Day05_01Dec2020/Day05_01Dec2020.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://apssdc.in\"><img src=\"https://camo.githubusercontent.com/e7501c5948d48f88dad8ab2ab6bd448e1cfd6c79/68747470733a2f2f64726976652e676f6f676c652e636f6d2f75633f6578706f72743d646f776e6c6f61642669643d3135414b51365f2d42697857344b366d4c36525070684635454b58715946327a6a\" width=\"800\" align=\"center\"></a>\n",
    "\n",
    "<h1><center>Data Analysis with Pandas</center></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Day Objectives\n",
    "\n",
    "- Importing of Data from Multiple files\n",
    "- Combining/merging of DataFrames (JOIN)\n",
    "- Grouping \n",
    "- Statistics\n",
    "- Sorting data\n",
    "- Data Visualization\n",
    "- Data Cleaning / Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>adult_families_in_shelter</th>\n",
       "      <th>adults_in_families_with_children_in_shelter</th>\n",
       "      <th>children_in_families_with_children_in_shelter</th>\n",
       "      <th>date_of_census</th>\n",
       "      <th>families_with_children_in_shelter</th>\n",
       "      <th>individuals_in_adult_families_in_shelter</th>\n",
       "      <th>single_adult_men_in_shelter</th>\n",
       "      <th>single_adult_women_in_shelter</th>\n",
       "      <th>total_adults_in_shelter</th>\n",
       "      <th>total_children_in_shelter</th>\n",
       "      <th>total_individuals_in_families_with_children_in_shelter_</th>\n",
       "      <th>total_individuals_in_shelter</th>\n",
       "      <th>total_single_adults_in_shelter</th>\n",
       "      <th>individuals_in_shelter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1796</td>\n",
       "      <td>14607</td>\n",
       "      <td>21314</td>\n",
       "      <td>2013-08-21T00:00:00.000</td>\n",
       "      <td>10261</td>\n",
       "      <td>3811.0</td>\n",
       "      <td>7231</td>\n",
       "      <td>2710</td>\n",
       "      <td>28359</td>\n",
       "      <td>21314</td>\n",
       "      <td>35921</td>\n",
       "      <td>49673</td>\n",
       "      <td>9941</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1803</td>\n",
       "      <td>14622</td>\n",
       "      <td>21324</td>\n",
       "      <td>2013-08-22T00:00:00.000</td>\n",
       "      <td>10274</td>\n",
       "      <td>3827.0</td>\n",
       "      <td>7201</td>\n",
       "      <td>2716</td>\n",
       "      <td>28366</td>\n",
       "      <td>21324</td>\n",
       "      <td>35946</td>\n",
       "      <td>49690</td>\n",
       "      <td>9917</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1802</td>\n",
       "      <td>14611</td>\n",
       "      <td>21291</td>\n",
       "      <td>2013-08-23T00:00:00.000</td>\n",
       "      <td>10266</td>\n",
       "      <td>3826.0</td>\n",
       "      <td>7149</td>\n",
       "      <td>2671</td>\n",
       "      <td>28257</td>\n",
       "      <td>21291</td>\n",
       "      <td>35902</td>\n",
       "      <td>49548</td>\n",
       "      <td>9820</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1801</td>\n",
       "      <td>14650</td>\n",
       "      <td>21343</td>\n",
       "      <td>2013-08-24T00:00:00.000</td>\n",
       "      <td>10291</td>\n",
       "      <td>3824.0</td>\n",
       "      <td>7110</td>\n",
       "      <td>2690</td>\n",
       "      <td>28274</td>\n",
       "      <td>21343</td>\n",
       "      <td>35993</td>\n",
       "      <td>49617</td>\n",
       "      <td>9800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1804</td>\n",
       "      <td>14694</td>\n",
       "      <td>21400</td>\n",
       "      <td>2013-08-25T00:00:00.000</td>\n",
       "      <td>10324</td>\n",
       "      <td>3830.0</td>\n",
       "      <td>7230</td>\n",
       "      <td>2704</td>\n",
       "      <td>28458</td>\n",
       "      <td>21400</td>\n",
       "      <td>36094</td>\n",
       "      <td>49858</td>\n",
       "      <td>9934</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   adult_families_in_shelter  adults_in_families_with_children_in_shelter  \\\n",
       "0                       1796                                        14607   \n",
       "1                       1803                                        14622   \n",
       "2                       1802                                        14611   \n",
       "3                       1801                                        14650   \n",
       "4                       1804                                        14694   \n",
       "\n",
       "   children_in_families_with_children_in_shelter           date_of_census  \\\n",
       "0                                          21314  2013-08-21T00:00:00.000   \n",
       "1                                          21324  2013-08-22T00:00:00.000   \n",
       "2                                          21291  2013-08-23T00:00:00.000   \n",
       "3                                          21343  2013-08-24T00:00:00.000   \n",
       "4                                          21400  2013-08-25T00:00:00.000   \n",
       "\n",
       "   families_with_children_in_shelter  \\\n",
       "0                              10261   \n",
       "1                              10274   \n",
       "2                              10266   \n",
       "3                              10291   \n",
       "4                              10324   \n",
       "\n",
       "   individuals_in_adult_families_in_shelter  single_adult_men_in_shelter  \\\n",
       "0                                    3811.0                         7231   \n",
       "1                                    3827.0                         7201   \n",
       "2                                    3826.0                         7149   \n",
       "3                                    3824.0                         7110   \n",
       "4                                    3830.0                         7230   \n",
       "\n",
       "   single_adult_women_in_shelter  total_adults_in_shelter  \\\n",
       "0                           2710                    28359   \n",
       "1                           2716                    28366   \n",
       "2                           2671                    28257   \n",
       "3                           2690                    28274   \n",
       "4                           2704                    28458   \n",
       "\n",
       "   total_children_in_shelter  \\\n",
       "0                      21314   \n",
       "1                      21324   \n",
       "2                      21291   \n",
       "3                      21343   \n",
       "4                      21400   \n",
       "\n",
       "   total_individuals_in_families_with_children_in_shelter_  \\\n",
       "0                                              35921         \n",
       "1                                              35946         \n",
       "2                                              35902         \n",
       "3                                              35993         \n",
       "4                                              36094         \n",
       "\n",
       "   total_individuals_in_shelter  total_single_adults_in_shelter  \\\n",
       "0                         49673                            9941   \n",
       "1                         49690                            9917   \n",
       "2                         49548                            9820   \n",
       "3                         49617                            9800   \n",
       "4                         49858                            9934   \n",
       "\n",
       "   individuals_in_shelter  \n",
       "0                     NaN  \n",
       "1                     NaN  \n",
       "2                     NaN  \n",
       "3                     NaN  \n",
       "4                     NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"dhs_daily_report.csv\", index_col = 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['adult_families_in_shelter',\n",
       "       'adults_in_families_with_children_in_shelter',\n",
       "       'children_in_families_with_children_in_shelter', 'date_of_census',\n",
       "       'families_with_children_in_shelter',\n",
       "       'individuals_in_adult_families_in_shelter',\n",
       "       'single_adult_men_in_shelter', 'single_adult_women_in_shelter',\n",
       "       'total_adults_in_shelter', 'total_children_in_shelter',\n",
       "       'total_individuals_in_families_with_children_in_shelter_',\n",
       "       'total_individuals_in_shelter', 'total_single_adults_in_shelter',\n",
       "       'individuals_in_shelter'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2356"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['adult_families_in_shelter'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "983"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['adult_families_in_shelter'].argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2016-06-06T00:00:00.000'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date_of_census'].iloc[983]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1796"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['adult_families_in_shelter'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['adult_families_in_shelter'].argmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df['adult_families_in_shelter'] == df['adult_families_in_shelter'].min()).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    .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>adult_families_in_shelter</th>\n",
       "      <th>adults_in_families_with_children_in_shelter</th>\n",
       "      <th>children_in_families_with_children_in_shelter</th>\n",
       "      <th>families_with_children_in_shelter</th>\n",
       "      <th>individuals_in_adult_families_in_shelter</th>\n",
       "      <th>single_adult_men_in_shelter</th>\n",
       "      <th>single_adult_women_in_shelter</th>\n",
       "      <th>total_adults_in_shelter</th>\n",
       "      <th>total_children_in_shelter</th>\n",
       "      <th>total_individuals_in_families_with_children_in_shelter_</th>\n",
       "      <th>total_individuals_in_shelter</th>\n",
       "      <th>total_single_adults_in_shelter</th>\n",
       "      <th>individuals_in_shelter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2074.955000</td>\n",
       "      <td>16487.932000</td>\n",
       "      <td>23273.873000</td>\n",
       "      <td>11588.83500</td>\n",
       "      <td>4367.502503</td>\n",
       "      <td>8299.773000</td>\n",
       "      <td>3173.107000</td>\n",
       "      <td>32328.857000</td>\n",
       "      <td>23273.882000</td>\n",
       "      <td>39761.805000</td>\n",
       "      <td>55602.739000</td>\n",
       "      <td>11472.88000</td>\n",
       "      <td>4919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>148.020238</td>\n",
       "      <td>848.363772</td>\n",
       "      <td>926.243984</td>\n",
       "      <td>626.41371</td>\n",
       "      <td>298.694822</td>\n",
       "      <td>766.782607</td>\n",
       "      <td>353.227146</td>\n",
       "      <td>2150.583637</td>\n",
       "      <td>926.247187</td>\n",
       "      <td>1677.972788</td>\n",
       "      <td>2745.294235</td>\n",
       "      <td>1113.66412</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1796.000000</td>\n",
       "      <td>14607.000000</td>\n",
       "      <td>21291.000000</td>\n",
       "      <td>10261.00000</td>\n",
       "      <td>3811.000000</td>\n",
       "      <td>6949.000000</td>\n",
       "      <td>2588.000000</td>\n",
       "      <td>28127.000000</td>\n",
       "      <td>21291.000000</td>\n",
       "      <td>35902.000000</td>\n",
       "      <td>49462.000000</td>\n",
       "      <td>9610.00000</td>\n",
       "      <td>4919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1906.000000</td>\n",
       "      <td>15831.500000</td>\n",
       "      <td>22666.000000</td>\n",
       "      <td>11060.00000</td>\n",
       "      <td>4026.000000</td>\n",
       "      <td>7590.500000</td>\n",
       "      <td>2790.000000</td>\n",
       "      <td>30184.250000</td>\n",
       "      <td>22666.000000</td>\n",
       "      <td>38775.500000</td>\n",
       "      <td>53196.500000</td>\n",
       "      <td>10381.75000</td>\n",
       "      <td>4919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2129.000000</td>\n",
       "      <td>16836.000000</td>\n",
       "      <td>23285.500000</td>\n",
       "      <td>11743.00000</td>\n",
       "      <td>4473.000000</td>\n",
       "      <td>8458.500000</td>\n",
       "      <td>3185.000000</td>\n",
       "      <td>33142.000000</td>\n",
       "      <td>23285.500000</td>\n",
       "      <td>40026.000000</td>\n",
       "      <td>56713.500000</td>\n",
       "      <td>11633.50000</td>\n",
       "      <td>4919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2172.250000</td>\n",
       "      <td>17118.250000</td>\n",
       "      <td>23610.000000</td>\n",
       "      <td>12146.00000</td>\n",
       "      <td>4566.500000</td>\n",
       "      <td>8883.250000</td>\n",
       "      <td>3538.000000</td>\n",
       "      <td>33940.750000</td>\n",
       "      <td>23610.000000</td>\n",
       "      <td>40529.500000</td>\n",
       "      <td>57872.250000</td>\n",
       "      <td>12437.50000</td>\n",
       "      <td>4919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2356.000000</td>\n",
       "      <td>17733.000000</td>\n",
       "      <td>25490.000000</td>\n",
       "      <td>12413.00000</td>\n",
       "      <td>4944.000000</td>\n",
       "      <td>9599.000000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>35294.000000</td>\n",
       "      <td>25490.000000</td>\n",
       "      <td>43208.000000</td>\n",
       "      <td>59068.000000</td>\n",
       "      <td>13270.00000</td>\n",
       "      <td>4919.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       adult_families_in_shelter  adults_in_families_with_children_in_shelter  \\\n",
       "count                1000.000000                                  1000.000000   \n",
       "mean                 2074.955000                                 16487.932000   \n",
       "std                   148.020238                                   848.363772   \n",
       "min                  1796.000000                                 14607.000000   \n",
       "25%                  1906.000000                                 15831.500000   \n",
       "50%                  2129.000000                                 16836.000000   \n",
       "75%                  2172.250000                                 17118.250000   \n",
       "max                  2356.000000                                 17733.000000   \n",
       "\n",
       "       children_in_families_with_children_in_shelter  \\\n",
       "count                                    1000.000000   \n",
       "mean                                    23273.873000   \n",
       "std                                       926.243984   \n",
       "min                                     21291.000000   \n",
       "25%                                     22666.000000   \n",
       "50%                                     23285.500000   \n",
       "75%                                     23610.000000   \n",
       "max                                     25490.000000   \n",
       "\n",
       "       families_with_children_in_shelter  \\\n",
       "count                         1000.00000   \n",
       "mean                         11588.83500   \n",
       "std                            626.41371   \n",
       "min                          10261.00000   \n",
       "25%                          11060.00000   \n",
       "50%                          11743.00000   \n",
       "75%                          12146.00000   \n",
       "max                          12413.00000   \n",
       "\n",
       "       individuals_in_adult_families_in_shelter  single_adult_men_in_shelter  \\\n",
       "count                                999.000000                  1000.000000   \n",
       "mean                                4367.502503                  8299.773000   \n",
       "std                                  298.694822                   766.782607   \n",
       "min                                 3811.000000                  6949.000000   \n",
       "25%                                 4026.000000                  7590.500000   \n",
       "50%                                 4473.000000                  8458.500000   \n",
       "75%                                 4566.500000                  8883.250000   \n",
       "max                                 4944.000000                  9599.000000   \n",
       "\n",
       "       single_adult_women_in_shelter  total_adults_in_shelter  \\\n",
       "count                    1000.000000              1000.000000   \n",
       "mean                     3173.107000             32328.857000   \n",
       "std                       353.227146              2150.583637   \n",
       "min                      2588.000000             28127.000000   \n",
       "25%                      2790.000000             30184.250000   \n",
       "50%                      3185.000000             33142.000000   \n",
       "75%                      3538.000000             33940.750000   \n",
       "max                      3779.000000             35294.000000   \n",
       "\n",
       "       total_children_in_shelter  \\\n",
       "count                1000.000000   \n",
       "mean                23273.882000   \n",
       "std                   926.247187   \n",
       "min                 21291.000000   \n",
       "25%                 22666.000000   \n",
       "50%                 23285.500000   \n",
       "75%                 23610.000000   \n",
       "max                 25490.000000   \n",
       "\n",
       "       total_individuals_in_families_with_children_in_shelter_  \\\n",
