strava_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import config\n",
    "import requests\n",
    "import pandas as pd\n",
    "import json\n",
    "import time\n",
    "from pandas import json_normalize\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme(style='whitegrid', palette='dark')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authenticate with Strava"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('strava_tokens.json') as json_file:\n",
    "    strava_tokens = json.load(json_file)\n",
    "\n",
    "if strava_tokens['expires_at'] < time.time():\n",
    "    response = requests.post(\n",
    "                        url = 'https://www.strava.com/oauth/token',\n",
    "                        data = {\n",
    "                                'client_id': config.client_id,\n",
    "                                'client_secret': config.client_secret,\n",
    "                                'grant_type': 'refresh_token',\n",
    "                                'refresh_token': strava_tokens['refresh_token']\n",
    "                                }\n",
    "                    )\n",
    "    new_strava_tokens = response.json()\n",
    "\n",
    "    with open('strava_tokens.json', 'w') as outfile:\n",
    "        json.dump(new_strava_tokens, outfile)\n",
    "\n",
    "    strava_tokens = new_strava_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('strava_tokens.json') as check:\n",
    "  data = json.load(check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the tokens from the file to connect to Strava\n",
    "with open('strava_tokens.json') as json_file:\n",
    "    strava_tokens = json.load(json_file)\n",
    "\n",
    "# Loop through all activities\n",
    "url = \"https://www.strava.com/api/v3/activities\"\n",
    "access_token = strava_tokens['access_token']\n",
    "\n",
    "# Get the first page of activities from Strava with all fields\n",
    "r = requests.get(url + '?access_token=' + access_token)\n",
    "r = r.json()\n",
    "    \n",
    "df = json_normalize(r)\n",
    "df.to_csv('strava_activities_all_fields.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Retrieval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the Strava API with a specific header to retrive activity data\n",
    "header = {'Authorization': 'Bearer ' + access_token}\n",
    "param = {'per_page': 200, 'page': 1}\n",
    "r = requests.get(url, headers=header, params=param).json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "activities = json_normalize(r)\n",
    "\n",
    "# new dataframe with desired columns\n",
    "columns = [\n",
    "            \"id\",\n",
    "            \"name\",\n",
    "            \"start_date_local\",\n",
    "            \"type\",\n",
    "            \"distance\",\n",
    "            \"moving_time\",\n",
    "            \"elapsed_time\",\n",
    "            \"total_elevation_gain\",\n",
    "            \"end_latlng\",\n",
    "            \"location_country\",\n",
    "            \"flagged\",\n",
    "            \"has_heartrate\",\n",
    "            \"average_heartrate\",\n",
    "            \"max_heartrate\",\n",
    "            \"average_speed\",\n",
    "    ]\n",
    "\n",
    "activities = activities[columns]\n",
    "\n",
    "# Export activities file as a csv \n",
    "# to the folder this script runs in\n",
    "activities.to_csv('strava_activities.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "\n",
    "Now that that activity data is retrieved and stored as a .csv file, and as a pandas DataFrame (df), we can inspect the head of the df to get an idea of how the data looks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>start_date_local</th>\n",
       "      <th>type</th>\n",
       "      <th>distance</th>\n",
       "      <th>moving_time</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>total_elevation_gain</th>\n",
       "      <th>end_latlng</th>\n",
       "      <th>location_country</th>\n",
       "      <th>flagged</th>\n",
       "      <th>has_heartrate</th>\n",
       "      <th>average_heartrate</th>\n",
       "      <th>max_heartrate</th>\n",
       "      <th>average_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6598004451</td>\n",
       "      <td>Morning Run</td>\n",
       "      <td>2022-01-29T09:57:57Z</td>\n",
       "      <td>Run</td>\n",
       "      <td>6444.5</td>\n",
       "      <td>1978</td>\n",
       "      <td>2030</td>\n",
       "      <td>52.1</td>\n",
       "      <td>[41.960498029366136, 3.0399949103593826]</td>\n",
       "      <td>Spain</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>144.9</td>\n",
       "      <td>168.0</td>\n",
       "      <td>3.258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6589161188</td>\n",
       "      <td>Lunch Run</td>\n",
       "      <td>2022-01-27T12:17:24Z</td>\n",
       "      <td>Run</td>\n",
       "      <td>9846.4</td>\n",
