strava_part2_medium.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 959,
   "id": "b7793eb4-3a70-457d-8c84-f30c8d0be6ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Once deleted, variables cannot be recovered. Proceed (y/[n])?  y\n"
     ]
    }
   ],
   "source": [
    "%reset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82324bc0-a425-47c5-809f-a5813f2fd34f",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Import the activities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 960,
   "id": "84bda020-86f1-44eb-bdf1-0af85dd9e905",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "\n",
    "def get_token():\n",
    "    response = requests.post(\n",
    "                        url = 'https://www.strava.com/oauth/token',\n",
    "                        data = {\n",
    "                                'client_id': '[YOUR_CLIENT_ID]',\n",
    "                                'client_secret': '[YOUR_CLIENT_SECRET]',\n",
    "                                'refresh_token': '[YOUR_REFRESH_TOKEN]',\n",
    "                                'code': '[YOUR_AUTHORIZATION_CODE]',\n",
    "                                'grant_type': 'refresh_token'\n",
    "                                }\n",
    "                    )\n",
    "    access_token = response.json()['access_token']\n",
    "    print(access_token)\n",
    "    return access_token\n",
    "\n",
    "def get_activities(access_token, from_date, to_date):\n",
    "    url = f'https://www.strava.com/api/v3/athlete/activities?after={from_date}&before={to_date}&page=1&per_page=200'\n",
    "    payload={}\n",
    "    headers = {\n",
    "        'Authorization': 'Bearer ' + access_token\n",
    "        }\n",
    "    response = requests.request(\"GET\", url, headers=headers, data=payload)\n",
    "    activities = response.json()\n",
    "    return activities\n",
    "\n",
    "def get_kudos(access_token, activity_id):\n",
    "    url = f'https://www.strava.com/api/v3/activities/{activity_id}/kudos?page=1&per_page=200'\n",
    "    payload={}\n",
    "    headers = {\n",
    "        'Authorization': 'Bearer ' + access_token\n",
    "        }\n",
    "    response = requests.request(\"GET\", url, headers=headers, data=payload)\n",
    "    data = response.json()\n",
    "    return data\n",
    "\n",
    "def transform(access_token, activities):\n",
    "    for act in activities:\n",
    "        if act[\"private\"] == False:\n",
    "            activity_id = act['id']\n",
    "            nb_kudos = act['kudos_count']\n",
    "            #kudos_list = [item['firstname'] + ' ' + item['lastname']  for item in get_kudos(access_token, activity_id)]\n",
    "            distance = act['distance']\n",
    "            moving_time = act['moving_time']\n",
    "            sport_type = act['sport_type']\n",
    "            total_elevation_gain = act['total_elevation_gain']\n",
    "            start_date_local = act['start_date_local']\n",
    "            average_speed = act['average_speed']\n",
    "            suffer_score = act['suffer_score']\n",
    "            if act['has_heartrate'] == True:\n",
    "                average_heartrate = act['average_heartrate']\n",
    "            else:\n",
    "                average_heartrate = None\n",
    "\n",
    "            stats = {\n",
    "                'activity_id' : activity_id,\n",
    "                'nb_kudos' : nb_kudos,\n",
    "                #'kudos_list': kudos_list,\n",
    "                'distance': distance,\n",
    "                'moving_time': moving_time,\n",
    "                'sport_type': sport_type,\n",
    "                'total_elevation_gain': total_elevation_gain,\n",
    "                'start_date_local': start_date_local,\n",
    "                'average_speed': average_speed,\n",
    "                'average_heartrate': average_heartrate,\n",
    "                'suffer_score': suffer_score\n",
    "            }\n",
    "            statlist.append(stats)\n",
    "    return\n",
    "\n",
    "def to_1D(series):\n",
    "    return pd.Series([x for list in series for x in list]) #series of lists to list of elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 961,
   "id": "9647158b-9e5a-472f-8456-9b6cecf1af14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get access token\n",
    "access_token = get_token()\n",
    "\n",
    "# Define the from date and to date\n",
    "from_date = datetime(2022, 1, 1) #datetime(year, month, day)\n",
    "from_date = int(from_date.timestamp()) #epoch of from_date\n",
    "to_date = datetime(2022, 12, 31) #datetime(year, month, day)\n",
    "to_date = int(to_date.timestamp()) #epoch of to_date\n",
    "\n",
    "# Get activities in scope\n",
    "activities = get_activities(access_token, from_date, to_date)\n",
    "\n",
    "# Put the relevant data in a Dataframe\n",
    "statlist = []\n",
    "transform(access_token, activities)\n",
    "df = pd.DataFrame(statlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 962,
   "id": "18e00cf2-0695-48d5-9013-f8ee323b860d",
   "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_id</th>\n",
       "      <th>nb_kudos</th>\n",
       "      <th>distance</th>\n",
       "      <th>moving_time</th>\n",
       "      <th>sport_type</th>\n",
       "      <th>total_elevation_gain</th>\n",
       "      <th>start_date_local</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>average_heartrate</th>\n",
       "      <th>suffer_score</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6455205724</td>\n",
       "      <td>7</td>\n",
       "      <td>4800.6</td>\n",
       "      <td>1589</td>\n",
       "      <td>Run</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2022-01-01T12:37:07Z</td>\n",
       "      <td>3.021</td>\n",
       "      <td>164.7</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>6635652530</td>\n",
       "      <td>11</td>\n",
       "      <td>49516.6</td>\n",
       "      <td>8161</td>\n",
       "      <td>Ride</td>\n",
       "      <td>442.4</td>\n",
       "      <td>2022-02-05T14:26:41Z</td>\n",
       "      <td>6.067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>351.0</td>\n",
       "      <td>2022-02-05</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6745119897</td>\n",
       "      <td>6</td>\n",
       "      <td>6753.2</td>\n",
       "      <td>2321</td>\n",
       "      <td>Run</td>\n",
       "      <td>10.9</td>\n",
       "      <td>2022-02-27T11:13:28Z</td>\n",
       "      <td>2.910</td>\n",
       "      <td>167.9</td>\n",
       "      <td>107.0</td>\n",
       "      <td>2022-02-27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6788573623</td>\n",
       "      <td>12</td>\n",