       "count                                        1000.000000         \n",
       "mean                                        39761.805000         \n",
       "std                                          1677.972788         \n",
       "min                                         35902.000000         \n",
       "25%                                         38775.500000         \n",
       "50%                                         40026.000000         \n",
       "75%                                         40529.500000         \n",
       "max                                         43208.000000         \n",
       "\n",
       "       total_individuals_in_shelter  total_single_adults_in_shelter  \\\n",
       "count                   1000.000000                      1000.00000   \n",
       "mean                   55602.739000                     11472.88000   \n",
       "std                     2745.294235                      1113.66412   \n",
       "min                    49462.000000                      9610.00000   \n",
       "25%                    53196.500000                     10381.75000   \n",
       "50%                    56713.500000                     11633.50000   \n",
       "75%                    57872.250000                     12437.50000   \n",
       "max                    59068.000000                     13270.00000   \n",
       "\n",
       "       individuals_in_shelter  \n",
       "count                     1.0  \n",
       "mean                   4919.0  \n",
       "std                       NaN  \n",
       "min                    4919.0  \n",
       "25%                    4919.0  \n",
       "50%                    4919.0  \n",
       "75%                    4919.0  \n",
       "max                    4919.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1000 entries, 0 to 999\n",
      "Data columns (total 14 columns):\n",
      " #   Column                                                   Non-Null Count  Dtype  \n",
      "---  ------                                                   --------------  -----  \n",
      " 0   adult_families_in_shelter                                1000 non-null   int64  \n",
      " 1   adults_in_families_with_children_in_shelter              1000 non-null   int64  \n",
      " 2   children_in_families_with_children_in_shelter            1000 non-null   int64  \n",
      " 3   date_of_census                                           1000 non-null   object \n",
      " 4   families_with_children_in_shelter                        1000 non-null   int64  \n",
      " 5   individuals_in_adult_families_in_shelter                 999 non-null    float64\n",
      " 6   single_adult_men_in_shelter                              1000 non-null   int64  \n",
      " 7   single_adult_women_in_shelter                            1000 non-null   int64  \n",
      " 8   total_adults_in_shelter                                  1000 non-null   int64  \n",
      " 9   total_children_in_shelter                                1000 non-null   int64  \n",
      " 10  total_individuals_in_families_with_children_in_shelter_  1000 non-null   int64  \n",
      " 11  total_individuals_in_shelter                             1000 non-null   int64  \n",
      " 12  total_single_adults_in_shelter                           1000 non-null   int64  \n",
      " 13  individuals_in_shelter                                   1 non-null      float64\n",
      "dtypes: float64(2), int64(11), object(1)\n",
      "memory usage: 117.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>adult_families_in_shelter</th>\n",
       "      <th>adults_in_families_with_children_in_shelter</th>\n",
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       "    <tr>\n",
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       "      <td>1803</td>\n",
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       "    <tr>\n",
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       "      <td>1802</td>\n",
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       "      <td>49548</td>\n",
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       "    <tr>\n",
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       "      <td>1801</td>\n",
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       "      <td>7110</td>\n",
       "      <td>2690</td>\n",
       "      <td>28274</td>\n",
       "      <td>21343</td>\n",
       "      <td>35993</td>\n",
       "      <td>49617</td>\n",
       "      <td>9800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1804</td>\n",
       "      <td>14694</td>\n",
       "      <td>21400</td>\n",
       "      <td>2013-08-25T00:00:00.000</td>\n",
       "      <td>10324</td>\n",
       "      <td>3830.0</td>\n",
       "      <td>7230</td>\n",
       "      <td>2704</td>\n",
       "      <td>28458</td>\n",
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       "      <td>36094</td>\n",
       "      <td>49858</td>\n",
       "      <td>9934</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   adult_families_in_shelter  adults_in_families_with_children_in_shelter  \\\n",
       "0                       1796                                        14607   \n",
       "1                       1803                                        14622   \n",
       "2                       1802                                        14611   \n",
       "3                       1801                                        14650   \n",
       "4                       1804                                        14694   \n",
       "\n",
       "   children_in_families_with_children_in_shelter           date_of_census  \\\n",
       "0                                          21314  2013-08-21T00:00:00.000   \n",
       "1                                          21324  2013-08-22T00:00:00.000   \n",
       "2                                          21291  2013-08-23T00:00:00.000   \n",
       "3                                          21343  2013-08-24T00:00:00.000   \n",
       "4                                          21400  2013-08-25T00:00:00.000   \n",
       "\n",
       "   families_with_children_in_shelter  \\\n",
       "0                              10261   \n",
       "1                              10274   \n",
       "2                              10266   \n",
       "3                              10291   \n",
       "4                              10324   \n",
       "\n",
       "   individuals_in_adult_families_in_shelter  single_adult_men_in_shelter  \\\n",
       "0                                    3811.0                         7231   \n",
       "1                                    3827.0                         7201   \n",
       "2                                    3826.0                         7149   \n",
       "3                                    3824.0                         7110   \n",
       "4                                    3830.0                         7230   \n",
       "\n",
       "   single_adult_women_in_shelter  total_adults_in_shelter  \\\n",
       "0                           2710                    28359   \n",
       "1                           2716                    28366   \n",
       "2                           2671                    28257   \n",
       "3                           2690                    28274   \n",
       "4                           2704                    28458   \n",
       "\n",
       "   total_children_in_shelter  \\\n",
       "0                      21314   \n",
       "1                      21324   \n",
       "2                      21291   \n",
       "3                      21343   \n",
       "4                      21400   \n",
       "\n",
       "   total_individuals_in_families_with_children_in_shelter_  \\\n",
       "0                                              35921         \n",
       "1                                              35946         \n",
       "2                                              35902         \n",
       "3                                              35993         \n",
       "4                                              36094         \n",
       "\n",
       "   total_individuals_in_shelter  total_single_adults_in_shelter  \\\n",
       "0                         49673                            9941   \n",
       "1                         49690                            9917   \n",
       "2                         49548                            9820   \n",
       "3                         49617                            9800   \n",
       "4                         49858                            9934   \n",
       "\n",
       "   individuals_in_shelter  \n",
       "0                     NaN  \n",
       "1                     NaN  \n",
       "2                     NaN  \n",
       "3                     NaN  \n",
       "4                     NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['date_of_census'] = df['date_of_census'].astype('datetime64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>49617</td>\n",
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       "      <th>2</th>\n",
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       "      <td>14611</td>\n",
       "      <td>21291</td>\n",
       "      <td>2013-08-23</td>\n",
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       "      <td>35946</td>\n",
       "      <td>49690</td>\n",
       "      <td>9917</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1803</td>\n",
       "      <td>14647</td>\n",
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       "      <td>2013-08-28</td>\n",
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       "    <tr>\n",
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       "      <td>2353</td>\n",
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       "      <td>39811</td>\n",
       "      <td>57913</td>\n",
       "      <td>13160</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>2356</td>\n",
       "      <td>17152</td>\n",
       "      <td>22649</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>12339</td>\n",
       "      <td>4944.0</td>\n",
       "      <td>9366</td>\n",
       "      <td>3728</td>\n",
       "      <td>35190</td>\n",
       "      <td>22649</td>\n",
       "      <td>39801</td>\n",
       "      <td>57839</td>\n",
       "      <td>13094</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     adult_families_in_shelter  adults_in_families_with_children_in_shelter  \\\n",
       "0                         1796                                        14607   \n",
       "3                         1801                                        14650   \n",
       "2                         1802                                        14611   \n",
       "1                         1803                                        14622   \n",
       "7                         1803                                        14647   \n",
       "..                         ...                                          ...   \n",
       "997                       2352                                        17202   \n",
       "986                       2353                                        17186   \n",
       "984                       2353                                        17125   \n",
       "985                       2355                                        17145   \n",
       "983                       2356                                        17152   \n",
       "\n",
       "     children_in_families_with_children_in_shelter date_of_census  \\\n",
       "0                                            21314     2013-08-21   \n",
       "3                                            21343     2013-08-24   \n",
       "2                                            21291     2013-08-23   \n",
       "1                                            21324     2013-08-22   \n",
       "7                                            21325     2013-08-28   \n",
       "..                                             ...            ...   \n",
       "997                                          22666     2016-06-20   \n",
       "986                                          22707     2016-06-09   \n",
       "984                                          22617     2016-06-07   \n",
       "985                                          22666     2016-06-08   \n",
       "983                                          22649     2016-06-06   \n",
       "\n",
       "     families_with_children_in_shelter  \\\n",
       "0                                10261   \n",
       "3                                10291   \n",
       "2                                10266   \n",
       "1                                10274   \n",
       "7                                10284   \n",
       "..                                 ...   \n",
       "997                              12385   \n",
       "986                              12382   \n",
       "984                              12336   \n",
       "985                              12358   \n",
       "983                              12339   \n",
       "\n",
       "     individuals_in_adult_families_in_shelter  single_adult_men_in_shelter  \\\n",
       "0                                      3811.0                         7231   \n",
       "3                                      3824.0                         7110   \n",
       "2                                      3826.0                         7149   \n",
       "1                                      3827.0                         7201   \n",
       "7                                      3831.0                         7229   \n",
       "..                                        ...                          ...   \n",
       "997                                    4931.0                         9367   \n",
       "986                                    4937.0                         9383   \n",
       "984                                    4941.0                         9358   \n",
       "985                                    4942.0                         9404   \n",
       "983                                    4944.0                         9366   \n",
       "\n",
       "     single_adult_women_in_shelter  total_adults_in_shelter  \\\n",
       "0                             2710                    28359   \n",
       "3                             2690                    28274   \n",
       "2                             2671                    28257   \n",
       "1                             2716                    28366   \n",
       "7                             2721                    28428   \n",
       "..                             ...                      ...   \n",
       "997                           3758                    35258   \n",
       "986                           3744                    35250   \n",
       "984                           3733                    35157   \n",
       "985                           3756                    35247   \n",
       "983                           3728                    35190   \n",
       "\n",
       "     total_children_in_shelter  \\\n",
       "0                        21314   \n",
       "3                        21343   \n",
       "2                        21291   \n",
       "1                        21324   \n",
       "7                        21325   \n",
       "..                         ...   \n",
       "997                      22666   \n",
       "986                      22707   \n",
       "984                      22617   \n",
       "985                      22666   \n",
       "983                      22649   \n",
       "\n",
       "     total_individuals_in_families_with_children_in_shelter_  \\\n",
       "0                                                35921         \n",
       "3                                                35993         \n",
       "2                                                35902         \n",
       "1                                                35946         \n",
       "7                                                35972         \n",
       "..                                                 ...         \n",
       "997                                              39868         \n",
       "986                                              39893         \n",
       "984                                              39742         \n",
       "985                                              39811         \n",
       "983                                              39801         \n",
       "\n",
       "     total_individuals_in_shelter  total_single_adults_in_shelter  \\\n",
       "0                           49673                            9941   \n",
       "3                           49617                            9800   \n",
       "2                           49548                            9820   \n",
       "1                           49690                            9917   \n",
       "7                           49753                            9950   \n",
       "..                            ...                             ...   \n",
       "997                         57924                           13125   \n",
       "986                         57957                           13127   \n",
       "984                         57774                           13091   \n",
       "985                         57913                           13160   \n",
       "983                         57839                           13094   \n",
       "\n",
       "     individuals_in_shelter  \n",
       "0                       NaN  \n",
       "3                       NaN  \n",
       "2                       NaN  \n",
       "1                       NaN  \n",
       "7                       NaN  \n",
       "..                      ...  \n",
       "997                     NaN  \n",
       "986                     NaN  \n",
       "984                     NaN  \n",
