Portfolio 1.ipynb

{
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   "cell_type": "code",
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   "source": [
    "# importing libraries\n",
    "import pandas as pd\n",
    "import pandas.util.testing as tm\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from datetime import timedelta\n",
    "plt.style.use('seaborn')\n",
    "%matplotlib inline\n",
    "import datetime\n",
    "from datetime import datetime\n",
    "import statistics as st\n",
    "import scipy\n",
    "from pylab import rcParams\n",
    "import seaborn as sb\n",
    "from scipy.stats.stats import pearsonr\n",
    "rcParams['figure.figsize'] = 10,9\n",
    "sb.set_style('whitegrid')\n",
    "from scipy.stats import ttest_ind\n",
    "pd.set_option('mode.chained_assignment', None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of Cycling Data\n",
    "\n",
    "## Loading Data\n",
    "\n",
    "The first dataset is an export of my ride data from [Strava](https://strava.com/), an online social \n",
    "network site for cycling and other sports.  This data is a log of every ride since the start of 2018\n",
    "and contains summary data like the distance and average speed.  It was exported using \n",
    "the script `scripts/stravaget.py` which uses the stravalib module to read data. Some details of\n",
    "the fields exported by that script can be seen in [the documentation for stravalib](https://pythonhosted.org/stravalib/api.html#stravalib.model.Activity). \n",
    "\n",
    "The exported data is a CSV file so that's easy to read, however the date information in the file is \n",
    "recorded in a different timezone (UTC) so we need to do a bit of conversion.  In reading the data I'm\n",
    "setting the index of the data frame to be the datetime of the ride. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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>average_heartrate</th>\n",
       "      <th>average_temp</th>\n",
       "      <th>average_watts</th>\n",
       "      <th>device_watts</th>\n",
       "      <th>distance</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>elevation_gain</th>\n",
       "      <th>kudos</th>\n",
       "      <th>moving_time</th>\n",
       "      <th>workout_type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02 20:47:51+00:00</th>\n",
       "      <td>100.6</td>\n",
       "      <td>21.0</td>\n",
       "      <td>73.8</td>\n",
       "      <td>False</td>\n",
       "      <td>15.2</td>\n",
       "      <td>94</td>\n",
       "      <td>316.00 m</td>\n",
       "      <td>10</td>\n",
       "      <td>73</td>\n",
       "      <td>Ride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04 01:36:53+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>131.7</td>\n",
       "      <td>False</td>\n",
       "      <td>18.0</td>\n",
       "      <td>52</td>\n",
       "      <td>236.00 m</td>\n",
       "      <td>5</td>\n",
       "      <td>46</td>\n",
       "      <td>Ride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04 02:56:00+00:00</th>\n",
       "      <td>83.1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00 m</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Ride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04 05:37:04+00:00</th>\n",
       "      <td>110.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>113.6</td>\n",
       "      <td>False</td>\n",
       "      <td>22.9</td>\n",
       "      <td>77</td>\n",
       "      <td>246.00 m</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>Ride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05 19:22:46+00:00</th>\n",
       "      <td>110.9</td>\n",
       "      <td>20.0</td>\n",
       "      <td>147.7</td>\n",
       "      <td>True</td>\n",
       "      <td>58.4</td>\n",
       "      <td>189</td>\n",
       "      <td>676.00 m</td>\n",
       "      <td>12</td>\n",
       "      <td>144</td>\n",
       "      <td>Ride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-19 20:22:49+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>166.8</td>\n",
       "      <td>True</td>\n",
       "      <td>58.2</td>\n",
       "      <td>174</td>\n",
       "      <td>666.00 m</td>\n",
       "      <td>16</td>\n",
       "      <td>136</td>\n",
       "      <td>Ride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-26 20:12:56+00:00</th>\n",
       "      <td>120.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>166.9</td>\n",
       "      <td>True</td>\n",
       "      <td>59.6</td>\n",
       "      <td>185</td>\n",
       "      <td>663.00 m</td>\n",
       "      <td>15</td>\n",
       "      <td>139</td>\n",
       "      <td>Ride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-27 20:42:59+00:00</th>\n",
       "      <td>122.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>168.4</td>\n",
       "      <td>True</td>\n",
       "      <td>19.8</td>\n",
       "      <td>49</td>\n",
       "      <td>247.00 m</td>\n",
       "      <td>6</td>\n",
       "      <td>47</td>\n",
       "      <td>Ride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-27 21:47:24+00:00</th>\n",