       "      <td>10861.0</td>\n",
       "      <td>3527</td>\n",
       "      <td>Run</td>\n",
       "      <td>14.4</td>\n",
       "      <td>2022-03-07T18:44:12Z</td>\n",
       "      <td>3.079</td>\n",
       "      <td>140.9</td>\n",
       "      <td>112.0</td>\n",
       "      <td>2022-03-07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>6816517211</td>\n",
       "      <td>11</td>\n",
       "      <td>36252.1</td>\n",
       "      <td>5490</td>\n",
       "      <td>Ride</td>\n",
       "      <td>254.7</td>\n",
       "      <td>2022-03-13T10:18:00Z</td>\n",
       "      <td>6.603</td>\n",
       "      <td>147.3</td>\n",
       "      <td>112.0</td>\n",
       "      <td>2022-03-13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    activity_id  nb_kudos  distance  moving_time sport_type  \\\n",
       "54   6455205724         7    4800.6         1589        Run   \n",
       "53   6635652530        11   49516.6         8161       Ride   \n",
       "52   6745119897         6    6753.2         2321        Run   \n",
       "51   6788573623        12   10861.0         3527        Run   \n",
       "50   6816517211        11   36252.1         5490       Ride   \n",
       "\n",
       "    total_elevation_gain      start_date_local  average_speed  \\\n",
       "54                   6.1  2022-01-01T12:37:07Z          3.021   \n",
       "53                 442.4  2022-02-05T14:26:41Z          6.067   \n",
       "52                  10.9  2022-02-27T11:13:28Z          2.910   \n",
       "51                  14.4  2022-03-07T18:44:12Z          3.079   \n",
       "50                 254.7  2022-03-13T10:18:00Z          6.603   \n",
       "\n",
       "    average_heartrate  suffer_score        date  month  \n",
       "54              164.7          62.0  2022-01-01      1  \n",
       "53                NaN         351.0  2022-02-05      2  \n",
       "52              167.9         107.0  2022-02-27      2  \n",
       "51              140.9         112.0  2022-03-07      3  \n",
       "50              147.3         112.0  2022-03-13      3  "
      ]
     },
     "execution_count": 962,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a date and month column for further use\n",
    "df['date'] = df['start_date_local'].apply(pd.to_datetime, format='%Y-%m-%d').dt.date\n",
    "df['month'] = df['start_date_local'].apply(pd.to_datetime, format='%Y-%m-%d').dt.month\n",
    "df = df.sort_values(by=\"start_date_local\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7492f6bb-9f32-45be-8c86-2d26c3c8ab2b",
   "metadata": {
    "tags": []
   },
   "source": [
    "### RUNS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e5798a5-a0dd-44fe-b8ec-859d89498838",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Let's look at the runs - Distance over the months - Data prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 963,
   "id": "f49e4698-86f1-46d2-828a-01eaba521827",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get all the runs distance and suffer score by month\n",
    "runs = df[df['sport_type'] == 'Run'][['date', 'suffer_score', 'distance']] \n",
    "runs = (runs.set_index('date')\n",
    "  .reindex(pd.date_range('2022-01-01', '2022-12-31', freq='D')) # to have every day in the dataframe\n",
    "  .rename_axis(['date'])\n",
    "  .fillna(0)\n",
    "  .reset_index())\n",
    "\n",
    "df_runs_mth = runs.groupby([pd.Grouper(key='date', freq='MS')])[['suffer_score', 'distance']].sum().reset_index().sort_values('date') # group by month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 964,
   "id": "3e77306c-825c-4396-9ced-3297b4fec49a",
   "metadata": {},
   "outputs": [],
   "source": [
    "average_runs = df_runs_mth['distance'].mean()\n",
    "under_average_runs = df_runs_mth[df_runs_mth['distance']<average_runs]\n",
    "over_average_runs = df_runs_mth[df_runs_mth['distance']>=average_runs]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f18362c5-ddfe-4c2c-bc39-3b8db26297e0",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 965,
   "id": "3eebfcc1-6d2c-4e42-95e6-0a0ac00e1ced",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_plot(original_data, low_series, high_series, average):\n",
    "    # Setup plot size\n",
    "    fig, ax = plt.subplots(figsize=(13.33,7.5), dpi = 96)\n",
    "    \n",
    "    # Colors\n",
    "    colors_high = [\"#e5383b\",\"#660708\"]\n",
    "    colors_low = [\"#f5f3f4\",\"#b1a7a6\"]\n",
    "    # Colormap\n",
    "    cmap_low = mpl.colors.LinearSegmentedColormap.from_list(\"my color\", colors_low, N=256)\n",
    "    cmap_high = mpl.colors.LinearSegmentedColormap.from_list(\"my color\", colors_high, N=256)\n",
    "    norm_low = mpl.colors.Normalize(low_series['distance'].min(), average)\n",
    "    norm_high = mpl.colors.Normalize(average, high_series['distance'].max())\n",
    "\n",
    "    # Plot bars and average (horizontal) line\n",
    "    bar1 = ax.bar(low_series['date'], low_series['distance'], color=cmap_low(norm_low(low_series['distance'])), width=20, label='Below Average', zorder=2)\n",
    "    bar2 = ax.bar(high_series['date'], high_series['distance'], color=cmap_high(norm_high(high_series['distance'])), width=20, label='Above Average', zorder=2)\n",
    "    plt.axhline(y=average, color = 'grey', linewidth=3)\n",
    "    #ax.text(x=0.12, y=0.8, s=\"Average\", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)\n",
    "    \n",
    "    # Determine the y-limits of the plot\n",
    "    ymin, ymax = ax.get_ylim()\n",
    "    # Calculate a suitable y position for the text label\n",
    "    y_pos = average/ymax + 0.03\n",
    "    # Annotate the average line\n",
    "    ax.text(0.98, y_pos, f'Average = {average/1000:.2f} km', ha='right', va='center', transform=ax.transAxes, size=8, zorder=3)\n",
    "\n",
    "    \n",
    "\n",
    "    # Create grid \n",
    "    ax.grid(which=\"major\", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)\n",
    "    ax.grid(which=\"major\", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)\n",
    "\n",
    "    # Remove splines\n",
    "    ax.spines[['top','right','bottom']].set_visible(False)\n",
    "\n",
    "    # Make left spine slightly thicker\n",
    "    ax.spines['left'].set_linewidth(1.1)\n",
    "\n",
    "    # Add label on top of each bar\n",
    "    #rects = ax.patches\n",
    "    #for rect in rects:\n",
    "     #   height = rect.get_height()\n",
    "      #  ax.text(rect.get_x() + rect.get_width() / 2, height + 1, int(height), ha='center', va='bottom', size=10) \n",
    "    \n",
    "    # Add label on top of each bar - alternative\n",
    "    ax.bar_label(bar1, labels=[f'{e/1000:,.2f}' for e in low_series['distance']], padding=3, color='black', fontsize=8)    \n",