       "985                     NaN  \n",
       "983                     NaN  \n",
       "\n",
       "[1000 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('adult_families_in_shelter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method sort_values in module pandas.core.frame:\n",
      "\n",
      "sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False) method of pandas.core.frame.DataFrame instance\n",
      "    Sort by the values along either axis.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "            by : str or list of str\n",
      "                Name or list of names to sort by.\n",
      "    \n",
      "                - if `axis` is 0 or `'index'` then `by` may contain index\n",
      "                  levels and/or column labels.\n",
      "                - if `axis` is 1 or `'columns'` then `by` may contain column\n",
      "                  levels and/or index labels.\n",
      "    \n",
      "                .. versionchanged:: 0.23.0\n",
      "    \n",
      "                   Allow specifying index or column level names.\n",
      "    axis : {0 or 'index', 1 or 'columns'}, default 0\n",
      "         Axis to be sorted.\n",
      "    ascending : bool or list of bool, default True\n",
      "         Sort ascending vs. descending. Specify list for multiple sort\n",
      "         orders.  If this is a list of bools, must match the length of\n",
      "         the by.\n",
      "    inplace : bool, default False\n",
      "         If True, perform operation in-place.\n",
      "    kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort'\n",
      "         Choice of sorting algorithm. See also ndarray.np.sort for more\n",
      "         information.  `mergesort` is the only stable algorithm. For\n",
      "         DataFrames, this option is only applied when sorting on a single\n",
      "         column or label.\n",
      "    na_position : {'first', 'last'}, default 'last'\n",
      "         Puts NaNs at the beginning if `first`; `last` puts NaNs at the\n",
      "         end.\n",
      "    ignore_index : bool, default False\n",
      "         If True, the resulting axis will be labeled 0, 1, …, n - 1.\n",
      "    \n",
      "         .. versionadded:: 1.0.0\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    sorted_obj : DataFrame or None\n",
      "        DataFrame with sorted values if inplace=False, None otherwise.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> df = pd.DataFrame({\n",
      "    ...     'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],\n",
      "    ...     'col2': [2, 1, 9, 8, 7, 4],\n",
      "    ...     'col3': [0, 1, 9, 4, 2, 3],\n",
      "    ... })\n",
      "    >>> df\n",
      "        col1 col2 col3\n",
      "    0   A    2    0\n",
      "    1   A    1    1\n",
      "    2   B    9    9\n",
      "    3   NaN  8    4\n",
      "    4   D    7    2\n",
      "    5   C    4    3\n",
      "    \n",
      "    Sort by col1\n",
      "    \n",
      "    >>> df.sort_values(by=['col1'])\n",
      "        col1 col2 col3\n",
      "    0   A    2    0\n",
      "    1   A    1    1\n",
      "    2   B    9    9\n",
      "    5   C    4    3\n",
      "    4   D    7    2\n",
      "    3   NaN  8    4\n",
      "    \n",
      "    Sort by multiple columns\n",
      "    \n",
      "    >>> df.sort_values(by=['col1', 'col2'])\n",
      "        col1 col2 col3\n",
      "    1   A    1    1\n",
      "    0   A    2    0\n",
      "    2   B    9    9\n",
      "    5   C    4    3\n",
      "    4   D    7    2\n",
      "    3   NaN  8    4\n",
      "    \n",
      "    Sort Descending\n",
      "    \n",
      "    >>> df.sort_values(by='col1', ascending=False)\n",
      "        col1 col2 col3\n",
      "    4   D    7    2\n",
      "    5   C    4    3\n",
      "    2   B    9    9\n",
      "    0   A    2    0\n",
      "    1   A    1    1\n",
      "    3   NaN  8    4\n",
      "    \n",
      "    Putting NAs first\n",
      "    \n",
      "    >>> df.sort_values(by='col1', ascending=False, na_position='first')\n",
      "        col1 col2 col3\n",
      "    3   NaN  8    4\n",
      "    4   D    7    2\n",
      "    5   C    4    3\n",
      "    2   B    9    9\n",
      "    0   A    2    0\n",
      "    1   A    1    1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(df.sort_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df2 = df.sort_values(['adult_families_in_shelter', 'children_in_families_with_children_in_shelter'], ascending = [True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adult_families_in_shelter</th>\n",
       "      <th>adults_in_families_with_children_in_shelter</th>\n",
       "      <th>children_in_families_with_children_in_shelter</th>\n",
       "      <th>date_of_census</th>\n",
       "      <th>families_with_children_in_shelter</th>\n",
       "      <th>individuals_in_adult_families_in_shelter</th>\n",
       "      <th>single_adult_men_in_shelter</th>\n",
       "      <th>single_adult_women_in_shelter</th>\n",
       "      <th>total_adults_in_shelter</th>\n",
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       "      <td>7229</td>\n",
       "      <td>2721</td>\n",
       "      <td>28428</td>\n",
       "      <td>21325</td>\n",
       "      <td>35972</td>\n",
       "      <td>49753</td>\n",
       "      <td>9950</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1803</td>\n",
       "      <td>14622</td>\n",
       "      <td>21324</td>\n",
       "      <td>2013-08-22</td>\n",
       "      <td>10274</td>\n",
       "      <td>3827.0</td>\n",
       "      <td>7201</td>\n",
       "      <td>2716</td>\n",
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       "      <td>21324</td>\n",
       "      <td>35946</td>\n",
       "      <td>49690</td>\n",
       "      <td>9917</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   adult_families_in_shelter  adults_in_families_with_children_in_shelter  \\\n",
       "0                       1796                                        14607   \n",
       "3                       1801                                        14650   \n",
       "2                       1802                                        14611   \n",
       "7                       1803                                        14647   \n",
       "1                       1803                                        14622   \n",
       "\n",
       "   children_in_families_with_children_in_shelter date_of_census  \\\n",
       "0                                          21314     2013-08-21   \n",
       "3                                          21343     2013-08-24   \n",
       "2                                          21291     2013-08-23   \n",
       "7                                          21325     2013-08-28   \n",
       "1                                          21324     2013-08-22   \n",
       "\n",
       "   families_with_children_in_shelter  \\\n",
       "0                              10261   \n",
       "3                              10291   \n",
       "2                              10266   \n",
       "7                              10284   \n",
       "1                              10274   \n",
       "\n",
       "   individuals_in_adult_families_in_shelter  single_adult_men_in_shelter  \\\n",
       "0                                    3811.0                         7231   \n",
       "3                                    3824.0                         7110   \n",
       "2                                    3826.0                         7149   \n",
       "7                                    3831.0                         7229   \n",
       "1                                    3827.0                         7201   \n",
       "\n",
       "   single_adult_women_in_shelter  total_adults_in_shelter  \\\n",
       "0                           2710                    28359   \n",
       "3                           2690                    28274   \n",
       "2                           2671                    28257   \n",
       "7                           2721                    28428   \n",
       "1                           2716                    28366   \n",
       "\n",
       "   total_children_in_shelter  \\\n",
       "0                      21314   \n",
       "3                      21343   \n",
       "2                      21291   \n",
       "7                      21325   \n",
       "1                      21324   \n",
       "\n",
       "   total_individuals_in_families_with_children_in_shelter_  \\\n",
       "0                                              35921         \n",
       "3                                              35993         \n",
       "2                                              35902         \n",
       "7                                              35972         \n",
       "1                                              35946         \n",
       "\n",
       "   total_individuals_in_shelter  total_single_adults_in_shelter  \\\n",
       "0                         49673                            9941   \n",
       "3                         49617                            9800   \n",
       "2                         49548                            9820   \n",
       "7                         49753                            9950   \n",
       "1                         49690                            9917   \n",
       "\n",
       "   individuals_in_shelter  \n",
       "0                     NaN  \n",
       "3                     NaN  \n",
       "2                     NaN  \n",
       "7                     NaN  \n",
       "1                     NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>22629</td>\n",
       "      <td>39802</td>\n",
       "      <td>57839</td>\n",
       "      <td>13114</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2352</td>\n",
       "      <td>17202</td>\n",
       "      <td>22666</td>\n",
       "      <td>2016-06-20</td>\n",
       "      <td>12385</td>\n",
       "      <td>4931.0</td>\n",
       "      <td>9367</td>\n",
       "      <td>3758</td>\n",
       "      <td>35258</td>\n",
       "      <td>22666</td>\n",
       "      <td>39868</td>\n",
       "      <td>57924</td>\n",
       "      <td>13125</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2347</td>\n",
       "      <td>17219</td>\n",
       "      <td>22668</td>\n",
       "      <td>2016-06-19</td>\n",
       "      <td>12398</td>\n",
       "      <td>4922.0</td>\n",
       "      <td>9314</td>\n",
       "      <td>3750</td>\n",
       "      <td>35205</td>\n",
       "      <td>22668</td>\n",
       "      <td>39887</td>\n",
       "      <td>57873</td>\n",
       "      <td>13064</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2341</td>\n",
       "      <td>17223</td>\n",
       "      <td>22672</td>\n",
       "      <td>2016-06-18</td>\n",
       "      <td>12398</td>\n",
       "      <td>4910.0</td>\n",
       "      <td>9257</td>\n",
       "      <td>3717</td>\n",
       "      <td>35107</td>\n",
       "      <td>22672</td>\n",
       "      <td>39895</td>\n",
       "      <td>57779</td>\n",
       "      <td>12974</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1804</td>\n",
       "      <td>14694</td>\n",
       "      <td>21400</td>\n",
       "      <td>2013-08-25</td>\n",
       "      <td>10324</td>\n",
       "      <td>3830.0</td>\n",
       "      <td>7230</td>\n",
       "      <td>2704</td>\n",
       "      <td>28458</td>\n",
       "      <td>21400</td>\n",
       "      <td>36094</td>\n",
       "      <td>49858</td>\n",
       "      <td>9934</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1801</td>\n",
       "      <td>14650</td>\n",
       "      <td>21343</td>\n",
       "      <td>2013-08-24</td>\n",
       "      <td>10291</td>\n",
       "      <td>3824.0</td>\n",
       "      <td>7110</td>\n",
       "      <td>2690</td>\n",
       "      <td>28274</td>\n",
       "      <td>21343</td>\n",
       "      <td>35993</td>\n",
       "      <td>49617</td>\n",
       "      <td>9800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1802</td>\n",
       "      <td>14611</td>\n",
       "      <td>21291</td>\n",
       "      <td>2013-08-23</td>\n",
       "      <td>10266</td>\n",
       "      <td>3826.0</td>\n",
       "      <td>7149</td>\n",
       "      <td>2671</td>\n",
       "      <td>28257</td>\n",
       "      <td>21291</td>\n",
       "      <td>35902</td>\n",
       "      <td>49548</td>\n",
       "      <td>9820</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1803</td>\n",
       "      <td>14622</td>\n",
       "      <td>21324</td>\n",
       "      <td>2013-08-22</td>\n",
       "      <td>10274</td>\n",
       "      <td>3827.0</td>\n",
       "      <td>7201</td>\n",
       "      <td>2716</td>\n",
       "      <td>28366</td>\n",
       "      <td>21324</td>\n",
       "      <td>35946</td>\n",
       "      <td>49690</td>\n",
       "      <td>9917</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1796</td>\n",
       "      <td>14607</td>\n",
       "      <td>21314</td>\n",
       "      <td>2013-08-21</td>\n",
       "      <td>10261</td>\n",
       "      <td>3811.0</td>\n",
       "      <td>7231</td>\n",
       "      <td>2710</td>\n",
       "      <td>28359</td>\n",
       "      <td>21314</td>\n",
       "      <td>35921</td>\n",
       "      <td>49673</td>\n",
       "      <td>9941</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     adult_families_in_shelter  adults_in_families_with_children_in_shelter  \\\n",
       "999                       2346                                        17166   \n",
       "998                       2347                                        17173   \n",
       "997                       2352                                        17202   \n",
       "996                       2347                                        17219   \n",
       "995                       2341                                        17223   \n",
       "..                         ...                                          ...   \n",
       "4                         1804                                        14694   \n",
       "3                         1801                                        14650   \n",
       "2                         1802                                        14611   \n",
       "1                         1803                                        14622   \n",
       "0                         1796                                        14607   \n",
       "\n",
       "     children_in_families_with_children_in_shelter date_of_census  \\\n",
       "999                                          22614     2016-06-22   \n",
       "998                                          22629     2016-06-21   \n",
       "997                                          22666     2016-06-20   \n",
       "996                                          22668     2016-06-19   \n",
       "995                                          22672     2016-06-18   \n",
       "..                                             ...            ...   \n",
       "4                                            21400     2013-08-25   \n",
       "3                                            21343     2013-08-24   \n",
       "2                                            21291     2013-08-23   \n",
       "1                                            21324     2013-08-22   \n",
       "0                                            21314     2013-08-21   \n",
       "\n",
       "     families_with_children_in_shelter  \\\n",
       "999                              12359   \n",
       "998                              12360   \n",
       "997                              12385   \n",
       "996                              12398   \n",
       "995                              12398   \n",
       "..                                 ...   \n",
       "4                                10324   \n",
       "3                                10291   \n",
       "2                                10266   \n",
       "1                                10274   \n",
       "0                                10261   \n",
       "\n",
       "     individuals_in_adult_families_in_shelter  single_adult_men_in_shelter  \\\n",
       "999                                       NaN                         9309   \n",
       "998                                    4923.0                         9355   \n",
       "997                                    4931.0                         9367   \n",
       "996                                    4922.0                         9314   \n",
       "995                                    4910.0                         9257   \n",
       "..                                        ...                          ...   \n",
       "4                                      3830.0                         7230   \n",
       "3                                      3824.0                         7110   \n",
       "2                                      3826.0                         7149   \n",
       "1                                      3827.0                         7201   \n",
       "0                                      3811.0                         7231   \n",
       "\n",
       "     single_adult_women_in_shelter  total_adults_in_shelter  \\\n",
       "999                           3779                    35173   \n",
       "998                           3759                    35210   \n",
       "997                           3758                    35258   \n",
       "996                           3750                    35205   \n",
       "995                           3717                    35107   \n",
       "..                             ...                      ...   \n",
       "4                             2704                    28458   \n",
       "3                             2690                    28274   \n",