       "      <td>144.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>235.2</td>\n",
       "      <td>True</td>\n",
       "      <td>29.1</td>\n",
       "      <td>50</td>\n",
       "      <td>415.00 m</td>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "      <td>Race</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-27 23:55:13+00:00</th>\n",
       "      <td>109.6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>114.7</td>\n",
       "      <td>True</td>\n",
       "      <td>20.2</td>\n",
       "      <td>68</td>\n",
       "      <td>280.00 m</td>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>Ride</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>268 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           average_heartrate  average_temp  average_watts  \\\n",
       "date                                                                        \n",
       "2018-01-02 20:47:51+00:00              100.6          21.0           73.8   \n",
       "2018-01-04 01:36:53+00:00                NaN          24.0          131.7   \n",
       "2018-01-04 02:56:00+00:00               83.1          25.0           13.8   \n",
       "2018-01-04 05:37:04+00:00              110.1          24.0          113.6   \n",
       "2018-01-05 19:22:46+00:00              110.9          20.0          147.7   \n",
       "...                                      ...           ...            ...   \n",
       "2019-07-19 20:22:49+00:00                NaN           3.0          166.8   \n",
       "2019-07-26 20:12:56+00:00              120.8           7.0          166.9   \n",
       "2019-07-27 20:42:59+00:00              122.0           3.0          168.4   \n",
       "2019-07-27 21:47:24+00:00              144.4           3.0          235.2   \n",
       "2019-07-27 23:55:13+00:00              109.6          15.0          114.7   \n",
       "\n",
       "                          device_watts  distance  elapsed_time elevation_gain  \\\n",
       "date                                                                            \n",
       "2018-01-02 20:47:51+00:00        False      15.2            94       316.00 m   \n",
       "2018-01-04 01:36:53+00:00        False      18.0            52       236.00 m   \n",
       "2018-01-04 02:56:00+00:00        False       0.0             3         0.00 m   \n",
       "2018-01-04 05:37:04+00:00        False      22.9            77       246.00 m   \n",
       "2018-01-05 19:22:46+00:00         True      58.4           189       676.00 m   \n",
       "...                                ...       ...           ...            ...   \n",
       "2019-07-19 20:22:49+00:00         True      58.2           174       666.00 m   \n",
       "2019-07-26 20:12:56+00:00         True      59.6           185       663.00 m   \n",
       "2019-07-27 20:42:59+00:00         True      19.8            49       247.00 m   \n",
       "2019-07-27 21:47:24+00:00         True      29.1            50       415.00 m   \n",
       "2019-07-27 23:55:13+00:00         True      20.2            68       280.00 m   \n",
       "\n",
       "                           kudos  moving_time workout_type  \n",
       "date                                                        \n",
       "2018-01-02 20:47:51+00:00     10           73         Ride  \n",
       "2018-01-04 01:36:53+00:00      5           46         Ride  \n",
       "2018-01-04 02:56:00+00:00      2            2         Ride  \n",
       "2018-01-04 05:37:04+00:00      8           64         Ride  \n",
       "2018-01-05 19:22:46+00:00     12          144         Ride  \n",
       "...                          ...          ...          ...  \n",
       "2019-07-19 20:22:49+00:00     16          136         Ride  \n",
       "2019-07-26 20:12:56+00:00     15          139         Ride  \n",
       "2019-07-27 20:42:59+00:00      6           47         Ride  \n",
       "2019-07-27 21:47:24+00:00     15           50         Race  \n",
       "2019-07-27 23:55:13+00:00      6           59         Ride  \n",
       "\n",
       "[268 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strava = pd.read_csv('data/Portfolio1/strava_export.csv',index_col='date',parse_dates = True)\n",
    "strava.sort_index()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second dataset comes from an application called [GoldenCheetah](https://www.goldencheetah.org/) which provides\n",
    "some analytics services over ride data.  This has some of the same fields but adds a lot of analysis of the \n",
    "power, speed and heart rate data in each ride.  This data overlaps with the Strava data but doesn't include all \n",
    "of the same rides. \n",
    "\n",
    "Again we create an index using the datetime for each ride, this time combining two columns in the data (date and time) \n",
    "and localising to Sydney so that the times match those for the Strava data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cheetah = pd.read_csv('data/Portfolio1/cheetah.csv', skipinitialspace=True)\n",
    "cheetah.index = pd.to_datetime(cheetah['date'] + ' ' + cheetah['time'])\n",