    "    ax.bar_label(bar2, labels=[f'{e/1000:,.2f}' for e in high_series['distance']], padding=3, color='black', fontsize=8)    \n",
    "\n",
    "    # Add in red line and rectangle on top\n",
    "    ax.plot([0.12, .9],                  # Set width of line\n",
    "        [.98, .98],                  # Set height of line\n",
    "        transform=fig.transFigure,   # Set location relative to plot\n",
    "        clip_on=False, \n",
    "        color='#E3120B', \n",
    "        linewidth=.6)\n",
    "    ax.add_patch(plt.Rectangle((0.12,.98),          # Set location of rectangle by lower left corder\n",
    "                        0.04,                       # Width of rectangle\n",
    "                        -0.02,                      # Height of rectangle. Negative so it goes down.\n",
    "                        facecolor='#E3120B', \n",
    "                        transform=fig.transFigure, \n",
    "                        clip_on=False, \n",
    "                        linewidth = 0))\n",
    "    \n",
    "    # Add legend\n",
    "    ax.legend(loc=\"best\", ncol=2, bbox_to_anchor=[1, 1.07], borderaxespad=0, frameon=False, fontsize=8)\n",
    "\n",
    "    # Reformat x-axis tick labels\n",
    "    ax.xaxis.set_tick_params(labelsize=12) # Set tick label size\n",
    "    #ax.set_xticklabels(ax.get_xticklabels(), rotation=0) # Set rotation of labels\n",
    "    labels = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "    ax.set_xticks(original_data['date'], labels) # Add labels to x-ticks\n",
    "\n",
    "    # Reformat yaxis\n",
    "    ax.set_ylabel('Distance', fontsize=12, labelpad=10)\n",
    "    ax.yaxis.set_label_position(\"right\")\n",
    "    ax.yaxis.set_major_formatter(lambda s, i : f'{s/1e3:,.0f} km')\n",
    "    ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "    ax.yaxis.set_tick_params(pad=-2,        # Pad tick labels so they don't go over y-axis\n",
    "                        labeltop=True,      # Put x-axis labels on top\n",
    "                        labelbottom=False,  # Set no x-axis labels on bottom\n",
    "                        bottom=False,       # Set no ticks on bottom\n",
    "                        labelsize=12)       # Set tick label size                  \n",
    "\n",
    "    # Add in title and subtitle\n",
    "    ax.text(x=0.12, y=.93, s=\"Strava Stats 2022 - Runs\", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)\n",
    "    ax.text(x=0.12, y=.90, s=\"Number of km run each month from 01/01/2022 to 31/12/2022\", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)\n",
    "    \n",
    "    # Adjust the margins around the plot area\n",
    "    plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.85, wspace=None, hspace=None)\n",
    "    \n",
    "    # Export plot as high resolution PNG\n",
    "    plt.savefig('strava_2022_runs.png', bbox_inches=\"tight\", facecolor='white', pad_inches=0.5)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 966,
   "id": "9512a8ee-5e02-4290-805b-64a9ca7f36f5",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1279.68x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot(original_data=df_runs_mth, low_series=under_average_runs, high_series=over_average_runs, average=average_runs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeae2fc5-433c-4f4c-be7c-11ff612e9e99",
   "metadata": {
    "tags": []
   },
   "source": [
    "### RIDES"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f807f6b9-3770-46e6-8ac0-120d523bf0f6",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Let's look at the Rides - Distance over the months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 967,
   "id": "3e3c6177-b05a-4251-8553-764f85c2b35f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get all the rides distance and suffer score by month\n",
    "rides = df[df['sport_type'] == 'Ride'][['date', 'suffer_score', 'distance']]\n",
    "rides = (rides.set_index('date')\n",
    "  .reindex(pd.date_range('2022-01-01', '2022-12-31', freq='D')) # to have every day in the dataframe\n",
    "  .rename_axis(['date'])\n",
    "  .fillna(0)\n",
    "  .reset_index())\n",
    "\n",
    "df_rides_mth = rides.groupby([pd.Grouper(key='date', freq='MS')])[['suffer_score', 'distance']].sum().reset_index().sort_values('date') # group by month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 968,
   "id": "9dbfea69-3018-40bc-ba06-450c640abf06",
   "metadata": {},
   "outputs": [],
   "source": [
    "average_rides = df_rides_mth['distance'].mean()\n",
    "under_average_rides = df_rides_mth[df_rides_mth['distance']<average_rides]\n",
    "over_average_rides = df_rides_mth[df_rides_mth['distance']>=average_rides]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d69f5194-18bf-40bd-95e4-ea74d057d071",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 969,
   "id": "0d840405-bba1-43d0-9751-5d813ffeed99",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_plot(original_data, low_series, high_series, average):\n",
    "    # Setup plot size\n",
    "    fig, ax = plt.subplots(figsize=(13.33,7.5), dpi = 96)\n",
    "    \n",
    "    # Colors\n",
    "    colors_high = [\"#fff3b0\",\"#e09f3e\"]\n",
    "    colors_low = [\"#415a77\",\"#778da9\"]\n",
    "    # Colormap\n",
    "    cmap_low = mpl.colors.LinearSegmentedColormap.from_list(\"my color\", colors_low, N=256)\n",
    "    cmap_high = mpl.colors.LinearSegmentedColormap.from_list(\"my color\", colors_high, N=256)\n",
    "    norm_low = mpl.colors.Normalize(low_series['distance'].min(), average)\n",
    "    norm_high = mpl.colors.Normalize(average, high_series['distance'].max())\n",
    "\n",
    "    # Plot bars and average (horizontal) line\n",
    "    bar1 = ax.bar(low_series['date'], low_series['distance'], color=cmap_low(norm_low(low_series['distance'])), width=20, label='Below Average', zorder=2)\n",
    "    bar2 = ax.bar(high_series['date'], high_series['distance'], color=cmap_high(norm_high(high_series['distance'])), width=20, label='Above Average', zorder=2)\n",
    "    plt.axhline(y=average, color = 'grey', linewidth=3)\n",
    "    #ax.text(x=0.12, y=0.8, s=\"Average\", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)\n",
    "    \n",
    "    # Determine the y-limits of the plot\n",
    "    ymin, ymax = ax.get_ylim()\n",
    "    # Calculate a suitable y position for the text label\n",
    "    y_pos = average/ymax + 0.03\n",
    "    # Annotate the average line\n",
    "    ax.text(0.98, y_pos, f'Average = {average/1000:.2f} km', ha='right', va='center', transform=ax.transAxes, size=8, zorder=3)\n",
    "\n",
    "    \n",
    "\n",
    "    # Create grid \n",