       "      <td>2962</td>\n",
       "      <td>2972</td>\n",
       "      <td>12.6</td>\n",
       "      <td>[41.96275033056736, 3.0380767956376076]</td>\n",
       "      <td>Spain</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>155.7</td>\n",
       "      <td>168.0</td>\n",
       "      <td>3.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6573787184</td>\n",
       "      <td>Burn Baby</td>\n",
       "      <td>2022-01-24T12:48:09Z</td>\n",
       "      <td>Run</td>\n",
       "      <td>6444.1</td>\n",
       "      <td>1893</td>\n",
       "      <td>1934</td>\n",
       "      <td>11.4</td>\n",
       "      <td>[41.96095953695476, 3.038990255445242]</td>\n",
       "      <td>Spain</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>160.2</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3.404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6559064384</td>\n",
       "      <td>Afternoon Run</td>\n",
       "      <td>2022-01-21T14:38:44Z</td>\n",
       "      <td>Run</td>\n",
       "      <td>9944.9</td>\n",
       "      <td>3022</td>\n",
       "      <td>3510</td>\n",
       "      <td>56.3</td>\n",
       "      <td>[41.96046022698283, 3.040398834273219]</td>\n",
       "      <td>Spain</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>149.3</td>\n",
       "      <td>174.0</td>\n",
       "      <td>3.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6538818076</td>\n",
       "      <td>Afternoon Run</td>\n",
       "      <td>2022-01-17T17:06:26Z</td>\n",
       "      <td>Run</td>\n",
       "      <td>9663.8</td>\n",
       "      <td>2963</td>\n",
       "      <td>3133</td>\n",
       "      <td>54.1</td>\n",
       "      <td>[41.96291017346084, 3.0405751056969166]</td>\n",
       "      <td>Spain</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>149.5</td>\n",
       "      <td>169.0</td>\n",
       "      <td>3.261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id           name      start_date_local type  distance  \\\n",
       "0  6598004451    Morning Run  2022-01-29T09:57:57Z  Run    6444.5   \n",
       "1  6589161188      Lunch Run  2022-01-27T12:17:24Z  Run    9846.4   \n",
       "2  6573787184      Burn Baby  2022-01-24T12:48:09Z  Run    6444.1   \n",
       "3  6559064384  Afternoon Run  2022-01-21T14:38:44Z  Run    9944.9   \n",
       "4  6538818076  Afternoon Run  2022-01-17T17:06:26Z  Run    9663.8   \n",
       "\n",
       "   moving_time  elapsed_time  total_elevation_gain  \\\n",
       "0         1978          2030                  52.1   \n",
       "1         2962          2972                  12.6   \n",
       "2         1893          1934                  11.4   \n",
       "3         3022          3510                  56.3   \n",
       "4         2963          3133                  54.1   \n",
       "\n",
       "                                 end_latlng location_country  flagged  \\\n",
       "0  [41.960498029366136, 3.0399949103593826]            Spain    False   \n",
       "1   [41.96275033056736, 3.0380767956376076]            Spain    False   \n",
       "2    [41.96095953695476, 3.038990255445242]            Spain    False   \n",
       "3    [41.96046022698283, 3.040398834273219]            Spain    False   \n",
       "4   [41.96291017346084, 3.0405751056969166]            Spain    False   \n",
       "\n",
       "   has_heartrate  average_heartrate  max_heartrate  average_speed  \n",
       "0           True              144.9          168.0          3.258  \n",
       "1           True              155.7          168.0          3.324  \n",
       "2           True              160.2          181.0          3.404  \n",
       "3           True              149.3          174.0          3.291  \n",
       "4           True              149.5          169.0          3.261  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activities.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Conversion\n",
    "\n",
    "Often when working with raw data, some of the data types are not in a format that is convenient. \n",
    "\n",
    "For example, most long distance runners prefer to work in miles and not kilometers. This means that a new kilometers column is needed, as well as pace data to be converted from min/km to min/mile. \n",
    "\n",
    "Also, days of the week data could be useful, so a new day of the week column, and month of the year column can be created. \n",
    "\n",
    "Finally, the data types sometimes need to be changed, in the example below we change a datetime column into a timedelta column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>start_date_local</th>\n",
       "      <th>type</th>\n",
       "      <th>distance</th>\n",
       "      <th>moving_time</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>total_elevation_gain</th>\n",
       "      <th>end_latlng</th>\n",
