Minor Project .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q. The Cheif Minister of the state is wanting to have a detailed overview of COVID-19 in the state for the month of June 2020 to plan the stratergy for safeguarding the people !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### State alloted  - Delhi  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Used this raw_data.csv instead of covid19.csv since raw_data.csv provided more insight of state along with districts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawData1 = pd.read_csv(\"raw_data1.csv\")\n",
    "rawData2 = pd.read_csv(\"raw_data2.csv\") #converting date column dtype as date format  by importing again\n",
    "rawData3 = pd.read_csv(\"raw_data3.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entry_ID                                     float64\n",
       "State Patient Number                          object\n",
       "Date Announced                                object\n",
       "Age Bracket                                  float64\n",
       "Gender                                        object\n",
       "Detected City                                 object\n",
       "Detected District                             object\n",
       "Detected State                                object\n",
       "State code                                    object\n",
       "Num Cases                                    float64\n",
       "Current Status                                object\n",
       "Contracted from which Patient (Suspected)     object\n",
       "Notes                                         object\n",
       "Source_1                                      object\n",
       "Source_2                                      object\n",
       "Source_3                                      object\n",
       "Nationality                                  float64\n",
       "Type of transmission                         float64\n",
       "Status Change Date                           float64\n",
       "Patient Number                               float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 588,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawData1.dtypes #displaying dtype of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "metadata": {},
   "outputs": [],
   "source": [
    "dateCols = ['Date Announced']\n",
    "rawData1 = pd.read_csv(\"raw_data1.csv\",parse_dates=dateCols)\n",
    "rawData2 = pd.read_csv(\"raw_data2.csv\",parse_dates=dateCols) #converting date column dtype as date format  by importing again\n",
    "rawData3 = pd.read_csv(\"raw_data3.csv\",parse_dates=dateCols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entry_ID                                            float64\n",
       "State Patient Number                                 object\n",
       "Date Announced                               datetime64[ns]\n",
       "Age Bracket                                         float64\n",
       "Gender                                               object\n",
       "Detected City                                        object\n",
       "Detected District                                    object\n",
       "Detected State                                       object\n",
       "State code                                           object\n",
       "Num Cases                                           float64\n",
       "Current Status                                       object\n",
       "Contracted from which Patient (Suspected)            object\n",
       "Notes                                                object\n",
       "Source_1                                             object\n",
       "Source_2                                             object\n",
       "Source_3                                             object\n",
       "Nationality                                         float64\n",
       "Type of transmission                                float64\n",
       "Status Change Date                                  float64\n",
       "Patient Number                                      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 590,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawData1.dtypes #ensuring date format has been changed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Source_3</th>\n",
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       "      <th>Status Change Date</th>\n",
       "      <th>Patient Number</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>28356.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-23</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Manipur</td>\n",
       "      <td>MN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Hospitalized</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two cases added based on the total mentioned i...</td>\n",
       "      <td>https://twitter.com/ANI/status/126445191296063...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57292.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>28223.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>State Unassigned</td>\n",
       "      <td>UN</td>\n",
       "      <td>439.0</td>\n",
       "      <td>Hospitalized</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mohfw.gov.in</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57159.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>28224.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ganjam</td>\n",
       "      <td>Odisha</td>\n",
       "      <td>OR</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Hospitalized</td>\n",
       "      <td>NaN</td>\n",
       "      <td>All in quarantine centres.1 Surat returnee and...</td>\n",
       "      <td>https://health.odisha.gov.in/covid19-dashboard...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57160.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Entry_ID State Patient Number Date Announced  Age Bracket Gender  \\\n",
       "0   28356.0                  NaN     2020-05-23          NaN    NaN   \n",
       "1   28223.0                  NaN     2020-05-24          NaN    NaN   \n",
       "2   28224.0                  NaN     2020-05-24          NaN    NaN   \n",
       "\n",
       "  Detected City Detected District    Detected State State code  Num Cases  \\\n",
       "0           NaN               NaN           Manipur         MN        0.0   \n",
       "1           NaN        Unassigned  State Unassigned         UN      439.0   \n",
       "2           NaN            Ganjam            Odisha         OR        7.0   \n",
       "\n",
       "  Current Status Contracted from which Patient (Suspected)  \\\n",
       "0   Hospitalized                                       NaN   \n",
       "1   Hospitalized                                       NaN   \n",
       "2   Hospitalized                                       NaN   \n",
       "\n",
       "                                               Notes  \\\n",
       "0  Two cases added based on the total mentioned i...   \n",
       "1                                                NaN   \n",
       "2  All in quarantine centres.1 Surat returnee and...   \n",
       "\n",
       "                                            Source_1 Source_2 Source_3  \\\n",
       "0  https://twitter.com/ANI/status/126445191296063...      NaN      NaN   \n",
       "1                                       mohfw.gov.in      NaN      NaN   \n",
       "2  https://health.odisha.gov.in/covid19-dashboard...      NaN      NaN   \n",
       "\n",
       "   Nationality  Type of transmission  Status Change Date  Patient Number  \n",
       "0          NaN                   NaN                 NaN         57292.0  \n",
       "1          NaN                   NaN                 NaN         57159.0  \n",
       "2          NaN                   NaN                 NaN         57160.0  "
      ]
     },
     "execution_count": 591,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawData1.head(3)  #understanding complexity of the day using .head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>Institutional quarantine at Lunglei</td>\n",
       "      <td>https://twitter.com/dipr_mizoram/status/126873...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Falkawn</td>\n",
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       "      <td>Mizoram</td>\n",
       "      <td>MZ</td>\n",
       "      <td>4</td>\n",
       "      <td>Hospitalized</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ZMC</td>\n",
       "      <td>https://twitter.com/dipr_mizoram/status/126873...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78001</td>\n",
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       "      <td>48615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nagaland</td>\n",
       "      <td>NL</td>\n",
       "      <td>14</td>\n",
       "      <td>Hospitalized</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/pangnyu/status/12687473719...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78002</td>\n",
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      "text/plain": [
       "   Entry_ID State Patient Number Date Announced Age Bracket Gender  \\\n",
       "0     48613                  NaN     2020-05-06         NaN    NaN   \n",
       "1     48614                  NaN     2020-05-06         NaN    NaN   \n",
       "2     48615                  NaN     2020-05-06         NaN    NaN   \n",
       "\n",
       "  Detected City Detected District Detected State State code  Num Cases  \\\n",
       "0           NaN           Lunglei        Mizoram         MZ          1   \n",
       "1       Falkawn            Aizawl        Mizoram         MZ          4   \n",
       "2           NaN               NaN       Nagaland         NL         14   \n",
       "\n",
       "  Current Status Contracted from which Patient (Suspected)  \\\n",
       "0   Hospitalized                                       NaN   \n",
       "1   Hospitalized                                       NaN   \n",
       "2   Hospitalized                                       NaN   \n",
       "\n",
       "                                 Notes  \\\n",
       "0  Institutional quarantine at Lunglei   \n",
       "1                                  ZMC   \n",
       "2                                  NaN   \n",
       "\n",
       "                                            Source_1 Source_2 Source_3  \\\n",
       "0  https://twitter.com/dipr_mizoram/status/126873...      NaN      NaN   \n",
       "1  https://twitter.com/dipr_mizoram/status/126873...      NaN      NaN   \n",
       "2  https://twitter.com/pangnyu/status/12687473719...      NaN      NaN   \n",
       "\n",
       "   Nationality  Type of transmission  Status Change Date  Patient Number  \n",
       "0          NaN                   NaN                 NaN           78000  \n",
       "1          NaN                   NaN                 NaN           78001  \n",
       "2          NaN                   NaN                 NaN           78002  "
      ]
     },
     "execution_count": 592,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "rawData2.head(3)"
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  {
   "cell_type": "code",
   "execution_count": 593,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>3</td>\n",
       "      <td>Hospitalized</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dipr_mizoram/status/127415...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Serchhip</td>\n",
       "      <td>Mizoram</td>\n",
       "      <td>MZ</td>\n",
       "      <td>2</td>\n",
       "      <td>Hospitalized</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dipr_mizoram/status/127415...</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",
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      "text/plain": [
       "   Entry_ID State Patient Number Date Announced Age Bracket Gender  \\\n",
       "0     71973                  NaN     2020-06-20         NaN    NaN   \n",
       "1     71974                  NaN     2020-06-20         NaN    NaN   \n",
       "2     71975                  NaN     2020-06-20         NaN    NaN   \n",
       "\n",
       "  Detected City Detected District Detected State State code  Num Cases  \\\n",
       "0           NaN             Mamit        Mizoram         MZ          4   \n",
       "1           NaN          Champhai        Mizoram         MZ          3   \n",
       "2           NaN          Serchhip        Mizoram         MZ          2   \n",
       "\n",
       "  Current Status  Contracted from which Patient (Suspected) Notes  \\\n",
       "0   Hospitalized                                        NaN   NaN   \n",
       "1   Hospitalized                                        NaN   NaN   \n",
       "2   Hospitalized                                        NaN   NaN   \n",
       "\n",
       "                                            Source_1 Source_2  Source_3  \\\n",
       "0  https://twitter.com/dipr_mizoram/status/127415...      NaN       NaN   \n",
       "1  https://twitter.com/dipr_mizoram/status/127415...      NaN       NaN   \n",
       "2  https://twitter.com/dipr_mizoram/status/127415...      NaN       NaN   \n",
       "\n",
       "   Nationality  Type of transmission  Status Change Date  Patient Number  \n",
       "0          NaN                   NaN                 NaN             NaN  \n",
       "1          NaN                   NaN                 NaN             NaN  \n",
       "2          NaN                   NaN                 NaN             NaN  "
      ]
     },
     "execution_count": 593,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawData3.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entry_ID\n",
      "State Patient Number\n",
      "Date Announced\n",
      "Age Bracket\n",
      "Gender\n",
      "Detected City\n",
      "Detected District\n",
      "Detected State\n",
      "State code\n",
      "Num Cases\n",
      "Current Status\n",
      "Contracted from which Patient (Suspected)\n",
      "Notes\n",
      "Source_1\n",
      "Source_2\n",
      "Source_3\n",
      "Nationality\n",
      "Type of transmission\n",
      "Status Change Date\n",
      "Patient Number\n"
     ]
    }
   ],
   "source": [
    "for col in rawData1.columns:      #Getting columns from data \n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The columns of 3 data are Same, You are good to Go!\n"
     ]
    }
   ],
   "source": [
    "rawData1_col = []\n",
    "rawData2_col = []\n",
    "rawData3_col = []\n",
    "\n",
    "for col in rawData1.columns:\n",
    "    rawData1_col.append(col)\n",
    "    \n",
    "for col in rawData2.columns:                #To ensure the columns of 3 dataset are same using logical operator !\n",
    "    rawData2_col.append(col) \n",
    "    \n",
    "for col in rawData3.columns:\n",
    "    rawData3_col.append(col)\n",
    "    \n",
    "    \n",
    "if ((rawData1_col == rawData2_col) and (rawData2_col==rawData3_col) and (rawData3_col==rawData1_col)):\n",
    "    print(\"The columns of 3 data are Same, You are good to Go!\")\n",
    "else:\n",
    "    print(\"The coumns of 3 data is mismatched !\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entry_ID\n",
      "State Patient Number\n",
      "Date Announced\n",
      "Age Bracket\n",
      "Gender\n",
      "Detected City\n",
      "Detected District\n",
      "Detected State\n",
      "State code\n",
      "Num Cases\n",
      "Current Status\n",
      "Contracted from which Patient (Suspected)\n",
      "Notes\n",
      "Source_1\n",
      "Source_2\n",
      "Source_3\n",
      "Nationality\n",
      "Type of transmission\n",
      "Status Change Date\n",
      "Patient Number\n"
     ]
    }
   ],
   "source": [
    "for col in rawData3.columns:      #Once again printing columns to eliminate column which is not necessay  \n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Entry_ID</th>\n",
       "      <th>State Patient Number</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28356.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Manipur</td>\n",
       "      <td>MN</td>\n",