    "    ax.grid(which=\"major\", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)\n",
    "    ax.grid(which=\"major\", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)\n",
    "\n",
    "    # Remove splines\n",
    "    ax.spines[['top','right','bottom']].set_visible(False)\n",
    "\n",
    "    # Make left spine slightly thicker\n",
    "    ax.spines['left'].set_linewidth(1.1)\n",
    "\n",
    "    # Add label on top of each bar\n",
    "    #rects = ax.patches\n",
    "    #for rect in rects:\n",
    "     #   height = rect.get_height()\n",
    "      #  ax.text(rect.get_x() + rect.get_width() / 2, height + 1, int(height), ha='center', va='bottom', size=10) \n",
    "    \n",
    "    # Add label on top of each bar - alternative\n",
    "    ax.bar_label(bar1, labels=[f'{e/1000:,.2f}' for e in low_series['distance']], padding=3, color='black', fontsize=8)    \n",
    "    ax.bar_label(bar2, labels=[f'{e/1000:,.2f}' for e in high_series['distance']], padding=3, color='black', fontsize=8)    \n",
    "\n",
    "    # Add in red line and rectangle on top\n",
    "    ax.plot([0.12, .9],                  # Set width of line\n",
    "        [.98, .98],                  # Set height of line\n",
    "        transform=fig.transFigure,   # Set location relative to plot\n",
    "        clip_on=False, \n",
    "        color='#E3120B', \n",
    "        linewidth=.6)\n",
    "    ax.add_patch(plt.Rectangle((0.12,.98),          # Set location of rectangle by lower left corder\n",
    "                        0.04,                       # Width of rectangle\n",
    "                        -0.02,                      # Height of rectangle. Negative so it goes down.\n",
    "                        facecolor='#E3120B', \n",
    "                        transform=fig.transFigure, \n",
    "                        clip_on=False, \n",
    "                        linewidth = 0))\n",
    "    \n",
    "    # Add legend\n",
    "    ax.legend(loc=\"best\", ncol=2, bbox_to_anchor=[1, 1.07], borderaxespad=0, frameon=False, fontsize=8)\n",
    "\n",
    "    # Reformat x-axis tick labels\n",
    "    ax.xaxis.set_tick_params(labelsize=12) # Set tick label size\n",
    "    #ax.set_xticklabels(ax.get_xticklabels(), rotation=0) # Set rotation of labels\n",
    "    labels = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "    ax.set_xticks(original_data['date'], labels) # Add labels to x-ticks\n",
    "\n",
    "    # Reformat yaxis\n",
    "    ax.set_ylabel('Distance', fontsize=12, labelpad=10)\n",
    "    ax.yaxis.set_label_position(\"right\")\n",
    "    ax.yaxis.set_major_formatter(lambda s, i : f'{s/1e3:,.0f} km')\n",
    "    ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "    ax.yaxis.set_tick_params(pad=-2,        # Pad tick labels so they don't go over y-axis\n",
    "                        labeltop=True,      # Put x-axis labels on top\n",
    "                        labelbottom=False,  # Set no x-axis labels on bottom\n",
    "                        bottom=False,       # Set no ticks on bottom\n",
    "                        labelsize=12)       # Set tick label size                  \n",
    "\n",
    "    # Add in title and subtitle\n",
    "    ax.text(x=0.12, y=.93, s=\"Strava Stats 2022 - Rides\", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)\n",
    "    ax.text(x=0.12, y=.90, s=\"Number of km cycled each month from 01/01/2022 to 31/12/2022\", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)\n",
    "    \n",
    "    # Adjust the margins around the plot area\n",
    "    plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.85, wspace=None, hspace=None)\n",
    "    \n",
    "    # Export plot as high resolution PNG\n",
    "    plt.savefig('strava_2022_rides.png', bbox_inches=\"tight\", facecolor='white', pad_inches=0.5)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 970,
   "id": "5f736223-daa9-43ce-be79-34d48ffbb90b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1279.68x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot(original_data=df_rides_mth, low_series=under_average_rides, high_series=over_average_rides, average=average_rides)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87b28165-33b1-493c-be23-74ccea42a966",
   "metadata": {
    "tags": []
   },
   "source": [
    "### RUNS & RIDES"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68745389-e6eb-460c-8b00-b521ce2dbd02",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### In order to compare have both on the same graph, let's use the suffer score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 971,
   "id": "2d93f10c-aff2-44ca-906e-6f456befa38d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get all the runs and rides suffer score by month\n",
    "runs_rides = df[(df['sport_type'] == 'Run') | (df['sport_type'] == 'Ride')][['date', 'suffer_score', 'distance']]\n",
    "runs_rides = (runs_rides.set_index('date')\n",
    "  .reindex(pd.date_range('2022-01-01', '2022-12-31', freq='D')) # to have every day in the dataframe\n",
    "  .rename_axis(['date'])\n",
    "  .fillna(0)\n",
    "  .reset_index())\n",
    "\n",
    "df_runs_rides_mth = runs_rides.groupby([pd.Grouper(key='date', freq='MS')])[['suffer_score', 'distance']].sum().reset_index().sort_values('date') # group by month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 972,
   "id": "f4fc97c8-ab93-44ad-acdb-110f7e1a8c5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "average_rr = df_runs_rides_mth['suffer_score'].mean()\n",
    "under_average_rr = df_runs_rides_mth[df_runs_rides_mth['suffer_score']<average_rr]\n",
    "over_average_rr = df_runs_rides_mth[df_runs_rides_mth['suffer_score']>=average_rr]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1075f797-261d-40e4-877c-d903e274820f",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 973,
   "id": "5edc9e2d-1e23-4677-b282-2e8991834fa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_plot_suffer_score(original_data, low_series, high_series, average):\n",
    "    # Setup plot size\n",
    "    fig, ax = plt.subplots(figsize=(13.33,7.5), dpi = 96)\n",
    "    \n",
    "    # Colors\n",
    "    colors_high = [\"#f7b267\",\"#e36414\"]\n",
    "    colors_low = [\"#2196f3\",\"#bbdefb\"]\n",
    "    # Colormap\n",
    "    cmap_low = mpl.colors.LinearSegmentedColormap.from_list(\"my color\", colors_low, N=256)\n",
    "    cmap_high = mpl.colors.LinearSegmentedColormap.from_list(\"my color\", colors_high, N=256)\n",
    "    norm_low = mpl.colors.Normalize(low_series['suffer_score'].min(), average)\n",
    "    norm_high = mpl.colors.Normalize(average, high_series['suffer_score'].max())\n",
    "\n",
    "    # Plot bars and average (horizontal) line\n",