       "2                             2671                    28257   \n",
       "1                             2716                    28366   \n",
       "0                             2710                    28359   \n",
       "\n",
       "     total_children_in_shelter  \\\n",
       "999                      22614   \n",
       "998                      22629   \n",
       "997                      22666   \n",
       "996                      22668   \n",
       "995                      22672   \n",
       "..                         ...   \n",
       "4                        21400   \n",
       "3                        21343   \n",
       "2                        21291   \n",
       "1                        21324   \n",
       "0                        21314   \n",
       "\n",
       "     total_individuals_in_families_with_children_in_shelter_  \\\n",
       "999                                              39780         \n",
       "998                                              39802         \n",
       "997                                              39868         \n",
       "996                                              39887         \n",
       "995                                              39895         \n",
       "..                                                 ...         \n",
       "4                                                36094         \n",
       "3                                                35993         \n",
       "2                                                35902         \n",
       "1                                                35946         \n",
       "0                                                35921         \n",
       "\n",
       "     total_individuals_in_shelter  total_single_adults_in_shelter  \\\n",
       "999                         57787                           13088   \n",
       "998                         57839                           13114   \n",
       "997                         57924                           13125   \n",
       "996                         57873                           13064   \n",
       "995                         57779                           12974   \n",
       "..                            ...                             ...   \n",
       "4                           49858                            9934   \n",
       "3                           49617                            9800   \n",
       "2                           49548                            9820   \n",
       "1                           49690                            9917   \n",
       "0                           49673                            9941   \n",
       "\n",
       "     individuals_in_shelter  \n",
       "999                  4919.0  \n",
       "998                     NaN  \n",
       "997                     NaN  \n",
       "996                     NaN  \n",
       "995                     NaN  \n",
       "..                      ...  \n",
       "4                       NaN  \n",
       "3                       NaN  \n",
       "2                       NaN  \n",
       "1                       NaN  \n",
       "0                       NaN  \n",
       "\n",
       "[1000 rows x 14 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "d1 = pd.read_excel(\"Day01_25Nov2020.xls\", skiprows = 6)\n",
    "d2 = pd.read_excel(\"Day02_26Nov2020.xls\", skiprows = 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((75, 7), (70, 7))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1.shape, d2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cdf = pd.concat([d1, d2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145, 7)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cdf = pd.concat([d1, d2], axis = 'columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75, 14)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Name</th>\n",
       "      <th>Email Address</th>\n",
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       "      <th>Time in Session (minutes)</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Name</th>\n",
       "      <th>Email Address</th>\n",
       "      <th>Join Time</th>\n",
       "      <th>Leave Time</th>\n",
       "      <th>Time in Session (minutes)</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17X41A1202-Sai Harini-SRKIT</td>\n",
       "      <td>harini.akkineni@outlook.com</td>\n",
       "      <td>9:36 AM</td>\n",
       "      <td>12:14 PM</td>\n",
       "      <td>158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17X41A1202-Sai Harini-SRKIT</td>\n",
       "      <td>harini.akkineni@outlook.com</td>\n",
       "      <td>9:28 AM</td>\n",
       "      <td>12:21 PM</td>\n",
       "      <td>173.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17X41A1203-Tejaswini Alapati-SRKIT</td>\n",
       "      <td>alapatitejaswini999@gmail.com</td>\n",
       "      <td>9:29 AM</td>\n",
       "      <td>12:13 PM</td>\n",
       "      <td>164</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17X41A1203-Tejaswini Alapati-SRKIT</td>\n",
       "      <td>alapatitejaswini999@gmail.com</td>\n",
       "      <td>9:31 AM</td>\n",
       "      <td>12:20 PM</td>\n",
       "      <td>169.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17X41A1204-Karthik-X4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9:19 AM</td>\n",
       "      <td>12:14 PM</td>\n",
       "      <td>174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17X41A1204-Karthik-X4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9:26 AM</td>\n",
       "      <td>12:21 PM</td>\n",
       "      <td>174.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17X41A1207-Bollu Bhavana-X4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9:30 AM</td>\n",
       "      <td>11:52 AM</td>\n",
       "      <td>142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17X41A1207-Bollu Bhavana-X4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9:25 AM</td>\n",
       "      <td>12:20 PM</td>\n",
       "      <td>174.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17X41A1207-Bollu Bhavana-X4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11:59 AM</td>\n",
       "      <td>12:13 PM</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17X41A1208-Bhuvaneswari Bonthu-SRKIT</td>\n",
       "      <td>bhuvanabonthu123@gmail.com</td>\n",
       "      <td>9:29 AM</td>\n",
       "      <td>12:12 PM</td>\n",
       "      <td>162.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Name                  Email Address  \\\n",
       "0         17X41A1202-Sai Harini-SRKIT    harini.akkineni@outlook.com   \n",
       "1  17X41A1203-Tejaswini Alapati-SRKIT  alapatitejaswini999@gmail.com   \n",
       "2               17X41A1204-Karthik-X4                            NaN   \n",
       "3         17X41A1207-Bollu Bhavana-X4                            NaN   \n",
       "4         17X41A1207-Bollu Bhavana-X4                            NaN   \n",
       "\n",
       "  Join Time Leave Time  Time in Session (minutes)  Unnamed: 5  Unnamed: 6  \\\n",
       "0   9:36 AM   12:14 PM                        158         NaN         NaN   \n",
       "1   9:29 AM   12:13 PM                        164         NaN         NaN   \n",
       "2   9:19 AM   12:14 PM                        174         NaN         NaN   \n",
       "3   9:30 AM   11:52 AM                        142         NaN         NaN   \n",
       "4  11:59 AM   12:13 PM                         14         NaN         NaN   \n",
       "\n",
       "                                   Name                  Email Address  \\\n",
       "0           17X41A1202-Sai Harini-SRKIT    harini.akkineni@outlook.com   \n",
       "1    17X41A1203-Tejaswini Alapati-SRKIT  alapatitejaswini999@gmail.com   \n",
       "2                 17X41A1204-Karthik-X4                            NaN   \n",
       "3           17X41A1207-Bollu Bhavana-X4                            NaN   \n",
       "4  17X41A1208-Bhuvaneswari Bonthu-SRKIT     bhuvanabonthu123@gmail.com   \n",
       "\n",
       "  Join Time Leave Time  Time in Session (minutes)  Unnamed: 5  Unnamed: 6  \n",
       "0   9:28 AM   12:21 PM                      173.0         NaN         NaN  \n",
       "1   9:31 AM   12:20 PM                      169.0         NaN         NaN  \n",
       "2   9:26 AM   12:21 PM                      174.0         NaN         NaN  \n",
       "3   9:25 AM   12:20 PM                      174.0         NaN         NaN  \n",
       "4   9:29 AM   12:12 PM                      162.0         NaN         NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Leave Time</th>\n",
       "      <th>Time in Session (minutes)</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Name</th>\n",
       "      <th>Email Address</th>\n",
       "      <th>Join Time</th>\n",
       "      <th>Leave Time</th>\n",
       "      <th>Time in Session (minutes)</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Vikram Parmar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9:29 AM</td>\n",
       "      <td>9:30 AM</td>\n",
       "      <td>0</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>71</th>\n",
       "      <td>Vikram Parmar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9:53 AM</td>\n",
       "      <td>9:54 AM</td>\n",
       "      <td>1</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>72</th>\n",
       "      <td>Yashwanth</td>\n",
       "      <td>rajyashwanth27@gmail.com</td>\n",
       "      <td>9:31 AM</td>\n",
       "      <td>9:32 AM</td>\n",
       "      <td>0</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>73</th>\n",
       "      <td>navya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9:26 AM</td>\n",
       "      <td>9:29 AM</td>\n",
       "      <td>3</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>74</th>\n",
       "      <td>navya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9:29 AM</td>\n",
       "      <td>12:18 PM</td>\n",
       "      <td>168</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name             Email Address Join Time Leave Time  \\\n",
       "70  Vikram Parmar                       NaN   9:29 AM    9:30 AM   \n",
       "71  Vikram Parmar                       NaN   9:53 AM    9:54 AM   \n",
       "72      Yashwanth  rajyashwanth27@gmail.com   9:31 AM    9:32 AM   \n",
       "73          navya                       NaN   9:26 AM    9:29 AM   \n",
       "74          navya                       NaN   9:29 AM   12:18 PM   \n",
       "\n",
       "    Time in Session (minutes)  Unnamed: 5  Unnamed: 6 Name Email Address  \\\n",
       "70                          0         NaN         NaN  NaN           NaN   \n",
       "71                          1         NaN         NaN  NaN           NaN   \n",
       "72                          0         NaN         NaN  NaN           NaN   \n",
       "73                          3         NaN         NaN  NaN           NaN   \n",
       "74                        168         NaN         NaN  NaN           NaN   \n",
       "\n",
       "   Join Time Leave Time  Time in Session (minutes)  Unnamed: 5  Unnamed: 6  \n",
       "70       NaN        NaN                        NaN         NaN         NaN  \n",
       "71       NaN        NaN                        NaN         NaN         NaN  \n",
       "72       NaN        NaN                        NaN         NaN         NaN  \n",
       "73       NaN        NaN                        NaN         NaN         NaN  \n",
       "74       NaN        NaN                        NaN         NaN         NaN  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Email Address_x</th>\n",
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       "      <th>Email Address_y</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17X41A1202-Sai Harini-SRKIT</td>\n",
       "      <td>harini.akkineni@outlook.com</td>\n",
       "      <td>9:36 AM</td>\n",
       "      <td>12:14 PM</td>\n",
       "      <td>158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>harini.akkineni@outlook.com</td>\n",
       "      <td>9:28 AM</td>\n",
       "      <td>12:21 PM</td>\n",
       "      <td>173</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17X41A1203-Tejaswini Alapati-SRKIT</td>\n",
       "      <td>alapatitejaswini999@gmail.com</td>\n",
       "      <td>9:29 AM</td>\n",
       "      <td>12:13 PM</td>\n",
       "      <td>164</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alapatitejaswini999@gmail.com</td>\n",
       "      <td>9:31 AM</td>\n",
       "      <td>12:20 PM</td>\n",
       "      <td>169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17X41A1204-Karthik-X4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9:19 AM</td>\n",
       "      <td>12:14 PM</td>\n",
       "      <td>174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9:26 AM</td>\n",
       "      <td>12:21 PM</td>\n",
       "      <td>174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17X41A1207-Bollu Bhavana-X4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9:30 AM</td>\n",
       "      <td>11:52 AM</td>\n",
       "      <td>142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9:25 AM</td>\n",
       "      <td>12:20 PM</td>\n",
       "      <td>174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17X41A1207-Bollu Bhavana-X4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11:59 AM</td>\n",
       "      <td>12:13 PM</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9:25 AM</td>\n",
       "      <td>12:20 PM</td>\n",
       "      <td>174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Vedasri</td>\n",
       "      <td>vedasri.avirneni@gmail.com</td>\n",
       "      <td>11:36 AM</td>\n",
       "      <td>12:18 PM</td>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vedasri.avirneni@gmail.com</td>\n",
       "      <td>9:25 AM</td>\n",
       "      <td>12:23 PM</td>\n",
       "      <td>177</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Vedasri</td>\n",
       "      <td>vedasri.avirneni@gmail.com</td>\n",
       "      <td>12:17 PM</td>\n",
       "      <td>12:18 PM</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vedasri.avirneni@gmail.com</td>\n",
       "      <td>9:25 AM</td>\n",
       "      <td>12:23 PM</td>\n",
       "      <td>177</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Vedasri</td>\n",
       "      <td>vedasri.avirneni@gmail.com</td>\n",
       "      <td>9:44 AM</td>\n",
       "      <td>9:57 AM</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vedasri.avirneni@gmail.com</td>\n",
       "      <td>9:25 AM</td>\n",
       "      <td>12:23 PM</td>\n",
       "      <td>177</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Vedasri</td>\n",
       "      <td>vedasri.avirneni@gmail.com</td>\n",
       "      <td>9:59 AM</td>\n",
       "      <td>10:55 AM</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vedasri.avirneni@gmail.com</td>\n",
       "      <td>9:25 AM</td>\n",
       "      <td>12:23 PM</td>\n",
       "      <td>177</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Vedasri</td>\n",
       "      <td>vedasri.avirneni@gmail.com</td>\n",
       "      <td>10:55 AM</td>\n",
       "      <td>11:33 AM</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vedasri.avirneni@gmail.com</td>\n",
       "      <td>9:25 AM</td>\n",
       "      <td>12:23 PM</td>\n",
       "      <td>177</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Name                Email Address_x  \\\n",
       "0           17X41A1202-Sai Harini-SRKIT    harini.akkineni@outlook.com   \n",
       "1    17X41A1203-Tejaswini Alapati-SRKIT  alapatitejaswini999@gmail.com   \n",
       "2                 17X41A1204-Karthik-X4                            NaN   \n",
       "3           17X41A1207-Bollu Bhavana-X4                            NaN   \n",
       "4           17X41A1207-Bollu Bhavana-X4                            NaN   \n",
       "..                                  ...                            ...   \n",
       "98                              Vedasri     vedasri.avirneni@gmail.com   \n",
       "99                              Vedasri     vedasri.avirneni@gmail.com   \n",
       "100                             Vedasri     vedasri.avirneni@gmail.com   \n",
       "101                             Vedasri     vedasri.avirneni@gmail.com   \n",
       "102                             Vedasri     vedasri.avirneni@gmail.com   \n",
       "\n",
       "    Join Time_x Leave Time_x  Time in Session (minutes)_x  Unnamed: 5_x  \\\n",
       "0       9:36 AM     12:14 PM                          158           NaN   \n",
       "1       9:29 AM     12:13 PM                          164           NaN   \n",
       "2       9:19 AM     12:14 PM                          174           NaN   \n",
       "3       9:30 AM     11:52 AM                          142           NaN   \n",
       "4      11:59 AM     12:13 PM                           14           NaN   \n",
       "..          ...          ...                          ...           ...   \n",
       "98     11:36 AM     12:18 PM                           42           NaN   \n",
       "99     12:17 PM     12:18 PM                            0           NaN   \n",
       "100     9:44 AM      9:57 AM                           13           NaN   \n",
       "101     9:59 AM     10:55 AM                           55           NaN   \n",
       "102    10:55 AM     11:33 AM                           38           NaN   \n",
       "\n",
       "     Unnamed: 6_x                Email Address_y Join Time_y Leave Time_y  \\\n",
       "0             NaN    harini.akkineni@outlook.com     9:28 AM     12:21 PM   \n",
       "1             NaN  alapatitejaswini999@gmail.com     9:31 AM     12:20 PM   \n",
       "2             NaN                            NaN     9:26 AM     12:21 PM   \n",
       "3             NaN                            NaN     9:25 AM     12:20 PM   \n",
       "4             NaN                            NaN     9:25 AM     12:20 PM   \n",
       "..            ...                            ...         ...          ...   \n",
       "98            NaN     vedasri.avirneni@gmail.com     9:25 AM     12:23 PM   \n",
       "99            NaN     vedasri.avirneni@gmail.com     9:25 AM     12:23 PM   \n",
       "100           NaN     vedasri.avirneni@gmail.com     9:25 AM     12:23 PM   \n",
       "101           NaN     vedasri.avirneni@gmail.com     9:25 AM     12:23 PM   \n",
       "102           NaN     vedasri.avirneni@gmail.com     9:25 AM     12:23 PM   \n",
       "\n",
       "     Time in Session (minutes)_y  Unnamed: 5_y  Unnamed: 6_y  \n",
       "0                            173           NaN           NaN  \n",
       "1                            169           NaN           NaN  \n",
       "2                            174           NaN           NaN  \n",
       "3                            174           NaN           NaN  \n",
       "4                            174           NaN           NaN  \n",
       "..                           ...           ...           ...  \n",
       "98                           177           NaN           NaN  \n",
       "99                           177           NaN           NaN  \n",
       "100                          177           NaN           NaN  \n",
       "101                          177           NaN           NaN  \n",
       "102                          177           NaN           NaN  \n",
       "\n",
       "[103 rows x 13 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "d1.merge(d2, on = 'Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "Empty DataFrame\n",
       "Columns: [Name_x, Email Address_x, Join Time_x, Leave Time_x, Time in Session (minutes)_x, Unnamed: 5_x, Unnamed: 6_x, Name_y, Email Address_y, Join Time_y, Leave Time_y, Time in Session (minutes)_y, Unnamed: 5_y, Unnamed: 6_y]\n",
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   "source": [
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
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   "outputs": [],
   "source": [
    "group = d1.groupby('Name')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 37,
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    {
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       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000023572A699D0>"
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    "group"
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  {
   "cell_type": "code",
   "execution_count": 38,
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   "outputs": [
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       "      <th>17X41A1203-Tejaswini Alapati-SRKIT</th>\n",
       "      <td>164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1204-Karthik-X4</th>\n",
       "      <td>174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1207-Bollu Bhavana-X4</th>\n",
       "      <td>156</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1209-HARSHA-X4</th>\n",
       "      <td>145</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1210-Gopi Chand-SRKIT</th>\n",
       "      <td>167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1211-Sowmya-SRKIT(X4)</th>\n",
       "      <td>165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1212-yashwanth-X4</th>\n",
       "      <td>160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1213-FARHEEN-SRKIT</th>\n",
       "      <td>154</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1214-Gorantla Ram Gopal-X4</th>\n",
       "      <td>130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1215-Neelima-SRKIT</th>\n",
       "      <td>161</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1216-Abhinaya-X4</th>\n",
       "      <td>167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1218 Supriya X4</th>\n",
       "      <td>161</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1219-Navya Sri - SRKIT</th>\n",
       "      <td>164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1221-Girish-X4</th>\n",
       "      <td>166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1222-JASWANTH-X4</th>\n",
       "      <td>165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1223-Himaja-X4</th>\n",
       "      <td>165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1225-M N Sandhya-SRKIT</th>\n",
       "      <td>167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1226-Mounika Munagala-SRKIT</th>\n",
       "      <td>167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1227-Durga Rani Nandamuri-SRKIT</th>\n",
       "      <td>162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1228-Deepika-X4</th>\n",
       "      <td>130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1228-Deepika-srkit</th>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1230-P.P.SARADHI REDDY-SRKIT</th>\n",
       "      <td>165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1231-Navya P</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1231-Navya-SRKIT</th>\n",
       "      <td>181</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1232  Maheswari Srkit</th>\n",
       "      <td>170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1233</th>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1234-Divyasri-SRKIT</th>\n",
       "      <td>173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1235 R.V.Lahari SRKIT</th>\n",
       "      <td>163</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1236-sk anjum Kousar-SRKIT</th>\n",
       "      <td>158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1239-Sheereen-SRKIT</th>\n",
       "      <td>165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1243-syed yasmeen-SRKIT</th>\n",
       "      <td>165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1247-V Ramya Sri - X4</th>\n",
       "      <td>166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17x41a1224-Gopichand(Srkit)-x4</th>\n",
       "      <td>52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17x41a1237 - Aslam- X4</th>\n",
       "      <td>39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17x41a1237-sk.aslam-x4</th>\n",
       "      <td>133</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abinaya</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amritha Mishra</th>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anil Kumar Teegala [APSSDC]</th>\n",
       "      <td>361</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhuvaneswari Bonthu 1208</th>\n",
       "      <td>158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Durga Rani Nandamuri</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karthik</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sairam</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Surisetti Jayasai</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vedasri</th>\n",
       "      <td>148</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vikram Parmar</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yashwanth</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>navya</th>\n",
       "      <td>171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Time in Session (minutes)  Unnamed: 5  \\\n",
       "Name                                                                           \n",
       "17X41A1202-Sai Harini-SRKIT                                  158         0.0   \n",
       "17X41A1203-Tejaswini Alapati-SRKIT                           164         0.0   \n",
       "17X41A1204-Karthik-X4                                        174         0.0   \n",
       "17X41A1207-Bollu Bhavana-X4                                  156         0.0   \n",
       "17X41A1209-HARSHA-X4                                         145         0.0   \n",
       "17X41A1210-Gopi Chand-SRKIT                                  167         0.0   \n",
       "17X41A1211-Sowmya-SRKIT(X4)                                  165         0.0   \n",
       "17X41A1212-yashwanth-X4                                      160         0.0   \n",
       "17X41A1213-FARHEEN-SRKIT                                     154         0.0   \n",
       "17X41A1214-Gorantla Ram Gopal-X4                             130         0.0   \n",
       "17X41A1215-Neelima-SRKIT                                     161         0.0   \n",
       "17X41A1216-Abhinaya-X4                                       167         0.0   \n",
       "17X41A1218 Supriya X4                                        161         0.0   \n",
       "17X41A1219-Navya Sri - SRKIT                                 164         0.0   \n",
       "17X41A1221-Girish-X4                                         166         0.0   \n",
       "17X41A1222-JASWANTH-X4                                       165         0.0   \n",
       "17X41A1223-Himaja-X4                                         165         0.0   \n",
       "17X41A1225-M N Sandhya-SRKIT                                 167         0.0   \n",
       "17X41A1226-Mounika Munagala-SRKIT                            167         0.0   \n",
       "17X41A1227-Durga Rani Nandamuri-SRKIT                        162         0.0   \n",
       "17X41A1228-Deepika-X4                                        130         0.0   \n",
       "17X41A1228-Deepika-srkit                                      11         0.0   \n",
       "17X41A1230-P.P.SARADHI REDDY-SRKIT                           165         0.0   \n",
       "17X41A1231-Navya P                                             4         0.0   \n",
       "17X41A1231-Navya-SRKIT                                       181         0.0   \n",
       "17X41A1232  Maheswari Srkit                                  170         0.0   \n",
       "17X41A1233                                                    26         0.0   \n",
       "17X41A1234-Divyasri-SRKIT                                    173         0.0   \n",
       "17X41A1235 R.V.Lahari SRKIT                                  163         0.0   \n",
       "17X41A1236-sk anjum Kousar-SRKIT                             158         0.0   \n",
       "17X41A1239-Sheereen-SRKIT                                    165         0.0   \n",
       "17X41A1243-syed yasmeen-SRKIT                                165         0.0   \n",
       "17X41A1247-V Ramya Sri - X4                                  166         0.0   \n",
       "17x41a1224-Gopichand(Srkit)-x4                                52         0.0   \n",
       "17x41a1237 - Aslam- X4                                        39         0.0   \n",
       "17x41a1237-sk.aslam-x4                                       133         0.0   \n",
       "Abinaya                                                        0         0.0   \n",
       "Amritha Mishra                                                14         0.0   \n",
       "Anil Kumar Teegala [APSSDC]                                  361         0.0   \n",
       "Bhuvaneswari Bonthu 1208                                     158         0.0   \n",
       "Durga Rani Nandamuri                                           0         0.0   \n",
       "Karthik                                                        0         0.0   \n",
       "Sairam                                                         0         0.0   \n",
       "Surisetti Jayasai                                              5         0.0   \n",
       "Vedasri                                                      148         0.0   \n",
       "Vikram Parmar                                                  1         0.0   \n",
       "Yashwanth                                                      0         0.0   \n",
       "navya                                                        171         0.0   \n",
       "\n",
       "                                       Unnamed: 6  \n",
       "Name                                               \n",
       "17X41A1202-Sai Harini-SRKIT                   0.0  \n",
       "17X41A1203-Tejaswini Alapati-SRKIT            0.0  \n",
       "17X41A1204-Karthik-X4                         0.0  \n",
       "17X41A1207-Bollu Bhavana-X4                   0.0  \n",
       "17X41A1209-HARSHA-X4                          0.0  \n",
       "17X41A1210-Gopi Chand-SRKIT                   0.0  \n",
       "17X41A1211-Sowmya-SRKIT(X4)                   0.0  \n",
       "17X41A1212-yashwanth-X4                       0.0  \n",
       "17X41A1213-FARHEEN-SRKIT                      0.0  \n",
       "17X41A1214-Gorantla Ram Gopal-X4              0.0  \n",
       "17X41A1215-Neelima-SRKIT                      0.0  \n",
       "17X41A1216-Abhinaya-X4                        0.0  \n",
       "17X41A1218 Supriya X4                         0.0  \n",
       "17X41A1219-Navya Sri - SRKIT                  0.0  \n",
       "17X41A1221-Girish-X4                          0.0  \n",
       "17X41A1222-JASWANTH-X4                        0.0  \n",
       "17X41A1223-Himaja-X4                          0.0  \n",
       "17X41A1225-M N Sandhya-SRKIT                  0.0  \n",
       "17X41A1226-Mounika Munagala-SRKIT             0.0  \n",
       "17X41A1227-Durga Rani Nandamuri-SRKIT         0.0  \n",
       "17X41A1228-Deepika-X4                         0.0  \n",
       "17X41A1228-Deepika-srkit                      0.0  \n",
       "17X41A1230-P.P.SARADHI REDDY-SRKIT            0.0  \n",
       "17X41A1231-Navya P                            0.0  \n",
       "17X41A1231-Navya-SRKIT                        0.0  \n",
       "17X41A1232  Maheswari Srkit                   0.0  \n",
       "17X41A1233                                    0.0  \n",
       "17X41A1234-Divyasri-SRKIT                     0.0  \n",
       "17X41A1235 R.V.Lahari SRKIT                   0.0  \n",
       "17X41A1236-sk anjum Kousar-SRKIT              0.0  \n",
       "17X41A1239-Sheereen-SRKIT                     0.0  \n",
       "17X41A1243-syed yasmeen-SRKIT                 0.0  \n",
       "17X41A1247-V Ramya Sri - X4                   0.0  \n",
       "17x41a1224-Gopichand(Srkit)-x4                0.0  \n",
       "17x41a1237 - Aslam- X4                        0.0  \n",
       "17x41a1237-sk.aslam-x4                        0.0  \n",
       "Abinaya                                       0.0  \n",
       "Amritha Mishra                                0.0  \n",
       "Anil Kumar Teegala [APSSDC]                   0.0  \n",
       "Bhuvaneswari Bonthu 1208                      0.0  \n",
       "Durga Rani Nandamuri                          0.0  \n",
       "Karthik                                       0.0  \n",
       "Sairam                                        0.0  \n",
       "Surisetti Jayasai                             0.0  \n",
       "Vedasri                                       0.0  \n",
       "Vikram Parmar                                 0.0  \n",
       "Yashwanth                                     0.0  \n",
       "navya                                         0.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Email Address</th>\n",
       "      <th>Join Time</th>\n",
       "      <th>Leave Time</th>\n",
       "      <th>Time in Session (minutes)</th>\n",
       "      <th>Unnamed: 5</th>\n",
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       "      <th>17X41A1202-Sai Harini-SRKIT</th>\n",
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       "      <th>17X41A1207-Bollu Bhavana-X4</th>\n",
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       "      <td>2</td>\n",
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       "      <th>17X41A1209-HARSHA-X4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17X41A1210-Gopi Chand-SRKIT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1211-Sowmya-SRKIT(X4)</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1212-yashwanth-X4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1213-FARHEEN-SRKIT</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1214-Gorantla Ram Gopal-X4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1215-Neelima-SRKIT</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1216-Abhinaya-X4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1218 Supriya X4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1219-Navya Sri - SRKIT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1221-Girish-X4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1222-JASWANTH-X4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1223-Himaja-X4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1225-M N Sandhya-SRKIT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1226-Mounika Munagala-SRKIT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1227-Durga Rani Nandamuri-SRKIT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1228-Deepika-X4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1228-Deepika-srkit</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1230-P.P.SARADHI REDDY-SRKIT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1231-Navya P</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1231-Navya-SRKIT</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1232  Maheswari Srkit</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1233</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1234-Divyasri-SRKIT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1235 R.V.Lahari SRKIT</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1236-sk anjum Kousar-SRKIT</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1239-Sheereen-SRKIT</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1243-syed yasmeen-SRKIT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1247-V Ramya Sri - X4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17x41a1224-Gopichand(Srkit)-x4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17x41a1237 - Aslam- X4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17x41a1237-sk.aslam-x4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abinaya</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amritha Mishra</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anil Kumar Teegala [APSSDC]</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhuvaneswari Bonthu 1208</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Durga Rani Nandamuri</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karthik</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sairam</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Surisetti Jayasai</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vedasri</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vikram Parmar</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yashwanth</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>navya</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Email Address  Join Time  Leave Time  \\\n",