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       "      <td>Hospitalized</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two cases added based on the total mentioned i...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57292.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28223.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>State Unassigned</td>\n",
       "      <td>UN</td>\n",
       "      <td>439.0</td>\n",
       "      <td>Hospitalized</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mohfw.gov.in</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57159.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Entry_ID State Patient Number Date Announced  Age Bracket Gender  \\\n",
       "0   28356.0                  NaN     2020-05-23          NaN    NaN   \n",
       "1   28223.0                  NaN     2020-05-24          NaN    NaN   \n",
       "\n",
       "  Detected City Detected District    Detected State State code  Num Cases  \\\n",
       "0           NaN               NaN           Manipur         MN        0.0   \n",
       "1           NaN        Unassigned  State Unassigned         UN      439.0   \n",
       "\n",
       "  Current Status Contracted from which Patient (Suspected)  \\\n",
       "0   Hospitalized                                       NaN   \n",
       "1   Hospitalized                                       NaN   \n",
       "\n",
       "                                               Notes  \\\n",
       "0  Two cases added based on the total mentioned i...   \n",
       "1                                                NaN   \n",
       "\n",
       "                                            Source_1 Source_2 Source_3  \\\n",
       "0  https://twitter.com/ANI/status/126445191296063...      NaN      NaN   \n",
       "1                                       mohfw.gov.in      NaN      NaN   \n",
       "\n",
       "   Nationality  Type of transmission  Status Change Date  Patient Number  \n",
       "0          NaN                   NaN                 NaN         57292.0  \n",
       "1          NaN                   NaN                 NaN         57159.0  "
      ]
     },
     "execution_count": 597,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawData1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 598,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entry_ID</th>\n",
       "      <th>State Patient Number</th>\n",
       "      <th>Date Announced</th>\n",
       "      <th>Age Bracket</th>\n",
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       "      <th>State code</th>\n",
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       "      <th>Patient Number</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18258</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <th>2176</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13335</th>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15247</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9268</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13427</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10599</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10853</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10299</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Entry_ID  State Patient Number  Date Announced  Age Bracket  Gender  \\\n",
       "18258     False                 False           False         True    True   \n",
       "2176      False                  True           False        False   False   \n",
       "13335     False                  True           False        False   False   \n",
       "15247     False                 False           False        False   False   \n",
       "9268      False                  True           False        False   False   \n",
       "13427     False                  True           False        False   False   \n",
       "10599     False                  True           False        False   False   \n",
       "10853     False                 False           False        False   False   \n",
       "5000      False                 False           False        False   False   \n",
       "10299     False                  True           False        False   False   \n",
       "\n",
       "       Detected City  Detected District  Detected State  State code  \\\n",
       "18258           True              False           False       False   \n",
       "2176            True              False           False       False   \n",
       "13335           True              False           False       False   \n",
       "15247           True              False           False       False   \n",
       "9268            True              False           False       False   \n",
       "13427           True              False           False       False   \n",
       "10599          False              False           False       False   \n",
       "10853           True              False           False       False   \n",
       "5000            True              False           False       False   \n",
       "10299           True              False           False       False   \n",
       "\n",
       "       Num Cases  Current Status  Contracted from which Patient (Suspected)  \\\n",
       "18258      False           False                                       True   \n",
       "2176       False           False                                       True   \n",
       "13335      False           False                                       True   \n",
       "15247      False           False                                       True   \n",
       "9268       False           False                                       True   \n",
       "13427      False           False                                       True   \n",
       "10599      False           False                                       True   \n",
       "10853      False           False                                       True   \n",
       "5000       False           False                                       True   \n",
       "10299      False           False                                       True   \n",
       "\n",
       "       Notes  Source_1  Source_2  Source_3  Nationality  Type of transmission  \\\n",
       "18258   True     False      True      True         True                  True   \n",
       "2176    True     False      True      True         True                  True   \n",
       "13335   True     False      True      True         True                  True   \n",
       "15247  False     False      True      True         True                  True   \n",
       "9268    True     False      True      True         True                  True   \n",
       "13427   True     False      True      True         True                  True   \n",
       "10599   True     False      True      True         True                  True   \n",
       "10853  False     False      True      True         True                  True   \n",
       "5000   False     False      True      True         True                  True   \n",
       "10299   True     False      True      True         True                  True   \n",
       "\n",
       "       Status Change Date  Patient Number  \n",
       "18258                True           False  \n",
       "2176                 True           False  \n",
       "13335                True           False  \n",
       "15247                True           False  \n",
       "9268                 True           False  \n",
       "13427                True           False  \n",
       "10599                True           False  \n",
       "10853                True           False  \n",
       "5000                 True           False  \n",
       "10299                True           False  "
      ]
     },
     "execution_count": 598,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawData1[rawData1_col].sample(10).isnull()  #getting null values of columns from random rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 599,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawData1.drop([\"State Patient Number\",\"Source_1\",\"Source_2\",\"Source_3\",\"Contracted from which Patient (Suspected)\",\"Detected City\",\"Patient Number\",\"Status Change Date\"],axis=1,inplace=True)\n",
    "rawData2.drop([\"State Patient Number\",\"Source_1\",\"Source_2\",\"Source_3\",\"Contracted from which Patient (Suspected)\",\"Detected City\",\"Patient Number\",\"Status Change Date\"],axis=1,inplace=True)\n",
    "rawData3.drop([\"State Patient Number\",\"Source_1\",\"Source_2\",\"Source_3\",\"Contracted from which Patient (Suspected)\",\"Detected City\",\"Patient Number\",\"Status Change Date\"],axis=1,inplace=True)\n",
    "\n",
    "#droping unwanted columns which doesn't have any influence over result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entry_ID</th>\n",
       "      <th>Date Announced</th>\n",
       "      <th>Age Bracket</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Detected District</th>\n",
       "      <th>Detected State</th>\n",
       "      <th>State code</th>\n",
       "      <th>Num Cases</th>\n",
       "      <th>Current Status</th>\n",
       "      <th>Notes</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Type of transmission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28356.0</td>\n",
       "      <td>2020-05-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manipur</td>\n",
       "      <td>MN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Hospitalized</td>\n",
       "      <td>Two cases added based on the total mentioned i...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28223.0</td>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>State Unassigned</td>\n",
       "      <td>UN</td>\n",
       "      <td>439.0</td>\n",
       "      <td>Hospitalized</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Entry_ID Date Announced  Age Bracket Gender Detected District  \\\n",
       "0   28356.0     2020-05-23          NaN    NaN               NaN   \n",
       "1   28223.0     2020-05-24          NaN    NaN        Unassigned   \n",
       "\n",
       "     Detected State State code  Num Cases Current Status  \\\n",
       "0           Manipur         MN        0.0   Hospitalized   \n",
       "1  State Unassigned         UN      439.0   Hospitalized   \n",
       "\n",
       "                                               Notes  Nationality  \\\n",
       "0  Two cases added based on the total mentioned i...          NaN   \n",
       "1                                                NaN          NaN   \n",
       "\n",
       "   Type of transmission  \n",
       "0                   NaN  \n",
       "1                   NaN  "
      ]
     },
     "execution_count": 600,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawData1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        False\n",
      "1        False\n",
      "2        False\n",
      "3        False\n",
      "4        False\n",
      "         ...  \n",
      "20433    False\n",
      "20434    False\n",
      "20435    False\n",
      "20436    False\n",
      "20437    False\n",
      "Name: Nationality, Length: 20438, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "print(rawData1[\"Nationality\"].notnull()) #Detecting is there any value in Nationality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        True\n",
      "1        True\n",
      "2        True\n",
      "3        True\n",
      "4        True\n",
      "         ... \n",
      "23418    True\n",
      "23419    True\n",
      "23420    True\n",
      "23421    True\n",
      "23422    True\n",
      "Name: Nationality, Length: 23423, dtype: bool \n",
      "\n",
      "0        True\n",
      "1        True\n",
      "2        True\n",
      "3        True\n",
      "4        True\n",
      "         ... \n",
      "22765    True\n",
      "22766    True\n",
      "22767    True\n",
      "22768    True\n",
      "22769    True\n",
      "Name: Nationality, Length: 22770, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "print(rawData2[\"Nationality\"].isnull(),\"\\n\") #Ensuring Nationality is NaN in other data's\n",
    "print(rawData3[\"Nationality\"].isnull()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        True\n",
      "1        True\n",
      "2        True\n",
      "3        True\n",
      "4        True\n",
      "         ... \n",
      "20433    True\n",
      "20434    True\n",
      "20435    True\n",
      "20436    True\n",
      "20437    True\n",
      "Name: Type of transmission, Length: 20438, dtype: bool \n",
      "\n",
      "0        True\n",
      "1        True\n",
      "2        True\n",
      "3        True\n",
      "4        True\n",
      "         ... \n",
      "23418    True\n",
      "23419    True\n",
      "23420    True\n",
      "23421    True\n",
      "23422    True\n",
      "Name: Type of transmission, Length: 23423, dtype: bool \n",
      "\n",
      "0        True\n",
      "1        True\n",
      "2        True\n",
      "3        True\n",
      "4        True\n",
      "         ... \n",
      "22765    True\n",
      "22766    True\n",
      "22767    True\n",
      "22768    True\n",
      "22769    True\n",
      "Name: Type of transmission, Length: 22770, dtype: bool \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(rawData1[\"Type of transmission\"].isnull(),\"\\n\") \n",
    "print(rawData2[\"Type of transmission\"].isnull(),\"\\n\")\n",
    "print(rawData3[\"Type of transmission\"].isnull(),\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawData1.drop([\"Nationality\",\"Type of transmission\",\"Nationality\",\"Notes\",\"Entry_ID\"],axis = 1,inplace = True)\n",
    "rawData2.drop([\"Nationality\",\"Type of transmission\",\"Nationality\",\"Notes\",\"Entry_ID\"],axis = 1,inplace = True) \n",
    "rawData3.drop([\"Nationality\",\"Type of transmission\",\"Nationality\",\"Notes\",\"Entry_ID\"],axis = 1,inplace = True)\n",
    "\n",
    "#again dropping unwanted columns as ensuring the affect of NaN value in column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawData1.rename(columns={\"Detected District\":\"district\",\"Detected State\":\"state\"},inplace=True)\n",
    "rawData2.rename(columns={\"Detected District\":\"district\",\"Detected State\":\"state\"},inplace=True)\n",
    "rawData3.rename(columns={\"Detected District\":\"district\",\"Detected State\":\"state\"},inplace=True)\n",
    "\n",
    "#renaming column name for better understability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date Announced</th>\n",
       "      <th>Age Bracket</th>\n",
       "      <th>Gender</th>\n",
       "      <th>district</th>\n",
       "      <th>state</th>\n",
       "      <th>State code</th>\n",
       "      <th>Num Cases</th>\n",
       "      <th>Current Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16821</th>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>44.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Udupi</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>KA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1832</th>\n",
       "      <td>2020-05-25</td>\n",
       "      <td>9.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Udupi</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>KA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10695</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kalaburagi</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>KA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15213</th>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Malappuram</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>KL</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12563</th>\n",
       "      <td>2020-05-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kamrup Metropolitan</td>\n",
       "      <td>Assam</td>\n",
       "      <td>AS</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13570</th>\n",
       "      <td>2020-05-31</td>\n",
       "      <td>40.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>TN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14429</th>\n",
