EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "from missingno import missingno as msno\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets to explore:\n",
    "#### Daily\n",
    "- dailyActivity_merged.csv\n",
    "- dailyCalories_merged.csv\n",
    "- dailyIntensities_merged.csv\n",
    "- dailySteps_merged.csv\n",
    "- sleepDay_merged.csv\n",
    "- weightLogInfo_merged.csv\n",
    "\n",
    "#### Hourly\n",
    "- hourlyCalories_merged.csv\n",
    "- hourlySteps_merged.csv\n",
    "\n",
    "#### Minutes\n",
    "- minuteCaloriesNarrow_merged.csv\n",
    "- minuteIntensitiesNarrow_merged.csv\n",
    "- minuteMETsNarrow_merged.csv\n",
    "- minuteSleep_merged.csv\n",
    "- minuteStepsNarrow_merged.csv\n",
    "\n",
    "#### Seconds\n",
    "- heartrate_seconds_merged.csv\n",
    "\n",
    "\n",
    "`df = pd.read_csv('FitBit-Tracker-Data/Fitabase Data 4.12.16-5.12.16/...')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read(name):\n",
    "    return pd.read_csv(f'FitBit-Tracker-Data/Fitabase Data 4.12.16-5.12.16/{name}.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Daily\n",
    "activity_df = read('dailyActivity_merged')\n",
    "sleepday_df = read('sleepDay_merged')\n",
    "weight_df    = read('weightLogInfo_merged')\n",
    "\n",
    "# Hourly\n",
    "calhour_df  = read('hourlyCalories_merged')\n",
    "stephour_df = read('hourlySteps_merged')\n",
    "\n",
    "# Minutes\n",
    "calmin_df   = read('minuteCaloriesNarrow_merged')\n",
    "intmin_df   = read('minuteIntensitiesNarrow_merged')\n",
    "mets_df     = read('minuteMETsNarrow_merged')\n",
    "sleepmin_df = read('minuteSleep_merged')\n",
    "stepmin_df  = read('minuteStepsNarrow_merged')\n",
    "\n",
    "# Seconds\n",
    "heart_df    = read('heartrate_seconds_merged')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General categories:\n",
    "- Calories\n",
    "- Sleep\n",
    "- Exercise (Intensity, METs, Steps)\n",
    "- Weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Daily"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Daily Activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ActivityDate</th>\n",
       "      <th>TotalSteps</th>\n",
       "      <th>TotalDistance</th>\n",
       "      <th>TrackerDistance</th>\n",
       "      <th>LoggedActivitiesDistance</th>\n",
       "      <th>VeryActiveDistance</th>\n",
       "      <th>ModeratelyActiveDistance</th>\n",
       "      <th>LightActiveDistance</th>\n",
       "      <th>SedentaryActiveDistance</th>\n",
       "      <th>VeryActiveMinutes</th>\n",
       "      <th>FairlyActiveMinutes</th>\n",
       "      <th>LightlyActiveMinutes</th>\n",
       "      <th>SedentaryMinutes</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>13162</td>\n",
       "      <td>8.50</td>\n",
       "      <td>8.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.55</td>\n",
       "      <td>6.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>328</td>\n",
       "      <td>728</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/13/2016</td>\n",
       "      <td>10735</td>\n",
       "      <td>6.97</td>\n",
       "      <td>6.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.69</td>\n",
       "      <td>4.71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>217</td>\n",
       "      <td>776</td>\n",
       "      <td>1797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/14/2016</td>\n",
       "      <td>10460</td>\n",
       "      <td>6.74</td>\n",
       "      <td>6.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>181</td>\n",
       "      <td>1218</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/15/2016</td>\n",
       "      <td>9762</td>\n",
       "      <td>6.28</td>\n",
       "      <td>6.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.14</td>\n",
       "      <td>1.26</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "      <td>34</td>\n",
       "      <td>209</td>\n",
       "      <td>726</td>\n",
       "      <td>1745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/16/2016</td>\n",
       "      <td>12669</td>\n",
       "      <td>8.16</td>\n",
       "      <td>8.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.71</td>\n",
       "      <td>0.41</td>\n",
       "      <td>5.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36</td>\n",
       "      <td>10</td>\n",
       "      <td>221</td>\n",
       "      <td>773</td>\n",
       "      <td>1863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id ActivityDate  TotalSteps  TotalDistance  TrackerDistance  \\\n",
       "0  1503960366    4/12/2016       13162           8.50             8.50   \n",
       "1  1503960366    4/13/2016       10735           6.97             6.97   \n",
       "2  1503960366    4/14/2016       10460           6.74             6.74   \n",
       "3  1503960366    4/15/2016        9762           6.28             6.28   \n",
       "4  1503960366    4/16/2016       12669           8.16             8.16   \n",
       "\n",
       "   LoggedActivitiesDistance  VeryActiveDistance  ModeratelyActiveDistance  \\\n",
       "0                       0.0                1.88                      0.55   \n",
       "1                       0.0                1.57                      0.69   \n",
       "2                       0.0                2.44                      0.40   \n",
       "3                       0.0                2.14                      1.26   \n",
       "4                       0.0                2.71                      0.41   \n",
       "\n",
       "   LightActiveDistance  SedentaryActiveDistance  VeryActiveMinutes  \\\n",
       "0                 6.06                      0.0                 25   \n",
       "1                 4.71                      0.0                 21   \n",
       "2                 3.91                      0.0                 30   \n",
       "3                 2.83                      0.0                 29   \n",
       "4                 5.04                      0.0                 36   \n",
       "\n",
       "   FairlyActiveMinutes  LightlyActiveMinutes  SedentaryMinutes  Calories  \n",
       "0                   13                   328               728      1985  \n",
       "1                   19                   217               776      1797  \n",
       "2                   11                   181              1218      1776  \n",
       "3                   34                   209               726      1745  \n",
       "4                   10                   221               773      1863  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note:* `dailyActivity_merged.csv` is a combination of `dailyCalories_merged.csv`, `dailyIntensities_merged.csv`, and `dailySteps_merged.csv`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sleep Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Id</th>\n",
       "      <th>SleepDay</th>\n",
       "      <th>TotalSleepRecords</th>\n",
       "      <th>TotalMinutesAsleep</th>\n",
       "      <th>TotalTimeInBed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:00:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>327</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/13/2016 12:00:00 AM</td>\n",
       "      <td>2</td>\n",
       "      <td>384</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/15/2016 12:00:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>412</td>\n",
       "      <td>442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/16/2016 12:00:00 AM</td>\n",
       "      <td>2</td>\n",
       "      <td>340</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/17/2016 12:00:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "           Id               SleepDay  TotalSleepRecords  TotalMinutesAsleep  \\\n",
       "0  1503960366  4/12/2016 12:00:00 AM                  1                 327   \n",
       "1  1503960366  4/13/2016 12:00:00 AM                  2                 384   \n",
       "2  1503960366  4/15/2016 12:00:00 AM                  1                 412   \n",
       "3  1503960366  4/16/2016 12:00:00 AM                  2                 340   \n",
       "4  1503960366  4/17/2016 12:00:00 AM                  1                 700   \n",
       "\n",
       "   TotalTimeInBed  \n",
       "0             346  \n",
       "1             407  \n",
       "2             442  \n",
       "3             367  \n",
       "4             712  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sleepday_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a `TotalTimeAwake` column\n",
    "sleepday_df['TotalTimeAwake'] = sleepday_df.TotalTimeInBed - sleepday_df.TotalMinutesAsleep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weight Log Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>WeightKg</th>\n",
       "      <th>WeightPounds</th>\n",
       "      <th>Fat</th>\n",
       "      <th>BMI</th>\n",
       "      <th>IsManualReport</th>\n",
       "      <th>LogId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>5/2/2016 11:59:59 PM</td>\n",
       "      <td>52.599998</td>\n",
       "      <td>115.963147</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.650000</td>\n",
       "      <td>True</td>\n",
       "      <td>1462233599000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>5/3/2016 11:59:59 PM</td>\n",
       "      <td>52.599998</td>\n",
       "      <td>115.963147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.650000</td>\n",
       "      <td>True</td>\n",
       "      <td>1462319999000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1927972279</td>\n",
       "      <td>4/13/2016 1:08:52 AM</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>294.317120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.540001</td>\n",
       "      <td>False</td>\n",
       "      <td>1460509732000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2873212765</td>\n",
       "      <td>4/21/2016 11:59:59 PM</td>\n",
       "      <td>56.700001</td>\n",
       "      <td>125.002104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.450001</td>\n",
       "      <td>True</td>\n",
       "      <td>1461283199000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2873212765</td>\n",
       "      <td>5/12/2016 11:59:59 PM</td>\n",
       "      <td>57.299999</td>\n",
       "      <td>126.324875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.690001</td>\n",
       "      <td>True</td>\n",
       "      <td>1463097599000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id                   Date    WeightKg  WeightPounds   Fat  \\\n",
       "0  1503960366   5/2/2016 11:59:59 PM   52.599998    115.963147  22.0   \n",
       "1  1503960366   5/3/2016 11:59:59 PM   52.599998    115.963147   NaN   \n",
       "2  1927972279   4/13/2016 1:08:52 AM  133.500000    294.317120   NaN   \n",
       "3  2873212765  4/21/2016 11:59:59 PM   56.700001    125.002104   NaN   \n",
       "4  2873212765  5/12/2016 11:59:59 PM   57.299999    126.324875   NaN   \n",
       "\n",
       "         BMI  IsManualReport          LogId  \n",
       "0  22.650000            True  1462233599000  \n",
       "1  22.650000            True  1462319999000  \n",
       "2  47.540001           False  1460509732000  \n",
       "3  21.450001            True  1461283199000  \n",
       "4  21.690001            True  1463097599000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((940, 15), (413, 6), (67, 8))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity_df.shape, sleepday_df.shape, weight_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     0.61194\n",
       "False    0.38806\n",
       "Name: IsManualReport, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_df.IsManualReport.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine Daily Datasets\n",
    "We will combine the data from `activity_df`, `sleepday_df`, and `weight_df` into a single dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new column with just the date (excluding the time)\n",
    "sleepday_df['ActivityDate'] = sleepday_df['SleepDay'].apply(lambda x: x[:9].strip())\n",
    "\n",
    "# weight_df.rename(columns={'Date':'DateTime'}, inplace=True)\n",
    "weight_df['ActivityDate'] = weight_df['Date'].apply(lambda x: x[:9].strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for any duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SleepDay</th>\n",
       "      <th>TotalSleepRecords</th>\n",
       "      <th>TotalMinutesAsleep</th>\n",
       "      <th>TotalTimeInBed</th>\n",
       "      <th>TotalTimeAwake</th>\n",
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       "      <th>160</th>\n",
       "      <td>4388161847</td>\n",
       "      <td>5/5/2016 12:00:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>471</td>\n",
       "      <td>495</td>\n",
       "      <td>24</td>\n",
       "      <td>5/5/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>4388161847</td>\n",
       "      <td>5/5/2016 12:00:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>471</td>\n",
       "      <td>495</td>\n",
       "      <td>24</td>\n",
       "      <td>5/5/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>4702921684</td>\n",
       "      <td>5/7/2016 12:00:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>520</td>\n",
       "      <td>543</td>\n",
       "      <td>23</td>\n",
       "      <td>5/7/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>4702921684</td>\n",
       "      <td>5/7/2016 12:00:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>520</td>\n",
       "      <td>543</td>\n",
       "      <td>23</td>\n",
       "      <td>5/7/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>8378563200</td>\n",
       "      <td>4/25/2016 12:00:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>388</td>\n",
       "      <td>402</td>\n",
       "      <td>14</td>\n",
       "      <td>4/25/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>8378563200</td>\n",
       "      <td>4/25/2016 12:00:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>388</td>\n",
       "      <td>402</td>\n",
       "      <td>14</td>\n",
       "      <td>4/25/2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Id               SleepDay  TotalSleepRecords  TotalMinutesAsleep  \\\n",
       "160  4388161847   5/5/2016 12:00:00 AM                  1                 471   \n",
       "161  4388161847   5/5/2016 12:00:00 AM                  1                 471   \n",
       "222  4702921684   5/7/2016 12:00:00 AM                  1                 520   \n",
       "223  4702921684   5/7/2016 12:00:00 AM                  1                 520   \n",
       "379  8378563200  4/25/2016 12:00:00 AM                  1                 388   \n",
       "380  8378563200  4/25/2016 12:00:00 AM                  1                 388   \n",
       "\n",
       "     TotalTimeInBed  TotalTimeAwake ActivityDate  \n",
       "160             495              24     5/5/2016  \n",
       "161             495              24     5/5/2016  \n",
       "222             543              23     5/7/2016  \n",
       "223             543              23     5/7/2016  \n",
       "379             402              14    4/25/2016  \n",
       "380             402              14    4/25/2016  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sleepday_df[sleepday_df[['Id', 'ActivityDate']].duplicated(keep=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(410, 7)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop duplicates from sleepday_df\n",
    "sleepday_df.drop_duplicates(inplace=True)\n",
    "sleepday_df.reset_index(drop=True, inplace=True)\n",
    "sleepday_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>WeightKg</th>\n",
       "      <th>WeightPounds</th>\n",
       "      <th>Fat</th>\n",
       "      <th>BMI</th>\n",
       "      <th>IsManualReport</th>\n",
       "      <th>LogId</th>\n",
       "      <th>ActivityDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Id, Date, WeightKg, WeightPounds, Fat, BMI, IsManualReport, LogId, ActivityDate]\n",
       "Index: []"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_df[weight_df[['Id', 'ActivityDate']].duplicated(keep=False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sleepday_df.drop(['SleepDay'], axis=1, inplace=True)\n",
    "weight_df.drop(['Date', 'LogId'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine `activity_df` with `sleepday_df`\n",
    "daily_df = pd.merge(activity_df, sleepday_df, on=['Id', 'ActivityDate'], how='outer')\n",
    "\n",
    "# Combine `weight_df` with the previous dataframe\n",
    "daily_df = pd.merge(daily_df, weight_df, on=['Id', 'ActivityDate'], how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ActivityDate</th>\n",
       "      <th>TotalSteps</th>\n",
       "      <th>TotalDistance</th>\n",
       "      <th>TrackerDistance</th>\n",
       "      <th>LoggedActivitiesDistance</th>\n",
       "      <th>VeryActiveDistance</th>\n",
       "      <th>ModeratelyActiveDistance</th>\n",
       "      <th>LightActiveDistance</th>\n",
       "      <th>SedentaryActiveDistance</th>\n",
       "      <th>...</th>\n",
       "      <th>Calories</th>\n",
       "      <th>TotalSleepRecords</th>\n",
       "      <th>TotalMinutesAsleep</th>\n",
       "      <th>TotalTimeInBed</th>\n",
       "      <th>TotalTimeAwake</th>\n",
