:45:17 PM</td>\n",
       "      <td>14.97</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4648.0</td>\n",
       "      <td>9.3</td>\n",
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       "      <th>3</th>\n",
       "      <td>Jan 21, 2016, 8:21:13 PM</td>\n",
       "      <td>13.33</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3341.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112.025002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>778.500000</td>\n",
       "      <td>853.700012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.518700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jan 22, 2016, 8:33:20 PM</td>\n",
       "      <td>13.52</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2665.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>111.398003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>778.400024</td>\n",
       "      <td>850.099976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.242699</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1303</th>\n",
       "      <td>Sep 23, 2021, 9:17:07 PM</td>\n",
       "      <td>25.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>11.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>115.495552</td>\n",
       "      <td>160.108063</td>\n",
       "      <td>163.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>Sep 25, 2021, 12:04:06 PM</td>\n",
       "      <td>80.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>10032.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.995524</td>\n",
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       "      <td>135.690186</td>\n",
       "      <td>158.168060</td>\n",
       "      <td>169.0</td>\n",
       "      <td>3.75</td>\n",
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       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>Sep 26, 2021, 2:08:31 PM</td>\n",
       "      <td>43.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5419.0</td>\n",
       "      <td>15.2</td>\n",
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       "      <td>224.0</td>\n",
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       "      <td>841.599976</td>\n",
       "      <td>83.144585</td>\n",
       "      <td>128.509323</td>\n",
       "      <td>161.173279</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3.92</td>\n",
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       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>Sep 28, 2021, 10:09:07 AM</td>\n",
       "      <td>43.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5752.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>7.535014</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>210.0</td>\n",
       "      <td>803.000000</td>\n",
       "      <td>833.799988</td>\n",
       "      <td>80.088715</td>\n",
       "      <td>130.684631</td>\n",
       "      <td>168.662201</td>\n",
       "      <td>189.0</td>\n",
       "      <td>3.50</td>\n",
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       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>Sep 29, 2021, 10:17:32 AM</td>\n",
       "      <td>43.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5597.0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>7.743398</td>\n",
       "      <td>219.000000</td>\n",
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       "      <td>79.728714</td>\n",
       "      <td>125.696442</td>\n",
       "      <td>158.627121</td>\n",
       "      <td>162.0</td>\n",
       "      <td>1.36</td>\n",
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      ],
      "text/plain": [
       "                  Activity Date  Distance  Athlete Weight  Bike Weight  \\\n",
       "0       May 9, 2015, 7:07:25 PM     13.39            80.0          NaN   \n",
       "1      May 23, 2015, 7:29:40 PM     13.74            80.0          NaN   \n",
       "2      Jan 20, 2016, 8:45:17 PM     14.97            80.0          NaN   \n",
       "3      Jan 21, 2016, 8:21:13 PM     13.33            80.0          NaN   \n",
       "4      Jan 22, 2016, 8:33:20 PM     13.52            80.0          NaN   \n",
       "...                         ...       ...             ...          ...   \n",
       "1303   Sep 23, 2021, 9:17:07 PM     25.89             NaN          8.5   \n",
       "1304  Sep 25, 2021, 12:04:06 PM     80.21             NaN          8.5   \n",
       "1305   Sep 26, 2021, 2:08:31 PM     43.34             NaN          8.5   \n",
       "1306  Sep 28, 2021, 10:09:07 AM     43.34             NaN          8.5   \n",
       "1307  Sep 29, 2021, 10:17:32 AM     43.34             NaN          8.5   \n",
       "\n",
       "      Moving Time  Max Speed  Average Speed  Elevation Gain  Elevation Loss  \\\n",
       "0          2927.0       12.7            NaN      109.188004             NaN   \n",
       "1          3843.0       12.4            NaN      106.164001             NaN   \n",
       "2          4648.0        9.3            NaN      127.360001             NaN   \n",
       "3          3341.0       14.6            NaN      112.025002             NaN   \n",
       "4          2665.0       13.5            NaN      111.398003             NaN   \n",
       "...           ...        ...            ...             ...             ...   \n",
       "1303       3600.0       11.0       7.193500        0.000000             0.0   \n",
       "1304      10032.0       18.0       7.995524      800.000000           816.0   \n",
       "1305       5419.0       15.2       7.998007      229.000000           224.0   \n",
       "1306       5752.0       13.2       7.535014      208.000000           210.0   \n",
       "1307       5597.0       13.4       7.743398      219.000000           218.0   \n",
       "\n",
       "      Elevation Low  Elevation High  Average Cadence  Average Heart Rate  \\\n",
       "0        778.400024      853.599976              NaN                 NaN   \n",
       "1        778.400024      852.900024              NaN                 NaN   \n",
       "2        778.400024      853.900024              NaN                 NaN   \n",
       "3        778.500000      853.700012              NaN                 NaN   \n",
       "4        778.400024      850.099976              NaN                 NaN   \n",
       "...             ...             ...              ...                 ...   \n",
       "1303     775.000000      779.400024        81.734726          115.495552   \n",
       "1304     742.599976      898.400024        80.780617          135.690186   \n",
       "1305     803.200012      841.599976        83.144585          128.509323   \n",
       "1306     803.000000      833.799988        80.088715          130.684631   \n",
       "1307     792.200012      828.799988        79.728714          125.696442   \n",
       "\n",
       "      Average Watts  Weighted Average Power  Wind Speed  \n",
       "0               NaN                     NaN         NaN  \n",
       "1         49.228699                     NaN         NaN  \n",
       "2         48.465000                     NaN         NaN  \n",
       "3         62.518700                     NaN         NaN  \n",
       "4         83.242699                     NaN         NaN  \n",
       "...             ...                     ...         ...  \n",
       "1303     160.108063                   163.0         NaN  \n",
       "1304     158.168060                   169.0        3.75  \n",
       "1305     161.173279                   165.0        3.92  \n",
       "1306     168.662201                   189.0        3.50  \n",
       "1307     158.627121                   162.0        1.36  \n",
       "\n",
       "[911 rows x 16 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['Activity Date', 'Distance', 'Athlete Weight', 'Bike Weight', 'Moving Time', 'Max Speed', 'Average Speed', 'Elevation Gain', 'Elevation Loss', 'Elevation Low', 'Elevation High', 'Average Cadence', 'Average Heart Rate', 'Average Watts', 'Weighted Average Power', 'Wind Speed']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a1180350-1024-41b3-af53-d234731a9eb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 911 entries, 0 to 1307\n",
