Fitbit Exploration.ipynb

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   "source": [
    "# Fitbit Data Exploration and its Relation to Real Events\n",
    "\n",
    "Stephen Hsu <br>\n",
    "M.S. Candidate in Data Science <br>\n",
    "Data Scientist <br>\n",
    "January 5, 2017\n",
    "\n",
    "In this exploration, I will use Fitbit data collected over the period of November 18, 2017 to January 5, 2017 to examine intraday heart activities (hourly, daily, and in relation to events such as tests and work) as well as a sleep summary. \n",
    "\n",
    "Link to video setting up Fitbit collection <br>\n",
    "Link to my code to collect Fitbit data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import \n",
    "\n",
    "Import the necessary packages and their use cases for this project:\n",
    "   \n",
    ">pandas: data frames and data manipulation <br>\n",
    ">numpy: summary statistics <br>\n",
    ">matplotlib: used for visualizations <br>\n",
    ">seasborn: fancy visualizations <br>\n",
    ">os: used to read in the files from the folder <br>\n",
    ">datetime: turn the dates into datetime objects / get day of week \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import datetime\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_files_from_path(path): #function to read files and add date components\n",
    "    list_ = []\n",
    "    my_dir = os.listdir(path)\n",
    "    csvs = [d for d in my_dir if d[-4:] == \".csv\"]\n",
    "    for c in csvs:\n",
    "        df = pd.read_csv(path+c,index_col=None, header=0)\n",
    "        df['date'] = c[-12:-4]\n",
    "        year, month, day = int(c[-12:-8]),int(c[-8:-6]),int(c[-6:-4])\n",
    "        df['year'] = year\n",
    "        df['month'] = month\n",
    "        df['day'] = day\n",
    "        df['DOW'] = datetime.date(year, month, day).strftime(\"%A\")\n",
    "        df['DOW'] = pd.Categorical(df['DOW'],\n",
    "                    categories=['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday', 'Sunday'], ordered=True)\n",
    "        list_.append(df)\n",
    "    frame = pd.concat(list_)\n",
    "    return frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DOW</th>\n",
       "      <th>Heart Rate</th>\n",
       "      <th>Time</th>\n",
       "      <th>date</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9679</th>\n",
       "      <td>Friday</td>\n",
       "      <td>73</td>\n",
       "      <td>23:58:19</td>\n",
       "      <td>20171208</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9680</th>\n",
       "      <td>Friday</td>\n",
       "      <td>75</td>\n",
       "      <td>23:58:24</td>\n",
       "      <td>20171208</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9681</th>\n",
       "      <td>Friday</td>\n",
       "      <td>73</td>\n",
       "      <td>23:58:34</td>\n",
       "      <td>20171208</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9682</th>\n",
       "      <td>Friday</td>\n",
       "      <td>75</td>\n",
       "      <td>23:58:39</td>\n",
       "      <td>20171208</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9683</th>\n",
       "      <td>Friday</td>\n",
       "      <td>76</td>\n",
       "      <td>23:58:54</td>\n",
       "      <td>20171208</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DOW  Heart Rate      Time      date  day  month  year\n",
       "9679  Friday          73  23:58:19  20171208    8     12  2017\n",
       "9680  Friday          75  23:58:24  20171208    8     12  2017\n",
       "9681  Friday          73  23:58:34  20171208    8     12  2017\n",
       "9682  Friday          75  23:58:39  20171208    8     12  2017\n",
       "9683  Friday          76  23:58:54  20171208    8     12  2017"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = read_files_from_path('/Users/shsu/Desktop/HealthData/Heart/')\n",
    "frame.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the time aspect might be a little bit too specific. Let's add in some columns for the hour/minute format and hour format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DOW</th>\n",
       "      <th>Heart Rate</th>\n",
       "      <th>Time</th>\n",
       "      <th>date</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>HourMinute</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>58</td>\n",