    "cheetah.index = cheetah.index.tz_localize('Australia/Sydney')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GoldenCheetah data contains many many variables (columns) and I won't go into all of them here. Some\n",
    "that are of particular interest for the analysis below are:\n",
    "\n",
    "Here are definitions of some of the more important fields in the data. Capitalised fields come from the GoldenCheetah data\n",
    "while lowercase_fields come from Strava. There are many cases where fields are duplicated and in this case the values\n",
    "should be the same, although there is room for variation as the algorithm used to calculate them could be different\n",
    "in each case. \n",
    "\n",
    "  * Duration - overall duration of the ride, should be same as elapsed_time\n",
    "  * Time Moving - time spent moving (not resting or waiting at lights), should be the same as moving_time\n",
    "  * Elevation Gain - metres climbed over the ride\n",
    "  * Average Speed - over the ride\n",
    "  * Average Power - average power in watts as measured by a power meter, relates to how much effort is being put in to the ride, should be the same as  * average_watts' from Strava\n",
    "  * Nonzero Average Power - same as Average Power but excludes times when power is zero from the average\n",
    "  * Average Heart Rate - should be the same as average_heartrate\n",
    "  * Average Cadence - cadence is the rotations per minute of the pedals\n",
    "  * Average Temp - temperature in the environment as measured by the bike computer (should be same as average_temp)\n",
    "  * VAM - average ascent speed - speed up hills\n",
    "  * Calories (HR) - Calorie expendature as estimated from heart rate data\n",
    "  * 1 sec Peak Power - this and other  'Peak Power' measures give the maximum power output in the ride over this time period.  Will be higher for shorter periods. High values in short periods would come from a very 'punchy' ride with sprints for example.\n",
    "  * 1 min Peak Hr - a similar measure relating to Heart Rate\n",
    "  * NP - Normalised Power, a smoothed average power measurement, generally higher than Average Power \n",
    "  * TSS - Training Stress Score, a measure of how hard a ride this was\n",
    "  * device_watts - True if the power (watts) measures were from a power meter, False if they were estimated\n",
    "  * distance - distance travelled in Km\n",
    "  * kudos - likes from other Strava users (social network)\n",
    "  * workout_type - one of  'Race',  'Workout' or  'Ride'\n",
    "  \n",
    "  \n",
    "Some of the GoldenCheetah parameters are defined [in thier documentation](https://github.com/GoldenCheetah/GoldenCheetah/wiki/UG_Glossary).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "__The first task is to combine these two data frames using the [`join` method of Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index).   The goal is to keep only those rows of data \n",
    "that appear in both data frames so that we have complete data for every row.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Since the strava timezone is in different format, I am parsing the date field of strava to Australian local time using lambda function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "strava = pd.read_csv('data/Portfolio1/strava_export.csv',parse_dates=[3],date_parser=lambda x: pd.Timestamp(x,  tz= 'Australia/Sydney'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* I need to use an inner join operation to retain matched data between Cheetah and Strava data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "Merge_Strava_Cheetah = strava.join(cheetah, how= 'inner',on='date' , rsuffix= True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* I am retaining one copy of data set so that I can validate the data if there is any filter applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_Strava_Cheetah_copy = strava.join(cheetah, how= 'inner',on='date' , rsuffix= True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Question 1. Remove rides with no measured power (where device_watts is False) - these are commutes or MTB rides__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Storing the result of both device_watts as false and workout_type as 'Ride' in a field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "Device_Watts_False = Merge_Strava_Cheetah[Merge_Strava_Cheetah.device_watts == 0]\n",
    "Device_Watts_False_With_Ride = Device_Watts_False[Device_Watts_False.workout_type == 'Ride']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Storing the result in an index and applying drop logic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = Device_Watts_False[Device_Watts_False.workout_type == 'Ride'].index \n",
    "Merge_Strava_Cheetah.drop(idx,inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Applying assert function to validate whether the drop operation worked or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert len(merge_Strava_Cheetah_copy) - len(Merge_Strava_Cheetah) == len(Device_Watts_False_With_Ride), \"Drop didn't work\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__2. Look at the distributions of some key variables: time, distance, average speed, average power, TSS. Are they normally distributed? Skewed?__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Plotting a histogram to check whether moving_time variable is normally distributed or not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82.67924528301887\n"