       "Name                                                                          \n",
       "17X41A1202-Sai Harini-SRKIT                        1          1           1   \n",
       "17X41A1203-Tejaswini Alapati-SRKIT                 1          1           1   \n",
       "17X41A1204-Karthik-X4                              0          1           1   \n",
       "17X41A1207-Bollu Bhavana-X4                        0          2           2   \n",
       "17X41A1209-HARSHA-X4                               0          2           2   \n",
       "17X41A1210-Gopi Chand-SRKIT                        1          1           1   \n",
       "17X41A1211-Sowmya-SRKIT(X4)                        0          1           1   \n",
       "17X41A1212-yashwanth-X4                            1          1           1   \n",
       "17X41A1213-FARHEEN-SRKIT                           0          2           2   \n",
       "17X41A1214-Gorantla Ram Gopal-X4                   2          2           2   \n",
       "17X41A1215-Neelima-SRKIT                           0          2           2   \n",
       "17X41A1216-Abhinaya-X4                             0          3           3   \n",
       "17X41A1218 Supriya X4                              1          1           1   \n",
       "17X41A1219-Navya Sri - SRKIT                       1          1           1   \n",
       "17X41A1221-Girish-X4                               0          1           1   \n",
       "17X41A1222-JASWANTH-X4                             0          3           3   \n",
       "17X41A1223-Himaja-X4                               0          1           1   \n",
       "17X41A1225-M N Sandhya-SRKIT                       1          1           1   \n",
       "17X41A1226-Mounika Munagala-SRKIT                  1          1           1   \n",
       "17X41A1227-Durga Rani Nandamuri-SRKIT              1          1           1   \n",
       "17X41A1228-Deepika-X4                              0          1           1   \n",
       "17X41A1228-Deepika-srkit                           0          1           1   \n",
       "17X41A1230-P.P.SARADHI REDDY-SRKIT                 1          1           1   \n",
       "17X41A1231-Navya P                                 0          3           3   \n",
       "17X41A1231-Navya-SRKIT                             0          4           4   \n",
       "17X41A1232  Maheswari Srkit                        1          1           1   \n",
       "17X41A1233                                         1          1           1   \n",
       "17X41A1234-Divyasri-SRKIT                          1          1           1   \n",
       "17X41A1235 R.V.Lahari SRKIT                        2          2           2   \n",
       "17X41A1236-sk anjum Kousar-SRKIT                   0          1           1   \n",
       "17X41A1239-Sheereen-SRKIT                          0          2           2   \n",
       "17X41A1243-syed yasmeen-SRKIT                      1          1           1   \n",
       "17X41A1247-V Ramya Sri - X4                        2          2           2   \n",
       "17x41a1224-Gopichand(Srkit)-x4                     2          2           2   \n",
       "17x41a1237 - Aslam- X4                             0          1           1   \n",
       "17x41a1237-sk.aslam-x4                             0          1           1   \n",
       "Abinaya                                            0          1           1   \n",
       "Amritha Mishra                                     0          1           1   \n",
       "Anil Kumar Teegala [APSSDC]                        1          3           3   \n",
       "Bhuvaneswari Bonthu 1208                           1          1           1   \n",
       "Durga Rani Nandamuri                               1          1           1   \n",
       "Karthik                                            0          1           1   \n",
       "Sairam                                             1          1           1   \n",
       "Surisetti Jayasai                                  2          2           2   \n",
       "Vedasri                                            5          5           5   \n",
       "Vikram Parmar                                      0          2           2   \n",
       "Yashwanth                                          1          1           1   \n",
       "navya                                              0          2           2   \n",
       "\n",
       "                                       Time in Session (minutes)  Unnamed: 5  \\\n",
       "Name                                                                           \n",
       "17X41A1202-Sai Harini-SRKIT                                    1           0   \n",
       "17X41A1203-Tejaswini Alapati-SRKIT                             1           0   \n",
       "17X41A1204-Karthik-X4                                          1           0   \n",
       "17X41A1207-Bollu Bhavana-X4                                    2           0   \n",
       "17X41A1209-HARSHA-X4                                           2           0   \n",
       "17X41A1210-Gopi Chand-SRKIT                                    1           0   \n",
       "17X41A1211-Sowmya-SRKIT(X4)                                    1           0   \n",
       "17X41A1212-yashwanth-X4                                        1           0   \n",
       "17X41A1213-FARHEEN-SRKIT                                       2           0   \n",
       "17X41A1214-Gorantla Ram Gopal-X4                               2           0   \n",
       "17X41A1215-Neelima-SRKIT                                       2           0   \n",
       "17X41A1216-Abhinaya-X4                                         3           0   \n",
       "17X41A1218 Supriya X4                                          1           0   \n",
       "17X41A1219-Navya Sri - SRKIT                                   1           0   \n",
       "17X41A1221-Girish-X4                                           1           0   \n",
       "17X41A1222-JASWANTH-X4                                         3           0   \n",
       "17X41A1223-Himaja-X4                                           1           0   \n",
       "17X41A1225-M N Sandhya-SRKIT                                   1           0   \n",
       "17X41A1226-Mounika Munagala-SRKIT                              1           0   \n",
       "17X41A1227-Durga Rani Nandamuri-SRKIT                          1           0   \n",
       "17X41A1228-Deepika-X4                                          1           0   \n",
       "17X41A1228-Deepika-srkit                                       1           0   \n",
       "17X41A1230-P.P.SARADHI REDDY-SRKIT                             1           0   \n",
       "17X41A1231-Navya P                                             3           0   \n",
       "17X41A1231-Navya-SRKIT                                         4           0   \n",
       "17X41A1232  Maheswari Srkit                                    1           0   \n",
       "17X41A1233                                                     1           0   \n",
       "17X41A1234-Divyasri-SRKIT                                      1           0   \n",
       "17X41A1235 R.V.Lahari SRKIT                                    2           0   \n",
       "17X41A1236-sk anjum Kousar-SRKIT                               1           0   \n",
       "17X41A1239-Sheereen-SRKIT                                      2           0   \n",
       "17X41A1243-syed yasmeen-SRKIT                                  1           0   \n",
       "17X41A1247-V Ramya Sri - X4                                    2           0   \n",
       "17x41a1224-Gopichand(Srkit)-x4                                 2           0   \n",
       "17x41a1237 - Aslam- X4                                         1           0   \n",
       "17x41a1237-sk.aslam-x4                                         1           0   \n",
       "Abinaya                                                        1           0   \n",
       "Amritha Mishra                                                 1           0   \n",
       "Anil Kumar Teegala [APSSDC]                                    3           0   \n",
       "Bhuvaneswari Bonthu 1208                                       1           0   \n",
       "Durga Rani Nandamuri                                           1           0   \n",
       "Karthik                                                        1           0   \n",
       "Sairam                                                         1           0   \n",
       "Surisetti Jayasai                                              2           0   \n",
       "Vedasri                                                        5           0   \n",
       "Vikram Parmar                                                  2           0   \n",
       "Yashwanth                                                      1           0   \n",
       "navya                                                          2           0   \n",
       "\n",
       "                                       Unnamed: 6  \n",
       "Name                                               \n",
       "17X41A1202-Sai Harini-SRKIT                     0  \n",
       "17X41A1203-Tejaswini Alapati-SRKIT              0  \n",
       "17X41A1204-Karthik-X4                           0  \n",
       "17X41A1207-Bollu Bhavana-X4                     0  \n",
       "17X41A1209-HARSHA-X4                            0  \n",
       "17X41A1210-Gopi Chand-SRKIT                     0  \n",
       "17X41A1211-Sowmya-SRKIT(X4)                     0  \n",
       "17X41A1212-yashwanth-X4                         0  \n",
       "17X41A1213-FARHEEN-SRKIT                        0  \n",
       "17X41A1214-Gorantla Ram Gopal-X4                0  \n",
       "17X41A1215-Neelima-SRKIT                        0  \n",
       "17X41A1216-Abhinaya-X4                          0  \n",
       "17X41A1218 Supriya X4                           0  \n",
       "17X41A1219-Navya Sri - SRKIT                    0  \n",
       "17X41A1221-Girish-X4                            0  \n",
       "17X41A1222-JASWANTH-X4                          0  \n",
       "17X41A1223-Himaja-X4                            0  \n",
       "17X41A1225-M N Sandhya-SRKIT                    0  \n",
       "17X41A1226-Mounika Munagala-SRKIT               0  \n",
       "17X41A1227-Durga Rani Nandamuri-SRKIT           0  \n",
       "17X41A1228-Deepika-X4                           0  \n",
       "17X41A1228-Deepika-srkit                        0  \n",
       "17X41A1230-P.P.SARADHI REDDY-SRKIT              0  \n",
       "17X41A1231-Navya P                              0  \n",
       "17X41A1231-Navya-SRKIT                          0  \n",
       "17X41A1232  Maheswari Srkit                     0  \n",
       "17X41A1233                                      0  \n",
       "17X41A1234-Divyasri-SRKIT                       0  \n",
       "17X41A1235 R.V.Lahari SRKIT                     0  \n",
       "17X41A1236-sk anjum Kousar-SRKIT                0  \n",
       "17X41A1239-Sheereen-SRKIT                       0  \n",
       "17X41A1243-syed yasmeen-SRKIT                   0  \n",
       "17X41A1247-V Ramya Sri - X4                     0  \n",
       "17x41a1224-Gopichand(Srkit)-x4                  0  \n",
       "17x41a1237 - Aslam- X4                          0  \n",
       "17x41a1237-sk.aslam-x4                          0  \n",
       "Abinaya                                         0  \n",
       "Amritha Mishra                                  0  \n",
       "Anil Kumar Teegala [APSSDC]                     0  \n",
       "Bhuvaneswari Bonthu 1208                        0  \n",
       "Durga Rani Nandamuri                            0  \n",
       "Karthik                                         0  \n",
       "Sairam                                          0  \n",
       "Surisetti Jayasai                               0  \n",
       "Vedasri                                         0  \n",
       "Vikram Parmar                                   0  \n",
       "Yashwanth                                       0  \n",
       "navya                                           0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "group = d1.groupby(['Name', 'Email Address'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th>Time in Session (minutes)</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th>Email Address</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17X41A1202-Sai Harini-SRKIT</th>\n",
       "      <th>harini.akkineni@outlook.com</th>\n",
       "      <td>158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1203-Tejaswini Alapati-SRKIT</th>\n",
       "      <th>alapatitejaswini999@gmail.com</th>\n",
       "      <td>164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1210-Gopi Chand-SRKIT</th>\n",
       "      <th>gopichandcherukuri121@gmail.com</th>\n",
       "      <td>167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1212-yashwanth-X4</th>\n",
       "      <th>rajyashwanth27@gmail.com</th>\n",
       "      <td>160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17X41A1214-Gorantla Ram Gopal-X4</th>\n",
       "      <th>ramgopalgorantla43@gmail.com</th>\n",
       "      <td>130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                    Time in Session (minutes)  \\\n",
       "Name                               Email Address                                                \n",
       "17X41A1202-Sai Harini-SRKIT        harini.akkineni@outlook.com                            158   \n",
       "17X41A1203-Tejaswini Alapati-SRKIT alapatitejaswini999@gmail.com                          164   \n",
       "17X41A1210-Gopi Chand-SRKIT        gopichandcherukuri121@gmail.com                        167   \n",
       "17X41A1212-yashwanth-X4            rajyashwanth27@gmail.com                               160   \n",
       "17X41A1214-Gorantla Ram Gopal-X4   ramgopalgorantla43@gmail.com                           130   \n",
       "\n",
       "                                                                    Unnamed: 5  \\\n",
       "Name                               Email Address                                 \n",
       "17X41A1202-Sai Harini-SRKIT        harini.akkineni@outlook.com             0.0   \n",
       "17X41A1203-Tejaswini Alapati-SRKIT alapatitejaswini999@gmail.com           0.0   \n",
       "17X41A1210-Gopi Chand-SRKIT        gopichandcherukuri121@gmail.com         0.0   \n",
       "17X41A1212-yashwanth-X4            rajyashwanth27@gmail.com                0.0   \n",
       "17X41A1214-Gorantla Ram Gopal-X4   ramgopalgorantla43@gmail.com            0.0   \n",
       "\n",
       "                                                                    Unnamed: 6  \n",
       "Name                               Email Address                                \n",
       "17X41A1202-Sai Harini-SRKIT        harini.akkineni@outlook.com             0.0  \n",
       "17X41A1203-Tejaswini Alapati-SRKIT alapatitejaswini999@gmail.com           0.0  \n",
       "17X41A1210-Gopi Chand-SRKIT        gopichandcherukuri121@gmail.com         0.0  \n",
       "17X41A1212-yashwanth-X4            rajyashwanth27@gmail.com                0.0  \n",