       "      <td>2020-05-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shivamogga</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>KA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15269</th>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>32.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Udupi</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>KA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12556</th>\n",
       "      <td>2020-05-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Udalguri</td>\n",
       "      <td>Assam</td>\n",
       "      <td>AS</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1549</th>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aligarh</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>UP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date Announced  Age Bracket Gender             district          state  \\\n",
       "16821     2020-02-06         44.0      M                Udupi      Karnataka   \n",
       "1832      2020-05-25          9.0      F                Udupi      Karnataka   \n",
       "10695     2020-05-30          NaN    NaN           Kalaburagi      Karnataka   \n",
       "15213     2020-01-06          NaN    NaN           Malappuram         Kerala   \n",
       "12563     2020-05-31          NaN    NaN  Kamrup Metropolitan          Assam   \n",
       "13570     2020-05-31         40.0      F              Chennai     Tamil Nadu   \n",
       "14429     2020-05-31          NaN    NaN           Shivamogga      Karnataka   \n",
       "15269     2020-01-06         32.0      F                Udupi      Karnataka   \n",
       "12556     2020-05-31          NaN    NaN             Udalguri          Assam   \n",
       "1549      2020-05-24          NaN    NaN              Aligarh  Uttar Pradesh   \n",
       "\n",
       "      State code  Num Cases Current Status  \n",
       "16821         KA        1.0   Hospitalized  \n",
       "1832          KA        1.0   Hospitalized  \n",
       "10695         KA        1.0      Recovered  \n",
       "15213         KL       14.0   Hospitalized  \n",
       "12563         AS        1.0   Hospitalized  \n",
       "13570         TN        1.0   Hospitalized  \n",
       "14429         KA        1.0      Recovered  \n",
       "15269         KA        1.0   Hospitalized  \n",
       "12556         AS        7.0   Hospitalized  \n",
       "1549          UP        1.0       Deceased  "
      ]
     },
     "execution_count": 606,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawData1.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 607,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(rawData1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date Announced    datetime64[ns]\n",
       "Age Bracket              float64\n",
       "Gender                    object\n",
       "district                  object\n",
       "state                     object\n",
       "State code                object\n",
       "Num Cases                float64\n",
       "Current Status            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 608,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawData1.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now slecting value falling under the date range June 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date= '06/01/2020'\n",
    "end_date = '06/30/2020'\n",
    "\n",
    "\n",
    "\n",
    "rawData1J = rawData1['Date Announced'].between(start_date, end_date)\n",
    "rawData1 = rawData1.loc[rawData1J]\n",
    "\n",
    "rawData2J = rawData2['Date Announced'].between(start_date, end_date)\n",
    "rawData2 = rawData2.loc[rawData2J]\n",
    "\n",
    "rawData3J = rawData3['Date Announced'].between(start_date, end_date)\n",
    "rawData3 = rawData3.loc[rawData3J]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date Announced</th>\n",
       "      <th>Age Bracket</th>\n",
       "      <th>Gender</th>\n",
       "      <th>district</th>\n",
       "      <th>state</th>\n",
       "      <th>State code</th>\n",
       "      <th>Num Cases</th>\n",
       "      <th>Current Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1549</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>State Unassigned</td>\n",
       "      <td>UN</td>\n",
       "      <td>582</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jajpur</td>\n",
       "      <td>Odisha</td>\n",
       "      <td>OR</td>\n",
       "      <td>19</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Khordha</td>\n",
       "      <td>Odisha</td>\n",
       "      <td>OR</td>\n",
       "      <td>9</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jharsuguda</td>\n",
       "      <td>Odisha</td>\n",
       "      <td>OR</td>\n",
       "      <td>1</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nayagarh</td>\n",
       "      <td>Odisha</td>\n",
       "      <td>OR</td>\n",
       "      <td>7</td>\n",
       "      <td>Hospitalized</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23418</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pulwama</td>\n",
       "      <td>Jammu and Kashmir</td>\n",
       "      <td>JK</td>\n",
       "      <td>1</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23419</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reasi</td>\n",
       "      <td>Jammu and Kashmir</td>\n",
       "      <td>JK</td>\n",
       "      <td>2</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23420</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kishtwar</td>\n",
       "      <td>Jammu and Kashmir</td>\n",
       "      <td>JK</td>\n",
       "      <td>-2</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23421</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>East Sikkim</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>SK</td>\n",
       "      <td>-3</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23422</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Sikkim</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>SK</td>\n",
       "      <td>3</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12798 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date Announced Age Bracket Gender      district              state  \\\n",
       "1549      2020-06-06         NaN    NaN    Unassigned   State Unassigned   \n",
       "1550      2020-06-06         NaN    NaN        Jajpur             Odisha   \n",
       "1551      2020-06-06         NaN    NaN       Khordha             Odisha   \n",
       "1552      2020-06-06         NaN    NaN    Jharsuguda             Odisha   \n",
       "1553      2020-06-06         NaN    NaN      Nayagarh             Odisha   \n",
       "...              ...         ...    ...           ...                ...   \n",
       "23418     2020-06-19         NaN    NaN       Pulwama  Jammu and Kashmir   \n",
       "23419     2020-06-19         NaN    NaN         Reasi  Jammu and Kashmir   \n",
       "23420     2020-06-19         NaN    NaN      Kishtwar  Jammu and Kashmir   \n",
       "23421     2020-06-19         NaN    NaN   East Sikkim             Sikkim   \n",
       "23422     2020-06-19         NaN    NaN  South Sikkim             Sikkim   \n",
       "\n",
       "      State code  Num Cases Current Status  \n",
       "1549          UN        582   Hospitalized  \n",
       "1550          OR         19   Hospitalized  \n",
       "1551          OR          9   Hospitalized  \n",
       "1552          OR          1   Hospitalized  \n",
       "1553          OR          7   Hospitalized  \n",
       "...          ...        ...            ...  \n",
       "23418         JK          1   Hospitalized  \n",
       "23419         JK          2      Recovered  \n",
       "23420         JK         -2      Recovered  \n",
       "23421         SK         -3      Recovered  \n",
       "23422         SK          3      Recovered  \n",
       "\n",
       "[12798 rows x 8 columns]"
      ]
     },
     "execution_count": 610,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawData2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting value falling under Sate code of DL - \"Delhi\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date Announced</th>\n",
       "      <th>Age Bracket</th>\n",
       "      <th>Gender</th>\n",
       "      <th>district</th>\n",
       "      <th>state</th>\n",
       "      <th>State code</th>\n",
       "      <th>Num Cases</th>\n",
       "      <th>Current Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>1320</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>349</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3001</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>53</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13789</th>\n",
       "      <td>2020-06-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>1547</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13790</th>\n",
       "      <td>2020-06-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>57</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13791</th>\n",
       "      <td>2020-06-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2134</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15044</th>\n",
       "      <td>2020-06-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2224</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15045</th>\n",
       "      <td>2020-06-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>878</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15046</th>\n",
       "      <td>2020-06-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>56</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16589</th>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>73</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16590</th>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>1647</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16591</th>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>604</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18368</th>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>1859</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18369</th>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>73</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18370</th>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>437</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20152</th>\n",
       "      <td>2020-06-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2414</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20153</th>\n",
       "      <td>2020-06-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>957</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20154</th>\n",
       "      <td>2020-06-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>67</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21565</th>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2877</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21566</th>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>3884</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21567</th>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>65</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23324</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>3137</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23325</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2228</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23326</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>66</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date Announced Age Bracket Gender district  state State code  Num Cases  \\\n",
       "2999      2020-06-06         NaN    NaN      NaN  Delhi         DL       1320   \n",
       "3000      2020-06-06         NaN    NaN      NaN  Delhi         DL        349   \n",
       "3001      2020-06-06         NaN    NaN      NaN  Delhi         DL         53   \n",
       "13789     2020-06-13         NaN    NaN      NaN  Delhi         DL       1547   \n",
       "13790     2020-06-13         NaN    NaN      NaN  Delhi         DL         57   \n",
       "13791     2020-06-13         NaN    NaN      NaN  Delhi         DL       2134   \n",
       "15044     2020-06-14         NaN    NaN      NaN  Delhi         DL       2224   \n",
       "15045     2020-06-14         NaN    NaN      NaN  Delhi         DL        878   \n",
       "15046     2020-06-14         NaN    NaN      NaN  Delhi         DL         56   \n",
       "16589     2020-06-15         NaN    NaN      NaN  Delhi         DL         73   \n",
       "16590     2020-06-15         NaN    NaN      NaN  Delhi         DL       1647   \n",
       "16591     2020-06-15         NaN    NaN      NaN  Delhi         DL        604   \n",
       "18368     2020-06-16         NaN    NaN      NaN  Delhi         DL       1859   \n",
       "18369     2020-06-16         NaN    NaN      NaN  Delhi         DL         73   \n",
       "18370     2020-06-16         NaN    NaN      NaN  Delhi         DL        437   \n",
       "20152     2020-06-17         NaN    NaN      NaN  Delhi         DL       2414   \n",
       "20153     2020-06-17         NaN    NaN      NaN  Delhi         DL        957   \n",
       "20154     2020-06-17         NaN    NaN      NaN  Delhi         DL         67   \n",
       "21565     2020-06-18         NaN    NaN      NaN  Delhi         DL       2877   \n",
       "21566     2020-06-18         NaN    NaN      NaN  Delhi         DL       3884   \n",
       "21567     2020-06-18         NaN    NaN      NaN  Delhi         DL         65   \n",
       "23324     2020-06-19         NaN    NaN      NaN  Delhi         DL       3137   \n",
       "23325     2020-06-19         NaN    NaN      NaN  Delhi         DL       2228   \n",
       "23326     2020-06-19         NaN    NaN      NaN  Delhi         DL         66   \n",
       "\n",
       "      Current Status  \n",
       "2999    Hospitalized  \n",
       "3000       Recovered  \n",
       "3001        Deceased  \n",
       "13789      Recovered  \n",
       "13790       Deceased  \n",
       "13791   Hospitalized  \n",
       "15044   Hospitalized  \n",
       "15045      Recovered  \n",
       "15046       Deceased  \n",
       "16589       Deceased  \n",
       "16590   Hospitalized  \n",
       "16591      Recovered  \n",
       "18368   Hospitalized  \n",
       "18369      Recovered  \n",
       "18370       Deceased  \n",
       "20152   Hospitalized  \n",
       "20153      Recovered  \n",
       "20154       Deceased  \n",
       "21565   Hospitalized  \n",
       "21566      Recovered  \n",
       "21567       Deceased  \n",
       "23324   Hospitalized  \n",
       "23325      Recovered  \n",
       "23326       Deceased  "
      ]
     },
     "execution_count": 611,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawData1_Delhi = rawData1['State code'] == 'DL'\n",
    "rawData1_Delhi = rawData1.loc[rawData1_Delhi]\n",
    "\n",
    "rawData2_Delhi = rawData2['State code'] == 'DL'\n",
    "rawData2_Delhi = rawData2.loc[rawData2_Delhi]\n",
    "\n",
    "rawData3_Delhi = rawData3['State code'] == 'DL'\n",
    "rawData3_Delhi = rawData3.loc[rawData3_Delhi]\n",
    "\n",
    "\n",
    "rawData2_Delhi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenation of rawdata2 & rawdata3 \n",
    "### leaving behind rawdata1 since rawdata1 doesn't have data as per requirement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date Announced</th>\n",
       "      <th>Age Bracket</th>\n",
       "      <th>Gender</th>\n",
       "      <th>district</th>\n",
       "      <th>state</th>\n",
       "      <th>State code</th>\n",
       "      <th>Num Cases</th>\n",
       "      <th>Current Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Date Announced, Age Bracket, Gender, district, state, State code, Num Cases, Current Status]\n",
       "Index: []"
      ]
     },
     "execution_count": 612,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawData1_Delhi #as we see here there no value which meet the criteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {},
   "outputs": [],
   "source": [
    "delhi_data=pd.concat([rawData2_Delhi,rawData3_Delhi])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date Announced</th>\n",
       "      <th>Age Bracket</th>\n",
