{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Today we are going to visualize my life using Fitbit and Matplotlib. \n",
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"\n",
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"# What is Fitbit\n",
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"\n",
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"[Fitbit](https://www.fitbit.com) is a fitness watch that tracks your sleep, heart rate, and activity.\n",
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"Fitbit is able to track your steps, however, it is also able to detect multiple types of activity\n",
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"like running, walking, \"sport\" and biking."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# What is Matplotlib\n",
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"\n",
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"[Matplotlib](https://matplotlib.org/) is a python visualization library that enables you to create bar graphs, line graphs, distributions and many more things.\n",
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"Being able to visualize your results is essential to any person working with data at any scale.\n",
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"Although I like [GGplot](https://ggplot2.tidyverse.org/) in R more than Matplotlib, Matplotlib is still my go to graphing library for Python. "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Getting Your Data\n",
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"\n",
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"There are two main ways that you can get your Fitbit data:\n",
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"\n",
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"- Fitbit API\n",
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"- Data Archival Export\n",
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"\n",
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"\n",
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"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",
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"You can export your data here: [https://www.fitbit.com/settings/data/export](https://www.fitbit.com/settings/data/export).\n",
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"\n",
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"![Data export on fitbit's website](dataExport.png)\n",
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"\n",
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"The Fitbit data archive was very organized and kept meticulous records of everything. \n",
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"All of the data was organized in separate JSON files labeled by date.\n",
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"Fitbit keeps around 1MB of data on you per day; most of this data is from the heart rate sensors.\n",
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"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",
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"When I downloaded the compressed file it was 20MB, but when I extracted it, it was 380MB!\n",
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"I've only been using Fitbit for 11 months at this point. \n",
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"\n",
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"![compressed data](compression.png)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Sleep\n",
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"\n",
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"Sleep is something fun to visualize.\n",
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"No matter how much of it you get you still feel tired as a college student.\n",
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"\n"
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]
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},
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{
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|
"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Resting Heart Rate"
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|
]
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},
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{
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|
"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Steps"
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|
]
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|
}
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|
],
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"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
|
|
}
|