       "      <td>00:00:10</td>\n",
       "      <td>20171211</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>00:00</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>56</td>\n",
       "      <td>00:00:15</td>\n",
       "      <td>20171211</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>00:00</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Monday</td>\n",
       "      <td>55</td>\n",
       "      <td>00:00:20</td>\n",
       "      <td>20171211</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>00:00</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monday</td>\n",
       "      <td>56</td>\n",
       "      <td>00:00:25</td>\n",
       "      <td>20171211</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>00:00</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Monday</td>\n",
       "      <td>56</td>\n",
       "      <td>00:00:40</td>\n",
       "      <td>20171211</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>00:00</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DOW  Heart Rate      Time      date  day  month  year HourMinute Hour\n",
       "0  Monday          58  00:00:10  20171211   11     12  2017      00:00   00\n",
       "1  Monday          56  00:00:15  20171211   11     12  2017      00:00   00\n",
       "2  Monday          55  00:00:20  20171211   11     12  2017      00:00   00\n",
       "3  Monday          56  00:00:25  20171211   11     12  2017      00:00   00\n",
       "4  Monday          56  00:00:40  20171211   11     12  2017      00:00   00"
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['HourMinute'] = frame['Time'].str.slice(start=0, stop=5)\n",
    "frame['Hour'] = frame['Time'].str.slice(start=0, stop=2)\n",
    "frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summary_stat(df, col_name): #function to provide median, min, and max of data\n",
    "    df = pd.DataFrame({'Mean ' : str(round(np.mean(df[col_name]),2)),\n",
    "         'Median' : str(np.median(df[col_name])),\n",
    "         'Min' : str(np.min(df[col_name])),\n",
    "         'Max' : str(np.max(df[col_name]))}, index=[0])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Max</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Median</th>\n",
       "      <th>Min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>185</td>\n",
       "      <td>68.14</td>\n",
       "      <td>64.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Max  Mean  Median Min\n",
       "0  185  68.14   64.0  42"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_stat(frame, 'Heart Rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the summary statistics implies a right skew (mean > median), it doesn't provide much information thereafter. We cannot conclude that this is the resting heart rate as the data simply averages over the course of the approximate three months. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(50,-3000,u'Data source: www.fitbit.com | Author: Randy Olson (randalolson.com / @randal_olson)')"
      ]
     },
     "execution_count": 563,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cAwAAAIYgnAMAAACGIJwDAAAAhiCcAwAAAIYgnAMAAACGIJwDAAAAhiCcAwAAAIYgnAMA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\nYmJZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1311e9cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HR = frame['Heart Rate']\n",
    "plt.figure(figsize=(12, 9)) \n",
    "plt.hist(HR, range=(np.min(HR),np.max(HR)),\n",
    "         linewidth=2,color=\"#3F5D7D\", bins=100,edgecolor='black')\n",
    "\n",
    "ax = plt.subplot(111)  \n",
    "ax.spines[\"top\"].set_visible(False)  \n",
    "ax.spines[\"right\"].set_visible(False)  \n",
    "\n",
    "ax.get_xaxis().tick_bottom()  \n",
    "ax.get_yaxis().tick_left()\n",
    "plt.xticks(fontsize=14)  \n",
    "plt.yticks(fontsize=14)  \n",
    "\n",
    "plt.title('Heart Rate Histogram', fontsize=16)\n",
    "plt.xlabel('Heart Rate', fontsize=16)\n",
    "plt.ylabel('Counts')\n",
    "\n",
    "plt.text(50,-3000, \"Data source: www.fitbit.com | \"  \n",
    "         \"Author: Randy Olson (randalolson.com / @randal_olson)\", \n",
    "         fontsize=10)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the above graph, the fitbit data confirms there is a right skew and a center around 63 bpm. Again, we aren't able to determine much else besides this, so let's take a deeper look. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's next take a look at more specific graphs rather than an aggregated total, more specifically:\n",