     ]
    }
   ],
   "source": [
    "mean = Merge_Strava_Cheetah['moving_time'].mean()\n",
    "std = Merge_Strava_Cheetah['moving_time'].std()\n",
    "count = Merge_Strava_Cheetah['moving_time'].size\n",
    "plt.title('Histogram of Moving time')\n",
    "counts = np.random.normal(mean,std,count)\n",
    "plt.hist(counts,bins =40)\n",
    "plt.show()\n",
    "print(mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ Plotting the histogram based on mean, standard deviation and count of moving_time field. The mean is 82.67. The graph looks normally distributed with gaps between -50 to 0 and beyond 200."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34.676841981132085\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = Merge_Strava_Cheetah['Distance'].mean()\n",
    "std = Merge_Strava_Cheetah['Distance'].std()\n",
    "count = Merge_Strava_Cheetah['Distance'].size\n",
    "print(mean)\n",
    "plt.title('Histogram of Distance')\n",
    "counts = np.random.normal(mean,std,count)\n",
    "plt.hist(counts,bins =40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ Plotting the histogram based on mean, standard deviation and count of moving_time field. The mean is 34.67. Since the tail is inclined on the left side, the graph is left skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25.12897849056604\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = Merge_Strava_Cheetah['Average Speed'].mean()\n",
    "std = Merge_Strava_Cheetah['Average Speed'].std()\n",
    "count = Merge_Strava_Cheetah['Average Speed'].size\n",
    "print(mean)\n",
    "plt.title('Histogram of Average Speed')\n",
    "counts = np.random.normal(mean,std,count)\n",
    "plt.hist(counts,bins =40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ Plotting the histogram based on mean, standard deviation and count of moving_time field. The mean is 25.12. The graph approximately looks like a symmetric distribution. Therefore, we can say that the graph is normally distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160.08522169811323\n"
     ]
    },
    {
     "data": {
      "image/png": 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2wxLvu5zHHr3dMDpWdOWtK1d7oZoL9Xnk8Ca+PGEo3d3dgH5nq621tTXr8qLG3DO8l3Z7CPBWRvvKeHmu9qyam5tztqVSqZwdCIn62YdmLUu86fKOTl59Y3XWthHDmhLvt5zHHjGsiX++mb+uXO3F1JyvzwAtLRMA/c7WsiSflllkZhPj2wcBj2a0LwAOztMuIiJ9LMmZ+/eAG8xsY+BF4G4AM5sLHApcD9xiZvOBNcBRFapVRESKVFS4u/srwPj49mJgvyzrfCa+uQY4skL1iYhIArqISUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3ESlJ+vQE5Vz9K30ryefcRWQAW296grSrf0cOb2Lq5AlVqkzSKdxFpGSFpieQ6tOwjIhIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIB0vQDEoR15jrJMHq7Yf1cjUj1KdwlCPnmOhkxrKmfqxGpPg3LiIgEKNGZu5kdDxwf3x0M7Aps5e5vxe1XA58AVsXrHO7uK8qqVEREipYo3N19OjAdwMyuA27uDfbYWOBAd3+j3AJFRKR0ZQ3LmNnuQLO7T0tbtgGwAzDNzBaY2aQyaxQRkRKV+wfVc4ELM5ZtClwDXAkMAuaZ2VPu/ly+HbW3t9PV1ZWzPZVKlVlqfVA/S6eveqstbW1tdHd3V7uMiqvV/5utra1ZlycOdzPbHPi4u8/LaOoErnL3zni9h4AxQN5wb25uztmWSqVydiAk6mcZ0r7qTaqrpaWl2iVUXD3+3yxnWGZf4IEsy3cE5pvZIDPbCNgbeLqMxxERkRKVMyxjwEv/vmN2JrDE3e8xs18DjwPvALe6e3t5ZYqISCkSh7u7X5Zx/8q025cCl5ZRl4iIlEEXMYmIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgEq98s6RERKMmXaQpZ3dK63fOTwJqZOnlCFisKkcBeRfrW8o5NX31hd7TKCp2EZEZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCVDiz7mb2SJgRXz3ZXc/Ia3tm8BJwFrgInf/Y1lViohISRKFu5kNBnD3iVnatgJOA3YHBgPzzex+d+8uo04RkappbGysdgklS3rmPgZoMrO58T7OdffH47Y9gQVxmHeb2RJgF+DJfDtsb2+nq6srZ3sqlUpYan1RP0tXj//xQtbW1kZ3d/ZzuULHKt+21dLY2MhvF67kmlkPZG0fObyJL08YWrW6W1tbsy5PGu6dwOXAjcAOwGwzM3dfCwzl/eEagFXAZoV22NzcnLMtlUrl7EBI1M8yzFpW2f1JYi0tLflXyHOsCm5bJdfMeiDvlAktLbU3J07ScF8MLHH3HmCxmXUAHwL+DqwEhqStOwR4q6wqRUSkJEnDfRKwM3CKmW1NdLb+Wtz2BPCTeFy+EdgJaCu3UBERKV7Sj0LeBGxuZvOBO4nC/jQz+5y7LwOuBh4FHgJ+5O65B9NFRKTiEp25u/sa4KiMxQvT2m8AbiijLhERKYMuYhIRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXfqdpgsIV0NDtSuQXolnhZSBacq0hSzv6MzaNnJ4E1Mn574Me51tMy5BH73dMDpWdCXet9SGLbdoyvs7Mnq7Yf1c0cClcJeSLO/ozDvHRtJtRwxr4p9vJt+31I5Cx1n6h4ZlREQCpHAXEQmQwl1EJEAKdxGRACncRUQCpHAXEQmQwl1EJEAKdxGRACncpWJ06blI7dAVqn2onEv1q/m4ubYvdOl4vkvPddm5VELFpr/IkG/6i3r93VW496FyLtWv5uPm2r6YS8fL2VakkGpMf1Gvv7salhERCVCiM3cz2wi4GRgFNAIXufs9ae1nAt8AXo8XneTuXl6pIiJSrKTDMkcDHe5+jJkNBxYB96S1jwWOdfdUuQWKiEjpkob7XcDdaffXZrS3Aj80s62Ae9394oSPIyIiCSQKd3d/G8DMhhCF/HkZq/wGuA5YCcw0s0Pd/Y/59tne3k5XV1fO9lSqvt4EFPNtQ21tbXR3d6+zrNx+Jn3cUravllx113LNUppyfzer9TuSr+6+1tramnV54k/LmNk2wEzg5+5+R9ryBuBn7r4ivn8vsBuQN9ybm5tztqVSqZwdqGkZ3zaUqaWlZZ37FetniY9b6vbVkrfuGq1ZSlPu72a1fkcK1l0FSf+gOhKYC5zq7g9mNA8F2sxsJ2A18CmiP76KiEg/SXrmfi6wBXC+mZ0fL7sB2NTdp5nZucA8oBt40N3/p/xSRUSkWEnH3E8HTs/TfhtwW9KiRESkPLqISeqC5q0Jn45xZWn6AakLmrcmfPmOMdT3cc7Vr76cY0rhLnVD89aEr9D8L/WqGvNMaVhGRCRACncRkQAp3EVEAqRwFxEJkMJdRCRACncRkQAp3EVEAqRwFxEJkMJdRIJQrekLanXahCCuUM13yXJfXt5bTboUX2Rd1ZqiolanTQgi3KtxaW+16VJ8kfVV6/9FLU6boGEZEZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCVCiz7mb2QbAz4ExQDdworsvSWv/JnASsBa4yN3/WIFaRUSkSEnP3D8PDHb3vYAfAFf0NpjZVsBpwCeAA4GLzayx3EJFRKR4Sa9Q3RuYA+Duj5vZ7mltewIL3L0b6DazJcAuwJNlVZrHyOG5rwDL19bXSq2rsbH418Bc+/7g5oPzznVRzPORdN/52svZti/3Xat19eW+VVft1NWX+ZQ03IcCK9Luv2tmG7r72ixtq4DNithnzqentbU174a1OndMqXW1tLT02b5LUavPp4gUL+mwzEpgSPp+4mDP1jYEeCvh44iISAJJw30BcDCAmY0Hnk9rewLYx8wGm9lmwE5AW1lViohISRp6enpK3ijt0zK7EA2nnEAU9kvc/Z740zKTiV48/tPdZ1SuZBERKSRRuIuISG3TRUwiIgFSuIuIBEjhLiISoJr+mr1C0xzUOzNbxPvXBLwM/BK4imjahrnufmG1aiuXmY0DLnH3iWb2MWA60EP0yalvu/t7ZnYBcAhRf89w9yeqVnBCGf0cC8wC/hI3X+/ud9ZzP81sI+BmYBTQCFwEvEBgxzNHP/9BHR/Pmg530qY5iD9yeQVweJVrqggzGwzg7hPTlj0DfBF4CbjXzMa6+9PVqTA5MzsHOAbo/VLJK4Hz3P1hM/sFcLiZ/RXYDxgHbAPMAPaoRr1JZennWOBKd0+fjmMs9d3Po4EOdz/GzIYDi4BnCO94ZuvnVOr4eNb6sMw60xwAu+dfva6MAZrMbK6ZPWRm+wKN7r7U3XuA+4D9q1tiYkuBI9LutwKPxLdnAwcQHdu57t7j7n8DNjSzLfu3zLJl6+chZvYnM7vJzIZQ//28Czg/7f5awjyeufpZt8ez1sM96zQH1SqmwjqBy4kmV/sW8Kt4Wa9ip22oOfF1De+kLWqIX7Dg/X4lnaaiZmTp5xPA2e6+L9G7rwuo8366+9vuvioOtruB8wjweOboZ10fz1oP93zTHNS7xcDt8RnAYqJfmGFp7SFN2/Be2u3efoU4TcVMd0/13gZ2I4B+mtk2wDzgNne/g0CPZ5Z+1vXxrPVwzzfNQb2bRDxVspltDTQBq81sezNrIDqjf7SK9VXSIjObGN8+iKhfC4ADzWwDM9uW6IX7jWoVWCH3mdme8e39gRR13k8zGwnMBb7v7jfHi4M7njn6WdfHs9aHOGYCnzazhbw/zUEobgKmm9l8ok8dTCI6I/o1MIhoXO/PVayvkr4H3GBmGwMvAne7+7tm9ijwGNFJxrerWWCFnAxca2ZrgGXAZHdfWef9PBfYAjjfzHrHpE8Hrg7seGbr55nAz+r1eGr6ARGRANX6sIyIiCSgcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQP8PFm7vf7RSAiQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = Merge_Strava_Cheetah['Average Power'].mean()\n",
    "std = Merge_Strava_Cheetah['Average Power'].std()\n",
    "count = Merge_Strava_Cheetah['Average Power'].size\n",
    "print(mean)\n",
    "plt.title('Histogram of Average Power')\n",
    "counts = np.random.normal(mean,std,count)\n",
    "plt.hist(counts,bins =40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ Plotting the histogram based on mean, standard deviation and count of moving_time field. The mean is 160.08. Since the tail is inclined on the left side, the graph is left skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97.21823254716979\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = Merge_Strava_Cheetah['TSS'].mean()\n",
    "std = Merge_Strava_Cheetah['TSS'].std()\n",
    "count = Merge_Strava_Cheetah['TSS'].size\n",
    "print(mean)\n",
    "plt.title('Histogram of TSS')\n",
    "counts = np.random.normal(mean,std,count)\n",
    "plt.hist(counts,bins =40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ Plotting the histogram based on mean, standard deviation and count of moving_time field. The mean is 97.21. Since the tail is inclined on the left side, the graph is left skewed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__3. Explore the relationships between the following variables. Are any of them corrolated with each other (do they vary together in a predictable way)? Can you explain any relationships you observe?__\n",
    "    * Distance\n",
    "    * Moving Time\n",
    "    * Average Speed\n",
    "    * Heart Rate\n",
    "    * Power (watts)\n",
    "    * Normalised power (NP)\n",
    "    * Training Stress Score\n",
    "    * Elevation Gain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Step1:-__ Creating a new datafame that contains specific fields from the large set of data columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "rt = pd.DataFrame(Merge_Strava_Cheetah,columns = ['Distance','Time Moving','Average Speed','Average Heart Rate','Average Power','NP','TSS','Elevation Gain'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Step2:-__ Checking correlationship between specific fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Distance</th>\n",
       "      <th>Time Moving</th>\n",
       "      <th>Average Speed</th>\n",
       "      <th>Average Heart Rate</th>\n",
       "      <th>Average Power</th>\n",
       "      <th>NP</th>\n",
       "      <th>TSS</th>\n",
       "      <th>Elevation Gain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Distance</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.934185</td>\n",
       "      <td>0.234375</td>\n",
       "      <td>0.173256</td>\n",
       "      <td>0.164562</td>\n",
       "      <td>0.287567</td>\n",
       "      <td>0.917104</td>\n",
       "      <td>0.807242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time Moving</th>\n",
       "      <td>0.934185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.040434</td>\n",
       "      <td>0.037252</td>\n",
       "      <td>-0.045643</td>\n",
       "      <td>0.101212</td>\n",
       "      <td>0.866582</td>\n",
       "      <td>0.812870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Speed</th>\n",
       "      <td>0.234375</td>\n",
       "      <td>-0.040434</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.386003</td>\n",
       "      <td>0.770931</td>\n",
       "      <td>0.650656</td>\n",
       "      <td>0.160946</td>\n",
       "      <td>0.027793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Heart Rate</th>\n",
       "      <td>0.173256</td>\n",
       "      <td>0.037252</td>\n",
       "      <td>0.386003</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.387190</td>\n",
       "      <td>0.273245</td>\n",
       "      <td>0.174781</td>\n",
       "      <td>0.050323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Power</th>\n",