       "      <th>Gender</th>\n",
       "      <th>district</th>\n",
       "      <th>state</th>\n",
       "      <th>State code</th>\n",
       "      <th>Num Cases</th>\n",
       "      <th>Current Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>1320</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>349</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3001</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>53</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13789</th>\n",
       "      <td>2020-06-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>1547</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13790</th>\n",
       "      <td>2020-06-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>57</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13791</th>\n",
       "      <td>2020-06-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2134</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15044</th>\n",
       "      <td>2020-06-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2224</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15045</th>\n",
       "      <td>2020-06-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>878</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15046</th>\n",
       "      <td>2020-06-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>56</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16589</th>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>73</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16590</th>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>1647</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16591</th>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>604</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18368</th>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>1859</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18369</th>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>73</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18370</th>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>437</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20152</th>\n",
       "      <td>2020-06-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2414</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20153</th>\n",
       "      <td>2020-06-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>957</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20154</th>\n",
       "      <td>2020-06-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>67</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21565</th>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2877</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21566</th>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>3884</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21567</th>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>65</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23324</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>3137</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23325</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2228</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23326</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>66</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1586</th>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>3630</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587</th>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>77</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1588</th>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>7725</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2795</th>\n",
       "      <td>2020-06-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>3000</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2796</th>\n",
       "      <td>2020-06-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>1719</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2797</th>\n",
       "      <td>2020-06-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>63</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4473</th>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2909</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4474</th>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>3589</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4475</th>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>58</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6003</th>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>3947</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6004</th>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2711</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6005</th>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>68</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6884</th>\n",
       "      <td>2020-06-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>3788</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6885</th>\n",
       "      <td>2020-06-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2124</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6886</th>\n",
       "      <td>2020-06-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>64</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10185</th>\n",
       "      <td>2020-06-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>3390</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10186</th>\n",
       "      <td>2020-06-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>3328</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10187</th>\n",
       "      <td>2020-06-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>64</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11837</th>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>3460</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11838</th>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2326</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11839</th>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>63</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13051</th>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2948</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13052</th>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2210</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13053</th>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>66</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15546</th>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2889</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15547</th>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>3306</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15548</th>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>65</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19842</th>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2084</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19843</th>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>3628</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19844</th>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>57</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22725</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2199</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22726</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>2113</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22727</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DL</td>\n",
       "      <td>62</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date Announced Age Bracket Gender district  state State code  Num Cases  \\\n",
       "2999      2020-06-06         NaN    NaN      NaN  Delhi         DL       1320   \n",
       "3000      2020-06-06         NaN    NaN      NaN  Delhi         DL        349   \n",
       "3001      2020-06-06         NaN    NaN      NaN  Delhi         DL         53   \n",
       "13789     2020-06-13         NaN    NaN      NaN  Delhi         DL       1547   \n",
       "13790     2020-06-13         NaN    NaN      NaN  Delhi         DL         57   \n",
       "13791     2020-06-13         NaN    NaN      NaN  Delhi         DL       2134   \n",
       "15044     2020-06-14         NaN    NaN      NaN  Delhi         DL       2224   \n",
       "15045     2020-06-14         NaN    NaN      NaN  Delhi         DL        878   \n",
       "15046     2020-06-14         NaN    NaN      NaN  Delhi         DL         56   \n",
       "16589     2020-06-15         NaN    NaN      NaN  Delhi         DL         73   \n",
       "16590     2020-06-15         NaN    NaN      NaN  Delhi         DL       1647   \n",
       "16591     2020-06-15         NaN    NaN      NaN  Delhi         DL        604   \n",
       "18368     2020-06-16         NaN    NaN      NaN  Delhi         DL       1859   \n",
       "18369     2020-06-16         NaN    NaN      NaN  Delhi         DL         73   \n",
       "18370     2020-06-16         NaN    NaN      NaN  Delhi         DL        437   \n",
       "20152     2020-06-17         NaN    NaN      NaN  Delhi         DL       2414   \n",
       "20153     2020-06-17         NaN    NaN      NaN  Delhi         DL        957   \n",
       "20154     2020-06-17         NaN    NaN      NaN  Delhi         DL         67   \n",
       "21565     2020-06-18         NaN    NaN      NaN  Delhi         DL       2877   \n",
       "21566     2020-06-18         NaN    NaN      NaN  Delhi         DL       3884   \n",
       "21567     2020-06-18         NaN    NaN      NaN  Delhi         DL         65   \n",
       "23324     2020-06-19         NaN    NaN      NaN  Delhi         DL       3137   \n",
       "23325     2020-06-19         NaN    NaN      NaN  Delhi         DL       2228   \n",
       "23326     2020-06-19         NaN    NaN      NaN  Delhi         DL         66   \n",
       "1586      2020-06-20         NaN    NaN      NaN  Delhi         DL       3630   \n",
       "1587      2020-06-20         NaN    NaN      NaN  Delhi         DL         77   \n",
       "1588      2020-06-20         NaN    NaN      NaN  Delhi         DL       7725   \n",
       "2795      2020-06-21         NaN    NaN      NaN  Delhi         DL       3000   \n",
       "2796      2020-06-21         NaN    NaN      NaN  Delhi         DL       1719   \n",
       "2797      2020-06-21         NaN    NaN      NaN  Delhi         DL         63   \n",
       "4473      2020-06-22         NaN    NaN      NaN  Delhi         DL       2909   \n",
       "4474      2020-06-22         NaN    NaN      NaN  Delhi         DL       3589   \n",
       "4475      2020-06-22         NaN    NaN      NaN  Delhi         DL         58   \n",
       "6003      2020-06-23         NaN    NaN      NaN  Delhi         DL       3947   \n",
       "6004      2020-06-23         NaN    NaN      NaN  Delhi         DL       2711   \n",
       "6005      2020-06-23         NaN    NaN      NaN  Delhi         DL         68   \n",
       "6884      2020-06-24         NaN    NaN      NaN  Delhi         DL       3788   \n",
       "6885      2020-06-24         NaN    NaN      NaN  Delhi         DL       2124   \n",
       "6886      2020-06-24         NaN    NaN      NaN  Delhi         DL         64   \n",
       "10185     2020-06-25         NaN    NaN      NaN  Delhi         DL       3390   \n",
       "10186     2020-06-25         NaN    NaN      NaN  Delhi         DL       3328   \n",
       "10187     2020-06-25         NaN    NaN      NaN  Delhi         DL         64   \n",
       "11837     2020-06-26         NaN    NaN      NaN  Delhi         DL       3460   \n",
       "11838     2020-06-26         NaN    NaN      NaN  Delhi         DL       2326   \n",
       "11839     2020-06-26         NaN    NaN      NaN  Delhi         DL         63   \n",
       "13051     2020-06-27         NaN    NaN      NaN  Delhi         DL       2948   \n",
       "13052     2020-06-27         NaN    NaN      NaN  Delhi         DL       2210   \n",
       "13053     2020-06-27         NaN    NaN      NaN  Delhi         DL         66   \n",
       "15546     2020-06-28         NaN    NaN      NaN  Delhi         DL       2889   \n",
       "15547     2020-06-28         NaN    NaN      NaN  Delhi         DL       3306   \n",
       "15548     2020-06-28         NaN    NaN      NaN  Delhi         DL         65   \n",
       "19842     2020-06-29         NaN    NaN      NaN  Delhi         DL       2084   \n",
       "19843     2020-06-29         NaN    NaN      NaN  Delhi         DL       3628   \n",
       "19844     2020-06-29         NaN    NaN      NaN  Delhi         DL         57   \n",
       "22725     2020-06-30         NaN    NaN      NaN  Delhi         DL       2199   \n",
       "22726     2020-06-30         NaN    NaN      NaN  Delhi         DL       2113   \n",
       "22727     2020-06-30         NaN    NaN      NaN  Delhi         DL         62   \n",
       "\n",
       "      Current Status  \n",
       "2999    Hospitalized  \n",
       "3000       Recovered  \n",
       "3001        Deceased  \n",
       "13789      Recovered  \n",
       "13790       Deceased  \n",
       "13791   Hospitalized  \n",
       "15044   Hospitalized  \n",
       "15045      Recovered  \n",
       "15046       Deceased  \n",
       "16589       Deceased  \n",
       "16590   Hospitalized  \n",
       "16591      Recovered  \n",
       "18368   Hospitalized  \n",
       "18369      Recovered  \n",
       "18370       Deceased  \n",
       "20152   Hospitalized  \n",
       "20153      Recovered  \n",
       "20154       Deceased  \n",
       "21565   Hospitalized  \n",
       "21566      Recovered  \n",
       "21567       Deceased  \n",
       "23324   Hospitalized  \n",
       "23325      Recovered  \n",
       "23326       Deceased  \n",
       "1586    Hospitalized  \n",
       "1587        Deceased  \n",
       "1588       Recovered  \n",
       "2795    Hospitalized  \n",
       "2796       Recovered  \n",
       "2797        Deceased  \n",
       "4473    Hospitalized  \n",
       "4474       Recovered  \n",
       "4475        Deceased  \n",
       "6003    Hospitalized  \n",
       "6004       Recovered  \n",
       "6005        Deceased  \n",
       "6884    Hospitalized  \n",
       "6885       Recovered  \n",
       "6886        Deceased  \n",
       "10185   Hospitalized  \n",
       "10186      Recovered  \n",
       "10187       Deceased  \n",
       "11837   Hospitalized  \n",
       "11838      Recovered  \n",
       "11839       Deceased  \n",
       "13051   Hospitalized  \n",
       "13052      Recovered  \n",
       "13053       Deceased  \n",
       "15546   Hospitalized  \n",
       "15547      Recovered  \n",
       "15548       Deceased  \n",
       "19842   Hospitalized  \n",
       "19843      Recovered  \n",
       "19844       Deceased  \n",
       "22725   Hospitalized  \n",
       "22726      Recovered  \n",
       "22727       Deceased  "
      ]
     },
     "execution_count": 614,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delhi_data #concated data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forced to delete column due to Nan value over columns\n",
    "### I used this data since I thought I got the data along with District ,Age & Gender but unfortunately since I got NaN value after Data wrangling I'm forced to delete column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {},
   "outputs": [],
   "source": [
    "delhi_data.drop(columns=[\"Age Bracket\",\"Gender\",\"district\",\"State code\"],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 57 entries, 2999 to 22727\n",
      "Data columns (total 4 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   Date Announced  57 non-null     datetime64[ns]\n",
      " 1   state           57 non-null     object        \n",
      " 2   Num Cases       57 non-null     int64         \n",
      " 3   Current Status  57 non-null     object        \n",
      "dtypes: datetime64[ns](1), int64(1), object(2)\n",
      "memory usage: 2.2+ KB\n"
     ]
    }
   ],
   "source": [
    "delhi_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date Announced</th>\n",
       "      <th>state</th>\n",
       "      <th>Num Cases</th>\n",