       "      <th>WeightKg</th>\n",
       "      <th>WeightPounds</th>\n",
       "      <th>Fat</th>\n",
       "      <th>BMI</th>\n",
       "      <th>IsManualReport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>13162</td>\n",
       "      <td>8.50</td>\n",
       "      <td>8.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.55</td>\n",
       "      <td>6.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1985</td>\n",
       "      <td>1.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/13/2016</td>\n",
       "      <td>10735</td>\n",
       "      <td>6.97</td>\n",
       "      <td>6.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.69</td>\n",
       "      <td>4.71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1797</td>\n",
       "      <td>2.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/14/2016</td>\n",
       "      <td>10460</td>\n",
       "      <td>6.74</td>\n",
       "      <td>6.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1776</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/15/2016</td>\n",
       "      <td>9762</td>\n",
       "      <td>6.28</td>\n",
       "      <td>6.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.14</td>\n",
       "      <td>1.26</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1745</td>\n",
       "      <td>1.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/16/2016</td>\n",
       "      <td>12669</td>\n",
       "      <td>8.16</td>\n",
       "      <td>8.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.71</td>\n",
       "      <td>0.41</td>\n",
       "      <td>5.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1863</td>\n",
       "      <td>2.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id ActivityDate  TotalSteps  TotalDistance  TrackerDistance  \\\n",
       "0  1503960366    4/12/2016       13162           8.50             8.50   \n",
       "1  1503960366    4/13/2016       10735           6.97             6.97   \n",
       "2  1503960366    4/14/2016       10460           6.74             6.74   \n",
       "3  1503960366    4/15/2016        9762           6.28             6.28   \n",
       "4  1503960366    4/16/2016       12669           8.16             8.16   \n",
       "\n",
       "   LoggedActivitiesDistance  VeryActiveDistance  ModeratelyActiveDistance  \\\n",
       "0                       0.0                1.88                      0.55   \n",
       "1                       0.0                1.57                      0.69   \n",
       "2                       0.0                2.44                      0.40   \n",
       "3                       0.0                2.14                      1.26   \n",
       "4                       0.0                2.71                      0.41   \n",
       "\n",
       "   LightActiveDistance  SedentaryActiveDistance  ...  Calories  \\\n",
       "0                 6.06                      0.0  ...      1985   \n",
       "1                 4.71                      0.0  ...      1797   \n",
       "2                 3.91                      0.0  ...      1776   \n",
       "3                 2.83                      0.0  ...      1745   \n",
       "4                 5.04                      0.0  ...      1863   \n",
       "\n",
       "   TotalSleepRecords  TotalMinutesAsleep  TotalTimeInBed  TotalTimeAwake  \\\n",
       "0                1.0               327.0           346.0            19.0   \n",
       "1                2.0               384.0           407.0            23.0   \n",
       "2                NaN                 NaN             NaN             NaN   \n",
       "3                1.0               412.0           442.0            30.0   \n",
       "4                2.0               340.0           367.0            27.0   \n",
       "\n",
       "   WeightKg  WeightPounds  Fat  BMI  IsManualReport  \n",
       "0       NaN           NaN  NaN  NaN             NaN  \n",
       "1       NaN           NaN  NaN  NaN             NaN  \n",
       "2       NaN           NaN  NaN  NaN             NaN  \n",
       "3       NaN           NaN  NaN  NaN             NaN  \n",
       "4       NaN           NaN  NaN  NaN             NaN  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert date from string to datetime object\n",
    "daily_df['ActivityDate'] = daily_df['ActivityDate'].apply(lambda x: pd.to_datetime(x, format='%m/%d/%Y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(daily_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_df.to_csv('FitBit-Tracker-Data/Generated-Data/daily.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Per Hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract date and time\n",
    "Special function to separate the date and time from datetime column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_date_hour(dataframe, datetime):\n",
    "    # Extract date\n",
    "    dataframe['Date'] = dataframe[datetime].apply(lambda x: x[:9].strip())\n",
    "    \n",
    "    # Extract time\n",
    "    dataframe['FullTime'] = dataframe[datetime].apply(lambda x: (x[9:]).strip())\n",
    "\n",
    "    # Extract AM/PM\n",
    "    dataframe['AmPm'] = dataframe.FullTime.apply(lambda x: x[-2:])\n",
    "\n",
    "    # Extract specific hour\n",
    "    dataframe['Hour'] = dataframe.FullTime.apply(lambda x: x[:2].replace(':', ''))\n",
    "\n",
    "    # Combine hour with am/pm\n",
    "    dataframe['FullHour'] = dataframe.Hour + dataframe.AmPm\n",
    "\n",
    "    # Drop the extra columns that are no longer needed\n",
    "    dataframe.drop(['FullTime', 'AmPm', 'Hour', datetime], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calories burned per hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ActivityHour</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:00:00 AM</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 1:00:00 AM</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 2:00:00 AM</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 3:00:00 AM</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 4:00:00 AM</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id           ActivityHour  Calories\n",
       "0  1503960366  4/12/2016 12:00:00 AM        81\n",
       "1  1503960366   4/12/2016 1:00:00 AM        61\n",
       "2  1503960366   4/12/2016 2:00:00 AM        59\n",
       "3  1503960366   4/12/2016 3:00:00 AM        47\n",
       "4  1503960366   4/12/2016 4:00:00 AM        48"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calhour_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_date_hour(calhour_df, 'ActivityHour')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steps taken per hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ActivityHour</th>\n",
       "      <th>StepTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:00:00 AM</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 1:00:00 AM</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 2:00:00 AM</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 3:00:00 AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 4:00:00 AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id           ActivityHour  StepTotal\n",
       "0  1503960366  4/12/2016 12:00:00 AM        373\n",
       "1  1503960366   4/12/2016 1:00:00 AM        160\n",
       "2  1503960366   4/12/2016 2:00:00 AM        151\n",
       "3  1503960366   4/12/2016 3:00:00 AM          0\n",
       "4  1503960366   4/12/2016 4:00:00 AM          0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stephour_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_date_hour(stephour_df, 'ActivityHour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((22099, 4), (22099, 4))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calhour_df.shape, stephour_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Combine the hourly datasets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Calories</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "      <th>StepTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>81</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>61</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>1AM</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>59</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>2AM</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>47</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>3AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>48</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>4AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id  Calories       Date FullHour  StepTotal\n",
       "0  1503960366        81  4/12/2016     12AM        373\n",
       "1  1503960366        61  4/12/2016      1AM        160\n",
       "2  1503960366        59  4/12/2016      2AM        151\n",
       "3  1503960366        47  4/12/2016      3AM          0\n",
       "4  1503960366        48  4/12/2016      4AM          0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour_df = pd.merge(calhour_df, stephour_df, on=['Id', 'Date', 'FullHour'], how='outer')\n",
    "hour_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id           0\n",
       "Calories     0\n",
       "Date         0\n",
       "FullHour     0\n",
       "StepTotal    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert date and time from string to datetime object\n",
    "hour_df['Date'] = hour_df['Date'].apply(lambda x: pd.to_datetime(x, format='%m/%d/%Y'))\n",
    "hour_df['Hour'] = hour_df['FullHour'].apply(lambda x: datetime.strptime(x, '%I%p').hour)\n",
    "hour_df.drop(['FullHour'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Calories</th>\n",
       "      <th>Date</th>\n",
       "      <th>StepTotal</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>81</td>\n",
       "      <td>2016-04-12</td>\n",
       "      <td>373</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>61</td>\n",
       "      <td>2016-04-12</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>59</td>\n",
       "      <td>2016-04-12</td>\n",
       "      <td>151</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>47</td>\n",
       "      <td>2016-04-12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>48</td>\n",
       "      <td>2016-04-12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id  Calories       Date  StepTotal  Hour\n",
       "0  1503960366        81 2016-04-12        373     0\n",
       "1  1503960366        61 2016-04-12        160     1\n",
       "2  1503960366        59 2016-04-12        151     2\n",
       "3  1503960366        47 2016-04-12          0     3\n",
       "4  1503960366        48 2016-04-12          0     4"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(hour_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "hour_df.to_csv('FitBit-Tracker-Data/Generated-Data/hourly.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Per Minute\n",
    "For each DataFrame, we will group by `Id`, `Date`, and `FullHour`. We will use `groupby` to find the minimum, mean, maximum, and sum of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_df(dataframe, data):\n",
    "    # Grouped dataframe\n",
    "    grouped_df = dataframe.groupby(['Id', 'Date', 'FullHour'], as_index=False).mean()\n",
    "    grouped_df.drop([data], axis=1, inplace=True)\n",
    "\n",
    "    # Average data\n",
    "    grouped_df[f'Avg{data}'] = dataframe.groupby(['Id', 'Date', 'FullHour'], as_index=False).mean()[data]\n",
    "\n",
    "    # Lowest data\n",
    "    grouped_df[f'Min{data}'] = dataframe.groupby(['Id', 'Date', 'FullHour'], as_index=False).min()[data]\n",
    "\n",
    "    # Highest data\n",
    "    grouped_df[f'Max{data}'] = dataframe.groupby(['Id', 'Date', 'FullHour'], as_index=False).max()[data]\n",
    "\n",
    "    # Sum data\n",
    "    grouped_df[f'Sum{data}'] = dataframe.groupby(['Id', 'Date', 'FullHour'], as_index=False).sum()[data]\n",
    "    \n",
    "    return grouped_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calories burned per minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ActivityMinute</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:00:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:01:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:02:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:03:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:04:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id         ActivityMinute  Calories\n",
       "0  1503960366  4/12/2016 12:00:00 AM    0.7865\n",
       "1  1503960366  4/12/2016 12:01:00 AM    0.7865\n",
       "2  1503960366  4/12/2016 12:02:00 AM    0.7865\n",
       "3  1503960366  4/12/2016 12:03:00 AM    0.7865\n",
       "4  1503960366  4/12/2016 12:04:00 AM    0.7865"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calmin_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_date_hour(calmin_df, 'ActivityMinute')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1325580, 4)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calmin_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "      <th>AvgCalories</th>\n",
       "      <th>MinCalories</th>\n",
       "      <th>MaxCalories</th>\n",
       "      <th>SumCalories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10AM</td>\n",
       "      <td>1.649028</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>4.5617</td>\n",
       "      <td>98.941697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10PM</td>\n",
       "      <td>1.080127</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>2.2022</td>\n",
       "      <td>64.807598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11AM</td>\n",
       "      <td>1.266265</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>4.2471</td>\n",
       "      <td>75.975898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11PM</td>\n",
       "      <td>1.358023</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>2.6741</td>\n",
       "      <td>81.481398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "      <td>1.355402</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>2.8314</td>\n",
       "      <td>81.324098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id       Date FullHour  AvgCalories  MinCalories  MaxCalories  \\\n",
       "0  1503960366  4/12/2016     10AM     1.649028       0.7865       4.5617   \n",
       "1  1503960366  4/12/2016     10PM     1.080127       0.7865       2.2022   \n",
       "2  1503960366  4/12/2016     11AM     1.266265       0.7865       4.2471   \n",
       "3  1503960366  4/12/2016     11PM     1.358023       0.7865       2.6741   \n",
       "4  1503960366  4/12/2016     12AM     1.355402       0.7865       2.8314   \n",
       "\n",
       "   SumCalories  \n",
       "0    98.941697  \n",
       "1    64.807598  \n",
       "2    75.975898  \n",
       "3    81.481398  \n",
       "4    81.324098  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_cal_df = group_df(calmin_df, 'Calories')\n",
    "grouped_cal_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intensity level per minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ActivityMinute</th>\n",
       "      <th>Intensity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:00:00 AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:01:00 AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:02:00 AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:03:00 AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:04:00 AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id         ActivityMinute  Intensity\n",
       "0  1503960366  4/12/2016 12:00:00 AM          0\n",
       "1  1503960366  4/12/2016 12:01:00 AM          0\n",
       "2  1503960366  4/12/2016 12:02:00 AM          0\n",
       "3  1503960366  4/12/2016 12:03:00 AM          0\n",
       "4  1503960366  4/12/2016 12:04:00 AM          0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intmin_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_date_hour(intmin_df, 'ActivityMinute')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "      <th>AvgIntensity</th>\n",
       "      <th>MinIntensity</th>\n",
       "      <th>MaxIntensity</th>\n",
       "      <th>SumIntensity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10AM</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10PM</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11AM</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11PM</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id       Date FullHour  AvgIntensity  MinIntensity  MaxIntensity  \\\n",
       "0  1503960366  4/12/2016     10AM      0.483333             0             1   \n",
       "1  1503960366  4/12/2016     10PM      0.150000             0             1   \n",
       "2  1503960366  4/12/2016     11AM      0.200000             0             1   \n",
       "3  1503960366  4/12/2016     11PM      0.350000             0             1   \n",
       "4  1503960366  4/12/2016     12AM      0.333333             0             1   \n",
       "\n",
       "   SumIntensity  \n",
       "0            29  \n",
       "1             9  \n",
       "2            12  \n",
       "3            21  \n",
       "4            20  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_intensity_df = group_df(intmin_df, 'Intensity')\n",
