jrtechs-RandomScripts/notebooks/fitbit/GraphingMyLifeWithFitbit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do a deep dive and start visualizing my life using Fitbit and Matplotlib. \n",
    "\n",
    "# What is Fitbit\n",
    "\n",
    "[Fitbit](https://www.fitbit.com) is a fitness watch that tracks your sleep, heart rate, and activity.\n",
    "Fitbit is able to track your steps, however, it is also able to detect multiple types of activity\n",
    "like running, walking, \"sport\" and biking."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is Matplotlib\n",
    "\n",
    "[Matplotlib](https://matplotlib.org/) is a python visualization library that enables you to create bar graphs, line graphs, distributions and many more things.\n",
    "Being able to visualize your results is essential to any person working with data at any scale.\n",
    "Although I like [GGplot](https://ggplot2.tidyverse.org/) in R more than Matplotlib, Matplotlib is still my go to graphing library for Python. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Your Fitbit Data\n",
    "\n",
    "There are two main ways that you can get your Fitbit data:\n",
    "\n",
    "- Fitbit API\n",
    "- Data Archival Export\n",
    "\n",
    "\n",
    "Since connecting to the API and setting up all the web hooks can be a pain, I'm just going to use the data export option because this is only for one person.\n",
    "You can export your data here: [https://www.fitbit.com/settings/data/export](https://www.fitbit.com/settings/data/export).\n",
    "\n",
    "![Data export on fitbit's website](dataExport.png)\n",
    "\n",
    "The Fitbit data archive was very organized and kept meticulous records of everything. \n",
    "All of the data was organized in separate JSON files labeled by date.\n",
    "Fitbit keeps around 1MB of data on you per day; most of this data is from the heart rate sensors.\n",
    "Although 1MB of data may sound like a ton of data, it is probably a lot less if you store it in formats other than JSON. \n",
    "When I downloaded the compressed file it was 20MB, but when I extracted it, it was 380MB!\n",
    "I've only been using Fitbit for 11 months at this point. \n",
    "\n",
    "![compressed data](compression.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sleep\n",
    "\n",
    "Sleep is something fun to visualize.\n",
    "No matter how much of it you get you still feel tired as a college student.\n",
    "In the \"sleep_score\" folder of the exported data you will find a single CSV file with your resting heart rate and Fitbit's computed sleep scores.\n",
    "Interesting enough, this is the only file that comes in the CSV format, everything else is JSON file. \n",
    "\n",
    "We can read in all the data using a single liner with the [Pandas](https://pandas.pydata.org/) python library.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "sleep_score_df = pd.read_csv('data/sleep/sleep_score.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sleep_log_entry_id             timestamp  overall_score  \\\n",
      "0           26093459526  2020-02-27T06:04:30Z             80   \n",
      "1           26081303207  2020-02-26T06:13:30Z             83   \n",
      "2           26062481322  2020-02-25T06:00:30Z             82   \n",
      "3           26045941555  2020-02-24T05:49:30Z             79   \n",
      "4           26034268762  2020-02-23T08:35:30Z             75   \n",
      "..                  ...                   ...            ...   \n",
      "176         23696231032  2019-09-02T07:38:30Z             79   \n",
      "177         23684345925  2019-09-01T07:15:30Z             84   \n",
      "178         23673204871  2019-08-31T07:11:00Z             74   \n",
      "179         23661278483  2019-08-30T06:34:00Z             73   \n",
      "180         23646265400  2019-08-29T05:55:00Z             80   \n",
      "\n",
      "     composition_score  revitalization_score  duration_score  \\\n",
      "0                   20                    19              41   \n",