       "      <th>location_country</th>\n",
       "      <th>...</th>\n",
       "      <th>has_heartrate</th>\n",
       "      <th>average_heartrate</th>\n",
       "      <th>max_heartrate</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>distance_miles</th>\n",
       "      <th>minute_mile</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>month_of_year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6598004451</td>\n",
       "      <td>Morning Run</td>\n",
       "      <td>2022-01-29 09:57:57+00:00</td>\n",
       "      <td>Run</td>\n",
       "      <td>6444.5</td>\n",
       "      <td>0 days 00:00:00.000001978</td>\n",
       "      <td>0 days 00:00:00.000002030</td>\n",
       "      <td>52.1</td>\n",
       "      <td>[41.960498029366136, 3.0399949103593826]</td>\n",
       "      <td>Spain</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>144.9</td>\n",
       "      <td>168.0</td>\n",
       "      <td>3.258</td>\n",
       "      <td>4.004425</td>\n",
       "      <td>8.232658</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6589161188</td>\n",
       "      <td>Lunch Run</td>\n",
       "      <td>2022-01-27 12:17:24+00:00</td>\n",
       "      <td>Run</td>\n",
       "      <td>9846.4</td>\n",
       "      <td>0 days 00:00:00.000002962</td>\n",
       "      <td>0 days 00:00:00.000002972</td>\n",
       "      <td>12.6</td>\n",
       "      <td>[41.96275033056736, 3.0380767956376076]</td>\n",
       "      <td>Spain</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>155.7</td>\n",
       "      <td>168.0</td>\n",
       "      <td>3.324</td>\n",
       "      <td>6.118267</td>\n",
       "      <td>8.069194</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6573787184</td>\n",
       "      <td>Burn Baby</td>\n",
       "      <td>2022-01-24 12:48:09+00:00</td>\n",
       "      <td>Run</td>\n",
       "      <td>6444.1</td>\n",
       "      <td>0 days 00:00:00.000001893</td>\n",
       "      <td>0 days 00:00:00.000001934</td>\n",
       "      <td>11.4</td>\n",
       "      <td>[41.96095953695476, 3.038990255445242]</td>\n",
       "      <td>Spain</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>160.2</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3.404</td>\n",
       "      <td>4.004177</td>\n",
       "      <td>7.879553</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6559064384</td>\n",
       "      <td>Afternoon Run</td>\n",
       "      <td>2022-01-21 14:38:44+00:00</td>\n",
       "      <td>Run</td>\n",
       "      <td>9944.9</td>\n",
       "      <td>0 days 00:00:00.000003022</td>\n",
       "      <td>0 days 00:00:00.000003510</td>\n",
       "      <td>56.3</td>\n",
       "      <td>[41.96046022698283, 3.040398834273219]</td>\n",
       "      <td>Spain</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>149.3</td>\n",
       "      <td>174.0</td>\n",
       "      <td>3.291</td>\n",
       "      <td>6.179472</td>\n",
       "      <td>8.150106</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6538818076</td>\n",
       "      <td>Afternoon Run</td>\n",
       "      <td>2022-01-17 17:06:26+00:00</td>\n",
       "      <td>Run</td>\n",
       "      <td>9663.8</td>\n",
       "      <td>0 days 00:00:00.000002963</td>\n",
       "      <td>0 days 00:00:00.000003133</td>\n",
       "      <td>54.1</td>\n",
       "      <td>[41.96291017346084, 3.0405751056969166]</td>\n",
       "      <td>Spain</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>149.5</td>\n",
       "      <td>169.0</td>\n",
       "      <td>3.261</td>\n",
       "      <td>6.004805</td>\n",
       "      <td>8.225084</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id           name          start_date_local type  distance  \\\n",
       "0  6598004451    Morning Run 2022-01-29 09:57:57+00:00  Run    6444.5   \n",
       "1  6589161188      Lunch Run 2022-01-27 12:17:24+00:00  Run    9846.4   \n",
       "2  6573787184      Burn Baby 2022-01-24 12:48:09+00:00  Run    6444.1   \n",
       "3  6559064384  Afternoon Run 2022-01-21 14:38:44+00:00  Run    9944.9   \n",
       "4  6538818076  Afternoon Run 2022-01-17 17:06:26+00:00  Run    9663.8   \n",
       "\n",
       "                moving_time              elapsed_time  total_elevation_gain  \\\n",
       "0 0 days 00:00:00.000001978 0 days 00:00:00.000002030                  52.1   \n",
       "1 0 days 00:00:00.000002962 0 days 00:00:00.000002972                  12.6   \n",
       "2 0 days 00:00:00.000001893 0 days 00:00:00.000001934                  11.4   \n",
       "3 0 days 00:00:00.000003022 0 days 00:00:00.000003510                  56.3   \n",
       "4 0 days 00:00:00.000002963 0 days 00:00:00.000003133                  54.1   \n",
       "\n",
       "                                 end_latlng location_country  ...  \\\n",
       "0  [41.960498029366136, 3.0399949103593826]            Spain  ...   \n",