       "      <td>0.164562</td>\n",
       "      <td>-0.045643</td>\n",
       "      <td>0.770931</td>\n",
       "      <td>0.387190</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.875896</td>\n",
       "      <td>0.272301</td>\n",
       "      <td>0.002386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NP</th>\n",
       "      <td>0.287567</td>\n",
       "      <td>0.101212</td>\n",
       "      <td>0.650656</td>\n",
       "      <td>0.273245</td>\n",
       "      <td>0.875896</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.450335</td>\n",
       "      <td>0.236570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSS</th>\n",
       "      <td>0.917104</td>\n",
       "      <td>0.866582</td>\n",
       "      <td>0.160946</td>\n",
       "      <td>0.174781</td>\n",
       "      <td>0.272301</td>\n",
       "      <td>0.450335</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.826334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elevation Gain</th>\n",
       "      <td>0.807242</td>\n",
       "      <td>0.812870</td>\n",
       "      <td>0.027793</td>\n",
       "      <td>0.050323</td>\n",
       "      <td>0.002386</td>\n",
       "      <td>0.236570</td>\n",
       "      <td>0.826334</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Distance  Time Moving  Average Speed  Average Heart Rate  \\\n",
       "Distance            1.000000     0.934185       0.234375            0.173256   \n",
       "Time Moving         0.934185     1.000000      -0.040434            0.037252   \n",
       "Average Speed       0.234375    -0.040434       1.000000            0.386003   \n",
       "Average Heart Rate  0.173256     0.037252       0.386003            1.000000   \n",
       "Average Power       0.164562    -0.045643       0.770931            0.387190   \n",
       "NP                  0.287567     0.101212       0.650656            0.273245   \n",
       "TSS                 0.917104     0.866582       0.160946            0.174781   \n",
       "Elevation Gain      0.807242     0.812870       0.027793            0.050323   \n",
       "\n",
       "                    Average Power        NP       TSS  Elevation Gain  \n",
       "Distance                 0.164562  0.287567  0.917104        0.807242  \n",
       "Time Moving             -0.045643  0.101212  0.866582        0.812870  \n",
       "Average Speed            0.770931  0.650656  0.160946        0.027793  \n",
       "Average Heart Rate       0.387190  0.273245  0.174781        0.050323  \n",
       "Average Power            1.000000  0.875896  0.272301        0.002386  \n",
       "NP                       0.875896  1.000000  0.450335        0.236570  \n",
       "TSS                      0.272301  0.450335  1.000000        0.826334  \n",
       "Elevation Gain           0.002386  0.236570  0.826334        1.000000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = rt.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ The degree of correlation ranges from -1 to 1. Here, -1 represent strong negative degree of correlation and 1 represent strong positive degree of correlation. The \"Distance\" vs \"Time moving\" represent strong positive degree of correlation with value 0.93. The 'Time Moving' vs 'Avg speed', 'Time Moving' vs 'Average heart rate','Time Moving' vs  'Avg Power','Time Moving' vs  'Np' represent the lowest degree of correlation as there value is extremely negatively low. From the relationship table, we can see that its quite hard to figureout the degree of correlationship based on the numerical values presented on the table. We need to draw a heatmap diagram that shows the visualization of correlationship between these fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Heatmap diagram that reveals relationship between variables')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.heatmap(corr, xticklabels = corr.columns.values, yticklabels = corr.columns.values)\n",
    "plt.title('Heatmap diagram that reveals relationship between variables')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ The light orange color shows the strongest correlation and the black color shows the weakest correlation. The light orange color that has the strongest correlations can be seen for 'Distance Vs Time Moving'. The black color that has the weakest correlation can be seen for 'Time Moving vs Average speed', 'Average speed vs Time Moving', and so on. The heatmap visualization techniques provide a convenient and clear picture of the relationship between fields."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__4. Some of the rides are designated as \"Race\" in the `workout_type` field, these are where I am racing and you might expect that these would be the most challenging rides.  Normalised Power (NP) is a good measure of how hard a ride is.  Explore the values of `NP` for races vs the overall set of rides to see if this hypothesis is supported (use graphs and summary statistics).  Are races more challenging than rides in general?__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Step1:-__ Storing the result of workout_type field as Race and checking the summary statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>NP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>251.003800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>50.774197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>244.716250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>257.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>272.654250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>302.396000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               NP\n",
