Repository where I mostly put random python scripts.
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{
"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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\n",
"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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\n",
"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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\n",
"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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\n",
"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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\n",
"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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\n",
"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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\n",
"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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\n",
"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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\n",
"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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\n",
"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": 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k5eXRo0cP5s+fn5HtpqrOCk2T2pqIyGHMnz+fiRMnMn36dPr168eKFSsYM2YMAMOGDcuYdlNVZyXuXufHhRde6A0VdZWbBg8e7IsXLw5dRsP9vE1Km1+5cqX37ds3pX1IciTjddy9e3dftmxZpWHLli3z7t27Z1S7yWwPWO3VZKpF4+qmV69evnr16gYFvZk1aL5sUp91nVHuPAbu3Jay5svKyrjkkktS1r4k15Fux3l5eezdu5dmzZodHLZv3z5atmxJeXl5xrSbzPbM7CV371V1eFqPAVf3DpALj8GDB7N48eJ0rupGp2/fvsH/n/Q4/CMZunbtyooVKyoNW7FiBV27ds2odlNVZyKdhBORtJo4cSJjxoxh+fLl7Nu3j+XLlzNmzBgmTpyYUe2mqs5EOgknImlVcQLre9/7HuvWraNr16788pe/POITW8luN1V1JlIAi0jaDRs2LKlBlqp2U1VnBR2CEBEJRAEsIhKIAlhEJBAFsIhIIApgEZFAFMAiIoEogEVEAlEAi4gEogAWEQlEASwiEogCWEQkEAWwiEggCmARkUAUwCIigSiARUQCUQCLiASiABYRCUQBLCISiAJYRCQQBbCISCAKYBGRQBTAIiKBKIBFRAJRAIuIBKIAFhEJRAEsIhKIAlhEJBAFsIhIIApgEZFAFMAiIoEogEVEAlEAi4gEogAWEQlEASwiEogCWEQkEAWwiEggCmARkUAUwCIigSiARUQCUQCLiASiABYRCUQBLCISiAJYRCQQBbCISCAKYKnVs88+C4C7B65EJPukLYD1Am6cfv/739P07l08//zzoUuRDKDXcXJpD1hqtH79ep5//nnuuusuioqKQpcjknWahi5AMtekSZO49dZbyc/Pp1OnTmzatImTTz45dFkiWUN7wFKtPXv2MH36dPLz8znmmGMYOnQoU6ZMCV2WSFZRAEu1Fi5cSO/evencuTMABQUFTJ48mX379gWuTCR7KIDlEO5OUVERBQUFB4f16NGDs88+m8WLFwesTCS7KIDlEKtWrWLLli0MHDiw0vDCwkKdjBNJIgWwHKK4uJgJEyaQl5dXafjgwYN5//33eeWVVwJVJpJdFMBSySeffMLDDz/M6NGjDxnXrFkzxo0bR3FxcYDKRLKPAlgqmTZtGjfccAPHH398tePHjh3L/fffz9atW9NcmUj2UQDLQfv372fSpEmVTr5VddJJJ3HVVVcxc+bM9BUmkqUUwHLQww8/zGmnncb5559f63SFhYUUFxdz4MCBNFUmkp0UwHJQcXFxrXu/FS6++GKOPvpoli5dmoaqRLKXAlgAWLduHa+99hpDhgw57LRmRkFBgU7GiRwhBbAA0d7vbbfdRvPmzes0/bBhwygrK+ODDz5IcWUi2UsBLGzfvp158+Yxbty4Os/TqlUrRo0aRWlpaQorE8luCmBh9uzZXHbZZXTo0KFe802YMIEZM2awe/fuFFUmkt0UwDnO3et88q2qs846iz59+rBgwYIUVCaS/RTAOW758uU0adKE/v37N2j+goICioqKdKcEkQZQAOe4ir1fM2vQ/N/61rfYvn07L7zwQpIrE8l+CuAc9uGHH7J8+XJGjBjR4DaaNGmir6SJNJACOIdNnjyZESNG0Lp16yNqZ9SoUTz66KNs3rw5SZWJ5AYFcI764osvmDZtGvn5+Ufc1nHHHceNN97I1KlTk1CZSO5QAOeoBx54gJ49e3LOOeckpb2KWxbt378/Ke2J5AIFcI5q6FfPatKzZ086derEgw8+mLQ2RbKdAjgHrV69mo0bN3LNNdcktV3dskikfhTAOai4uJj8/PxDbjl0pK6//nreeustXn/99aS2K5KtFMA55vPPP2fJkiWMGTMm6W03b96c2267TV9JE6kjBXCOmT59OoMHD6Zt27Ypaf+2225jwYIFbNu2LSXti2QTBXAOKS8vp7S0NKkn36o65ZRTuOKKK5g1a1bK+hDJFgrgHPLYY4/Rvn17evfundJ+Km5ZpOtDiNROAZxDioqKKCwsTHk//fr1o3nz5jz99NMp70ukMVMA54i3336btWvXcuONN6a8LzPTV9JE6kABnCNKSkoYM2YMLVu2TEt/t9xyCytWrGDDhg1p6U+kMVIA54CdO3cye/Zsxo8fn7Y+v/KVrzBixAgmTZqUtj5FGhsFcA6YO3cul156Kaeffnpa+83Pz2f69Ons3bs3rf2KNBYK4CxXccuhdJx8q+qrX/0qF1xwAffff3/a+xZpDBTAWe65555j3759DBgwIEj/OhknUjMFcJYrKio6olsOHamrrrqKzz77jFWrVgXpXySTKYCz2D//+U+eeuopbr311mA15OXlMWHCBF0fQqQaTUMXIKkzZcoUhg0bRps2bYLWMXr0aDp37synn35Ku3btgtYikkm0B5ylvvzyS6ZOnZrS6z7U1QknnMD111/P9OnTQ5ciklEUwFnqL3/5C126dKFbt26hSwGik3GlpaWUl5eHLkUkYyiAs1S6rvtQVxdccAEdOnTgkUceCV2KSMZQAGehtWvXsmHDBq699trQpVRSUFCgr6SJJFAAZ6Hi4mLGjx9P06aZdY51yJAhvPrqq7z55puhSxHJCArgLLNlyxYWLVrE2LFjQ5dyiBYtWvDd736XkpKS0KWIZAQFcJaZMWMGgwYNon379qFLqda4ceOYM2cOO3bsCF2KSHCZ9Rk1Sz344IO8+OKLPPnkkynva8mSJfz5z39OeT8NddpppzFgwACGDh1Kp06dAPj444/DFiUSiAI4DQoKCmjRogVnnnlmyvvq378/ffr0SXk/R+KPf/wjS5YsOfh3t27dMubrciLpZPW5b1evXr189erVKSxHRCT7mNlL7t6r6nAdAxYRCUQBLCISiAJYRCQQBbCISCAKYBGRQBTAIiKBKIBFRAJRAIuIBFKvH2KY2afAhtSVU622wGdp7jOT5Pryg9YBaB005uX/DMDdB1YdUa8ADsHMVlf3C5JckevLD1oHoHWQrcuvQxAiIoEogEVEAmkMATwldAGB5fryg9YBaB1k5fJn/DFgEZFs1Rj2gEVEspICWEQkkLQHsJmdZmbLzWydmb1uZrfHw483syfN7J343+MS5vmxmb1rZm+Z2bcShl9oZq/G4/5kZpbu5amvWpb/TjP7p5mtjR9XJ8yTNcsPYGYtzWyVmb0cr4NfxMNzYhuAWtdBzmwHAGaWZ2Z/N7NH4r9zZhsAwN3T+gBOBi6Inx8NvA10A34L/Cge/iPgN/HzbsDLQAvgDOA9IC8etwq4GDDgceCqdC9PEpf/TuDfqpk+q5Y/rtuA1vHzZsDfgL65sg0cZh3kzHYQ134HMA94JP47Z7YBd0//HrC7b3L3NfHzHcA6oAMwGLgvnuw+4Lr4+WBggbt/4e4fAO8CF5nZyUAbdy/z6H9hVsI8GauW5a9JVi0/gEd2xn82ix9OjmwDUOs6qEnWrQMzOxUYBExLGJwz2wAEPgZsZp2A84ne/U90900QhRRQcV/1DsBHCbP9Ix7WIX5edXijUWX5AQrN7BUzuzfho1dWLn/80XMt8AnwpLvn3DZQwzqA3NkO/gD8O3AgYVhObQPBAtjMWgN/Br7v7ttrm7SaYV7L8EahmuUvBc4CzgM2Af9ZMWk1szf65Xf3cnc/DziVaE+mRy2T59I6yIntwMyuAT5x95fqOks1wxrt8lcIEsBm1owofOa6+1/iwZvjjxPE/34SD/8HcFrC7KcCG+Php1YzPONVt/zuvjl+QR4ApgIXxZNn3fIncvetwDPAQHJoG0iUuA5yaDv4F+BaM1sPLAAGmNkccmwbCPEtCAOmA+vc/fcJox4CRsbPRwIPJgwfamYtzOwM4KvAqvjjyQ4z6xu3eWvCPBmrpuWv2Ohi1wOvxc+zavkBzKydmR0bPz8KuBx4kxzZBqDmdZAr24G7/9jdT3X3TsBQYJm7DyeHtgEgyLcg+hF9RHgFWBs/rgZOAJ4G3on/PT5hnolEZz3fIuEMJ9CLaAN9Dygi/mVfJj9qWf7ZwKvx8IeAk7Nx+eO6vwb8PV7W14D/iIfnxDZwmHWQM9tBQv3f4L+/BZEz24C766fIIiKh6JdwIiKBKIBFRAJRAIuIBKIAFhEJRAEsIhKIAljqzMxOMrMFZvaemb1hZo+Z2dmHmWdnbeNrmGdlw6s82MZMM/sgvqLYm2b28yNtswE1fKPiKl8i1VEAS53EX3JfDDzj7me5ezfgJ8CJSewjD8DdL0lSkz/06Ke+5wEj4y/w17WWpkmqQaRGCmCpq28C+9x9UsUAd1/r7s+ZWWsze9rM1sTXZR1cdWaL/M7MXounuTke/g2Lro88j+gHCJX2ms3sh2b2Ynxxmopr5n7FzB616Fq6r1W0VYuW8b+74vnXm1nb+HkvM3smfn6nmU0xs6XALDMbZWZ/MbP/Z9H1aX+bUNeVZlYWL/MD8bU9MLOB8R73CuBf67WGJefoXV7qqgdQ04VT9gLXu/v2ONheMLOHvPKvfP6VaE+0J9AWeNHM/hqPuwjo4dFlBg8ysyuJfnJ6EdFFVx4ys0uBdsBGdx8UT3dMDXX9zsx+CnQG/uTun9QwXaILgX7uvsfMRsU1nw98AbxlZvcAe4CfApe7+y4z+5/AHXFATwUGEF0ucWEd+pMcpj1gSQYDfmVmrwBPEV0OsOqhiX7AfI8uNLMZeBboHY9bVTV8Y1fGj78Da4AuRIH8KnC5mf3GzL7u7ttqqKviEMRJwGVmVpdDGw+5+56Ev592923uvhd4A+hIdOH0bsDzFl1OcmQ8vAvwgbu/E7/5zKlDf5LDtAcsdfU6MKSGcbcQ7ZVe6O774itctawyTW23idlVw3ADfu3ukw8ZYXYh0TU0fm1mS939f9XUuLvvjA8z9ANWAvv5752PqnVWreWLhOflRK8ZI7p+77AqNZ1HI7oUooSnPWCpq2VACzMbWzHAzHqbWX/gGKJru+4zs28S7Q1W9VfgZosuQt4OuJToVjK1eQIYnXB8tYOZtTezU4Dd7j4H+D/ABbU1Ep9Q60N0sRaA9USHGgBuOEwN1XkB+Bcz6xy33yr+NsibwBlmdlY83bCaGhABBbDUUfyR+nrgivhraK8T3b9sIzAX6GVmq4n2ht+sponFRFf4epkozP/d3T8+TJ9Lie4XVmZmrwKLiO6jdy6wKv74PxG4u4YmfhdP8wrRYYuKa0//AvijmT1HtFdbL+7+KTAKmB8fdnkB6BIfprgNeDQ+Cbehvm1LbtHV0EREAtEesIhIIApgEZFAFMAiIoEogEVEAlEAi4gEogAWEQlEASwiEsj/B9FXGcEba0J3AAAAAElFTkSuQmCC\n",
"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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\n",
"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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\n",
"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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\n",
"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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\n",
"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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\n",
"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
}