      "1                   22                    21              40   \n",
      "2                   22                    21              39   \n",
      "3                   17                    20              42   \n",
      "4                   20                    16              39   \n",
      "..                 ...                   ...             ...   \n",
      "176                 20                    20              39   \n",
      "177                 22                    21              41   \n",
      "178                 18                    21              35   \n",
      "179                 17                    19              37   \n",
      "180                 21                    21              38   \n",
      "\n",
      "     deep_sleep_in_minutes  resting_heart_rate  restlessness  \n",
      "0                       65                  60      0.117330  \n",
      "1                       85                  60      0.113188  \n",
      "2                       95                  60      0.120635  \n",
      "3                       52                  61      0.111224  \n",
      "4                       43                  59      0.154774  \n",
      "..                     ...                 ...           ...  \n",
      "176                     88                  56      0.170923  \n",
      "177                     95                  56      0.133268  \n",
      "178                     73                  56      0.102703  \n",
      "179                     50                  55      0.121086  \n",
      "180                     61                  57      0.112961  \n",
      "\n",
      "[181 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "print(sleep_score_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the Pandas library you can generate Matplotlib graphs.\n",
    "Although you can directly use Matplotlib, the wrapper functions using Pandas makes it easier to use.\n",
    "\n",
    "## Sleep Score Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc2c0a270d0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sleep_score_df.hist(column='overall_score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heart Rate\n",
    "\n",
    "Fitbit keeps their calculated heart rates in the sleep scores file rather than heart.\n",
    "Knowing your resting heart rate is useful because it is a good indicator of your overall health.\n",
    "\n",
    "![](restingHeartRate.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc2917a6090>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sleep_score_df.hist(column='resting_heart_rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resting Heart Rate Time Graph\n",
    "\n",
    "Using the pandas wrapper we can quickly create a heart rate graph over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc28f609b50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sleep_score_df.plot(kind='line', y='resting_heart_rate', x ='timestamp', legend=False, title=\"Resting Heart Rate(BPM)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, as we notice with the graph above, the time axis is wack.\n",
    "In the pandas data frame everything was stored as a string timestamp.\n",
    "We can convert this into a datetime object by telling pandas to parse the date as it reads it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc28f533510>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sleep_score_df = pd.read_csv('data/sleep/sleep_score.csv', parse_dates=[1])\n",
    "sleep_score_df.plot(kind='line', y='resting_heart_rate', x ='timestamp', legend=False, title=\"Resting Heart Rate(BPM)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fully manipulate the graphs, we need to use some matplotlib code to do things like setting the axis labels or make multiple plots right next to each other.\n",