    "\n",
    "   1. Activity by the hour\n",
    "    \n",
    "   2. Activity by the day\n",
    "    \n",
    "   3. Night versus Day\n",
    "    \n",
    "   4. Day of the Week Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'Heart Rate')"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s8Pbbb+P69euIjY3F6dOnsXXrVqxevRr3338/rK2tUVBQ0O5u76a/bDtDT08Pb7zxBgwN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\n0kDqq/M8vhcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x152a2d150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12,7))\n",
    "\n",
    "ax = plt.subplot(121)  \n",
    "Hourlydf = frame.groupby(by='Hour').mean()\n",
    "meanHeart = Hourlydf['Heart Rate']\n",
    "\n",
    "plt.plot(meanHeart, linestyle='-', marker='o', markersize=10)\n",
    "\n",
    "ax.spines[\"top\"].set_visible(False)  \n",
    "ax.spines[\"right\"].set_visible(False)  \n",
    "\n",
    "ax.get_xaxis().tick_bottom()  \n",
    "ax.get_yaxis().tick_left()\n",
    "ax.set_ylim([np.min(meanHeart)-10, np.max(meanHeart)+10])\n",
    "plt.xticks(fontsize=10)  \n",
    "plt.yticks(fontsize=14)  \n",
    "\n",
    "plt.title('Heart Rate vs. Hour of the Day', fontsize=15)\n",
    "plt.xlabel(meanHeart.index.name)\n",
    "plt.xlabel('Hour of the Day', fontsize=14)\n",
    "plt.ylabel('Heart Rate', fontsize=14)\n",
    "\n",
    "#############\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "DOWdf = frame.groupby(by='DOW').mean()\n",
    "meanDOW = DOWdf['Heart Rate']\n",
    "meanDOW.plot(linestyle='-', marker='o', markersize=10)\n",
    "  \n",
    "ax2.spines[\"top\"].set_visible(False)  \n",
    "ax2.spines[\"right\"].set_visible(False)  \n",
    "\n",
    "ax2.get_xaxis().tick_bottom()  \n",
    "ax2.get_yaxis().tick_left()\n",
    "ax2.set_ylim([np.min(meanHeart)-10, np.max(meanHeart)+10])\n",
    "\n",
    "plt.xticks(fontsize=10)  \n",
    "plt.yticks(fontsize=14)  \n",
    "\n",
    "plt.title('Heart Rate vs. Day of the Week', fontsize=15)\n",
    "plt.xlabel(meanDOW.index.name, fontsize=14)\n",
    "plt.xlabel('Day of the Week')\n",
    "plt.ylabel('Heart Rate', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beginning with the right graph, while it looks like the Day of the Week has no significant spikes, minue a peak on Wednesdays and Fridays, we can note that for further examination.\n",
    "\n",
    "With the left graph, we can see that the time of day has a significant difference between the hours of 1AM to 7AM versus 8AM to 12AM. Let's start with this analysis as it appears to be more significant than the former. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Day n Nite\n",
    "\n",
    "On the topic of Day n nite: let's see how \"the lonely coder seems to free his mind at night\" by first splitting the data into day time and night time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame['Hour'] = frame['Hour'].astype(int)\n",
    "Nine_to_fivedf = frame[(frame['Hour'] >= 8) & (frame['Hour'] <= 17) & (frame['DOW'] != 'Saturday') & (frame['DOW'] != 'Sunday')] \n",
    "Free_timedf = frame[~((frame['Hour'] >= 8) & (frame['Hour'] <= 17) & (frame['DOW'] != 'Saturday') & (frame['DOW'] != 'Sunday'))] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HR when Awake: 76.18\n",
      "HR when Awake: 64.52\n"
     ]
    }
   ],
   "source": [
    "print('HR when Awake: '+str(round(Nine_to_fivedf['Heart Rate'].mean(),2)))\n",
    "print('HR when Awake: '+str(round(Free_timedf['Heart Rate'].mean(),2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This doesn't seem quite right given that we are sleeping most of the time I'm not at work or school. Let's look closer into the free time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HR when Free & Awake: 66.81\n",
      "HR when Free & Asleep: 61.62\n"
     ]
    }
   ],
   "source": [
    "Free_time_wokedf = Free_timedf[(Free_timedf['Hour']>=17)|(Free_timedf['Hour']<=1)]\n",
    "Free_time_sleepdf = Free_timedf[~(Free_timedf['Hour']>=17)|(Free_timedf['Hour']<=1)]\n",
    "print('HR when Free & Awake: '+str(round(Free_time_wokedf['Heart Rate'].mean(),2)))\n",