    "grouped_intensity_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We don't need `SumIntensity`\n",
    "grouped_intensity_df.drop(['SumIntensity'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### METs per minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ActivityMinute</th>\n",
       "      <th>METs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:00:00 AM</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:01:00 AM</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:02:00 AM</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:03:00 AM</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:04:00 AM</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id         ActivityMinute  METs\n",
       "0  1503960366  4/12/2016 12:00:00 AM    10\n",
       "1  1503960366  4/12/2016 12:01:00 AM    10\n",
       "2  1503960366  4/12/2016 12:02:00 AM    10\n",
       "3  1503960366  4/12/2016 12:03:00 AM    10\n",
       "4  1503960366  4/12/2016 12:04:00 AM    10"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mets_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_date_hour(mets_df, 'ActivityMinute')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "      <th>AvgMETs</th>\n",
       "      <th>MinMETs</th>\n",
       "      <th>MaxMETs</th>\n",
       "      <th>SumMETs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10AM</td>\n",
       "      <td>20.966667</td>\n",
       "      <td>10</td>\n",
       "      <td>58</td>\n",
       "      <td>1258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10PM</td>\n",
       "      <td>13.733333</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11AM</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>10</td>\n",
       "      <td>54</td>\n",
       "      <td>966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11PM</td>\n",
       "      <td>17.266667</td>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "      <td>1036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "      <td>17.233333</td>\n",
       "      <td>10</td>\n",
       "      <td>36</td>\n",
       "      <td>1034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id       Date FullHour    AvgMETs  MinMETs  MaxMETs  SumMETs\n",
       "0  1503960366  4/12/2016     10AM  20.966667       10       58     1258\n",
       "1  1503960366  4/12/2016     10PM  13.733333       10       28      824\n",
       "2  1503960366  4/12/2016     11AM  16.100000       10       54      966\n",
       "3  1503960366  4/12/2016     11PM  17.266667       10       34     1036\n",
       "4  1503960366  4/12/2016     12AM  17.233333       10       36     1034"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_mets_df = group_df(mets_df, 'METs')\n",
    "grouped_mets_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We don't need `SumMETs`\n",
    "grouped_mets_df.drop(['SumMETs'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sleep per minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>date</th>\n",
       "      <th>value</th>\n",
       "      <th>logId</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 2:47:30 AM</td>\n",
       "      <td>3</td>\n",
       "      <td>11380564589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 2:48:30 AM</td>\n",
       "      <td>2</td>\n",
       "      <td>11380564589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 2:49:30 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>11380564589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 2:50:30 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>11380564589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 2:51:30 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>11380564589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id                  date  value        logId\n",
       "0  1503960366  4/12/2016 2:47:30 AM      3  11380564589\n",
       "1  1503960366  4/12/2016 2:48:30 AM      2  11380564589\n",
       "2  1503960366  4/12/2016 2:49:30 AM      1  11380564589\n",
       "3  1503960366  4/12/2016 2:50:30 AM      1  11380564589\n",
       "4  1503960366  4/12/2016 2:51:30 AM      1  11380564589"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sleepmin_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "sleepmin_df.drop(['logId'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_date_hour(sleepmin_df, 'date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Id</th>\n",
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       "      <th>FullHour</th>\n",
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       "      <th>Minvalue</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>2AM</td>\n",
       "      <td>1.923077</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>3AM</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>4AM</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>5AM</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>6AM</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id       Date FullHour  Avgvalue  Minvalue  Maxvalue  Sumvalue\n",
       "0  1503960366  4/12/2016      2AM  1.923077         1         3        25\n",
       "1  1503960366  4/12/2016      3AM  1.083333         1         3        65\n",
       "2  1503960366  4/12/2016      4AM  1.016667         1         2        61\n",
       "3  1503960366  4/12/2016      5AM  1.100000         1         2        66\n",
       "4  1503960366  4/12/2016      6AM  1.000000         1         1        60"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_sleep_df = group_df(sleepmin_df, 'value')\n",
    "grouped_sleep_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We don't need `Sumvalue`\n",
    "grouped_sleep_df.drop(['Sumvalue'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steps taken per minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ActivityMinute</th>\n",
       "      <th>Steps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:00:00 AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:01:00 AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:02:00 AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:03:00 AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:04:00 AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id         ActivityMinute  Steps\n",
       "0  1503960366  4/12/2016 12:00:00 AM      0\n",
       "1  1503960366  4/12/2016 12:01:00 AM      0\n",
       "2  1503960366  4/12/2016 12:02:00 AM      0\n",
       "3  1503960366  4/12/2016 12:03:00 AM      0\n",
       "4  1503960366  4/12/2016 12:04:00 AM      0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepmin_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_date_hour(stepmin_df, 'ActivityMinute')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "      <th>AvgSteps</th>\n",
       "      <th>MinSteps</th>\n",
       "      <th>MaxSteps</th>\n",
       "      <th>SumSteps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10AM</td>\n",
       "      <td>11.266667</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10PM</td>\n",
       "      <td>1.483333</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11AM</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11PM</td>\n",
       "      <td>5.633333</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "      <td>6.216667</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id       Date FullHour   AvgSteps  MinSteps  MaxSteps  SumSteps\n",
       "0  1503960366  4/12/2016     10AM  11.266667         0        88       676\n",
       "1  1503960366  4/12/2016     10PM   1.483333         0        18        89\n",
       "2  1503960366  4/12/2016     11AM   6.000000         0        78       360\n",
       "3  1503960366  4/12/2016     11PM   5.633333         0        34       338\n",
       "4  1503960366  4/12/2016     12AM   6.216667         0        41       373"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_steps_df = group_df(stepmin_df, 'Steps')\n",
    "grouped_steps_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((22093, 7), (22093, 6), (22093, 6), (3574, 6), (22093, 7))"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_cal_df.shape, grouped_intensity_df.shape, grouped_mets_df.shape, grouped_sleep_df.shape, grouped_steps_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Combine the grouped dataframes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine calories per minute with intensity per minute\n",
    "minute_df = pd.merge(grouped_cal_df, grouped_intensity_df, on=['Id', 'Date', 'FullHour'], how='outer')\n",
    "\n",
    "# Combine METs per minute with previous dataframe\n",
    "minute_df = pd.merge(minute_df, grouped_mets_df, on=['Id', 'Date', 'FullHour'], how='outer')\n",
    "\n",
    "# Combine sleep value per minute with previous dataframe\n",
    "minute_df = pd.merge(minute_df, grouped_sleep_df, on=['Id', 'Date', 'FullHour'], how='outer')\n",
    "\n",
    "# Combine steps per minute with previous dataframe\n",
    "minute_df = pd.merge(minute_df, grouped_steps_df, on=['Id', 'Date', 'FullHour'], how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "      <th>AvgCalories</th>\n",
       "      <th>MinCalories</th>\n",
       "      <th>MaxCalories</th>\n",
       "      <th>SumCalories</th>\n",
       "      <th>AvgIntensity</th>\n",
       "      <th>MinIntensity</th>\n",
       "      <th>MaxIntensity</th>\n",
       "      <th>AvgMETs</th>\n",
       "      <th>MinMETs</th>\n",
       "      <th>MaxMETs</th>\n",
       "      <th>Avgvalue</th>\n",
       "      <th>Minvalue</th>\n",
       "      <th>Maxvalue</th>\n",
       "      <th>AvgSteps</th>\n",
       "      <th>MinSteps</th>\n",
       "      <th>MaxSteps</th>\n",
       "      <th>SumSteps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10AM</td>\n",
       "      <td>1.649028</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>4.5617</td>\n",
       "      <td>98.941697</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.966667</td>\n",
       "      <td>10.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.266667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10PM</td>\n",
       "      <td>1.080127</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>2.2022</td>\n",
       "      <td>64.807598</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.733333</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.483333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11AM</td>\n",
       "      <td>1.266265</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>4.2471</td>\n",
       "      <td>75.975898</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11PM</td>\n",
       "      <td>1.358023</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>2.6741</td>\n",
       "      <td>81.481398</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.266667</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.633333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "      <td>1.355402</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>2.8314</td>\n",
       "      <td>81.324098</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.233333</td>\n",
       "      <td>10.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.216667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>373.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id       Date FullHour  AvgCalories  MinCalories  MaxCalories  \\\n",
       "0  1503960366  4/12/2016     10AM     1.649028       0.7865       4.5617   \n",
       "1  1503960366  4/12/2016     10PM     1.080127       0.7865       2.2022   \n",
       "2  1503960366  4/12/2016     11AM     1.266265       0.7865       4.2471   \n",
       "3  1503960366  4/12/2016     11PM     1.358023       0.7865       2.6741   \n",
       "4  1503960366  4/12/2016     12AM     1.355402       0.7865       2.8314   \n",
       "\n",
       "   SumCalories  AvgIntensity  MinIntensity  MaxIntensity    AvgMETs  MinMETs  \\\n",
       "0    98.941697      0.483333           0.0           1.0  20.966667     10.0   \n",
       "1    64.807598      0.150000           0.0           1.0  13.733333     10.0   \n",
       "2    75.975898      0.200000           0.0           1.0  16.100000     10.0   \n",
       "3    81.481398      0.350000           0.0           1.0  17.266667     10.0   \n",
       "4    81.324098      0.333333           0.0           1.0  17.233333     10.0   \n",
       "\n",
       "   MaxMETs  Avgvalue  Minvalue  Maxvalue   AvgSteps  MinSteps  MaxSteps  \\\n",
       "0     58.0       NaN       NaN       NaN  11.266667       0.0      88.0   \n",
       "1     28.0       NaN       NaN       NaN   1.483333       0.0      18.0   \n",
       "2     54.0       NaN       NaN       NaN   6.000000       0.0      78.0   \n",
       "3     34.0       NaN       NaN       NaN   5.633333       0.0      34.0   \n",
       "4     36.0       NaN       NaN       NaN   6.216667       0.0      41.0   \n",
       "\n",
       "   SumSteps  \n",
       "0     676.0  \n",
       "1      89.0  \n",
       "2     360.0  \n",
       "3     338.0  \n",
       "4     373.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minute_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22114, 20)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minute_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE:** We won't be saving this dataset yet because we are going to add the heartrate dataset to it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Per Second\n",
    "### Heartrate per second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Time</th>\n",
       "      <th>Value</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:00 AM</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:05 AM</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:10 AM</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:20 AM</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:25 AM</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id                  Time  Value\n",
       "0  2022484408  4/12/2016 7:21:00 AM     97\n",
       "1  2022484408  4/12/2016 7:21:05 AM    102\n",
       "2  2022484408  4/12/2016 7:21:10 AM    105\n",
       "3  2022484408  4/12/2016 7:21:20 AM    103\n",
       "4  2022484408  4/12/2016 7:21:25 AM    101"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2483658, 3)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_date_hour(heart_df, 'Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Value</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>97</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>7AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>102</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>7AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>105</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>7AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>103</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>7AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>101</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>7AM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id  Value       Date FullHour\n",
       "0  2022484408     97  4/12/2016      7AM\n",
       "1  2022484408    102  4/12/2016      7AM\n",
       "2  2022484408    105  4/12/2016      7AM\n",
       "3  2022484408    103  4/12/2016      7AM\n",
       "4  2022484408    101  4/12/2016      7AM"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "      <th>AvgValue</th>\n",
       "      <th>MinValue</th>\n",
       "      <th>MaxValue</th>\n",
       "      <th>SumValue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10AM</td>\n",
       "      <td>106.716075</td>\n",
       "      <td>60</td>\n",
       "      <td>134</td>\n",
       "      <td>51117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11AM</td>\n",
       "      <td>67.767157</td>\n",
       "      <td>55</td>\n",
       "      <td>90</td>\n",
       "      <td>27649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12PM</td>\n",
       "      <td>66.230570</td>\n",
       "      <td>56</td>\n",
       "      <td>89</td>\n",
       "      <td>25565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>1PM</td>\n",
       "      <td>83.687500</td>\n",
       "      <td>60</td>\n",
       "      <td>112</td>\n",
       "      <td>17407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>2PM</td>\n",
       "      <td>80.577093</td>\n",
       "      <td>59</td>\n",
       "      <td>107</td>\n",
       "      <td>36582</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id       Date FullHour    AvgValue  MinValue  MaxValue  SumValue\n",
       "0  2022484408  4/12/2016     10AM  106.716075        60       134     51117\n",
       "1  2022484408  4/12/2016     11AM   67.767157        55        90     27649\n",
       "2  2022484408  4/12/2016     12PM   66.230570        56        89     25565\n",
       "3  2022484408  4/12/2016      1PM   83.687500        60       112     17407\n",
       "4  2022484408  4/12/2016      2PM   80.577093        59       107     36582"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_heartrate_df = group_df(heart_df, 'Value')\n",
    "grouped_heartrate_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We don't need the sum of heartrates\n",
    "grouped_heartrate_df.drop(['SumValue'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine Minutes and Seconds datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine heartrate dataframe with grouped minutes dataframe\n",