      "Data columns (total 16 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Activity Date           911 non-null    object \n",
      " 1   Distance                911 non-null    float64\n",
      " 2   Athlete Weight          688 non-null    float64\n",
      " 3   Bike Weight             850 non-null    float64\n",
      " 4   Moving Time             911 non-null    float64\n",
      " 5   Max Speed               911 non-null    float64\n",
      " 6   Average Speed           308 non-null    float64\n",
      " 7   Elevation Gain          868 non-null    float64\n",
      " 8   Elevation Loss          563 non-null    float64\n",
      " 9   Elevation Low           878 non-null    float64\n",
      " 10  Elevation High          878 non-null    float64\n",
      " 11  Average Cadence         790 non-null    float64\n",
      " 12  Average Heart Rate      780 non-null    float64\n",
      " 13  Average Watts           910 non-null    float64\n",
      " 14  Weighted Average Power  516 non-null    float64\n",
      " 15  Wind Speed              193 non-null    float64\n",
      "dtypes: float64(15), object(1)\n",
      "memory usage: 121.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7039a787-3434-4f12-b484-7b598644335e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L92/7LTZcusgaxwZan0UTd69H55V7OcKpM5MN19jwYNEYMjuvvxDJ6KpJJ0rzbf/z0kI89y4iaaaLyUvpdPOEMO/JFg922WB89VQFn9q5H7fvPtRUYHPb6MGG6sEkXYKiNHbw0zd7Vujvl6phDNgT0/c/v+BhzbbHjLFpoFqe/4Xh5Rh69znaqk8/wcKxqDF/E8HnRsmzD8bg5xc8vHlmsj5J+6+xsNWBybMP4p90bM23vRzwzrcXWpYtQ9wFLbGGhoZ4bGxm1DnZTvI12x4zGmddAU7w9dSJG9xM7LQgmC4Uojaj+kPGfMGmRyLnkXcbBOC5bVdYH5P25/SfM3FDUcFzJ8rrBOUdTL+/CvmYZIt1z01yToddY6PjJWzddai++lrQ52HLldXQ3+JNjzi9h/+zhP2uz4ecF2EQ0T5mHtLdJ2GZGMQNnYRtbEUNX+hCFmA0CCHZLoJ2CYD5l9RAY5bBRLnSFMP2L/F7QV/e5TPML4TLyUZBedFrtj0GoOpN9ju8R45gPHfU7+jyOv7fULf6Cq6kTJvsUVN7w87psLRHv2EHqqvCkQcOYPBz37Z/4MBrqc8S9tu38tqTsExEkoROwja2ovZGNWlH982e1dRAIbhiWLt0IR7aV0ql2Mllya3SJ02eUxB1EYZJtKaNynTRbYjGwTWOGqMgNZSg/MK8ObNCuwlNM3C3JeyhfkeXYif1G7puSPrTKwHU001tr+3H5dq0pT2OPHhAu1lemebImUTquh5Zt8QazgkqVqa5whbjHpEkmR9hnnnUjABXTz8Ydy5NlLUqdkFvy6SzkWSScL3Q1YQWjJm2OkRzqjKNgpfH3etXWuOlLkSRFJ5weB+v5lZHSd8EzmrluGqvhxma4DlwyhfD9nNefwGj4yVjPF+HSq8MI0fUFN8PuzZHx0t48/Sk8TWjfq9hlCbKGB4sYuyFk6E6SK0Q3xPjHpEkmR9hnnlUOV5XlTydIbeVlpu8HwBN99kmCd24Xcv9/ROav2jK1fNPQrkyhVt27neaSPI5wpQm4TlqXNjWRGWauX4uAHaP1oRr/0/A/vvpvGMdXp6wdunC2FIKYei6UJnGokIu7VwBAtXw1uh4qb4hvfnhp6wb8mmf17KhGpE4m56KtDeHXF4vqjE05TSreHmU1yIgVGHPhCk00ImL1MQCQ5ekPFGk5idA9HOjHRvNwcbXLh6owssR3jZ3VqKVTxT6Cx7eeGvSOJGE5eq3Cv+5cNHmb4WOwRYS02HbUBXjHpGkBtqmCx0nVSzseVGMgJcjbTm/wlZubUOXdRHmfeomS5fnRSXuZyr2F4xhIpfsGB1RzoE0VjD9BQ+vv1WxVloKdvq8HOZ4eeskFuUcc3ESG15bjHu6JNVJCYtbA+mlMKYVxsgT4T/Mn6t9LZeTV+fNjo6XjPHVoIHsJo+dANy1fqVxool6gZoI0xh3zbvWob7fbvpes4bfy07rOovqGIhxbzO2cnmdITcZR5ORiDK5pHnx3r1+pXbVcu2qYl261HY26SasZZ/9R21l5oI+ryHjpx2xdlfWXHQOnv9puS4c5v/MaU3KthUiEB53D4qn6Sj64vhJJoqZivrti4brOg5peu6yoZoyuu41CtMGZFgqmf+1g3m4pYkyRh440FCdGdSJdjW+Nor9BacNX5sR1m3UefkcqtL/jQR9jm4x7AUvhx8efa3++zIaL3JTIU7UlZ6tQ9TpyWmrESEA6395EXY8cSy0onTjzv1Y0OcZ9w8EM/7ss4f2lXDtqqLznoQJkR/oYsLK5aMYV11vSN1r+/NwlU60Ivh3juxqdjr8WiVhcr9heenBCcuUnuc/fttoeoJaSdFlOyjD7ve4grLAfkPgktNs6xAVxvyC19T+zRY6e/VUBV6OnLx9QY9Sj0xCmLBcVMS4R8DFA4sr2alb3kfpDelKrM0zAsZeOOnkfapjJh2SYEGWSzrnjifcNdc7hU5i1+/d+3HJaXZNGQ1S8PIggrEi1ERlmtFf8DBvzqz6Z4lzqszOExjp54xngeMTZRRj/m43rh6wCsvFQeQHHHHVJHcpNQ8WIxa8PG5YPdBQ1j03oAmedgPqKAWRlSnGfRG6xgwPFrHh0kXa9zh1ZrLheTrVvuDEZgstFLw8FvSlW7pvwyTyxwAW10rJXZpehBkA0/cS9lnnernY4ZXXypW6DnpU00yoGqh//eJH6nr6UWnn79gKlNMTxaiq7y1tww6IcXcmrTZfypDrmiK/FtC08BvQtDVWol68tmrWIKPjJTy0r6R9j+DncpFyzVtq8++4ZjmueN+5ET9NfMJWPqWJcFlghU2q2PS9bLlymfX8evVUJXJ3KEXcc2xBn4e7fJLGqlmGzcBf/M55DZ/t7vUrMf7Zy510a9pBn5eDpedKE8ohGR4s4s/Xr3R+HgPY+YNjndGWIaKvAPgogFeY+b21Y1sB/B4AFWT6Y2b+Vu2+zQA+geou2X9l5kdTH3UHiNvmK5gtY9pwM1V63nr/Adyycz/mFzxtTLTPy6EyzR1ZBpv0PcKkYcPCEsEQ0OoLF+DxZ5v7rOdzhLEXTuKhfe3r5ZkmpiYXLhK3tjZycWsR/Kul/oIXGt83bSArbKvN73zqg9rjW69aZmyf2E4YhO0fWwkgPDMp+D0oKQTX8ExlilOXHgAcUiGJ6AMAfgbg7wLG/WfM/GeBx74HwA4AlwA4D8D/AfALzGxdo2YhFTJJZarCFLN3TfNTVX9hmuVJ6C94ePP0pNPFpdtEdE27jJJnXfDymGZuaDas6FTlYZoQqlk4JrGyVlaq6qqIgepvaTKyrumeca8Z12K1uAVo/ue71itELV6MmoIct/AtUSokM3+PiBY7vtfVAL7GzKcBPEdEz6Bq6L/vOthuJW6bL8Ccwqi8Ntd4uknxcc/hE7FipLM0jaS3XrXMSTBL99mjbPqqEIDLc2z3Z92wA1UDZVOhVCmQOsegP0EKo83I+legpYlyqO66jrjXjF950mYgk/7yN9SyU25xaK8XVfdJ93iTwBrQGmnrJNkyf0hEvwVgDMCtzPwqgCKAvb7HvFg71gQR3QzgZgAYGBhIMIz2EPbj2mQFTCeoCrtEOUl1E0GczVau/S+oH2I72f1cu6o5JdJ1GUqoNpsGkm8U94Ln7oK/fZ/KT3cVNzPhamTjEtUg2p6fdp1DPkcYevc5ANwyttR4onwfwcebVkNenlLNb1fENe5/BeDzqNqIzwO4E8DvQp+EoT3/mPkeAPcA1bBMzHG0FdOPa9ORDvNMoxomJaPqv2BM3ptKbTNdGKaVgEsaXjCnN0ouOgP13HvXlL/+gtdUvKOqY8MqA5UOzPyChzdOT2pVHLNIkk/RX/Da0p0rjQnCFrokai54A6p7UQvmzTGeW1PTZ+PcSVblUVDfg6nTU9rEMu7M/LK6TURfAvDN2p8vAljke+j5AI7HHl1GsFUTumpou1Dw8nUZVf9EYipAIUJou6/SRLneR1N5ViPrloR6hcG87vsMlXkEoG92XisxcO/eo7hx9YBT2TYRGipt/RK4D+170fg8f+hhzbbHIknf9ioq/JaUpBpLUTAZYNN5U65M48ebLrPuSahzOO4KI87nTzrZRSFWKiQR+XPPfgPAj2q3dwG4nojmENEFAC4G8GSyIXYXujZetmrC/oS5u3mihlS4PYdPaLsvzZs9C4VA7pY/7dA2jmD+OlCNR9rwL1m3P3rEeAExoDXsinv3HsWcWbmG1oA3BnL+1We5d+9RTJw6Uz829sJJjDxwwKiRHVzudouEQTtR36ct1TQOrnUfaWFKDTWlW6rz0xbLVqvgNdseq4ci71q/Eo9vuszJsLfz88fBJVtmB4APAngHgJcBbKn9vRLVa/d5AL/PzC/VHv8ZVEM0kwA2MvM/hA0iC9kygHnH3FY4ogsnRCG4i27zRGwCZG+enozktRb7C/j3n53WZqgAjYp4tjG5VuzF0aEPy5YoeDmcM29OPSTTbV570myPMPJEePaOj6TyWq7dl9JSxIwyLlsWiy3Ovf6XF8VWY00jey4NEjXIZuYNzHwuM3vMfD4zf5mZf5OZlzPz+5j5KmXYa4//IjNfxMxLXAx7ljCFX2wt0ibKFdxxzXJrIY6NYONkkyeSJ7IuP6OGh0oTZUxPM/KaksygBoZpTITqctq1ofLGnfvrqyGXCSHMMJYr043Nw9vEjasHQn/vgpfHXetX4saQFVIS0tpo1nmpJmcm7UrqMMKK4IYHi9j+sRUN5+CCPg/br1uhXQXbivP8JOnI1i5EWyYCph/OdgmpS3w65oU2Ua5g8aZH6iloUWOPwFnjGzUsEdQbMcUVdWMinE01A4BP7dwPl5bTSuUyy+x44hg2XNrsFSpPvb9W2LZx5/7Y1aQuxJEA0BEnxbWdhMWxTfe7pECaiNrMvhOI/EAE4vxwjOrF4aQ5Y7nS/fHwKLFHAPVJwVa2bmLCpzdiikXqvCd/OToAvD1CWXmnqxMJVc32uEwx1yVgg9/J3etX4vTkdN3zTeOT5jXnTZrZHq7eaCsyTFqJ6Zp0uVZdNJE6jXjuEQiTszVxfKKMuzSNLvyEed/A2SWjycjqYosqrKKTRXAJVbiGk1zTRLMAA3j+p40GLU+EuV7OujnsR0nABuOva7Y9ltp34e9u1crMFZOX6rKq62aSpEAmzeFvB2LcIxAmZ2viPF+jC3+Oq9JWVyEXl2INkxc1PFjUVpZOTXNdn8afPqhWAWEEP+dtowfrTSDyRFh94QI8/9Oy8QR3UUhMi3mGlMs4+H8HArDh0kWRGzEkLThT0gB9s3P4yStvNtwX3PhrZYqdyQhuvao1+dntIo0iq27+/GLcI6J+TF2M2dQs2e8J+LNPprlRTU73ukFsS0bTxq4y0Cq0M9fLRdK8GB0v1TVs/AZuirlB0CvYiGJ0vNTW7j5RDXs1dZRCvwt/0VUU1G/l96p1Ql86gm0G25lTHiQLXmpcut1AJ0GMuwXTBaU72U0eN6Px4jDtzvtf19bP0rZkdKn2LFemInnSas9geLDo1DTD/3lcsg46yeQ0Y/0vn489h0+kngOvJu1gWMp1xTdRrtQnVaDzRqjT7y9ERzZUDaj8WH/618gDBxp0yP0bjaYNzaLPezMZkKBAkW1z9Pbdh4yFEnE3TcNQ43M1TMcnys7pjJ2kMsXY8cQxjKxbkkrWSrDgTE1wuslUPdYEM7quKEbIFmLcDWzddahpc7Iyzdi661DDMVXhpnpl+gl6byaCoRabkX71VAUjDx7QXvQqayWM/oIXaRJQ43PdXJ1f8Jxj+iZM79Vf8FJt6DDFjFt27sfsWckvhWnmpqwiU4xdPdY2kbvmXAvtRVel3o2IcTdgyiTxH/cXdwDVEIYySf4OS7fef8CaJRMMtSgjbTJwlSluKPgJPjfMCKsem+pxtsf7x7fh0kXGx9Vf2/f6ptebN9s+sRT7C7jz4yu0qWZbr1qG/Vsux42rB1LLEWfAWIkbBd1+SFi6XdiqoZuKYoRsyA4oxLgnQLfkZpzNftn88FPYuHO/NZwR7JWqGB4shhY+mU4s2/vlc1Tf5JxiRsHLY8Oli7Se/II+D9euqoYWLtj0CPYcPoE1F51jnQwYsG6iXruqiFMhG5+Lf75Q/27VexX7C01jef9F57S0CCgKphS6sHzo4cGiNde9m4piBPd2m92AbKgaWGBpgqA2ukxelYrPuxTj+FutAY2btC6NGHQt60x6LkRokrtV+dh3XLO8afMYaMzeKU2UcfLNM0251Sok5RKRf2hfKfRz/cuzJ+uvpSagtUsXNlR8liaq6ZedLHfKE2Ga2Zo94tIHwDXTSug8WZAdUIhxN/Cec39O27cTqIZZABiNFFG0KstyZQqbH34Kb1Wm6xd5aaLsvKwKnlhRJQqOT5S12RC6gptgdk+UNoHq+QBbx6PrJatLRey0Krua5MKwZZrYFDVvCGj4CJ0nC7IDCgnL1PBvkvzif/8Ho2EHqt7kyAMHjGJccWRkyj7DrnCNAs/1cg0bPEA0iQLTiRnmpfg3k6NQrkzj2lXhewOdZkGfhwUGqeS0ml3YPL49h090/abdTCMLsgOKGeu5+3PY5xc8vHlmst7swqQP7qfT+id+lPohcDYOf+0qjTxBxHJrm5dia6Dswp7DJ3Dnx1e0VZrAtSWfv0+oSVI2jWYXgPk7JqDpNwUgnnyHyVJB14z03IM73hPlSlMXoyxTrkzhvr1HtU04/B58nqgeZtF5hjYvRZcqGgUVCmqnBx9HmTNMUjYpuu9YF4Pv1k27mUiwxqUbDTvg0KyjHbS7WUecUEIv4O+pGjQgpiYFpirdxZseSTSWYn8Ba5cuxH17j7Ytdh6lmbZr04Y0CH7HpnMz2LhFSEYnJR3SwtasY0aGZdLY2fZyBBBa5vHbeo/GZaJcqefpmzzDYLf2Vpz8BLTdsAP6FFFTporu+2gVwQ1Xk/PRjZt2WcXW1D5rBt7EjAzLxLlIvBwalubbP7YC269bYdVgT0J/n5eqYXch2PTaVqxh2mh0gVGNuadt2KOGd/JEuGv9SmOufKfS27K0aZdVspSvHpcZadzjaImoPVbVQBeopbG1yPVMqqYYZ84JNr22nfxbrlwGT9clwoFifyF1w0mopiY+v+0K3L1+ZX0ithn8aWYMDxYTNW1oBa2O8wvZylePS2hYhoi+AuCjAF5h5vfWjm0HcCWAMwCeBfA7zDxBRIsBPA1ATX97mfmTrRh4EoYHi1blRRPKex174WRTC7VuQ1XKhjU0VgQLZsJO/mDWgEnKVhfbd9Wu76tV755yyF7yq2/6wxwXWPYG/BIAcZs2tApRYWwtWcpXj4uL5/63AD4cOPYdAO9l5vcB+FcAm333PcvMK2v/dZ1hV8TN0ChXprDjiWOxDbuXo8ger1+v5sbVAw0enSk8ooy12tHfcuUyq1iY3zgCbi3I/FkDJi2YGwLjVR6oSRzN35D7VGUaDHJqIh01h98/mYmnPPOYCaGvUM+dmb9X88j9x77t+3MvgOtSHlfLSdIZPslzZ89yb9WmUF54sGUbUI2N37Jzf1P82q/DDoR3kQoax6jebNT8X93jdasLJY9gklQIG5dL8241HjHmM4cs5avHxSkVsmbcv6nCMoH7dgPYycz31h53CFVv/nUAtzHzP4e9frtTIYFk6ZBRUurSwpYGZ0pL1D1HV5SjQifF/mbdk3ae/BdsesSosWLqQZsj4D9fOtDQjDtIL6S8CYKOlqVCEtFnAEwCuK926CUAA8z8UyJaBWCUiJYx8+ua594M4GYAGBgIX3anzci6JaFxd126I0HvuXs5wtvmzmpZWzl/eCForEwiZzmihm4+QKPHEsx3V4Jnt+8+hIlTlbYbQpNWj/rsc2Y1twec5qoY2dC7z7GuEsSYCzON2NkyRHQTqhutN3DN/Wfm08z809rtfahutv6C7vnMfA8zDzHz0MKFC+MOo2XkibD+kkXYft2KesjCqnxIwBXvO7clnZCAal44oE9R/Nlbk9o4/hSzVhJYxcqL/YWmz1OZZrx6qtJ2rerR8RJ+9tZk03EvT1i7dCE2P3zQqLFfrkzh1vv1DUwEYaYSy7gT0YcBfBrAVcx8ynd8IRHla7cvBHAxgH9LY6BpE5bPOsWM+/YexdgLJzGybgnyRNa87MoU16Vz/ZNBWuz8wbG6xx70XivTjHmzZ2k3icuVKWNjD5e0r3bl/m5/9IhWzmBymnHv3qOhG9imiUwQopKVTkthuKRC7gDwQQDvIKIXAWxBNTtmDoDvUNWgqJTHDwD4HBFNApgC8ElmNssrdhAXw6a63u948phTjD0onevXO3fBy5FRr6UyxfVQjA6TQqVCV4Hn0lAbaE/ur+k9omxttLOqVOhNeqly1SVbZoPm8JcNj30IwENJB9UOXA0b0NzgwvaaftTJYIvt9xe8erghLGZv0x7JEeHtBfvzy5UpbN11qEEN08tTqIRCO3J/o/weNnqpCEVoP7bivawZ9xlZoQrYm1DHpTRRxoWbH8Fi33Lu9t2HjI/vL3gNvTvDNmPVBqdu3FPMxti7n4lypUENE1yVEqDaeILPb1fub5yqYR29VIQitJ9eqlydscY9WLjSX4ivleJHOfkq88RmsCtT05GKoVTmiql5ti32bhzDNKNv9iw8t+0K7N9yeX0D2V/MA6DlMciwXqJ+iv0FrNH0T+21IhSh/XSbFEUSZqQqpCKYInfDl75v7cAUlTC98yjFTP7OP8ODRdxiCPW8Vq5EFuTyeyXB7yRuDDJObrmtUElx9/qVAKq9Xf2fk1Btvp21pbPQXXSjFEVcMu+5p7WzPTpeStWwp02w84/Jk5hf8CKHN2xeSRz1vDBFSRMj65ZYw0pqgtONSSlNCkISekmKItOee5o722HpftYc95gQgFkOG5o5AsZeONngCa9durBJvKzg5VGZau7FaiPMK4kTg0y0KWUYvJej+gTXS3FRofvolaK3THvuaWoyhxmGghf9qwoTCePa/9SGZrG/gHmzmzdLpxlNbfN2PnkM/sz7BX0erl1VDA319Bc8Z69kdLyEnCF+b/P24xpfU647gIaigW6Oi/ZKjrSQfTLtuZvis3FS6sJS8VxkZ/0onRbALNYFnN3QHP/s5QDMErW6SlK/IXyrMo1HnnrJOibV2NnFK1GrIt24w7z9sMbapli8zfirPH+lKKnTxylNlLFm22Md044JNg1Xm+pA9nKkheyTac/dlhXi6jUpTyutnqrF/gKe9zXOHR4shjZm9hu1uN5nuTJlzcxZ0OdFih3qVkVA9TsPex2TnKqSETDF4sM+e2miXNfKCVYC+/VxOlWpqmsaXplmbN1lTocVhFaRaeNuqxp1ucj9G39pYPJow4yW/36dYUya/91f8DD+2csjeY8mL1p1L7Jh2pTac/iENYzmkpGgflObPk6n2qWZtG9MxwWhlWTauJsaNCjCLvLbdx/Seqe2JhhB/I00TB6trWAqOCHoDOMNqwdiF1x5+epGZNRYcNK4tr+Rh1rFpLERGvxNZXNVEPRkOuaui70GMV3ko+MlYxjD1ZMP6p+bCMrsKj140/N1u/VD7z6nQTbgzTOToVk2ADBvdvUnjppV1Ip8X1MsnlENo50606wKqSMYxkqrXVpS3XeT9HKSZuKCEJdMG/eg0dRhushtHj1RuGCVMnS2TJOtuw7Vl+QL+jxsudJtM1OHrrjItlGreK1ciZWamFanGr/BtGnZRAmNBcNYcSch/9jmejmUfZvmcdJqt1y5DCMPHmj4fF6esOXKZZZnCUJryLRxB84aPVOHIaWDDjRezDaT6KJE6A8PBA0ggIasCaCqGzPyYHqZE+o1wlYu59WaZOsIC10kzfcN/iZqops3Ox+51aDCy1NTGAuIPgkFx1bWZENFFYyaCa3bhOyQeeOuGB4sYuyFk7hv79G64Wac7dIDhBvCqCjvLhjumOvltPna/nS+NFCfeccTekli5cGaVjamVU1abelMGTenYhp2ANoipziTkGlsQaLG7nulAEbIPj1j3IFq+bkuc8IlfBGHPJE23BFnDyAOo+MlPLSv1PDZTP1QgysJL0fa0EWaVb9GjXbE70NbmU5ngnT9HbqhMEoQ4pDpbJkgpgs2qWE3SeHGed00jYVJY6XYX6hnqNQJ5lMa8it1GURxUwttn3WKOXYGUBoTpMvvQHBLzxSEbqSnjHscw6nK8U0U+wtGKdywVMwgwXhxUlxj6dsfPdK0ialCRH5sGURxDKpNoz3udwikM0GG6fkTgBtWD0iIRcgsPRWWcUmNDPLRFefiC8PLtRuy/qwLUyxVt4mr8+eJgO3XrUjVWLimAUaZBGzvZcIWow9moQCNmUamzXATacmvBjc/5xc8EAETpyqyESr0BD1l3MM2GHUomVhbpoO/F6qKFat/+wse5nq5ulEwpvRx+voirmmASScB9V46TDH6sRdO1lQrGw27LiVU3Q7bG3GtK3BFNj+FXqanjLtugzEMJTZlygwJGi/12upffx57nMyUJLim3iWdBPyNQhS25t/lypRxgn31VKW+QtAZeFO/WQLw+KbLtPcJgtBMaMydiL5CRK8Q0Y98x84hou8Q0U9q/y7w3beZiJ4hoiNEtK5VA9fhmt7mR6kJKiGrjTv3Y/Bz366X57u+5qunKtj88EGsXbpQK5rVqo05XZm/7jEuDQhMgl/BRiGj4yWMPHjAWngUR/dneLBorOaUrBVBiIbLhurfAvhw4NgmAN9l5osBfLf2N4joPQCuB7Cs9py/JKJ0u1BbsIUV+guedgNNZ4KUoR4dL0XWPdnxxDGUK1N1xcpOd3JRmjKqLd9d61cmngRu330oVPogrI+rKQNny5XL2jo5CkKvEhqWYebvEdHiwOGrAXywdvurAP4JwKdrx7/GzKcBPEdEzwC4BMD3UxqvFVvM+7VyBe+/6BznVnrK+ITpvAfxh23CJApajS1nHdCHc1zi0DZpYaBqjK9dVWzqFBVEN3FKlaeQVhHdTCduzP1dzPwSADDzS0T0ztrxIoC9vse9WDvWBBHdDOBmABgYGIg5jCq2+K9ifsHDv0TskXp8ooy71q+MXdkatXw9bUyaMlt3HcLpyelYhUphapJ5Ily7qogvDC+vi51F1f2Rjc6ZS5pFdDOdtPPcdWtx7fqdme9h5iFmHlq4cKHuIU64aLIXvHxVDCzia5/XX2hqDBGVTkrPmt57olwxGv0wWeCwYqYpZjy0r9SguX73+pUSahGcSLN15kwnrnF/mYjOBYDav6/Ujr8IYJHvcecDOB5/eOHYOgb548YTIaGEIMEc98c3XRaraUYnNwKjvvdEuWLskqRwmayCF2NwglSyDdsfPSI9RoUGRJ8/PeIa910AbqrdvgnAN3zHryeiOUR0AYCLATyZbIh2bB2D/Bkk/Q6a2sEJIbgMDDOWQePfCu80StMNU/aLq764zmNynTBUSzyF6n3ql23oZEs8oTvp5ubnWcMlFXIHqhuiS4joRSL6BIBtAH6NiH4C4Ndqf4OZDwG4H8CPAfwjgD9g5vRkGDWYfvQcUd0A3jZ6ED97y94IouDlcefHV1hTCsM6Kt2weiA00yQJ/hCUzbtWDA8Wce2qYj1zRcXDdRkpJoKTp01SIEhwbKYl98ad+5173gq9jckhkRBedIhboJYYlaGhIR4bG4v13Cil60GophUQpfRcV62aduWkCVMjbyUUFswyWLt0YVPGSsHL445rlgNoLL039flUr+3nttGDuHfvUacxF7wcnv78rwMALtj0iHXfQ41NNs5mNpIt4w4R7WPmIe19WTfuQOPJkIsgJUuANhumW42MyTiaPoeJoME2TRrqdXXfw+JNjziP+8bVA/jC8HLj+9jGJgiCGZtx7wlVSH+V5nSEyeq8/oJ1dz5qU+lWY4tHRqnODYZabLrrpgkuSvbQjieOAQhXYrSNRRCEaPSEcQfObjRGWYeMrFtiNCYqnu0a324HtnhkFKMYnCRMk4bNgEeJgaqVlEtaqWycCUI69IRxd8l1D1LwchgeLGJ+QZ85QoSu2/yzyQNEMYqnzkw2rEbibGINDxbR57mfPreNni1Ekdx3QWg9PRFzd4nlBvHyhO3XrcDtuw+FltPrSCMun+bGUdyNZd0Ga5Qm08H2fTZU7N3/fNk4E4T49PSG6uh4ySgTG0axv4DjtbBL3OfH3fwzNQdJMmH4jeX8goc3z0yGCnwByT+Hyh4yNSpR5Inw7B0fifU+giA0YzPumdZzVwYyLspjNGmY+/VXTM+Pi20jN65xD2qyjI6XnJqDJ/kc6j1dVk+taFIuCIKeTMfcbRkiXo6QC6m2UaEAk4Z5Kzf/2lFmPTxYdMoeSmMT02XcYTLAgiCkR6Y9d6tBIWB62nx3UI7XFPs19fi0bf65xJJdW98lJUyyOK1NTBdp5A2XLrLeLwhCemTaczcZwjyRNdYcVRrAtYkF4C4R0K4ya937KP85TYkEWw57nqhpM1UQhNaS6Q3V20YP4r69Rxs28Qpe3hiqURWXriX6cYxemESAn3Zli/Ta+wiCUKUns2V0oRICcMPqAew5fMJ5k9SW4RFHM8YmEfDctiucX6cXEGMvCK2lJ+UHdJupDGDP4RNYu1Tf/KMy1Zz9Ypva4lSlimRplagKloIgpEtmjbst22TP4RPa+948E79VnisiWVpFOuoIQmfJbLaMKTtDeYlRCCu+iZKemGaD5yyHNaSjjiB0lswa95F1SyKX2xe8HABq2jy9dlXRGKcHoodU0mjwnPVGwe1K9RQEQU9mjbsycFGkB85MMTZccj72HD6h9Yaj5rOb0DXNML2niVZUsCYlykpCN/nOxPCUIHSKzBp3oGrgoxj3qWnGjieO4c6Pr9AapTRCKjqP29+1yNUD77awRtSVRJrhKUEQopNp4w6g3urOlSnmUKOUxAC5NM1w8cC7LawRZyWRRnhKEIR4xM6WIaIlRLTf99/rRLSRiLYSUcl3vKUygKsvXBD5Oa3M2nD1rMMe55J1085OUd22khAEwU5sz52ZjwBYCQBElAdQAvB1AL8D4C5m/rM0BmhjdLyEHx59LdZzXTNqomasuGisqMeFMWdWru4tL+jzsOXKZcb9gVZvuHbbSkIQBDtp5bl/CMCzzPxCSq/nRJS+oUFcFArjFOK49AkN21hU7ztRPttE5K1Kowpau/PIJX9fELJFWsb9egA7fH//IRE9RURfISJt3ISIbiaiMSIaO3FCX3QURpKQgEucPo4B1YmM3bh6wEl0LMr7tjtMEkU8TRCEzpN4Q5WIZgO4CsDm2qG/AvB5VOuCPg/gTgC/G3weM98D4B6gqi0T571dQyAmRsdLDWGOYPjF1YDqnhu3s5Hu9XXHOxEm6fUN0iwXjQlCkDQ8918H8ENmfhkAmPllZp5i5mkAXwJwSQrvocUlBGLj9t2HAJjDL/19+ubZfgPaCg0VF30aCZOki2jhCL1GGsZ9A3whGSI613ffbwD4UQrvocUfKgDO6pQD1Q3IebPthl81xjaFQZhhNaCqjV3asW8Xwy1hknQRLRyh10gUliGiPgC/BuD3fYf/lIhWohqWeT5wX+oMDxYx9sLJJl33tyr2/qd+TGGQ18qVJv13tVRXnp4pdp+0LykQXgDU62GSdiKpnkKvkci4M/MpAD8fOPabiUYUkdHxUpNhB6peFxFg2zftL1TDLv19Xt2L93Nef8FoQMMydYKhm6ixXDHc7UVSPYVeI7OSv4rtjx4xKjoyA15en/Lo5Qhbr1qG0fESfvbWZPP9ebLGr20eXTB0I7Hc7kf2MIReI/PGPWzZPCtHCJp3ArD+kkUYHixi+6NHUJlunh7mzZ4VWqykI0/UEPuWWG42kD0ModfIvLZMWDpkOVD8A5zt2ATY4+02TKqHQYMgsdzsIKEwoZfIvOc+sm5Jk2fugjKucdviuXp60nZPEIROkHnjPjxYtHZRMqVDKuOaJNY6PFjE45suw3PbrsDjmy7Ten0SyxUEoRNkPiwDVL1mU2jGy+dQ8NAU9z51ZrKhQrVVlYmiay4IQicgjqCF3iqGhoZ4bGws8vNUimGYBMHd61di665DDUJcgD5GLgiCkBWIaB8zD+nuy2xYxp9iaINQ9Z7nzWlepMTNWmmnjrogCEIcMhuWcZX7ZVSNcVpZK1lvXC0Iwswgs557FKO8/dEjqWWtSN66IAhZILPGPYpRPj5RTi1rRfLWBUHIApk17lHkfucXvNQqEF1kgAVBEDpNZmPu/hTDsE3VN31pj0ni4nF1aARBENpNZj134GwR0d3rV1q9+MoUpxITv333oVg6NIIgCO0m08ZdEWzaoaM0UU6Uujg6XtLKAgPhOjSCIAjtpieMO3DWi7cZ+CSSuzbPP0ckue6CIHQVmY25m9CpNQZRqYtRQim2bJgp5nquOyBSA4IgdJ6eM+5BLReTuELU1MVwaeEpbN11CKcnp6XASRCEjtMzYRk/frVGU5gmauri2qULQx8zUa5IgZMgCF1Bpj13XW9S4Gx6ZJ4IU8wgoMGDj1O8pJp7xEEKnARBaDeJjDsRPQ/gDQBTACaZeYiIzgGwE8BiAM8D+Dgzv5psmM3oNF5GHjgAUDX1EajGwoGqYVcGvhgzDh5moAteHnO9nLHRtiAIQjtJIyyzlplX+mQnNwH4LjNfDOC7tb9TR6fxUpnmumEPogy7qalGGDYDrapdt1y5TBpzCILQFbQiLHM1gA/Wbn8VwD8B+HTabxJWlaojSXjEtWcqINkygiB0nqTGnQF8m4gYwP9k5nsAvIuZXwIAZn6JiN6peyIR3QzgZgAYGBhIOAw3koRHXDsqSZNlQRC6gaTGfQ0zH68Z8O8Q0WHXJ9YmgnuAaiemhOMIJY3wiBhuQRCyQqKYOzMfr/37CoCvA7gEwMtEdC4A1P59JekgdeSJtMeV4qP/MXEVIAVBELJKbM+diOYByDHzG7XblwP4HIBdAG4CsK327zfSGGiQDZcuwr17jzYdv2H1AL4wvLwVbykIgpAZkoRl3gXg61T1jmcB+Htm/kci+gGA+4noEwCOAvhY8mE2owz4jieOYYoZeSJsuHSRGHZBEAQAxNzycHcoQ0NDPDY21ulhCIIgZAoi2udLQ28g0xWqNnTVqxJzFwRhptCTxl1XvSoCXoIgzCR6UjhMV70qAl6CIMwketK4mypRRcBLEISZQk8ad1Mlqgh4CYIwU+hJ4z6ybokIeAmCMKPpyQ1VVx0YQRCEXqUnjTsgOjCCIMxsejIsIwiCMNMR4y4IgtCDiHEXBEHoQcS4C4Ig9CBi3AVBEHqQrlCFJKITAF6I+fR3APj3FIfTTmTs7Ser4wayO/asjhvo/rG/m5kX6u7oCuOeBCIaM0ledjsy9vaT1XED2R17VscNZHvsEpYRBEHoQcS4C4Ig9CC9YNzv6fQAEiBjbz9ZHTeQ3bFnddxAhsee+Zi7IAiC0EwveO6CIAhCADHugiAIPUimjTsRfZiIjhDRM0S0qdPj8UNEi4hoDxE9TUSHiOiPasfPIaLvENFPav8u8D1nc+2zHCGidZ0bPUBEeSIaJ6Jv1v7Oyrj7iehBIjpc++5/JUNjv6V2rvyIiHYQ0dxuHTsRfYWIXiGiH/mORR4rEa0iooO1+/4HEVEHxr29dr48RURfJ6L+bht3LJg5k/8ByAN4FsCFAGYDOADgPZ0el2