       "      <th>Current Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>1320</td>\n",
       "      <td>Hospitalized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>349</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3001</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>53</td>\n",
       "      <td>Deceased</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date Announced  state  Num Cases Current Status\n",
       "2999     2020-06-06  Delhi       1320   Hospitalized\n",
       "3000     2020-06-06  Delhi        349      Recovered\n",
       "3001     2020-06-06  Delhi         53       Deceased"
      ]
     },
     "execution_count": 617,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delhi_data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating piviot table to our requirement for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Current Status</th>\n",
       "      <th>Deceased</th>\n",
       "      <th>Hospitalized</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date Announced</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-06-06</th>\n",
       "      <td>53</td>\n",
       "      <td>1320</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-13</th>\n",
       "      <td>57</td>\n",
       "      <td>2134</td>\n",
       "      <td>1547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-14</th>\n",
       "      <td>56</td>\n",
       "      <td>2224</td>\n",
       "      <td>878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-15</th>\n",
       "      <td>73</td>\n",
       "      <td>1647</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-16</th>\n",
       "      <td>437</td>\n",
       "      <td>1859</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-17</th>\n",
       "      <td>67</td>\n",
       "      <td>2414</td>\n",
       "      <td>957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-18</th>\n",
       "      <td>65</td>\n",
       "      <td>2877</td>\n",
       "      <td>3884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-19</th>\n",
       "      <td>66</td>\n",
       "      <td>3137</td>\n",
       "      <td>2228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-20</th>\n",
       "      <td>77</td>\n",
       "      <td>3630</td>\n",
       "      <td>7725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-21</th>\n",
       "      <td>63</td>\n",
       "      <td>3000</td>\n",
       "      <td>1719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-22</th>\n",
       "      <td>58</td>\n",
       "      <td>2909</td>\n",
       "      <td>3589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-23</th>\n",
       "      <td>68</td>\n",
       "      <td>3947</td>\n",
       "      <td>2711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-24</th>\n",
       "      <td>64</td>\n",
       "      <td>3788</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-25</th>\n",
       "      <td>64</td>\n",
       "      <td>3390</td>\n",
       "      <td>3328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-26</th>\n",
       "      <td>63</td>\n",
       "      <td>3460</td>\n",
       "      <td>2326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-27</th>\n",
       "      <td>66</td>\n",
       "      <td>2948</td>\n",
       "      <td>2210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-28</th>\n",
       "      <td>65</td>\n",
       "      <td>2889</td>\n",
       "      <td>3306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-29</th>\n",
       "      <td>57</td>\n",
       "      <td>2084</td>\n",
       "      <td>3628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>62</td>\n",
       "      <td>2199</td>\n",
       "      <td>2113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Current Status  Deceased  Hospitalized  Recovered\n",
       "Date Announced                                   \n",
       "2020-06-06            53          1320        349\n",
       "2020-06-13            57          2134       1547\n",
       "2020-06-14            56          2224        878\n",
       "2020-06-15            73          1647        604\n",
       "2020-06-16           437          1859         73\n",
       "2020-06-17            67          2414        957\n",
       "2020-06-18            65          2877       3884\n",
       "2020-06-19            66          3137       2228\n",
       "2020-06-20            77          3630       7725\n",
       "2020-06-21            63          3000       1719\n",
       "2020-06-22            58          2909       3589\n",
       "2020-06-23            68          3947       2711\n",
       "2020-06-24            64          3788       2124\n",
       "2020-06-25            64          3390       3328\n",
       "2020-06-26            63          3460       2326\n",
       "2020-06-27            66          2948       2210\n",
       "2020-06-28            65          2889       3306\n",
       "2020-06-29            57          2084       3628\n",
       "2020-06-30            62          2199       2113"
      ]
     },
     "execution_count": 618,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.pivot_table(delhi_data,index='Date Announced',columns='Current Status',values=\"Num Cases\")\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insight from Delhi June 2020 Covid-19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Percentage distribution Of Covid-19 Infected, Recovered, Deceased as on June 2020 of Delhi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "infected = sum(table[\"Hospitalized\"])\n",
    "deceased = sum(table[\"Deceased\"])\n",
    "recovered = sum(table[\"Recovered\"])\n",
    "\n",
    "labels = ['Infected', 'Recovered', 'Deceased']\n",
    "sizes = [infected, recovered, deceased]\n",
    "colors = ['lightcoral', 'yellowgreen', 'red']\n",
    "explode = (0, 0, 0) \n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True)\n",
    "plt.title(\"Percentage distribution Of Covid-19 Infected, Recovered, Deceased as on June 2020 of Delhi\")\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relation between Infected people & Deceased personal of Delhi 2020 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 620,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index1 = [\"06 Jun\",\"13 Jun\",\"14 Jun\",\"15 Jun\",\"16 Jun\",\"17 Jun\",\"18 Jun\",\n",
    "         \"19 Jun\",\"20 Jun\",\"21 Jun\",\"22 Jun\",\"23 Jun\",\"24 Jun\",\"25 Jun\",\n",
    "         \"26 Jun\",\"27 Jun\",\"28 June\",\"29 Jun\",\"30 Jun\"]\n",
    "\n",
    "x = np.arange(len(index1))\n",
    "\n",
    "plt.scatter(index1,table[\"Hospitalized\"],label=\"Infected\")\n",
    "plt.scatter(index1,table[\"Deceased\"],label=\"Deceased\")\n",
    "plt.title(\"Relation between Infected people & Deceased personal of Delhi 2020 \")\n",
    "plt.xticks(x,index1,rotation=55)\n",
    "plt.ylabel(\"Number of Deceased people\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relation between Infected , Recovered & Deceased personal of Delhi 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(len(index1))\n",
    "\n",
    "plt.plot(index1,table[\"Hospitalized\"],label=\"Infected\")\n",
    "plt.plot(index1,table[\"Recovered\"],label=\"Recovered\")\n",
    "plt.plot(index1,table[\"Deceased\"],label=\"Deceased\")\n",
    "plt.title(\"Relation between Infected , Recovered & Deceased personal of Delhi 2020 \")\n",
    "plt.ylabel(\"Number of  people\")\n",
    "plt.xticks(x,index1,rotation=55)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COVID-19 Recovery rate Calculation Weekly for Delhi June 2020  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[26.43939393939394,\n",
       " 72.49297094657919,\n",
       " 39.47841726618705,\n",
       " 57.3,\n",
       " 114.43406022845275]"
      ]
     },
     "execution_count": 622,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1 = table.iloc[0]\n",
    "week2 = table.iloc[1] \n",
    "week3 = table.iloc[2:9]\n",
    "week4 = table.iloc[9:16]\n",
    "week5 = table.iloc[16:19]\n",
    "\n",
    "week1R=week1['Recovered']\n",
    "week1I=week1['Hospitalized']\n",
    "\n",
    "week2R=week2['Recovered']\n",
    "week2I=week2['Hospitalized']\n",
    "\n",
    "week3R=week3['Recovered'].iloc[0]\n",
    "week3I=week3['Hospitalized'].iloc[0]\n",
    "\n",
    "week4R=week4['Recovered'].iloc[0]\n",
    "week4I=week4['Hospitalized'].iloc[0]\n",
    "\n",
    "week5R=week5['Recovered'].iloc[0]\n",
    "week5I=week5['Hospitalized'].iloc[0]\n",
    "\n",
    "RR_weeks = []\n",
    "\n",
    "\n",
    "recovered = week1R\n",
    "infected = week1I\n",
    "recovery_rate = (recovered/infected) * 100\n",
    "RR_weeks.append(recovery_rate)\n",
    "        \n",
    "        \n",
    "recovered=week2R\n",
    "infected = week2I\n",
    "recovery_rate = (recovered/infected) * 100\n",
    "RR_weeks.append(recovery_rate)\n",
    "        \n",
    "        \n",
    "recovered=week3R\n",
    "infected = week3I\n",
    "recovery_rate = (recovered/infected) * 100\n",
    "RR_weeks.append(recovery_rate) \n",
    "  \n",
    "        \n",
    "\n",
    "recovered=week4R\n",
    "infected = week4I\n",
    "recovery_rate = (recovered/infected) * 100\n",
    "RR_weeks.append(recovery_rate)\n",
    "        \n",
    "        \n",
    "\n",
    "recovered=week5R\n",
    "infected = week5I\n",
    "recovery_rate = (recovered/infected) * 100\n",
    "RR_weeks.append(recovery_rate)\n",
    "       \n",
    "        \n",
    "        \n",
    "RR_weeks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COVID-19 Mortality rate Calculation weekly for Delhi June 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4.015151515151516,\n",
       " 2.671040299906279,\n",
       " 2.5179856115107913,\n",
       " 2.1,\n",
       " 2.249913464866736]"
      ]
     },
     "execution_count": 623,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "week1 = table.iloc[0]\n",
    "week2 = table.iloc[1] \n",
    "week3 = table.iloc[2:9]\n",
    "week4 = table.iloc[9:16]\n",
    "week5 = table.iloc[16:19]\n",
    "\n",
    "week1D=week1['Deceased']\n",
    "week1I=week1['Hospitalized']\n",
    "\n",
    "week2D=week2['Deceased']\n",
    "week2I=week2['Hospitalized']\n",
    "\n",
    "week3D=week3['Deceased'].iloc[0]\n",
    "week3I=week3['Hospitalized'].iloc[0]\n",
    "\n",
    "week4D=week4['Deceased'].iloc[0]\n",
    "week4I=week4['Hospitalized'].iloc[0]\n",
    "\n",
    "week5D=week5['Deceased'].iloc[0]\n",
    "week5I=week5['Hospitalized'].iloc[0]\n",
    "\n",
    "MR_weeks = []\n",
    "\n",
    "\n",
    "deceased = week1D\n",
    "infected = week1I\n",
    "mortality_rate = (deceased/infected) * 100\n",
    "MR_weeks.append(mortality_rate)\n",
    "        \n",
    "        \n",
    "deceased=week2D\n",
    "infected = week2I\n",
    "mortality_rate = (deceased/infected) * 100\n",
    "MR_weeks.append(mortality_rate)\n",
    "        \n",
    "        \n",
    "deceased=week3D\n",
    "infected = week3I\n",
    "mortality_rate = (deceased/infected) * 100\n",
    "MR_weeks.append(mortality_rate) \n",
    "  \n",
    "        \n",
    "\n",
    "deceased=week4D\n",
    "infected = week4I\n",
    "mortality_rate = (deceased/infected) * 100\n",
    "MR_weeks.append(mortality_rate)\n",
    "        \n",
    "        \n",
    "\n",
    "deceased=week5D\n",
    "infected = week5I\n",
    "mortality_rate = (deceased/infected) * 100\n",
    "MR_weeks.append(mortality_rate)\n",
    "       \n",
    "        \n",
    "        \n",
    "MR_weeks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relation between Recovery Rate & Mortality Rate of Delhi June weekly 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index2 = [\"1-6 June\",\"7-13 June\",\"14-20 June\",\"21-27 June\",\"28-39 June\"]\n",
    "x = np.arange(len(index2))\n",
    "\n",
    "plt.plot(index2,RR_weeks,label=\"Recovery Rate\")\n",
    "plt.plot(index2,MR_weeks,label=\"Mortality Rate \")\n",
    "\n",
    "plt.title(\"Relation between Recovery Rate & Mortality Rate of Delhi June weekly 2020 \")\n",
    "plt.ylabel(\"Percentage %\")\n",
    "plt.xticks(x,index2,rotation=55)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Star : Problem Statement \n",
    "## The cheif minister of the state is wanting to have detailed overview of COVID-19 in the neighbouring states for the month of June 2020 to plan the strategy for safeguarding the people of the state\n",
    "### Alloated States = Haryana\n",
    "### Neigbouring States = Haryana"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using covid-19 dataset instead of rawdata since for delhi there was a NaN value for district as cleaning up the data so it would be irrelavent if haryana is compared in terms of it's own district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sno</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>State/UnionTerritory</th>\n",
       "      <th>ConfirmedIndianNational</th>\n",
       "      <th>ConfirmedForeignNational</th>\n",
       "      <th>Cured</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sno       Date     Time State/UnionTerritory ConfirmedIndianNational  \\\n",
       "0    1 2020-01-30  6:00 PM               Kerala                       1   \n",
       "1    2 2020-01-31  6:00 PM               Kerala                       1   \n",
       "\n",
       "  ConfirmedForeignNational  Cured  Deaths  Confirmed  \n",
       "0                        0      0       0          1  \n",
       "1                        0      0       0          1  "
      ]
     },
     "execution_count": 625,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dateCols = ['Date']\n",
    "data = pd.read_csv(\"covid-19.csv\",parse_dates=dateCols)\n",
    "df = pd.DataFrame(data)\n",
    "df.head(2)  #same as earlier here we are converting the data type of date for better sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sno                                  int64\n",
       "Date                        datetime64[ns]\n",
       "Time                                object\n",
       "State/UnionTerritory                object\n",
       "ConfirmedIndianNational             object\n",
       "ConfirmedForeignNational            object\n",
       "Cured                                int64\n",
       "Deaths                               int64\n",
       "Confirmed                            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 626,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3711 entries, 0 to 3710\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype         \n",
      "---  ------                    --------------  -----         \n",
      " 0   Sno                       3711 non-null   int64         \n",
      " 1   Date                      3711 non-null   datetime64[ns]\n",
      " 2   Time                      3711 non-null   object        \n",
      " 3   State/UnionTerritory      3711 non-null   object        \n",
      " 4   ConfirmedIndianNational   3711 non-null   object        \n",
      " 5   ConfirmedForeignNational  3711 non-null   object        \n",
      " 6   Cured                     3711 non-null   int64         \n",
      " 7   Deaths                    3711 non-null   int64         \n",
      " 8   Confirmed                 3711 non-null   int64         \n",
      "dtypes: datetime64[ns](1), int64(4), object(4)\n",
      "memory usage: 261.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Sno', 'Date', 'Time', 'State/UnionTerritory', 'ConfirmedIndianNational', 'ConfirmedForeignNational', 'Cured', 'Deaths', 'Confirmed']\n"
     ]
    }
   ],
   "source": [
    "col = []\n",
    "for i in df.columns:\n",
    "    col.append(i)\n",
    "    \n",
    "print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Sno','Time',],inplace = True) #dropping unwanted data column which is irrelevant for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={\"State/UnionTerritory\":\"State\"},inplace=True) #again renaming for better understandability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>State</th>\n",
       "      <th>ConfirmedIndianNational</th>\n",
       "      <th>ConfirmedForeignNational</th>\n",
       "      <th>Cured</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   State ConfirmedIndianNational ConfirmedForeignNational  Cured  \\\n",