    "    bar1 = ax.bar(low_series['date'], low_series['suffer_score'], color=cmap_low(norm_low(low_series['suffer_score'])), width=20, label='Below Average', zorder=2)\n",
    "    bar2 = ax.bar(high_series['date'], high_series['suffer_score'], color=cmap_high(norm_high(high_series['suffer_score'])), width=20, label='Above Average', zorder=2)\n",
    "    plt.axhline(y=average, color = 'grey', linewidth=3)\n",
    "    #ax.text(x=0.12, y=0.8, s=\"Average\", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)\n",
    "    \n",
    "    # Determine the y-limits of the plot\n",
    "    ymin, ymax = ax.get_ylim()\n",
    "    # Calculate a suitable y position for the text label\n",
    "    y_pos = average/ymax + 0.03\n",
    "    # Annotate the average line\n",
    "    ax.text(0.98, y_pos, f'Average = {average:.0f}', ha='right', va='center', transform=ax.transAxes, size=8, zorder=3)\n",
    "\n",
    "    # Create grid \n",
    "    ax.grid(which=\"major\", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)\n",
    "    ax.grid(which=\"major\", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)\n",
    "\n",
    "    # Remove splines\n",
    "    ax.spines[['top','right','bottom']].set_visible(False)\n",
    "\n",
    "    # Make left spine slightly thicker\n",
    "    ax.spines['left'].set_linewidth(1.1)\n",
    "\n",
    "    # Add label on top of each bar\n",
    "    #rects = ax.patches\n",
    "    #for rect in rects:\n",
    "     #   height = rect.get_height()\n",
    "      #  ax.text(rect.get_x() + rect.get_width() / 2, height + 1, int(height), ha='center', va='bottom', size=10) \n",
    "    \n",
    "    # Add label on top of each bar - alternative\n",
    "    ax.bar_label(bar1, labels=[f'{e:,.0f}' for e in low_series['suffer_score']], padding=3, color='black', fontsize=8)    \n",
    "    ax.bar_label(bar2, labels=[f'{e:,.0f}' for e in high_series['suffer_score']], padding=3, color='black', fontsize=8)    \n",
    "\n",
    "    # Add in red line and rectangle on top\n",
    "    ax.plot([0.12, .9],                  # Set width of line\n",
    "        [.98, .98],                  # Set height of line\n",
    "        transform=fig.transFigure,   # Set location relative to plot\n",
    "        clip_on=False, \n",
    "        color='#E3120B', \n",
    "        linewidth=.6)\n",
    "    ax.add_patch(plt.Rectangle((0.12,.98),          # Set location of rectangle by lower left corder\n",
    "                        0.04,                       # Width of rectangle\n",
    "                        -0.02,                      # Height of rectangle. Negative so it goes down.\n",
    "                        facecolor='#E3120B', \n",
    "                        transform=fig.transFigure, \n",
    "                        clip_on=False, \n",
    "                        linewidth = 0))\n",
    "    \n",
    "    # Add legend\n",
    "    ax.legend(loc=\"best\", ncol=2, bbox_to_anchor=[1, 1.07], borderaxespad=0, frameon=False, fontsize=8)\n",
    "\n",
    "    # Reformat x-axis tick labels\n",
    "    ax.xaxis.set_tick_params(labelsize=12) # Set tick label size\n",
    "    #ax.set_xticklabels(ax.get_xticklabels(), rotation=0) # Set rotation of labels\n",
    "    labels = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "    ax.set_xticks(original_data['date'], labels) # Add labels to x-ticks\n",
    "\n",
    "    # Reformat yaxis\n",
    "    ax.set_ylabel('Suffer Score', fontsize=12, labelpad=10)\n",
    "    ax.yaxis.set_label_position(\"right\")\n",
    "    ax.yaxis.set_major_formatter(lambda s, i : f'{s:,.0f}')\n",
    "    ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "    ax.yaxis.set_tick_params(pad=-2,        # Pad tick labels so they don't go over y-axis\n",
    "                        labeltop=True,      # Put x-axis labels on top\n",
    "                        labelbottom=False,  # Set no x-axis labels on bottom\n",
    "                        bottom=False,       # Set no ticks on bottom\n",
    "                        labelsize=12)       # Set tick label size                  \n",
    "\n",
    "    # Add in title and subtitle\n",
    "    ax.text(x=0.12, y=.93, s=\"Strava Stats 2022 - Runs and Rides\", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)\n",
    "    ax.text(x=0.12, y=.90, s=\"Suffer score each month from 01/01/2022 to 31/12/2022\", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)\n",
    "    \n",
    "    # Adjust the margins around the plot area\n",
    "    plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.85, wspace=None, hspace=None)\n",
    "    \n",
    "    # Export plot as high resolution PNG\n",
    "    plt.savefig('strava_2022_runs&rides.png', bbox_inches=\"tight\", facecolor='white', pad_inches=0.5)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 974,
   "id": "04108c1e-af6a-4da2-a7e9-671e64e3d6b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1279.68x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot_suffer_score(original_data=df_runs_rides_mth, low_series=under_average_rr, high_series=over_average_rr, average=average_rr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56f0c9d4-cb69-436c-a361-26e52ee71356",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Split between rides and runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 975,
   "id": "be325b1b-345e-40b1-819b-6aa9c49fa552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1279.68x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13.33,7.5), dpi = 96)\n",
    "\n",
    "labels = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "x = np.arange(len(labels))  # the label locations\n",
    "width = 0.35  # the width of the bars\n",
    "rides = ax.bar(x - width/2, df_rides_mth['suffer_score'], width, label='rides', color='#e5383b')\n",
    "runs = ax.bar(x + width/2, df_runs_mth['suffer_score'], width, label='runs', color='#b1a7a6')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Suffer score')\n",
    "ax.set_xticks(x, labels)\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "ax.bar_label(rides, padding=3)\n",
    "ax.bar_label(runs, padding=3)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 976,
   "id": "9666985e-d965-4cd2-a63d-eb7e22aff3c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# From raw value to percentage\n",
    "totals = [i+j for i,j in zip(df_runs_mth['suffer_score'], df_rides_mth['suffer_score'])]\n",
    "runs_prop = [i / j * 100 for i,j in zip(df_runs_mth['suffer_score'], totals)]\n",
    "rides_prop = [i / j * 100 for i,j in zip(df_rides_mth['suffer_score'], totals)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 987,