    "We can create grab the current axis being used by matplotlib by using plt.gca()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "sleep_score_df.plot(kind='line', y='resting_heart_rate', x ='timestamp', legend=False, title=\"Resting Heart Rate Graph\", ax=ax, figsize=(10, 5))\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Resting Heart Rate (BPM)\")\n",
    "plt.show()\n",
    "#plt.savefig('restingHeartRate.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same thing can be done with sleep scores.\n",
    "It is interesting to note that  the sleep scores rarely vary anything between 75 and 85."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "sleep_score_df.plot(kind='line', y='overall_score', x ='timestamp', legend=False, title=\"Sleep Score Time Series Graph\", ax=ax)\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Fitbit's Sleep Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Pandas we can generate a new column with a specific date attribute like year, day, month, or weekday.\n",
    "If we add a new column for weekday, we can then group by weekday and collapse them all into a single column by summing or averaging the value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sleep_log_entry_id                 timestamp  overall_score  \\\n",
      "0           26093459526 2020-02-27 06:04:30+00:00             80   \n",
      "1           26081303207 2020-02-26 06:13:30+00:00             83   \n",
      "2           26062481322 2020-02-25 06:00:30+00:00             82   \n",
      "3           26045941555 2020-02-24 05:49:30+00:00             79   \n",
      "4           26034268762 2020-02-23 08:35:30+00:00             75   \n",
      "..                  ...                       ...            ...   \n",
      "176         23696231032 2019-09-02 07:38:30+00:00             79   \n",
      "177         23684345925 2019-09-01 07:15:30+00:00             84   \n",
      "178         23673204871 2019-08-31 07:11:00+00:00             74   \n",
      "179         23661278483 2019-08-30 06:34:00+00:00             73   \n",
      "180         23646265400 2019-08-29 05:55:00+00:00             80   \n",
      "\n",
      "     composition_score  revitalization_score  duration_score  \\\n",
      "0                   20                    19              41   \n",
      "1                   22                    21              40   \n",
      "2                   22                    21              39   \n",
      "3                   17                    20              42   \n",
      "4                   20                    16              39   \n",
      "..                 ...                   ...             ...   \n",
      "176                 20                    20              39   \n",
      "177                 22                    21              41   \n",
      "178                 18                    21              35   \n",
      "179                 17                    19              37   \n",
      "180                 21                    21              38   \n",
      "\n",
      "     deep_sleep_in_minutes  resting_heart_rate  restlessness  weekday  \n",
      "0                       65                  60      0.117330        3  \n",
      "1                       85                  60      0.113188        2  \n",
      "2                       95                  60      0.120635        1  \n",
      "3                       52                  61      0.111224        0  \n",
      "4                       43                  59      0.154774        6  \n",
      "..                     ...                 ...           ...      ...  \n",
      "176                     88                  56      0.170923        0  \n",
      "177                     95                  56      0.133268        6  \n",
      "178                     73                  56      0.102703        5  \n",
      "179                     50                  55      0.121086        4  \n",
      "180                     61                  57      0.112961        3  \n",
      "\n",
      "[181 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "temp = pd.DatetimeIndex(sleep_score_df['timestamp'])\n",