985wL4pdrtnwPwrwDeA+BPAWyqHd8E4E9qt99T+wxzAFxQ+2z5Do7/UwD+HsA3a39nZdxfBfBfardnA+jPwtgBFAE8B6BQ+/t+AL/drWMH8AEAvwTgR75jkccK4EkAvwKAAPwDgF/vwLgvBzCrdvtPunHccf7Lsud+CYBnmPnfmPkMgK8BuLrDY6rDzC8x8w9rt98A8DSqF/DVqBog1P4drt2+GsDXmPk0Mz8H4BlUP2PbIaLzAVwB4G98h7Mw7rejevF+GQCY+QwzTyADY68xC0CBiGYB6ANwHF06dmb+HoCTgcORxkpE5wJ4OzN/n6sW8+98z2nbuJn528w8WftzL4Dzu23ccciycS8COOb7+8Xasa6DiBYDGATwBIB3MfNLQHUCAPDO2sO66fPcDeC/AZj2HcvCuC8EcALA/6qFlP6GiOYhA2Nn5hKAPwNwFMBLAF5j5m8jA2P3EXWsxdrt4PFO8ruoeuJAtsbdRJaNuy7G1XV5nUT0NgAPAdjIzK/bHqo51vbPQ0QfBfAKM+9zfYrmWKd+h1moLrn/ipkHAbyJanjARNeMvRafvhrV5f95AOYR0Y22p2iOdd35X8M01q76DET0GQCTAO5ThzQP67pxm8iycX8RwCLf3+ejuoztGojIQ9Ww38fMD9cOv1xb1qH27yu1493yedYAuIqInkc11HUZEd2L7h+3GsuLzPxE7e8HUTX2WRj7rwJ4jplPMHMFwMMA3o9sjF0Rdawv4mwIxH+87RDRTQA+CuCGWqgFyMC4bWTZuP8AwMVEdAERzQZwPYBdHR5Tndru+ZcBPM3Mf+67axeAm2q3bwLwDd/x64loDhFdAOBiVDdt2gozb2bm85l5Marf6WPMfCO6fNwAwMz/D8AxIlpSO/QhAD9GBsaOajhmNRH11c6dD6G6T5OFsSsijbUWunmDiFbXPvNv+Z7TNojowwA+DeAqZj7lu6urxx1Kp3d0k/wH4COoZqE8C+AznR5PYGz/EdWl2lMA9tf++wiAnwfwXQA/qf17ju85n6l9liPogt13AB/E2WyZTIwbwEoAY7XvfRTAggyN/XYAhwH8CMD/RjVLoyvHDmAHqnsDFVQ92U/EGSuAodrnfRbAX6BWNd/mcT+DamxdXad/3W3jjvOfyA8IgiD0IFkOywiCIAgGxLgLgiD0IGLcBUEQehAx7oIgCD2IGHdBEIQeRIy7IAhCDyLGXRAEoQf5/8J/VvuzqujMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y=df['Average Watts'], x=df.index)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c093834e-54b6-4e01-ac08-021f7f93e6a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y=df['Weighted Average Power'], x=df.index)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad41811c-898b-4d6b-850e-5934284c8e75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method IndexOpsMixin.value_counts of 0         NaN\n",
       "1         NaN\n",
       "2         NaN\n",
       "3         NaN\n",
       "4         NaN\n",
       "        ...  \n",
       "1303    163.0\n",
       "1304    169.0\n",
       "1305    165.0\n",
       "1306    189.0\n",
       "1307    162.0\n",
       "Name: Weighted Average Power, Length: 911, dtype: float64>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Weighted Average Power'].value_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b93563-6019-4427-94ea-8b083ac85ce0",
   "metadata": {},
   "source": [
    "Comparing \"Average Watts\" and \"Weighted Average Power\", we notice that the first ~500 activities were with estimated power, so those should be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5a843031-8ffd-4565-849e-62dc307c5634",
   "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>Activity Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Athlete Weight</th>\n",
       "      <th>Bike Weight</th>\n",
       "      <th>Moving Time</th>\n",
       "      <th>Max Speed</th>\n",
       "      <th>Average Speed</th>\n",
       "      <th>Elevation Gain</th>\n",
       "      <th>Elevation Loss</th>\n",
       "      <th>Elevation Low</th>\n",
       "      <th>Elevation High</th>\n",
       "      <th>Average Cadence</th>\n",
       "      <th>Average Heart Rate</th>\n",
       "      <th>Average Watts</th>\n",
       "      <th>Weighted Average Power</th>\n",
       "      <th>Wind Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>Aug 2, 2018, 10:07:41 PM</td>\n",
       "      <td>28.79</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3599.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.418564</td>\n",
       "      <td>128.630737</td>\n",
       "      <td>160.117538</td>\n",
       "      <td>193.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>Dec 20, 2018, 9:21:47 PM</td>\n",
       "      <td>28.50</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3649.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>774.400024</td>\n",
       "      <td>837.000000</td>\n",
       "      <td>81.804916</td>\n",
       "      <td>136.374344</td>\n",
       "      <td>141.124146</td>\n",
       "      <td>158.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>Dec 21, 2018, 11:44:34 AM</td>\n",
       "      <td>60.17</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7936.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>564.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>712.799988</td>\n",
       "      <td>870.000000</td>\n",
       "      <td>84.583115</td>\n",
       "      <td>144.088074</td>\n",
       "      <td>130.858368</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>Dec 27, 2018, 12:11:00 PM</td>\n",
       "      <td>102.46</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>13926.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>708.799988</td>\n",
       "      <td>879.400024</td>\n",
       "      <td>82.143440</td>\n",
       "      <td>133.484985</td>\n",
       "      <td>121.892433</td>\n",
       "      <td>132.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>Dec 28, 2018, 8:00:09 AM</td>\n",
       "      <td>151.54</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>22486.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1809.0</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>465.600006</td>\n",
       "      <td>872.400024</td>\n",
       "      <td>80.559204</td>\n",
       "      <td>124.585190</td>\n",
       "      <td>105.306503</td>\n",
       "      <td>126.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>Sep 23, 2021, 9:17:07 PM</td>\n",
       "      <td>25.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.193500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>775.000000</td>\n",
       "      <td>779.400024</td>\n",
       "      <td>81.734726</td>\n",
       "      <td>115.495552</td>\n",
       "      <td>160.108063</td>\n",
       "      <td>163.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>Sep 25, 2021, 12:04:06 PM</td>\n",
       "      <td>80.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>10032.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.995524</td>\n",
       "      <td>800.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>742.599976</td>\n",
       "      <td>898.400024</td>\n",
       "      <td>80.780617</td>\n",
       "      <td>135.690186</td>\n",
       "      <td>158.168060</td>\n",
       "      <td>169.0</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>Sep 26, 2021, 2:08:31 PM</td>\n",
       "      <td>43.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5419.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>7.998007</td>\n",
       "      <td>229.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>803.200012</td>\n",
       "      <td>841.599976</td>\n",
       "      <td>83.144585</td>\n",
       "      <td>128.509323</td>\n",
       "      <td>161.173279</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>Sep 28, 2021, 10:09:07 AM</td>\n",
       "      <td>43.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5752.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>7.535014</td>\n",
       "      <td>208.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>803.000000</td>\n",
       "      <td>833.799988</td>\n",
       "      <td>80.088715</td>\n",
       "      <td>130.684631</td>\n",
       "      <td>168.662201</td>\n",
       "      <td>189.0</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>Sep 29, 2021, 10:17:32 AM</td>\n",
       "      <td>43.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5597.0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>7.743398</td>\n",
       "      <td>219.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>792.200012</td>\n",
       "      <td>828.799988</td>\n",
       "      <td>79.728714</td>\n",
       "      <td>125.696442</td>\n",
       "      <td>158.627121</td>\n",
       "      <td>162.0</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>516 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Activity Date  Distance  Athlete Weight  Bike Weight  \\\n",
       "401    Aug 2, 2018, 10:07:41 PM     28.79            70.0          7.5   \n",
       "499    Dec 20, 2018, 9:21:47 PM     28.50            70.0          7.5   \n",
       "500   Dec 21, 2018, 11:44:34 AM     60.17            70.0          7.5   \n",
       "502   Dec 27, 2018, 12:11:00 PM    102.46            70.0          7.5   \n",