   "id": "8a7b0b61-875e-47f6-b800-e1c884081bad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_plot_suffer_score_stacked(original_data, bar1, bar2):\n",
    "    # Setup plot size\n",
    "    fig, ax = plt.subplots(figsize=(13.33,7.5), dpi = 96)\n",
    "\n",
    "    # Plot bars and average (horizontal) line\n",
    "    width = 20\n",
    "    runs = ax.bar(df_runs_mth['date'], runs_prop, color='#b1a7a6', width=width, label='runs', alpha=0.7, zorder=2)\n",
    "    rides = ax.bar(df_rides_mth['date'], rides_prop, bottom=runs_prop, color='#415a77', edgecolor='white', width=width, label='rides', alpha=0.7, zorder=2)    \n",
    "\n",
    "    # Create grid \n",
    "    ax.grid(which=\"major\", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)\n",
    "    ax.grid(which=\"major\", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)\n",
    "\n",
    "    # Remove splines\n",
    "    ax.spines[['top','right','bottom']].set_visible(False)\n",
    "\n",
    "    # Make left spine slightly thicker\n",
    "    ax.spines['left'].set_linewidth(1.1)\n",
    "\n",
    "    # Add label on top of each bar\n",
    "    #rects = ax.patches\n",
    "    #for rect in rects:\n",
    "     #   height = rect.get_height()\n",
    "      #  ax.text(rect.get_x() + rect.get_width() / 2, height + 1, int(height), ha='center', va='bottom', size=10) \n",
    "    \n",
    "    # Add label on top of each bar - alternative\n",
    "    ax.bar_label(runs, labels=[f'{e:,.0f}%' for e in runs_prop], padding=3, color='black', fontsize=8, label_type='center')    \n",
    "    ax.bar_label(rides, labels=[f'{e:,.0f}%' for e in rides_prop], padding=3, color='black', fontsize=8, label_type='center')    \n",
    "\n",
    "    # Add in red line and rectangle on top\n",
    "    ax.plot([0.12, .9],                  # Set width of line\n",
    "        [.98, .98],                  # Set height of line\n",
    "        transform=fig.transFigure,   # Set location relative to plot\n",
    "        clip_on=False, \n",
    "        color='#E3120B', \n",
    "        linewidth=.6)\n",
    "    ax.add_patch(plt.Rectangle((0.12,.98),          # Set location of rectangle by lower left corder\n",
    "                        0.04,                       # Width of rectangle\n",
    "                        -0.02,                      # Height of rectangle. Negative so it goes down.\n",
    "                        facecolor='#E3120B', \n",
    "                        transform=fig.transFigure, \n",
    "                        clip_on=False, \n",
    "                        linewidth = 0))\n",
    "    \n",
    "    # Add legend\n",
    "    ax.legend(loc=\"best\", ncol=2, bbox_to_anchor=[1, 1.07], borderaxespad=0, frameon=False, fontsize=8)\n",
    "\n",
    "    # Reformat x-axis tick labels\n",
    "    ax.xaxis.set_tick_params(labelsize=12) # Set tick label size\n",
    "    #ax.set_xticklabels(ax.get_xticklabels(), rotation=0) # Set rotation of labels\n",
    "    labels = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "    ax.set_xticks(original_data['date'], labels) # Add labels to x-ticks\n",
    "\n",
    "    # Reformat yaxis\n",
    "    ax.yaxis.set_major_formatter(lambda s, i : f'{s:,.0f}%')\n",
    "    ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "    ax.yaxis.set_tick_params(pad=-2,        # Pad tick labels so they don't go over y-axis\n",
    "                        labeltop=True,      # Put x-axis labels on top\n",
    "                        labelbottom=False,  # Set no x-axis labels on bottom\n",
    "                        bottom=False,       # Set no ticks on bottom\n",
    "                        labelsize=12)       # Set tick label size                  \n",
    "\n",
    "    # Add in title and subtitle\n",
    "    ax.text(x=0.12, y=.93, s=\"Strava Stats 2022 - Runs and Rides\", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)\n",
    "    ax.text(x=0.12, y=.90, s=\"Suffer score proportion each month from 01/01/2022 to 31/12/2022\", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)\n",
    "    \n",
    "    # Adjust the margins around the plot area\n",
    "    plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.85, wspace=None, hspace=None)\n",
    "    \n",
    "    # Export plot as high resolution PNG\n",
    "    plt.savefig('strava_2022_split_runs_rides.png', bbox_inches=\"tight\", facecolor='white', pad_inches=0.5)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 988,
   "id": "91c60303-1635-4852-9ba3-6efbae414a94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1279.68x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot_suffer_score_stacked(df_runs_rides_mth, df_runs_mth, df_rides_mth)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "460b8a0e-8f71-46b6-9d57-ba4b86345125",
   "metadata": {},
   "source": [
    "### Typical activity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16037e7c-60f8-4242-b46b-ee2cb13b2cce",
   "metadata": {},
   "source": [
    "#### Runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 989,
   "id": "8ce1f37d-2bf0-4f72-97f7-b6a61449b7af",
   "metadata": {},
   "outputs": [],
   "source": [
    "runs = df[df['sport_type'] == 'Run'][['suffer_score', 'distance', 'moving_time', 'nb_kudos', 'total_elevation_gain', 'average_speed', 'average_heartrate']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 990,
   "id": "a1ca3265-0a41-4bc4-893e-ff420c316756",
   "metadata": {},
   "outputs": [],
   "source": [
    "runs[\"average_heartrate\"] = runs[\"average_heartrate\"].fillna(150)\n",
    "runs['dummy'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 991,
   "id": "415ef8b9-81f2-4bab-8114-f6203dac3b84",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>suffer_score</th>\n",
       "      <th>distance</th>\n",
       "      <th>moving_time</th>\n",
       "      <th>nb_kudos</th>\n",
       "      <th>total_elevation_gain</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>average_heartrate</th>\n",
       "      <th>dummy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>118.857143</td>\n",
       "      <td>9016.503571</td>\n",
       "      <td>2947.357143</td>\n",
       "      <td>12.214286</td>\n",
       "      <td>21.589286</td>\n",
       "      <td>3.083571</td>\n",
       "      <td>163.203571</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>34.754924</td>\n",
       "      <td>2207.356759</td>\n",
       "      <td>765.219175</td>\n",
       "      <td>3.083494</td>\n",
       "      <td>21.130149</td>\n",
       "      <td>0.279098</td>\n",
       "      <td>9.119149</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>4800.600000</td>\n",
       "      <td>1452.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>2.277000</td>\n",