       "count   30.000000\n",
       "mean   251.003800\n",
       "std     50.774197\n",
       "min      0.000000\n",
       "25%    244.716250\n",
       "50%    257.170000\n",
       "75%    272.654250\n",
       "max    302.396000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Race = Merge_Strava_Cheetah[Merge_Strava_Cheetah['workout_type'] == 'Race']\n",
    "Race_NP = Race[['NP','workout_type']]\n",
    "Race_NP.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ From the summary statistics, we can see that the average value of NP for races is 251.003 and count is 30."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>NP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>212.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>215.759731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>49.747963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>203.262000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>222.906500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>239.860750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>302.396000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               NP\n",
       "count  212.000000\n",
       "mean   215.759731\n",
       "std     49.747963\n",
       "min      0.000000\n",
       "25%    203.262000\n",
       "50%    222.906500\n",
       "75%    239.860750\n",
       "max    302.396000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_totalrecords_NP=Merge_Strava_Cheetah[['NP','workout_type']]\n",
    "merge_totalrecords_NP.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ From the summary statistics, we can see that the average value of NP for over all ride is 251.75 and the count is 212. Comparing overall rides vs values in NP,  only 14.15% of total participants are in race."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Values of NP Vs Overall set of data')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = Race_NP.plot(kind='scatter', x= 'workout_type', y='NP')    \n",
    "ax2 = merge_totalrecords_NP.plot(kind='scatter',x= 'workout_type', y='NP', ax=ax1)  \n",
    "plt.xlabel('Work Type')\n",
    "plt.ylabel('Values of NP')\n",
    "plt.title('Values of NP Vs Overall set of data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ Plotting scatter diagram between Values of NP vs Overall set of data, we can see that a larger number of participants preferred Ride comparing to Race and Workout. We can conclude that participants mostly enjoyed going to Ride. We also see a considerably low number of participants preferring Race work type. Also, the participants who preferred the Race work type seem to have considerably large values of NP. Since the larger values of NP is a good measure of how hard the ride is. We can say that Race is more challenging than Ride."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__5. It is useful to see a summary of rides over the year. Generate a plot that summarises the number of km ridden each month over the period of the data. Overlay this with the _sum_ of the Training Stress Score and the _average_ of the Average Speed to generate an overall summary of activity.__\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Step1:-__ Storing the result of a ride in a variable and calculating total distance covered based on each month using groupby function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>351.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>618.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>757.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>907.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>632.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>467.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>103.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>160.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>388.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>490.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>415.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month  distance\n",
       "0       1     351.1\n",
       "1       2     618.5\n",
       "2       3     757.1\n",
       "3       4     907.5\n",
       "4       5     632.8\n",
       "5       6     481.0\n",
       "6       7     467.2\n",
       "7       8     103.1\n",
       "8       9     160.6\n",
       "9      10     388.8\n",
       "10     11     490.4\n",
       "11     12     415.2"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ride_take = Merge_Strava_Cheetah[Merge_Strava_Cheetah['workout_type']=='Ride']\n",
    "Ride_take['date'] = pd.to_datetime(Ride_take['date'])\n",
    "Ride_take['Month'] = Ride_take['date'].dt.month\n",