    "print('HR when Free & Asleep: '+str(round(Free_time_sleepdf['Heart Rate'].mean(),2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based off this data, it looks like nine to five has on average, the highest heart rate. We could stop there, but let's go deeper again. What about the minimums and maximums?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,u'Day of the Week')"
      ]
     },
     "execution_count": 567,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12db033d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OsLAwLFq0CPn5+dwuvdJ07NgR58+fR5cuXUTaO3fujOvXr6N58+Yiu6WcnJxw7NgxBAQE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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1323164d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12,7))\n",
    "Free_time_wokedf.boxplot(column = 'Heart Rate', by = 'DOW')\n",
    "\n",
    "ax = plt.subplot(111)  \n",
    "ax.spines[\"top\"].set_visible(False)  \n",
    "ax.spines[\"right\"].set_visible(False) \n",
    "ax.set_ylim([0, 180])\n",
    "plt.xticks(fontsize=10)  \n",
    "plt.yticks(fontsize=14) \n",
    "\n",
    "plt.title('')\n",
    "plt.suptitle('Day of the Week Heart Rate Boxplot')\n",
    "plt.ylabel('Heart Rate')\n",
    "plt.xlabel('Day of the Week')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By investigating the extremes of the data, we gained insightful information. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moving on, we can go further to see if more specific real world events are reflected in the data. Specifically some fun events I wanted to check were:\n",
    "    \n",
    "   1. when taking a test (Dec 8, Dec 11, Dec 12) <br>\n",
    "   2. when at work (Tuesdays and Thursdays October to December ~9am:5pm)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Taking a Test\n",
    "\n",
    "As a graduate student in Data Science, tests are often a stressful time. And since I know that my exam dates were December 8 2PM-4PM, December 11 10AM-12PM, and December 12 10AM-1PM, we can look closer at those days. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "metadata": {},
   "outputs": [],
   "source": [
    "Testingpeakdf = frame[((frame['date']=='20171208') & (frame['Hour']<=16) & (frame['Hour'] >=14)) | \\\n",
    "                    ((frame['date']=='20171211') & (frame['Hour']<=12) & (frame['Hour'] >=10)) | \\\n",
    "                    ((frame['date']=='20171212') & (frame['Hour']<=13) & (frame['Hour'] >=10))]\n",
    "nonTestingpeakdf = frame[((frame['date']!='20171208') & (frame['Hour']<=16) & (frame['Hour'] >=14)) | \\\n",
    "                    ((frame['date']!='20171211') & (frame['Hour']<=12) & (frame['Hour'] >=10)) | \\\n",
    "                    ((frame['date']!='20171212') & (frame['Hour']<=13) & (frame['Hour'] >=10))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Max  Mean  Median Min\n",
      "0  133  76.23   70.0  54\n",
      "   Max  Mean  Median Min\n",
      "0  185  76.48   71.0  49\n"
     ]
    }
   ],
   "source": [
    "print(summary_stat(Testingpeakdf, \"Heart Rate\"))\n",
    "print(summary_stat(nonTestingpeakdf, \"Heart Rate\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Work \n",
    "\n",
    "On top of being a graduate student, I also worked part time as a data science intern. The work days were Monday and Wednesday from October to early December. Let's take a look at those days:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "metadata": {},
   "outputs": [],
   "source": [
    "Workdf = frame[((frame['DOW']=='Monday') | (frame['DOW']=='Wednesday')) & \\\n",
    "                (frame['date'].astype(int) < 20171204) & (frame['Hour'].astype(int) < 17) & \\\n",
    "              (frame['Hour'].astype(int) > 8)] \n",
    "Afterworkdf = frame[((frame['DOW']=='Monday') | (frame['DOW']=='Wednesday')) & \\\n",
    "                (frame['date'].astype(int) < 20171204) & (frame['Hour'].astype(int) > 17)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Max  Mean  Median Min\n",
      "0  141  69.43   68.0  55\n",
      "   Max  Mean  Median Min\n",
      "0  131  71.59   68.5  49\n"
     ]
    }
   ],
   "source": [
    "print(summary_stat(Workdf, 'Heart Rate'))\n",
    "print(summary_stat(Afterworkdf, 'Heart Rate'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "metadata": {},
   "outputs": [],
   "source": [
    "NotWorkdf = frame[((frame['DOW']=='Monday') | (frame['DOW']=='Wednesday')) & \\\n",