       "      <td>140.900000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>99.750000</td>\n",
       "      <td>7261.050000</td>\n",
       "      <td>2338.000000</td>\n",
       "      <td>10.750000</td>\n",
       "      <td>13.275000</td>\n",
       "      <td>2.985250</td>\n",
       "      <td>160.200000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>115.500000</td>\n",
       "      <td>9129.900000</td>\n",
       "      <td>2973.500000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>15.900000</td>\n",
       "      <td>3.095500</td>\n",
       "      <td>165.150000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>147.000000</td>\n",
       "      <td>10288.300000</td>\n",
       "      <td>3338.750000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>19.750000</td>\n",
       "      <td>3.266250</td>\n",
       "      <td>168.100000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>181.000000</td>\n",
       "      <td>13537.800000</td>\n",
       "      <td>4603.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>103.300000</td>\n",
       "      <td>3.547000</td>\n",
       "      <td>176.900000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       suffer_score      distance  moving_time   nb_kudos  \\\n",
       "count     28.000000     28.000000    28.000000  28.000000   \n",
       "mean     118.857143   9016.503571  2947.357143  12.214286   \n",
       "std       34.754924   2207.356759   765.219175   3.083494   \n",
       "min       40.000000   4800.600000  1452.000000   6.000000   \n",
       "25%       99.750000   7261.050000  2338.000000  10.750000   \n",
       "50%      115.500000   9129.900000  2973.500000  12.500000   \n",
       "75%      147.000000  10288.300000  3338.750000  14.000000   \n",
       "max      181.000000  13537.800000  4603.000000  19.000000   \n",
       "\n",
       "       total_elevation_gain  average_speed  average_heartrate  dummy  \n",
       "count             28.000000      28.000000          28.000000   28.0  \n",
       "mean              21.589286       3.083571         163.203571    1.0  \n",
       "std               21.130149       0.279098           9.119149    0.0  \n",
       "min                6.100000       2.277000         140.900000    1.0  \n",
       "25%               13.275000       2.985250         160.200000    1.0  \n",
       "50%               15.900000       3.095500         165.150000    1.0  \n",
       "75%               19.750000       3.266250         168.100000    1.0  \n",
       "max              103.300000       3.547000         176.900000    1.0  "
      ]
     },
     "execution_count": 991,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runs.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "699c0ea4-9467-4d77-8ace-ad16c7bbf0d9",
   "metadata": {},
   "source": [
    "#### Rides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 992,
   "id": "bb86968a-a131-41cd-8c44-231a7b6aa0fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "rides = df[df['sport_type'] == 'Ride'][['sport_type', 'suffer_score', 'distance', 'moving_time', 'nb_kudos', 'total_elevation_gain', 'average_speed', 'average_heartrate']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 993,
   "id": "effc88c2-db80-48ce-b557-d1a4b8b55436",
   "metadata": {},
   "outputs": [],
   "source": [
    "rides[\"average_heartrate\"] = rides[\"average_heartrate\"].fillna(150)\n",
    "rides['dummy'] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 994,
   "id": "07e56c71-bc3d-49b7-b111-b36194695808",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>suffer_score</th>\n",
       "      <th>distance</th>\n",
       "      <th>moving_time</th>\n",
       "      <th>nb_kudos</th>\n",
       "      <th>total_elevation_gain</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>average_heartrate</th>\n",
       "      <th>dummy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>191.058824</td>\n",
       "      <td>50380.952941</td>\n",
       "      <td>8163.764706</td>\n",
       "      <td>14.647059</td>\n",
       "      <td>624.417647</td>\n",
       "      <td>6.264235</td>\n",
       "      <td>147.664706</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>87.924307</td>\n",
       "      <td>16775.654333</td>\n",
       "      <td>2943.028107</td>\n",
       "      <td>4.372373</td>\n",
       "      <td>434.744234</td>\n",
       "      <td>0.756075</td>\n",
       "      <td>6.855193</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>36073.300000</td>\n",
       "      <td>5490.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>138.100000</td>\n",
       "      <td>4.538000</td>\n",
       "      <td>134.200000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>136.000000</td>\n",
       "      <td>41277.800000</td>\n",
       "      <td>6700.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>256.800000</td>\n",
       "      <td>5.673000</td>\n",
       "      <td>145.300000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>174.000000</td>\n",
       "      <td>49516.600000</td>\n",
       "      <td>7426.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>508.100000</td>\n",
       "      <td>6.359000</td>\n",
       "      <td>148.500000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>206.000000</td>\n",
       "      <td>52134.200000</td>\n",
       "      <td>8105.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>940.400000</td>\n",
       "      <td>6.811000</td>\n",
       "      <td>152.600000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>364.000000</td>\n",
       "      <td>110063.000000</td>\n",
       "      <td>17646.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1754.500000</td>\n",
       "      <td>7.384000</td>\n",
       "      <td>157.600000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       suffer_score       distance   moving_time   nb_kudos  \\\n",
       "count     17.000000      17.000000     17.000000  17.000000   \n",
       "mean     191.058824   50380.952941   8163.764706  14.647059   \n",
       "std       87.924307   16775.654333   2943.028107   4.372373   \n",
       "min       78.000000   36073.300000   5490.000000   9.000000   \n",
       "25%      136.000000   41277.800000   6700.000000  11.000000   \n",
       "50%      174.000000   49516.600000   7426.000000  15.000000   \n",
       "75%      206.000000   52134.200000   8105.000000  16.000000   \n",
       "max      364.000000  110063.000000  17646.000000  23.000000   \n",
       "\n",
       "       total_elevation_gain  average_speed  average_heartrate  dummy  \n",
       "count             17.000000      17.000000          17.000000   17.0  \n",
       "mean             624.417647       6.264235         147.664706    2.0  \n",