    "sleep_score_df['weekday'] = temp.weekday\n",
    "\n",
    "print(sleep_score_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         sleep_log_entry_id  overall_score  composition_score  \\\n",
      "weekday                                                         \n",
      "0              2.483733e+10      79.576923          20.269231   \n",
      "1              2.485200e+10      77.423077          20.423077   \n",
      "2              2.490383e+10      80.880000          21.120000   \n",
      "3              2.483418e+10      76.814815          20.370370   \n",
      "4              2.480085e+10      79.769231          20.961538   \n",
      "5              2.477002e+10      78.840000          20.520000   \n",
      "6              2.482581e+10      77.230769          20.269231   \n",
      "\n",
      "         revitalization_score  duration_score  deep_sleep_in_minutes  \\\n",
      "weekday                                                                \n",
      "0                   19.153846       40.153846              88.000000   \n",
      "1                   19.000000       38.000000              83.846154   \n",
      "2                   19.400000       40.360000              93.760000   \n",
      "3                   19.037037       37.407407              82.592593   \n",
      "4                   19.346154       39.461538              94.461538   \n",
      "5                   19.080000       39.240000              93.720000   \n",
      "6                   18.269231       38.692308              89.423077   \n",
      "\n",
      "         resting_heart_rate  restlessness  \n",
      "weekday                                    \n",
      "0                 58.576923      0.139440  \n",
      "1                 58.538462      0.142984  \n",
      "2                 58.560000      0.138661  \n",
      "3                 58.333333      0.135819  \n",
      "4                 58.269231      0.129791  \n",
      "5                 58.080000      0.138315  \n",
      "6                 58.153846      0.147171  \n"
     ]
    }
   ],
   "source": [
    "print(sleep_score_df.groupby('weekday').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sleep Score Based on Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "sleep_score_df.groupby('weekday').mean().plot(kind='line', y='overall_score', ax = ax)\n",
    "plt.ylabel(\"Sleep Score\")\n",
    "plt.title(\"Sleep Scores on Varying Days of Week\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sleep Score Based on Days of Week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "sleep_score_df.groupby('weekday').mean().plot(kind='line', y='resting_heart_rate', ax = ax)\n",
    "plt.ylabel(\"Resting heart rate (BPM)\")\n",
    "plt.title(\"Resting Heart Rate Varying Days of Week\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calories\n",
    "\n",
    "Fitbit keeps all of their calorie data in JSON files representing sequence data at 1 minute increments.\n",
    "To extrapolate calorie data we need to group by day and then sum the days to get the total calories burned per day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "calories_df = pd.read_json(\"data/calories/calories-2019-07-01.json\",  convert_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 dateTime  value\n",
      "0     2019-07-01 00:00:00   1.07\n",
      "1     2019-07-01 00:01:00   1.07\n",
      "2     2019-07-01 00:02:00   1.07\n",
      "3     2019-07-01 00:03:00   1.07\n",
      "4     2019-07-01 00:04:00   1.07\n",
      "...                   ...    ...\n",
      "43195 2019-07-30 23:55:00   1.07\n",
      "43196 2019-07-30 23:56:00   1.07\n",
      "43197 2019-07-30 23:57:00   1.07\n",
      "43198 2019-07-30 23:58:00   1.07\n",
      "43199 2019-07-30 23:59:00   1.07\n",
      "\n",
      "[43200 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(calories_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           dateTime  value date_minus_time\n",
