Fitbit Analytics.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def import_intraday_steps(filename):\n",
    "    with open(filename) as file:\n",
    "        data = json.load(file)\n",
    "        \n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read JSON from file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "testfilename = \"intraday_data/activities/steps/2013-01-05.json\"\n",
    "data = import_intraday_steps(testfilename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract day's data from JSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "activities_steps = data[\"activities-steps\"]\n",
    "if len(activities_steps) != 1:\n",
    "    print(\"WARNING: Unexpected activities-steps length\")\n",
    "day = activities_steps[0]['dateTime']\n",
    "total_steps = int(activities_steps[0]['value'])\n",
    "\n",
    "dataset = data['activities-steps-intraday']['dataset']\n",
    "df = pd.DataFrame(dataset)\n",
    "if total_steps != df.value.sum():\n",
    "    print(\"WARNING: Intraday step count total doesn't equal summary total\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Massage the dataframe to our liking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Date'] = day + ' ' + str(df['time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['time'].astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'one' : pd.Series(np.random.randn(3), index=['a', 'b', 'c']),\n",
    "                   'two' : pd.Series(np.random.randn(4), index=['a', 'b', 'c', 'd']),\n",
    "                   'three' : pd.Series(np.random.randn(3), index=['b', 'c', 'd'])})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = df.ix[1]\n",
    "df.sub(row, axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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