       "std              434.744234       0.756075           6.855193    0.0  \n",
       "min              138.100000       4.538000         134.200000    2.0  \n",
       "25%              256.800000       5.673000         145.300000    2.0  \n",
       "50%              508.100000       6.359000         148.500000    2.0  \n",
       "75%              940.400000       6.811000         152.600000    2.0  \n",
       "max             1754.500000       7.384000         157.600000    2.0  "
      ]
     },
     "execution_count": 994,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d807a7b-f00a-4e18-8cd6-31b4e63b7b43",
   "metadata": {},
   "source": [
    "#### Suffer score comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 997,
   "id": "0e5f2c74-3a3f-4811-b893-b8d57abcb3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatter_plot(runs, rides):\n",
    "    # Setup plot size\n",
    "    fig, ax = plt.subplots(figsize=(13.33,7.5), dpi = 96)\n",
    "\n",
    "    # Plot bars and average (horizontal) line\n",
    "    width = 20\n",
    "    size_runs = runs['distance']/13\n",
    "    runs_graph = ax.scatter(runs['suffer_score'], runs['dummy'], color='#b1a7a6', zorder=2, alpha=0.3, s=size_runs)\n",
    "    size_rides = rides['distance']/13\n",
    "    rides_graph = ax.scatter(rides['suffer_score'], rides['dummy'], color='#415a77', zorder=2, alpha=0.3, s=size_rides)\n",
    "    runs_mean = ax.scatter(runs['suffer_score'].mean(), runs['dummy'].iloc[0], color='red', label = 'mean', marker='X', zorder=2, alpha=1, s=100)\n",
    "    rides_mean = ax.scatter(rides['suffer_score'].mean(), rides['dummy'].iloc[0], color='red', marker='X', zorder=2, alpha=1, s=100)\n",
    "    \n",
    "    # Annotate the categories\n",
    "    ax.text(1.05, 0.75, 'Rides', ha='right', va='center', transform=ax.transAxes, size=12, zorder=3)\n",
    "    ax.text(1.05, 0.25, 'Runs', ha='right', va='center', transform=ax.transAxes, size=12, zorder=3)\n",
    "\n",
    "    # Create grid \n",
    "    ax.grid(which=\"major\", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)\n",
    "    ax.grid(which=\"major\", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)\n",
    "\n",
    "    # Remove splines\n",
    "    ax.spines[['top','right','bottom']].set_visible(False)\n",
    "\n",
    "    # Make left spine slightly thicker\n",
    "    ax.spines['left'].set_linewidth(1.1)\n",
    "\n",
    "    # Add label on top of each bar\n",
    "    #rects = ax.patches\n",
    "    #for rect in rects:\n",
    "     #   height = rect.get_height()\n",
    "      #  ax.text(rect.get_x() + rect.get_width() / 2, height + 1, int(height), ha='center', va='bottom', size=10) \n",
    "    \n",
    "    # Add label on top of each bar - alternative\n",
    "    #ax.bar_label(runs, labels=[f'{e:,.0f}%' for e in runs_prop], padding=3, color='black', fontsize=8, label_type='center')    \n",
    "    #ax.bar_label(rides, labels=[f'{e:,.0f}%' for e in rides_prop], padding=3, color='black', fontsize=8, label_type='center')    \n",
    "\n",
    "    # Add in red line and rectangle on top\n",
    "    ax.plot([0.12, .9],                  # Set width of line\n",
    "        [.98, .98],                  # Set height of line\n",
    "        transform=fig.transFigure,   # Set location relative to plot\n",
    "        clip_on=False, \n",
    "        color='#E3120B', \n",
    "        linewidth=.6)\n",
    "    ax.add_patch(plt.Rectangle((0.12,.98),          # Set location of rectangle by lower left corder\n",
    "                        0.04,                       # Width of rectangle\n",
    "                        -0.02,                      # Height of rectangle. Negative so it goes down.\n",
    "                        facecolor='#E3120B', \n",
    "                        transform=fig.transFigure, \n",
    "                        clip_on=False, \n",
    "                        linewidth = 0))\n",
    "    \n",
    "    # Add legend\n",
    "    ax.legend(loc=\"best\", ncol=2, bbox_to_anchor=[1, 1.07], borderaxespad=0, frameon=False, fontsize=8)\n",
    "\n",
    "    # Reformat x-axis tick labels\n",
    "    ax.set_xlabel('Suffer Score', fontsize=12)\n",
    "    ax.xaxis.set_tick_params(labelsize=12) # Set tick label size\n",
    "    #ax.set_xticklabels(ax.get_xticklabels(), rotation=0) # Set rotation of labels\n",
    "    #labels = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "    #ax.set_xticks(original_data['date'], labels) # Add labels to x-ticks\n",
    "\n",
    "    # Reformat yaxis\n",
    "    ax.set_ylim(0.5,  2.5)\n",
    "    #ax.yaxis.set_major_formatter(lambda s, i : f'{s:,.0f}')\n",
    "    #ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "    ax.set_yticklabels([])\n",
    "    ax.yaxis.set_tick_params(pad=-2,        # Pad tick labels so they don't go over y-axis\n",
    "                        labeltop=True,      # Put x-axis labels on top\n",
    "                        labelbottom=False,  # Set no x-axis labels on bottom\n",
    "                        bottom=False,       # Set no ticks on bottom\n",
    "                        labelsize=12)       # Set tick label size                  \n",
    "\n",
    "    # Add in title and subtitle\n",
    "    ax.text(x=0.12, y=.93, s=\"Strava Stats 2022 - Runs and Rides\", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)\n",
    "    ax.text(x=0.12, y=.90, s=\"Suffer score for each activity - the size of the bubble represents the distance of the activity\", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)\n",
    "    \n",
    "    # Adjust the margins around the plot area\n",
    "    plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.85, wspace=None, hspace=None)\n",
    "    \n",
    "    # Export plot as high resolution PNG\n",
    "    plt.savefig('strava_2022_suffer_score_comparison.png', bbox_inches=\"tight\", facecolor='white', pad_inches=0.5)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 998,
   "id": "b39c6f30-4b22-4515-aab0-27442d442ab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1279.68x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "runs_rides = df[(df['sport_type'] == 'Run') | (df['sport_type'] == 'Ride')][['sport_type', 'suffer_score', 'distance', 'moving_time', 'nb_kudos', 'total_elevation_gain', 'average_speed', 'average_heartrate']] \n",
    "runs_rides['dummy'] = np.where(runs_rides['sport_type'] == 'Run', 1, 2)\n",
    "scatter_plot(runs,rides)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8605368-76c4-429b-bf86-2ceac0edb540",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b58083c-0b0c-4bb4-bc1e-a045f60d1008",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}