       "0 2020-01-30  Kerala                       1                        0      0   \n",
       "1 2020-01-31  Kerala                       1                        0      0   \n",
       "\n",
       "   Deaths  Confirmed  \n",
       "0       0          1  \n",
       "1       0          1  "
      ]
     },
     "execution_count": 631,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"ConfirmedIndianNational\",\"ConfirmedForeignNational\"],inplace= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting value falling under Sate of  - \"Haryana\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>State</th>\n",
       "      <th>Cured</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2461</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>940</td>\n",
       "      <td>19</td>\n",
       "      <td>1721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>176</td>\n",
       "      <td>3</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3182</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>4556</td>\n",
       "      <td>134</td>\n",
       "      <td>9218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>439</td>\n",
       "      <td>11</td>\n",
       "      <td>818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>2020-10-04</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3650</th>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>10499</td>\n",
       "      <td>240</td>\n",
       "      <td>14941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3290</th>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>5557</td>\n",
       "      <td>160</td>\n",
       "      <td>10635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1167</th>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>127</td>\n",
       "      <td>3</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2641</th>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>1089</td>\n",
       "      <td>23</td>\n",
       "      <td>2954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date    State  Cured  Deaths  Confirmed\n",
       "2461 2020-05-30  Haryana    940      19       1721\n",
       "1360 2020-04-27  Haryana    176       3        289\n",
       "3182 2020-06-19  Haryana   4556     134       9218\n",
       "1946 2020-05-15  Haryana    439      11        818\n",
       "811  2020-10-04  Haryana     29       3        169\n",
       "3650 2020-02-07  Haryana  10499     240      14941\n",
       "3290 2020-06-22  Haryana   5557     160      10635\n",
       "780  2020-09-04  Haryana     29       3        169\n",
       "1167 2020-04-21  Haryana    127       3        254\n",
       "2641 2020-04-06  Haryana   1089      23       2954"
      ]
     },
     "execution_count": 633,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "haryana = df[\"State\"] == \"Haryana\"\n",
    "haryana=df.loc[haryana]\n",
    "haryana.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting value falling under Haryana of June 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>State</th>\n",
       "      <th>Cured</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2020-06-03</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>2020-06-04</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>2020-06-05</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>256</td>\n",
       "      <td>6</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2713</th>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>2134</td>\n",
       "      <td>24</td>\n",
       "      <td>3597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2966</th>\n",
       "      <td>2020-06-13</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>2475</td>\n",
       "      <td>70</td>\n",
       "      <td>6334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3002</th>\n",
       "      <td>2020-06-14</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>2803</td>\n",
       "      <td>78</td>\n",
       "      <td>6749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3038</th>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>3003</td>\n",
       "      <td>88</td>\n",
       "      <td>7208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3074</th>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>3565</td>\n",
       "      <td>100</td>\n",
       "      <td>7722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3110</th>\n",
       "      <td>2020-06-17</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>3748</td>\n",
       "      <td>118</td>\n",
       "      <td>8272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3146</th>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>3952</td>\n",
       "      <td>130</td>\n",
       "      <td>8832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3182</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>4556</td>\n",
       "      <td>134</td>\n",
       "      <td>9218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3218</th>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>4889</td>\n",
       "      <td>144</td>\n",
       "      <td>9743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3254</th>\n",
       "      <td>2020-06-21</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>5128</td>\n",
       "      <td>149</td>\n",
       "      <td>10223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3290</th>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>5557</td>\n",
       "      <td>160</td>\n",
       "      <td>10635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3326</th>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>5916</td>\n",
       "      <td>169</td>\n",
       "      <td>11025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3362</th>\n",
       "      <td>2020-06-24</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>6498</td>\n",
       "      <td>178</td>\n",
       "      <td>11520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3398</th>\n",
       "      <td>2020-06-25</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>6925</td>\n",
       "      <td>188</td>\n",
       "      <td>12010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3434</th>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>7380</td>\n",
       "      <td>198</td>\n",
       "      <td>12463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3470</th>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>8016</td>\n",
       "      <td>211</td>\n",
       "      <td>12884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3506</th>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>8472</td>\n",
       "      <td>218</td>\n",
       "      <td>13427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3542</th>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>8917</td>\n",
       "      <td>223</td>\n",
       "      <td>13829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3578</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>9502</td>\n",
       "      <td>232</td>\n",
       "      <td>14210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date    State  Cured  Deaths  Confirmed\n",
       "52   2020-06-03  Haryana      0       0          2\n",
       "688  2020-06-04  Haryana     25       1         84\n",
       "1649 2020-06-05  Haryana    256       6        548\n",
       "2713 2020-06-06  Haryana   2134      24       3597\n",
       "2966 2020-06-13  Haryana   2475      70       6334\n",
       "3002 2020-06-14  Haryana   2803      78       6749\n",
       "3038 2020-06-15  Haryana   3003      88       7208\n",
       "3074 2020-06-16  Haryana   3565     100       7722\n",
       "3110 2020-06-17  Haryana   3748     118       8272\n",
       "3146 2020-06-18  Haryana   3952     130       8832\n",
       "3182 2020-06-19  Haryana   4556     134       9218\n",
       "3218 2020-06-20  Haryana   4889     144       9743\n",
       "3254 2020-06-21  Haryana   5128     149      10223\n",
       "3290 2020-06-22  Haryana   5557     160      10635\n",
       "3326 2020-06-23  Haryana   5916     169      11025\n",
       "3362 2020-06-24  Haryana   6498     178      11520\n",
       "3398 2020-06-25  Haryana   6925     188      12010\n",
       "3434 2020-06-26  Haryana   7380     198      12463\n",
       "3470 2020-06-27  Haryana   8016     211      12884\n",
       "3506 2020-06-28  Haryana   8472     218      13427\n",
       "3542 2020-06-29  Haryana   8917     223      13829\n",
       "3578 2020-06-30  Haryana   9502     232      14210"
      ]
     },
     "execution_count": 634,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date= '06/01/2020'\n",
    "end_date = '06/30/2020'\n",
    "\n",
    "date=haryana['Date'].between(start_date, end_date)\n",
    "haryana=haryana.loc[date]\n",
    "haryana\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insight from Haryana June 2020 Covid-19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Percentage distribution Of Covid-19 Infected, Recovered, Deceased as on June 2020 of Haryana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "infected = sum(haryana[\"Confirmed\"])\n",
    "deceased = sum(haryana[\"Deaths\"])\n",
    "recovered = sum(haryana[\"Cured\"])\n",
    "\n",
    "labels = ['Infected', 'Recovered', 'Deceased']\n",
    "sizes = [infected, recovered, deceased]\n",
    "colors = ['lightcoral', 'yellowgreen', 'red']\n",
    "explode = (0, 0, 0) \n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True)\n",
    "plt.title(\"Percentage distribution Of Covid-19 Infected, Recovered, Deceased as on June 2020 of Haryana\")\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relation between Infected people & Deceased personal of Haryana 2020 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index1 = [\"03 Jun\",\"04 Jun\",\"05 Jun\",\"06 Jun\",\"13 Jun\",\"14 Jun\",\"15 Jun\",\n",
    "         \"16 Jun\",\"17 Jun\",\"18 Jun\",\"19 Jun\",\"20 Jun\",\"21 Jun\",\"22 Jun\",\n",
    "         \"23 Jun\",\"24 Jun\",\"25 June\",\"26 Jun\",\"27 Jun\",\"28 Jun\",\"29 Jun\",\"30 Jun\"]\n",
    "\n",
    "x = np.arange(len(index1))\n",
    "\n",
    "plt.scatter(index1,haryana[\"Confirmed\"],label=\"Infected\")\n",
    "plt.scatter(index1,haryana[\"Deaths\"],label=\"Deceased\")\n",
    "plt.title(\"Relation between Infected people & Deceased personal of Haryana 2020 \")\n",
    "plt.xticks(x,index1,rotation=55)\n",
    "plt.ylabel(\"Number of Deceased people\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relation between Infected , Recovered & Deceased personal of Haryana 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 637,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(len(index1))\n",
    "\n",
    "plt.plot(index1,haryana[\"Confirmed\"],label=\"Infected\")\n",
    "plt.plot(index1,haryana[\"Cured\"],label=\"Recovered\")\n",
    "plt.plot(index1,haryana[\"Deaths\"],label=\"Deceased\")\n",
    "plt.title(\"Relation between Infected , Recovered & Deceased personal of Haryana 2020 \")\n",
    "plt.ylabel(\"Number of  people\")\n",
    "plt.xticks(x,index1,rotation=55)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COVID-19 Recovery rate Calculation Weekly for Haryana June 2020  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 39.074834227976005,\n",
       " 41.532078826492814,\n",
       " 50.16140076298542,\n",
       " 63.096745363819174]"
      ]
     },
     "execution_count": 638,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1 = haryana.iloc[0:4]\n",
    "week2 = haryana.iloc[4] \n",
    "week3 = haryana.iloc[5:12]\n",
    "week4 = haryana.iloc[12:19]\n",
    "week5 = haryana.iloc[19:22]\n",
    "\n",
    "week1R=week1['Cured'].iloc[0]\n",
    "week1I=week1['Confirmed'].iloc[0]\n",
    "\n",
    "week2R=week2['Cured']\n",
    "week2I=week2['Confirmed']\n",
    "\n",
    "week3R=week3['Cured'].iloc[0]\n",
    "week3I=week3['Confirmed'].iloc[0]\n",
    "\n",
    "week4R=week4['Cured'].iloc[0]\n",
    "week4I=week4['Confirmed'].iloc[0]\n",
    "\n",
    "week5R=week5['Cured'].iloc[0]\n",
    "week5I=week5['Confirmed'].iloc[0]\n",
    "\n",
    "RR_weeksHR = []\n",
    "\n",
    "\n",
    "recovered = week1R\n",
    "infected = week1I\n",
    "recovery_rate = (recovered/infected) * 100\n",
    "RR_weeksHR.append(recovery_rate)\n",
    "        \n",
    "        \n",
    "recovered=week2R\n",
    "infected = week2I\n",
    "recovery_rate = (recovered/infected) * 100\n",
    "RR_weeksHR.append(recovery_rate)\n",
    "        \n",
    "        \n",
    "recovered=week3R\n",
    "infected = week3I\n",
    "recovery_rate = (recovered/infected) * 100\n",
    "RR_weeksHR.append(recovery_rate) \n",
    "  \n",
    "        \n",
    "\n",
    "recovered=week4R\n",
    "infected = week4I\n",
    "recovery_rate = (recovered/infected) * 100\n",
    "RR_weeksHR.append(recovery_rate)\n",
    "        \n",
    "        \n",
    "\n",
    "recovered=week5R\n",
    "infected = week5I\n",
    "recovery_rate = (recovered/infected) * 100\n",
    "RR_weeksHR.append(recovery_rate)\n",
    "       \n",
    "        \n",
    "        \n",
    "RR_weeksHR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COVID-19 Mortality rate Calculation weekly for Haryana June 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 1.1051468266498263,\n",
       " 1.1557267743369388,\n",
       " 1.4574977990805047,\n",
       " 1.6235942503910032]"
      ]
     },
     "execution_count": 639,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "week1 = haryana.iloc[0:4]\n",
    "week2 = haryana.iloc[4]  \n",
    "week3 = haryana.iloc[5:12]\n",
    "week4 = haryana.iloc[12:19]\n",
    "week5 = haryana.iloc[19:22]\n",
    "\n",
    "week1D=week1['Deaths'].iloc[0]\n",
    "week1I=week1['Confirmed'].iloc[0]\n",
    "\n",
    "week2D=week2['Deaths']\n",
    "week2I=week2['Confirmed']\n",
    "\n",
    "week3D=week3['Deaths'].iloc[0]\n",
    "week3I=week3['Confirmed'].iloc[0]\n",
    "\n",
    "week4D=week4['Deaths'].iloc[0]\n",
    "week4I=week4['Confirmed'].iloc[0]\n",
    "\n",
    "week5D=week5['Deaths'].iloc[0]\n",
    "week5I=week5['Confirmed'].iloc[0]\n",
    "\n",
    "MR_weeksHR = []\n",
    "\n",
    "\n",
    "deceased = week1D\n",
    "infected = week1I\n",
    "mortality_rate = (deceased/infected) * 100\n",
    "MR_weeksHR.append(mortality_rate)\n",
    "        \n",
    "        \n",
    "deceased=week2D\n",
    "infected = week2I\n",
    "mortality_rate = (deceased/infected) * 100\n",
    "MR_weeksHR.append(mortality_rate)\n",
    "        \n",
    "        \n",
    "deceased=week3D\n",
    "infected = week3I\n",
    "mortality_rate = (deceased/infected) * 100\n",
    "MR_weeksHR.append(mortality_rate) \n",
    "  \n",
    "        \n",
    "\n",
    "deceased=week4D\n",
    "infected = week4I\n",
    "mortality_rate = (deceased/infected) * 100\n",
    "MR_weeksHR.append(mortality_rate)\n",
    "        \n",
    "        \n",
    "\n",
    "deceased=week5D\n",
    "infected = week5I\n",
    "mortality_rate = (deceased/infected) * 100\n",
    "MR_weeksHR.append(mortality_rate)\n",
    "       \n",
    "        \n",
    "        \n",
    "MR_weeksHR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relation between Recovery Rate & Mortality Rate of Haryana June weekly 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index2 = [\"1-6 June\",\"7-13 June\",\"14-20 June\",\"21-27 June\",\"28-39 June\"]\n",
    "x = np.arange(len(index2))\n",
    "\n",
    "plt.plot(index2,RR_weeks,label=\"Recovery Rate\")\n",
    "plt.plot(index2,MR_weeksHR,label=\"Mortality Rate \")\n",
    "\n",
    "plt.title(\"Relation between Recovery Rate & Mortality Rate of Delhi June weekly 2020 \")\n",
    "plt.ylabel(\"Percentage %\")\n",