       "503    Dec 28, 2018, 8:00:09 AM    151.54            70.0          7.5   \n",
       "...                         ...       ...             ...          ...   \n",
       "1303   Sep 23, 2021, 9:17:07 PM     25.89             NaN          8.5   \n",
       "1304  Sep 25, 2021, 12:04:06 PM     80.21             NaN          8.5   \n",
       "1305   Sep 26, 2021, 2:08:31 PM     43.34             NaN          8.5   \n",
       "1306  Sep 28, 2021, 10:09:07 AM     43.34             NaN          8.5   \n",
       "1307  Sep 29, 2021, 10:17:32 AM     43.34             NaN          8.5   \n",
       "\n",
       "      Moving Time  Max Speed  Average Speed  Elevation Gain  Elevation Loss  \\\n",
       "401        3599.0       12.3            NaN             0.0             NaN   \n",
       "499        3649.0       16.1            NaN           189.0           196.0   \n",
       "500        7936.0       16.4            NaN           564.0           577.0   \n",
       "502       13926.0       17.5            NaN          1056.0          1034.0   \n",
       "503       22486.0       20.6            NaN          1809.0          1827.0   \n",
       "...           ...        ...            ...             ...             ...   \n",
       "1303       3600.0       11.0       7.193500             0.0             0.0   \n",
       "1304      10032.0       18.0       7.995524           800.0           816.0   \n",
       "1305       5419.0       15.2       7.998007           229.0           224.0   \n",
       "1306       5752.0       13.2       7.535014           208.0           210.0   \n",
       "1307       5597.0       13.4       7.743398           219.0           218.0   \n",
       "\n",
       "      Elevation Low  Elevation High  Average Cadence  Average Heart Rate  \\\n",
       "401             NaN             NaN        87.418564          128.630737   \n",
       "499      774.400024      837.000000        81.804916          136.374344   \n",
       "500      712.799988      870.000000        84.583115          144.088074   \n",
       "502      708.799988      879.400024        82.143440          133.484985   \n",
       "503      465.600006      872.400024        80.559204          124.585190   \n",
       "...             ...             ...              ...                 ...   \n",
       "1303     775.000000      779.400024        81.734726          115.495552   \n",
       "1304     742.599976      898.400024        80.780617          135.690186   \n",
       "1305     803.200012      841.599976        83.144585          128.509323   \n",
       "1306     803.000000      833.799988        80.088715          130.684631   \n",
       "1307     792.200012      828.799988        79.728714          125.696442   \n",
       "\n",
       "      Average Watts  Weighted Average Power  Wind Speed  \n",
       "401      160.117538                   193.0         NaN  \n",
       "499      141.124146                   158.0         NaN  \n",
       "500      130.858368                   150.0         NaN  \n",
       "502      121.892433                   132.0         NaN  \n",
       "503      105.306503                   126.0         NaN  \n",
       "...             ...                     ...         ...  \n",
       "1303     160.108063                   163.0         NaN  \n",
       "1304     158.168060                   169.0        3.75  \n",
       "1305     161.173279                   165.0        3.92  \n",
       "1306     168.662201                   189.0        3.50  \n",
       "1307     158.627121                   162.0        1.36  \n",
       "\n",
       "[516 rows x 16 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna(subset=['Weighted Average Power'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "63c9d455-7457-4209-ad5d-1ad09a8f1010",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 516 entries, 401 to 1307\n",
      "Data columns (total 16 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Activity Date           516 non-null    object \n",
      " 1   Distance                516 non-null    float64\n",
      " 2   Athlete Weight          293 non-null    float64\n",
      " 3   Bike Weight             515 non-null    float64\n",
      " 4   Moving Time             516 non-null    float64\n",
      " 5   Max Speed               516 non-null    float64\n",
      " 6   Average Speed           308 non-null    float64\n",
      " 7   Elevation Gain          473 non-null    float64\n",
      " 8   Elevation Loss          478 non-null    float64\n",
      " 9   Elevation Low           483 non-null    float64\n",
      " 10  Elevation High          483 non-null    float64\n",
      " 11  Average Cadence         516 non-null    float64\n",
      " 12  Average Heart Rate      496 non-null    float64\n",
      " 13  Average Watts           516 non-null    float64\n",
      " 14  Weighted Average Power  516 non-null    float64\n",
      " 15  Wind Speed              193 non-null    float64\n",
      "dtypes: float64(15), object(1)\n",
      "memory usage: 68.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "466c4d6e-062d-499c-b8d4-85c68a591dda",
   "metadata": {},
   "source": [
    "Now we should check every feature that have null values in order to fix them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "08d55c42-8f9a-44a9-9beb-766ab644828a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67.0    192\n",
       "70.0    101\n",
       "Name: Athlete Weight, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Athlete Weight'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5678eb96-a19b-4915-9abf-d20c9511a909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y=df['Athlete Weight'], x=df.index)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "59b13148-d79d-4255-939c-fda0705c7f97",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-15-619230eb946b>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Athlete Weight'] = df['Athlete Weight'].fillna(df['Athlete Weight'].mode()[0])\n"
     ]
    }
   ],
   "source": [
    "df['Athlete Weight'] = df['Athlete Weight'].fillna(df['Athlete Weight'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1833c437-8de5-4586-8bee-c13f6da471b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y=df['Athlete Weight'], x=df.index)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab8889d-5152-47c1-8ca9-7b5992a6084c",
   "metadata": {},
   "source": [
    "The bike weight that is lacking (just one) is between two 8.5kg, so i'll fulfill it with this value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d9572f20-6175-4b18-86f8-0e4ec7e99f79",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-c01e764875d9>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Bike Weight'] = df['Bike Weight'].fillna(8.5)\n"
     ]
    }
   ],
   "source": [
    "df['Bike Weight'] = df['Bike Weight'].fillna(8.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0ecc9b58-ada7-4b84-9560-0c9de576603f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y=df['Average Speed'], x=df.index)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "05635753-3f8f-4c9f-af05-226b170f04b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns = ['Average Speed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cae7838e-d92a-4765-963e-b9cce4fbe8ea",
   "metadata": {},
   "source": [
    "activities that doesn't have \"Elevation Gain\" and \"Elevation Loss\" are indoor activities so they should be dropped.\n",
    "\n",
    "indoor average speed are meaningless"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "be8187a0-d483-4ab9-8167-48ee074dd355",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>Activity Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Athlete Weight</th>\n",
       "      <th>Bike Weight</th>\n",
       "      <th>Moving Time</th>\n",
       "      <th>Max Speed</th>\n",
       "      <th>Elevation Gain</th>\n",
       "      <th>Elevation Loss</th>\n",
       "      <th>Elevation Low</th>\n",
       "      <th>Elevation High</th>\n",
       "      <th>Average Cadence</th>\n",
       "      <th>Average Heart Rate</th>\n",
       "      <th>Average Watts</th>\n",
       "      <th>Weighted Average Power</th>\n",