    "                (frame['date'].astype(int) > 20171204) & (frame['Hour'].astype(int) < 17) & \\\n",
    "              (frame['Hour'].astype(int) > 8)] \n",
    "AfterNotWorkdf = frame[((frame['DOW']=='Monday') | (frame['DOW']=='Wednesday')) & \\\n",
    "                (frame['date'].astype(int) > 20171204) & (frame['Hour'].astype(int) > 17)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Max  Mean  Median Min\n",
      "0  185  78.64   71.0  46\n",
      "   Max  Mean  Median Min\n",
      "0  147  70.79   67.0  49\n"
     ]
    }
   ],
   "source": [
    "print(summary_stat(NotWorkdf, 'Heart Rate'))\n",
    "print(summary_stat(AfterNotWorkdf, 'Heart Rate'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [],
   "source": [
    "Weekenddf = frame[((frame['DOW']=='Saturday') | (frame['DOW']=='Sunday')) & \\\n",
    "                (frame['date'].astype(int) > 20171204) & (frame['Hour'].astype(int) < 17) & \\\n",
    "              (frame['Hour'].astype(int) > 8)] \n",
    "AfterWeekenddf = frame[((frame['DOW']=='Saturday') | (frame['DOW']=='Sunday')) & \\\n",
    "                (frame['date'].astype(int) > 20171204) & (frame['Hour'].astype(int) > 17)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Max  Mean  Median Min\n",
      "0  174  76.58   71.0  49\n",
      "   Max  Mean  Median Min\n",
      "0  173  70.36   66.0  44\n"
     ]
    }
   ],
   "source": [
    "print(summary_stat(Weekenddf, 'Heart Rate'))\n",
    "print(summary_stat(AfterWeekenddf, 'Heart Rate'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the conclusion of those events, we can see that both the extremes lie both in MWF instead of on Tuesdays and Thursdays, with also an increase in means. \n",
    "    \n",
    "At this point, you might be wondering about Fitbit sleep tracking, so let's go to sleep! (but not actual sleep sleep)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sleep Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While we could repeat the above process for sleep data, Fitbit API fortunately offers a detailed sleep summary. Similarly, with the power of hindsight used on real world events, I wanted to see if I could extract any patterns regarding test season and sleep. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Awakenings</th>\n",
       "      <th>Date</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Efficiency</th>\n",
       "      <th>MainSleep</th>\n",
       "      <th>Minutes Asleep</th>\n",
       "      <th>Minutes Awake</th>\n",
       "      <th>Restless Count</th>\n",
       "      <th>Restless Duration</th>\n",
       "      <th>Time in Bed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>11/18/17</td>\n",
       "      <td>26400000</td>\n",
       "      <td>92</td>\n",
       "      <td>True</td>\n",
       "      <td>405</td>\n",
       "      <td>35</td>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>11/19/17</td>\n",
       "      <td>29040000</td>\n",
       "      <td>96</td>\n",
       "      <td>True</td>\n",
       "      <td>465</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>11/20/17</td>\n",
       "      <td>28440000</td>\n",
       "      <td>96</td>\n",
       "      <td>True</td>\n",
       "      <td>453</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>11/21/17</td>\n",
       "      <td>26280000</td>\n",
       "      <td>91</td>\n",
       "      <td>True</td>\n",
       "      <td>400</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>11/22/17</td>\n",
       "      <td>25320000</td>\n",
       "      <td>93</td>\n",
       "      <td>True</td>\n",
       "      <td>391</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>29</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Awakenings      Date  Duration  Efficiency  MainSleep  Minutes Asleep  \\\n",
       "0           2  11/18/17  26400000          92       True             405   \n",
       "1           0  11/19/17  29040000          96       True             465   \n",
       "2           1  11/20/17  28440000          96       True             453   \n",
       "3           0  11/21/17  26280000          91       True             400   \n",
       "4           2  11/22/17  25320000          93       True             391   \n",
       "\n",
       "   Minutes Awake  Restless Count  Restless Duration  Time in Bed  \n",