      "date_minus_time                                           \n",
      "2019-07-01      2019-07-01 00:00:00   1.07      2019-07-01\n",
      "2019-07-01      2019-07-01 00:01:00   1.07      2019-07-01\n",
      "2019-07-01      2019-07-01 00:02:00   1.07      2019-07-01\n",
      "2019-07-01      2019-07-01 00:03:00   1.07      2019-07-01\n",
      "2019-07-01      2019-07-01 00:04:00   1.07      2019-07-01\n",
      "...                             ...    ...             ...\n",
      "2019-07-30      2019-07-30 23:55:00   1.07      2019-07-30\n",
      "2019-07-30      2019-07-30 23:56:00   1.07      2019-07-30\n",
      "2019-07-30      2019-07-30 23:57:00   1.07      2019-07-30\n",
      "2019-07-30      2019-07-30 23:58:00   1.07      2019-07-30\n",
      "2019-07-30      2019-07-30 23:59:00   1.07      2019-07-30\n",
      "\n",
      "[43200 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "calories_df['date_minus_time'] = calories_df[\"dateTime\"].apply( lambda calories_df : \n",
    "    datetime.datetime(year=calories_df.year, month=calories_df.month, day=calories_df.day))\t\n",
    "\n",
    "calories_df.set_index(calories_df[\"date_minus_time\"],inplace=True)\n",
    "\n",
    "print(calories_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   value\n",
      "date_minus_time         \n",
      "2019-07-01       3422.68\n",
      "2019-07-02       2705.85\n",
      "2019-07-03       2871.73\n",
      "2019-07-04       4089.93\n",
      "2019-07-05       3917.91\n",
      "2019-07-06       2762.55\n",
      "2019-07-07       2929.58\n",
      "2019-07-08       2698.99\n",
      "2019-07-09       2833.27\n",
      "2019-07-10       2529.21\n",
      "2019-07-11       2634.25\n",
      "2019-07-12       2953.91\n",
      "2019-07-13       4247.45\n",
      "2019-07-14       2998.35\n",
      "2019-07-15       2846.18\n",
      "2019-07-16       3084.39\n",
      "2019-07-17       2331.06\n",
      "2019-07-18       2849.20\n",
      "2019-07-19       2071.63\n",
      "2019-07-20       2746.25\n",
      "2019-07-21       2562.11\n",
      "2019-07-22       1892.99\n",
      "2019-07-23       2372.89\n",
      "2019-07-24       2320.42\n",
      "2019-07-25       2140.87\n",
      "2019-07-26       2430.38\n",
      "2019-07-27       3769.04\n",
      "2019-07-28       2036.24\n",
      "2019-07-29       2814.87\n",
      "2019-07-30       2077.82\n"
     ]
    }
   ],
   "source": [
    "calories_per_day = calories_df.resample('D').sum()\n",
    "print(calories_per_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "calories_per_day.plot(kind='hist', title=\"Calorie Distribution\", legend=False, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "calories_per_day.plot(kind='line', y='value', legend=False, title=\"Calories Per Day\", ax=ax)\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Calories\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calories Per Day Box Plot\n",
    "\n",
    "Using this data we can turn this into a boxplot to make it easier to visualize the distribution of calories burned during the month of July."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEWCAYAAABYGk2QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAdpklEQVR4nO3deZgU9b3v8feXYZMgboALKqhE2QwuIOghkuASFB/RIypEEAJBYGbymHhPzk1CcmKuJrlJ7slN4iysguwoCbhfUUEjMgaR4Iq7oAmIy2FfFIbv/aNqOD3DzDAzdPevp/vzep5+6Knl9/tWUf3p6qruKnN3REQk/ZqELkBEJFcpgEVEAlEAi4gEogAWEQlEASwiEogCWEQkEAVwljAzN7PODZz3FjNbmuyaEtr/iZlNS2J7O83szPj5TDO7O4ltTzKznyWrvYR2zcxmmNkWM1uV7Pbr0P96M7s83f1K7RTAGcTMvm1mq+OA2WRmj5tZv1T36+5z3f3KhsxrZs+Y2V4z22Fm283sJTP7kZm1SGj/V+7+3Tq2ddjp3L21u7/fkHqr9DfKzFZUaXu8u991pG1Xox9wBXCqu1+UjAaP5E1XMoMCOEOY2R3AH4BfAScCpwMlwOAU99s0Cc0UuvvRwMnA/wCGAo+ZmSWh7YOSVGsoHYH17r6rvjM28uWW2ri7HoEfwDHATuDGWqa5CCgDtgKbgCKgecJ4BzontDcL+BTYAPwUaBKPGwU8D/xf4L+Au+NhKxLa6gI8GY9/C7iplrqeAb5bZdjpwG7gmvjvO4E58fOWwBzg83hZXiR6w/klUA7sjddFUcJyFQDvAB9Us6wzgUlxvTuAZ4GO8bhO8bRNq9YLdI37Ko/725rQ3t0J048F3o3XxUPAKVXW+fi4ti1AMWDVrKMxVfr6RR3brrTc1bRbdT0k1v0N4B8Jf