       "      <th>Wind Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>Dec 20, 2018, 9:21:47 PM</td>\n",
       "      <td>28.50</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3649.0</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>189.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>774.400024</td>\n",
       "      <td>837.000000</td>\n",
       "      <td>81.804916</td>\n",
       "      <td>136.374344</td>\n",
       "      <td>141.124146</td>\n",
       "      <td>158.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>Dec 21, 2018, 11:44:34 AM</td>\n",
       "      <td>60.17</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7936.0</td>\n",
       "      <td>16.400000</td>\n",
       "      <td>564.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>712.799988</td>\n",
       "      <td>870.000000</td>\n",
       "      <td>84.583115</td>\n",
       "      <td>144.088074</td>\n",
       "      <td>130.858368</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>Dec 27, 2018, 12:11:00 PM</td>\n",
       "      <td>102.46</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>13926.0</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>708.799988</td>\n",
       "      <td>879.400024</td>\n",
       "      <td>82.143440</td>\n",
       "      <td>133.484985</td>\n",
       "      <td>121.892433</td>\n",
       "      <td>132.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>Dec 28, 2018, 8:00:09 AM</td>\n",
       "      <td>151.54</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>22486.0</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>1809.0</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>465.600006</td>\n",
       "      <td>872.400024</td>\n",
       "      <td>80.559204</td>\n",
       "      <td>124.585190</td>\n",
       "      <td>105.306503</td>\n",
       "      <td>126.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>Dec 29, 2018, 10:21:38 AM</td>\n",
       "      <td>79.98</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11130.0</td>\n",
       "      <td>19.299999</td>\n",
       "      <td>816.0</td>\n",
       "      <td>812.0</td>\n",
       "      <td>641.599976</td>\n",
       "      <td>872.799988</td>\n",
       "      <td>79.305138</td>\n",
       "      <td>129.399734</td>\n",
       "      <td>114.944656</td>\n",
       "      <td>133.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Sep 19, 2021, 6:52:30 PM</td>\n",
       "      <td>64.91</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7777.0</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>463.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>794.599976</td>\n",
       "      <td>865.200012</td>\n",
       "      <td>82.040230</td>\n",
       "      <td>144.109299</td>\n",
       "      <td>152.081268</td>\n",
       "      <td>156.0</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>Sep 25, 2021, 12:04:06 PM</td>\n",
       "      <td>80.21</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>10032.0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>800.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>742.599976</td>\n",
       "      <td>898.400024</td>\n",
       "      <td>80.780617</td>\n",
       "      <td>135.690186</td>\n",
       "      <td>158.168060</td>\n",
       "      <td>169.0</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>Sep 26, 2021, 2:08:31 PM</td>\n",
       "      <td>43.34</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5419.0</td>\n",
       "      <td>15.200000</td>\n",
       "      <td>229.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>803.200012</td>\n",
       "      <td>841.599976</td>\n",
       "      <td>83.144585</td>\n",
       "      <td>128.509323</td>\n",
       "      <td>161.173279</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>Sep 28, 2021, 10:09:07 AM</td>\n",
       "      <td>43.34</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5752.0</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>208.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>803.000000</td>\n",
       "      <td>833.799988</td>\n",
       "      <td>80.088715</td>\n",
       "      <td>130.684631</td>\n",
       "      <td>168.662201</td>\n",
       "      <td>189.0</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>Sep 29, 2021, 10:17:32 AM</td>\n",
       "      <td>43.34</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5597.0</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>219.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>792.200012</td>\n",
       "      <td>828.799988</td>\n",
       "      <td>79.728714</td>\n",
       "      <td>125.696442</td>\n",
       "      <td>158.627121</td>\n",
       "      <td>162.0</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>332 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Activity Date  Distance  Athlete Weight  Bike Weight  \\\n",
       "499    Dec 20, 2018, 9:21:47 PM     28.50            70.0          7.5   \n",
       "500   Dec 21, 2018, 11:44:34 AM     60.17            70.0          7.5   \n",
       "502   Dec 27, 2018, 12:11:00 PM    102.46            70.0          7.5   \n",
       "503    Dec 28, 2018, 8:00:09 AM    151.54            70.0          7.5   \n",
       "504   Dec 29, 2018, 10:21:38 AM     79.98            70.0          7.5   \n",
       "...                         ...       ...             ...          ...   \n",
       "1300   Sep 19, 2021, 6:52:30 PM     64.91            67.0          8.5   \n",
       "1304  Sep 25, 2021, 12:04:06 PM     80.21            67.0          8.5   \n",
       "1305   Sep 26, 2021, 2:08:31 PM     43.34            67.0          8.5   \n",
       "1306  Sep 28, 2021, 10:09:07 AM     43.34            67.0          8.5   \n",
       "1307  Sep 29, 2021, 10:17:32 AM     43.34            67.0          8.5   \n",
       "\n",
       "      Moving Time  Max Speed  Elevation Gain  Elevation Loss  Elevation Low  \\\n",
       "499        3649.0  16.100000           189.0           196.0     774.400024   \n",
       "500        7936.0  16.400000           564.0           577.0     712.799988   \n",
       "502       13926.0  17.500000          1056.0          1034.0     708.799988   \n",
       "503       22486.0  20.600000          1809.0          1827.0     465.600006   \n",
       "504       11130.0  19.299999           816.0           812.0     641.599976   \n",
       "...           ...        ...             ...             ...            ...   \n",
       "1300       7777.0  15.100000           463.0           507.0     794.599976   \n",
       "1304      10032.0  18.000000           800.0           816.0     742.599976   \n",
       "1305       5419.0  15.200000           229.0           224.0     803.200012   \n",
       "1306       5752.0  13.200000           208.0           210.0     803.000000   \n",
       "1307       5597.0  13.400000           219.0           218.0     792.200012   \n",
       "\n",
       "      Elevation High  Average Cadence  Average Heart Rate  Average Watts  \\\n",
       "499       837.000000        81.804916          136.374344     141.124146   \n",
       "500       870.000000        84.583115          144.088074     130.858368   \n",
       "502       879.400024        82.143440          133.484985     121.892433   \n",
       "503       872.400024        80.559204          124.585190     105.306503   \n",
       "504       872.799988        79.305138          129.399734     114.944656   \n",
       "...              ...              ...                 ...            ...   \n",
       "1300      865.200012        82.040230          144.109299     152.081268   \n",
       "1304      898.400024        80.780617          135.690186     158.168060   \n",
       "1305      841.599976        83.144585          128.509323     161.173279   \n",
       "1306      833.799988        80.088715          130.684631     168.662201   \n",
       "1307      828.799988        79.728714          125.696442     158.627121   \n",
       "\n",
       "      Weighted Average Power  Wind Speed  \n",
       "499                    158.0         NaN  \n",
       "500                    150.0         NaN  \n",