    "MonthVsdistance = Ride_take.groupby(\"Month\")['distance'].sum()\n",
    "MonthVsdistanceFinal = MonthVsdistance.reset_index()\n",
    "MonthVsdistanceFinal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Step2:-__ Similarly, calculating total TSS based on each month using groupby function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>TSS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>966.2920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1683.9476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2494.5250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2517.4567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1733.9907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1432.3450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1219.7014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>331.4360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>564.7220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>951.4079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1337.7984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1225.9295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month        TSS\n",
       "0       1   966.2920\n",
       "1       2  1683.9476\n",
       "2       3  2494.5250\n",
       "3       4  2517.4567\n",
       "4       5  1733.9907\n",
       "5       6  1432.3450\n",
       "6       7  1219.7014\n",
       "7       8   331.4360\n",
       "8       9   564.7220\n",
       "9      10   951.4079\n",
       "10     11  1337.7984\n",
       "11     12  1225.9295"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MonthVsTSS = Ride_take.groupby(\"Month\")['TSS'].sum()\n",
    "MonthVsTSSFinal = MonthVsTSS.reset_index()\n",
    "MonthVsTSSFinal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Step3:-__ Similarly, calculating Average of Average speed based on each month using groupby function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Month</th>\n",
       "      <th>Average Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20.172441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>23.742950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23.752011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>22.012100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>25.163340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>24.184394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>23.706800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>24.603700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>24.427367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>25.648018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>22.911415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>24.643389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month  Average Speed\n",
       "0       1      20.172441\n",
       "1       2      23.742950\n",
       "2       3      23.752011\n",
       "3       4      22.012100\n",
       "4       5      25.163340\n",
       "5       6      24.184394\n",
       "6       7      23.706800\n",
       "7       8      24.603700\n",
       "8       9      24.427367\n",
       "9      10      25.648018\n",
       "10     11      22.911415\n",
       "11     12      24.643389"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MonthVsAverageSpeed = Ride_take.groupby('Month')['Average Speed'].mean()\n",
    "MonthVsAverageSpeedFinal = MonthVsAverageSpeed.reset_index()\n",
    "MonthVsAverageSpeedFinal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Step4:-__  As we need to show the relation between month based on distance, TSS and Avg of Average speed. Plotting and interpreting the data in a single graph can be confusing. We need to use subplots to plot data in separate graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax3 = fig.add_subplot(212)\n",
    "\n",
    "ax1.plot(MonthVsdistanceFinal['Month'],MonthVsdistanceFinal['distance']); ax1.set_title('Month VS distance');ax1.ylabel ='Distance';ax1.xlabel ='Month';\n",
    "ax2.plot(MonthVsdistanceFinal['Month'],MonthVsTSSFinal['TSS']); ax2.set_title('Month VS TSS')\n",
    "ax3.plot(MonthVsdistanceFinal['Month'],MonthVsAverageSpeedFinal['Average Speed']); ax3.set_title('Month VS Average Speed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpretation:-__ \n",
    "1. While comparing Month Vs distance, April is the month where most people traveled to maximum distance and from April to August, we see a drastic drop in total distance traveled and from September onwards, we again see an increase in total distance.\n",
    "2. While comparing Month Vs TSS, March is the month where most people has training stress score and from April to June, we see a drastic drop in training stress score and from July onwards, we again see an increase in TSS trend.\n",
    "3. While comparing Month Vs Average Speed, we see a fluctuation of average speed throughout the year. From January, we see a rise in average speed and in march we again see a drop in average speed. Likewise in April, we see a drastic increase in average speed. In conclusion, throughout the month we see fluctuation in the data."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}