68HLgdOiv9vTkgYd2G8rTQL/VrItYf2gDPDxUTBtLiWacqBHwBt4+kvA/JrmPYeohA+E+gP3Ap8J2F8H+B9oD1R8B1kZl8hCrN58fhhQImZda/rwrj7h8Bq4OvVjB4Z13YacAJRgO1x94nAc0R7063dvTBhnuvimrvV0OUtwF1E62YtMLcONa6L+y6L+zu26jRmNgD4NXAT0d79BmBBlcmuAXoDPePpvlVNX9Or9PXzOrZ9uOWuN3f/mOhN6KaEwcOBBe6+L1n9SN0ogDPDCcBn7r6/pgnc/SV3f8Hd97v7emAyUbhWYmZ5wM3Aj919RzztfwIjEibb6O73xG3tqdLENUQflWfE49cAfwaG1HOZNgLHVzN8H9Hydnb38ni5th+mrV+7+39VU2uFR939r+7+BTARuNjMTqtnvdW5BbjX3dfEbf84brtTwjT/2923xm86y4Hzktj24Za7oe4jCt2K7WUYMDvJfUgdKIAzw+dA29qO9ZnZ2Wb2iJl9bGbbiY4Vt61m0rZAc6I9qgobgA4Jf39USy0dgT5mtrXiQRQWJ9VxWSp0IPpoXdVs4AlggZltNLPfmlmzw7RVW72Vxrv7zrjfU+pTbA1OIWE9xm1/TuV1+XHC891A6yS2fbjlbqgHgW7xN0muALa5e9q/mSEK4ExRRnSM8LpapikF3gS+6u5tgJ8A1Z3k+oxoL7NjwrDTgX8m/F3bJfA+Ap5192MTHq3dfUIdlgOAeO/zQqJDCpW4+z53/4W7dwMuIdrjvvUwdR3ukn0H93bNrDXRnvdGoOKEV6uEaRPfSA7X7kYS1mN8eOYEKq/LhqpL2/W5VOEual7OStx9L3A/0RvrCLT3G4wCOAO4+zbgP4BiM7vOzFqZWTMzu8rMfhtPdjSwHdhpZl2AagPR3cuJXly/NLOjzawjcAfRia+6eAQ428xGxDU0M7PeZtb1cDPGdfcn2sNaBTxWzTTfNLNz44++24neLMrj0ZuJjlvX19Vm1s/MmhMdC/6bu3/k7p8SBdpwM8szs9HAWQnzbQZOjeerzjzgO2Z2Xvy1ul/Fba9vQI2pbnst0Xo43sxOAr5/mOlnEZ18vZa6bxuSZArgDOHuvycKyp8SnZH+CCgElsST/BvwbaIz/VOBhbU09z2iPaL3gRVEL/Z761jHDuBKoq+SbST6iP0boEUtsxWZ2Q6iQPsD0THjge5+oJppTwIWEYXvOqJvLVQEwB+BIfGPFf5Ul3pj84CfEx16uJBoz67CWOCHRB/vuwMrE8YtA14HPjazz6o26u5PAz+Ll2cTUXgPrUddNUpi2xV7ybOBl4m+7bCU2rcP3P154ACwJklvKNIA5q4Lsos0NmbWBtgGHOfuWxvYxjJgnrsn7VeKUj/6grdI43Qz8N4RhG9v4AJS/EMfqZ0CWKSRMbOVwLFEPyhpyPz3EZ3wvT0+5CSB6BCEiEggOgknIhJIvQ5BtG3b1jt16pSiUkREsk/btm154oknnnD3gVXH1SuAO3XqxOrVq5NXmYhIDjCz6n61qkMQIiKhKIBFRAJRAIuIBKIAFhEJRAEsIhKIAlhEJBAFsIhIILoWRBp8+OGHvPvuu2npq23btnzta19LS18NtXv3bl544YVKw04//XQ6d9Yd1iW3KIDT4Ac/+AFvv/027dq1S3lfa9asoaysjK5dD3v99GBuv/12ysrKaN++PQDbtm3jqKOOYsWKFYErE0kvBXAalJeXc9ddd3HddbXdcSg5fvazn1FSUsI999yT8r4aYsuWLSxatIi33nrrYACXlZVxxx13BK5MJP10DDjLjBs3jrlz57JjR2ZeZXDGjBkMGjToYPiK5DIFcJY59dRTGTBgALNnZ959Fg8cOEBJSQmFhYWhSxHJCArgLFRYWEhxcTGZdq3nJ554gmOPPZY+ffqELkUkIyiAs1D//v0xM5555pnQpVRSVFREQUEBZha6FJGMoADOQmZGQUEBRUVFoUs56L333mPVqlUMHZqUmwqLZAUFcJYaPnw4y5cv56OPPgpdCgClpaWMHj2ao446KnQpIhlDAZyljj76aIYPH87kyZNDl8Lu3buZOXMm48ePD12KSEZRAGex/Px8pk2bxhdffBG0jnnz5nHJJZdwxhlnBK1DJNMogLNYly5dOPfcc1m0aFGwGtyd4uJiCgoKgtUgkqkUwFmusLAw6Mm4lStXsmvXLq644opgNYhkKgVwlrvmmmvYuHEjL730UpD+K/Z+mzTRpiZSlV4VWS4vL48JEyZQXFyc9r43bdrE448/zsiRI9Pet0hjoADOAWPGjGHx4sV8/vnnae136tSp3HzzzRx77LFp7VeksVAA54B27dpx7bXXcu+996atz3379jF58mSdfBOphQI4RxQWFlJSUkJ5eXla+luyZAmdO3fm3HPPTUt/Io2RAjhH9O7dm3bt2vH444+npb/i4mJd9UzkMBTAOSRdX0l79dVXeeedd9JyAXqRxkwBnENuuukm1qxZwzvvvJPSfoqLixk3bhzNmjVLaT8ijZ0COIe0bNmSMWPGUFJSkrI+tm7dysKFCxk7dmzK+hDJFgrgHDN+/HhmzZrFrl27UtL+fffdx8CBAzn55JNT0r5INlEA55iOHTvy9a9/nblz5ya97QMHDujkm0g9KIBzUMXJuGTfsuipp56iVatWXHLJJUltVyRbKYBz0GWXXcaXX37JihUrktpuUVERhYWFuuWQSB0pgHNQKm5ZtH79elauXMm3v/3tpLUpku0UwDlq5MiRPPnkk2zcuDEp7ZWWljJy5EhatWqVlPZEcoECOEe1adOGoUOHMmXKlCNua8+ePdx7771MmDAhCZWJ5A4FcA4rKChgypQpfPnll0fUzsKFC+nduzedO3dOUmUiuUEBnMO6d+/OOeecw+LFixvchrsfPPkmIvWjAM5xR3p9iFWrVrFlyxYGDhyYxKpEcoMCOMcNHjyYDz74gJdffrlB8xcVFZGfn69bDok0gF41Oa5p06aMHz++Qbcs+uSTT3jkkUf4zne+k4LKRLKfAlgYO3YsDzzwAFu2bKnXfNOmTeOGG27g+OOPT1FlItlNASyceOKJXH311cycObPO8+zfv5/S0lLdckjkCCiABYh
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.set_title('Calorie Distribution for July')\n",
    "ax.boxplot(calories_per_day['value'], vert=False,manage_ticks=False, notch=True)\n",
    "plt.xlabel(\"Calories Burned\")\n",
    "ax.set_yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Steps\n",
    "\n",
    "Fitbit is known for taking the amount of steps someone takes per day.\n",
    "Similar to calories burned, steps taken is stored in time series data at 1 minute increments.\n",
    "Since we are interested at the day level data, we need to first remove the time component of the dataframe so that we can group all the data by date.\n",
    "Once we have everything grouped by date, we can sum and produce steps per day. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           dateTime  value date_minus_time\n",
      "date_minus_time                                           \n",