       "502                    132.0         NaN  \n",
       "503                    126.0         NaN  \n",
       "504                    133.0         NaN  \n",
       "...                      ...         ...  \n",
       "1300                   156.0        1.51  \n",
       "1304                   169.0        3.75  \n",
       "1305                   165.0        3.92  \n",
       "1306                   189.0        3.50  \n",
       "1307                   162.0        1.36  \n",
       "\n",
       "[332 rows x 15 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna(subset=['Elevation Gain'])\n",
    "df = df.dropna(subset=['Elevation Loss'])\n",
    "df= df[df['Elevation Gain'] != 0.0]\n",
    "df= df[df['Elevation Loss'] != 0.0]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcd526aa",
   "metadata": {},
   "source": [
    "There was an empty Heart Rate that is fullfiled with the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0179629c-c9a9-4b3d-bf35-1fd2c6808b56",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Average Heart Rate'] = df['Average Heart Rate'].fillna((df['Average Heart Rate'].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4ab20d6",
   "metadata": {},
   "source": [
    "Transforming the max speed and wind speed from meters per second to kilometers per hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "157bb6de",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Max Speed'] = df['Max Speed'] * 3.6\n",
    "df['Wind Speed'] = df['Wind Speed'] * 3.6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5abd6260",
   "metadata": {},
   "source": [
    "It's better to sum the bike weight to rider weight in order to reduce one feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a397104b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Total Weight'] = df['Athlete Weight'] + df['Bike Weight']\n",
    "df = df.drop(columns = ['Athlete Weight', 'Bike Weight'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c12808b",
   "metadata": {},
   "source": [
    "transforming the date into datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "67034490",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Activity Date'] = pd.to_datetime(df['Activity Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "019f3219-31a4-4824-b74d-abd3a509d19b",
   "metadata": {},
   "source": [
    "average grade is a percentage of elevation gain divided by the distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a420cb4d-c083-47e7-8abf-514ba627a129",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Average Grade'] = df['Elevation Gain'] /  (df['Distance'] * 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd0ec63-2086-4248-b3d9-a03c765c1164",
   "metadata": {},
   "source": [
    "The average speed for some reason just started to show after the activity #734.\n",
    "\n",
    "Luckily, we can calculate that. \n",
    "\n",
    "It's the \"Distance\" (measured in kilometers) divided by the \"Moving Time\" (measured in seconds)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7fdc34b1-94ba-4fc9-adf1-acce79786cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "499     28.117292\n",
       "500     27.294859\n",
       "502     26.486859\n",
       "503     24.261496\n",
       "504     25.869542\n",
       "          ...    \n",
       "1300    30.047062\n",
       "1304    28.783493\n",
       "1305    28.792028\n",
       "1306    27.125174\n",
       "1307    27.876362\n",
       "Name: Average Speed, Length: 332, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Average Speed'] = (df['Distance'] * 60 * 60) / df['Moving Time']\n",
    "df['Average Speed']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f95e07a",
   "metadata": {},
   "source": [
    "There was 2 rows that were indoor activities. They shall be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "375b3845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Activity Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Moving Time</th>\n",
       "      <th>Max Speed</th>\n",
       "      <th>Elevation Gain</th>\n",
       "      <th>Elevation Loss</th>\n",
       "      <th>Elevation Low</th>\n",
       "      <th>Elevation High</th>\n",
       "      <th>Average Cadence</th>\n",
       "      <th>Average Heart Rate</th>\n",
       "      <th>Average Watts</th>\n",
       "      <th>Weighted Average Power</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Total Weight</th>\n",
       "      <th>Average Grade</th>\n",
       "      <th>Average Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>2018-12-20 21:21:47</td>\n",
       "      <td>28.50</td>\n",
       "      <td>3649.0</td>\n",
       "      <td>57.960001</td>\n",
       "      <td>189.0</td>\n",
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       "      <td>774.400024</td>\n",
       "      <td>837.000000</td>\n",
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       "      <td>141.124146</td>\n",
       "      <td>158.0</td>\n",
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       "      <td>77.5</td>\n",
       "      <td>0.663158</td>\n",
       "      <td>28.117292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>2018-12-21 11:44:34</td>\n",
       "      <td>60.17</td>\n",
       "      <td>7936.0</td>\n",
       "      <td>59.039999</td>\n",
       "      <td>564.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>712.799988</td>\n",
       "      <td>870.000000</td>\n",
       "      <td>84.583115</td>\n",
       "      <td>144.088074</td>\n",
       "      <td>130.858368</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5</td>\n",
       "      <td>0.937344</td>\n",
       "      <td>27.294859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>2018-12-27 12:11:00</td>\n",
       "      <td>102.46</td>\n",
       "      <td>13926.0</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>708.799988</td>\n",
       "      <td>879.400024</td>\n",
       "      <td>82.143440</td>\n",
       "      <td>133.484985</td>\n",
       "      <td>121.892433</td>\n",
       "      <td>132.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5</td>\n",
       "      <td>1.030646</td>\n",
       "      <td>26.486859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>2018-12-28 08:00:09</td>\n",
       "      <td>151.54</td>\n",
       "      <td>22486.0</td>\n",
       "      <td>74.160001</td>\n",
       "      <td>1809.0</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>465.600006</td>\n",
       "      <td>872.400024</td>\n",
       "      <td>80.559204</td>\n",
       "      <td>124.585190</td>\n",
       "      <td>105.306503</td>\n",
       "      <td>126.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5</td>\n",
       "      <td>1.193744</td>\n",
       "      <td>24.261496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>2018-12-29 10:21:38</td>\n",
       "      <td>79.98</td>\n",
       "      <td>11130.0</td>\n",
       "      <td>69.479997</td>\n",
       "      <td>816.0</td>\n",
       "      <td>812.0</td>\n",
       "      <td>641.599976</td>\n",
       "      <td>872.799988</td>\n",
       "      <td>79.305138</td>\n",
       "      <td>129.399734</td>\n",
       "      <td>114.944656</td>\n",
       "      <td>133.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5</td>\n",
       "      <td>1.020255</td>\n",
       "      <td>25.869542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>2021-09-19 18:52:30</td>\n",
       "      <td>64.91</td>\n",
       "      <td>7777.0</td>\n",
       "      <td>54.360001</td>\n",
       "      <td>463.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>794.599976</td>\n",
       "      <td>865.200012</td>\n",
       "      <td>82.040230</td>\n",
       "      <td>144.109299</td>\n",
       "      <td>152.081268</td>\n",
       "      <td>156.0</td>\n",
       "      <td>5.436</td>\n",
       "      <td>75.5</td>\n",
       "      <td>0.713295</td>\n",
       "      <td>30.047062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>2021-09-25 12:04:06</td>\n",
       "      <td>80.21</td>\n",
       "      <td>10032.0</td>\n",
       "      <td>64.800000</td>\n",