       "0             35              15                 26          440  \n",
       "1             19              16                 19          484  \n",
       "2             21              11                 17          474  \n",
       "3             38              18                 38          438  \n",
       "4             31              17                 29          422  "
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2 = pd.read_csv('/Users/shsu/Desktop/HealthData/sleepsummary.csv')\n",
    "frame2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice here that the Date appears to be in MM/DD/YYYY format as apposted to the YYYYMMDD format seen on the intraday heart data. As a result, we must handle the date again. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Awakenings</th>\n",
       "      <th>Date</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Efficiency</th>\n",
       "      <th>MainSleep</th>\n",
       "      <th>Minutes Asleep</th>\n",
       "      <th>Minutes Awake</th>\n",
       "      <th>Restless Count</th>\n",
       "      <th>Restless Duration</th>\n",
       "      <th>Time in Bed</th>\n",
       "      <th>Dow</th>\n",
       "      <th>dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>11/18/17</td>\n",
       "      <td>26400000</td>\n",
       "      <td>92</td>\n",
       "      <td>True</td>\n",
       "      <td>405</td>\n",
       "      <td>35</td>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "      <td>440</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2017-11-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>11/19/17</td>\n",
       "      <td>29040000</td>\n",
       "      <td>96</td>\n",
       "      <td>True</td>\n",
       "      <td>465</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>484</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2017-11-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>11/20/17</td>\n",
       "      <td>28440000</td>\n",
       "      <td>96</td>\n",
       "      <td>True</td>\n",
       "      <td>453</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>474</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2017-11-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>11/21/17</td>\n",
       "      <td>26280000</td>\n",
       "      <td>91</td>\n",
       "      <td>True</td>\n",
       "      <td>400</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>438</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2017-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>11/22/17</td>\n",
       "      <td>25320000</td>\n",
       "      <td>93</td>\n",
       "      <td>True</td>\n",
       "      <td>391</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>29</td>\n",
       "      <td>422</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2017-11-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Awakenings      Date  Duration  Efficiency  MainSleep  Minutes Asleep  \\\n",
       "0           2  11/18/17  26400000          92       True             405   \n",
       "1           0  11/19/17  29040000          96       True             465   \n",
       "2           1  11/20/17  28440000          96       True             453   \n",
       "3           0  11/21/17  26280000          91       True             400   \n",
       "4           2  11/22/17  25320000          93       True             391   \n",
       "\n",
       "   Minutes Awake  Restless Count  Restless Duration  Time in Bed        Dow  \\\n",
       "0             35              15                 26          440   Saturday   \n",
       "1             19              16                 19          484     Sunday   \n",
       "2             21              11                 17          474     Monday   \n",
       "3             38              18                 38          438    Tuesday   \n",
       "4             31              17                 29          422  Wednesday   \n",
       "\n",
       "           dt  \n",
       "0  2017-11-18  \n",
       "1  2017-11-19  \n",
       "2  2017-11-20  \n",
       "3  2017-11-21  \n",
       "4  2017-11-22  "
      ]
     },
     "execution_count": 494,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3 = pd.DataFrame(frame2.Date.str.split('/').tolist(),\n",