       "1   [41.96275033056736, 3.0380767956376076]            Spain  ...   \n",
       "2    [41.96095953695476, 3.038990255445242]            Spain  ...   \n",
       "3    [41.96046022698283, 3.040398834273219]            Spain  ...   \n",
       "4   [41.96291017346084, 3.0405751056969166]            Spain  ...   \n",
       "\n",
       "   has_heartrate  average_heartrate  max_heartrate  average_speed  \\\n",
       "0           True              144.9          168.0          3.258   \n",
       "1           True              155.7          168.0          3.324   \n",
       "2           True              160.2          181.0          3.404   \n",
       "3           True              149.3          174.0          3.291   \n",
       "4           True              149.5          169.0          3.261   \n",
       "\n",
       "   distance_miles  minute_mile  day_of_week month_of_year  week_of_year  year  \n",
       "0        4.004425     8.232658     Saturday             1             4  2022  \n",
       "1        6.118267     8.069194     Thursday             1             4  2022  \n",
       "2        4.004177     7.879553       Monday             1             4  2022  \n",
       "3        6.179472     8.150106       Friday             1             3  2022  \n",
       "4        6.004805     8.225084       Monday             1             3  2022  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a distance in miles column\n",
    "conv_fac_kilometers_miles = 0.621371 # conversion factor\n",
    "activities['distance_miles'] = (activities['distance']*conv_fac_kilometers_miles)/1e3\n",
    "\n",
    "# Convert m/s to min/mile\n",
    "activities['minute_mile'] = 1/(activities['average_speed']/26.822)\n",
    "\n",
    "# Convert dates to datetime type\n",
    "activities['start_date_local'] = pd.to_datetime(activities['start_date_local'])\n",
    "\n",
    "# Create a day of the week and month of the year columns\n",
    "activities['day_of_week'] = activities['start_date_local'].dt.day_name()\n",
    "activities['month_of_year'] = activities['start_date_local'].dt.month\n",
    "activities['week_of_year'] = activities['start_date_local'].dt.isocalendar().week\n",
    "\n",
    "# Convert times to timedeltas\n",
    "activities['moving_time'] = pd.to_timedelta(activities['moving_time'])\n",
    "activities['elapsed_time'] = pd.to_timedelta(activities['elapsed_time'])\n",
    "\n",
    "# Convert start date to years\n",
    "activities['year'] = pd.DatetimeIndex(activities['start_date_local']).year\n",
    "\n",
    "activities.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Only Half Marathon Training Activities\n",
    "\n",
    "Next, two dfs are needed, the actual race day data, and the training data excluding the race day data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# date range for the training of the half marathon, excluding the actual race day\n",
    "start_date_training = '2021-10-23'\n",
    "end_date_training = '2021-12-04'\n",
    "\n",
    "mask_training = (activities['start_date_local'] > start_date_training) & (activities['start_date_local'] <= end_date_training)\n",
    "df_training = activities.loc[mask_training]\n",
    "\n",
    "# date for the race day\n",
    "date_race = '2021-12-05'\n",
    "date_race2 = '2021-12-06'\n",
    "mask_race = (activities['start_date_local'] >= date_race) & (activities['start_date_local'] <= date_race2)\n",
    "df_race = activities.loc[mask_race]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graphing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weekly Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct dataframe with week number column and values column\n",
    "df2 = df_training.loc[:, [\"distance_miles\"]]\n",
    "df2[\"week_of_year\"] = activities['week_of_year']\n",
    "\n",
    "# Sum up weekly values\n",
    "df3 = (\n",
    "    df2\n",
    "    .groupby(\"week_of_year\")\n",
    "    .sum()\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "ax = sns.barplot(x='week_of_year', y='distance_miles', data=df3, color = 'black')\n",
    "ax.set(xlabel=\"week_of_year\", ylabel = \"distance, miles\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total miles ran in training: 139\n"
     ]
    }
   ],
   "source": [
    "print(\"Total miles ran in training:\", round(sum(df_training['distance_miles'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Weekly Distance Plan\n",
    "\n",
    "The bar chart above shows that the training consisted of an easy first week, followed by 4 weeks of 25 miles or above per week, peaking at around 31 miles a week. The training ends with an aggressive two week taper running at a distance of approximately 60 percent less than the proceeding 4 weeks average. A total of 139 miles was ran in the training period before the Half Marathon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday' ]\n",