    "minute_df = pd.merge(minute_df, grouped_heartrate_df, on=['Id', 'Date', 'FullHour'], how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert date and time from string to datetime object\n",
    "minute_df['Date'] = minute_df['Date'].apply(lambda x: pd.to_datetime(x, format='%m/%d/%Y'))\n",
    "minute_df['Hour'] = minute_df['FullHour'].apply(lambda x: datetime.strptime(x, '%I%p').hour)\n",
    "minute_df.drop(['FullHour'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                  0\n",
       "Date                0\n",
       "AvgCalories        29\n",
       "MinCalories        29\n",
       "MaxCalories        29\n",
       "SumCalories        29\n",
       "AvgIntensity       29\n",
       "MinIntensity       29\n",
       "MaxIntensity       29\n",
       "AvgMETs            29\n",
       "MinMETs            29\n",
       "MaxMETs            29\n",
       "Avgvalue        18548\n",
       "Minvalue        18548\n",
       "Maxvalue        18548\n",
       "AvgSteps           29\n",
       "MinSteps           29\n",
       "MaxSteps           29\n",
       "SumSteps           29\n",
       "AvgValue        16109\n",
       "MinValue        16109\n",
       "MaxValue        16109\n",
       "Hour                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minute_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I4FskEbNSkRGeE5LFUTdgTSTl80GM8WwjaM/UY+EEI9ovCSF8ACyCZEaOK2RiYow/50SuQ32GPhpDHkTv1pchhH/EGL+MMR5ntu4HnBFCuAUF9noDf7bvdgKmBtaMMf5nkhrRAoo+GSSlNQT1u4uw9w/AiPYOSHbqPBsf77B7BcEech0nHQ6Hw+Fw5InEh+wF3AZ8jWQ6r6+0Yg6Hw/ErgcvFOByOFmESBncDN8QY/2rXioz245CkwQwhhPWBmxDBfoJloe6JDifcpsgULpFrlaNRfSzr4x7gYeAYI5xnAZ4FbkcHZo60Z2cDVgfuijF+MGlrP+4IOqx1KPBvy16fFXgI6Ifab3O7PgNwDLAqsEbuOtBBh7euig6GbAb+hMj0a2KMm9szfVFgaA7gLeD9ZLdFNkhkRnqhwy+7IDmmW5KAwm7AGSgwcnyj4E7uckyhXpapN7APcBiy9R8xxi/t3j6IgO8JvAysZdIyhcRMdtnelp1/C9Ad2Ab4j9ncG+0cKWxbD0n9LAcsH2McUlWdHQ6Hw+H4tSDxRbJal/yvKOyxnXRPAR8C/wDujzH+UG3tHA6H49cBz2R3OBwtIsb4aQhhpxjj05ZVO0uM8XW7PQjYKYTwEJJV2TfGeIrdmx/4HfAKMDz5e9k4skZi/j2EcFKM8W27FoBp0dbKC4xgnwdls98N/NFkVA5A2cWDkQZ9liiISPu9LzAXsDXwXQjheuADJDNyjN3bPkirfElgWaRznR3BXiJoV0FBgo2Ab2KMP1gwAeDIEAIxxs1jjCOAp61kC1v09UCZz8OQrMgTKZEcYzzLMqFPRXIw/4h26Gn6dyZlvduCJLuqB3BmCOHFGOPJMcavbbcBiGgnyWg/OYRwG9pN8roF+DoW/To3gt3QD5gZODzG+D5ACOH/gN2BWUIIz8cYN4wx3m5BvVdIMt0dDodjckJooA3d3snN9l7/XzMSX6Q7sE8I4eUY441V12tCwAj24jyiL5E04jDznfojXyrEGN/2PuyY2Jgcx36Hoy1wkt3hcDREIivytJHPlwOrhhBWNumYF1H282YoW+I0I4wWReRfBP5sDl+OE+rmQH8SLWer63BEei0XQngWHb54D7CTke4LoczTzxo5D7nA/ucFwX4s8G9gO+AkFBhoijFeHkK4EUn+/B2RgCDCb4UY46uTvOJtQEKwH4EWDMOBh2zR1BR14G5KtP8cY9zGvpNjXxwNe9eORXI2OwAfGfE+DzAl0DHG+FCM8fQQQjM60PRt4PkW/2gGsMBIsX35MeBz4MMQQtcY4w8xxm9DCCcDARHt0Yj2ETHGN5K/M1oiKGP8CMyAxpD/ovduB+AatItiqxDCiTHG/WOM12NbuHPdfeBwOBzji1JQvB8a43+KMX5Sbc3GH0kWdEegTyHz5cgfJV/kISSl8lOalDIZoCs6i+jBGOP7IYQuIYRNUdJGL6BrCGHjGOM9rf6VzJC7/+6oR2nsL+QsvwDeBH6qsm4Ox8SGk+wOh6MlNAHNJnHwHSKIZgeuCiFsGWMcGkI43J5bD5FHI5FcxwiUBT0qV+IoxvjPEMLFMcafQgh/QtnCQ1H9H0fBg02Bu2OMmwRhCmAvlKU6OGOCfTT5H0I4FfgD0pF/A8lyNKFDFwGuijrUdOUgeaDvgJ9zX2wESfjsjA5vfRQt3EHBHRKi/RfgmBDCDzHGXXJ30C3QMxPwcYzxPyGEbiGE7YBDkGTKyBDCeTHGo2KMZ1pQ6JYq69wWGCHRFbgL+BjYFUn2jAohdIkx/mgZ7ceitjwQ6BNCODCaPJP9nazeuRbGtxFoe/bBKJj3IfBby1yfEgUiO5T/Vo7jpKN9YXIkIXL1IRxjR4lkORvtkJsZ+CaEcDxwc8zszJCxIdQfbH0B8EYI4YoY42tV121CYHJ/38wX6Y4I9v+ic19ez93nHUc0Iz9r1RDC/sBiwCbAicB/gC2AI0IIQ4CRuc4Zk3tfnJxh69Bi7L8UWBHtFO+IeIRLYoz3VllHh2Niwkl2h8PREImsw1DgihjjESZRcSBwhRHtT4cQ9kMH6qwJfI+I3JuKLJ8cs05D7UDIn0IIK6KDJG8IIRwTY3wuhLAnsABaEL4dpFG+OLAx0plfIcY4rDIDxoKEYF8ImAlJxDxr1z8MOrAV4DyUMXyDZRN/Wk2NxwvDgA1QZv5SwNohhDtSh9yI9guAn9HhT1nD3q8O6D0aEEL4JzANsA5wHAomHAT8NoRwRozxixjjDfbdLN+1ElYCpgO2jDWJppWB39hYcwswGDgBBRSWQP+LLJGQLV2BZZB+/mMWLDgdBba6A8NjjB9a+06FDmzN/gwHR/tCOgaEEHrFGL+puk7/K0K9rMPv0LkarwFv2I66do00KDI5BkgSkuVKJCv4d7s1K3AOsEAI4aiY4fkojRDGPNj6SzRnvVt6rl22Zag/dH1VpOM9cmzfa4fYAmV77w28askNSyK/vzdwYYzx6yor2FY0IqKjZBMvQ+fZ7IHOlFotxni/fWdpoHeM8btJXuE2ItTLC+6Dzlz6OoRwd5wMpH3a6xjRViTr0IuBlVEbfox84KvR2L9tjPHFqurocExMOMnucDjqUCLr/g68B1wLYPIiIKL98hDCFpb9/YiV9O90yJH0s0VSIaOyaYzxaiOdCy3o4y1LfzXgKmBDYF8kqfIRItizdwrMsZkPkZVPRx0S2SHG+IsRfnuhjOGzgC4hhH/n2F7Q4iIihhCeR21zEXAK8EUI4bESafHfINmRrDKgYUy77PdfQggnoEzohdDifbUY46P2neWBtVHggOS7WbZdCdMiB/vbEMICaLfIAUjqZg7gN8AqMca3QgiHAt9bO2e3GLE6FWTLvYj86wO8ZzsPHokxvpw83x1YEDgZycmcMelr3TpCxvJXjsYIIXRGY8FoGaUQQjGX3RgbHIrcXlAiNB8DeqCgVRdEthwWY7y00kr+D2gwr3ViMtxCH0LYAAVMtwcesDadBxGczUC7CQZF6Vp3AW5FWdCF3nVBTP8QDe1tPA31Mip3oPnsSOCGSis2cTAr0CXG+EoIoV8IYUvgCNSmswBroeSGrBFqskXdgZ2QpOCnwDUxxhtCCINR0OCHGOMXQYey90e7kh+trOJjQWnsfxL5TN8BnYFdbFfMsbGdSk6Vdvh0R3Pbl8kcnp3POz4IISyGzvjaH7jFEtt6oN3UjyHZGIdjskSYDN5hh8MxgWGT4Jook+XZGOOFpYyrLRHR3gnYzLK/m2wBkq1zULLheuRobh1jfMky8g9GGuwF0d4BOdwDgNfRwZpfVlT9cYJl459qHzeIMd5q11MpmRkRQT0/MF+OmTslZ/T/0OKoEzAoxviCXV8EuAyRL9uhbOIs+2CBZHHUA5ENUwEvA3dGycRMAXwLdLPM6I7AbMjO52OMu1RW+Tag0TgQdKjuC6j9RiDCbH/gOpSxPwzYIsZ4VWt/p2okY11H4EqgL3rX+gJ/BOZB5Mtt1sZ9kBbqGkj/dbU06FWNFfUIIXS2BVBXNB48k9v/3VGPICm3d4ADY4wX2LWr0KL2XOCyGOPwVv5E9rAgwl3AKOAvMcZnQwjzo3MM5gLmjjG2u4V6aV47DAXg5kYJDffEGB+vsn4TEuaL7A8sH6UPPQARZ3cBO8QYvw8hzNle2tEynq8AdowxPmjX1kZjfm/guRjjARVWcZxRzLPmjzyN5uLjgCfLmew5zsmtoQVfZGXgbuRz/YzGkr+ggPlS6AyjZXN+D5M264narDfyrXpRO2fpihjjT/Z8T7Sb5DDkey1hRHaW7Wn+1bUo2POnaHJMIYQb0K6m5YDHc6x7ayiN/aci+cCFgIeBW2OM51dZvwmJEMJvkH+/QozxqRDC3GgH0L3AtjHGkSGENZC/6edaOCYrNFVdAYfDkSX2QYvY3ZDOenpiPTHGy5Gkww/A/bZAai6eq6bKrcOIsYJgnx7JVuyNRdJjjCehRcWqwEEhhEUt6/tdtOj9IFeCPYQQkt87AsQYTwe2sst7GRFdZGIV7TgckdJLZ0qwp5p+V6FDPg+28kwI4bAQwnRRsgFbI2mRC4CV0v9JjjDytSciG7ZH2t0nIimmuayvjTKCvS+wOnApWkT9CerbPScE7YaJIYSOIYQZQgg9rC0/QwTgeShDbo0Y4xUom3FO9C5+mP6tHMeTJJtxMSTvc0KM8XYbF7dDC96LgPWsjfqgoMJVKFP/Z/sfVUqwhxCWDCHsDGAEe5HBeBZajLd7BGEM/fv2DiPYh6JDum+za/9EskVbA+fGGIeXx4hcx4xWMDcKdJ8NFAdxz4OCrQfEGN8s5rz2hGReuw6dLQJqy93ReSlbtfTdnFH4FvZ78d5ND3Qwgr0fmvMGAzsbwb4NcF4IYbpJX+PxwihEZM4XQhgYQjgNjZs97Pr2IYQdqqzguMLm64CI2eLQ9YeMBJvT5ooFi7m92tq2HYkv0lQaJx5EGuVvoEz9VWKM58UY37L7rwHZZkmndqHdcf9B2feLIL9kFHAssIXZ3g34G/BXlLyxZKydmZVre/ZBu3FvBAp5wU3RjseDYoxDkD/crpCM/VejndIPoySNDmgcPKbC6o03Uj8r8TO6or74UQhhVkSwD0bB1ZEhhPXQmQgzTtraOhyTADFGL168eKkrKKvj7yjD4zygU3KvKfl9Z5Tx0aHqOo+DbWeiyPrDwFR2rUNyfz+0yLgaGFh1fdtgT4fS5x6lzzsjEvM6YOFG7Zh7QQ7oB2j77vQo8+N0s+sYJH3WZNeHIfKpW9X1bsGWjsnvB6NsqvmQg70PWtw9D8xpz3RBhw6/graodyz/nZxK0a8Q2TAILWI/QLIwAxo83xllcT4I3NMe+qX1tYeRHu+zSNs0vT8bcCfwObB+YWdyv/LxElgBSVMcaZ97Wt8bjLLEulddx//RvjH+xzYWrlV13SaAbT2RjNSdwMx2bU4bN7ZMnusHHI+y2vesut7jaevvbZyfwT5vaZ8PSv4XhzcaW3IvwJ9tvloO6GnXdjH7jshhnBhHe0Lye5/k9+WAr1AA/DOUBd7H7s2IzvS5Lv1OLqXRfGTv1Q1IX3iYzc0b2r1ZkOTIXlXXfTztvRG42H7vAvwBSUZ+abb+MW3nnEvx/tgYcQXwBNpJdygwb/EMtr5BhO3c6EDUG3P3RVBQZ020u3HH0r2+wIs2J0xv1zYDtkn+L1n6kFa3JmCgjYXL27WtSmN/D+ASlCRUeZ3H0b7NkV+8ItDVrq1l9l2Sc9u0wba5k997ogDQI8gfvgLzLdHu1QvRGmjqquvtxcuELpVXwIsXL9WWlhxJRLSfbZP+XmP7TntYECLd58dtofdYag/1wYNCK/QSEnIst0J9cOBwlE31JjpQbLViMWQLpYJoX7DqerfBrpSInsoWRn8tPdOEMnN+AVayawFtu5y9ahvGYl9PJLd0OLBr6d7OSJroOWpE+1zAptQI7Cwd8KR+XWxxNwQ4Gi1Yv0fZ3XMlz0+NgnmPAE9RW+y2h7FkNeB9e682L9cZZdrebvdXqLq+pbqtgDROT0PbxptQsPQBYGZqi/BZzI5+Vdd5HO3rjYiHFZJrlyEy7HxEQLQLoqiBbT1RYOcHYNrk+pwoQ3EXFODaCUkTvYqCJ83ATlXXfyy2jTGuIaJlBPB/lAh2u7+hvWdLVF3/8bD3YhsbOydt+F/gcmpExExV13M87Po78Ba1wMj0ZtNI4MXkuf6IZBkOzFN1vRvYUQS0u1g/XIIaYdkPBSPXw/wNFOxfAPkrW1dd/3G0tQkFvO9EZPSJaDfrjygjejUbdx4l0wSGkj2F79sN+VPP2xxwGyL7nsL8RntuSmBP5Is8m7R9tkQ78n+b7b36fXK98KMWsvbbtcF3s/KxqPkcaaCuB8pgPxP5WM3AIcn9lVFA5P+qrv942Hs48BK14Oqc1i8vS8b+Raqu53jYdYC100rJtZ2Rr/wJMBMKbM2P1tefAvNXXW8vXiZGqbwCXrx4qa6UFhFLoUj6ssn9/sA/aUy0t1eSYnlq5NeeyfUy0b47SUQ+54LI82H28wqUNfYBsFey2Cgy5Abn6NQgsu9WYBr7XPTNWRFBe4B9LhYQAZGAbyOCsNOkrvP/YOufrC1+QWcagA7hKu7vjIixZ4AFSt/NamBdi/UAACAASURBVHGU1Ksg2DuhoMA1wKzJ/WNQNty/MaIdSccMQrtlss3Qp+VA5HK2SBhCg2wqdJbDP3KyCVgcEbSnJdc6IXLhuMJeJDnyIiL9hgDrVV33NtrX28a+B4Fedu1SpFG7Ku04Y8pse9veo8+Q5npxb0ZEZH6KCM7/IEm3bvY+vgH8vWob2mBjD6RtXcwDU6BAwceIMDrErjchYuJhJMOULRnWgp0d0c6d2+zzALSD7ipsNxqwK7BHOje0hwIcZO/bY1iQAO3Wutn67d02PzyA5MGyI5Oo35H1mL1XI4GPUDb31KXnu9rY+ggiorOcp5P6Nqwf8reGIt/jeiyL2O4dC9xPabdkboWEIAe2QD7vLMn97RDp/gTmCyP/+D7kP2fri5TsnAUR0M1ol8gUpfv9EXF7UBX1Gw97uqNdVwvZ524o0etL5CunY/9c9p7dkvvYT4O1MpL4GZa0YzH2F6T7Zsgvnqbq+o+jrcvYGPEZsLJdmwYlrn2GdsW8hCTD3iLZXe3Fy+RWKq+AFy9eqinUMgd6mbNSZGX+hMjaYht6f5vsf6EdbTlvbZEDLIsWet+QZPdRItrbQ0HBgI+AVZI27YeycYaRZHnYs9+QYWYq2vb6I8o6miq53hEdTjUouZZKbzwNXF91/cfR1n5Ik/xr4OYW7NoRZXBeYp+zD2qhYN0gtEh/CGWspLstjrIF06WYvAPKZi8CQdkRE9QW3J1QRua81GdbrWQ2PUwr25bJYMGOAozf2Ti/Y2LbtLbwuQJJFl2KdDTPQOdyfIaybrPug9QOfBtMjdxbE5HuaybPTQ2sj3aHTFtFXcfDtt5IImYQypY9CS3ML0+eGYgIwD2RxnBxfVq0qN3XPmfbjtSyMw8s2gZlZH5o/XBLdJ7Kxij4k33WKfXB+3TsuBgFP1ZDZNg1mPQUIl5uQ3I/Oe+mS+0ZnY2KggPvop2D/ez6rCh4dyMicA8hw11n1Aj2zsg3ftjqvaWNkc3o3IoZ7bmprN8+bs9nvSMreV96oMzT09DBtEsk17tSk/TpgAJaTwFnV13/NtrYA+2SuB64qtwe6BycL9Ch0aAg5QJJ22fVdi2NbWjn2SXId/4jMGVybzGUPTxGJnuOxd6xkWg3xQJ2bXpE2n6OEr4WRTJbj6PdnrmP/en4mK5r/oD8xv3Ntquol9C6xvpvtgGtVvrkYiiAOgLzQ2w86Y+kmo5BQYSZq7bBi5eJWSqvgBcvXqorKFPgOZT193tEwhyFshcfpbY4GoAkSJqBTaqudxvsSp3pLdHWvKOAjZLrKyDCYgT1RHuWBARa8PUq1xNlsDyROJvF1vMZrB1vKv2dvlXb0lKboYOo3kfZmoVefkdzqpuBE0vf6WfO9gkoQJJd27W0WLPF0dFm17/Sdk5+/21L38+xIPLrQVvYDUmup1n6RyGy7Hbqs8uybTtE3t6OMm9GoCy/32PZjEhX8wsUWFiq6nq3YMvyKIB6NiLUP0b6rF3s/gZ27Q3gXupJ2ivIXKMWyag8Z2Wm5Pp2KAg5BSKq10HBx+H27t1JSVM/t4ICPM+iTMtiTp4SZcN9AVzZynf7I4mcYcBsVdvSRnuvROfBHEyNaF8YZbQPs3Z7HmVGZ01oluw6C+kKF3P0XNY3Cym34l2cHvgXChjNUXW9W7AlNBqzE9sCCva8g+boGauuc1vtsp+d7N0ZhA6JTJ85jER+CQVeL0d+SHvJgu6VvE/D0JxcELWpDzIFCgINQcHzwr7s5uuSfQOsjZqB65Lr6flSdwHPNPhuVvMc9TuOV0A7jpdK7k9rY+aPSJJvE7Rb8hE0H2Y/Nia27G1jxt3UMtqnR+vPV83GZ1CAsl28a1bHS1DywmxFndGc3oyCCEVwtZ+N/cNIpBVzKuX3gwa7A9GungdQIGGlquvsxUsVpfIKePHiZdIW6rOqtkRZfosli4vuwLpoe+y1ybMDUNQ9a4eG+syBa82O95DkyPfY9my7XxDtnwJ7VF33VmzqaAucC0lIckQsX41Is97J9WKhuzfKDJkr58VR0vdSov0daiTm7Gb7KFtMrID0UC9GWSC5OqPF/7wbIvd2tgVSIWMxIzWi/aLke11Kfyf7RRK1DLCZ7L1rBk5uZBMiB+8sO+s5FpQR9zIKHvzRxsxbrC8eQk0/c3kU1HqdzOSYEEkyCjjFPvdE+uSfoIy+4uCt2VCW92jJJhs7ngWOrdqOVuzrjc6iaDa7lkzuLWa234FIlW/QlvQl0DzXjB1Om2NB83FfpEk+XeneVEiOqI5oT8bTHRERPZw8JTnK5xikY8RVmEQANaJ9CnRI8jrWV7M+o6Jk25QocWEE0pHvhOa7rZB/8gzS0T/Q2uxzMtxKb2NC8X8v+tklwAXJMylJuw/yQR6mpmeerS+S2PgU2j3xPCbDQX3yxpVIkmk6+9wzuZflfJ2+J8jvuL/oY0i6rdixukvRjijA+jYKMhftlqt9RX8s6jkXChw3Y7J8Rf2R73yN9css7Un/1ygo8ri1xUhENp9HTe5mOuQjN6N1zoXA6bm2WXnMxnwQ+31f5P/fQ41o74r86AWQ/xIa/Z1cC9ot0mxtUpy11M/G/c+QFvs/USD9Y/Kcr7tSCmqj3Y5DaRDARxK0r5k9y9q1kP704mVyLpVXwIsXLxO/AIsAOyefi0XSYWjRN23penfgr+asjZGZmaNjU5600eFbw5CcQw+0yD3GHNS7k+eWRpqb75HpgXjm3ByFtJRPpp5o38ectw0afO9gtBCcYlLV9X9tP1sAbYqI9nepEX5zIomVIvPvU3TAWHZERGGH/eyFsoneRJnEnyLSttgOOx01ov2Cquo7Hva1SJTY4uEGRO4dn1xPyZeivbMk2pP6HYIIl/mSextZe21b+s7qKGiXzYIWESV/QzteuiX9sjf1RHvn0vd6ocDBY2Z/dmN+YsdbKPt+XZTt9oqN60Uf3Rjt9jkT2Dz57rL2Xi5bRd3bYFsvNAef1uBe0T9Tov2K5P7iiES6kgzPFknq341E758xifYimDV9C38ny/GjhbrOAtyEzkzZyK51tfdsSNJ3LwHmrbq+Derf1ca3I5JxZBobW+p2mpXG+lsQefsadhhqzgWReAcgn/ALGyfqJM1szPyOEhlGhv5jqX497H06D9i9dG86tOvlR2rk++wosFzYnd08QCvzLdpl8CUKfm+RXJ/D+uNFVde/DfZ1Q2TsA+hckWWQj9yMdlAUuz5nQjvVfkFyHN3G9v+ZxHbU+XxonZkeYp2O/fsgX//udCykBemtnAr1iV5pYO6v1mZnUjssuRsi4G+19j0OI+FzKigofDcKFBc7rjoj3+ozmxfGkP5Ca7ZiR8nyk7LOXrxUXSqvgBcvXiZuMUfmepvkdind29EcsqXs82hNcqTx3QysWrUNY7EvdWKKundDGSrnUr/Y640O5RoJ7JdcX5xEuiLHYu24v7XXKYlj3Q0RTJ8gkq9wgKZDWe73k6kcAi1r+rVEtPdEi/oNkP5w1nrKiJR4DGXkLIuyhFdH2bRvUgtuTYeCKM3AwVXXuw12FQvunmhRdyeSSjkAkzdAW+0bEe3pdu0sF0klW6+2xU+RwbOlvYMH2ec+QP+W/kcV130ZJFHRl/osxsKWNKN9u+R6d6QH/bL13SwlOez9+gjtMijepSUQefIKys4sFvZdqJ8rpkNE0/O0QOBWbFtvavryDc/QYOxEe/+cxn5EeNURJkiG6BNgy+R6SrbcjbK6D6aUyZ9rae09QUT7Leg8jo1JssKtT/YgUw12G0desX65L7Ug1qzAiTZ//SN5vrj/LxREeJwMJYsazUPIz/gTCojcYON8Sp7tYP12oarrP462HmTz14/A1nYtfd+WtnFkrwbfzWr8tzql+vKHIJ//HORvTWn3FkREezPaJXIj8oufJ/NdFVa3Le29G5iM+RubPXuVnu2HgpPfo/Vd96rrb/VaFO3UKQ51brIxvRk4I3ku7Yv/sPt3kWFmdwt2pj5G0VbpGrRIqDmTDMn0Vuzqau0xzOavYp3ZBfgNmqMHo6BcOk7ub+PnFcA8VdvhxcukLJVXwIsXLxO/ILLlRpsId02uL4Kyp57GDiK06wHJdrwDDKy6/q3Y1R1l6m1Vqvs0aBF7QnKtIAb7It3ha6qu/3jamxLtxTbmJVFQ4QdECp5vjukIYMGq692CLakzOgcix/ok7dQRZeMURPsYun+5F2Blq/tq1CR8tkIL3OLArcIR72eLkOwyxUo2FfXtae9RoY95l40Xr2BZ3yiz6nokSZXtgWmJTeWtsB3Nrtvt85a2QCrargMil04mM2IMSSo1A8c26lPUyIWUaN82+R+sjTSVs8xgRJlVqwB/IdF7trF+SUS0v4q2LJc1RDdB2cJfkCFJRu2Q03uxzN8GNpQlOwqi/ZOiv+ZUkvHiNep3hSyB5M5exEg/u14s4neglgm3TdV2tMHONJh1CLBCg2eKQ02/QDJA3aqudxvsKvrbVCio9T6wH/VEe0GKpTJhfVC27W+xg/1yKkn9O1hdp0xsnRId4Po98p9XQPrXy6HdPfeV38vci/W9M82mO5PrxfvWhALjJ1ZRv/Hsk71sXHkRHfD8NsquPR07XBGY28bGZuSvbFjuA7mUBmP9EWhXRXEw5mbU+yFTUb8baAY0vzWnY2rFNt1n9dmFmsRefyQ38hmJf0gtA38BFET/hga7uXIpNDgPCh1MfgQ1/ykl2o+jJB2Tc6HmY3Sntpv4NuqJ9t9SI9rntf9JH6RD/xdKEphevPwaSuUV8OLFy6QpaDF7q02Ef0yu74ei008jQmJ6+/kEWuRnu4hAQYLvzLFOnebOiBx7GYuep44QymZ5kAwzc9pgcw9qRPtpmBaoOdYnIgmVF1HwYb4q69qKDSnBfhZaIH2HZVBhuwqoJ9pfxzLacy3l/oQy4UZS07wuk7R9UDZP99L3slr0NbCzyZznIcCsyfVLzb4tqA8ePGDvXHbZYijotiO1DPy+SGNyUfu8q9l0CiZdkXx3QZQRd3TVdpRsWgEFck6mAYGXtE2ZaP8IEe1Znwlg780DiKAcQ4LIfi+I9nJG+7rWbx/FJJtyKogweh3NvYXec0EmdSY5XLFBe06FMjnfJTn8NYeCgh+rIQLsSUSgFATEItZWL1EihWw+2N/GySzHRTQnX5x87ogy+pqtny1Ver4JZdp+igKTm+f2jrVgZ9FeU6OdS42I9r9TO8j1MCRZ9ClGduZUEnt6IX/pGRSYG0RNN7kXItq/QokbL9j9e6kFJLP0kVvqU2hOPsva6cLSvcWQhvLeVde/jTZ2QnrxD6FzowqC9hmzY6WknedBGe1DqCelK/dLqBGWxbuUapQfBnxuv/+Geh8yoLNiHqT+IPmZkMZ3NtnDVsevrL7FuUT90G7Iz6kn2gPyI8/NeXxEO4kHAWtQm4eLQ4XfR7I3jYj2u1Fw4V9kvova6lv4ID1omWhfD51L9AbKXh+E1nTZBxK8eJkYpfIKePHiZdIV6on23ZLr+yJCutmcoGFoa2+2i4jEoVkWLVSHUk+074k0sM/ECDS7Ph01fd7KnetW7Gtty3kv6on2Psm9qREZ03Vi1/F/aTf7/Upru60RuVksjE6gloHUEWWefoMOYBwja6Rie3pSv8W1O6aBDKyJyM6BwO/t/SpkRordIreT6W6Dsdj8BHBkcm1Ds29f+9yD2sG101DKvM2lIDLsYVsYLGw/n6J2SN+cSDLlZ+BSu9YJbX9+HHiEjLacI4L9J+BUaoRD8b+fghLpTj3R/qK14bpV29GKfb0RUfs4Ywm6USLak//BEmQoPYKyaR+wNli0dK8zIqEfpcEZG9TmwynJVEbLxrwV0c6Wloj2F1FgqwkFsR4B/pz8jeyIdmpnNDxUur40ms8eA5Yu3Wuycecb5G/1qtqONtpajBctEe0zWfsNQweHDiXD3SKJPT0QITYEZZ6eiBJOPgfWtmf6Aruh822eIwkYkMif5VSS9uiKdtRtg2QRizl5FkRuNqPs052Ava2vPpPje9aCnXPY+L5tcm19s2s/+9yZWvb0Aohofx74v6rrn/TBg7Bzs9AcN5KaL7WkvUtPUJKIAeZHc8Y5jJlNnQUxTf3unkepEe1FgtDM1Ij2i1HC0PKIlE/lp7Kwp2Tb7GgN8w6a24r3bhprl/fR+rqY54p5+iq0O+EbMpSra60daZloD2jHyCDkn92JHczrxcuvsVReAS9evEycQj2ZmTo5S1Ij2ndPri+Ashj3R+RfljIBjWxE23cLon2T5P7p5pTeg0jALdHW5S/JKMOjgV1ppvfGaLvdHiQLdeqJ9tEa7e2loAXd84VN5oj+YG3VjIj2/navI8pazSojAhHquwGnJ9feRBnETfZOvYMW5j9Rfw7A3IhAupgMyNmx2FkE24r3bRqUnXiIfS62LxcBhG7mhG9RGntyDNZ1Roujd9BuisfK7xKSTrnbbPy3/f40Igqz0StHxH8zOuC5I/VnbCyOFunLNPhesXjqbeNj5ba0YF+hU34PNRmVVt8dakT7C2R6wGlS1+5o98s31s+KQGMnRLAPoQV9dnsuu/erQR0Lov19xiTaFzYbRyBC813aAeGHSIddrN6Plu4tbWPlY+m7h7Jqr0MBn2xJltb6FC0Q7XavOyLLsjkToEH9A5qrh1CfiHGAjaOpDOG0wO4oaH45GcsfUJ+h/4y9R80oc3YItSSAmVBG+/coiHwa2jGS1YGZJdvKu3iWsPqvZ583p94X6YUywRdM/i/z2jOPYRrhFds0lf3vm4EDqe1k6m/3u9r9Edae3awsh+b0J8go0D+2dqMx0T4TcLxdb7Yx8ykyDWKVbJvP2uADNLcVPuE0KJD6PvW+/wzAZcCMQN+q69+KXa0levVA50jVEe12LyDfM8tELy9eJlWpvAJevHiZ8CVxJjsgEmnG0v2laJDR3tLfyblQI5FSon3T5P4h1LL0Pyb/zKqm5Pd/m+P2LMpkeZ56qZ+CaP8e6bDnqHnaCRFdvyn6ofXL/alp5he6pxva5xsR0XQMDU6sz6WYM3k02hJ5NcpMGUx9ptv21vdeQhrSHYF10GJ3aLI4yoogQxmxS1PLfOuDiPOZUdbzs8C1iBQsFofFu7gsWkhloQfaBlt7Am+ZHa8VfY76YNdcKEPzVuACFFzJJhCJFuKFDutRpXuLogDCxbRADpVtyG3st7HuXZKDQJP+1olWpLEQEfMJyq7KbuFXGvO7IfmiH6yfzWpjx2M0kNwgU1JlbPYiGYdGGe2zAn9ApNJByfiYVX8s//8RqbwrIokaEe2f2Ji5vdn+L0SYZSuBVhr/VkHB1M1Kz5SJ9izbqRUb70EyZwUxtilKXNjfPvdN7k2BfJWvbR7ImWjvauPd3dbf+ppt3yFfsq89NyPKgh4OnJN8P5tzAqzuM1I716YX8qEC2mn2HfJDigz2g5L3cnVEgK5ln4txZm4s2FChXen7NQvKbi58kKb0GRRgPoPaDpFi188DZBToL9nXYmY9jYn23tT8rI3JyL9qg63zo7msEdH+AAoaX4IS2a6y9y07Ca0W2mpr4FC0O3JWapnrPakR7bdSkjzy4uXXXiqvgBcvXiZsSRyTnjapD0U6aXWHrFAj2j8F/lB1vcfBvtYyq1oi2mdA29EH0GCrfS6F+t0H55stK9vnk1C20bvAHslzvdA258/ITALB+uBNKIv0HWufQp98JiTdMxvK/t6bWgbVttQOuzsyd6cN6UaOMse5OAMgzdzZwdrtU0TIv4q2Uma5OLI6/R8invdBB1QNRxkrxeJ886SN/pp8b260gLorR7tasHVGtJ1+KyRF8hJ2EHTZhrF9rtiOhVCG1NfAEXZtQURCnEtJ+z/5XlYBngb164YkAd7FSMnk3emMSIeLWhsnkGTTHBO7ruNhW0+UTbpRyd6dENE+Ci3ex8hgt7H/KBrsTsihtPZuoMBImWhv2A9zesdaqF9biPb5bTz93uaAd4CFq657KzalgZ9L7f371N7BR7CdJHa/INrfRmRMlu1FzfdI2+tt4Cz7XMxphd51J0RsHp18pw/aWfgRmZ17ULJ1DetjK1Ejv7awMWX0wd32s5Dr+IrMDihHCRk7IqJyfkS4f44C/IW/eCryjZup351b7Ba8qdSfK++fSObmD8DGybVbkB/fTL1EVjHXdUMB4wPRzs/1yZSIJgmOouDUtIx51ssYRHuj9q/alnGwuUy0F0GhqdB67n0bQ5/LfOxPs9KvQBKKT6KgTnGGSHEIb0G0f2BzQOcq6uzFS46l8gp48eJlwhVq2Q89EZF3P/BnRPI1owj64snzS6EDCZuB31Vd/zbYl0bXB9oCYvnSM8vTQDom90I9wf5blIW0qn0+2BYRB6JsuM+AXZPnewJTVm1DyZ7iAL/7EGHbMACADiL8qtQvd0Kk4HFkrOmXLH5uRFnsn6Ct5L3tenrQ0QLo4L/tbaFUvKtZLY5K9t2EZG4+R4vcvtTIhg5oofcLkj3YAwVEhlofzTmAUKePmVzvYv3xbbT7ZUByb0ZgYPI5iwxidHBYt+TzfDbOf23v0DcoU3EMzWeUZTV11Ta0wcb+aBfSG8COyfXuaOH3KO3g8LAGdnU0m5qRjum6yb1uKNg4wt7DGUrf7Wnt2gzMW7UtjWyznz1QgP8uK7sl42NKtD+e81jfwL6WAgI9EGnUiGjvbePLerQi+5NTQcGr9xFp2wPthmlGWfj9k+emRsH0F8jIF0EE36ZJn5sKZa8Xu5VOR0H+o20uS7Ogl0A+9H7U+2e9ySxZo9wfUdLCt9T8jOLQ9UJGpTdaGxRk2YzU5BVPqdqexI6AdmK9Zu30IcrOnz55ZlG0k/BnlBSwAlrzPIV8kax2C6KdLS/aO1TorjchYnYZagfI7518p7WAZTY+lrVXes7LldYGrwLXU9q5g+buL1HAoXLpnjba2NLYH5Cf3yijvSvKAl+EDH0uG9vPpd6XPA+ta5azz0dQ2xG+LfVE+4n2jmabne/Fy6QulVfAixcvE7agzL4b0bb6QurhKpQ9NRItdFNCcwXg7zk5ai3YlRLs/0KLua9RRtFZJNp91Ij2J4Atqq57KzZ1M+fyHERSptq05yMSYhtEtGxp91ZGi8E3gYOrtqEFu7qghexoXUm7PgYxiQ4H/Qw7zAkRf1cAp1ZtRyv2lcnZedBuiTNQxvfl1DK+WySbW3LWqy4kW5XNqf4R6WUWNhUkRCeUHTcUbYe9z/4HxSIruwBC8o4VmpLnIuKvIF26IRLsLSTPtDjKEH+OEmlWdUEyTM2MuTiaDy1uvwaeLdtuv8+CJIsuqtqONto6L9ph8AbKpGpCRMXYdMqzCIa0Ur9LERn2MyKa107u9aQmHXMZNbmt7sA/0Q6FRauo91hsKsaPXmiefgo4Fu2c+wTN36PHR2pnIrwLzFZ1/dtgX/oeTW/vW0jGxZ60QLS3p4LkYZ4BVrHP+6Cg698QifQ0SXALEdj9q653yYZlrZ73ojn6TaSTXOyIWdvmrmbgPLvWydp0iM1pDYOyVRcbB9JxvxsWcEMHrX+DfJNNGPPQ9W1QNvhcSb/th3ZMViqjYnXpRZLdbGNEs71T6zd4fjGU0f41Im2fRn5kVr4ISmz6Cs3ZK7bwzLxIKrJMtM+E7ULIraAgTdpePakd1L0dClT9gojY6UvffYj2mei1kI0fc2FBN2pnMaVEexZ9byx2HWRtUIz1a5kNhczSX2zs3x4FuT6xMaSYx3uQYfDAi5cqS+UV8OLFy/gXc0RnJzmozyb4G4DV7fPVSMdvXmAjmyivBZZs8PfagzNQ6JSvj6RGbqN2IGHX5LnlzJl9gAZZnFUXa7shiFi5F2meTpHet5+DUUZB7+R776HssqFklDWW1H1lc7DXZSxEMtL+vhstdJ8CHkQBoSyzGqnfBtuX+q2VHYEzqRHtPez6jIh8nqvq+o+DnU1ou/XN1j7FToop7X6a2dfd2jHN3M82aIcWgC8jwuUDFMR6AtvCi7KO1kPZV6Ns/HyajA7hQsHU86hJ9pxf6ovzUyPaDy+16yxI4/uVnGxq0EZ7lq7NZ+32hrXJYzTQtEYLvs3IOLudGrG1EZIJ2A+RYs9jC1u7n0rHXIY0iM9DAfPsCPak3l1s7roX28WEfJFv0AL9ImqZcJ1QpvQNOY8bVteUZDkNBXq+R0G5baglNqRE+4NV13s8bOuIdJELaZGdUFBnE+uTh9m48wAZB0asHQ6wPvcNykzvSr2c23bI/3gDHbh+MfJFhpLpjiwb4/YkOX/DxvPD7fdFbOx/AgXJD7DrgZqMyr+ScWj0DrUMbFvA6nYzMK1d2xIFR96wsgwNfEskw7IQ8rkKm7JY1yBZnheRj9jqgZdo/i6I9iPQjp9HUUJKVskZ1p8+xmTLbHy43sb+Iph1LUqI+hStXaYt/Y1zc2mnVuxMx8cLkP/4g9lzMSZHRz3R/jY6FyCrAF0D22awvnWRfZ7Hxs2uSJP9G2qJXiugxICX0YHf2a2vvXjJoVReAS9evIxfQXIp15rT8i9qC9kpUHZwB2B3RMgub/emtIVDMyJ4K89YaaOtRSbRLigzrrBnb3NyLrKfl5LoDqNtmQMmdX3bYE8h53MvypRtqGOHNMs/pX4htRDKCFyTTLfmAYdbvdtE4Jmdp6JdFlfTyiGGFduVZmjeSi2zdjtMnxWRn2eibc23o4yyR63fVr6AHYt9rW1JvhkR7QeREO0o0NC/9Gx2CwrqSZV9EAFYZP3tiha/rwGLJO04AAUW9iezjDiry85ocXs+IvMuoZ5oT6VjDrNrM6DA3svUCKRsbLL6dEnmqRNL9+azd+lnYL/keiqVdiEizbKV5KA+6/llRO7NbG31LI2J9q+tnb8iU4I9sWt9FDCdzz7fgAIji6MAZDMiKopMuJTAyHKcpD6weBUiUHZFckbvIKJ9fyzpwdp2F7P1zqrrPw52Fm02tY0XfVGQ8TBqXTA2DQAAIABJREFUOsMzoQBls40n2YwhyJ8YiGXMIr93OMqkfQjzEUtj5TrAKYiovoHkLJicbCvZeRoKtp2D/Pz7SHTiqclEPo8yqJuQ3zjE2rOwL5v5Gu08eB/5Tgcn17ugwMJANFe/Yc+mWuvdGVM2JyfbVkc+xpL2uVjX9EVJQXugXXXF2QFzUwuk/xf5kcWcnQ3RbmPBj9R2gsyPdhEU67Rr0Xy8aNInX6VEtNuzWb1r1qeWK137Nxr7N7TP9yES+g7s3DN71+a3fvoiGR0k3MDGwnc6HpHnRTZ7X7NjEFqfFf2yF1r7fIfmgD5V2+DFS46l8gp48eJl3AuSQxmBMtt2QAuhdAFYTJrXADeWvnsLImXuJN/FbBdzoPdNbOmCFrT72efdELG+EZIYucict7Np4YC/HAoKflyIMsBmT643klLpgDJChiN90EWQTMAzZKhfSI1gORF4xX5vSLQnzy5qTlux4OgyMes4AWzrQk2m4iwUGBiFttDPWtiMJJj+Y+/pw2SaEZfYVyy4u6HAwGZp/7R7tyBy8y+IyJgakdWHVF3/NrZdD6S9ewTJ4cF2b2tbOIwm2hv8nSzarjTWD0WLoF1QRu2FNCbav0JSAPeTZLCT2aLW6rQ8WrS/j4jZs0r357G2ep16jfY+iJT4Flisajsa2NWR+l1nxZi3DiJR5kFSYd/ZGF8m2ndFi/vsDk2jJKlhY/o+aJF+BJKCWdru9U3a9jZaOPQu14Iy/F5M7NnL+utzyCfZn1pGey/ko7WXhIYDEHGbHmw6F5LgSM+B+S0irNcjo0x2JBtyHyIk17drCwDHoDn6Y7Qzq9hlVj6MsVvpcxZjflKfjqXPVyZj5QJ2rSl5D3dAwbkP0NkqL5v92fkjiED/AiUpzJ1cT6Ugm1CgrtjRtCTyt6ZB/smaVdvRin27ojl6tuTavMiH+p7arrRnCvuRHNWyyB/L9ZDTbijB6SNrmy5IRrAjWqe9TUJUox1ohQzhVFXVuw12BRQgGFZqw6epT/QahZKDvkTBoUJ6sIO1bzbj41jsXQ75Hn9PrvVCGfv/TK4NRPP27JTOismlUO8jZxNo8/LrKpVXwIsXL+NWgAXR1tfTaEEDzZyDzjbhP0It83RhtPhYLHk2m4wIq08vlBHwrNm5YXJvANoGOrM52Xsni4WVEKHZDFxctR2t2NcbZWIe2BZnGWUEPkftwJlPyJBkKdX5j1bfVokuc8IfBbZJrmXnEKWLG5QRdoP1wWIhe4rZ+w9qRHtHtLgfnW3VlvauyL6UGHsGLcZ/tvdpM+p3h9xsi4pHENH0FhlKjtj/fk2SbD2UDVwsZLe16ykhvbXZ9DLJuRU5laStinFvG7QteU0kOfIz2tlUJtqL7OEXyJhgT+p8PiJcLkAE9Oml+/NR20myI9rWfBaZyqjYu3UvOiR4s9K9OZGsw9H2eUkaE+09MdmwHAvK+juH0u4xROqdQS0LelpE+g1FQeSsfJAGdqUL9q4o4+8o+/xnRKxvYJ+HoDl6P2pSF9nNaa3YuhXahbZFcq2fzQnnI4m+WZG8w/VkFCBBc+0XKNN0hwb3uwKHUiPae9r1DtYns9v1WKr/XGg3wRrUsvEfsPb6EmWbFrsoUqJ9EeA3iCAcLbOS0/iPAuC3oMShhhKI1M9pi9r4/6GNLU+hpIZsbGpQ//URmX4qsKGNEV9Znz3Xxv290Xw3uIW/kU1QpFSvpRBpXsgVFX3vEpTQVchdNiHf8VIksZKlPcX/mlrQcXO7thdwhv2+K5qni4z2C5CPdQuZ7sZtYGN558dZKEmhCBT0tbFyKEpoG2jzwLNkGiDJuU95+XWVyivgxcukLOUJpT0V7NR2RK7fTiIVQguLOLT98Cd0EOpJ5og+kuskhEiE1xEZsT4tZ0EvjQjA3yXXtjNnYGsy1r5G2QLN1PSfW1yAUyN3ZzP7diKzg8VasfFblGXV2qGEy9tCKefso6INutl7dA8wyK6lW5X/Ye16UqM2yvidK+zrgHaDDLZ2WRdl5/yAgibpoVanAjfZ89ltqUcBxhfQYnWtpI4zILmbL4C70+eT37e0711RtR0lmxZBh2j1KV0fgAjLfezzX2hMtC9M+5BAKPrjcjZX7Y8Wdd9SOgyZGtH+KpKsyJJgt7oW+rrvoSDV9WjBXhBef0QETCHVsThawD8J/Lbq+rfRxm3MxpXscxPaXfABcGny3CIoUDkrNTImS9+MeoK9kOSbA2WYzmvtuQeWAW1t2oykSfYgITtzLiU7hwCPlO5vi3zJT83mz4CFqq53Ur/5Ecl6GokMRXmcQ2Tu4YhoH4ykZGa28eO2qu1oxb6lUPD3NeC45Prs6MDZS1EgZHTyDa3Lv2X1vlk7vAPslrabvWd7oeD+ULQ7Zja7NzdaK7yC/JHssvMb2HmWjQ1FsP9G4PelZ4oAc3YZ0Ml4PUamMCLUP0vmsI5o59yTRZ+zsfNeYNXk+zm31/RoJ/G1ybWZ7Z17BTiE2q6YRcz+L1AGfKfcxn4b/zakdF4NNb9rTbTz5ThqQfGFUTDre7PvQzJP9LJ6XwecUnU9vPx6S+UV8OJlYpacJ+/xtKcrIhROHstzqQN0MNru9bY5dFk6otQy7+9DGqctHsREjaj+sznn06Asgn+Ssfad1X0Jq/tvWrGvsH1B4A9ktiBqo51nmZ3HUdMrL/peQGcHXIIW9GNoM1Zc94EYkW6fm1Cm21uIdL43uZcStCdR0xrOyqYGNqYBgq72Tl1DQujZAuMCRK6Uifb0kOHsCFu0AC+2lK9DfRbtIYiIvqaFdlwrp/EREUjFovwS4C92vVgY7Y8WgnOibOL9rM0uoIH8Um7tRYOFKNrx87CN6X3s5zfAaaXn5kXk2ndkRPq10B+fQoTzQYg0egbJPGyHAihXIm32grAdSO1wyezkwVqwcwjwUNq2wLHWH09E2X9PIKmRrAn2kl0XAsfY7wUBuBkKyKXSFnsjkuzfZJzN2Gh8K8YK4HeIaNmmdH9lJOVxBJkkMiR96HBEmve3Pldc74wIsQWx4LeNkYda2/0XJXaM3uGTW0FJJV/ZGLhIcr0D9b7+v6kR7cVZBzMhibSs5YqsniOo7RApJCNfszFwOAruFP7VFMl3Z0naO6u5Lalj6m/9HwpIrpO+h9QCruej4Gp2Y365TiW7NkV+1Z7JtXVQ8HuQjR3PoHmwsDUrErqRbUi29GdMfsquLY0C/ysm1zZDAYQDyXBXDFrHnENtR+NRNjZ2Kj032N67Hsm12VGgdTtst25upfQuHWTj+uq5juteJv9SeQW8eJlYpTTg7gD8FW0rXIaMNbvHYtPsKANnW/vc8MBMu9eNWubVDEg/OVtHFJF8L5Jkp4/l+ZNRRsiL5jB8iWlS5lwQefQpdoq7XWtIMqCF/fVV13ks9qTvWSjdu84cuouBBZPrS6JM26/S6zkUtED/k9V7cOne9Ogwv2bggOR6StBegIimLBcPKHu02GpekLS3mk2vM2aGSxHA+gEFfMqLrCzttLoNQCR7mWifBhHto6gn2sv6vJUT7WhhtDsiGUahRdJbSBd0PxvX+6NMzD/ad6ZFC8ORKGM420UGyqy6HMlU9CzdW9FsWMHa7HxE/pWJ9gGUzg/IsVg930YSWRuioMjZKEPzPXvH7kckYDFXLwzMU3XdG9gymsgs+qn93A3Jpayb3Jvf2u47FBC5hwwP8BuLvVcizeFUU39j5HdsYZ+nQbt/sj6jomTXtsAKpWv9EclyZdLWWbcT0gi+v4EdZ9u79Yu11TZ2rxuwOSKkTybTHT7Id3/Sxv2GMiql5y9H/uUlwAbIFxmaw1w2lnr3Qtm/n6EzRK618fBtFBBpsvfraBSwW6rB38iqj1IvVdfiHFwaR+dHB9WeNjHrNp72LGbjwn7UnyeVEu13okSwwvbeaHfxO2jX2Y3Jvazaq6V+hBKjPkN+cOEzz46CQqfb534oCHZ+rnZZPWe0ceEBe7/eQHKXaaBqZST9c2S5f7aHgnymQ5HUT7uqu5fJq1ReAS9eJkYpOS3XoWyxlxGJNAo4HZi/6nqOh109zem8IrlWJjbTiXIQYx7klKUDgEihT0kyVMbyfBPaOnojOuxu3qptaKWuhWPWhLYQnm39cI/0mVK/nQNlFByaq6NAPcG+uzlrO6ZtYY7pj2bvY+akv2AOd5ZbDm1hsBvKVLmvdG8GtHB9H9gruZ4S7VlmaCLS4V2UsZ7qrM+Dskub0dbsrqXvTWMLiGbaGASr0MayPMCcNu63RLT/BFxVdb3HYlNfJD0x3PpePxQwfhYLuiIi5VlqC9gpkTzMg7n1w8SuzohwLrL0r0EHFHZCxEQ3dLBwoYE6O1rEfsVYdnPlUiiRW9Yf37I+uapdWxjtFHkbZcL1s+u5tltBqDfKhp4KEekXl673sjltkeT7WRGaY7F5LXv/9k7G99lQsOst66eDUSZxu8hgB1az924YCoQvSk2nfHuUvbl61XVug01dkB/4gvWvWVAQ6yMb3wcjIr0422adFv5Odv0RycS8g0nqJe/OVNZ+x6OkgFR+43xECo6w8TXbgFapPy6MglTDUEDkZEpkOgq4NgNbVV33ttiF1mynWv/cj/qdCKODVzbnDbT2eoaEoK/alqS+21rdfrEx72yUeNIteWYL5O/vU/puD+STZJnoZWPIAqVrafDgRBTwn8M+90ISkd9bf30ZycTkvJsuXV9OgxLbBts48Q1av62M/LJH0E7C6aqu9zjaWJwH9gPJQd1evFRRKq+AFy8TsyCpivdRRlxxCFWxXWqTnByYNtrTBWUmfkKy+CnbYc7b0YiQyW7LYQu2FQdStXiwW+K4rpQ4p+1C89TqWuiwD0R6fv/BNChLz02FFkrvkKEuY4P6XmmO2lv2bj1M/dbKddH2vUsQ6b4xJiGTW0kWAb2APWlMtM+IMofLRHvH5PccF7Td0MFiHyA99XQ76AAU+HgLWLu8CAKmo42H9WbQdj2BZZPrrRHtB1mfPbrq+o/Fpt5IHmskcINd64Mkip6gtpU+zSDuRaYBH6tTF+AK63NPIrmDJ6x//sX63MZoATiXfWdWdEhcM3B81TaMg60LJb/PaX3xbXSIYUGozJTruNjAnq4osHM+sEbp3h4oa32NVr6fXX+0epWDIikJeB/wVOn+IjamPo52BLWL5I2iPyJf5ACks1volC9tdt1n72evquvbBnuWRITXx9Qy158kIVrMrs+RH9Kp3NY5FmAXRFoOSK4thHa8/EAtQDmc+sNql0LrntGHtldtSwPbUl9rOft9auT/lg9PLp7dBiUKjJHJnltBO5JesPntKWunRymdsYEypc+zue9hMpX0tDr1RIGOQSjYPdzm48WT+8+jTOmGwVgyW6/ZWPASCuz8DUm71R0Ob230X5RsUshqzWD98VIUSMlakimxd+HS5xWRlM8IG1NOQvN6M3aoa66FMXcdLGz9cZT9bHG3vxcvE7tUXgEvXiZWMcftIaQHWpAqAxBBfTk13dOsJvwGdhROcuFkLo6IljtJDnmj/pC7+VDW8AlV178N9hV27YyyjjZow3eeB06quu7jaOcJKNuhcNpWRFndP6LAzzIoK2QbtLD9kiTrJddi9X7cHO8u6OCc4SjDb6NGbZ1rafCu9aZ1on0ICoQcVnXd22BbseDphhZ0H9GYaH8DEdLr0MLCvKXrORQUdLsFk/pJ7B5AY6J9OnvnsrOpQX/sY/3xe+Ce5LmBiIw+DtPiLf2d7N67Un+8wMbGK1GW/l8RQTbCbPoK7T4rFrezIZmH9rKoPQEt4tPMuIb9MedCPeHcHxESw1Em3+2I6Oxr79R72BxNhmRRG2xdusG1FRAJsXvpeuFLtgsZQkq+iF3rinTNH7Px5TLrmx+TqRRTMj4WY8lSNvbfhgKSZemzJjRfn1113cfBxjmQL3g5krc5CgUdP0eZxHMhneThyA+boaX/U46F2nx9d9qWyf20j86JyOqby8/lUqg/D2ADFHgrsp9XR4k1Q0l2BNo4+hpKjMoyKELNXyps64v8jssQ+fwL8idXMrt/Ign65F7sHbrG3q3/IOJ8DmrSigEdrvsBDQLhubVXK3aO9kXK4wLKbD8MZeR/iEj2B1Fme3Y+ZKnu86e/o0DqKGD7quvm5ddbKq+AFy8TqpQnORRl/i+wn32exyaPq5OJcz9gyarr3gbbugOrUL+V93vgbuwAzeTZxdBC6Sky3HLYio3ToADIHUCfVp5bGhEw61Vd53G07xTg+dK1geaYfkUtI+l9lCWSZUZcA8dsbaSfmQZ5VjNn9Gng9+Xv5twfk3etGCPGRrS/DVyfs01JfdtCtBdZ368hiYR2sXgo2Xl1ua0S294w29amRGzmaGsr/fE74MHSs9lmrbdgW9Efu6MMsY/RgclNaBG/G7Vt2BeRZNTm2Fat2DnG2G/XU6K9Xbxr1lYrJb5FfyT19hLSy38AZf2diSQrZq26zuNh45E2F19l7VJkNU6LttEPQkkcTZS06dtDKfdH6oMn3e29uwuRFM3YgaE5lmR87GGfG2aoW1utCLwJ/KHqeo+DfZ3RWSipj3gVyY4le+6viNico+o6j4eNDefr5H4PFIh8GEn+ZKnpXa4Xkt47v/RMS75x/+T3/2fvzuMsOct6gf+qZyCEJOybigoY2QM9oOxbh1WEsF/ZEvBiuOx0ZIlIUCACgqDDIBcxl7DI1asBlXBF9mlE9uA0WwzCFVEWWYWww8zU/eOtkzlz0jPTfdLn1Dlvf7+fT32m5yzTzzNV9Z46T7311EyeFOnWw0X72tDjt0q5GfLXUorUF3Sf2X+f5Cp9x72B/C6fUqB9Y8p30e+mXF13r+75G3c5vmToPTO1Da4jx7W+h45eCf8zKcX2czOj30NH4t2ZcmX43YYeu0HKZLW9SR7Zd4yWrbn0HoDFckmWlD7W1xh57O7dn5dJuQHLWSm94L6RUgw8rnv+uilfCB83i1+SRr74vCplVt99c2BGwcndAc2FKTcSe0HKbLI9KUX2mb3kcI1cBwenj0vpA/rqjNwEr3v+Ct3/xUezxoydWVnW+j9P6RF6YUq7g+05UBA7JmUG+z279ftzOcxJhp7zGp6J+YyU2aR/luT13WOXyoEvGIMvEx9M8mt9x77e9TW0r90v6yu0XyVzcPJgKN7BvnakGe3nd/8PM31Z9iH2tSenzEa6fC5+8nU4t4vNVp2FZYPb4+6h187sDU4Pk+vo9vjVlJntgxPKv5DyxX5eZq0faewfPUl5fMqJhK8luXPf8R8pp257vLDbHgeFzcFn2WkpJ/73phx77c8c3QR0KMdbplxZ9/9Sii0fTLlCa1uSE7u87t53nBtdd0OPXWx7HP3sSpmkcvskv9h3DofLaWR8POjq1KGxZVvK8f77Uwq1M39MPJLvpbpx4gFJbjf6f9Etg57zM13UHOPzejFlNu35KVcozOoNaod7sL8mZVb0O9LdOyRDs4FTjo0/nzJZ6OEj/85MHUMeYl+7b0bu29M9f6NuXX6sGyPfOmv5rBHz8JUHw/3xb5TSNuWLKbP0z+k+396Q0tbnun3HvpF1N/TYIY9FRv4Ptmdo4tQsLylX+Hw5pYXWoQrtp/Qdp2XrLb0HYLFckqU7WNmd5E7d39+eMhvuGt3fn9wNsHu7A4TBAdpVkpyd0i/v5/qI/Qh5DeI8JuVGki/tDlo+mYML7TdOmfn3gZQv6m9MKb7M5CWHQ/mt+SUnpVXAi7r19aaUyysHhaXbdQev384M31xmJJ8TUma8bU+5qdj+dDfXmfV1dIS8/jLlpNVnU2as7E1yh0E+QwetSykzbndnjZMms7AcZl/7RA7+4j4obH4n3aXNI//OTH5xz2FOAKTMAjxUof36KTPmZjKvkTyOTil+Xb37+e4pbZh+tnt+9BL0G8xqbpu1Pc7qcrj/85Ht8TU5UGifufW0jjwPN/YvjPx5/ZSZjTM3C3Ud2+PoTZKPSXJSSqu+i4pis7qsY3t8XEpv8h+mzGL/zZSi+3sy4wXNkVzWsz02c7C+Drc93jcjhaGUYtJjUgrsH82MTz453Of1yOuGJzxct8vtrEnFtck5HunzevgGjTdLOYHwiKHtdCa30S6XT6V8Tzsv5YTxj5PcehB3Dhwbn9g996q+4z5MPuva1zJyX6zu/+G/r3db7jG/I44B3fb3+JSTQF/Igat7/lvf8W8gz/WO/Rf93HfMh8ll4RA/3yfluPE9uXih/c+6nB/Sd/yWrbUMBnuYS03T3CilyHyzlA/+n0ry4LZtP9w9f3zKZZQnpRSRXpTSN/SBKe0C7ti27cd7CP2QmqZp2rZtm6Y5NmWmw1dSWr8cl3LJ5LaUG4u9vW3b7zdNc6m2bX/SNM0V2rb91tC/s61t23195HA4w3E1TfPUlML6QpLntW37laZprpFyZvqMlIO1L6XMbt+XclD6yFlbZ2tpmuaslF7P30w5ODs/5eqDM1NmuXyjbdv/7C/C9RtZZ3dM2ad+O2Xm1N1T1tUvppzs+mjTNNuT7Ou24zsk+XLbtp/pKfxDWse+tpBSyHxb27Y/aJrmcinrdFeSP27b9kk9hb4ug/XW5feHKS2Zvpfkt9q2/UL3msumXEJ6UsrlvU9s2/Z7a/07041+fZqm2ZZyYH2LlC/q/5XSD/p2Kfva25N8s23bf+5ev71t273D75+V3LbK9tj9PBj7m5Sx/6vd45dNufz33ikz4Z7Qtu33mqZZaNt2f0+hb8g4Y//gc3zasR7OBrbHv2/b9kcj77lqkq93Px+0z82KIx2LjLz215PcK8mvpsxKPT/J7du2/a/pRr1xtRyLbHR7bJrmit3rLpVyov/Utm33zvr2OPJ5/f0kpw8+r0ded1RK0ewlKYXQX+rya9oZ/XK/0c/r7j0X5TNLn9fJQeuiSbkvyslJntS27eeaprl3ShuV62XtY+ObJ1mdpXwGxtjXBq8/aP3M+r7W/bzmscjI63825Rj5AUnulOSGbdteML2Ix1PL2J9cbBw4rm3b7wwfNzVNc9+U+1T8a5Jnt237zu7xG6dMuPzD4XEFJk2RnbnXNM31Uz78j0pyWtu2Lx95/kYpHyqPSTkLfWHKQd2T27b9xHSjXZ+maRZS2tz8cpL7t2372e7x66fcAOmqKR8ab23b9geD98xLESJJmqY5J+XA+tsp7VK+k3JJ1+7u+WsmeXhK3+ukzCb74BwdENwmB24O9LMp6/IGKTPiLp+S9+dSeuu/JmVGy0yvv6ZpfiflcthrJ3lY27Y/7h6/a8oNda6bA18mLpVk76x+2RvYwL72tu6k1hWS3DXJ38zil4dRXdHyoyknqL6Xsu72pcwMe2/btj8eKrTfM2UG1kPatv1hTyFvWNM0N+l+vEXKWHKHlD6h+1L2tX0pl/0upBQs/qKPONej9u0xWXPsvzBle3zfyPb4KymXZj+ibdvv9xXvRm1g7P9hysn/s2ex0JKse3t8UsqxyA9Hj0Pm4bjkMNvjP4wUjI5NaSPwlJQv8ef3EO6G1XQsMsb2eNMkV0y5f8XFioCz5gif17sH66Vpml9MGSMHJ87v3k22men8kg19Xm9L8vRZ/rxOLlpnZ6dMBvpe27aPGXruiMfGs7rONrqv9RboJXCYsf89Q/va6Gfa1dYqxM+imsb+gaZpXpIyO//hbdt+daTQfr+UK/o/kOQ5bdu+vXv80oPvqzA17QxMp7dYxllycH+7D6ccmF6Y5K7Dz3c/b0/5AD0xyXWSXKHv+NeR3+4kb1nj8eun9PP7ZMrl2peedmxj5jPc2+9XunV2y5QDtVuk9Mr8SsoVBnPVW3h4WzvMa66Scpn5uV3e/z3Jn2R+bi5z5W797E/yzjWev0tKgfa/0l0eOy/LBva1o0een9XLli+64XHKZb5vSTnAvmzKzLf3dOvy7jnQeurolDZA52aGb+a0nthSTsx9LmU24LVSitBnpBQlZnKdjcRf2/a4nrH/P9fYHv93yk3UfrrvHA6T21YY+7fischge7zY5ekzPj7aHg89Ps7ketvg53WTMjP/4Sn3YvqdzHDrwS3yef3TKVfc7k/yhjWev0vKRLBvJblN3/FuMDdj/4HXz+T4MRRf9WN/l8OfJPmPJH+R5GrdY9uHPp9/P+V76HmZ0fvcWLbGYiY7c2f0UsjukvlLpRyc/X7KB8eD2rZ929BrZvos5hqX2B2b8qG3L92B9cjz/yvlw/GClFl+H5nlGWPDsTVN8+iUS/OuleRRbZl906Tc2OmslLPsp6QUcodznslLYEcuOzwupUft14afG/rz7CTXadv2TkPvn8n1ttbsmqZprpVS+LpFype8vxner5qmuXOSP025quT4JD+atXW2SfvaTM48Sg66zPeYlEuVr5Ry+fXTB8/n4vva7rbMID4qyU/att0/i9vl4NLj7pLrq6WcLD2/e64ZvK7L/20p7Soedqh/Z2qBH8YW2B7HGfsH2+NlUtbxTF69VOPY71hkze3xHe3BMxkdi0zJJdgeP51yZeSsb48b/rxu2/Zt3Wzo49q2/Wb3upn7DKjx8zo55LHxz6dMUrhxypjx5nao/Vd3bHx2ks+0bXuXaca7Xsb+I4/9s6rGsT+5WIuY4Z9flNJW9v0pLS6/OpTfzpSrE45J8itt2/5bT+GzxS30HQBsRDeIXnSJXdM0l2vb9sK2bb/Rtu1HU/pEfzjJOd1BTbrC0cuapnlUf5EfXvfBcNmmad7QNM1V27b9bspZ2junnDDY15RehgPfTpn18ZOUPvOZtQ/IpmmOa5rm+cmB2JrSG+1PUmap7BschLbFZ5KcmuSfU25Se8+mXK6YwWumnMIRjRzYPC9lBtIHm6Z5d9M0107SJmX9dm/5cpJfHPmCMVPrbWAor99qSn/ddAcrD0s5qH5Bkrt1X/YG73lXykys27dt+8NZXGebtK/N2pfZWzdN84rkoP3kpkmemvIl6KAvtEP72vkpX/y4GsCSAAAgAElEQVR+pfsi+6MZLrBv676wH5uyvt6d5J1N03yoKZcvN11ug/y/mPKF92Jm6Qt7pdvjJR37796US4B/OCcF9mrGfscia26Pv+pYpB+XYHv8cWZ3e7wkn9evbprmPintRgYF9oOKn7Og1s/r5KBt8n8NHRt/PsmDU1qRvjjdZ9jQe96V0s/77j2EvC7G/iOP/bOo1rF/pN6zkOSy3Qm7tOUk5F8luW2SP26a5srd9nmllPZgy0lupsBOr9oZmE5vsaxnycGXeT0/yf9NuZHHi5Pcbui5X0q5jPI73XPnpFzGd/O+czhCfvft4nx49/erpXyI7Es5eBtcUnqDlMu9HpnkfyT5bpIdfce/Rj7PSfmwv8zI4yemXPr65ZRi7Oj7jk+5oeankxzTdx6HyW/4MvK/Srnc9YyUs+vfS7lU7fY5uG3Rr6Vcxnb1vuNfZ473SLk51UqSKw09fq0kn0q5wcy9Mn/tfarZ11Iuk3xpkj9Y47k7Jflakv+XNVr4dPvaJ1NmXfWey2FyHFwGemxKoWGlW0/36NbjP62xrz095ZLSmR1DatweuziN/XM89tsebY+ztNS0Pcbn9dx/XnfxnpJybPzuJFceevxaOcKxcYa+y87aUtO+1sVp7J/DsT8H13uem+Tvun3qtSn3Bxg896Ik/97tcy/q1vU3khzfdw4WS+8BWDZhJZbWDNfpO44J5zj8AfF/usH2ed2HyddTbo5296HX3DTJ61P65H0kyU36zmEdOW5Psifl8rTBY7dIuQRxf0pvvHck+Zck/9Q9/+iU/nE/33f8w3l0f15+cGCTconr8GvuktI//51Z4+RHSt/8mclpKK5tSS478thzU2Z237L7+9NSDr6/0B3w3D4HemberTsAmNmDm9F1mTK76nMpfUHX+jLx6ST3zxwV2iva167S/XnM4M8kZ4y85i4pJxwPta9dMzP4pS/lHgDHDv390imzqt45lPcbUmbA/Uv3mXD7ofHn11O+BM50H83KtkdjfwVjv+3R9jhLS0Xbo8/rCj6vh7bJp6T0JV/r2PgTST6T5IGzuL6OkFcN+5qxv46x/43dmPF/UlqV/kfKVUpPHHrNY5K8PeXk5LuSnNB33BZL2yqyz/3SfSB+IuXs3Q36jmcK+T5/5IPkUd0H/7+nnHW+y9BrmyS/kKEZuLOyZOQGJekKlCk3kPluSn+7wXNX6g5A39Z9kOzs1nuT5M0pN2q5Yt85dbEe1x1wPjkHvkg8tFtHrxx57d1ymC8Ts7YM5fbEdF8mklw9ySuSPL77+1O7A4BfS7k3wJeSfCylP1yTcvPdmbyJX0ZuMJUDNyDc3h2w/XuX//CM9p/vctyToS9Ys7RUvK/dtvu/f8LQ9nh6t6+9YuS1R9zXMkNfBLvcvpjkcUO5XSvJK5Pcq/v7X6YccC92r/9mygy523XPXz0zeKPCirdHY/8cjv22R9vjLC0Vb48+r+fw87qLZ9xj468lOafv+A+TV637mrF/zsb+HDyRcnC/yMekXG1wm6F97tYpJ+v2J3nY0Hsu3eU1k99DLVtz6T0AyyasxDKg7k85y3fDvuOZYJ7Hp5xMOKX7+9NSesHdK+UmjPtSTjjcpc8415HH4EByW5KjRp67dsrJgr841IFd9/MNUi6b+nZm5Kxt9+H92m5b/HrKJYRNkp9K8rLugPNQBzhvTXfiZBaXQ+R2dMoNd3815c70t075ovEbQ+/78+49X80al//OypKDLzm87+g2lwMz2r+YMlPgikOv+bnM6JU0Fe9rR6UcaA+2x8d2j18j5VL0bxxiX7sw5UvSLG+Lo7k9JsllusdP7Pa5U5J8Nskdh9731u4930ly06HHm2nGv0W3R2P/HI79tkfb4ywtFW+PPq/n8PN6ZJs8NsljRre5HCi0fzmlUDt8bHyNzNDJkEPkVdu+Zuyfs7E/5Yqes5LcaeTxP0o5OXB0Dv6OevOUE3TvTbmZcu85WCxrLb0HYLkEK68MuIPLfx7XDaKvyxy0RjlCXtu6D8QHJjlp5MDsASkzBJZSznD++tBzr+4+eD6b5MS+8xjJ6dIjfz86pVfaQf3Fuuce263L23Z/Xxj58xbder5g1tZ1ygH2p1NuPvvjJE/uHr9qd4DzzTUOcO7a5fs3owd7s7SskdsTRz74f7NbJz879NhLUg5U/y7JdfvOYSSfbUmuOfLYyd26ePbQY4MvE5dO+UK4vzsYvUrfORwir+r3tZSblj8j5aTiu1NONg72tautY197ad85bDC30X3tZd1+eLmhx/4iyR8k2ZUZ+mK7FbbHLjZj/xyM/bZH26Ptceo5+ryek8/rLrYrJ7neyGPP6NbF7w49Nnxs/Kfd829PctWR985EflthX+tiM/bPwdg/FN9tuv/7dya5zdDjr0vypaG/bx/6+andWPNzfcdvsRxq6T0AyyVYeQffGOLSKUXmHyQ5O8mN+o5vzJyOSfLHST7aDbr7k+xNuRxtcEf6pPRiPy/lAHXwoX9OStuK9ya5dt+5DOW0I+Us7b1H1tdLUg5Mf5jk3CQPS5kBckxKj/m/ytBB6dB7L59ytvpn+s5tjdiul3Ly4wUpPfP3JnlS99zhvkycmOT6fcc/Rm7Dl/6+MKUv4Q26v185yZu6g6CZOMgeymWwn70hyYOGHj8+ycu7/e45Q48f1f15lZRemj9Kuapk1i7r3Ur72o1TZrr99gb3tVtkZHbSrC1r5PaTJI8f2tf+LKUn6C+knGy+bpJ/TPLAoX+j931ui22Pxv4ZH/ttj7ZH22Nvufq8nvHP6y6OxW6be0uSuw49fny3jg51bHzTlN7XP07yP/vOY428ttK+Zuyf8bF/KJ9BTedu3bjx7hw4sfMb3f60nAM99gcTSx+f0m/+Gn3nYLEcauk9AMuYK+7iNwL9YMoNST7THQS8PnPWOial19hHUy49fGaSmyS5Y8olQ19P8smUs81Nd2DwnaH3Xrn7f7hzZuhO4Cl97D6VAycM/irJI3LgxMDPJflvSc5P6Zt2XpK7p/Qv/FSSX+g7hw3kOsjpaUn+LckdUvrE/STdTUpGDnBm7kB0jNx+nAN98E7ocl1JmfH9hpTL82ZqHXb72Z5uzHhhRg6gU3pprlVob5LcM2W2wVNnMK8tsa9162GQ05kpX5BumdL/9KIbAg3ta9/ISM/X7vmZ++K+jtye0D13s5T2YB/p1t9q9/PMfInYKttjl4uxf8bHftuj7XGW1uFW2R7j8zqZg8/rLs7bpLQT+et0LX1Gnr921jg27p77jZTv3Q+dwby2xL7W5WLsn/Gxf428BoX2X005abA7yS+nXGnx4ZRWTKcOvf4qKZMq/zHJcX3Hb7Ecauk9AMslXIGl2PyfKXfJvmpKD7jfzoEe7XMxoz3J5VJmyL4jyQ1z8EmEY5Pco3v+UykzBu7QfUB+MMnTk/xtd3A0U72hUy6lHPRD+9vuAGZ/Sp+xU5Jcq3vdcd3B2Xu6PD7Rve6MvnM4TG5HdQdvg0smB2eYb5VysuSUbr2+Lhf/MrGzy++P+s5jk3IbzJJYSmlX9Lnu4GAm+hQO5XXZLq53pMw+Omh2wNDrjk/yP7t19IIu1+t0j70qM3g5ZeX72vFJfiYHbuI02B7v2o2J9+nW7Vr72qDFz9P7zmOTchvsa3dI+TL4oW69r7kt2x4nkpuxf/7Gftuj7bH3fLbI9ujzev4+r2+a0st6Z5KfGnr8cMfGL0w5Lr5lSlHzzEO9r+fcat7XjP1zNvYfItdBjvfu1slbUq54uXySj6dcKfOelLZMK0m+NQ95Wbb20nsAlnWuqHLp1v8YeewK3QB61hqvf0o3UL1m1geiLrd/S+llN3xwM9xnbCHlcqKvJPnb7rHHdgcJX06ZFTFrfeEGZ52PTzmT/Icpl+c9PQfa4fxrkiclOX7ofQ9LKWS+KzM4e2VonX0l5cqJnUkWR57/30ku6H4+NqWV0fABzjWSvCgjfQ9nYdmE3C4/WPrOZY3cnt7tKydkjRtMZaiXZMqM9helXEb6X90++rUM3aRqVpbK97UdXfyfSemdeauR5/82yZ7u58sdYl87bRbz24TcLpOD+zTORI6Vb4/G/kPnNpNjv+3R9th3Lltoe/R5fejcZvXzelvKzOY3p2t/kgOza7ennDQ4PgfP/H5+SnvWn6RcffCRJJfqO5c1cqt5XzP2Hzq3WR3716plDU64HZ1yQ9PPpFz58o/ddntMt799MOUq7Nena4djsczy0nsAlnWuqHIzi/1JnjX02GVTzvD92dBjCymX8F06pa3Dt5O8MTPcZywHbrDyB0d43TEpPcj2J7lTl+uVukH4itOIdcz8rpByadO3011ZkHKG+teTfKDL54KUg7yfzoEzuoODvFk8cHtMF/ePk7yt+/Ml6W6ek1Kg/XiSx3V/v3p3wPa9dDN0MmP9vC9hbmcPcktydN85HCa3v0zyjjUe/41uG/1Eys1xbtg9fqWUS2hfmuT0JL/Ydw5HyK+qfS3ly98ZXdzfSPkSuy/lcuxHdK+5WcqB50OHtsdXpXwBfMbIvzczX5Y2IbffSnKZoX/vYieN+l5q2x67mIz9czj22x5tj7O21LY9xuf1XH5epxTSz0vy2pHHr5nk91IKf19JKfrt6J47LuVq0OUMtYiZpXU2kktV+1oXk7F/zsb+jNSycqDAflTKlTAf6PbHu6W0jnlvhiaKptS9ZnIfs1hGl94DsKxzRZUzki/K0N3NU4rpf5NyFvpiN/pM6cV1QZLvZAZvTjIU5xVTLrvbn+R3jvDaX0qZOfDrfce9wRzv1uX3jKHHjk3y+e7gbqU7+Nmf5DeHXjMzB6JrrLPnpNwA84+SnJxyKdd/pNxk5f4pfdXOHnrP1VJ6AX41s31S5JLk9pVZza07cHljkven3MNgcAOqd3Xb3TdTvkx8vxtTrtV3zGPmWeu+9pOUvqcPSJlx9Z8pB9+nphxsv3zoPVdN6Sv6nlnNaxNyW5nl3LbA9mjsb+dj7Lc92h5ndal4e/R53c7H53XKDPv3pLSo+KmUAvqvdvvZ/pTj4rekXMn5rxm64nrk35mZFjGHiK/Wfc3Y387H2J+Da1nP7R47KuWeex9I8rNDr71HSqH9nUmW+o7dYtno0nsAlg2srHL53YtHBqcbdAPqmzN0l+XuQOGvU/qbH9t37BvM7XfXeH5wydtVk3w3B3qPzeSH/yFyHPSNv3LKrPzzUy7X+6nusRt1/wdzcZa2+7D8g26dPa7L4w4pPRffkVKs3Z/kdkPvucqhDlBnaak1tyS36OJ+V0rB/Ssps5Je2m2Dl05yUspsiD/t3jM3+9hQnjXua4Px8QkpVxjcpFuPf989/r0MtfJJmbk0GDdndh3WnFvl22N142Ptudke52+d1Zxb5dtjlZ9pteaW5LY5UFD/cEpx7+NJntw9f6kk9005wfD8Wc7lCHnWuK9VOT7WmlsOrvecmdL29wNJrjn0msF4MTgxdG5mdHa+xXKoZXuYG23bXtg0zXO7v57RNM3+JM9N8tSUnl1/3zTNn6fcIOIuSW6X5Adt2363l4A3YCS3322aJm3bPmfo+f3djyelzJp4e/d4O91IL5G3JLlXSu+7h6RcYfCwtm2/3D3/jZR1maZptrdtu7eXKNepbdtvN01zZsplpH+c5Cpt2z63aZrbJPmVlMtGF5N8feg9X1/zH5sxtebWtu2Hm6a5Y8olkyckeXfKTZzOa9v2+0nSNM07Uw7Cj+7eM0/72ECN+9pzU1pk7UrZHp/dNM3dktw65eqEa6b0zR+851tJ0jTNwtD4OXNqzm1IjdtjdeNjUnduQ2yPc7LOas5tSI3bY5WfabXm1rbt+5qmuV3KLNsrJvndJG9o2/bT3fM/aZrmoyknEH7UPebYuGc1j4+15jZS73lqysTJm7Zt+6Wh1+xvmqZp2/btTdPcNcmX2rb9QR/xwtj6rvJbNr7k4LOAz0iZfXqrlLPvX0v5kPxYZvAGhRvM7XdHnvuZlJ5y5yS5XN+xbiCnZujn93W5rSS5duZwJsQh1tlLurzOHHnuCn3HJ7c187pskquv8XiTMivp40meOnis73g3kNdW2NcOuppp6LlBD82Z7DG5FXPbIttjdeNjrbnZHvuPUW4Hxb0VtseqPtNqzy2lreLFrv5OOalwjyT/kuTB3WNzs41ukX2tqvGx9txSZuoP7rO3ZqvgGrZNy9ZdzGSfQ+2Bs4BNygB1qbac3bxtkl/oXva1tm2/0VuQY2ovPqO97XI7NmVmwW2TnNi27YX9Rbkxbdu23RnZNuWu2DdO8t62bT/Xc2iboltnz0nSJnlm0zT72rZ9dvf03KyntdSaW1tmrQ9mrl+6bdsfd09dIeVGTsemnMxKt93OhS2yrx10NdPQ9viTLveZnCl2JDXmtkW2x+rGx6TO3GyP86vG3LbI9ljVZ9pArbm1bbu3aZrvJQdmcjdNs5DkOinfQb8Qx8Yzp8bxcaDW3NoyU/+FKT3Zn91tn88Zec3c7GMwSpF9Tg0NukkZnBa6QfeCHsPaFCMHb89umubSKb3iHp7kNm3bzl2OQx8U5yZ5ZpJbJsnQQc9cG1lnv9M0zd62bX9vHg+yR9WcW5IMCuxN09w5ySkpLZnu1Lbt53sNbExbbF97VnfAfabcZtMW2x6rGh9rzM32OL9qzG2LbY9VfKYN1JrbIP6uwH6VlNYcj0m5Qeod2rbd1zTNtrZt9/UZ50ZtsX2tivFxoNbc2otPrNzXtu3v9RoUbBJF9jk2VGjfmzLofr9t2xf1HddmGBp49yX57ZTLiX65bdvVfiO7ZNq2/WLTNM9P8sdN05zYtu27+45ps4yss+c2TfPjSrfHqnJrmuaolLvUXyHJt5Lcvm3bT/Yb1SW3Rfa1/Ume0zTNF9u2PbvvuDZDrbltke2xuvGx1txsj/Op1ty2yPZY1WdaUnduTdNcIcmnUnpefzzJyV3hfeZ7lR/OFtnXqhofk3pzqzUvUGSfc93g9PwkP07yf/uOZzN1ub0gybeT/PU8zmA/hLckeWOSf+g7kM22BbbH6nJr2/ZHTdP8ZpKbJfnbdujmMxWofV97XpJ/T/K6vuPZTBXnVvv2WN34mFSdm+1xDlWcW+3bY42fadXm1rbtt5pyQ9efS/J3bbkZ47Z5LrAPqX1fq3F8rDa3WvNia2squEKIzPZd2y+pynOb6xkRh1L5Oqsyt1ouGT2UWve1gZrzqzG3GnNK6h0fk+pzsz3Omcpzq3J7HKg5v1pzq3V/s77mT6251ZoXW5MiOwAAAAAAjGmh7wCmrWmaBzZN87Kmad7bNM2FTdO0TdO8vu+4AAAAAACYP1uxJ/sZSW6a5LtJvpDk+v2GAwAAAADAvNpyM9mTnJbkukkul+SxPccCAAAAAMAc23Iz2du23T34uWmaPkMBAAAAAGDObcWZ7AAAAAAAsCkU2QEAAAAAYEyK7AAAAAAAMKYt15N9s9zpTndq+45hEnbu3JkkWV5e7jmSzVVrXonc5lGteSVym0e15pXIbR7VmldScltcXOw7DLjI6upqtftaUt84UmteSf25GfuZJbWO/UmysrJS440Xq6k9Li0tjfW+3bt3H/lF6zfRbcRMdgAAAAAAGJOZ7AAAMAW1zh6rdRZqrXklB3IDAGBzmMkOAAAAAABjMpMdAACmYHFxMSsrK32HMTG15lZrXqurq32HAABQjS1XZG+a5r5J7tv99Rrdn7dumuY13c9fb9v2qVMPDAAAAACAubPliuxJFpM8YuSx63RLknw+iSI7AACbSk/2+VJrXome7AAAm23LFdnbtn12kmf3HAYAAFuMdjHzqda8tIsBANg8bnwKAAAAAABj2nIz2QEAoA/axcyXWvNKtIsBANhsiuwAADAF2sXMp1rz0i4GAGDzaBcDAAAAAABjUmQHAAAAAIAxKbIDAAAAAMCYFNkBAAAAAGBMiuwAAAAAADAmRXYAAAAAABiTIjsAAAAAADNjcXGx7xA2ZHvfAQAAAAAAUKcdO3Zk79692bVrV9+hTIyZ7AAAAAAAMCZFdgAAAAAAGJMiOwAAAAAAjEmRHQAAAAAAxqTIDgAAAAAAY1JkBwAAAACAMSmyAwAAAADAmBTZAQAAAABgTIrsAAAAAAAwJkV2AAAAAAAYkyI7AAAAAACMSZEdAAAAAADGpMgOAAAAAABjUmQHAAAAAIAxKbIDAAAAAMCYFNkBAAAAAGBMiuwAAAAAADAmRXYAAAAAABiTIjsAAAAAAIxJkR0AAAAAAMakyA4AAAAAAGNSZAcAAAAAgDEpsgMAAAAAwJgU2QEAAAAAYEyK7AAAAAAAMCZFdgAAAAAAJqZt27Rt23cYE7O97wAAAAAAAKjTnj17kiQnnnjiut9zj3vcI6effvqkQtp0ZrIDAAAAADAz3vrWt/YdwoYosgMAAAAAwJgU2QEAAAAAYEx6sgMAAAAAMBEnnHBCPvGJT+TmN795FhYWsrCwkG3btqVpmov+3jRNklz055Of/OQ+Q94wRXYAAAAAACZi+/btOeGEE/LiF7+471AmRpEdAACmYHV1NcvLy32Hsel27tyZJNXlVmteyYHcAACmYc+ePUmSpaWldb/n2c9+du54xztOKqRNp8gOAABTsLi4mJWVlb7DmJhac6s1r9XV1b5DAAA4pO985zt9h7AhbnwKAAAAAMDM2LZtW98hbIgiOwAAAAAAM+NFL3pR3yFsiCI7AAAAAAAz4/a3v33fIWyInuwAAAAAAEzEjh07snfv3uzatavvUCbGTHYAAAAAABiTIjsAAAAAAIxJkR0AAAAAAMakyA4AAAAAAGNSZAcAAAAAgDFt7zsAAAAAAADqtGfPniTJ0tLSut/zwAc+MI9//OMnFdKmU2QHAIApWF1dzfLyct9hbLqdO3cmSXW51ZpXciA3AIBZ9YY3vEGRHQAAONji4mJWVlb6DmNias2t1rxWV1f7DgEAoBqK7AAAAAAATMTi4mJWV1fzyEc+Mk3TZGFhIQsLC2maJtu2bbvo56ZpLnrPPe95zx4j3jhFdgAAAAAAJqJpmpxwwgl5xCMe0XcoE7PQdwAAAAAAADCvFNkBAAAAAGBMiuwAAAAAADAmPdkBAAAAAJiIPXv2JEmWlpbW/Z6b3OQmeelLXzqpkDadmewAAAAAAMyMj3/8432HsCGK7AAAAAAAMCZFdgAAAAAAZsbDHvawvkPYED3ZAQAAAACYiB07dmTv3r3ZtWtX36FMjJnsAAAAAAAwJkV2AAAAAAAYk3YxAAAAAABMxA9+8INccMEFedWrXpW2bdO2bZKkbdvs37//op8HS9M0Ofnkk3Pcccf1GfaGKLIDAMAUrK6uZnl5ue8wNt3OnTuTpLrcas0rOZAbAMA0XHDBBUmS17/+9et+zznnnJPdu3dPKqRNp8gOAABTsLi4mJWVlb7DmJhac6s1r9XV1b5DAACohp7sAAAAAADMjPvd7359h7AhZrIDAAAAADARO3bsyIUXXpinP/3pWVhYyMLC4ed9t22ba1/72lOKbnMosgMAAAAAMDGXvexlc93rXrfvMCZGuxgAAAAAABiTIjsAAAAAAIxJkR0AAAAAAMakyA4AAAAAAGNSZAcAAAAAgDFt7zsAAAAAgBqtrq5meXm57zA21c6dO5OkurySrZEb9GHPnj1JkqWlpQ29b/fu3ZMIZyIU2QEAAAAmYHFxMSsrK32HMRG15pXUm9vq6mrfIUC1FNkBAAAAJsBM9vmyFXIDJkNPdgAAAAAAGJOZ7AAAAAAToF3MfKo1N+1iYHIU2QEAAAAmQLuY+bIVcgMmQ7sYAAAAAAAYk5nsAAAAABOgXcx8qjU37WJgchTZAQAAACZAu5j5shVyAyZDuxgAAAAAABiTmewAAAAAE6BdzHyqNTftYmByFNkBAAAAJkC7mPmyFXIDJkO7GAAAAAAAGJMiOwAAAAAAjEmRHQAAAAAAxqQnOwAAAMAEuPHpfKo1Nzc+hclRZAcAAACYADc+nS9bITdgMhTZAQAAACbATPb5VGtuZrLD5CiyAwDAFNQ4mzGpd9ZfrXklZjMCAGw2Nz4FAAAAAIAxmckOAABTUHPLgKTeS+trzUvLAACAzaPIDgAAU6BdzHypNa9EuxgAYD4sLS2t+7WveMUrcv3rX3+C0RyedjEAAAAAAMyt8847r9ffbyY7AABMgXYx86nWvLSLAQBm3bvf/e40TdN3GOuiyA4AAFOgXcx8qTWvRLsYAGA+zEuBPVFkBwCAqTCTfT7VmpeZ7AAAm0dPdgAAAAAAGJOZ7AAAMAXaxcyXWvNKtIsBANhsiuwAADAF2sXMp1rz0i4GAGDzKLIDAMAUmMk+X2rNKzGTHQBgsymyAwDAFJjJPp9qzctMdgCAzePGpwAAAAAAMCZFdgAAAAAAGJMiOwAAAAAAjEmRHQAAAAAAxqTIDgAAAAAAY1JkBwAAAACAMSmyAwAAAAAwM+5973v3HcKGbO87AAAAAAAA6rRjx47s3bs3u3bt6juUiTGTHQAAAAAAxqTIDgAAAAAAY1JkBwAAAACAMSmyAwAAAADAmNz4FAAAAACAidizZ0+SZGlpad3vOeOMM3LnO995UiFtOjPZAQAAAACYGT/5yU/6DmFDzGQHAAAAAGAiduzYkb1792bXrl19hzIxZrIDAAAAAMCYFNkBAAAAAGBMiuwAAAAAADAmRXYAAAAAABiTIjsAAAAAAIxJkR0AAAAAAMakyA4AAAAAAGNSZAcAAAAAgDFt7zsAAADYClZXV7O8vNx3GJtu586dSVJdbrXmlRzIDZi8Gsf+rTA+1pwb9GHPnj1JkqWlpYn9jhe+8IW5xS1uMbF//0gU2QEAYAoWFxezsrLSdxgTU2tutea1urradwgAAJvm9NNPz+7du3v7/YrsAAAwBTXOZkzqnfVXa16J2YwAwHQ9/vGPz8tf/vK+w5goPdkBAAAAAJiI17zmNX2HMG7JYygAACAASURBVHFmsgMAwBRoFzOfas1LuxgAYFoe9ahHZdeuXX2HMVGK7AAAAAATUPMJ1lrzSurNzQlW+lJ7gT1RZAcAAACYiBrvx7EV7llRc27Qh1vd6lb54Ac/2HcYE6XIDgAAAADARLzgBS/Y0OuXlpYmFMnkKLIDAMAU1DibMal31l+teSVmMwIAbDZFdgAAmIKa+/Im9favrTUvfXkBADaPIjsAAEyBmezzpda8EjPZAYDpOv/88/OhD30obduua5lHiuwAADAFZrLPp1rzMpMdpqPmsb/WvJJ6czP205dXv/rVOe+889I0zWGXJFlYWBjrd9zoRjfazJA3TJEdAAAAAICJ2LdvX0444YTs2rWr71AmZrxTAwAAAAAAgCI7AAAAAACMS5EdAAAAAADGpMgOAAAAAABjUmQHAAAAAIAxKbIDAAAAAMCYFNkBAAAAAGBMiuwAAAAAADAmRXYAAAAAABiTIjsAAAAAAIxJkR0AAAAAAMakyA4AAAAAAGNSZAcAAAAAgDEpsgMAAAAAwJgU2QEAAAAAYEyK7AAAAAAAMCZFdgAAAAAAGJMiOwAAAAAAjEmRHQAAAAAAxqTIDgAAAAAAY1JkBwAAAACAMSmyAwAAAADAmBTZAQAAAABgTIrsAAAAAAAwJkV2AAAAAAAYkyI7AAAAAACMSZEdAAAAAADGtL3vAAAAYCtYXV3N8vJy32Fsup07dyZJdbnVmldyIDcAgGnYs2dPkmRpaWnd7zn11FPz0Ic+dFIhbTpFdgAAmILFxcWsrKz0HcbE1JpbrXmtrq72HQIAwCGdddZZc1Vk1y4GAAAAAADGZCY7AABMgXYx86XWvBLtYgAANpsiOwAATIF2MfOp1ry0iwEA2DzaxQAAAAAAwJjMZAcAgCnQLma+1JpXol0MAMBmU2QHAIAp0C5mPtWal3YxAACbR7sYAAAAAAAYk5nsAAAwBdrFzJda80q0iwEA2GxmsgMAAAAAwJjMZAcAgCnQk30+1ZqXnuwAAJtHkR0AAKZAu5j5UmteiXYxAACbTbsYAAAAAAAYk5nsAAAwBdrFzKda89IuBgBg8yiyAwDAFGgXM19qzSvRLgYAYLNpFwMAAAAAAGMykx0AAKZAu5j5VGte2sXAdNR4FdNWuNKn5txgniwtLa37tU95ylNyr3vda4LRHJ4iOwAAAMAE1HyCtda8knpzc4KVmu3atUuRHQAAAKA2ZrLPl62QG8yTU045JQsLC1lYWEjTNId83cLCQh70oAdNMbKLU2QHAAAAmAAz2edTrbmZyc68ed3rXrfu137961/Pk570pAlGc3iK7AAAAAAAzJTb3va2SXLYWeyD5x/+8IdPI6RDUmQHAAAAAGAibnjDG+b888/Pox/96CRJ27ZrLoPnkuRBD3pQjjnmmN5i3ihFdgAAAAAAJuKoo47KCSeckIc85CF9hzIxC30HAAAAAAAA80qRHQAAAAAAxqTIDgAAAAAAY1JkBwAAAACAMSmyAwAAAADAmLb3HQAAAABAjVZXV7O8vNx3GJtq586dSVJdXsnWyA368M1vfjOf//zns7S0NLHfcY973COnn376xP79I1FkBwAAAJiAxcXFrKys9B3GRNSaV1Jvbqurq32HwBb1+c9/fuK/4+Mf//jEf8fhKLIDAMAU1DibMal31l+teSVmM8I01Tj2b4XxsebcoFZf+tKXev39iuwAADAFNc9mTOqd9VdrXmYzAgBsHkV2AACYghpnMyb1zvqrNa/EbEYAgM220HcAAAAAAAAwr8xkBwCAKdAuZj7Vmpd2MTAdNY/9teaV1JubsR8mR5EdAAAAAICZcdxxx+UBD3hAmqa5aBnYv39/2rZN27bZv39/kuTBD35wX6EmUWQHAAAAAGBCduzYkb1792bXrl19hzIxiuwAAAAAAEzEnj17kiRLS0vrfs9JJ52U0047bVIhbTpFdgAAmILV1dUsLy/3Hcam27lzZ5JUl1uteSUHcgMAmFXnnnvuXBXZF/oOAAAAAAAA5pWZ7AAAMAWLi4tZWVnpO4yJqTW3WvNaXV3tOwQAgGoosgMAwBRoFzNfas0r0S4GAGCzKbIDAMAUmMk+n2rNy0x2AIDNoyc7AAAAAACMyUx2AACYAu1i5kuteSXaxQAAbDZFdgAAmALtYuZTrXlpFwMAsHm0iwEAAAAAgDGZyQ4AAFOgXcx8qTWvRLsYAIDNpsgOAABToF3MfKo1L+1iAAA2j3YxAAAAAAAwJjPZAQBgCrSLmS+15pVoFwMAsNkU2QEAYAq0i5lPtealXQwAwObRLgYAAAAAAMZkJjsAAEyBdjHzpda8Eu1iAAA2myI7AABMgXYx86nWvLSLAQDYPIrsAAAAABNQ41VMW+FKn5pzg3mytLS07tfu2rUrJ5xwwgSjOTxFdgAAmIIaCy1JvQWJWvNKFFoAgPo86UlPyu7du3v7/W58CgAAAADARJx++ul9hzBxiuwAAAAAAEzEC1/4wr5DmDjtYgAAYArc+HQ+1ZqXG58CANPy2Mc+Nq94xSv6DmOiFNkBAAAAJqDmE6y15pXUm5sTrPSl9gJ7osgOAAAAMBE13vR6K9wYuubcgMnQkx0AAAAAgIm48pWv3HcIE2cmOwAATEGNsxmTemf91ZpXYjYjADBdy8vLedazntV3GBOlyA4AAFNQc1/epN7+tbXmpS8vADAttRfYE+1iAAAAAACYkKtd7Wp9hzBxZrIDAMAUaBczX2rNK9EuBgCYrle+8pV55StfmbZtL3qsbdu0bZt9+/Zl//79By3vf//7e4x2PIrsAAAwBdrFzKda89IuBgCYlvvd7359hzBx2sUAAAAAAMCYzGQHAIAp0C5mvtSaV6JdDAAwXbe85S3zoQ99qO8wJkqRHQAApkC7mPlUa17axQAA01J7gT3RLgYAAAAAgAm53vWu13cIE2cmOwAATIF2MfOl1rwS7WIAgOn69Kc/3XcIE6fIDgAAADABNbcKqzWvpN7ctAqjZk972tN6/f2K7AAAMAU1F1qSegsSteal0AIAzLrdu3f3HcK66ckOAAAAAABjUmQHAAAAAIAxKbIDAAAAAMCYFNkBAAAAAGBMiuwAAAAAADAmRXYAAAAAAGZG0zR9h7AhiuwAAAAAAMyMtm37DmFDtvcdAAAAbAWrq6tZXl7uO4xNt3PnziSpLrda80oO5AYAwOZQZAcAgClYXFzMyspK32FMTK251ZrX6upq3yEAAFRDuxgAAAAAABiTIjsAAAAAAIxJkR0AAAAAAMakJzsAAEyBG5/Ol1rzStz4FABgsymyAwDAFLjx6XyqNS83PgUA2DzaxQAAAAAAwJjMZAcAgCnQLma+1JpXol0MAMBmU2QHAIAp0C5mPtWal3YxMB01nmDdCicha84NmAztYgAAAAAAYEyK7AAAAAAAMCZFdgAAAAAAGJOe7AAAAAAAzJSzzjorTdNctCRZ8+/btm3L/e9//xx99NG9xarIDgAAAADATPnzP//zdb/2q1/9ak477bQJRnN4iuwAAAAAAMyURzziEWnbNm3bHvT48GNt22ZhYSEPechD+gjxIorsAAAAAADMlNe+9rXrfu2FF17Y60x2Nz4FAAAAAGBufeITn+j19yuyAwAAAAAwtz73uc/1+vsV2QEAAAAAmFuLi4u9/n5FdgAAAAAA5tbq6mqvv1+RHQAAAACAufWKV7yi19+/vdffDgAAAAAAI3bv3t13COumyA4AAAAwAYuLi1lZWek7jImoNa+k3tz6bqcBNVNkBwAAAJiA1dXVLC8v9x3Gptq5c2eSVJdXsjVyg3lyxhlnZGFhIU3TXLSs9fft27fn1FNPzeUvf/neYlVkBwAAAJgAM9nnU625mcnOvHnf+9637td+6EMfyjnnnDPBaA5PkR0AAABgAsxkny9bITeo1cknn9zr71dkBwAAAJgAM9nnU625mcnOvJmnG58u9B0AAAAAAADMK0V2AAAAAAAYkyI7AAAAAACMSZEdAAAAAADGpMgOAAAAAABjUmQHAAAAAIAxKbIDAAAAAMCYFNkBAAAAAGBMiuwAAAAAADAmRXYAAAAAABiTIjsAAAAAAIxJkR0AAAAAAMakyA4AAAAAAGNSZAcAAAAAgDFt7zsAAADYClZXV7O8vNx3GJtu586dSVJdbrXmlRzIDQCAzaHIDgAAU7C4uJiVlZW+w5iYWnOrNa/V1dW+QwAAqIYiOwAAAAAAM2VpaWndrz3zzDNzu9vdboLRHJ4iOwAATIF2MfOl1rwS7WJgmmoc+7fC+FhzbtCHk046Keeee+5Ef8eznvWs7N69e6K/43AU2QEAAAAAmIjTTjstp5122rpfv5EZ7LNCkR0AAKZAT/b5VGteerIDANNy8skn5wtf+ELfYUyUIjsAAExBjS0Dknovra81r0TLAABgumovsCfJQt8BAAAAAABQp1e96lV9hzBxZrIDAMAUaBczn2rNS7sYAGBarnOd62zopqR6sgMAAGvSLma+1JpXol0MAMBmU2QHAAAAAGAi9u3bl0996lNp2/aix4Z/roEiOwAAAAAAE/Gyl70sb3rTm/oOY6IU2QEAYAr0ZJ9PtealJzsAMC0PfvCD87nPfW5dr22aJh/72McmHNHmU2QHAIAp0JN9vtSaV6InOwAwXde4xjXy0pe+dN2vd+NTAABgTWayz6da8zKTHQBg8yiyAwDAFJjJPl9qzSsxkx0AmK7PfvazefKTn5z9+/cf9nXzfDNURXYAAJgCM9nnU615mckOAEzLu971rnz/+9/vO4yJUmQHAAAAAGAiHv3oR+fe9773RTPV27Y96Of9+/df9Nj+/ftz6qmn9hnuWBTZAQBgCrSLmS+15pVoFwMATNcTnvCEnH/++X2HMVELfQcAAAAAAECdai+wJ2ayAwDAVOjJPp9qzUtPdgBgWp73vOflmc98Zt9hTJQiOwAATIF2MfOl1rwS7WIAgOmqvcCeaBcDAAAAAMCEXPva1+47hIkzkx0AAAAAgIk4++yzN/T6paWlCUUyOWayAwAAAADAmMxkBwAAAABgIj75yU/miU98Yt9hTJQiOwAATMHi4mJWVlb6DmNias2t1rxWV1f7DgEA2CL+6Z/+qe8QJk6RHQAAAACAiTjllFNyyimnrPv189iTXZEdAACmYHV1NcvLy32Hsel27tyZJNXlVmteyYHcAADYHIrsAAAAAABMxN69e3PeeeelbduLHmuaZs2f55UiOwAATIGe7POp1rz0ZAcApmXXrl1585vf3HcYE6XIDgAAU6BdzHypNa9EuxgAYLpOPvnkfPWrX13zueHZ7YO/f+QjH5lGWJtKkR0AAKbATPb5VGteZrIDANNy1ateNb//+7+/7te78SkAALAmM9nnS615JWayAwDT9cxnPjPvf//7+w5johb6DgAAAAAAgDp985vf7DuEiTOTHQAApkC7mPlUa17axQAA03LBBRf0HcLEKbIDAAAATEDNJ1hrzSupNzcnWKnZmWee2evv1y4GAAAAAIC59c///M+9/n5FdgAAAAAA5lbfN1bVLgYAAAAAgIk4+uij84Mf/GBD77nPfe6Tu9zlLkmStm2P+Pob3vCGY8W2WRTZAQBgClZXV7O8vNx3GJtu586dSVJdbrXmlRzIDZi8Gsf+rTA+1pwb9GGjBfYkedOb3pQ3velN6379He5whzznOc/Z8O/ZLIrsAAAwBTXf/C6p9yZxtebl5ncwHTWP/bXmldSbm7Gfmv3DP/xDr79fkR0AAKagxtmMSb2z/mrNKzGbEaapxrF/K4yPNecG/P/27j/Y0rq+D/j7ey6y2HY7GYmiaKwNMAxW8Dp2dgRJZNsxWulsXQGBCqZlLaNR08UfixRpNBsz4cfEK1TCYLBGMqHBMWmbkmLT5N7MEMisafeCzELKjlZLSmioDBrCYrN8+8e9rJf9cffeZ8/zPHue+3rNnLnnPOd7zvl8Znae2Xmf7/k87XDhUwAAAAAAWnHjjTf2XULr7GQHAIAODHlkQDLcn9YPtS8jAwCArtxzzz19l9A6ITsAAHRgiCMDkuH+tH6ofSVGBgAA3brzzjv7LqF1QnYAAOiAneyTaah92ckO3RjyuX+ofSXD7c25H9ojZAcAgA7YyT5ZhtpXYic7dGmI5/61cH4ccm/Qh7POOiv33ntv32W0yoVPAQAAAABoxbnnntt3Ca0TsgMAAAAA0Iqrr7667xJaZ1wMAAB0YMhzeZPhzq8dal/m8kI3hnzuH2pfyXB7c+5nyM4666xeP99OdgAAAAAAJlbfM9/tZAcAgA4M8eJ3yXAvEjfUvhIXv4MuDfHcvxbOj0PuDWiHkB0AADow5JEByXB/Wj/UvowMAAC68u53vzt33nln32W0SsgOAAAA0IIhf8E61L6S4fbmC1b68uSTT/ZdQuuE7AAAAAAAtOKJJ57IiSeemAsvvDCj0SillJRSMhotXC506bHnb0899dQBx2utB9ySZGpqKm95y1v6bFHIDgAAAABAe44//vhs2rSp7zJaM+q7AAAAAAAAmFRCdgAAAAAAaEjIDgAAAAAADQnZAQAAAACgISE7AAAAAAA0JGQHAAAAAICGhOwAAAAAANCQkB0AAAAAABoSsgMAAAAAQENCdgAAAAAAaEjIDgAAAAAADQnZAQAAAACgISE7AAAAAAA0dEzfBQAAAAAAMFyPPfZY5ubmMhqN9t1KKQd9PDU1lST7jiXZ91wpZd9t6ZrXvOY1+471QcgOAAAdmJ+fz9atW/suY+xmZmaSZHC9DbWv5Ie9Ae0b4rl/LZwfh9wb9GHnzp1Jkk9/+tOtfs7s7Gyr778cITsAAHRgeno6c3NzfZfRmqH2NtS+5ufn+y4BAGAwzGQHAAAAAICG7GQHAAAAaMGQf8U01L6S4fbmV0xMmj7Hv6yWkB0AAACgBWayT5a10BvQDiE7AAAAQAvsZJ9MQ+3NTnZoj5nsAAAAAAAcNY477ri+S1gVO9kBAAAAWmBczGRZC73BpNizZ0/fJayKkB0AAACgBcbFTKah9mZcDLRHyA4AAADQAjvZJ8ta6A1oh5nsAAAAAADQkJ3sAAAAAC0wLmYyDbU342KgPXayAwAAAADQitFo9RH0DTfc0EIl7RGyAwAAAADQimOPPXbVr5mammqhkvYYFwMAAADQAhc+nSxroTfow549e1b9miuuuGJV67ds2ZJLLrlk1Z8zLkJ2AADowBCDlmS4gcRQ+0oELQBAt4477rhGQftq3HbbbUJ2AAAYuiFf/C4Z7kXihtqXi98BAF2544478oUvfOGA46WUg66/66672i5p7ITsAAAAAAC0opSSY445eAy9NGg/VOg+CYTsAADQAeNiJstQ+0qMiwEAuvWe97wnTz/9dN9ltErIDgAAHTAuZjINtS/jYgCArmzZsiU33nhj32W0SsgOAAAdsJN9sgy1r8ROdgCgW5s3b87mzZtXvH7jxo0tVtOOUd8FAAAAAADApLKTHQAAOmBczGQaal/GxQAAjI+QHQAAOmBczGQZal+JcTEAQLduvvnmfOUrX+m7jFYJ2QEAoAN2sk+mofZlJzsA0JWhB+yJmewAAAAAANCYkB0AAAAAgFacccYZfZfQOiE7AAAAAACtOPXUU/suoXVmsgMAQAdc+HSyDLWvxIVPAYBufeADH8gFF1xwwPFa6wH3a625+OKLO6ttXITsAADQARc+nUxD7cuFTwGArszOzmb79u19l9EqITsAAHTATvbJMtS+EjvZAYBufeYzn+m7hNYJ2QEAoAN2sk+mofZlJzsA0JXrr78+H/3oR/suo1UufAoAAAAAQCuGHrAnQnYAAAAAACZY3+PwjIsBAAAAAOCo8rGPfeyQz5VS9t0fjUY57bTTuijpkITsAAAAAAAcVW644YYVr33ooYdyxRVXtFjN8oTsAAAAAAAcVd7xjnek1nrA8f2PjUajXHbZZV2VdVBCdgAAAAAAjiof//jH+y5hxYTsAAAAAC2Ynp7O3Nxc32W0Yqh9JcPtbX5+vu8SYLCE7AAAAAAtmJ+fz9atW/suY6xmZmaSZHB9JWujN6Ado74LAAAAAACASWUnOwAAAEALjIuZTEPtzbgYaI+QHQAAAKAFxsVMlrXQG9AOITsAAAAAAEeViy666AWPa60HvT81NZVrr702r371qzurbX9CdgAAAAAAjiqPP/74itfefffdufzyy1usZnlCdgAAAAAAjip33HHHAcdKKQe9/9KXvrSTmg5FyA4AAAAAwFHl5S9/ed8lrJiQHQAAOjDEi98lw71I3FD7Slz8Dro0PT2dubm5vstoxVD7Sobb2/z8fN8lwGAJ2QEAoANDDlqS4QYSQ+1L0AIAMD5CdgAA6ICd7JNlqH0ldrIDAIybkB0AADpgJ/tkGmpfdrIDAEezSy+9tO8SVkXIDgAAHbCTfbIMta/ETnYA4Oh3++2357LLLuu7jBUTsgMAQAfsZJ9MQ+3LTnYAgPERsgMAQAfsZJ8sQ+0rsZMdAGDcRn0XAAAAAAAAk8pOdgAA6IBxMZNpqH0ZFwMAMD5CdgAA6IBxMZNlqH0lxsUAAIybkB0AADpgJ/tkGmpfdrIDAIyPkB0AADpgJ/tkGWpfiZ3sAADjJmQHAIAO2Mk+mYbal53sAADjI2QHAIAO2Mk+WYbaV2InOwDAuAnZAQCgA3ayT6ah9mUnOwDA+Iz6LgAAAAAAACaVkB0AAAAAABoSsgMAAAAAQENCdgAAAAAAaMiFTwEAAAAAaMX09HTm5+dzySWXpJSSJCml7Ls9bzQa7Xt83nnn9VJrU0J2AAAAAABaUUrJ6aefni1btvRdSmuMiwEAAAAAgIbsZAcAAAAAoBXf//73s3v37nz4wx/O1NRURqPRvr/JC0fHlFIyGo2ybdu2rF+/vufKV07IDgAAAABAK3bv3p0kefDBB1f8mnvuuSezs7NtlTR2xsUAAAAAAHDUmLQLnwrZAQAAAAA4anz1q1/tu4RVEbIDAAAAANCKl73sZat+zc0339xCJe0xkx0AAAAAgFa88pWvzAknnJAbb7yx71JaYyc7AAAAAAA0JGQHAAAAAICGhOwAAAAAANCQkB0AAAAAABoSsgMAAAAAQENCdgAAAAAAaOiYvgsAAAAAAGCY9u7dm0ceeSR33HHHYdeWUlJKyaZNm/LiF7+4g+rGQ8gOAAAAAEArHnjggSTJrbfeuuLXfPvb3862bdvaKmnshOwAAAAAALTida97XR588MFceOGFK37NJZdc0mJF4ydkBwAAAACgFS960Yty+umn5/3vf3/fpbTGhU8BAAAAAKAhITsAAAAAADQkZAcAAAAAgIaE7AAAAAAA0JCQHQAAAAAAGhKyAwAAAABAQ0J2AAAAAABoSMgOAAAAAAANHdN3AQAAAAAADNOzzz6bXbt2ZcuWLUmSUsq+2/6PSyk57rjj8slPfjIveclL+ix7VYTsAAAAAAC0YteuXUmSb37zmyt+zXnnnZfZ2dm2Sho742IAAAAAAKAhO9kBAKAD8/Pz2bp1a99ljN3MzEySDK63ofaV/LA3AADGw052AAAAAABoyE52AADowPT0dObm5vouozVD7W2ofc3Pz/ddAgDAYAjZAQCgA8bFTJah9pUYFwMAMG7GxQAAAAAAQEN2sgMAQAeMi5lMQ+3LuBgAgPERsgMAQAeMi5ksQ+0rMS4GAGDcjIsBAAAAAICG7GQHAIAOGBczmYbal3ExAADjI2QHAIAOGBczWYbaV2JcDADAuBkXAwAAAAAADQnZAQAAAACgIeNiAACgA2ayT6ah9mUmOwDA+NjJDgAAAAAADQnZAQAAAACgIeNiAACgA/Pz89m6dWvfZYzdzMxMkgyut6H2lfywNwAAxkPIDgAAHTCTfTINtS8z2QEAxkfIDgAAAABAK6anp/Poo4/mE5/4RGqt+25JDvr4ueeey5ve9KY+S141ITsAAAAAAK0opeQVr3hF3vjGN/ZdSmtc+BQAAAAAABoSsgMAAAAAQENCdgAAAAAAaEjIDgAAAAAADQnZAQAAAACgISE7AAAAAAA0JGQHAAAAAICGhOwAAAAAANCQkB0AAAAAABoSsgMAAAAAQENCdgAAAAAAaEjIDgAAAAAADQnZAQAAAACgISE7AAAAAAA0dEzfBQAAAAAAMFzf+MY3ctVVV+17XEo56P0kuffee1f9/pdffnkuvvji5gUeISE7AAB0YH5+Plu3bu27jLGbmZlJksH1NtS+kh/2BrRviOf+tXB+HHJv0Iezzz47jz/+eL7zne8kSWqt+25LHezYSt16661CdgAAGLrp6enMzc31XUZrhtrbUPuan5/vuwQAYI246aab+i6hdUJ2AAAAgBYM+QvWofaVDLc3X7BCe4TsAAAAAAC04g1veEOeeuqpfOQjH0kpJVNTUxmNRge9X0rJaDQ64H6yMLt9//ntycKYmfXr13fd1gsI2QEAoANDnMubDHd+7VD7SszlhS4N8dy/Fs6PQ+4N+rBz584kyYc+9KFWP2d2drbV91+OkB0AADow5JEByXB/Wj/UvowMgG4M+dw/1L6S4fbm3A/tEbIDAEAHhribMRnurr+h9pXYzQhdGuK5fy2cH4fcG/ThjDPOyAMPPNB3Ga0SsgMAQAeGvJsxGe6uv6H2ZTcjdGPI5/6h9pUMtzfnfvrSRcC+efPm1j9jOUJ2AAAAgBbYyT5Z1kJvMFQnnXTSYdeUUq5K8q4kpyZ5NskfJ7mq1vrgkjXbk1yQ5MeS/CDJf09yTa313uXeW8gOAAAdGGLQkgw3kBhqX4mgBQDo1nvf+958+ctfbvUzbrjhhpx77rmHW3ZOkpuTfD1JSfLzSf5rKeW1tdbvLq750yQfTPKtJC9OckWSu0spp9RaHz/UGwvZAQCgA0MeGZAM96f1Q+3LyAAAoCvr16/vu4QkSa31bUsfl1IuTfJUkjcn+Z3FNb++35qPnQVadQAACQRJREFUJNmSZDrJ1w713kJ2AAAAAABacf755+f8889f8fqNGzc2+pz77rsvGzZsyNTU1Epfsj7JKMmTB3uylHJsksuTfC/JsjsUhOwAANAB42Imy1D7SoyLAQC6dfvtt+eLX/xi65+zffv2nHbaabnuuutWGrR/Lgvh+X1LD5ZS/nGSf5fkbyR5LMlblxsVkwjZAQCgE8bFTKah9mVcDHRjyOf+ofaVDLc353760kXAniTPPPNMdu3alR07duTMM89cdm0p5ZeTnJ3k7Frr3v2ens3CeJgfTfIvktxZSjmz1vrYod5PyA4AAAAAQCte//rX5/7778+mTZtSSsloNEqSlFJecEuS0Wh00Pv7ry2l5P7778/OnTtf8FnPPvtsdu/evWzIXkr5bJKLkmystX5z/+drrU8n2b14++NSyiNJ3pdk+6HeU8gOAAAAAEArng/KTzzxxH3B+dTU1L7AfWmYniRTU1PZuHFj1q1bt+z73nfffXn44YfzzDPP7Du2bt26nHzyycvV8rksBOzn1FofXmELoyTLFiNkBwAAAACgFc+PKrrllltW/Jprr702s7Ozy67ZsGFDTjvttOzatSvPPvts1q1bl9e+9rXZsGHDQdeXUj6f5NIk70zyZCnl5YtP/WWt9S9LKX87ybYkv5OFWewvTfLBJK9KcudytQjZAQAAAFowxIter4ULQw+5NxiSqampXHfdddmxY0d2796dk08+ORs2bFjuoqc/s/j39/c7/ukkn0ry10n+XpLLkhyf5P8m+XqSn6y1PrBcLUJ2AAAAAAAmztTUVM4888zDXug0SWqt5TDP/1WSzU3qELIDAAAAtGB6ejpzc3N9l9GKofaVDLe350d2AOMnZAcAAABogXExk2Ut9Aa0Y9R3AQAAAAAAMKnsZAcAAABogXExk2movRkXA+0RsgMAAAC0wLiYybIWegPaYVwMAAAAAAA0JGQHAAAAAICGhOwAAAAAANCQkB0AAAAAABoSsgMAAAAAQEMrCtlLKceXUt5XSvntUsruUsozpZSnSin3lFK2lFJG+60/pZRyZSnlD0op/6uU8oNSyuOllP9QStl4iM94VSnl6lLKVxY/47lSSi2lnHyI9S8qpWwupdxWSnmwlPK9UspflVK+UUr5+VLK+iPtAwAAAACgDaWUV5RSfq2U8hellD2llF2llLf0XRerd8wK112Q5FeSPJZkNsl3kpyQ5F1JfjXJPyqlXFBrrYvrtye5MMmuJL+b5LtJTk2yKcmmUsq/rLXeuN9n/P0kv5CkJvlWkqeS/MgyNZ2U5LeSPL1Y011J/laStyW5JsmFpZQ311qfOII+AAAAABqZnp7O3Nxc32W0Yqh9JcPtbX5+vu8SWKKU8iNJ/ijJPUnOTfIXSX48yf/psy6aWWnI/j+yEJDfVWt97vmDpZR/lWRHkvOyEFR/dfGpu5NcW2vdufRNFr+J+b0k15dSvlJrfWzJ03+S5CeT3F9r/V4pZS7Jct/cfD/JB5P8Wq316SWfcWwWwvdzk/xckg8fQR8AAAAAAOO2Lcljtdb3Ljn2rb6K4cisKGSvtf7BIY7/eSnlliSfSXJOFsPpWuuXDrH+DxfD87cmOStLwuxa66NJHl1p4bXWP0ty80GO/6CU8otZCNnPOZI+AAAAAJqan5/P1q1b+y5jrGZmZpJkcH0la6M3jirvTHJ3KeU3k2xM8r+zMGnj88tN2di7d2927NiRRx55JKeccko2bNiQqampjkrmUFa6k305/2/x71+3tL6JJp/RRV0AAADAGmFczGQaam/GxRx1fjzJzyT5bJJfSjKd5KbF5/7NwV6wd+/ebNu2LQ899FD27NmTY489NieddFKuvPLKFwTttdY8n9Pvn9f38dxaUI6k0VLKMUl2JnldkrfXWr92mPV/J8mfJtmb5FW11ieXWTuXhXExp9Rad6+yrl9J8v4kv1RrvWoF61fVR5Kcc845g/wXMtRvbWdmZjI9Pd13GQDAGjbE3YzJsP//mAyvr8T/jQHWqqH+XyRJ5ubmSt81rFYp5QdJ/qTWetaSY7+YZHOt9bQsXLfyBe67775s3749zzzzTIeV9md2dnacb9fqv5EjDdlvSPLRJL9baz33MGvXJfn9JG9Osq3Wev1h1s+lQcheStmU5N8n+bMkZywX5C95zYr7AAAAAAA4EqWUbyf5vVrr+5YcuzTJLbXWv3mw12zcuPGaJJ9KMlpy+LkkPzc7O/sLLZbLYTQO2UspP5vkc0keTvLmWut3l1k7leSOJBck+c0kFy83W2jxNXNZZcheSjkryX/Jwjc9/7DWumOcfQAAAAAAHKlSym8k+bFa608sObY9yXm11tf2VxlNjA6/5ECllA9mIZjelWTjCgL2X89CwH5nkksOF7A3rOnMJP85C9/evH2FAfuK+wAAAAAAGJPPJnlTKeXqUsrJpZQLkvxsks/3XBcNrDpkL6VszcLw/QezEEz/+TJrj8nCDvaLkvxGkn9aax37hUVLKT+R5GtZ2MH+U7XWP1rBa1bcBwAAAADAuNRav57knUnenYV88jNJrklyc5910cwxq1lcSrkyC1e7nU/y1lrrE8usPTYLO9f/SZIvJ/nntdbnjqDWQ33OP0jyH5P8IMnbFv+BHu41K+4DAAAAAGDcaq13Jbmr7zo4civeyV5KuSYLwfR/y8K88+UC9nVJfjsLAfttaS9g/6kk/ynJnsWaVhKwr7gPAAAAAABYzooufFpK+ekkX0qyN8lNSZ46yLL/WWv90uL6f5vknyV5Igs/cTjYh8zVWuf2+5wvLXn49iQnJPmtJN9fPPartdZ7FteemoWd6Mcl+WoWflZxgFrrp5r2AQAAAAAAy1npuJi/u/h3KsnWQ6z5wywE2EvX/2iSf73M+87t9/inD7LmXfutv2fx/iuyELAnyXmLt4P51JL7q+0DAAAAAAAOaUU72QEAAAAAgAOteCY7AAAAAADwQkJ2AAAAAABoSMgOAAAAAAANCdkBAAAAAKAhITsAAAAAADQkZAcAAAAAgIaE7AAAAAAA0JCQHQAAAAAAGhKyAwAAAABAQ0J2AAAAAABo6P8D0LKufOaFhU0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(minute_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "minute_df.to_csv('FitBit-Tracker-Data/Generated-Data/minute.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STOP HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STOOOOOOOOOOOOOOOOOOOOOOOOP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate date and time from sleepmin_df\n",