      "2019-07-01      2019-07-01 04:00:00      0      2019-07-01\n",
      "2019-07-01      2019-07-01 04:01:00      0      2019-07-01\n",
      "2019-07-01      2019-07-01 04:02:00      0      2019-07-01\n",
      "2019-07-01      2019-07-01 04:03:00      0      2019-07-01\n",
      "2019-07-01      2019-07-01 04:04:00      0      2019-07-01\n",
      "...                             ...    ...             ...\n",
      "2019-07-31      2019-07-31 03:55:00      0      2019-07-31\n",
      "2019-07-31      2019-07-31 03:56:00      0      2019-07-31\n",
      "2019-07-31      2019-07-31 03:57:00      0      2019-07-31\n",
      "2019-07-31      2019-07-31 03:58:00      0      2019-07-31\n",
      "2019-07-31      2019-07-31 03:59:00      0      2019-07-31\n",
      "\n",
      "[41116 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "steps_df = pd.read_json(\"data/steps-2019-07-01.json\",  convert_dates=True)\n",
    "\n",
    "steps_df['date_minus_time'] = steps_df[\"dateTime\"].apply( lambda steps_df : \n",
    "    datetime.datetime(year=steps_df.year, month=steps_df.month, day=steps_df.day))\t\n",
    "\n",
    "steps_df.set_index(steps_df[\"date_minus_time\"],inplace=True)\n",
    "print(steps_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 value\n",
      "date_minus_time       \n",
      "2019-07-01       11285\n",
      "2019-07-02        4957\n",
      "2019-07-03       13119\n",
      "2019-07-04       16034\n",
      "2019-07-05       11634\n",
      "2019-07-06        6860\n",
      "2019-07-07        3758\n",
      "2019-07-08        9130\n",
      "2019-07-09       10960\n",
      "2019-07-10        7012\n",
      "2019-07-11        5420\n",
      "2019-07-12        4051\n",
      "2019-07-13       15980\n",
      "2019-07-14       23109\n",
      "2019-07-15       11247\n",
      "2019-07-16       10170\n",
      "2019-07-17        4905\n",
      "2019-07-18       10769\n",
      "2019-07-19        4504\n",
      "2019-07-20        5032\n",
      "2019-07-21        8953\n",
      "2019-07-22        2200\n",
      "2019-07-23        9392\n",
      "2019-07-24        5666\n",
      "2019-07-25        5016\n",
      "2019-07-26        5879\n",
      "2019-07-27       19492\n",
      "2019-07-28        4987\n",
      "2019-07-29        9943\n",
      "2019-07-30        3897\n",
      "2019-07-31         166\n"
     ]
    }
   ],
   "source": [
    "steps_per_day = steps_df.resample('D').sum()\n",
    "print(steps_per_day)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Steps Per Day Histogram\n",
    "\n",
    "After the data is in the form that we want, graphing the data is straight forward.\n",
    "Two added things I like to do for normal box plots is to set the displays to horizontal add the notches. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.set_title('Steps Distribution for July')\n",
    "ax.boxplot(steps_per_day['value'], vert=False,manage_ticks=False, notch=True)\n",
    "plt.xlabel(\"Steps Per Day\")\n",
    "ax.set_yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wrapping that all into a single function we get something like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readFileIntoDataFrame(fName):\n",
    "    steps_df = pd.read_json(fName,  convert_dates=True)\n",
    "\n",
    "    steps_df['date_minus_time'] = steps_df[\"dateTime\"].apply( lambda steps_df : \n",
    "        datetime.datetime(year=steps_df.year, month=steps_df.month, day=steps_df.day))\t\n",
    "\n",
    "    steps_df.set_index(steps_df[\"date_minus_time\"],inplace=True)\n",
    "    return steps_df.resample('D').sum()\n",
    "\n",
    "def graphBoxAndWhiskers(data, title, xlab):\n",
    "    ax = plt.gca()\n",
    "    ax.set_title(title)\n",
    "    ax.boxplot(data['value'], vert=False, manage_ticks=False, notch=True)\n",
    "    plt.xlabel(xlab)\n",
    "    ax.set_yticks([])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphBoxAndWhiskers(readFileIntoDataFrame(\"data/steps-2020-01-27.json\"), \"Steps In January\", \"Steps Per Day\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is cool, but, what if we could view the distribution for each month in the same graph?\n",