       "17X41A1214-Gorantla Ram Gopal-X4   ramgopalgorantla43@gmail.com            0.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = group.sum()\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x235745ec220>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pnsEpyKWuOQFrcLyduF2OQdcWctgsXDanipU7GyMLzIVIFxLgiJT6/OxKlt96Ot9cNH7QheMS7dKaSpSCJ1abn8V5cvUexg/LZ0aMVXCHskRWM/a0duJO0zU4ABVFuTR4O/EHgqY+bn1L7NvujysKFdNsSmCFaSFiIQGOSCm71UJRmk4JHFecx6kTynlyda2pHyhbD3r5cPehcAAlCzMHy+3KSUiA0+4L0NEVTO8MTmEOWkO919wszkD6UPWlOPz9e6gtcU1QhYiFBDhCHMMVc6o40NzBsi31pj3mU2tqsVoUF80aadpjDiWJmqLyhNf1pHUGJ9zKwuxpqlCAE1t15BJXYrfuCxErCXCEOIbTT6igLN9h2mJjfyDIMx/WsnBCeb99l0R0JS4HTa0+tFE22CRNraEMxGAX2ibT8KLQe+bAYfMWGofaNMSewSkJZ3AkwBHpRgIcIY7BYbPw+dmVLN14kIMm1B95Z0sDB1s6ubSm0oTRDU2lLgf+oKa5w9xFrUYGpzTGTEYyDAu3azDjvWhoDU/NlRXEOkUVer1kikqkGwlwhgCl1J+UUgeVUp/0OHaHUmqvUmpt+M95PW77N6XUVqXUJqXU2akZdfq4vKaKQFDz1JrauB/ryTV7cLscnH5CAvoMDBFGnyiz1+EYGYh0zuCUuXKwWhR1Jm4VH2wV4yMV50kGR6QnCXCGhgeBc6Ic/7XWemb4zxIApdRk4ApgSvg+v1NKDelCLePK85k71s3jq/bENS3S1Orj9fUHuXDmcQmrjjwUuPONAMfcdSiN4Smq0jReZGyxKIYV5JjarqG7yF9sgZ3NGqpNJBkckW7kp+wQoLVeBjQO8PQLgce01p1a6x3AVmBuwgaXIa6YU8UuTxvvbx/oy3i059fuxRcIculsqX0Tj14NN03U2NqJ1aIoyE19HaZjGVaYa+oUVSxVjI9U4nRIBkekHQlwhrZblFLrwlNYJeFjI4GeK2prw8eOopS6QSm1Wim1ur7evF1G6ejcqSMoyLXx+KrdMT/Gk2tqmXJcIZOPKzRxZENPoqaoGlu7KHE6EtIk1EzDC3PMnaIKB4rlMa7BgdBC4ybJ4Ig0IwHO0PV7oBqYCewH/it8PNpP96jzMlrr+7XWNVrrmvLy8sSMMk3kOaxcPGskSz45wOEYfpCv39fMp/uauXS2LC6OlzGFZPZW8cY0L/JnqCjMNXUXlZHBMQLHWBQ7HRzKggzO39ft44WP96V6GMIkEuAMUVrrOq11QGsdBP6P7mmoWqDnHEolIN/xwOVzqvD5gzy3du+g7/vkmj04rBYunCm1b+KV57CSZ7eav8i4tSuuD/lkqSjMpbnDT7svYMrjNXg7KXHasVtj/zgIZXAyP8D5v2Xb+eM721M9DGESCXCGKKXUiB5fXgwYO6xeAK5QSuUopcYC44GVyR5fOppyXBHTRhbx6Mrdg1ps7PMHeX7tPs6YPCxSFE3Exx2uhWOmUJuG9P//qSgM1cIxax1OQxxtGgzFTgeHWjN/isrT6ktYGxCRfBLgDAFKqUeBFcBEpVStUurLwK+UUv9USq0DTgP+FUBr/SnwBLAe+Adws9banF8Vs8Dlc6rYeKCFdbWHB3yfNzbW0djqk8XFJnInoJpxU1tXWm8RN1SEa+GYNU0VT5E/Q4nTQUunny6Te2QlW6MEOFklvbcLCFNora+McviBY5x/F3BX4kaUuS6ceRx3vbSBx1btYUZV8YDu8+TqWoYV5HDK+LIEj27oMLvhZiCoaWrzRXZopTMjg1PXYs5W8QZvJ9MqB/Ze7kuJq7sfVTyLlVOpoytAW3jar6MrQK59SFfHyAqSwRFiEApy7Zw/fQQvrN1La2f/lXQPtnTw1uZ6LjmxElscaxxEb6UmBziH27vQmoyYQowEOKZlcHwx18AxdFczztzsR8+MYDasJxIS4AgxaFfMqaLVF+Clf+7v99xnP9xLIKilNYPJQlNU5hW7a2yNfydRshTm2si1W0zZKt7RFcDb6TdhisqoZpy563Aae9RVMrvGkkgNCXCEGKTZo0uoLnfx+KpjN+DUWvPkmlpOHFVMdXl+kkY3NLjzHXR0BWnzmdOPyqhinAkBjlKK4YW5pkxR1Ycfo9yENTiQ2ZmPngFzJl+H6CYBjhCDpJTiijmjWLOriS11LX2et3bPIbYe9HJpjSwuNpvZ1YyN6a5MCHAgVM3YjCkqowZOvA1Gjam9TJ6i6jnlKQuNs4MEOELE4JITR2K3Kh47RhbnqTW15NotXDB9RJ/niNi4w8X+zPogyrQAp6IwlzoTtokbVYxliqr3e8nsEgQiNSTAESIGpfk5nDV5OM98WEun/+hd9B1dAV74eF+4xUP6V8fNNEbF4UaTMgaZ0Em8J6NdQzzNX6FHH6o4dz7l2a04bJaMntrxtPqwWRRKSQYnW0iAI0SMLp9TRVNbF6+trzvqtlc+PUBLh19aMyRIJINj0hSVx+vD5bBmzNbgisJcOrqCNLfHtwapIbwGJ97t8UopSpz2jC721+j14XY5KM6zmxY4i9SSAEeIGH32+DJGFudFXWz81JpaRhbncdK40hSMLPuZ3XCzqc2HO851KMk0LFILJ75pqgZvJwW5NlMCu0zvKO5pDQU4oSrZmRuoiW4S4AgRI4tFcVlNFe9saWBPY1vk+N5D7Szf2sDnZ1emfWfqTFWYa8NuVaZVM/a0+nBnyPQUwHAjwIlzq3iD1xf3DipDsdPOoYxeg9NJab7D9BIEInUkwBEiDpfWVGJR8OTq7izOM2tq0RqZnkqg0JSII1K/Jl5Nrb6MKPJnMKtdQ70JbRoMmZ7BaWz14XblhK5DMjhZQQIcIeJwXHEep04o54nVtfgDQbTWPPVhLSeNc1PldqZ6eFnNzHYNjeHpiUzR3XAzvgCvwdtJWYE5113sdGT0LipPa6hVR2m+Q9bgZAkJcISI0+VzRnGguYNlW+pZuaORXZ42aayZBKX55jXcbMywKapcu5WiPHvcGZyGFjMzOHYOtfni3tmVCj5/kJYOP26XI5zByczrEL1Js00h4rRo0jDK8h08tnIPhXl28nNsnDtteKqHlfXcrhz2Nh2K+3HafQHauwIZtcgYQtNU8azB6fQHaO6Iv02DocTpwB/UeDv9GVcawZhac7scdHQF8Ac1zR1+ivIy6zpEbxLgCBEnu9XC52dX8sd3duCwWlg84zicDvnWSjS3025KBseYjsikDA4Yxf5in6IypvfMCnCKnd0dxTMtwDEqYpe6HJGO4k2tPglwMpxMUQlhgivmjCIQ1LR3BaSxZpK4XTm0dPjx+YNxPU5ThlUxNlTE2a6hocUIcMy57kzuR9WzkrWRyTNr+lOkjgQ4QphgbJmL+ceXMn5YPrNHl6R6OEOC8UEU7weqJ2MDnBzqvZ0EgrGtFTGrirGhxJW57RqMbeGl+Y5IJk/aNWQ+yaMLYZLffXE2/kAQpaT2TTL0bLhp7CqKRaZmcIYX5hIIajytnQwrGPz113vN6SRuKHZmbsPN7gxODjm2UHVo2UmV+STAEcIkMl+fXGZVM87UDE6kmvHh2AKcSAbHxEXGkJl9nBpbfVgUFOfZybGFJjYkg5P5ZIpqCFBK/UkpdVAp9UmPY/+plNqolFqnlHpWKVUcPj5GKdWulFob/nNf6kYuRN8iGZw4i/01tfqwWhSFGbYwtiLOasYNLaH+W3kOc/pvFeXZUSpTp6h8lDgdWCwKpyPUODQTAzXRmwQ4Q8ODwDlHHHsNmKq1ng5sBv6tx23btNYzw39uTNIYhRgUMzM4JU57xrXVGB5nP6pQkT9zsjdAJEjMyCkqb3ehR6UUbqd5RSRF6kiAMwRorZcBjUcce1VrbbQifh+QrT8ioxQ7HaGMQZwfRE3h394zTVl+6Ppj3UnV4O2Mu4v4kUqc9ozM4BxZydrtyuy2EyJEAhwBcD3wco+vxyqlPlJKva2UOqWvOymlblBKrVZKra6vr0/8KIXowWpRFOfFXwunsS2z2jQYbFYLZfk51DXHNkXXYGIfKkOx05GRGRxPuNGmIdRwM/OuQ/QmAc4Qp5T6EeAHHgkf2g+M0lrPAr4D/E0pVRjtvlrr+7XWNVrrmvLy8uQMWIgezOhHlWl9qHoaXpjLgVjX4Hh9pk5RgZHBybzA4Mj3QInLIYuMs4AEOEOYUuoa4ALgizrceEVr3am19oT/vQbYBkxI3SiF6FupKyfu37SbMjjAibVdgz8QpKnNZ3oGJxM7cQeCmkPtXbhd3a9FqYmNXEXqSIAzRCmlzgFuAxZrrdt6HC9XSlnD/x4HjAe2p2aUQhxbvBmcYFDTlKFTVBDaSRVLR/HGVh9aQ7nJ/bcycYqqqS30WvRcj1TidNDc4acrEF+VbJFaEuAMAUqpR4EVwESlVK1S6svAb4EC4LUjtoMvANYppT4GngJu1Fo3Rn1gIVLMnR9fgHO4vYugzrwaOIaKwlwaW310+gODul+9yTVwDCVOO62+QNztM5KpMUodJHekKnNmBWuiNyn0NwRora+McviBPs59Gng6sSMSwhyl4d0ugaDGGsM270wt8meoKAwFKAebO6lyOwd8v4Zwc0mz1+AUu7qrGQ+Lo7p0MvVstGkocRntGrpiKqIo0oNkcIQQGcvtcqB17O0BjN/QM3GbOHQX+zs4yFo4DS2Jy+BAZhX7i2RwjthFBfEXkRSpJQGOECJjGR9EsU4lGL+9Z24GJxTgHDg8uA/i7jYNZtfBybyO4o3hIObIOjhAxi2YFr1JgCOEyFiR37S98WVwMj3AGexOqgZvJzk2C/k55q5SKA5ncDJpobExTdkzi2d0FJeGm5lNAhwhRMaKt11DtAWmmaTEacdhtQy6XUODN7RFXClz21N0Z3AyJ/PR2OqjKM+O3dr9cdi9BkcCnEwmAY4QImOVhmuXxFoLp7HVh9NhJdduTsPJZFNKMawwZ9DtGszuQ2XIxCkqT6vvqJYVdquFglyb1MLJcBLgCCEyVkl4O2+sH0SZXOTPUFGYO+h2DQ1en+k1cADyHFZybBYOZVIGxxv9PWBGlWyRWipcwFaIuNTU1OjVq1enehhiCJp2+yt8fnYldyye0ut4V1cXtbW1dHT0nd1o8HYS1DqjtwI3tvroCgQj63EGYv/hDnLtloTsHtt/uINcmyUyzZPu6po7sFkUpUfsKDvY0olFmbfTLDc3l8rKSux2e6/jSqk1WusaU55E9CJ1cIQQGc2dH70xYm1tLQUFBYwZM6bPtSZbDrZgs1gYW+ZK9DATZt+hdhpbfUwaWTSg87XW+Pc2U16Qw/Ai8wM7a10LDquFMRnymup9zRTm2ags6V1HKK+hla5AkPEVBfE/h9Z4PB5qa2sZO3Zs3I8nBkamqIQQGc3dR2PEjo4OSktLj7mQNhDQ2GIoEJhO7FZFUGsCwYFl4wNBjUZjsybmum0WNeCxpJoOv27R3gNWi8Jv0nUopSgtLT1mNlGYTwIcIURGczujZ3CAfncJ+WOsgJxObOHdPwPtm2R8aCcqsDMzMEg0I9izWo7+KLRZQ4GaWcs4zN6xJvonAY4QIqOFFoMOvuJsMKgJ6izI4ITH7x9ogBM+zxblQ90M1gzK4ESCvSjZLKsllBnLkEsRUUiAI4TIaEbDzcH+pm18uFkTMFXj8XiYOXMmM2fOZPjw4YwcOZKZM2eSn5/P17/+dVOfy2618MRf/sRf/vKXAZ3vD2qCwSC3fu9fmTp1KtOmTWPOnDns2LHDlPF88vGH3PXv3zcl86G15vTTT6e5uXnA93nhhRf4xS9+MaBzA1GyWffeey9tbW2RADAQHHjj0N/+9rf8+c9/HvD5IrFkkbEQIqOVuhx0BTQtnX4Kc+393yHM+OBKRCajtLSUtWvXAnDHHXeQn5/P9773PdOfB0JTVJd96foBLxjuCmheeeEZ6vbvY926dVgsFmpra3G5zFkUPHv2HI47fioBrbHFOS2zZMkSZsyYQWFh4YDvs3jxYhYvXjygc6NN1917771cddVVOFxFkXMGuh/s+uuvZ/78+Vx33XUDHq9IHMngCCEymjtc7K9xkO0aEr0WJZq33nqLCy64AAgFPtdccw1nnXUWY8aM4ZlnnuHWW29l2rRpnHPOOXR1hWrJrFmzhlNPPclR9hUAACAASURBVJXZs2dz9tlns3///l6PabUo7rvnF/z23l8DsHDhQm677Tbmzp3LhAkTeOedd3qd7w8GaaivY8SIEVjCwV1lZSUlJSUAvPrqq8ybN48TTzyRSy+9FK/XC8APfvADJk+ezPTp0yPB2pNPPsnUqVOZMWMGCxYsAGDF8mXccu3lBIKaxsZGLrroIqZPn85JJ53EunXrItd+/fXXs3DhQsaNG8dvfvObqK/XI488woUXXgjAzp07OeGEE/jKV77C1KlT+eIXv8jrr7/O/PnzGT9+PCtXrgTgwQcf5JZbbgHg2muv5Zvf/CYnn3wy48aN46mnnur1/2BM133n29/iwQcf5De/+Q379u3jtNNO44JzzgTglUG8Hk6nkzFjxkTGIlJLMjhCiIxWGun87Otza/KdL37K+n29pzn8QU1nV4A8hxVLDJmGyccVcvvnpvR/4jFs27aNN998k/Xr1zNv3jyefvppfvWrX3HxxRfz0ksvcf755/ONb3yD559/nvLych5//HF+9KMf8ac//anX41iOWPfi9/tZuXIlS5Ys4c477+T111/vvi2gOW/xJVz3+XNZvnw5ixYt4qqrrmLWrFk0NDTws5/9jNdffx2Xy8Uvf/lL7rnnHm655RaeffZZNm7ciFKKQ4cOAfCTn/yEV155hZEjR0aOGYu2A0HN7bffzqxZs3juued44403uPrqqyOZrY0bN/Lmm2/S0tLCxIkTuemmm46qEfPuu+/yhz/8IfL11q1befLJJ7n//vuZM2cOf/vb31i+fDkvvPACP//5z3nuueeOeo3379/P8uXL2bhxI4sXL+YLX/hC92sRfs2MGPeb3/wm99xzD2+++SYFRSW8v2En//kf/zHg1wOgpqaGd955h7lz5w7wXSASRQIcIURGi7UflbFGJJW7W84991zsdjvTpk0jEAhwzjnnADBt2jR27tzJpk2b+OSTTzjzzFA2IRAIMGLEiKMex6oUgR5rXi655BIAZs+ezc6dO3ud6w9qKisr2bRpE2+88QZvvPEGixYt4sknn6S9vZ3169czf/58AHw+H/PmzaOwsJDc3Fy+8pWvcP7550eyUPPnz+faa6/lsssuizynESz6g5rly5fz9NNPA3D66afj8Xg4fPgwAOeffz45OTnk5OQwbNgw6urqqKys7DXWxsZGCgq669CMHTuWadOmATBlyhQWLVqEUiryekVz0UUXYbFYmDx5MnV1db1uCwQ1VqWivgesFsW6D1exaeOGAb8eAMOGDWPjxo1RxyKSSwIcIURGcw+gMWK0TMuBwx3Ut3QwdWRRyoKcnJzQ9JrFYsFut0fGYbFY8Pv9aK2ZMmUKK1asOObjWCyKYI8MjvG4VqsVv9/f61x/IIjNaiEnJ4dzzz2Xc889l4qKCp577jnOOusszjzzTB599NGjnmPlypUsXbqUxx57jN/+9re88cYb3HfffXzwwQe89NJLzJw5k7Vr12L0rOxri7VxjcYY+xongM1mIxgMRqbSet7HYrH0ev2i3f/I+xjjMR7XH9BYrSpqfRqrRYGGBaedzrNPPTGg1wNC9Zfy8vKijkUkl6zBEUJkNHePKarB8AeDWC2WtK5PMnHiROrr6yMBTldXF59++ulR51kUBDQD2rnkD2o2/HMt+/btAyAYDLJu3TpGjx7NSSedxLvvvsvWrVsBaGtrY/PmzXi9Xg4fPsx5553HvffeG5lm2rZtG5/5zGf4yU9+QllZGXv27MEafj0DAc2CBQt45JFHgNC6l7KyskEtGJ44cSLbt28f8PkDNXr0aNavX09rezvt3haWLl0aua2goICWlhaUUsyqmcvK91cM+PUA2Lx5M1OnTjV9zGLwJIMzBCil/gRcABzUWk8NH3MDjwNjgJ3AZVrrpvBt/wZ8GQgA39Rav5KCYQsxIM5wg8fB1sLpq4JtOnE4HDz11FN885vf5PDhw/j9fr797W8zZUrvjJTVEpqi6q/+jNYaf1BzqLGBz33um3R2hl6zuXPncsstt5Cbm8uDDz7IlVdeGbntZz/7GQUFBVx44YV0dHSgtebXvw4taP7+97/Pli1b0FqzaNEiZsyYwVtvvYUiFEjdcccdXHfddUyfPh2n08lDDz00qOs///zzeeuttzj++OMHdb/+VFVVcdlll3HewpMYN+54Zs2aFbnthhtu4Nxzz2XEiBH84bEXuPt/7hvw6wGhdUO33367qeMVsZFmm0OAUmoB4AUe7hHg/Apo1Fr/Qin1A6BEa32bUmoy8CgwFzgOeB2YoLUOHOs5pNmmSKWT/2MpJ1WXcs9lMyPHNmzYwKRJk/q8z7b60G6Y6vL8hI8v0Q63+djV2Mb4YQXkOax9nucPBFm/v5kRRXmUF5jTRDKa9fsOU5TnYGRJfFM1+/fv5+qrr+a1114zaWS9bdjfTH6OjSq3M+rt2+q9oKF62MDeIx999BH33HNPnzWJor0npdlm4sgU1RCgtV4GNB5x+ELA+HXqIeCiHscf01p3aq13AFsJBTtCpC2j2N9gZEMfKsNA2zUYu4bsCepDZbBaLIMqkNeXESNG8NWvfnVQhf4GyshmHasnl22QbScaGhr46U9/asbwhAlkimroqtBa7wfQWu9XSg0LHx8JvN/jvNrwsaMopW4AbgAYNWpUAocqxLG5XTmDDnD8QY0zSwIcI2Dx9xNU+APJqf1jZj+qyy67zJTHOVJQhxZBH+u1GGzjUGO3m0gPksERR4r23R71O1xrfb/WukZrXVNeXp7gYQnRt1KXA88gCv11d5HOjh+B3RmcY38YGwGQcX7CxpMB/aiMYC9ao02DkYmSpRyZKTu+u0Us6pRSIwDCfx8MH68FqnqcVwnsS/LYhBiUUMPNgQc4RhfpbJmisiiFzWIZ8BRVMjI4aR/gDOC1sFkVGtL+WkR0EuAMXS8A14T/fQ3wfI/jVyilcpRSY4HxgNQdF2nN7XLQ3hWg3XfMtfARiWy0mSo2q4pkJfriD2gUKlJtOFHMnKJKlEijzX7W4ABpfy0iOlmDMwQopR4FFgJlSqla4HbgF8ATSqkvA7uBSwG01p8qpZ4A1gN+4Ob+dlAJkWqRasZtPkY6+t+5E62LdKazWweSwQlitUav3Gsmm0UR1Jqg1jG1wUiGyHTdMd4DPdtOiMwjGZwhQGt9pdZ6hNbarrWu1Fo/oLX2aK0Xaa3Hh/9u7HH+XVrraq31RK31y6kcuxADEQlwBrgOJxlTNTt37jyq4Nsdd9zB3XffnZDns1tV/2twBrBzbCBj3LlzJ3l5ecycOZOZM2dy44039ro9EwKDSBbvGGtwJIOT2SSDI4TIeN0NNwdW7M/YwnysD7dMY7Na8AeDx8ya+E0sblhdXd2rgm+vsfQIDOx9l+VJKX8g9Doda7rOeH/0tztNpKfs+e4WQgxZg224mazt0seycOFCbrvtNubOncuECRN45513AHjwwQe55JJLOOeccxg/fjy33npr5D433XQTNTU1TJkypVe13DFjxvDLn/yYL114FnNq5vDhhx9y9tlnU11dzX333Rc57w//82suPutUpk+f3uv+d911FxMnTuSMM85g06ZNcV9bJIPTT0YplQZSydqWAdch+iYZHCFExit1hary9hngvPwDOPDPyJdF/gCuoMbiiONH4PBpcO4vYr8/4Pf7WblyJUuWLOHOO+/k9ddfB2Dt2rV89NFH5OTkMHHiRL7xjW9QVVXFXXfdhdvtJhAIsGjRItatW8f06dMBqKoaxV+ef5X7f3U71157Le+++y4dHR1MmTKFG2+8kVdeeYUd27fx8hvvMLwol8WLF7Ns2TJcLhePPfYYH330EX6/nxNPPJHZs2cDRIKjI6egAHbs2MGsWbMoLCzkZz/7GaecckrkNiPzYUaxv0TxB3W/i60tFoVFpf+CaRGdBDhCiIxXmGfDZlEDbripiV7wyUx9LeTtefySSy4BYPbs2ezcuTNyfNGiRRQVFQEwefJkdu3aRVVVFU888QT3338/fr+f/fv3s379+kiAs3jx52gDTpg8lc72NgoKCigoKCA3N5dDhw7xyquvsmLZG5x16jxsFoXX62XLli20tLRw8cUX43Q6w4+zODKOaIENhCoM7969m9LSUtasWcNFF13Ep59+GmmkmQlrV4yu6v3JhJo+IjoJcIQQGU8pRYnL0fci4yMyLfsbWvEHgoyvKEjYmEpLS2lqaup1rLGxkbFjx0a+zskJZZ6sVit+v/+o4z1v27FjB3fffTerVq2ipKSEa6+9lo6Ojsh5LmcebV2h4K3n/S0WC36/n2AwyPU3/yvf+cbNlISn9ADuvffeQe+qysnJiTzH7Nmzqa6uZvPmzdTUhFoqZcIi40BQk2vv/7ozYcu7iE7W4AghskKpy0Fj28AyOIHgwH57j0d+fj4jRoxg6dKlQCi4+cc//sFnP/vZmB6vubkZl8tFUVERdXV1vPxy7w2ONotCoQj28WF8+qKzeO7xR+hobwVg7969HDx4kAULFvDss8/S3t5OS0sLL774Yr9jqa+vJxAIVY/Yvn07W7ZsYdy4cZHbjamddA5wBjJFBaHF2+k81Sb6JhkcIURWKHEOvJqxP6DJsSV+gfHDDz/MzTffzHe/+10Abr/9dqqrq2N6rBkzZjBr1iymTJnCuHHjmD9/fq/blVLYrH0HFaeevojz1nzMmQtPQSlFfn4+f/3rXznxxBO5/PLLmTlzJqNHj+61lqavNTjLli3jxz/+MTabDavVyn333Yfb7e51TjpnPgLBUI2eYxX5M9gsis6u9LwOcWxKemwIM9TU1OjVq1enehhiCLv5bx+yfl8zb35vIQAbNmxg0qRJUc/9ZO9h3C4HxxX3XxQwk2w96MWiYFx5/lG3ebyd7D3UzqQRhdgTnL0C2FLXgt1qYUyZK+HPNVg+f4CNB1qoLMnD7co55rn7DrXT2Opj6siiuJ832ntSKbVGa10T94OLo8gUlRAiK4QabvZfBydo/PaeRVWMDfZjtGvoSnL15nTO4HQXehzYIuOg1n1O/Yn0JQGOECIruF0Omjv8A244mU19qAw2q4WuPtaL+ANBbBZLwts0GNK54WZ3J/GBLTKG9N4RJqKTAEcIkRWMasZN/azDCUR6EGXfjz97OKiIlm3wBwa25sQs6by9ejCtOozXTBYaZ57s+w4XQgxJxlqK/mrhJKMPVaoYO8OiZbHMbNMwEFZLaPdROq7zjAS5A1pkbLRrSL/rEMcmAY4QIiu4B5zBGfj0RKaxhz+wu6JlcILBpGatrBaFJj1r4fiDGqXUgDqdZ0JNHxGdBDhCiKzgjjTcHLoZHGN3lD9aBicFU1SQnoGB0VV9IOuRMqEqs4hOAhwhRFYYaMNNf0CjSHwGZ+fOnUydOrXXsTvuuIO77747Yc8ZyeAcsZNqMHVfBjrGdevWMW/ePKZMmcK0adN6VVWG9F6cGxhgkT9I7+sQxyaF/oQQWaHEaQf6z+AEgkGsSdxNlEyW8LTLkWtwzF5Y7ff7ueqqq/jLX/7CjBkz8Hg82O32XuekdQZnEOuRlFKhBdP97M4T6UcyOEKIrGCzWih22mlsPXYtnIGW6E+0hQsXcttttzF37lwmTJjAO++8A8CDDz7IJZdcwjnnnMP48eO59dZbI/e56aabqKmpYcqUKdx+++2R42PGjOGHP/whJ598Mlect5CPPvyQs88+m+rqau67775IRud///se5syZw/Tp03vd/6677mLixImcccYZbNq0qd+xv/rqq0yfPp0ZM2YAob5bVqu11znpvHZlsOuRrBaLZHAykGRwhBBZw+2K3q7hlyt/ycbGjQC0d4V6KOXZrUedNxgnuE/gtrm3xfUYfr+flStXsmTJEu68805ef/11ANauXctHH31ETk4OEydO5Bvf+AZVVVXcdddduN1uAoEAixYtYt26dZFu4lVVVaxYsYLrbryF797yNVZ9sIKOjg6mTJnCldd8mffefoMd27aycuVKtNYsXryYZcuW4XK5eOyxx/joo4/w+/2ceOKJzJ49G+i7VcPmzZtRSnH22WdTX1/PFVdc0SsQg/Se2gkENLbcgQe56bzlXfRNApwhTCk1EXi8x6FxwI+BYuCrQH34+A+11kuSPDwhBi1UzbifflQakjE71dcUWM/jl1xyCRDqyL1z587I8UWLFlFUFGoNMHnyZHbt2kVVVRVPPPEE999/P36/n/3797N+/fpIgLN48eLw+VPwtrRQUFBAQUEBubm5eDyNrFj2Jm8vfZ1Zs2YB4PV62bJlCy0tLVx88cU4nc5ejwNHBzYGv9/P8uXLWbVqFU6nk0WLFjF79mwWLVoUOcdqUSjSL4MT1JqAHlwWz2pR+GSKKuNIgDOEaa03ATMBlFJWYC/wLHAd8GutdeJWQwqRAG6Xg+31rUcd75lpWb+vmcI8G5UlzoSOpbS0lKampl7HGhsbGTt2bOTrnJxQ7R6r1Yrf7z/qeM/bduzYwd13382qVasoKSnh2muv7bWw17iP3WbFZs9B6/BWaIuFjq4utNbc9oMfcNMRQcu999476PVIlZWVnHrqqZSVlQFw3nnn8eGHH/YKcJRSkVo46cSoYjyYXXQ2q6JNGm5mHFmDIwyLgG1a612pHogQsXK7cmhq6zuDo7UmENRJqQeTn5/PiBEjWLp0KRAKbv7xj3/w2c9+NqbHa25uxuVyUVRURF1dHS+//HLU80L1Z3SvzEkgqPnsaYt48M9/xuv1ArB3714OHjzIggULePbZZ2lvb6elpYUXX3yx37GcffbZrFu3jra2Nvx+P2+//TaTJ0+OOpZ0m6IaTJE/g9WiCAR0WhYtFH2TDI4wXAE82uPrW5RSVwOrge9qrZuOvINS6gbgBoBRo0YlZZBCHIvbZaepravPxoiBoEaTvEXGDz/8MDfffDPf/e53Abj99tuprq6O6bFmzJjBrFmzmDJlCuPGjWP+/PlRz7Oq7rUvtvAyI38gyKmnnUH7wd3MmzcPCAVgf/3rXznxxBO5/PLLmTlzJqNHj+aUU06JPFZfa3BKSkr4zne+w5w5c1BKcd5553H++ecfPZY0XLsymEabBpvFgia01d6ahbvvspWSiFQopRzAPmCK1rpOKVUBNAAa+CkwQmt9/bEeo6amRq9evTrxgxXiGB5YvoOf/n09H/2/MzmwexuTJk3qdXtnV4BNdS1UuZ2UOB0pGmVitXb62VbvZWyZi4Lc0NbtbQe9oKC6PD+pY9nZ0IovEGRCRUFSn/dYmtp87GlsY0JFAbkDXGje1OpjT1MbEysKyIljcfqGDRuOek8qpdZorWtiflDRJ5miEgDnAh9qresAtNZ1WuuA1joI/B8wN6WjE2KASvupZpzNVYwNtijF/pLdh8qQlhmcGNbgpPOOMNE3CXAEwJX0mJ5SSo3ocdvFwCdJH5EQMeivmrHxAZUOdXASxW45ul2DPxiMNOJMpnTcXh0IBlGoQb0HujuKp9e1iGOTNThDnFLKCZwJfK3H4V8ppWYSmqLaecRtQqSt7gCnkwIrkZ1EBrMr+qYjiyX04W1kcIJBY2F1ajI4Qa0JBjWWNAkqjUKPg9k5ZkY/KlkOknwS4AxxWus2oPSIY19K0XCEiEtpfvcU1cRhuXg8HkpLSyMfZkNhigpCTTeNdg2Ra05io01Dz6kdR5q85rE0HbWGA+JYt7xrrfF4POTm5sZ0fxEbCXCEEFkjksHx+qg8cQy1tbXU19dHbj/c3kVrp59NLXmpGmJSNHg7CWpNe30uPn+Qgy2d+BsdHIyzevNgtfsCofVQh3Iinc5Trb4l1Moj0JjTz5m9HTzUTttBGw159v5PjiI3N5fKysqY7itiIwGOECJr5Nis5OfYaGzzYbfbexXVA/jOE2v5YPsh3v3BiSkaYXJ8/8mPeWeLh/d/uIilG+r46gurefbrJzNpVElSx7Fim4evPvo+f/vKZzj5+LKkPndfbr77LSYdV8j//suk/k/u4bqfL+WU8WX856WDu59InfQIqYUQwiQlLnufi4ybWn2RLE82qyjMpd7bSSCoafCGMhZl+YPLWJihxBXKdjS1dSX9ufviafVFdtsNRonLccwikiL9SIAjhMgqbldOnwFOY1sXJUMiwMkhENR4vJ00hHtzlRekIMAJ1xpKl8CgKxDkcHtXTEGu+xiBs0hPEuAIIbLKsRpuNrZ24nbGtoYik1QUhhaz1jV3Ut/SSX6ObcBF7cxUHH6tD6VJgGMEWrFkcI4VOIv0JAGOECKruF2OY0xRdeF2JT+TkWxGgHOguYMGbydl+anJWuXYrDgd1rSZojLeF7G8B9xOyeBkGglwhBBZpTQc4BxZd6TTH8Db6cftGkoZHCPASV1QV+JMn7UrjV4jwIltDU5zhz+y/V6kPwlwhBBZxe1y4AsE8Xb6ex1vau0K3579GZyyfAcWBQebO/B4fZH6QKlQ7LRzKE0yOEYLj1heD2NaK12uRfRPAhwhRFbpq12Dp7UzfHv2Z3BsVgtl+Tk9pqhSF9S502j3UfcUVWwZnJ6PIdKfBDhCiKxi/HZ+5AfRUMrgAAwvymXvoXaa2rpSGuAUOx1pk/XwtPpQipg6ybudEuBkGglwhBBZpaSPD6KhlMEBGFaQy4b9LQCUpWCLuKHEaU+jDE4nxXn2mJqtuvPTa8u76J8EOEKIrFIaztB4jsrgxL6DJhNVFHZvay5P6RocB4fbu9KiE3djHIUejQzOke8rkb4kwBFCZBV3H1NUjW1dKAVFMfYSyjTDC7sbO6Z2F5UdraG5PfXTVB6vLxIAD1axUbRQApyMIQGOECKruBxWHDbL0QFOHNMTmagibQKc9JnaiSeD47BZKMi1yRqcDCIBjhAiqyilolYzDhX5y/42DYZhhd1BTSrX4BjVjNOh2F9jqy+S4YvFsYpIivQjAY4QIuuEPog6ex3ztHYOqQBneFEog5Nrt+ByJL9Ng8HI4KS6XUMwqGlqi63RpiGdihaK/kmAI4TIOm6Xg8YjMgZDLYNTURAKcMryc1AqddNy3VNUqc3gHGrvIqhjq4FjKJUMTkaRAEcIkXVKo2ZwYl9/kYmKnXYcNktK198AFLvSo+Gmx2uUCYgjgyMBTkaxpXoAIrWUUjuBFiAA+LXWNUopN/A4MAbYCVymtW5K1RiFGKwSlyPSdwhA69D0xFAKcJRSjCjKZVgK198AFOTYsFlUyqd2Im0a4igT4O7R5yyVWTExMJLBEQCnaa1naq1rwl//AFiqtR4PLA1/LUTGKHU5aPUF6OgKANDc7icQ1DFVsM1k//mFGXzv7IkpHYNSimKnPeVTVPG0aTCUOB10+oO0h99XIr1JgCOiuRB4KPzvh4CLUjgWIQbNKOZnfKg1tsXeZDGTzR3rZkJFQaqHEW7XkCYZnDjeA6XSjyqjSIAjNPCqUmqNUuqG8LEKrfV+gPDfw6LdUSl1g1JqtVJqdX19fZKGK0T/jmy4afw91DI46aLEaY/0AksVY8oynveANNzMLLIGR8zXWu9TSg0DXlNKbRzoHbXW9wP3A9TU1KS+DrsQYcZv6Z4jApx41l+I2BU7HexpbEvpGBpbOynIteGwxf57vdHHTAKczCAZnCFOa70v/PdB4FlgLlCnlBoBEP77YOpGKMTgdWdwQjtnjPL6JUOk0Wa6KXHaU95R3NMaXw0c6J76TPWCaTEwEuAMYUopl1KqwPg3cBbwCfACcE34tGuA51MzQiFi071WIvSh6jFhgamIXToUyIunTYMh0nDTKwFOJpApqqGtAng2vN3RBvxNa/0PpdQq4Aml1JeB3cClKRyjEINWmBvqORXJ4LT5yLVbcDrkR14qFBu7j3wB8lJUVbmx1UdliTOuxyjItWFNgy3vYmDku30I01pvB2ZEOe4BFiV/REKYw2JRlDjtkbUSHq8v8tu3SL6SSD8qH3mOvJSMwdPqY0ZlcVyPEXpfOSKZQZHeZIpKCJGV3D0abja1xddkUcSnOMUdxbXWNMXZaNPgdtmPqpIt0pMEOEKIrNSz87On1SdbxFPIyOCkaqFxc7sff1DHvcgYwuuJJIOTESTAEUJkpVJXTiTAaTJhB42InVE/JlUZHE9r/H2oDKX5jkjhSJHeJMARQmQlt8sR2T3V1OqLfMiK5CuOrMFJTebDjDYNhtAaHAlwMoEEOEKIrOR2OTjc3kWbz09Lp18yOClUnBd67Q+lKDAwo9Gmwe0KtZ0IBKW2abqTAEcIkZWMasbb61sBJIOTQg6bhfwcW+ozOCYsMi5xOghqaG6XdTjpTgIcIURWMhYVb6v3Asg28RQrdtpT1nCzu1WHOWtwoDsrJNKXBDhCiKxkfJhtqQsHOJLBSalUVjP2eH04HVZy7fEXGSxJ8ZZ3MXAS4AghspIxHbH1oAQ46aDYaacxZVNUnab9/x/ZqV6kLwlwhBBZyfgg2nKwpdfXIjVKnI6UTVGZ0WjTYLyPmiTASXsS4AghspIxlbDL04ZS3dV0RWqUOO0pCwrMaLRpMN5XsgYn/UmAI4TISnarhaI8O/6gpjgv1HxTpE6x00Fzhx9/IJj05w4FOPFvEQfIc1jJs1slg5MBJMARQmQtY1pCtoinntGu4XCSt1drrUNTVCb2InO7pJpxJpAARwiRtYxpCSnyl3rd7RqSG+C0+gL4/EFT12D17HMm0pcEOEKIrGV8qEqjzdQz1kAle6Fxo9e8Ng2GEpdDpqgygAQ4QoisZWRuZAdV6pWkqB+V0WjTzCxeqUxRZQQJcIQQWcstAU7aSFWBPDMbbRpKnI5IZkikLwlwhBBZSwKc9GF0FE/2FJWZjTYNbpedVl+Ajq6AaY8pzCcBzhCmlKpSSr2plNqglPpUKfWt8PE7lFJ7lVJrw3/OS/VYhYiFsXNGApzUy8+xYbOopE9Rmdlo02BsOT+UosrMYmBsqR6ASCk/8F2t9YdKqQJgjVLqtfBtv9Za353CsQkRN+O3dglwUk8pX7EMYwAAIABJREFURXEKqhk3tvpw2Cy4HPH3oTK4XaFslKe1k+FFuaY9rjCXBDhDmNZ6P7A//O8WpdQGYGRqRyWEeU4aV8oPzj2BedWlqR6KwKhmnORFxt5QmwalzCv0GFlPlORrEYMjU1QCAKXUGGAW8EH40C1KqXVKqT8ppUr6uM8NSqnVSqnV9fX1SRqpEAPnsFm48dRqcmzm/fYuYpeKjuJmNto0RBpuyk6qtCYBjkAplQ88DXxba90M/B6oBmYSyvD8V7T7aa3v11rXaK1rysvLkzZeIURmKnbak75uxcw+VAZpuJkZJMAZ4pRSdkLBzSNa62cAtNZ1WuuA1joI/B8wN5VjFEJkh1RkcMzsJG4oyrOjlDTcTHcS4AxhKjQp/QCwQWt9T4/jI3qcdjHwSbLHJoTIPsWuUAZHa5205zSz0abBFm7kKhmc9CaLjIe2+cCXgH8qpdaGj/0QuFIpNRPQwE7ga6kZnhAim7idDnyBIG2+AK6cxH/8dHQFaPMFTG20aZCGm+lPApwhTGu9HIi2tWBJsscihMh+PasZJyPA8SSgirHBLdWM055MUQkhhEiK7mrGyVlonIhGm4YSV/LXE4nBkQBHCCFEUhjd3ZMVGCSi0aah1OWIVEkW6UkCHCGEEEmR7I7iiWi0aTAyOMlcMC0GRwIcIYQQSVEcXoOTrHYNjQlotGlwOx10BTQtnX7TH1uYQwIcIYQQSVGcF87gJKnFgafVh82iKMwzf0GzFPtLfxLgCCGESAqb1UJBri1pa3AavT5KTO5DZTACHCn2l74kwBFCCJE0JUnsKJ6IKsaGEsngpD0JcIQQQiRNidOexEXG5jfaNLjD64lkJ1X6kgBHCCFE0hQnMYOTiEabBnd+cre8i8GTAEcIIUTSJDODk8gpKpfDisNqkTU4aUwCHCGEEElTnKSO4j5/kJYOv+mNNg1KKUpc0nAznUmAI4QQImlKnA5aOvz4A8GEPo8RRLkT0GjT4Hbl0JikLe9i8CTAEUIIkTQlrnA/qvbEBgYer1HkL5EBjp3GcDsIkX4kwBFCCJE0yapmnMg2DYYSpyNp64nE4EmAI4QQImmS1Y8qkY02DdJwM72ZX79aCCGE6EOJM7EF8vYeauev7+/isZW7UQqGFeQm5HkgVOzvcHsXXYEgdqvkC9KNBDhCCCGSpjicwTlkYgZHa8372xt56L2dvLr+AABnTKrgK6eMoyj8fIlgTH8dauuivCAxu7VE7CTAEUIIkTSRDI4Ja3DafQGe/WgvD6/YycYDLRQ77dywoJqrThpFZYkz7sfvT6ThZptPApw0JAGOiEopdQ7w34AV+KPW+hcpHpIQIgs4wwXy4lmDs6exjb+8v4vHV+3hcHsXk0cU8qvPT2fxzOPItVtNHO2xGe0aPF4fVCTtacUASYAjjqKUsgL/C5wJ1AKrlFIvaK3Xp3ZkQohMp5Si2Gkf9C4qrTXLtzbw0Hu7WLqxDotSnDNlONfOH0PN6JKEdAzvT4lL2jWkMwlwRDRzga1a6+0ASqnHgAsB0wOcXz7xOTa27Tf7YYUQaWx0mZ+t9ZpL/zC4oCSoNQqYV23BbrXQ1qX43VsJGeKAaK2ZPcbP22uGc960V1I3EBGVBDgimpHAnh5f1wKfOfIkpdQNwA0Ao0aNSs7IhBAZL8dmwR/Ug7+fxZJWu5WUUtgsFuyW9BmT6CYBjogm2q9VR/000lrfD9wPUFNTM/ifVsBtl70Yy92EEEKIY5KwU0RTC1T1+LoS2JeisQghhBCDJgGOiGYVMF4pNVYp5QCuAF5I8ZiEEEKIAZMpKnEUrbVfKXUL8AqhbeJ/0lp/muJhCSGEEAMmAY6ISmu9BFiS6nEIIYQQsZApKiGEEEJkHQlwhBBCCJF1JMARQgghRNaRAEcIIYQQWUdpHVN9NiF6UUrVA7tivHsZ0GDicDLNUL7+oXztMLSvX649ZLTWujyVg8lWEuCIlFNKrdZa16R6HKkylK9/KF87DO3rl2sfmteeTDJFJYQQQoisIwGOEEIIIbKOBDgiHdyf6gGk2FC+/qF87TC0r1+uXSSUrMERQgghRNaRDI4QQgghso4EOEIIIYTIPlpr+ZOEP0Ae8DZgDX/9D+AQ8PcjznsHWBv+sw94Lnz8O8ADPc77IvDSEff9AqCBmh7HjOdZEuX5DwNB4GtHPP8BwBe+refzfwCsB9YBnwBvHPE8rVGefyXgDz/XcuD4HreVA13ALcAjwD/Dj7sHqAW8R1zfd3o8/1JC9SMARgNrwq/Zp8CNPe6zOHw9W4CngGXR/g+At4BNwMfhsW7o+X8ABIAOoBF4ElhEqO7PEsAZHv/W8PVr4JQeY3g3fOyDI64nEL5mHX7e4h633RX+P+g64j4rw8fbgTbgaWBkj9tbgHrgt+GvFwK7w4+/JzzG/CMe89rwfXaGr3E/8KvwbXeE/1930P2+HB7++3D49Q4C3vDjrwSuCd9vb/gatwDPAJOBYeHHGt7j+X8H/CD87xuBq4ELgI/C494XHvfHwDbAE37+9cCVPa6hI/zYG4Efhe9/HTCG0Pvq7B7X4A3/f68FHj7i9agBfmOMJXzsQeD28P/V58LHrgeaw2P6BLiwx7nG6/UxsKjHY78PbA3/+3uE3n9nh/+fPMCdwHa6vwf/GX6cX4TvswD4kND31BeOGPevwv8fG8LjN5Yg3EL3e7Osx/lFwObw+D8FrjviPfEA8N+Evt8+BP4IVHHE92WCfl4uIfz9EMvzARN7/F+vDf8/fTt8W2uP13UnsLbH/R4j9DPj8fA5qwi9l/PivJ4/ApN7fG/vOfK6ws/THH691wBL+/v5Fr49J/xeWhk+5wVg/iDG5iD0c9EWw3X9BPj/7Z15mF9Flf4/JztZSCAsYgITiICy77IbBVRGAYGwiaNxG5FlEHQcGAYkj6gsYmSAAQd/EHAQgQkqE4dV04R9k4SEBAIJgbAmQEjI1