       "      <td>800.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>742.599976</td>\n",
       "      <td>898.400024</td>\n",
       "      <td>80.780617</td>\n",
       "      <td>135.690186</td>\n",
       "      <td>158.168060</td>\n",
       "      <td>169.0</td>\n",
       "      <td>13.500</td>\n",
       "      <td>75.5</td>\n",
       "      <td>0.997382</td>\n",
       "      <td>28.783493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>2021-09-26 14:08:31</td>\n",
       "      <td>43.34</td>\n",
       "      <td>5419.0</td>\n",
       "      <td>54.719999</td>\n",
       "      <td>229.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>803.200012</td>\n",
       "      <td>841.599976</td>\n",
       "      <td>83.144585</td>\n",
       "      <td>128.509323</td>\n",
       "      <td>161.173279</td>\n",
       "      <td>165.0</td>\n",
       "      <td>14.112</td>\n",
       "      <td>75.5</td>\n",
       "      <td>0.528380</td>\n",
       "      <td>28.792028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>2021-09-28 10:09:07</td>\n",
       "      <td>43.34</td>\n",
       "      <td>5752.0</td>\n",
       "      <td>47.519999</td>\n",
       "      <td>208.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>803.000000</td>\n",
       "      <td>833.799988</td>\n",
       "      <td>80.088715</td>\n",
       "      <td>130.684631</td>\n",
       "      <td>168.662201</td>\n",
       "      <td>189.0</td>\n",
       "      <td>12.600</td>\n",
       "      <td>75.5</td>\n",
       "      <td>0.479926</td>\n",
       "      <td>27.125174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>2021-09-29 10:17:32</td>\n",
       "      <td>43.34</td>\n",
       "      <td>5597.0</td>\n",
       "      <td>48.239999</td>\n",
       "      <td>219.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>792.200012</td>\n",
       "      <td>828.799988</td>\n",
       "      <td>79.728714</td>\n",
       "      <td>125.696442</td>\n",
       "      <td>158.627121</td>\n",
       "      <td>162.0</td>\n",
       "      <td>4.896</td>\n",
       "      <td>75.5</td>\n",
       "      <td>0.505307</td>\n",
       "      <td>27.876362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>330 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Activity Date  Distance  Moving Time  Max Speed  Elevation Gain  \\\n",
       "499  2018-12-20 21:21:47     28.50       3649.0  57.960001           189.0   \n",
       "500  2018-12-21 11:44:34     60.17       7936.0  59.039999           564.0   \n",
       "502  2018-12-27 12:11:00    102.46      13926.0  63.000000          1056.0   \n",
       "503  2018-12-28 08:00:09    151.54      22486.0  74.160001          1809.0   \n",
       "504  2018-12-29 10:21:38     79.98      11130.0  69.479997           816.0   \n",
       "...                  ...       ...          ...        ...             ...   \n",
       "1300 2021-09-19 18:52:30     64.91       7777.0  54.360001           463.0   \n",
       "1304 2021-09-25 12:04:06     80.21      10032.0  64.800000           800.0   \n",
       "1305 2021-09-26 14:08:31     43.34       5419.0  54.719999           229.0   \n",
       "1306 2021-09-28 10:09:07     43.34       5752.0  47.519999           208.0   \n",
       "1307 2021-09-29 10:17:32     43.34       5597.0  48.239999           219.0   \n",
       "\n",
       "      Elevation Loss  Elevation Low  Elevation High  Average Cadence  \\\n",
       "499            196.0     774.400024      837.000000        81.804916   \n",
       "500            577.0     712.799988      870.000000        84.583115   \n",
       "502           1034.0     708.799988      879.400024        82.143440   \n",
       "503           1827.0     465.600006      872.400024        80.559204   \n",
       "504            812.0     641.599976      872.799988        79.305138   \n",
       "...              ...            ...             ...              ...   \n",
       "1300           507.0     794.599976      865.200012        82.040230   \n",
       "1304           816.0     742.599976      898.400024        80.780617   \n",
       "1305           224.0     803.200012      841.599976        83.144585   \n",
       "1306           210.0     803.000000      833.799988        80.088715   \n",
       "1307           218.0     792.200012      828.799988        79.728714   \n",
       "\n",
       "      Average Heart Rate  Average Watts  Weighted Average Power  Wind Speed  \\\n",
       "499           136.374344     141.124146                   158.0         NaN   \n",
       "500           144.088074     130.858368                   150.0         NaN   \n",
       "502           133.484985     121.892433                   132.0         NaN   \n",
       "503           124.585190     105.306503                   126.0         NaN   \n",
       "504           129.399734     114.944656                   133.0         NaN   \n",
       "...                  ...            ...                     ...         ...   \n",
       "1300          144.109299     152.081268                   156.0       5.436   \n",
       "1304          135.690186     158.168060                   169.0      13.500   \n",
       "1305          128.509323     161.173279                   165.0      14.112   \n",
       "1306          130.684631     168.662201                   189.0      12.600   \n",
       "1307          125.696442     158.627121                   162.0       4.896   \n",
       "\n",
       "      Total Weight  Average Grade  Average Speed  \n",
       "499           77.5       0.663158      28.117292  \n",
       "500           77.5       0.937344      27.294859  \n",
       "502           77.5       1.030646      26.486859  \n",
       "503           77.5       1.193744      24.261496  \n",
       "504           77.5       1.020255      25.869542  \n",
       "...            ...            ...            ...  \n",
       "1300          75.5       0.713295      30.047062  \n",
       "1304          75.5       0.997382      28.783493  \n",
       "1305          75.5       0.528380      28.792028  \n",
       "1306          75.5       0.479926      27.125174  \n",
       "1307          75.5       0.505307      27.876362  \n",
       "\n",
       "[330 rows x 16 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop([1169, 1225])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6e0248e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 330 entries, 499 to 1307\n",
      "Data columns (total 16 columns):\n",
      " #   Column                  Non-Null Count  Dtype         \n",
      "---  ------                  --------------  -----         \n",
      " 0   Activity Date           330 non-null    datetime64[ns]\n",
      " 1   Distance                330 non-null    float64       \n",
      " 2   Moving Time             330 non-null    float64       \n",
      " 3   Max Speed               330 non-null    float64       \n",
      " 4   Elevation Gain          330 non-null    float64       \n",
      " 5   Elevation Loss          330 non-null    float64       \n",
      " 6   Elevation Low           330 non-null    float64       \n",
      " 7   Elevation High          330 non-null    float64       \n",
      " 8   Average Cadence         330 non-null    float64       \n",
      " 9   Average Heart Rate      330 non-null    float64       \n",
      " 10  Average Watts           330 non-null    float64       \n",
      " 11  Weighted Average Power  330 non-null    float64       \n",
      " 12  Wind Speed              193 non-null    float64       \n",
      " 13  Total Weight            330 non-null    float64       \n",
      " 14  Average Grade           330 non-null    float64       \n",
      " 15  Average Speed           330 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(15)\n",
      "memory usage: 43.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8b5be43f-f44b-4e98-92cc-5c817cb54f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('clean_values.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60195f0f-3aaf-4e4d-a8b4-b76efcc8ef4a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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