    "Based on the two previous graphs, my step distribution during July looked distinctly different from my step distribution in January. \n",
    "The first difficultly would be to read in all the files since Fitbit creates a new file for every month.\n",
    "The next thing would be to group them by month and then graph it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['steps-2019-04-02.json', 'steps-2019-08-30.json', 'steps-2020-02-26.json', 'steps-2019-10-29.json', 'steps-2019-07-01.json', 'steps-2020-01-27.json', 'steps-2019-07-31.json', 'steps-2019-06-01.json', 'steps-2019-09-29.json', '.ipynb_checkpoints', 'steps-2019-12-28.json', 'steps-2019-05-02.json', 'calories', 'steps-2019-11-28.json', 'sleep']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "files = os.listdir(\"data\")\n",
    "print(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []\n",
    "for file in files: # this can take 15 seconds\n",
    "    if \"steps\" in file: # finds the steps files\n",
    "        dfs.append(readFileIntoDataFrame(\"data/\" + file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stepsPerDay = pd.concat(dfs)\n",
    "graphBoxAndWhiskers(stepsPerDay, \"Steps Per Day Last 11 Months\", \"Steps per Day\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "DatetimeIndex(['2019-04-03', '2019-04-04', '2019-04-05', '2019-04-06',\n",
      "               '2019-04-07', '2019-04-08', '2019-04-09', '2019-04-10',\n",
      "               '2019-04-11', '2019-04-12',\n",
      "               ...\n",
      "               '2019-12-19', '2019-12-20', '2019-12-21', '2019-12-22',\n",
      "               '2019-12-23', '2019-12-24', '2019-12-25', '2019-12-26',\n",
      "               '2019-12-27', '2019-12-28'],\n",
      "              dtype='datetime64[ns]', name='date_minus_time', length=342, freq=None)\n",
      "                 value  month  week_day\n",
      "date_minus_time                        \n",
      "2019-04-03         510      4         2\n",
      "2019-04-04       11453      4         3\n",
      "2019-04-05       12684      4         4\n",
      "2019-04-06       12910      4         5\n",
      "2019-04-07        3368      4         6\n",
      "...                ...    ...       ...\n",
      "2019-12-24        5779     12         1\n",
      "2019-12-25        4264     12         2\n",
      "2019-12-26        4843     12         3\n",
      "2019-12-27        9609     12         4\n",
      "2019-12-28        2218     12         5\n",
      "\n",
      "[342 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(type(stepsPerDay['value'].to_numpy()))\n",
    "print(stepsPerDay['value'].keys())\n",
    "\n",
    "stepsPerDay['month'] = pd.DatetimeIndex(stepsPerDay['value'].keys()).month \n",
    "stepsPerDay['week_day'] = pd.DatetimeIndex(stepsPerDay['value'].keys()).weekday\n",
    "\n",
    "print(stepsPerDay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graphing Steps by Month\n",
    "\n",
    "Now that we have columns for the total amount of steps per day and the months, we can plot all the data on a single plot using the group by operator in the plotting library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.set_title('Steps Distribution for July\\n')\n",
    "stepsPerDay.boxplot(column=['value'], by='month',ax=ax, notch=True)\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Steps Per Day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.set_title('Steps Distribution By Week Day\\n')\n",
    "stepsPerDay.boxplot(column=['value'], by='week_day',ax=ax, notch=True)\n",
    "plt.xlabel(\"Week Day\")\n",
    "plt.ylabel(\"Steps Per Day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Future Work\n",
    "\n",
    "Moving forward with this I would like to do more visualizations with sleep data and heart rate."
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}