    "extract_date_hour(sleepmin_df, 'date')\n",
    "\n",
    "# Separate date and time from complete minute dataset\n",
    "extract_date_hour(minute_df, 'ActivityMinute')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>date</th>\n",
       "      <th>value</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 2:47:30 AM</td>\n",
       "      <td>3</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>2AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 2:48:30 AM</td>\n",
       "      <td>2</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>2AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 2:49:30 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>2AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 2:50:30 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>2AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 2:51:30 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>2AM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id                  date  value       Date FullHour\n",
       "0  1503960366  4/12/2016 2:47:30 AM      3  4/12/2016      2AM\n",
       "1  1503960366  4/12/2016 2:48:30 AM      2  4/12/2016      2AM\n",
       "2  1503960366  4/12/2016 2:49:30 AM      1  4/12/2016      2AM\n",
       "3  1503960366  4/12/2016 2:50:30 AM      1  4/12/2016      2AM\n",
       "4  1503960366  4/12/2016 2:51:30 AM      1  4/12/2016      2AM"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sleepmin_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ActivityMinute</th>\n",
       "      <th>Calories</th>\n",
       "      <th>Intensity</th>\n",
       "      <th>METs</th>\n",
       "      <th>Steps</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:00:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:01:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:02:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:03:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:04:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id         ActivityMinute  Calories  Intensity  METs  Steps  \\\n",
       "0  1503960366  4/12/2016 12:00:00 AM    0.7865          0    10      0   \n",
       "1  1503960366  4/12/2016 12:01:00 AM    0.7865          0    10      0   \n",
       "2  1503960366  4/12/2016 12:02:00 AM    0.7865          0    10      0   \n",
       "3  1503960366  4/12/2016 12:03:00 AM    0.7865          0    10      0   \n",
       "4  1503960366  4/12/2016 12:04:00 AM    0.7865          0    10      0   \n",
       "\n",
       "        Date FullHour  \n",
       "0  4/12/2016     12AM  \n",
       "1  4/12/2016     12AM  \n",
       "2  4/12/2016     12AM  \n",
       "3  4/12/2016     12AM  \n",
       "4  4/12/2016     12AM  "
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minute_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete datetime column\n",
    "sleepmin_df.drop(['date'], axis=1, inplace=True)\n",
    "minute_df.drop(['ActivityMinute'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grouped dataframe\n",
    "grouped_sleep_df = sleepmin_df.groupby(['Id', 'Date', 'FullHour'], as_index=False).mean()\n",
    "grouped_sleep_df.drop(['value'], axis=1, inplace=True)\n",
    "\n",
    "# Average sleep score\n",
    "grouped_sleep_df['AvgValue'] = sleepmin_df.groupby(['Id', 'Date', 'FullHour'], as_index=False).mean()['value']\n",
    "\n",
    "# Lowest sleep score\n",
    "grouped_sleep_df['MinValue'] = sleepmin_df.groupby(['Id', 'Date', 'FullHour'], as_index=False).min()['value']\n",
    "\n",
    "# Highest sleep score\n",
    "grouped_sleep_df['MaxValue'] = sleepmin_df.groupby(['Id', 'Date', 'FullHour'], as_index=False).max()['value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "      <th>AvgValue</th>\n",
       "      <th>MinValue</th>\n",
       "      <th>MaxValue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>2AM</td>\n",
       "      <td>1.923077</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>3AM</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>4AM</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>5AM</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>6AM</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id       Date FullHour  AvgValue  MinValue  MaxValue\n",
       "0  1503960366  4/12/2016      2AM  1.923077         1         3\n",
       "1  1503960366  4/12/2016      3AM  1.083333         1         3\n",
       "2  1503960366  4/12/2016      4AM  1.016667         1         2\n",
       "3  1503960366  4/12/2016      5AM  1.100000         1         2\n",
       "4  1503960366  4/12/2016      6AM  1.000000         1         1"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sleepmin_df.groupby(['Id', 'Date', 'FullHour'], as_index=False).mean()['value']\n",
    "\n",
    "grouped_sleep_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Calories</th>\n",
       "      <th>Intensity</th>\n",
       "      <th>METs</th>\n",
       "      <th>Steps</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id  Calories  Intensity  METs  Steps       Date FullHour\n",
       "0  1503960366    0.7865          0    10      0  4/12/2016     12AM\n",
       "1  1503960366    0.7865          0    10      0  4/12/2016     12AM\n",
       "2  1503960366    0.7865          0    10      0  4/12/2016     12AM\n",
       "3  1503960366    0.7865          0    10      0  4/12/2016     12AM\n",
       "4  1503960366    0.7865          0    10      0  4/12/2016     12AM"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minute_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Min, Average, Max Calories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grouped dataframe\n",
    "grouped_cal_df = minute_df.groupby(['Id', 'Date', 'FullHour'], as_index=False).mean()\n",
    "grouped_cal_df.drop(['value'], axis=1, inplace=True)\n",
    "\n",
    "# Average sleep score\n",
    "grouped_cal_df['AvgValue'] = minute_df.groupby(['Id', 'Date', 'FullHour'], as_index=False).mean()['value']\n",
    "\n",
    "# Lowest sleep score\n",
    "grouped_cal_df['MinValue'] = minute_df.groupby(['Id', 'Date', 'FullHour'], as_index=False).min()['value']\n",
    "\n",
    "# Highest sleep score\n",
    "grouped_cal_df['MaxValue'] = minute_df.groupby(['Id', 'Date', 'FullHour'], as_index=False).max()['value']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heartrate Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "heart_df = pd.read_csv('FitBit-Tracker-Data/Fitabase Data 4.12.16-5.12.16/heartrate_seconds_merged.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Time</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:00 AM</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:05 AM</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:10 AM</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:20 AM</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:25 AM</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id                  Time  Value\n",
       "0  2022484408  4/12/2016 7:21:00 AM     97\n",
       "1  2022484408  4/12/2016 7:21:05 AM    102\n",
       "2  2022484408  4/12/2016 7:21:10 AM    105\n",
       "3  2022484408  4/12/2016 7:21:20 AM    103\n",
       "4  2022484408  4/12/2016 7:21:25 AM    101"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2483658, 3)"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "heart_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Extract the date\n",
    "2. Extract the hour by:\n",
    "    - Extract time\n",
    "    - Extract AM/PM\n",
    "    - Specific hour\n",
    "    - Combine hour with am/pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def extract_date_hour(dataframe, time):\n",
    "#     # Extract date\n",
    "#     dataframe['Date'] = dataframe[time].apply(lambda x: x[:9].strip())\n",
    "    \n",
    "#     # Extract time\n",
    "#     dataframe['FullTime'] = dataframe[time].apply(lambda x: (x[9:]).strip())\n",
    "\n",
    "#     # Extract AM/PM\n",
    "#     dataframe['AmPm'] = dataframe.FullTime.apply(lambda x: x[-2:])\n",
    "\n",
    "#     # Extract specific hour\n",
    "#     dataframe['Hour'] = dataframe.FullTime.apply(lambda x: x[:2].replace(':', ''))\n",
    "\n",
    "#     # Combine hour with am/pm\n",
    "#     dataframe['FullHour'] = dataframe.Hour + dataframe.AmPm\n",
    "\n",
    "#     # Drop the extra columns that are no longer needed\n",
    "#     dataframe.drop(['FullTime', 'AmPm', 'Hour'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_date_hour(heart_df, 'Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Time</th>\n",
       "      <th>Value</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:00 AM</td>\n",
       "      <td>97</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>7AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:05 AM</td>\n",
       "      <td>102</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>7AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:10 AM</td>\n",
       "      <td>105</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>7AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:20 AM</td>\n",
       "      <td>103</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>7AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:25 AM</td>\n",
       "      <td>101</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>7AM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id                  Time  Value       Date FullHour\n",
       "0  2022484408  4/12/2016 7:21:00 AM     97  4/12/2016      7AM\n",
       "1  2022484408  4/12/2016 7:21:05 AM    102  4/12/2016      7AM\n",
       "2  2022484408  4/12/2016 7:21:10 AM    105  4/12/2016      7AM\n",
       "3  2022484408  4/12/2016 7:21:20 AM    103  4/12/2016      7AM\n",
       "4  2022484408  4/12/2016 7:21:25 AM    101  4/12/2016      7AM"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by 'Id' and 'FullHour' and find the mean of 'Value'\n",
    "mean_heart_df = heart_df.groupby(['Id', 'Date', 'FullHour'], as_index=False)['Value'].mean()\n",
    "\n",
    "# Rename 'Value' column\n",
    "mean_heart_df.rename(columns={'Value':'MeanHeartRate'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "      <th>MeanHeartRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10AM</td>\n",
       "      <td>106.716075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11AM</td>\n",
       "      <td>67.767157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12PM</td>\n",
       "      <td>66.230570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>1PM</td>\n",
       "      <td>83.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>2PM</td>\n",
       "      <td>80.577093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id       Date FullHour  MeanHeartRate\n",
       "0  2022484408  4/12/2016     10AM     106.716075\n",
       "1  2022484408  4/12/2016     11AM      67.767157\n",
       "2  2022484408  4/12/2016     12PM      66.230570\n",
       "3  2022484408  4/12/2016      1PM      83.687500\n",
       "4  2022484408  4/12/2016      2PM      80.577093"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_heart_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6013, 4)"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_heart_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "# heart_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Merge the dataframes\n",
    "# mean_heart_df = pd.merge(heart_df, mean_df, on=['Id', 'FullHour'])\n",
    "\n",
    "# mean_heart_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calories Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "cal_df = pd.read_csv('FitBit-Tracker-Data/Fitabase Data 4.12.16-5.12.16/minuteCaloriesNarrow_merged.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ActivityMinute</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:00:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:01:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:02:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:03:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:04:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id         ActivityMinute  Calories\n",
       "0  1503960366  4/12/2016 12:00:00 AM    0.7865\n",
       "1  1503960366  4/12/2016 12:01:00 AM    0.7865\n",
       "2  1503960366  4/12/2016 12:02:00 AM    0.7865\n",
       "3  1503960366  4/12/2016 12:03:00 AM    0.7865\n",
       "4  1503960366  4/12/2016 12:04:00 AM    0.7865"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1325580, 3)"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_date_hour(cal_df, 'ActivityMinute')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ActivityMinute</th>\n",
       "      <th>Calories</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:00:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:01:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:02:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:03:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016 12:04:00 AM</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id         ActivityMinute  Calories       Date FullHour\n",
       "0  1503960366  4/12/2016 12:00:00 AM    0.7865  4/12/2016     12AM\n",
       "1  1503960366  4/12/2016 12:01:00 AM    0.7865  4/12/2016     12AM\n",
       "2  1503960366  4/12/2016 12:02:00 AM    0.7865  4/12/2016     12AM\n",
       "3  1503960366  4/12/2016 12:03:00 AM    0.7865  4/12/2016     12AM\n",
       "4  1503960366  4/12/2016 12:04:00 AM    0.7865  4/12/2016     12AM"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by 'Id' and 'FullHour' and find the mean of 'Calories'\n",
    "mean_cal_df = cal_df.groupby(['Id', 'Date', 'FullHour'], as_index=False)['Calories'].mean()\n",
    "\n",
    "# Rename 'Calories' column\n",
    "mean_cal_df.rename(columns={'Calories':'MeanCalories'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "      <th>MeanCalories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10AM</td>\n",
       "      <td>1.649028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10PM</td>\n",
       "      <td>1.080127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11AM</td>\n",
       "      <td>1.266265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11PM</td>\n",
       "      <td>1.358023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "      <td>1.355402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id       Date FullHour  MeanCalories\n",
       "0  1503960366  4/12/2016     10AM      1.649028\n",
       "1  1503960366  4/12/2016     10PM      1.080127\n",
       "2  1503960366  4/12/2016     11AM      1.266265\n",
       "3  1503960366  4/12/2016     11PM      1.358023\n",
       "4  1503960366  4/12/2016     12AM      1.355402"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_cal_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Merge the dataframes\n",
    "# mean_cal_df = pd.merge(cal_df, mean_df, on=['Id', 'Date', 'FullHour'])\n",
    "\n",
    "# mean_cal_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22093, 4)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_cal_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NOTE\n",