    "                                   columns = ['Month','Day','Year'])\n",
    "dow = []\n",
    "dt = []\n",
    "for index, row in frame3.iterrows():\n",
    "    dow.append(datetime.date(int(str(20)+str(row.Year)), \n",
    "                        int(row.Month), int(row.Day)).strftime(\"%A\"))\n",
    "    dt.append(datetime.date(int(str(20)+str(row.Year)), \n",
    "                        int(row.Month), int(row.Day)))\n",
    "frame2['Dow'] = dow\n",
    "frame2['dt'] = dt\n",
    "frame2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IsjFzJv3u6+v6Fg7A+RF9xn5oMaLeEp4ygVOOk5uYGJpke+6cvGNzJBER4rbL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\nd2so2NdpmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x138ecba10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2, ncols=2, sharex=True, figsize=(12,7))\n",
    "plt.suptitle('Sleep Summary', fontsize=20)\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.plot(frame2['dt'],frame2['Minutes Asleep'], linestyle='-', marker='o', markersize=10, color='red')\n",
    "plt.tick_params(labelbottom='off')\n",
    "plt.ylabel('Minutes Asleep', fontsize=12) \n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(frame2['dt'],frame2['Minutes Awake'], linestyle='-', marker='o', markersize=10, color='orange')\n",
    "plt.tick_params(labelbottom='off')\n",
    "plt.ylabel('Minutes Awake', fontsize=12)\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.plot(frame2['dt'],frame2['Restless Duration'], linestyle='-', marker='o', markersize=10, color='blue')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Minutes Restless', fontsize=12)\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.bar(frame2['dt'],frame2['Efficiency'], color='green')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Efficiency (Percentage)', fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is difficult to see everything on it's own, so let's combine it into a single plot - specifically the line plots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 736653.,  736660.,  736667.,  736674.,  736681.,  736688.,  736695.]),\n",
       " <a list of 7 Text xticklabel objects>)"
      ]
     },
     "execution_count": 496,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nOdFe8+bxm8B+/hmwsgKOHNF0RKS4ev0aWLhQmJ8zByhTJv/rmTABcHLi05mZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x128793150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12,5))\n",
    "plt.plot(frame2['dt'],frame2['Restless Duration'], linestyle='-', \n",
    "         markersize=10, color='blue', label='Restless', linewidth=3.0)\n",
    "plt.plot(frame2['dt'],frame2['Minutes Awake'], linestyle='-', \n",
    "         markersize=10, color='orange', label='Awake', linewidth=3.0, alpha=0.9)\n",
    "plt.plot(frame2['dt'],frame2['Minutes Asleep'], linestyle='-', \n",
    "         markersize=10, color='red', label='Asleep', linewidth=3.0)\n",
    "\n",
    "ax = plt.subplot(111)  \n",
    "ax.spines[\"top\"].set_visible(False)  \n",
    "ax.spines[\"right\"].set_visible(False)  \n",
    "ax.get_xaxis().tick_bottom()  \n",
    "ax.get_yaxis().tick_left()\n",
    "\n",
    "plt.xlabel('Date', fontsize=14)\n",
    "plt.ylabel('Minutes', fontsize=14)\n",
    "plt.title('Aspects of Sleep Over Oct to Dec (2017)', fontsize=16)\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this, we can see that sleep restlessness and sleep awake are highly correlated, if not the same. Additionally, with the power of hindsight, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Efficiency</th>\n",
       "      <th>Minutes Asleep</th>\n",
       "      <th>Minutes Awake</th>\n",
       "      <th>Restless Count</th>\n",
       "      <th>Restless Duration</th>\n",
       "      <th>Time in Bed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Efficiency</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.377399</td>\n",
       "      <td>-0.959147</td>\n",
       "      <td>-0.705870</td>\n",
       "      <td>-0.924118</td>\n",
       "      <td>0.163233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minutes Asleep</th>\n",
       "      <td>0.377399</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.159260</td>\n",
       "      <td>-0.118669</td>\n",
       "      <td>-0.124733</td>\n",
       "      <td>0.973329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minutes Awake</th>\n",
       "      <td>-0.959147</td>\n",