unu1O+P963cm2+6k45xQLHe5+mn77duLadOnTrn1Ll1bx3o6yZquv//WD5Orstrh/k6UdF7bZjHtkNTNozfaEjvSHDaNYy+9yWkgJ8FvlRLvx4pwMnA1UD3DvhTV8RTGmmq5TsJONWT5wxkIA/J/UMK5bKGMnOAc3zdzbzdBxiAFOgWtbz9LKQvASfW0nM7k4BTnXYocBVwBVKqTbX8X0HKcIrHZJTTrzXPn3Vd82q0NwHfMq/mIYOalfZi4Dqnz6Iy6hOAOyw3M4Cf1Gg4GjgGaG3gx8eB3r7+JnBjbYL29HVfpLQ+CJyInLJjazJ6UwNvTjMvm7D8Af8I3OrrMcjgLkBK9yXkRLyK5HwL4Ez35WXkAM0E9nL5E5BRewqY1dCfBaYxO5Vn1e4diwzVMqBPw7z4PFKYI5HsTQMGAuchwzfXadt6DG6vydgT2Jmo1TnC9b0IbIU+QHYzcA5wOVLyw5HhHo5k+VLkGA7xuO/kfn/BY/sbpHQXuI3ZwOto7p4A/JfTd0HzsgdWsEB383YwcIPb2tv3LnYfTgW2RAahu/sw1fQNQbL6r8CPgeOAyQ19Xj7enVSoo5GDM8vjORg5Brebx32BzWt5h9dk7NlaPQ+h+TUYyVCeQ8PQXNwNCPfxBWoOifMNAXZAc2p4LX1v5Eh39d+DwDDf29nlZrKig3MWcEFNp74F9PDvf0b65+ha2gHAY8DCzvLN5bquSf52yq+VQ2V+vEa1IFrOB8vTObW8ZzrvXh6HHkgf/AHrmD+l/YbfewKbNPaLFfXbT4BXVqXf/LsnsmlnAf2dtiVwJ3DEGtD4PeD4teRzE++eg9MbeGK1+TpR0UnYMPr3Coa5gzJjqFY+nTXMD7Gig9NhO1RGoelPEJy6YVwfGdf1gfV8vZ7v/b0FPJCS/WY7dCxXxDVh27oDnjwADGlIG0YHDo750gJ8rJa2t3n5n8DZDXX9FDkuKwmZ23mrnfafRFGV54BBVEq7v9tPwPbOOxoZjAnA/yDn8/6aYP8S+BFyYE6vtbvAPB+LlMfbVMrlXmAPp41sh+ZWaoYJGcxzff2Yef8AMrB7OP0CtzneNM0DfocexzYDDzhfF/f7MLTymY9W/ncjWZ1iHswF1nWdWyDl1+b0s13Xv6MV7ksu/xSVsxRIrj6PIgy9nH4+cl4WAv+FHIDnqAzj901vC1oljQd+bV7dafp3Ni2zqBzJNvPgEfPnRPP+68gYTzJ9j/v/PyLZuwkpzimm6X7kJC0wr4cjp3+haT3V9xa5H5ch5/hu5AAm/2/1uM1FMthCFaV7wX1c7LbvRFGLJa7zPvPpJPd7se/dANzlOhaY5mbfm+OyORpyh+tMbnsWch7mu/1XXLbV/B/jdp71WL/s6+f8+1W3/U1fzzNP3vT9qW7nTae/anpmU0U4F5nWaaa31XmaqeSmzfU87bEZ7fSZwA9d33TkII5z2aUe02+7jWtd5jLgGqrFx397bN7yuBwNbI4c0GloQfuS+dyPyql7FC2MEtJ9F9XG5HXz6VfAUVSy3WoeXu/+tJnWyWjh+Q5VdHQ6cCCS08zDe13XpR6bR933qR6/ej9akPxlWZmAnMEj/ftN96UVLWwvdN3vmG+nIlmfieTqAjRP7kaLh+loLg/3mMxynx9GjulYt3OB+zTD/XzC6VkvDnG/2pDe2LuWPtnXXwPeaseODHRfp6JF9X1owbCTx+hJpCc2QXN4PDAKzftm8+Q5j815KGp0LdKji4AjzJdJHofubvcc834ysj9RswvDa3ZgJQcH+JB5ONH9HYr04kWubxJwTE3v32P+TkPz/3ikzyYBQ2v1/hrr/Y7+OrMH53jgt/lHSun3SCDaRUT0Az6BVnCklFqRkr3cjLs6pTSjVuT7Tl9Sr2c17RyHJvHgiBhUK/NQSunVxswppXEppUX+mVdfoCjDXSmlt1JKc5Hi+rTL/G8yEHMHN9aLJn83NHFIKTWnlJ4xHw6JiIcj4omI+D16NDMzIkZExGUd9KuOw9FEXej6rkCGdHOkQC50+syI+A+0cu4NfBjxP98biQRyANDL6SMiYjRy7N5GwnQMenzwDprchwNtKaVJNZqmoYm1HdAHuM3pfVEY9Tg0ScbXyjyJhPQA4B+QkiUiNkWPKR5BCvf0iHgwIs6LiC1dNoBNI+LpiLg+VxgR5yDFNQBNkBOB6yJiATKm3ZBT85KLJGQEAtgtIqahiNNE83MImnhfQAr6h+7nQy5/P1qFzUCTqgtSjDdHRI6g7IA+v76h6bo6IjZAYf42ZEgDuCEiHkfKvdnp30KrzEHAxRHxDY9HclsnAvv6+rfAxmiyn0G1j+4GYDOk5EcgZ2wgWqGCopvDkHx0RQpnN+CfgCvR/BuEnK1mtz0EGbJdkAN9VK3v/4xWli8ieTnW7exHhTedFyRXF/v/xmjubOayS4Gvur3ZSIEvQ47mVsjw9DYf5zttKnpUBlKgr7pfA52vPxr/7dAceg7JZl/0+GEhkvcbUbTvdvNgKponc82XF13/NBTxbPH1scix6oMM7USkk/ZEMr4uMqKnUUVktjW96zjvBqZzoNvqYd7gdhajR1LbogVYxiwUTZyKDMxdKFLVisZwJJKrfSOiu8vsa153R48K10dztgdyfiabzn2QvPVE4/cx0zTQvOiDZKO/eb7UtF3rtkHGcCnSmxugaMRxyAHcyOOztcdjO+S8nI1k6wbgFPMwPyZfhJzn61NKuyNd0tX5s9P2EdOzP3qs9m3kWF2HFgizzK+TXPYK5FxORHNhJPBdJP/Pup9HosjNh4CPup3DXf9c0/wosGttbHohQ98FRUJfQYu4ycA3nGc2cBAa32OQXm/EZ5wPkL6MiCfdp7nArimlHZEs/Qzprd6+90E0X29G49yGdNL55v8k5ESMcPmhKAiwxP0fl1La3vR9xiRcllLaPaW0nct8th2aO8L1wOWmd280Tkcgp2xH5NReFBGbOP+OaM5vj2zGVimlPVCU+ZRavY+xor5ZCat0cCKiB4q2zFyDzhyOnh3OzwkppQeQcB2IDbPr3xnYNKU0trOVNxjGbJjXBF+lMsyDkOBnvEQ1SXN73RGTb2+sKKX0Fnre+UJE3BARx0dE5ul9wJ4ppZ2RMurWCdqOiYgJETEBrbr61u6dhQzUbCRg9YH9FPKcf9VOnW+gyMlSZPwztkRGHzQ5jkOT/XUUZfshmhh19EJGcQNkqC5yfz+ElPCv3U4dmyND/HukSNd3+rFo/EATfBry6NdHSmMzJJ+vIoW8BTKAmMYlSNbWQcq2N1qdTHKfRzfQ0R1N2DbkUHzX9IAU3g7IkbkMOQFZQX4NTf5eHpcRLpOQY/ZlZAReQY8FBiIFc3FK6Q2kwCe4TLP78lEka72QQ/QiUuILgX9Bk3s+lYMTyNnayPnvQyu0bdyfMN8mIYO1AMnJGGRYP2D6FiLl/QwyYlPQnooADnZ7PzBdj5r39yEj8qbbOt+0DUIyPQQ5KwM8Rj2o9nYtpdIxvVDUYz2PxVzff8s07uS+vW065vp/b2SIuzr/hubpcW7/O0geNnGeRU5f6nbedNtDTWc/qsdAPZCifggtCj6MFnQ54oJ5sLD2+2UqHTHO/fsAMqz/gqJQ65mWnyGl3GZeg2RgGXKmAZ5KKc1DK3qonLYuyMiOQ4urAVS41f9fQNGD+1xfd+SEHoXkcIr7N8D0tCDHZSAa3yepFgPfRnMpy+g95tH30FybCXwSzfU2NK/3dZuPuM28iLzf6VPd7nw0925PKb2JIhttKBJ8Kooi/gjN+YHIMA8wrRu5zm7ABZ6D/4tkrZ95/XRK6TXzdRmK2j1e6+vWyOm4LaV0K5Ktrq5jNnocNQrptTlUEb+t0JzG5ROaYxshnQmS3ydrY9OCbEWb6bgnpdSCZOiDztMdRcLWcV+3qZUnIr5gmpcHAlJKs1JKOyC9FkC/iNgQjcu65t0ItEDviaK7z7iNSa7rHhSpusZ8moF0520ppRxJ7Upl6yZR6dyPe8E+Cc2V7KyvEg54DEop/dr9WOJgw77ADSmltpTS66Ztdxd7NKX0qmmajuZUIz2gsfsgq8DqIjgbIIWzJjgOeeHLERF9kVLpjhQUNoyj0MRaE9QNYzbMnYIFZzdkSEGC0ojU8Ps/gPEppXvbyUtK6WtoRfEIUrZX+9Zg4A4LxAlI0FaHG1NKO7m+FqSEMo5Gg50fXZ1X68N6QFNEzET8PSMi8jkntyClBSsKx5ZoMnwCKcwd0YT/NFotrQd0j4hza2UuRwq4DzDRAtjPv7uj1dEA4Fa33x9Nvqm5f2jygcZthGm+FXnrk1JKJ6JVxJ5YYaWUliEnYWPTeLb7fQUrTraEjOUryAFchIxYuK4WZPzXQUrqPOQg90OGax/3fyqa7PuhEO7drv8m5Iwscb9eTilNQBNzMHJAs/G7IiL2QMrrICSrfZBDsQ5yijB9PV1uIFqJD0LKtZfz9PK9CcjpHWyaT6dyKAegx1CBZODDVAZiMDLiTyPnbz/k4AVSaN08Nv3QSrwnMozzkYPQEy0EuiED0M/8nOx2ZqIxbPEYnOKy6yId08P090WO8WhkfLs4/Ra0abCf+/5T4BdUzhvm+T9RPc74OYpYnEs1tvNR9KA+xsvcx9keu59QRSuWUDlKByOncyQr6oB8HWge7+A+X4jksBXJ0LVIDocjYzYTOcs/cPutricb4YfNj5aGdrpFxOamaTGSk4muI6O5VqY5pTQeRSpaUHRhb6pN4yPQ3Lqv1o8brGemoIXHTSi6ssB9y/OlF9I5LeblLCqbMRvpqTle7Y/MNKaUTnB/+yKZzVGfOt0gXXw8kp0rkVOSTNvbwC0ppY/U6D4JLWqa0aJzLHLY60imo8087EW1Ib+5lq8Fjdt6KMo6Cs2NbsjutVrv5MVws/u2jEomW1nRhvZCUe+EZCs/jsvIY3gacsoXmwc9ann6oMXsv1JFP+tYhHTVfqYjL0K7pJT+mFJajGQLNGZLWVH26vQso3p0D5rnLaY/3+8WEb2QHRzusb6KSjetDu3Z2FWl0w6NzbXreqAgj22HWJ2Ds5jOd4SIGIjCcb9ruDUSGa0fIEECKbPtqAzznlSGcVVoNIw71h5prIq2A5HgHGrDDFJom9ayDUbGMZf5HjIWp9fS7nCU5ec5LaU0KaU0ChmyI518KQrrbY8NjwWlMzgKTd5lbnNzJPDzkJMyBtgoIg7CK42U0pCU0hCk2M5PKT3mupqR4sorVpCT0h1N8BakTB5Em3ETWqX9N5qkuT8DXNccFN3ZIyIO8urzfqSIb0UT7nC3n0Pi+fHeQW5vKNo4O8g0H4/k4lhHDbdBCiC5nwN9vSfal9ULGak70WTbCRm3bBA3R6sX0HhugiZ5b9N4OfCOFemNLpNl4kv+fyFS+rs4X0IGtgeSm7nArh6D45GBHmteLkGGdz00x/4dOeZvo5D3MVRO2WDTfbJpmOm+fMv325Dcfsj090NO2POmoZvTP+kxbEaO33S0eBhgOrKDPpgqejLO5Vs9TkORMzfd19l4vIGM5gy04u6CxnELtw2SyxOQAh6FVmRv+l4O4fc0/Qe7ja5IoT+E5Du/cdUbRSS6+a8LWinfROUwfdp9faKWJ6GV4TzXdQoa/25oLgUykH2QE9LTvOmHjPEH0Fg2og8yele6T1eZ/i7m3ZFUEbC8F2ZTt5sfP+Twe1bQpznfthERrPgIfF3T24aM1B4Adpg3aCQuIv4OOSt5v+Agt/8HFP3alupx6xLgkIjYCDl8m7rO6WgBsRty8D/mvEORwZyBnJ/ZaP/FC7lfEbE+imT2Nz1DzYtrkew0ozHr5zI5KpMd5TlITncB2iLiqFrfdvTlHDR266Kxex1FVrd0vhwZXgx8ztcDEL8fQo4pEXEwVYTtFdfTgvT8RubV+kAXL8I/5bp2RXoONC5H45cJkAO5MR6nTiA/3gM5atnx2QZFJA6lFliIiMERkRfID5iWF1JKs5GuWwAsiYgv28Z8Bi1mvoTkdSaaqzk6uNJTD+vYOR3Qm+3WGw5WDO9kP/GTnJci4nNup2dE9EbbGI6JiK6ORO2PggRrgq2QHVglAavbZDwLb4yspQ2j/c2/J+CNbbW07RGzeyGF8AhwUDtlm2h/c+zY2u+tgWca8oxk5Q23jZuMd0YTeMuG9PWRoVjPf88D66dqk9cDrOL1QKQUh9V+H0i1SewJ9JwUFBJ81fdHoJXdvUigFiOD+WOqV3ubkAJvQgpnRyTEf48m0uvIgE5h5TcjXgO+m6oNiQ9SbWRciibsb6heJ87tn4AeE+2CFF/erLnYbeVHV6+jVdkSqghJk/s7Bym2xVRvUeWNptkTn4Och/za6x5odTnNdT6FFGqu621k3F6k2pOUIzF5X8VcZBgXoAl9s+s+FznWc03Tyygy8Y6v73Wdy9zWcyi615T76no+7vszkdKYbH61IiX5ZTRP3kTOSStSgKNN3/PuT5vbm4DkIYe8r/GYPOgxWojmSfJfHr8c2s/GL+9RyBunx5uHl5iOfU3fUmS0WqlCvu8ghd1k/oyhegzVZhquNF15I/Fc0zKbyonIvEvuU4vba6GKViykepW8xTT81NevUa1i3zaNWbYyHbn9NrezEM2X+VSv67e57C1IFt4xHc+5X4nKmF6CdNI85Ai/VOPlpa5nLJKfMabrGapNxj9Ej7qzE543BC90Xc1IXmcjWXnD9/5I7dV/Kod0tsdu+ScbXCYh2Z+O5kiODuXHjVnes0y2mVfPoujvMx73vMG71X/jqV6zX+o+TTYfX0TO6cvI+T/M994yPRPwW1TokWPeEL7Y9FzjMciPQC5BemCG6bgPzZVFyJg/SLXfaKbH53aqtwFzRO4O5LAt9njMRXNksa/vcvlpSL6fRfLxNnK+p3tsRrmf+fMUt3kc84bdF9HejqUer4tc791Ub5MtQAvAJ92vxWhR3wQsruU5F/hOTacv8vVVVI/B5gNLnX6f+503Ar/m9BPN/4lu8xaP3QQ0h0/1OM2n0ot5j12T/x/ivr2FopjX+95/oicPw9EeuQU1W1Kn/7xavddQvewxmnY2GaMIa77e0mOXX3/fglVvMq7b/Hqdjff+SMNbhivZ6E44OP8Pv+fu342G+VMNxHy69js8aAfX0nbz4PRYlYPTXjtm+PkN5XYApvj6Qudd5v95EO6mMswT8CvAvpdfi36OFb8DkY1BLnNOO7zph54H5+9p3F8bjMOQ8N/rgXwMhd1H0PA6uPOvlN4wuKORsvodEvARNSem7uCMphK45ffQyvS1jtpy+t8hQXza/bkL71qv1+vfR1J9p6FOZw+kYC6iHUe4HXqHAz9rh5YVyiKnMPf5PMvFw6w42ZbT0c4EbTIPfoeU6szGdpBs/tzXGzjfY2iyTkWKcgiVEzsAGZLD6jxFDvUU5KQ+2EDDbrX6Mw1dkNHM3wEah1Z5w5Chzf/rPKjX9UvTsJfpuQ1FccZ4PA9wvs/X2riwYQymu87LqN7YyW+TPY/2V4123p2RbDzh+q6meo31gA7SZ7rP4TG7EMlYE9Urr/tTvQmyADlov3V9T/n/59Bmza/U+N23Vv8ZSP7yvbwXZJXf+EDG7tB20peP97v1hxYxv28n/SJghzWsayy17/CsJu8dteuPIANykMdsOJo7n20o09f/e6O5sksHded8gR53nPZu8nQN+HWt5eUjNfk5pM53FHHJb0MOtez1eLdprfF9PHKCMk2bAV9dE/4jm9LuG8B/iX/WQb9Ybb4/V0Xlr1OD8hXW8psQf83td0DTGittl2tiDb+5YOP3BrD/e93vNaR7GKv+LMPGNi5HU31LZne0Mr95Ldu+FDn/W/25ZAmF4megjdj19F8ip+dp4EyndUWPmh6wwR0LfKKh3DEuN9lGeMPavcOB769F/4fw7js4uwM7rWUdA1A0408ef/TY7Eq00Mxv2Gy8ujHroK7TqKJG1+PPdvyl/aFo7D4ocvkEWqCeQe0bOGhh+xgglsj9AAAE4UlEQVRVROXg94pe07MO8G8oovU4ii7t3Fn+o0XpF99r3q9hnw+i4bMn7f116rDNiPgKevTU+FZNQUHBXwC8B+FMtOl6GVqFjkwpTX9PCXuP4f0cd6WU1vRliYKCgr9ylNPECwoKCgoKCt536MyH/goKCgoKCgoK/qpQHJyCgoKCgoKC9x2Kg1NQUNAuIiJFxMW1399p+PDj/3X750bEy/nr3v4bsPqSq633hIj4oq9HR0S73/WIiG4R8UZE/GgVdXV49IqPDikoKHiPUBycgoKCjtAMHOEztd4rjEop7VT7W+vNwimlK1NK13Ui6yfRJyCO9sf41hoR0XX1uQoKCv4cKA5OQUFBR2hFHwI7rfFGrHiY7N0RsbHTz42IayPiztBhr0dExIURMSkibs+HP0bErhFxT0Q87q+Db9LYRkdw1OQ3EfE/EfF8RJwcEaeblofyV20j4usR8WhETIyIMf6CaqbxO6tuBdBX0y9BH0jbs9b+lyNiWkTcg14pzumbhw6MfTQivl9LHxYR4yLil8Akf731Iud7MnS4KhGxSUSMd6RqckTs57yj/XtSRKw0FgUFBe2jODgFBQWrwuXA8RHRvyG9fpjsr9DZVhlD0efiD6Od04nt5FyKPhy5K/og4A86aP+02uOpcbX07dCHC/dw2UWm5UHgi85zS9IJyDui1+a/2tlO+9P4B6Dv7tyAz7yzIzYSOTYHseJBiZcAVySdeP1aQ5V7AGellLYxHfOcb3fg6z6O5fPoY3v5lOUJ6BiSQSml7czDazrbh4KCv3V05oTrgoKCv1GklOZHxHXoHK76wXaDgRtt8Hugrx1n3JZSagkdNNve6cRbIwflLj/56Up1Nk8jRqWUftxO+riU0jvAOxExD51MndvYwdfbRcR56KN3fdHn/juLz7qNRRExBjjb0ZOPAk0ppTkAEXEjOhMH5PTks9t+AVxQq++RlFLm0SeBHWp7f/qjz9k/ClxtB/A3KaUJETED2CIiLkUfMbyTgoKCTqE4OAUFBavDT9FXhOvRg0uBn6SUbo2IYehYjIzlJy9HxEqnE6PPxT+VUtprLWjqzInDo4HPpZQmRsQI9EXozuI4YJ/Qob6gU88/7utVfTyso3sLa9cBnJJSWsnhioj9UfTrFxFxUUrputCBk59Cp2kfjb4iXVBQsBqUR1QFBQWrREopn+Rdf8TTHx3ICNUJ7J3FM8CGEbEXQER0j4htfX1yRJy8liRn9ANedUSkvVPC20VErIvOwdospTQk6cT7k5DT8zAwLCIGut6jakXvR6fGs5r27gC+WduPtFVE9AmdCj47pXQVOgNwF2/w7pJSGgOcjQ7DLSgo6ASKg1NQUNAZXIwOs8w4F7g5Iu5F53t1GimlpejwxgsiYiLaa7K3b38YnZCdUd+DMyEihqxBU2cjh+QudFZSZ3EE8IeUUj1K9FvgUHQa87lor8/dKLKVcSpwUkQ8ihzAjvBzdCbQHyNiMvAzFHUaBkyIiCfQo65LgEFAU0RMQBGpM9egHwUFf9MoRzUUFBT8xSAixgJH2AkqKCgo+JNRHJyCgoKCgoKC9x3KI6qCgoKCgoKC9x2Kg1NQUFBQUFDwvkNxcAoKCgoKCgredygOTkFBQUFBQcH7DsXBKSgoKCgoKHjfoTg4BQUFBQUFBe87/H8XkvY3UCJ1dAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df5.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Finding and Removing Duplicates\n",
    "- Identifying and Elemenating Outliers\n",
    "- Identifying and working on missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying and Elemenating Outliers\n",
    "\n",
    "\n",
    "the data which is far away from the min/max value data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Open Price\n",
    "- Avg Price\n",
    "- Total Price\n",
    "- Closing Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.533333333333333"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "stock = np.array([5.5, 5.2, 5.3, 5.6, 2.2, 5.5, 5.2, 5.3,10])\n",
    "stock1 = np.array([5.5, 5.2, 5.3, 5.6, 5.525, 5.5, 5.2, 5.3])\n",
    "stock.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.3, 5.4)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(stock), np.median(stock1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.975, 5.390625)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.mean(), stock1.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x235778e12e0>,\n",
       "  <matplotlib.lines.Line2D at 0x235778e1640>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x235778e19a0>,\n",
       "  <matplotlib.lines.Line2D at 0x235778e1d00>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x235778d2f40>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x235778ea0d0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x235778ea3d0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.boxplot(df5['Time in Session (minutes)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(126.96, 162.0)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5['Time in Session (minutes)'].mean(), df5['Time in Session (minutes)'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x23577990970>,\n",
       "  <matplotlib.lines.Line2D at 0x23577990cd0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x2357799a0a0>,\n",
       "  <matplotlib.lines.Line2D at 0x2357799a400>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x23577990610>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x2357799a760>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x2357799aa60>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(stock)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "184.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df5['Time in Session (minutes)'].quantile(0.25)) - (1.5 * (df5['Time in Session (minutes)'].quantile(0.25) - df5['Time in Session (minutes)'].quantile(0.75)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Formulae: data < min and data > max\n",
    "\n",
    "data[~ data < min or data > max]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- IQR - InterQuantileRange - Q3-Q1 -> 0.25 - 0.75\n",
    "- Q1 = 0.25\n",
    "- Q3 = 0.75\n",
    "- min = Q1 - 1.5 * IQR = Q1 - 1.5 * (Q3 - Q1)\n",
    "- max = Q3 + 1.5 * IQR = Q1 - 1.5 * (Q3 - Q1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Q1 = df5['Time in Session (minutes)'].quantile(0.25)\n",
    "Q3 = df5['Time in Session (minutes)'].quantile(0.75)\n",
    "\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "mn = Q1 - 1.5 * IQR\n",
    "mx = Q3 + 1.5 * IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x = df5['Time in Session (minutes)'][~ ((df5['Time in Session (minutes)'] > mx) | (df5['Time in Session (minutes)'] < mn))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x235781854f0>,\n",
       "  <matplotlib.lines.Line2D at 0x235781859d0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x23578185cd0>,\n",
       "  <matplotlib.lines.Line2D at 0x235781042b0>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x23578185dc0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x23578104610>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x23578104910>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                                   Email Address                  \n",
       "17X41A1202-Sai Harini-SRKIT            harini.akkineni@outlook.com        False\n",
       "17X41A1203-Tejaswini Alapati-SRKIT     alapatitejaswini999@gmail.com      False\n",
       "17X41A1210-Gopi Chand-SRKIT            gopichandcherukuri121@gmail.com    False\n",
       "17X41A1212-yashwanth-X4                rajyashwanth27@gmail.com           False\n",
       "17X41A1214-Gorantla Ram Gopal-X4       ramgopalgorantla43@gmail.com       False\n",
       "17X41A1218 Supriya X4                  supriya9kesari@gmail.com           False\n",
       "17X41A1219-Navya Sri - SRKIT           navyakota2000@gmail.com            False\n",
       "17X41A1225-M N Sandhya-SRKIT           sandhyameegada4@gmail.com          False\n",
       "17X41A1226-Mounika Munagala-SRKIT      mounika.munagala143@gmail.com      False\n",
       "17X41A1227-Durga Rani Nandamuri-SRKIT  durganandamuri123@gmail.com        False\n",
       "17X41A1230-P.P.SARADHI REDDY-SRKIT     pardhu1818@gmail.com               False\n",
       "17X41A1232  Maheswari Srkit            mahipottetti1234@gmail.com         False\n",
       "17X41A1233                             potumahesh1234@gmail.com           False\n",
       "17X41A1234-Divyasri-SRKIT              divyasripushadapu@gmail.com        False\n",
       "17X41A1235 R.V.Lahari SRKIT            lahariaug@gmail.com                False\n",
       "17X41A1243-syed yasmeen-SRKIT          syedyasmeen77@gmail.com            False\n",
       "17X41A1247-V Ramya Sri - X4            ramyavagicharla@gmail.com          False\n",
       "17x41a1224-Gopichand(Srkit)-x4         m.gopichand7777@gmail.com          False\n",
       "Anil Kumar Teegala [APSSDC]            aps.sdc.ml@gmail.com               False\n",
       "Bhuvaneswari Bonthu 1208               bhuvanabonthu123@gmail.com         False\n",
       "Durga Rani Nandamuri                   durganandamuri123@gmail.com        False\n",
       "Sairam                                 sairam.ummadisetti@gmail.com       False\n",
       "Surisetti Jayasai                      jaisainaidu123@gmail.com           False\n",
       "Vedasri                                vedasri.avirneni@gmail.com         False\n",
       "Yashwanth                              rajyashwanth27@gmail.com           False\n",
       "Name: Time in Session (minutes), dtype: bool"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df5['Time in Session (minutes)'] > mx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                                   Email Address                  \n",
       "17X41A1202-Sai Harini-SRKIT            harini.akkineni@outlook.com        158\n",
       "17X41A1203-Tejaswini Alapati-SRKIT     alapatitejaswini999@gmail.com      164\n",
       "17X41A1210-Gopi Chand-SRKIT            gopichandcherukuri121@gmail.com    167\n",
       "17X41A1212-yashwanth-X4                rajyashwanth27@gmail.com           160\n",
       "17X41A1214-Gorantla Ram Gopal-X4       ramgopalgorantla43@gmail.com       130\n",
       "17X41A1218 Supriya X4                  supriya9kesari@gmail.com           161\n",
       "17X41A1219-Navya Sri - SRKIT           navyakota2000@gmail.com            164\n",
       "17X41A1225-M N Sandhya-SRKIT           sandhyameegada4@gmail.com          167\n",
       "17X41A1226-Mounika Munagala-SRKIT      mounika.munagala143@gmail.com      167\n",
       "17X41A1227-Durga Rani Nandamuri-SRKIT  durganandamuri123@gmail.com        162\n",
       "17X41A1230-P.P.SARADHI REDDY-SRKIT     pardhu1818@gmail.com               165\n",
       "17X41A1232  Maheswari Srkit            mahipottetti1234@gmail.com         170\n",
       "17X41A1234-Divyasri-SRKIT              divyasripushadapu@gmail.com        173\n",
       "17X41A1235 R.V.Lahari SRKIT            lahariaug@gmail.com                163\n",
       "17X41A1243-syed yasmeen-SRKIT          syedyasmeen77@gmail.com            165\n",
       "17X41A1247-V Ramya Sri - X4            ramyavagicharla@gmail.com          166\n",
       "Anil Kumar Teegala [APSSDC]            aps.sdc.ml@gmail.com               183\n",
       "Bhuvaneswari Bonthu 1208               bhuvanabonthu123@gmail.com         158\n",
       "Vedasri                                vedasri.avirneni@gmail.com         148\n",
       "Name: Time in Session (minutes), dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5['Time in Session (minutes)'][~(df5['Time in Session (minutes)'] < mn)]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}