    "Do **NOT** need to merge the dataset with the old dataset\n",
    "\n",
    "Just keep the mean datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.merge(cal_df, mean_df, on=['Id', 'Date', 'FullHour'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "      <th>MeanCalories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10AM</td>\n",
       "      <td>1.649028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10PM</td>\n",
       "      <td>1.080127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11AM</td>\n",
       "      <td>1.266265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11PM</td>\n",
       "      <td>1.358023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12AM</td>\n",
       "      <td>1.355402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id       Date FullHour  MeanCalories\n",
       "0  1503960366  4/12/2016     10AM      1.649028\n",
       "1  1503960366  4/12/2016     10PM      1.080127\n",
       "2  1503960366  4/12/2016     11AM      1.266265\n",
       "3  1503960366  4/12/2016     11PM      1.358023\n",
       "4  1503960366  4/12/2016     12AM      1.355402"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_cal_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Date</th>\n",
       "      <th>FullHour</th>\n",
       "      <th>MeanHeartRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>10AM</td>\n",
       "      <td>106.716075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>11AM</td>\n",
       "      <td>67.767157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>12PM</td>\n",
       "      <td>66.230570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>1PM</td>\n",
       "      <td>83.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>2PM</td>\n",
       "      <td>80.577093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id       Date FullHour  MeanHeartRate\n",
       "0  2022484408  4/12/2016     10AM     106.716075\n",
       "1  2022484408  4/12/2016     11AM      67.767157\n",
       "2  2022484408  4/12/2016     12PM      66.230570\n",
       "3  2022484408  4/12/2016      1PM      83.687500\n",
       "4  2022484408  4/12/2016      2PM      80.577093"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_heart_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(mean_heart_df, mean_cal_df, on=['Id', 'Date', 'FullHour'], how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "part_df = df[:2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
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       "           Id       Date FullHour  MeanHeartRate  MeanCalories\n",
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       "1  2022484408  4/12/2016     11AM      67.767157      1.656861\n",
       "2  2022484408  4/12/2016     12PM      66.230570      1.438944\n",
       "3  2022484408  4/12/2016      1PM      83.687500      2.059835\n",
       "4  2022484408  4/12/2016      2PM      80.577093      3.286050"
      ]
     },
     "execution_count": 243,
     "metadata": {},
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    "part_df.head()"
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   "execution_count": null,
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   "execution_count": null,
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   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('FitBit-Tracker-Data/Fitabase Data 4.12.16-5.12.16/minuteMETsNarrow_merged.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "           Id         ActivityMinute  METs\n",
       "0  1503960366  4/12/2016 12:00:00 AM    10\n",
       "1  1503960366  4/12/2016 12:01:00 AM    10\n",
       "2  1503960366  4/12/2016 12:02:00 AM    10\n",
       "3  1503960366  4/12/2016 12:03:00 AM    10\n",
       "4  1503960366  4/12/2016 12:04:00 AM    10"
      ]
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_date(row):\n",
    "    new_row = row[:9]\n",
    "    new_row = new_row.strip()\n",
    "    return new_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = df['ActivityMinute'].apply(lambda x: x[:9])  # Get the date from 'ActivityMinute' column\n",
    "df['Date'] = df['Date'].apply(lambda x: x.strip())  # Remove any white spaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4/12/2016', '4/13/2016', '4/14/2016', '4/15/2016', '4/16/2016',\n",
       "       '4/17/2016', '4/18/2016', '4/19/2016', '4/20/2016', '4/21/2016',\n",
       "       '4/22/2016', '4/23/2016', '4/24/2016', '4/25/2016', '4/26/2016',\n",
       "       '4/27/2016', '4/28/2016', '4/29/2016', '4/30/2016', '5/1/2016',\n",
       "       '5/2/2016', '5/3/2016', '5/4/2016', '5/5/2016', '5/6/2016',\n",
       "       '5/7/2016', '5/8/2016', '5/9/2016', '5/10/2016', '5/11/2016',\n",
       "       '5/12/2016'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['Date'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "           Id         ActivityMinute  METs       Date\n",
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       "1  1503960366  4/12/2016 12:01:00 AM    10  4/12/2016\n",
       "2  1503960366  4/12/2016 12:02:00 AM    10  4/12/2016\n",
       "3  1503960366  4/12/2016 12:03:00 AM    10  4/12/2016\n",
       "4  1503960366  4/12/2016 12:04:00 AM    10  4/12/2016"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "df.head()"
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  {
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   "execution_count": 29,
   "metadata": {},
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    {
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       "<p>5 rows × 31 columns</p>\n",
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      ],
      "text/plain": [
       "Date        4/12/2016  4/13/2016  4/14/2016  4/15/2016  4/16/2016  4/17/2016  \\\n",
       "Id                                                                             \n",
       "1503960366  17.528472  15.874306  15.686806  15.409722  16.454167  15.258333   \n",
       "1624580081  11.968056  11.798611  13.137500  11.231944  12.231944  12.990278   \n",
       "1644430081  15.811111  14.344444  15.944444  13.591667  17.263889  14.879167   \n",
       "1844505072  15.072222  13.805556  15.813889  12.809722  12.302778  13.313889   \n",
       "1927972279  10.832639  10.426389  11.550694  10.769444  10.000000  10.001389   \n",
       "\n",
       "Date        4/18/2016  4/19/2016  4/20/2016  4/21/2016  ...  5/11/2016  \\\n",
       "Id                                                      ...              \n",
       "1503960366  16.967361  17.974306  15.775000  15.678472  ...  17.502381   \n",
       "1624580081  13.408333  11.995833  12.084722  12.256944  ...  11.356944   \n",
       "1644430081  13.869444  16.081944  12.009722  10.577778  ...  11.652778   \n",
       "1844505072  13.461111  10.141667  10.015278  15.309722  ...  10.000000   \n",
       "1927972279  10.233333  10.000000  10.000000  10.004861  ...  10.000000   \n",
       "\n",
       "Date        5/12/2016   5/2/2016   5/3/2016   5/4/2016   5/5/2016   5/6/2016  \\\n",
       "Id                                                                             \n",
       "1503960366        NaN  17.815972  17.692361  16.171528  17.418056  16.855556   \n",
       "1624580081  12.008333  12.511111  11.150000  11.438889  11.455556  11.208333   \n",
       "1644430081        NaN  12.533333  16.411111  10.984722  12.175694  16.723611   \n",
       "1844505072  10.000000  10.005556  12.934722  11.501389  11.800000  10.029167   \n",
       "1927972279  10.000000  12.142361  10.643750  11.336111  10.000000  11.550694   \n",
       "\n",
       "Date         5/7/2016   5/8/2016   5/9/2016  \n",
       "Id                                           \n",
       "1503960366  16.188194  15.468750  16.172917  \n",
       "1624580081  12.318056  11.927778  11.102778  \n",
       "1644430081  16.821528  14.765278  14.866667  \n",
       "1844505072  10.001389  10.001389  10.000000  \n",
       "1927972279  10.809028  10.000694  10.000000  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_df = pd.pivot_table(df, index='Id', columns='Date', values='METs')\n",
    "\n",
    "pivot_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New\n",
    "`sleepDay_merged.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('FitBit-Tracker-Data/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SleepDay</th>\n",
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       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
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       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/13/2016 12:00:00 AM</td>\n",
       "      <td>2</td>\n",
       "      <td>384</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/15/2016 12:00:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>412</td>\n",
       "      <td>442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/16/2016 12:00:00 AM</td>\n",
       "      <td>2</td>\n",
       "      <td>340</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/17/2016 12:00:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id               SleepDay  TotalSleepRecords  TotalMinutesAsleep  \\\n",
       "0  1503960366  4/12/2016 12:00:00 AM                  1                 327   \n",
       "1  1503960366  4/13/2016 12:00:00 AM                  2                 384   \n",
       "2  1503960366  4/15/2016 12:00:00 AM                  1                 412   \n",
       "3  1503960366  4/16/2016 12:00:00 AM                  2                 340   \n",
       "4  1503960366  4/17/2016 12:00:00 AM                  1                 700   \n",
       "\n",
       "   TotalTimeInBed  \n",
       "0             346  \n",
       "1             407  \n",
       "2             442  \n",
       "3             367  \n",
       "4             712  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.pivot_table(df, index='Id', columns='Date', values='TotalMinutesAsleep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Date column\n",
    "df['Date'] = df['SleepDay'].apply(lambda x: x[:9])\n",
    "df['Date'] = df['Date'].apply(lambda x: x.strip())\n",
    "\n",
    "# Create column for how long person was awake while in bed\n",
    "df['TotalTimeAwake'] = df.TotalTimeInBed - df.TotalMinutesAsleep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Id</th>\n",
       "      <th>SleepDay</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>346</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/13/2016 12:00:00 AM</td>\n",
       "      <td>2</td>\n",
       "      <td>384</td>\n",
       "      <td>407</td>\n",
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       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
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       "      <td>442</td>\n",
       "      <td>4/15/2016</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/16/2016 12:00:00 AM</td>\n",
       "      <td>2</td>\n",
       "      <td>340</td>\n",
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       "      <td>4/16/2016</td>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
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       "      <td>4/17/2016 12:00:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>712</td>\n",
       "      <td>4/17/2016</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id               SleepDay  TotalSleepRecords  TotalMinutesAsleep  \\\n",
       "0  1503960366  4/12/2016 12:00:00 AM                  1                 327   \n",
       "1  1503960366  4/13/2016 12:00:00 AM                  2                 384   \n",
       "2  1503960366  4/15/2016 12:00:00 AM                  1                 412   \n",
       "3  1503960366  4/16/2016 12:00:00 AM                  2                 340   \n",
       "4  1503960366  4/17/2016 12:00:00 AM                  1                 700   \n",
       "\n",
       "   TotalTimeInBed       Date  TotalTimeAwake  \n",
       "0             346  4/12/2016              19  \n",
       "1             407  4/13/2016              23  \n",
       "2             442  4/15/2016              30  \n",
       "3             367  4/16/2016              27  \n",
       "4             712  4/17/2016              12  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>TotalMinutesAsleep</th>\n",
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       "      <th>Date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1644430081</td>\n",
       "      <td>5/2/2016 12:00:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>796</td>\n",
       "      <td>961</td>\n",
       "      <td>5/2/2016</td>\n",
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      ],
      "text/plain": [
       "            Id              SleepDay  TotalSleepRecords  TotalMinutesAsleep  \\\n",
       "27  1644430081  5/2/2016 12:00:00 AM                  1                 796   \n",
       "\n",
       "    TotalTimeInBed      Date  \n",
       "27             961  5/2/2016  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['TotalMinutesAsleep'] == 796]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.118644</td>\n",
       "      <td>419.467312</td>\n",
       "      <td>458.639225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.345521</td>\n",
       "      <td>118.344679</td>\n",
       "      <td>127.101607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>361.000000</td>\n",
       "      <td>403.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>433.000000</td>\n",
       "      <td>463.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>526.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>961.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TotalSleepRecords  TotalMinutesAsleep  TotalTimeInBed\n",
       "count         413.000000          413.000000      413.000000\n",
       "mean            1.118644          419.467312      458.639225\n",
       "std             0.345521          118.344679      127.101607\n",
       "min             1.000000           58.000000       61.000000\n",
       "25%             1.000000          361.000000      403.000000\n",
       "50%             1.000000          433.000000      463.000000\n",
       "75%             1.000000          490.000000      526.000000\n",
       "max             3.000000          796.000000      961.000000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['TotalSleepRecords', 'TotalMinutesAsleep', 'TotalTimeInBed']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.pivot_table(df, index='Id', columns='Date', values='TotalMinutesAsleep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1927972279</td>\n",
       "      <td>4/12/2016 12:00:00 AM</td>\n",
       "      <td>3</td>\n",
       "      <td>750</td>\n",
       "      <td>775</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>4388161847</td>\n",
       "      <td>4/24/2016 12:00:00 AM</td>\n",
       "      <td>3</td>\n",
       "      <td>552</td>\n",
       "      <td>595</td>\n",
       "      <td>4/24/2016</td>\n",
       "      <td>43</td>\n",
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       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>6962181067</td>\n",
       "      <td>4/13/2016 12:00:00 AM</td>\n",
       "      <td>3</td>\n",
       "      <td>630</td>\n",
       "      <td>679</td>\n",
       "      <td>4/13/2016</td>\n",
       "      <td>49</td>\n",
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       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "             Id               SleepDay  TotalSleepRecords  TotalMinutesAsleep  \\\n",
       "32   1927972279  4/12/2016 12:00:00 AM                  3                 750   \n",
       "152  4388161847  4/24/2016 12:00:00 AM                  3                 552   \n",
       "307  6962181067  4/13/2016 12:00:00 AM                  3                 630   \n",
       "\n",
       "     TotalTimeInBed       Date  TotalTimeAwake  \n",
       "32              775  4/12/2016              25  \n",
       "152             595  4/24/2016              43  \n",
       "307             679  4/13/2016              49  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['TotalSleepRecords'] == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>160</th>\n",
       "      <td>4388161847</td>\n",
       "      <td>5/5/2016 12:00:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>471</td>\n",
       "      <td>495</td>\n",
       "      <td>5/5/2016</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>4388161847</td>\n",
       "      <td>5/5/2016 12:00:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>471</td>\n",
       "      <td>495</td>\n",
       "      <td>5/5/2016</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Id              SleepDay  TotalSleepRecords  TotalMinutesAsleep  \\\n",
       "160  4388161847  5/5/2016 12:00:00 AM                  1                 471   \n",
       "161  4388161847  5/5/2016 12:00:00 AM                  1                 471   \n",
       "\n",
       "     TotalTimeInBed      Date  TotalTimeAwake  \n",
       "160             495  5/5/2016              24  \n",
       "161             495  5/5/2016              24  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Date'] == '5/5/2016') & (df['Id'] == 4388161847)]"
   ]
  },
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   "cell_type": "code",
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