       "      <td>-0.159260</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.754793</td>\n",
       "      <td>0.957484</td>\n",
       "      <td>0.066280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Restless Count</th>\n",
       "      <td>-0.705870</td>\n",
       "      <td>-0.118669</td>\n",
       "      <td>0.754793</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.787721</td>\n",
       "      <td>0.050943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Restless Duration</th>\n",
       "      <td>-0.924118</td>\n",
       "      <td>-0.124733</td>\n",
       "      <td>0.957484</td>\n",
       "      <td>0.787721</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.097250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time in Bed</th>\n",
       "      <td>0.163233</td>\n",
       "      <td>0.973329</td>\n",
       "      <td>0.066280</td>\n",
       "      <td>0.050943</td>\n",
       "      <td>0.097250</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Efficiency  Minutes Asleep  Minutes Awake  Restless Count  \\\n",
       "Efficiency           1.000000        0.377399      -0.959147       -0.705870   \n",
       "Minutes Asleep       0.377399        1.000000      -0.159260       -0.118669   \n",
       "Minutes Awake       -0.959147       -0.159260       1.000000        0.754793   \n",
       "Restless Count      -0.705870       -0.118669       0.754793        1.000000   \n",
       "Restless Duration   -0.924118       -0.124733       0.957484        0.787721   \n",
       "Time in Bed          0.163233        0.973329       0.066280        0.050943   \n",
       "\n",
       "                   Restless Duration  Time in Bed  \n",
       "Efficiency                 -0.924118     0.163233  \n",
       "Minutes Asleep             -0.124733     0.973329  \n",
       "Minutes Awake               0.957484     0.066280  \n",
       "Restless Count              0.787721     0.050943  \n",
       "Restless Duration           1.000000     0.097250  \n",
       "Time in Bed                 0.097250     1.000000  "
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrdf = frame2[['Efficiency','Minutes Asleep', 'Minutes Awake', \n",
    "        'Restless Count', 'Restless Duration', 'Time in Bed', 'Dow']]\n",
    "corrdf.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createheatmap(df, anchor_val):\n",
    "    sns.set(style = \"white\")\n",
    "    corrd = df.corr()\n",
    "    mask = np.zeros_like(corrd, dtype=np.bool)\n",
    "    mask[np.triu_indices_from(mask)] = True\n",
    "    f, ax = plt.subplots(figsize=(11, 9))\n",
    "    cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "    sns.heatmap(corrd, mask=mask, cmap=cmap, vmax=anchor_val,\n",
    "            square=True, xticklabels=True, yticklabels=True,\n",
    "            linewidths=.5, cbar_kws={\"shrink\": .5}, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5EBkZqQoVKigjI0PR0dGSLu3tUqNGDcvCAQAAB3JaaSpdurTuvvvuK0rSxYsX/R4KAAA4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\nHT9+XL169VKvXr08z7lu3TrNmzdPb77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      "text/plain": [
       "<matplotlib.figure.Figure at 0x152851a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "createheatmap(corrdf, .8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, we can see that minutes awake and restless count are all correlated "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion and future work:\n",
    "\n",
    "In conclusion, we can see that\n",
    "\n",
    "    data with context is important\n",
    "    data with explanation is important\n",
    "    work tends to impact heart rate \n",
    "    test tends to impact heart rate and sleep\n",
    "    \n",
    "So what's next?\n",
    "\n",
    "Future analyses with more data could include predictions of what days might be more active, holiday (NY resolutions, Christmas) effects on sleep, and/or the seasonality or trend over the year. Additionally, as time goes on, I'll be analyzing my data further to see if improvements to sleep and exercise will change my averages.\n",
    "\n",
    "If you made it this far, thanks for taking the time to go through my analyses and feel free to contact me / let me know of any improvements / errors! Thanks!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}