    "\n",
    "ax = sns.barplot(x='day_of_week', y='distance_miles', data=df_training, order=days_of_week, ci=None, color='black')\n",
    "ax.set(xlabel=\"day of week\", ylabel = \"distance, miles\", title = \"Average Miles per Day\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Days of the Week Analysis\n",
    "\n",
    "The first half of the week (Mondays to Wednesdays) where lighter distance days, when compared to the later half (Thursdays to Sundays). There was no predefined plan for this, it just happened that after 4 days of longer runs, the proceeding 3 days where run at a lower distance. This variation could have helped with recovery between long runs. However, the shorter runs where ran faster, in order to build speed. And speed runs also need their rest days afterwards."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pacing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target race pace was 7:30 min / mile. Most of the training that occurred in the 6 week period before race day was done above race pace. Two exceptions come in the form of tempo runs, when the average pace for these two runs was better than target race pace. These are designed to be run at speed.\n",
    "\n",
    "Running distance per session ranged from roughly 3 to 10 miles. The Half Marathon race distance (13.1 miles) was never run in training in the 6 weeks prior to race day. This was intentional. I have ran marathons before and many half marathons in training, so I knew I could cover the distance. I was training to run a personal best, to run faster than I had before.\n",
    "\n",
    "To achieve this, I ran long distances above race pace and shorter distances at speed, significantly faster than race pace. This variable training type is designed to get the heart stronger, get the legs able to move faster, as well as having the cardiovascular engine to cover the half marathon race distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training data\n",
    "ax = sns.scatterplot(x='distance_miles', y='minute_mile', data=df_training, color = 'black')\n",
    "\n",
    "# race day data\n",
    "ax1 = sns.scatterplot(x='distance_miles', y='minute_mile', data=df_race, color = 'red', s=100)\n",
    "\n",
    "ax.set(xlabel=\"distance, miles\", ylabel = \"speed, minute mile\")\n",
    "plt.axhline(y=7.5, color = \"black\", linestyle='--', label=\"target race pace\")\n",
    "\n",
    "# mark race data\n",
    "plt.text(12.5,7.7,'Race')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph above, it can be seen that the race pace target was actually achieved on race day. The red data point represents the race day pace. Also, most runs in training are significantly slower than the actual race pace, except two tempo runs. This was the plan and it appeared to work well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart Rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heart is a very valuable metric to observe when training for, and running, long distance races. Typically, the harder you run the more stress you put under the cardiovascular system. When running a long distance race, the trick is to keep your heart rate at a comfortably uncomfortable level, for me that is around 160 beats per minute (bpm). I kept my heart rate at about 160 bpm for the majority of the race, although likely towards the end it went over into the 170-180 bpm range, as for the last mile it is important to give it everything you have got left in the tank.\n",
    "\n",
    "The heart rate data below is average only, so on tempo runs that rate would vary significantly. Usually on tempo runs I ran 4 miles, the first mile slow, the second fast, the third slow, and the final mile fast.\n",
    "\n",
    "During the race, my heart rate was much more consistent, only spiking upwards towards the end of the race.\n",
    "\n",
    "The graph below demonstrates that during training, my average heart rate spanned a big range, from around 130 bpm to 180 bpm depending on the type of run. This was intentional, to expose my cardiovascular system to a wide range of heart rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training data\n",
    "ax = sns.scatterplot(x='average_heartrate', y='minute_mile', data=df_training, color = 'black')\n",
    "\n",
    "# race day data\n",
    "ax1 = sns.scatterplot(x='average_heartrate', y='minute_mile', data=df_race, color = 'red', s=100)\n",
    "\n",
    "ax.set(xlabel=\"average_heartrate\", ylabel = \"speed, minute mile\")\n",
    "plt.axhline(y=7.5, color = \"black\", linestyle='--', label=\"target race pace\")\n",
    "\n",
    "# mark race data\n",
    "plt.text(161,7.7,'Race')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "9a443516413c2881eaa45503843a41caec010d6420f7e7f4b04a65c7fd85d327"
  },
  "kernelspec": {
   "display_name": "Python 3.8.5 64-bit ('base': conda)",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}