    "plt.xticks(x,index2,rotation=55)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison btw Delhi & Haryana"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relation between Recovery Rate of Delhi & Haryana June weekly 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 641,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r1y7DMi9Pcl7dv4x+j2FhYdy9e5fmzZsD+u/mv8cv6P++XvW6bYL+mJ4+fTpt2rQBIDY2lsTExBTrc3NzY8CAAVSqVAlnZ2esra05ceIEV65c4ZNPPknz+z1z5gxVq1Y1lOZf7oetrS27d++mcOHCfPnll3z88ce0bduWcuXKJdt2ixYtiI+P56+//uLcuXN88cUXKeIDuH79OqNGjcLV1ZUpU6ZgamoK6E8YXpZuAZ4+fWq4Z5mUlMTUqVO5ffs2mzdvNmy7dOnSBAQEJIvXxsYmxfefFqPd8/Hw8KBOnTr4+voC0LJlS/z8/Az11KVLlzJ58uRky5w9e5batWvzwQcf0KRJEw4dOoRWqwXA1NQ0WVJ5qVWrVmzatAm1Wo1Op2Pjxo04OztnOt6XZ0KPHj3ir7/+onnz5jRv3pyTJ09y584dAI4ePYqXlxcJCQkp4nFzc+Orr76iefPmWFlZUalSJb799lvDGXRa+9+yZUt++OEHhBAkJSUxcuRIfv7550zvw6vKly/PiBEjWLBgAXFxcWnuS/PmzdmzZw9JSUnodDrmzp3L3r1702zbkiVLUqdOHWbPnm34kbOysqJevXp8//33gD6J9uvXj0OHDr02xgYNGmBiYsL69esz3HvR3NycOXPm8Msvv3Dt2jUCAwOxtbVl1KhRtGzZ0vCDpdVqDScQQggqV66MpaWlIfk8fvwYDw+PFL3RqlSpglKp5MCBA4D+j27//v2GunhmHD16FH9/f/r06ZPptskoKysrbG1tOX36NKC/kihcuDBKpfK191TTExwcjEql4uOPP8bFxYUzZ84YjovXcXd3JygoiP379xuu6k+ePEm7du3o378/tWvX5uDBg4a/49Sk9T2+KiQkhFmzZqFWq1GpVNy/f58KFSoA+pOul/fE9uzZk6H9bdmyJRs3bjTs46xZs/jqq69SfK5UqVIULVqUzZs34+zsTMuWLTlw4ABRUVHUqFEjze+3Xr16BAcHc/bsWQCCgoJwd3c3fD+VKlWiefPm+Pj4MGXKlBRtrVAo6N+/PzNmzMDDw+O1VY8nT57w/vvvM2rUKKZPn25IPKCvnGzduhWNRsOLFy/Yu3cvrq6uAEycOBGVSpUs8bxsl4CAAO7fvw/A5s2bDRWYjDLKlc9Ls2bNwsvLi+PHj9OrVy/CwsIMZ0elS5fm888/T/Z5Dw8PDhw4QKdOndDpdLRr147o6GhUKhXVqlXDwsKCnj17JrvJOXLkSL744gu6deuGRqOhTp06zJo1K9OxJiYm0r17d9RqNTNnzqRy5coAfPrpp/zvf/8znGGtWrWKQoUKJYvnt99+w9XVlXnz5jFx4kTg34O6QYMGAGnu/4wZM1iwYAGenp6o1WpatGjB0KFDs9TmrxoyZAg7duww3NxPbV/69u1LaGgo3t7eCCFo0qQJPj4+aDSaNNu2V69ejBs3jlWrVhneW7x4MfPmzcPT05OkpCQ8PDzw8vLi4cOHr43R29ub33//nRo1amR4vxo1aoSnpyeffvop3333HX5+fnTs2BGFQkGTJk2wtbUlODiYihUrUqdOHbp06cLGjRtZuXIlCxYsYN26dWg0GsaNG0fDhg2TrdvMzIyVK1cyf/58li9fjlarZfTo0TRr1izduM6dO0fXrl0B/Q9GiRIlWL9+PXZ2dllqm4wwNTVlzZo1LFiwgCVLlqDVahk7dixmZmYsWbKEDRs2ZKpU4uDgQNu2benUqRPm5ubY29tTrVo1goODX9uJwdzcHHd3dyIiIgylwr59+zJhwgQ8PT3RaDQ4Oztz4MCBVBMY6EuNqX2PVapUMXyuUaNGtGvXDi8vL9RqNX379sXe3h6AmTNn8umnn2JtbU2LFi0M7Z6WUaNG8cUXX9C9e3e0Wi01a9Y0lCD/y83Nje+++45atWphYmKCpaWl4UccUv9+AZYtW8bChQtJTExECMHChQspV64cf//9t2H5ESNGcPjwYdatW0fx4sWTbbt79+588cUX9OnT57WxrVy5kvj4eDZs2MCGDRsA/Xfz22+/0a9fPx48eEDXrl1Rq9X06dOHJk2acPHiRfbv30+lSpWSlfImTpxIq1at8PX1ZezYsajVaipUqJDqFVdqFCK9Go0kGYlGo+Gjjz7Cy8uLzp07GzscKQvi4uIYOHAgs2fPTnbvTHq79u7dy/bt21m3bp2xQ8kwOcKBlCPdvn2b5s2bU7Ro0WTPJkm5x/Hjx2nbti2tWrWSiecd8vHxYeXKlVmq6BiTvPKRJEmSst07vfJRqVR4eHgYata//vorHh4ehgfQXvY4CQoKwtvbG3d3d2bMmJHlnkOSJElS7vDOkk9AQAD9+vUz9Ia4d+8e69evZ/PmzezatQudTmd4QnnSpEnMnj2b/fv3I4Rgy5Yt7yosSZIkKQd4Z8lny5YtzJkzhxIlSgD/doG1srJCoVBgb2/Po0ePCA0NJSEhwVAT9vb2ztUDAUqSJEnpe2ddrRcsWJDsddmyZQ1PPj979oyNGzfi6+vL06dPk3V5tLOzy9LzB5IkSVLuke3P+YSFhTF06FB69OhB06ZNOX/+fIqhHV43bEN6nj+PRafLfN+JYsWsiIzM3FDg+Zlsr8yTbZY5sr0yJ6vtZWKioGjRtEcNeZeyNfncuXOHoUOH4uPjw+DBgwH9k8Hh4eGGz0RERBhKdZmh04ksJZ+Xy0oZJ9sr82SbZY5sr8zJje2Vbc/5qFQqhgwZwrhx4wyJB/TlOAsLC8MQ5jt37qR169bZFZYkSZJkBNl25ePn50dERATff/+9YXwjFxcXxo0bx+LFi5k5cyYqlQpHR8e3PrukJEmSlLPkmYdMIyNVKS49tVoNz5+Ho9EkpbKUfrTqtMaUkpKT7ZUxSqU5RYvaYWqqxM6uMOHhMcYOKdeQ7ZU5WW0vExMFxYoZbwZjow4s+q49fx6OpWVBChUqlWonBqXSBI1G/phmlGyv9AkhiI19wfPn4RQvnnL+FUmS8vjYbhpNEoUKWWep95wkZZVCoaBQIes0r7glKb/L08kHkIlHMgp53EnZQaPNvVWIPJ98JEmS8qKHT1WMX36Cc0G586H8PH3PJyd5/PgR/fp5U6mSfuKrxMQEnJzqMmLER9jappwT/tXlxowZjp/fbhYsmEv9+g3p3Nkz2Wd27PADoFu35NMi9+zpiaWlJUqlGQAqVQw1atRkxoxPDFP55nQXLpxjypTxlC1bHgCdTkt8fDwDBryXYn9fpVKpWLBgLr6+i7MrVEnKNnEJGlZsv4LS1ISq5WzQJKScxTmnk8knGxUvbscPP+gHUxVCsGbNCmbOnMLKlW82AVRaP8KLFi2ldOkyAKjVakaNGsK+fXvp3j31ZXIaB4eafPPNWsPrW7duMHToe7i5daRQodf31omJecGtWzeyK0RJyjZCCL7/PYjwqAQm969P0cKWhMvkI2WUQqFgyJDheHp24PbtW1SrVp0NG37gyJE/0Wp1NG3ajJEjx6ZY7tSpE2zf/hvPnj3jvfcG07WrN+vXrwFgyJDhaW5TpYpBpVJhbW0NwOnTp1i/fjUajYbSpcsyZcoMbGyKcPbsGb755muE0FGqVGnmzJlPgQIFWbbsS86f188z7+7emYEDBzF9+iQ6dOhI27b6+dsHDx7I1KkzKViwEIsX+/LiRTQWFpaMHz8Je/saLFgwl+joaEJDQxgx4iN++eUnVq36DoDff9/NtWuBTJw4Lc39ePz4MQUKFMDMzJzYWBW+vvMID39KREQ4jRo1YerUWXz99SIiIsKZNm0ivr6L+eOPPfz22yZ0OoGDQw3+978pr53rXpJyuv1/h3D+Zji921XDvnwRY4eTZfkm+Zy88pgTlx+neF+hgDd90qllndI4O2W+S62ZmRnly5cnOPg+ERHh3LgRxLff/oRCoWDevNkcOPAHdeoknwEyKSmJtWt/5N69O4wdO4KuXb3T3MakSeMwNTXl2bNnlChRkh49euPi4sbz589Zvfobli1bjbW1NTt2bGXVquX8739T+PTTWXz11XKqV3dg9epv+OOPPZiYmBIWFsbPP/9KfHwiY8YMo0qVari7d+bPP/+gbdv2hIQ8ICkpCXv7GowcOZjx4ydjb1+De/fuMn36RDZt2gaAjY0NCxcuQQjBsmVLCA19SNmy5di3by/Dh3+UYh9u3Ahi0KD+JCTEExPzgvr1G7JkyQrMzc05evQw1avbM3/+F6jVagYO7MWNG9f5+ONJjBkzHF/fxdy9e4fdu3ewatV3WFhYsHr1N2zatIFBg4Zm+juTJGO6GRKFn/8dGtrb4d6kvLHDeSP5JvnkXAosLCw4d+5vrl0LZMgQH0B/T6hkyVIpkk+rVm1QKBRUrlyVqKiodNf+suzm73+I5cuX0K6dKwqFgmvXAgkLe8LYsSMA/b0Ua2sb7t69jZ2dHdWrOwAwYoQ+GcycOZnOnT0wNTXF0tISN7dOnD//NyNGjGHJkoXExcVy8OB+3N07ERcXR1DQNT777FNDHPHx8URH6+OtVau2fs8VCjp16sL+/b/TubMXz549w9Gxdop9eFl2S0pKYt682RQqVIiaNR0BcHPryLVrgWzZ8gv3798jOjqa+Pg4bGxsDMtfvHiOhw9DGD78AwA0GjX29jUy8N1IUs4RrUpk1c5A7IpY8kHnmrm+R2W+ST7OTq+/OjHmQ5NqtZqQkGAqV67ChQtn6d27H337DgQgJiYGU1NTww/2S6ampkDmu/K2bduev/8+ja/vpyxevAydTkudOnX54oslACQmJhIfH094+FPg33WrVCri4l43YrhAq9ViZmaGs3MrTpw4xuHDf7Jo0VJ0Oh3m5haG+1sAT5+GYW2tTwivlrs6d/ZkwoQxmJub07Fj5zT3wdzcnClTZtKvnzeHDv1J+/Zu+Pltxt//MF5e3enZswn37t3hv4N2aLU6XFxc+fjjSQDExcWh1Woz1X6SZExanY7VO68Sn6BhQu96FLTM/T/dsqu1keh0OtavX0OtWk6ULVuOBg0as3//78TFxaHRaJg2bQL+/ofe6jY//HAkly8HcOrUCWrVqs3Vq1d48CAYgB9+WMeKFV9ToUJFoqKec+/eXQA2bvyRHTu20rBhI/74Yy9arZaEhAQOHNhH/fqNAP39n82bf8bGpgilSpXGysqKcuXKs3//7wCcPXua0aOHvTamUqVKY2dXgh07ttKxY5d098HKyoohQ4axYsXXJCYmcPbsGby8vOnQoRNJSUncunUTnU6HqampIcHUr9+QY8f8ef78GUIIvvzSly1bfklnS5KUc2w7dpcbIVH4uDtQroTxhsR5m3J/+sxFIiLCGTSoP6Avc1Wv7sDcufpJ91q2bM3t2zcZNmwQOp2Wpk1b0KmTB0+epLxPlVVFi9oyYMB7rFy5lB9+2MTUqbOZPXsaOp0WO7uSzJ79KRYWFsya9Snz589Bo1FTpkw5Zs36FHNzc0JCHuDj0xe1Wk2HDp1o06YdAHXq1EOlUiXrdTdnznwWLfqMX375CaXSjE8//SzVqzVX1w74+x+meHG71/77f3l4dMPP71c2b95I7979WbzYl59//p5ChayoXbsOjx8/ol69BpQsWYoxY4azfPkaPvjgQ8aOHYEQgmrV7Bk4cNAbt6ckZYeLN8P54/QD2tYrk6V7yzlVnh5Y9MmTYEqVqpjmcnKsssx52+2l0WiYN282Li6utGnj8tbWmxO8PP7kQJmZI9vrX0+fx/HJD+coUbQA0wc2wExpmuIzuXVgUVl2k4xGCEG3bp0wMTGhVau2xg5HknKUJLWWFdsDMVHA6G61X5t4cjNZdpOMRqFQsGfPn8YOQ5JypJ//vEnIUxUf96pD8SK5Y0SSzJBXPpIkSTnMsYBHnLj8GI8WlahTtbixw3knZPKRJEnKQYKfxPDzgZvUqlSUbi0rGzucd0YmH0mSpBwiNkHNiu1XKFzQjGFejpiY5O4HSdMik48kSVIOoBOC9XuCeB6TyMhutbEuaG7skN4pmXwkSZJygD9OB3PpdgR9XKpRraxN+gvkcrK3WzZ5dV6eV7Vs2YgTJ84ZKaq0rV+/hp07txnmG1Krk1AqlUyYMDXFmHM5WVbnNVq/fg2NGjWhbt362RWqlE8FBT9n27G7NKlZglNfo8EAACAASURBVPYNyxk7nGwhk4+Upq5dvZNN1eDnt4nly5fw7bc/GjGqzMvKvEYXL56nfv2G2RWilE89j0lkzc5AStkWZFCnGrl+wNCMyjfJR33zJOobx1K8r1AoUgxEmVlmDq0xs3d+o3WkNi/NxYvnWbVqGVqtjsqVKxMQcImvvvqGChUq/jOjZ082bdrGnj072LfvdxIS4jEzM2Pu3AVUqFCJnj09cXfvzN9//0V8fAIzZ35CjRo1uXjxPGvXriQxMYGYGBVjx45P90FPnU5HWNi/A4Q+exbJokWfERYWhomJCcOHj6Zx46a8eBGNr+88Hjy4j5mZOWPGjKdhw8acPHmcb79dhRA6ypQpy6RJ07l2LZBdu3awcKF+gFM/v808fPiQMWPGs3LlUi5ePI9Wq6NzZw/69BnAhQvnMtQeac3V8995jbZu/TVF2129GsiNG0F88cV8PvtsMRYWFq+dn0iS3oRGq2PVzkAS1Tom9XfC0jzf/CTnn+STE7w6ttt/nTp14rXz0gCEhDzAz28PVlZWrF+/hgMH/mDo0BH4+x+iRYuWaDRqjh07yjffrMHCwpJ161azdesWxo+fDOjnz/n225/w89vMhg3fsWDBIrZu/ZWpU2dRsWIlzp8/y9Kli1+bfHbu3Mbx40eJiXmBEAJn51ZMmzYbgKVLF9OlixctW7YhIiKCUaOG8MMPv/Dtt6spV648vr6LuXPnNgsXLuDzz79k0aLPWLVqPaVLl+GXX37iq68WMnfuAhYt8uXFixdYW1tz6NABxo6dwO7d2wH47ruNJCUl8b//fUSNGrUy1B6vSzypzWsUG6tKte327t3F4MHDqFq1WprzE0lSVvn53+H2w2iGezlStnghY4eTrfJN8jGzd37t1Ul2ju326jTaL7VsqR8ZOrV5aQDKl6+IlZV+DKbOnT35+ONRDB06gn379jJs2GgKFbJi7tz5HDx4gJCQB5w5c8owHw9A06YtAKhSpRpHjx4BYNaseZw6dZwjRw5y9eoV4uPjXxvzy7JbZGQE48aNpHZtJ4oX1z/0du7c3wQHB7NunX4mVY1GQ2joQy5dOs+cOfoBU6tWrcaaNd9z8uRxatZ0NJS+vLy82bDhB5RKJa1bt+Po0cM0btyM6OhoatZ0ZOPGH7l16ybnz+vvh8XHx3Hnzm0qVaqcbnu8TmrzGqXXdkCa8xPZ2OTemSQl4zp3/SkHzobQvmE5mtYqaexwst07TT4qlYq+ffuyevVqypUrx6lTp/D19SUxMZFOnToxfvx4AIKCgpgxYwaxsbE0atSITz75BKUy3+RFgDTnpXn1TL506TKUKlWao0cP8+xZJI6OtQkLe8KYMcPp0aM3zZq1wNa2GLdu3TAsY27+b5fNl+scPfpDGjRoSP36DWnYsDGffDIzzfiKFSvOlCkzmTBhDE5O9ShTpixarY5ly1YZynAREREULVoUpVKZrG4dHHwfIZIneCGEYcoDd/fOrFu3ipiYF3To0AnQz8EzatRYw2CjUVFRFChQgKtXr6TbHmn577xG6bUdkO78RJKUWY8jY/nu9yCqlrGmj0s1Y4djFO+sq3VAQAD9+vXj/v37ACQkJDB9+nRWrlzJ77//TmBgIEePHgVg0qRJzJ49m/379yOEYMuWLe8qrBwrtXlpXqdLFy++/nox7u76ydeuX79GuXLl6dNnADVr1uLYsSPodKlPlvbiRTQhIcEMGTKCZs2cOX78aKrbepWTU11atGjJypXLAGjYsBHbtv0GwL17d3nvvT4kJiZQt24DDh7cD+gTz4QJY6hVqzbXrl3h8eNHAOzatY0GDfQ382vXdiIiIoL9+3/Hza2jYd27du1Ao9EQFxfHqFFDuHr1SobaIz2vzmuUVtuZmirRarWZmp9IktKTmKRl5fZAlKYmjOxWG6Vp/nzi5Z3t9ZYtW5gzZw4lSpQA4PLly1SsWJHy5cujVCrx9PRk3759hIaGkpCQQL16+q673t7e7Nu3712FlWP17t2f779fy3vv9WHp0i8N89K8Tps27XjxItow+Vrjxs3Q6XQMHNiLwYMHUrFiJR49ev2yANbWNnh4dMXHpzcDBvQkLi6OhISEVEtvrxo1agynTh0nIOAS48dP5tq1QN5/vy9z5kxj1qxPKViwEEOGDCck5AHvv9+PTz6ZyaxZn2JrW4xJk2YwffpEBg7szcWL55k4cbphve3bu1GwYEHKltV3M+3WrSfly5fngw/6M3SoD507e9KgQaMMtUd6Xp3XKK22a9q0OYsX+3LlSgBz5sxn9+4dvP9+X1avXpHm/ESSlBohBD/tv86jiFiGezlia21p7JCM5p3P5+Pi4sJPP/3EpUuX8Pf3Z/HixQCcOnWKdevWMWbMGBYuXMimTZsACA4OZtiwYezfvz9T28kv8/kIITh9+iQ7dmw1TIGdnXJaexm7PdIi5/PJmrzcXkcuPGTDgZt0a1UZL+e3M25bbp3PJ9turOh0umRnikIIFApFqu9n1usa8elTE5TK9C/uMvKZnGLJkkWcOHGcJUuWGy3unNReOaE9UmNiYoKdXWEAw/+ljMmL7XXzwXM2HbpNwxol+MDL6a2O25Yb2yvbkk+pUqUIDw83vA4PD6dEiRIp3o+IiDCU6jLjdVc+Op0u3bP0nHYmn54xYyYwZswEAKPEndPay9jtkRadTkd4eEyePpN/F/Jie6ni1Xz2/d/YFDLnfXcHIiNVb23dufXKJ9tOFevWrcu9e/cIDg5Gq9WyZ88eWrduTdmyZbGwsOD8+fMA7Ny5k9atW7+17eaRWcKlXEYed9JLOiFYu/sq0bFJjOpeG6sCZsYOKUfItisfCwsLPv/8c8aMGUNiYiJt2rShY0d9z6bFixczc+ZMVCoVjo6OvPfee29lmyYmpmi1GsOYXpKUXbRaDSYmeWvaYylr9py8T+DdZ/i4O1C5tLWxw8kx3nmHg+zyurJbTEwUGo2aIkWKoVC8/iIvp5WRcjrZXukTQkdUVARKpTmFCxfJk2WkdykvtVfgvUiW/BpAM8dSDPWo+U56SObWsluefpLTysqG58/DCQt7CLw+x5qYmGToGRdJT7ZXRigwN7fEyko+hJqfRUYnsHbXNcrYFeK9jg6ya/5/5Onko1AosLVNu/NCXjrLyg6yvSQpfS8HDNVodYzu7oSFmSzB/lfO6psqSZKUB/x66DZ3H71gSJealLItaOxwciSZfCRJkt6i01efcOjCQzo0Lk9Dh8w/NpJfyOQjSZL0loRGxPLDvutUL2dDz7ZVjR1OjiaTjyRJ0lsQn6hh5fYrWJorGdE1/w4YmlGydSRJkt6QEIIf/rjOk2dxjPBypGjh1GfSlfRk8pEkSXpDB88/5Oz1p/RoU5UaFYsaO5xcQSYfSZKkN3D7YTRbDt+mXrXidGpawdjh5Boy+UiSJGXRi9gkVu0MxNba4p2NYJBXyeQjSZKUBTqdYM2uq6ji1Yzu7kRBSzmGZGbI5CNJkpQFO07cJSj4OQPd7KlQMvfNp2NsMvlIkiRlUsDtCPacCqZVndK0qlvG2OHkSjL5SJIkZUJ4VDzr9lyjQgkrBrjZGzucXEsmH0mSpAxSa7Ss3B6ITsAobyfM5YChWSaTjyRJUgb9cvAWwWExDPWoSYkiBYwdTq4mk48kSVIGnLzymKOXHtG5WUXqV7czdji5nkw+kiRJ6Qh5quKn/TeoUaEI3VtXNnY4eYJMPpIkSWmIS9CwYvsVCloqGd61NqYm8mfzbZCtKEmSlAohBOv3XiMiKoGRXWtjU8jc2CHlGTL5SJIkpWL/3yFcvBVB73ZVsS9fxNjh5Cky+UiSJL3GjQfP8fO/QyMHO9walzd2OHmOTD6SJEn/EaVKZPXOq9gVLcAHneWAoe+CTD6SJEmv0Op0rN55lfgkDaO716aAhdLYIeVJMvlIkiS9YtvRu9wMieJ99xqUs7Mydjh5lkw+kiRJ/7hwM5w/zjygXf2yNK9dytjh5GlGST47d+6kS5cudOnShS+++AKAoKAgvL29cXd3Z8aMGWg0GmOEJklSPhX2PI71e69RuXRh+ravbuxw8rxsTz7x8fEsWLCADRs2sHPnTs6dO8epU6eYNGkSs2fPZv/+/Qgh2LJlS3aHJklSPpWo1rJiWyAmCgUju9XGTCmLQu9atrewVqtFp9MRHx+PRqNBo9GgVCpJSEigXr16AHh7e7Nv377sDk2SpHxICMHPB24QGq5imJcjxW3kgKHZIdu7cVhZWTFu3Dg6depEgQIFaNy4MWZmZtjZ/TtQn52dHWFhYZlab7FiWb8xaGcnZyHMDNlemSfbLHOys732nw7m5JUn9HVzwKVppWzb7tuUG4+vbE8+169fZ+vWrRw5coTChQszceJETp48mawfvRAi0/3qIyNV6HQi0/HY2RUmPDwm08vlV7K9Mk+2WeZkZ3sFP4lh9bbLOFa2xbV+mVz5PWW1vUxMFG900v6msr3sduLECZo3b06xYsUwNzfH29ubM2fOEB4ebvhMREQEJUqUyO7QJEnKR2IT1KzYfgXrQmYM86yFiYl8kDQ7ZXvyqVGjBqdOnSIuLg4hBIcPH6ZJkyZYWFhw/vx5QN8brnXr1tkdmiRJ+YROCNbtvsbzmERGdqtN4YJywNDsluGyW2hoKEuXLiUhIYHhw4fj6OiYpQ22bNmSa9eu4e3tjZmZGU5OTgwbNgw3NzdmzpyJSqXC0dGR9957L0vrlyRJSs/vfwUTcCeSAW72VC1jY+xw8iWFECJDN0rGjBnDoEGDUCgUzJkzh927d7/r2DJF3vPJHrK9Mk+2Wea86/a6dv8ZX/56iSY1SzLMs1auH7ctz93zmTp1KpGRkYbXarWacuXKUb58eZKSkrIlOEmSpLfpeUwia3ZdpZRtQd7v6JDrE09ulmrZrWfPnowdOxZXV1fee+89Ro0axbhx41Cr1UyaNCk7Y5QkSXpjGq2OVTsCSdLo+MjbCUtzOWCoMaV65dOoUSM2bNiAmZkZAwcOJDY2ls2bN7N161ZcXV2zM0ZJkqQ39tuRO9wOjeaDTjUoXayQscPJ99Ls7RYfH0/Pnj1ZsWIFe/fuZdy4cTx+/Di7YpMkSXorzl5/yp/nQnBtWI4mNUsaOxyJNMpuq1atYu/evWi1WgYPHsz8+fO5fPkykydPplmzZowePTo745QkScqSx5GxfPd7EFXLWtPbpZqxw5H+keqVz44dO9izZw/bt2/np59+AqBOnTps2LBBPgAqSVKukJikZeX2QMxMTRjZtTZKUzlgaE6R6pWPra0ta9euJS4ujkqVKiX7t169er3ruKQc6OnzOHacvE+r2qUoZmNp7HAkKU1CCH7cd51HkbFM6FMPW2t5zOYkqZ4GrFq1igIFClC6dGkWLlyYnTFJOVBEdDyLNl1k1/G7zP3+by7djjB2SJKUpiMXQzl9LYxurapQq5KtscOR/iPV5FOkSBF8fHzo06cPBQrIIcbzsyhVIos3XyI+Ucu09xtTzNqSZX6X2XLkNhqtztjhSVIKdx+9YNPBW9SpWowuzSsaOxzpNWRHdylNMXFJLN58iejYJCb2qUezOmWoZFeQzYdvs+/MA249jGKEV21ZhpNyjJi4JFbuuELRwhYM9aiFiXyQNEeSd9+kVMUlqPny10uER8Xzcc86VC2rHwPLTGmKTwcHRnR1JDQ8VpbhpBxDpxN8u/saL2KTGNW9NlYFzIwdkpQKmXyk10pI0rDktwBCw2P5yNsJhwpFU3ymSc2SzPmgMcVs/inDHZZlOMm4dp+6T+C9Z/R3s6dSKWtjhyOlId3kExsbyyeffML7779PVFQUs2fPJjY2Njtik4wkSa1lmd9l7j2KYWS32jhVKZbqZ0sWLcgMn4a0a1CWfX8/4IuNF4iMTsjGaCVJ78rdSHaduIdz7VK0qVvG2OFI6Ug3+cyfPx9ra2siIyOxsLBApVIxe/bs7IhNMgK1RseK7YHceBDFUM+aNLC3S3eZZGW4iH/KcLdkGU7KPpHRCazddZWydlYMdJcDhuYG6SafoKAgxo8fj1KppECBAixevJigoKDsiE3KZlqdjrW7rnLlbiTvd6pBs1qlMrV8sjLcVlmGk7KHWqNj5Y5AdEIwunttLMxMjR2SlAHpJh8Tk+Qf0Wq1Kd6Tcj+dTrB+bxDnb4bTz7U6rbNYtnhdGS4iOv4tRytJ/9p8+Bb3Hr9gcOdalLQtaOxwpAxKN4s0btyYRYsWkZCQwPHjxxkzZgxNmzbNjtikbCKE4Kf9Nzh9NYwebarg1qj8G63vZRluZLfahEbE8sn3Z2UZTnonTl99wpELoXRsUoGGDumXiKWcI93kM3HiRAoWLEjhwoVZsmQJDg4OTJ48OTtik7KBEIJNh25xLOARHi0q0qV5pbe27sY1SiQrw/16+JYsw0lvTWi4ih/2Xce+fBF6tK1i7HCkTMrwNNo5nZxGO2u2HbvDnlPBuDUqT9/21dK9UZuV9lJrtPx6+DaHL4RStYw1w7s6Utwm/4yakd+PsczKSHvFJ2qY9+M54hI1zP2gMUWsLLIpupwnt06jne4IBy4uLsl+kBQKBQUKFKB69epMnTpVjnCdi+396z57TgXTpl6ZDCWerDJTmjKwgwMOFYry/e9BfPL9WYZ0qUW96sXfyfakvE0Iwfd/XOfp83gm9auXrxNPbpZu8nF1dSU2NpYBAwZgYmKCn58fsbGxODg4MHv2bFavXp0dcUpv2Z9nQ9h69C7NHUvik01dUxvXKEGFklas3nGVZVsv06FxeXq2rSqHuZcy5c9zDzl3/Sm92lV97cPPUu6Q7l/9uXPnWLBgAbVq1aJGjRrMnDmTW7duMWjQIEJDQ7MjRuktOxbwiE2HbtHQ3o7BXWpm69hXJYsWZLpPQ9o3KMeBsyF8LnvDSZlw62EUvx25Tf3qxenYpIKxw5HeQIZGOFCpVIbXKpWKhAT5BHtu9dfVJ/z4x3WcqhRjeFdHTI3Qbd5MacKADvaM6labx5H63nAXb4VnexxS7vIiNolVOwIpZm3JkC418/2DpLrY56jvnkWbkDtHnEm37NajRw969+5Nx44dEUJw4MABevXqxYYNG6hSRfYwyU3O3whn/Z4gHCoUYXR348/q2OifMtyqHVdZvvWKLMNJqdLpBGt2XSU2QcPHPnUpaJn/BgwVOh268LtoHgSgeRCALvIBAInFi4J17psePEO93Y4fP86xY8dQKpW0adOGZs2aERgYSKVKlbCyMl5viVfJ3m5pu3wnkuVbL1OpdGEm9KmHpXnWZtN4F+2l1ujYcvg2hy48pEoZa0bksd5w+eUYe1te115bj95h71/BDO5ck5Z1ShspsuwnEmPRhFxB8yAA7cNAREIMKEwwLVUd0/J1UVaoSymHGnmztxuAk5MT1apVQwiBVqvl5MmTODs7Z3mjhw8f5ptvviE+Ph5nZ2dmzpzJqVOn8PX1JTExkU6dOjF+/Pgsr19K7nrwc1Zsv0I5OyvG96qb5cTzrrwswzlUKML3fwQx97uzDOlSk/oZGFdOyvsu3Y5g71/BtK5bJs8nHiEEuueP9MkmJADtk1sgdCgsrDAt74SyQl2U5Z1QWBQydqhvLN1foaVLl7J27Vr9h5VKkpKSqFatGrt3787SBkNCQpgzZw6//fYbxYoV4/333+fo0aPMmTOHDRs2ULp0aYYPH87Ro0dp06ZNlrYh/etOaDRL/S5TokgB/tcnZ5crkpXhtskynARPo+JZt/saFUsWZoBbdWOH804ITRLaR9fRPLiE5kEAQhUJgEmxCpjX64KyQl1M7KqgyGPDmqWbfHbu3MmRI0f4/PPPmTx5MqdPn+bo0aNZ3uCff/5J586dKVVKP2jlkiVLCA4OpmLFipQvrx/WxdPTk3379snk84aCn8Tw1ZYAbKzMmdC3HoULmhs7pHSV+Kc33JYjtzlwNoRbD6MZ2dWR4kXyThlOyhi1Rsuq7YEAjOpeGzNl3hkwVKeKNNy70YYGgTYJlOYoyzpiWt8TZfk6mFjZGjvMdyrd5GNra0uJEiWoUqUK169fp1u3bnz77bdZ3mBwcDBmZmaMGDGCx48f07ZtW6pXr46d3b8llhIlShAWFpblbUgQGhHLl79eoqCFKZP61s9VD+KZKU0Y4GaPQ/l/ynDfyzJcfrTxz1sEh8Uwtmcd7HL5yYfQ6dA+vYP2QQCaB5fQPXsIgKKwHWY1WqOsUBfT0g4olDn/BPFtSTf5KJVKHjx4QJUqVTh37hwtW7YkMTExyxvUarWcO3eODRs2ULBgQUaOHImlpWWybpNCiEx3o3yTG2d2doWzvGxO9ChCxZItlzA3M+Gz0S0pU/zt3lTMrvbqZFeYejVLsXDDWZZvu0LX1lV5v0stzJS5r/yQ146xdy3g3nOOBTyiV/vquDWvbOxwskQbH0P8nUvE3T5P3N2L6OJVYGKKZfkaFKzvQsFqDTErVvatdBnPjcdXusln+PDhzJo1i1WrVrF06VJ27NhB27Zts7zB4sWL07x5c2xt9ZeUrq6u7Nu3D1PTfy+pw8PDMz1sj+ztphcRHc8XGy+QpNYxZUADzIR4q/uX3e2lBCb1rc+WI7fZeewOl2+F57oyXF47xt41lVrHyq0B1KxYFPeG5XJN2+k7CzzUl9IeXEYbdguEQGFZGNPydTGvUBdlOUcUFoVQA9ECiFClt9p05dmx3WrVqsWPP/4IwI4dOwgODn6j+XzatWvHlClTePHiBYUKFeL48eN07NiRtWvXEhwcTLly5dizZw89evTI8jbyqyhVIos3XyI+UcukfvUpWzz394iB15fhBnfJ2CyrUu4Sl6DGd8MFClkqGe7liIlJzn6QVGgS0T4KQhMcgCbk8r+dBYpXxLy+5z+dBSqjUOS+q/V3LdXkExUVBcCHH37Ihg0bePk4UPHixRk4cCD79u3L0gbr1q3L0KFD6d+/P2q1GmdnZ/r160eVKlUYM2YMiYmJtGnTho4dO2Zp/fnVi7gkFm++RHRsEhP71KNiqdx3GZ6eRjVKUKFUYVbtCOSbbVdwa1SeXu1kb7i8Ii5Bzbo9QTx9HseU/g2wLpQz73/oYiL+7SzwKAi0alBaoCzniGkDL31ngUJyzLn0pPqQ6ZAhQzh58mSK95VKJe7u7nz55ZfvPLjMyM9lt7gENQs3XeRxZBz/6133nQ62mBPaS63RseXIbQ6df0jl0oUZ2bV2ji7D5YQ2y6mEENx7HIP/xVD+DgojSaNjWDcnmtXIOVe1QqdFG3b7n84CAeie68e0VFiX0D9387KzgKlxHmPIrWW3dEc4mDZtGr6+vtkVT5bl1+STkKThy18vcf+xvleQU5Vi73R7Oam9zt94yne/X0cBOboMl5PaLKeIT9Rw5loY/hdDefBUhYWZKc0cS9K2XlkaOZUxenvpEmLQ/jOygOZhICTGgsIU09L2hoSjsCmVI8aXy7PJByA0NJTo6Ghe/aijo+M7DSyz8mPySVJr+fq3AG6GRDOyW+1smUY4p7XX06h4Vu8I5P6TGFwblaN3u2o5rgyX09rMmIKfxHD0Uih/XQsjMUlL+RJWtK1flma1SlLAQn8XwBjtJYRA9yzk33HTnt7RdxYoYI1p+Tr6hFPOEYV5wWyNKyNya/JJt8PBsmXLWL9+PcWK/XtGrVAoOHTo0DsNTEqbWqNjxfZAbjyI4kPPWvl2/voSRQowbWBDfjtym4PnHnInNJoRXWvn+udC8pJEtZa/g8Lwv/iIe49fYKY0oUnNErStX5Yqpa2NdvUg1IloQ6/pE07IZUTsMwBM7CpjXt/rn84ClWRngXck3eSzY8cODhw4QMmSJbMjHikDtDoda3dd5crdSAZ1qkEzx1LGDsmozJQm9HfTjw333e/X9b3hOtfMtwk5pwgNV+F/6RGnAp8Qn6ihdLGC9HOtTovapShkpGGedC/C/0k2LzsLaMDMEmVZR5QNu2FaoQ4mBYsYJbb8Jt3kU7p0aZl4chCdTrB+bxDnb4bTz7U6reuWMXZIOUZDhxKUL1mY1TsCWbH9So4tw+Vlao2WczfC8b8Yyq2H0ShNFTRy0F/lVC9nk+1XOUKnQfvkNpoHl9A+uIwu6hEACpuSmNVqr+8sUMoehWnOGmw3P0i3xZs3b87ChQtp3749lpaWhvdz2j2f/EAIwU/7b3D6ahg92lTBrVF5Y4eU4xjKcP6yDJednjyL4+ilUE5eeYIqXk2JogXo3a4azk6lsn1MQV38i1c6C1yBpHgwMcW0dA0sarbRl9Ns8ne1ICdIt8OBi4tLyoVy4D2fvN7hQAjBpkO3OHjuIR4tKuLduqpR4sgt7QX/9oYDjFqGy01tlhkarY4LN8M5eukRQcHPMTVRUL96cdrWL0uNikWzPD17ZttLCIEu8oFhVGjd03uAQFHABmWFOphWqIuyrCMK87x5ApJnOxwcPnw4O+KQ0rH9+F0OnnuIa6NydG8lZ5DNiIYOJahQUv9Q6ortV3BtWI5e7arlyrHhcpLwqHiOBTzieMAjXsSpKWZtiXfrKrSqUxqbbBrAVqgT0IRe/efZm8uIuChAoe8s0LCb/uqmeAXZWSAHSzf5xMbG8uWXX3Lnzh2WLl3KV199xZQpUyhUKG8M3ZIb7P3rPntO6SfT6te+eo54tiC3sPtPGe52aDQjutWmhCzDZYpWpyPgdiT+l0K5evcZKKBeteK0qVeW2pVts2UYHN2Lp/pSWvAltI9vgE4DZgVQlnPU37spXweTgjbvPA7p7Ug3+cyfP58SJUoQGRmJhYUFKpWK2bNn57gRDvKqP8+GsPXoXZo5luQ9dweZeLLATGlCf1d7HMoX5bvfg/jk+7MM7lyDhg6ZG7w2P3r2IkF/lXP5Mc9jEila2AJP50q0rlsGW2vL9FfwBoRWg/bJzX8G6gxAF/0EABObUpjVdv2ns0B1FCays0BulO63FhQUhK+vL0ePHqVAgQIsXrwYDw+P7Igt+Qwf/gAAIABJREFU3zsW8IhNh27R0N6OIV1q5vhBFnO6hg52VChpxeqdgazYHijLcKnQ6QSB9yLxv/iIgDsRIKB2lWIMdLOnTrVimL7DGTV1cdHEBJwl/uoZ/cgC6gQwUWJapgYWjvreaSbW8qQhL0g3+fx3BGutVvtGo1pLGfPX1Sf8+Md1nKoUY3hXx3f6B5+fvCzDbTkiy3D/FaVK5Pjlxxy79IjIFwlYFzKnc7OKtK5b5p30FtTFRaGLCEYbcf+f/wcjVJHEAoqCRTCr2gTTCvVQlq2FwuzdXmVJ2S/d5NO4cWMWLVpEQkICx48f5+eff6Zp06bZEVu+df5GOOv3BOFQoQiju9eWz6m8ZUpTWYZ7SScEQcHP8b8YyqVbEWh1gpoVi9LbpRr1qxd/K8eeEAIR+yxZktFFBP/TSUBPYVMK05LVMHVsT/HajYk2KS5LzHlcul2t1Wo1a9euxd/fH61WS6tWrRg1ahQWFjlrWua80tX68p1Ilm+9TKXShZnQpx6W5jmrnp3T2utNhUfFs3pnIPcex9C+of6h1LddhsuJbfYiLomTVx5z9OIjnkbFY1XAjJZOpWlTrwwlbbM+fpkQAhETnjLRJPyz/woFJkXKYFK8EqbFK2JSvCKmxSok6wadE9srJ8uzXa3NzMxo0qQJo0ePJioqinPnzuW4xJNXXA9+zortVyhrV4jxvermuMSTFxl6wx25w5/nQrgdqh+kNS+W4YQQ3AyJwv/SI87feIpGK7AvZ0O3VpVp6GCHmdI0/ZW8uj6dDt2LJ8mSjDbivv6hTgATU0yKlkNZsb4+yRSviEmx8iiU8vdDykDyWbJkCRcuXGDDhg0kJCSwdu1abt68yahRo7Ijvnzjdmg0S/0uY1ekABP61KOgkca+yo+Upib0c62uHxtubxCffP83H3SqSaMaeaMMF5ug5tSVJ/hfCuVxZBwFLJS0rVeWNvXK/L+9Ow+Lst7/P/6cGWZgAHFhFxEX3FFwxyX1uOSCW4ontU6nsjS3ykpPi1/L7HdsM8sW261cTq5Yaprmlru44EJuqbihbAICMzDb/fsDGdE0d+4B3o/r8pKBwXldH++Z931/7s9CiP+tnfkqDjuOrOQrhSYtCXvGabAVFD5B54a2SnX0taMLC41/DbSVQ1Tb40a4vpt2u/Xu3Zu4uDj0+sKDyGKxMGDAAJYvX14iAW9Vae52O3Uhh3f/t5cKnnpefqQZlUpoot6dcIX2up/uRzecWlsEHE++xMa959h5OBWrzUGtqj50igqhZYMA3PU3vspR7FYcmeeuFJn0UzgunincsRPAzR2db/UrRcYvDG2l4Hs25LmsH2P3WpntdrNarc7CA4XdcHIj8N45l57HtPkJeLrrGD+4qUsXnvKgtHfDmQtsbE+8wPq9yZxNy8XdoKNd42A6RVWleuBft1ZXbBYcGaeLdZudwpF5Fhz2wifojej8wtA36uK8R6P1CUIjoy/FXbpp8WnWrBkvvvgisbGxaDQali5dSmRkZElkK/NSMk28/+NedDoNLw1pim9FGU7qCkpjN1zShUts2JvMjj9SKLDaqR7ozWM96tG6wZVN2hSLGXvG6avu0TiykkFxAKBx90brXwNDaI/L92hqoKngJ0vUiPvipt1uJpOJGTNmsHXrVtzc3GjTpg1jxozBaHStM8HS1u2Wnm3mnbl7KLA6+M8jzQjxKx3LFZW3LpH0LDMzf0rk5PlLdGlWjX92vv1uuPvVZgUWOzsOFW5FnXQhB4ObllYNC7eirlFFi+OqQpOEIzsFKHyPaDwrXRkEUFRovKq4RK9GeTvG7laZ7XabOXMmL7/8cklkKTeycgt4/8cETAV2JgxpWmoKT3nkV8nIK482Y9GG46yOP8OfydmM7NeIgMrqbad8NjWXDQnn2JZ4AXOBnXBfLaNaaahfIRtd1kHsG0+Rl5PmfL7G2xedXxhuddqg87t8j0Y2TBMqu2nx2bBhAy+++GJJZCkXLpksvP9jAtm5Fl4cHEVY0F/74YVrcdNpGdylDnVDL3fDfRdf4t1wFqud+EMp7Eo4hi3tFGH6i4ypkkuwJh1dfhb8WXhNY/cJKBwE0KDTlXs0HnKMCddz0+JTrVo1nnzySZo1a3bVStZPPPHEfQ1WFpnyrXwwP4G0LDPjBkUSHiIr8JYmzer6Uz3Am5k/JfLZ0oN33A13KxRFQclNJ/3kUc4e/gNHxilqaNJpos2HCgAatMZgtH4Nrp6s6S5X0aJ0uGnxqVSp8PL83Llz9z1MWZZvsTF9wT7OpeXxbGwT6odVVjuSuAN/6YY7l83I/nfXDacoDpRLqc5BALa0JCypSbjZTBiBWoqGS+5+6AKaYAirg86/BjrfUFnvTJRqNx1wUOTSpUv4+Pjc7zx3zJUHHFisdj5cuI+jZwqH7aq1o+a9IDeDr9h7NI1vVhxCQeHxng1oeYNuuOJtpjgcOLLPXzUQwJ5+GqyFqwI4NDrO2ytzylKZTEMgwXUaENUikooV1bsxXNLkGLs9ZXbAwcmTJxk9ejQ5OTksWrSIxx9/nE8++YTatdXZxrm0sdocfBp3kCOns3i6T8NSXXjE1ZrW9eeNy91wM5ce5EizEB7uHI6bVgNWM4o1HyU/h5zkNPJPHi4sNhmnwWYp/Ad0BjRVQsnyj2LfRS92pHiQ6qhERHgA/2gaQreaVe54K2ohXN1Ni8+UKVN47bXXeO+99wgMDOTRRx9l0qRJzJ07965e+J133iEzM5O3336bQ4cO8dprr5GXl0eLFi2YPHkybm6lf10zu8PBlz8ncuBEBo/3rE90oyC1I4m/oTgcYMtHsZhRLGawmFGsZhRLPorFVFhQLn+tWPLBasbTYmacj4kcxyVsJ0xcSrJiwHbVv2sC0HsUTtasXzgQIMcjmI0nHfx+4ALZuRYqV3CnY5uqPBBZlcoVZKKxKPtu+gmflZVFu3bteO+99wB45JFHWLBgwV296LZt24iLi6NTp04AjB8/nrfeeouoqCheffVVFixYwNChQ+/qNdTmcCh8s+IQu4+mMaRLHTpEVlU7UpmlKA6wFlwpGtbCvxVrsSLyl59dLiiW/CvPs+bf2gvqPdAYPNEYPEBvROvuRaVQPzJMGnaeycOs6GlSvxrVq/mjcffCr3Y9suxeKIqG/Scy2LD3HAdOJIECjWv70ql7CI1rV5E9m0S5ckuXFwUFBc7JZ2lpaTgcjjt+waysLKZPn84zzzzD4cOHOXfuHPn5+URFRQEwYMAAZsyYUaqLj6Io/PDrEbYnpjCgQy26tQxVO5JLchYNa/7lImFyfv2XwlCsmFwpIvnFisYt3O/Te6AxGNHojWDwQKM3Fk6sNBhBbyz8mcEIl5/jfHzV1+43nPFfDfDIMvP5z4ks33WJzo4gHu4cTo6bO8s2HeP3fclcvFRARS8DMW0KN2nzq+hak7WFKCk3LT5Dhw5l2LBhZGRkMG3aNFasWMFTTz11xy84adIkxo0bx/nz5wFITU3F3//KfRB/f39SUlJu+9+9mxtn/v73bh6Eoih8/dNBft+XzKAudXisV8N79m+7Cj8/bxRrPo4CM44C0+U/ZhwWE8q137vm50VfK5f/vpWiodF7oHX3ROtuROfuidbLC627PxpD4fcK/3hd/tvT+Vyt8+eFVyka7e1tGXAn/P0r8P5zfvzwyx8s3Xic/ScucvFSPg6HQlRdf4Y/VIPWjYJkg8CbuJfvyfKgNLbXTYtPbGwsYWFhbNiwAZvNxpQpU2jXrt0dvdjChQsJDg6mTZs2LFmyBACHw3HVkh6KotzREh+uMtpt8cbjrNh2iq4tqtGjRbUyNWrHemwr1p0LsJuy4VYGSboZnFcQXL5y0HgFoKlsRGsw4na5+6rwSqPY10VXJQZj4dXK3xQNBbBf/nNdFsDi4PKdlxLTt00YoX6erNx+mg5RIbSs50fg5eHYmRfzSjRLaSOj3W5PmRztdvToUZKSkoiMjGT8+PF3/WK//PILaWlp9OvXj+zsbEwmExqNhrS0K0uBpKenExDgugs4/p3lW5NYse0UHSKrMqRLHZdYJ+teUBQFS8IKLPGLcA+ph67uA9d0R3mAwfNywbhcRG5SNMqDpnX8aVrHXz5MhbiOGxafxYsX88477xAWFsbp06eZNm0a7du3v6sXmzVrlvPrJUuWsHPnTqZOnUrv3r3ZvXs3zZs356effqJDhw539TpqWBN/hiW/nyC6USCPda9XdgqPw0HB1jlY/1iHW+1oqsY+T3rmLd6YF0KIG7hh8Zk9ezbLli0jMDCQvXv3Mn369LsuPjfy/vvvM3HiRHJzc2nUqBGPPfbYfXmd++X3fcn8b+0xmtX1Z1hMA7TaMlJ4bBby132OLWkP+iY9cW89CI2bHpDiI4S4O3/b7RYYGAhA06ZNyczMvKcvPGDAAAYMGABA/fr1WbRo0T3990vKtsQLfL/yMBG1qjCib6MyM1zWkZ+D+dePcKQcx73tIxgiuqkdSQhRhtyw+FzbbaTTle/+++vZfSSNb5Yfol71Sox5qPF9WWBSDY5LqZhWfoCSm45H11Hoa7VUO5IQooy55WUEyso9jHtl//EMPv/pIDWDKzB2YBMM+rJRnO1pSZhXfYDisGOMmYBbUF21IwkhyqAbFp8jR47QrFkz5+P8/HyaNWvmHAq9Z8+eEgnoig6fyuTTuAOE+Hsx7p+Rzm2KSzvbmf2Y13yKxsMbz94vo6ssqzIIIe6PG35qrlmzpiRzlBp/nsvmo0X78a9k5MWHo/D00Ksd6Z6wHtlE/u+z0FaphrHnC7LTpRDivrph8QkJCSnJHKXCqQs5TF+wj4peBl4aHEUFT4Pake6aoihY9v6MZVccupBGGLuNKZzDI4QQ91HZ6C8qAefS85g2PwGju46XhkRRybv0rzysOOwUbP4B6+GNuNVpi0eHJ9Ho5JAQQtx/8klzC1IyTbz/4150Wg3jhzQtE4tBKtYCzGs/w356H4ao3hhaDpRBJUKIEiPF5ybSs828/7+92O0K/xna1Lk+V2nmMF/CvOpDHOkncW//GIaGndWOJIQoZ6T4/I2s3ALe/zEBU4GdCUOaEuJf+rcydmSnYFo5DSUvE49uY9HXaHbzXxJCiHtMis8NXDJZeP/HBLJzLbw4OIqwoNK3ZPm17KknMK+aDoqCZ+//oAsMVzuSEKKckuJzHaZ8Kx/MTyAty8y4QZGEh1RUO9Jds51OwPzbZ2iMFfHs+SLaSrKltxBCPVJ8rmEusDF9wT7OpeUxdmAT6odVVjvSXbMc3kjBpu/R+lbH2ON5mcMjhFCdFJ9iLFY7Hy/ez8nzOYzs34gmtX3VjnRXFEXBsnsplj0/oQttjLHraDR6D7VjCSGEFJ8iVpuDT+MOcuR0Fk/1aUjzeqVzQ7siisNG/u/fYzu6Cbe6D+DR4d9otPLfLYRwDfJpBNgdDr78OZEDJzL4d496tGlUuu+HKNZ8zL99iv3MAQzN+mFo3l/m8AghXEq5Lz52h8I3Kw6x+2gaQ7rUoWNU6V5WyGHKxrxqOo6MU7g/8DiGBp3UjiSEEH9RrouPoijMXLyP7YkpDOhQi24tQ9WOdFccWRcK5/CYszE++BxuYVFqRxJCiOsq18Un8eRFft1+ipg2YfRuW0PtOHfFnvIn5lUfgkZTuB1CQC21IwkhxA2V6+JTr3pl3hrRluBKpXuRUGvSHvLXzkTjVblwDk/FQLUjCSHE3yrXxUfvpiWyrj9paTlqR7ljlj/WUbBlNlq/Ghh7jENr9FE7khBC3FS5Lj6lmaIoWOIXY0lYjq56JMYuo9DoS/cVnBCi/JDiUwopdhv5v8/CdmwL+vodcW//GBqtTu1YQghxy6T4lDKKxYx5zSfYzyViaPEQhqZ9ZQ6PEKLUkeJTijhMWZhXfoDj4lk8Og5DX+8BtSMJIcQdkeJTStgzkzGvnIaSn4uxx/O4hTZRO5IQQtwxrRov+sknnxATE0NMTAzvvvsuAFu3bqVPnz48+OCDTJ8+XY1YLst24Rimn/8f2K149nlFCo8QotQr8eKzdetWNm/eTFxcHEuXLiUxMZHly5fz6quv8tlnn/HLL79w8OBBNm7cWNLRXJL15C7MK95B4+GNZ7+J6PxrqB1JCCHuWokXH39/f15++WUMBgN6vZ7atWuTlJREWFgYoaGhuLm50adPH1atWlXS0VyO5eAa8td8itavBp79JqL1Kd0rbQshRJESLz516tQhKqpwzbGkpCRWrlyJRqPB39/f+ZyAgABSUlJKOprLUBQH+dvnU7B1Lm5hUXjGjEfrUfq38RZCiCKqDTg4duwYI0aMYMKECeh0OpKSkpw/UxTltocP+/p633EWf3/X+WBXbFbSln+KNXETPs2649t9mMvN4XGl9iotpM1uj7TX7SmN7aVK8dm9ezfPPvssr776KjExMezcuZO0tDTnz9PS0ggIuL0upoyMXBwO5baz+PtXcJnldRSLCfPqj7EnH8LQMhZHVAzpGSa1Y13FldqrtJA2uz3SXrfnTttLq9Xc1Un73Srxbrfz588zevRo3n//fWJiYgCIjIzk5MmTnDp1CrvdzvLly+nQoUNJR1OVIy8T089TsZ8/ikenp3Fv2lsmjwohyqwSv/L55ptvKCgo4O2333Z+b/Dgwbz99tuMHTuWgoICOnbsSI8ePUo6mmrsF88VzuGxmDD2HIdbtQi1IwkhxH2lURTl9vuqXFBp7XazJR/GvHoGGp0eY88X0PmFqZblVqjdXqWRtNntkfa6PaW1201WOFCR9fhO8td/idbHH2PPF9FW8FM7khBClAgpPiqx7P+Vgu3/QxdYB2P359B4qHcGIoQQJU2KTwlTFAcF2+djPfArbjWa49F5BBo3g9qxhBCiREnxKUGKzUL+hq+xndiJPqIb7tFD0GhVWV5PCCFUJcWnhCgFeZhXz8B+/gjurR9G36SHDKUWQpRbUnxKgCM3o3AfnuwLeHQegT68jdqRhBBCVVJ87jN7xpnCOTzWAow9X8QtpKHakYQQQnVSfO4j27k/MK/+GI3BA8++r6LzDVU7khBCuAQpPveJ9c9t5G/4Gm3FIIw9X0Dr7at2JCGEcBlSfO4xRVGw7l9JwY4F6ILrYXzwWTTuXmrHEkIIlyLF5x5SHA4Kts3DmvgbbrVa4fGPp9Ho9GrHEkIIlyPF5x5RbBby132BLWk3+sbdcY9+GI1G5vAIIcT1SPG5B5T8XMy/foQ95U/co4dgaNJd7UhCCOHSpPjcJUdOOuaV03BcSsOj60j0tVqpHUkIIVyeFJ+7YE8/hXnlByh2K8ZeL+FWtb7akYQQolSQ4nOHbGcPYl7zCRqDJ54xE9BVCVE7khBClBpSfO6A9egW8jd+i7ZycOE+PF6V1Y4khBClihSf26AoCpaEFVjiF6Gr2gDjg2PRGDzVjiWEEKWOFJ9bpDgcFGydg/WPdbiFR+PR8Sk0Omk+IYS4E/LpeQsUWwH5az/HdmovhsheGFrFyhweIYS4C1J8bsKRn4N51Yc4Uk/g3vZRDBFd1Y4khBClnhSfv+G4lIpp5TSU3Aw8uo1GX7OF2pGEEKJMkOJzA/a0JMyrPkBx2DHG/Ae3oDpqRxJCiDJDis912M7sx7zmUzQe3nj2eRldpapqRxJCiDJFis81rEc2kf/7LLRVqhXuw+NZSe1IQghR5kjxuUxRFCx7fsayOw5dSCOM3cagMRjVjiWEEGWSS40XXrZsGb169eLBBx9k7ty5Jfa6isNOwabvsOyOw61OO4w9x0nhEUKI+8hlrnxSUlKYPn06S5YswWAwMHjwYFq3bk14ePh9fV2HJR/z6hnYT+/D0LQPhhYD0Gg09/U1hRCivHOZK5+tW7cSHR1NpUqV8PT0pHv37qxateq+vqZSkMf5Oa9jP7Mf9/aP4d5yoBQeIYQoAS5z5ZOamoq/v7/zcUBAAPv377/l3/f19b7t1zSfPkNK5nkCY/+DV92Wt/375ZW/fwW1I5Q60ma3R9rr9pTG9nKZ4uNwOK666lAU5bauQjIycnE4lNt7UWMoYS98T3p6Lqa0nNv73XLK378CadJWt0Xa7PZIe92eO20vrVZzRyft94rLdLsFBQWRlpbmfJyWlkZAQMB9f13pZhNCiJLnMsWnbdu2bNu2jYsXL2I2m1m9ejUdOnRQO5YQQoj7wGW63QIDAxk3bhyPPfYYVquV2NhYmjRponYsIYQQ94HLFB+APn360KdPH7VjCCGEuM9cpttNCCFE+SHFRwghRImT4iOEEKLEudQ9n7uh1d75kOm7+d3ySNrr9kmb3R5pr9tzJ+2ldhtrFEW5zZmZQgghxN2RbjchhBAlToqPEEKIEifFRwghRImT4iOEEKLESfERQghR4qT4CCGEKHFSfIQQQpQ4KT5CCCFKnBQfIYQQJU6KjxBCiBInxUcIFyMrXt0eaa/b4yrtJcXnFrjKf1Zp4HA4rvu3uDWKopCXl6d2jFJF3p+3zmKxkJqaqnYMoAytan2v2e12dDod27dvZ/fu3Zw+fZpu3brRvn17PDw81I7nkhRFQastPJ/56quvuHjxIhUqVKBLly40aNBA5XSuq+hY27x5MytXrsTDwwOtVssLL7yA0WhUO57LKf7e3LBhAzqdDn9/f4YMGYK7u7va8VxOUXtt3LiRpUuXYjQayc7O5t1338XLy0u1XHLlcx2KoqDT6cjJyWHy5Ml07NiRvXv3cuzYMSwWi9rxXN6HH37IuXPnqFmzJosWLaJixYqkpKQAcpZ6PUXH2n//+18GDBjAmTNnMBqN5Obmsn//frXjuZTrvTe3bdtGXl4e+fn5nD17Vu2ILken05Gbm8t7773HsGHDAKhatSrZ2dmsX79etVxSfK5Doync52LevHk89NBD+Pv7U7FiRYYOHcoLL7zAvHnzVE7omjQaDampqfzxxx9MmjSJkydPMnToUC5dusTYsWMpKChwtq242rZt2+jfvz/16tUjPT2dp556ivnz57N79261o7mUouNnxYoVxMbG0qRJEzQaDY888ggffPABO3fuVDmhazp48CB9+vQhODiYw4cP8+yzz7J582YSEhJUyyTF5280a9aM3NxcRo0axciRI6lYsSLR0dHSJ/83AgICiIiI4LnnnuPkyZMMHz6c+vXrYzAYXKav2ZUUXQmGhISwbt06+vbty4svvoiPjw/u7u4cPXpU5YSupai9atasSUJCArGxsTz77LNUqlQJX19fTpw4oXJC11LUXkFBQSxbtox+/frxn//8hwoVKmC1Wvnzzz9VyybFpxi73e78u6CggPr16/PHH3+g1+upV68eCQkJxMXFERERoXJS11RUlGvVqsW5c+d48MEHOXz4MJ9++il6vZ7Q0FCVE7qOomOtqBs3PDycHj16ULduXZKSkti3bx+LFy+mf//+asZ0GUXtZbPZAGjYsCGBgYHUrl0bgGPHjrFixQoeeOAB1TK6kqL2MpvNmEwmAgMDGTt2LPXr12fjxo1s2rSJuXPn8u9//1u1jLKT6WWKojgv6d99912OHz9O586dGThwIG+88QYpKSnodDpat27NE088oXJa11H8ZnlcXByKojB+/Hg2b97Mjh07yM7OxsfHh9dee40qVaqoHdclFD/Wxo8fT3p6Om3atCEsLIyMjAz27t2Lw+GgRYsWDBkyROW06iveXm+99RanTp2ib9++VK5cme3bt3PmzBkKCgpo06aNqh+mrmjUqFGYTCbCw8Np2LAhvr6+rFu3Do1GQ2RkJA899JBq2aT4XFZ0gH/99dds27aNIUOGMGnSJBo3bsyMGTNwd3fHYrFgMBjUjuoyitosNzeXIUOGMHLkSNasWUNSUhLjxo0jIiICHx8fLBYLnp6easd1GUXtNmfOHPbu3Uvv3r1ZvXo1fn5+NGnShFatWlGxYkW1Y7qM4u/Nffv28cADD7Bs2TJat25Nhw4dCAsLw2g0ynvzsqL2WrJkCTt37mTo0KH8/vvvXLp0idDQUGJiYlziRFD3xhtvvKF2CFeg0WhITk5m7dq1jB49mpYtWzJs2DA2btzICy+8QLVq1WjUqJHaMV1K0dnoTz/9hF6v5+mnn6ZHjx54e3vzwQcfkJiYSKdOnTAajTLQoBiNRsOZM2eYOXMmEyZMoHnz5jRu3JjDhw+zY8cOsrKyiIiIcA5bL++K3ptz5szh9ddfp3Xr1rRq1YrffvuN9evX4+PjQ7169dSO6TI0Gg1JSUlMnTqVkSNH0rRpU8LDw8nJyeHgwYMcPHiQ5s2bo9frVc1Z7ouPw+FwfjAuXryYjRs3kpWVRWhoKJUrV+bBBx+kVq1a5OTk0LhxY5XTuo6is6vU1FTmz5/PgQMH8PLyolatWjRo0IBevXqxe/duunbtKh+ilxXvPjpx4gSbN29m1apVREZGUqNGDVq3bk1+fj7VqlWjRo0a6oZ1AcXba8uWLWzevJldu3bRqFEjwsLC6Nq1K6mpqVSsWJHw8HCV06qveHuZTCa2b9/OvHnzaNGiBTVq1KBhw4a4u7tTv359l7j/Kt1ul61fv55//OMfbN26lfnz5xMeHk7btm2JiIiQiWs3kJKSgs1mw9fXl++++45z585Ru3Zt/vGPfxAWFqZ2PJe1adMmAgMDcXd356effiI+Pp6YmBgGDx6sdjSXtGvXLsLDwzl+/Di//vor2dnZdO3alW7duqkdzSWtWbMGo9FIVFQUCxcuZM6cOQwcOJBRo0apHe0q5f7KBwpHaf3f//0fS5YsYeDAgc4iFB8fj06no3bt2tJtdB0//fQTw4YNw9/fnyeffBJFUdi3bx979uyhZs2aVKpUSe2ILsXhcJCbm8tbb73FoUOHqF69OtHR0VStWpW4uDj27NlDp06d5Fi7zOFwkJ2dzVNPPcWhQ4fo1q0bTZo0wWQysX79ek6fPk3z5s2lvS5zOByYTCbmzZvHH3/8QUFBAV26dKFDhw7MmjWLNWv778cgAAAUU0lEQVTW0Lt3b5dpr3J75VM0SquI1Wrlq6++YtGiRYwcOZJBgwYxZ84cGjVqRNOmTVVM6lqKX9oD7N+/n1GjRlG7dm0+//xzUlNT2bRpE48++qiKKV3Ltceaoih88skn7Nmzh86dO9O1a1fS0tIwGAzUr19fxaSuweFwXNVV63A4mDRpEgkJCYwePZr27duzefNmwsLCaNiwoYpJXcO17aUoCgsWLGD37t0EBwfTu3dvqlSpQkZGBnXr1lUx6TWUcm7nzp1Kenq68/GOHTuULl26KE899ZRiNptVTOa6zp49q2zcuFHJyclxfm/48OFKVFSUEh8fr2Iy1zZ37lxl8+bNzscbNmxQ2rVrp7z00kvKqVOnVEzmmlatWqUcOHDA+Xj58uVK48aNlcmTJyvZ2dkqJnNNM2bMUJYvX+58vHv3bqVfv37KiBEjlH379qmY7PrKZbfb4sWLqVq1Kmazmeeff56LFy8SFBSEl5cXYWFhmM1mzGYzDz74oNwsv2zXrl2cPn2aatWqsXjxYn777TcURcHDw4PKlSvTrl07EhISCAgIkFGBxXz55Zc0b94cgF9++YX4+HhSU1OpW7cu4eHhuLu7k5ycTGxsrMt0h6hp/vz5REREkJuby3fffUdSUpJzEEaDBg3Q6XQcPnyYAQMGSHsBX3zxBbVq1cLd3Z3Dhw/zyy+/cPToUZo2bUr16tUxGAzk5OTwz3/+0+Xaq9wVn6NHj7J06VL27NlDREQEsbGx/P7776xduxYvLy/MZjNz587l5ZdfpnLlymrHdQmKorB27Vq+++47rFYrjz32GAaDgd9++4309HQURWHFihUEBQU5Fy4s75TLWyNMmDCBBQsW0LFjR3r16oXFYmHPnj1s2bIFnU7H7NmzefbZZwkKClI7suouXLjAm2++ydq1a+nUqRMdOnTg5MmTJCYmcvLkSQAWLVrEhAkT8Pf3VzmtuhRFwWw289FHH7F8+XJq1KhB9+7dCQsLY8+ePfz444+4ubnx7bffMmLECEJCQtSO/BflrvhUqlSJoKAgLl68yPLly3F3d2fEiBHk5+ezePFiduzYQUREBL169VI7qsvQaDTUrl2bGjVqsHLlSjZu3MhDDz1EdHQ027ZtY9euXRw4cICJEyfKZNLLNBoNBoOBJ598krS0NJ5//nm8vb15+OGHCQ0N5fjx46xevZqOHTsSExOjdlyX4O3tTb9+/Th58iTvvvsuISEhDB06FLvdTkJCAitWrKBly5b07t1b7aiq02g06PV6YmNjycvLY9q0aZhMJrp27UrLli3Jyclh06ZNdO/enZ49e6od97rK7YCD8+fPs2PHDrZt24a/vz/PPPMMFosFrVYro7RuwG63c+rUKZYuXcrevXt5/PHH6dKlC9nZ2QAyK/8aNpsNN7fCLbMOHDjAqFGjqFmzJp999hne3t5X/VxcbcOGDUybNo2IiAjeeOMNNBoNFosFb29vtaO5jOIDWY4ePcqkSZPw8fHhjTfeoGrVqiqnu7lyW3wA8vPzOXDgAL/++isBAQEMHz5c7UilQlZWFlu3buXnn39m3LhxMrv8byiKgsPhcH5IFK3l9vHHH8sH6U1kZGTw9ttv4+HhwaRJk1Sfke+KFEW5ahPHt956i+3bt/P999/j6+urcrq/Vy7vphfVWw8PD1q2bEn//v3ZsmWL8/6F+Hs+Pj706tWL4OBgdu3apXYcl6bRaNDpdBQUFADw3nvvodFoyMnJUTmZ67jee85ut+Pr68trr71GSkqKtNcNaDQatFqt8/iaOHEiVapUcfZGuLJyec1fNOojKSmJnJwcfv31V3x8fPDz81M5mevLy8vj4MGDzhvnrtqfrBbl8jyoa+denD17lnXr1pGbm4vNZiM4OFjFlK6hqNvoeqOwzpw5w6FDhzh27Bh2u90lFsJ0BUXH17Vzx5KSkpg/fz4eHh7YbDZq1aqlYspbU+aLz41WonY4HOzbt4/NmzdjNpt58803VUjnmq49sIvLzc1ly5YtnD17lpiYGFq1alXC6VxbQUEBHh4eaLXaq9YNVBSFs2fPoigKM2bMUDmla3jzzTd5+OGH/zJR1G63c/78eTZt2kROTg7vvfeeSgldi91uJz09ncDAQHQ6HXa7Ha1Wi0ajITAwEDc3N7y8vJg5c6baUW9Jmb7ns3TpUtasWePcT+Z6fey5ubkYjcYbftiWN1arlU8++YTTp0/Tp08fOnfu/JfnXLp0CR8fHxXSua4lS5awZcsW5xYSw4YNu+7Z57VXROXVvHnzePPNN5kyZQqDBg36y8+LBmNYrVa51wMsXLiQ7du3A5Camsrrr7/uXEy1+DGlXLMCiSsrs++CEydO8NFHHzFs2DCGDBmCxWIhKSmJ5ORk526IUDi8UwrPFRMmTCAvL4/IyEimTp1KYmIihw8f5sKFC87nSOG52p9//snHH39MbGwsXbt2JTAwkJEjRzJv3ry/PFcKT2F7LV26lClTphAfH09+fv5fnlM0ClAKT+EurV9//TX//ve/GT9+PD4+PvTv35+vv/4aKDymiq4hSkvhgTI8z2f37t3o9XoGDx5Mamoqb731FnFxcSQlJWG1WqlTp06pOksoCcnJySxbtozp06cTFRXFggULOHDgANu3b+fIkSOEh4fLcOrrOHr0KFqtlqFDhxISEkKbNm2oWbMmCxcu5OLFi0RGRgKl64Phfho1ahSxsbEMGjSIb7/9FkC2pv8b8fHxGI1GBg4ciLe3N35+fgQEBPDbb7+RkZHhXFy1tB1fZfY0LCQkhPj4ePbu3cs333zDv/71L2bPnk29evVYsGABubm5pe4/634LDAzE09OTbdu2ceTIEerUqcM333zD888/T05ODomJiWpHdClFZ5v+/v6sXr2apUuXOq+io6OjGTZsGMeOHcNms8mxdllSUhI9evRwdrWNHj2aTZs2cebMGaDwvoYoVHR8+fn5ERcXx5o1awCIi4sjNDSUV199lWPHjl11b7E0KbNXPpUrVyYjI4MjR46g0Wjo2rUr/v7+REZG8vPPP9OgQQMCAgLUjulSFEUhKiqKiIgI/Pz8aN++PQaDgYCAADIzMzl+/DjR0dGl8kC/H0wmE1qtFj8/PyIiIvj6669JSkqiVatWuLm5ERYWxnfffUfNmjVLxaS/+81iseDr60t4eDgGgwG73U5AQAC7du3iyJEjtG/fXroli7FYLLi5uVG1alUCAgL48MMP2bhxIw6Hg5deeomqVasyd+5cGjZsWCqXGyrTAw7OnTvH559/zvbt2+nbty+RkZGkpKQwb9484uLi1I7nMtasWUNkZKSzGBe/2Xvo0CGCgoIYOXKkc5tsUTgwY/z48QAYjUb69u2L0Whkzpw5nDlzhkceeYQLFy6wfv16/ve//6mcVn1Wq5U333yT3NxcatasSc+ePalTpw4AaWlpjBs3Dq1Wy0cffSRrKlLYXlOmTOHSpUu0bt2aIUOGUFBQgMlkwmq1kpuby86dO1myZAkLFixQO+4dKbNXPoqi4OPjQ+fOnTGbzWRkZLB161by8vJk0dBrfPfdd7z22msEBATQsGFDtFotVquVCxcuMGfOHBYtWkR0dLTs0VPMhAkTCAoKYsiQIWRnZ7Nv3z4GDx5M27ZtMRqNrFq1Cn9/f0aOHCnLNVHYXhUrVqRr167OhULbtWuHw+HA29ubnj17cvjwYSIjI6lQoYLacVX34osv4uPjwwMPPMCsWbOoXr06Fy5cQKvV4u7uzjPPPIOXlxfPP/986Z0DVRL7NqjBZrMpiqIoCQkJyldffaVkZGQoNptNKSgoUDmZ65k9e7YyaNAgpW3btsoTTzzhbCOLxaKkpaUpubm5Kid0LVlZWcrIkSMVh8OhKErh/kb9+vWTPXluIDk5WXn88cedjw8dOqQMGjRIyczMvOp5FoulpKO5pMzMTGX48OHOxzExMcq//vUvZezYscrw4cOVM2fOKBaLxXn8lVZlooP1ejfDdTodVquVV155hZo1a1KlShV0Ot11J5yWd0FBQfTo0YMtW7ZgNBqJjo5m3bp16PV6/Pz88PLyUjuiS6lQoQI6nY4vv/yS/Px8goODURSFlJQUoHBPmvXr16uc0nUEBATg5eXFokWLsFgshIaGkpGRQWpqKlC4z1FCQoIMq77Mx8cHjUbD6tWrOXv2LNHR0fzwww9MnDiRWrVqsXnzZvR6fem/96p29btbDodDWbdundK4cWNlzpw5V509bd68WZk1a5Z64UoJm82m7Nixw/l46dKlSlRUlDJixAhFUZRSf4Z1P6Snpyt//vmn8/GTTz6prF+/Xjl06JDSt2/fv5zVl1dFx86BAweUY8eOOb//8MMPK/v27VP279+v9O7dW8nKylIroksqvouyyWRyfr106VLltddeU6xWqxqx7qlSf+Wj0Who2LAher2euXPn8vzzz3Pq1CkAWrVqxeOPPw4UzgIWVyiXx5kUrbjcqlUrHA4HiqLQr18/1q1b57wvVurPsO4DX19fwsLCnI8jIyM5deoU77//PiNGjJD7PJcVHTsRERFXtVdERATnzp1jxowZjBw5UuaPXcPDw8P5mWUwGNi0aRNJSUl8//33tG/fvkxsxVHqiw8UdrE9/PDDLFy4EH9/f4YPH86SJUuuuoyXIZxXK/pQKD47OjMzE41Gg81mo3LlykydOlXNiC6n+AmMw+HAzc0Nk8mExWKhZs2aTJ06ldDQUNmI8DoURUGv15Ofn4/dbqdGjRqMGzdO2qsYpdjA46Ilc0wmEykpKWzcuJH//ve/dO/evcyMOC0To92MRiMpKSlERkbSqVMngoODmTlzJrt27aJly5YYjUa1I7qMTZs2kZCQQP369Z17zWi1Wnbt2sXPP//snKMi/qpotWql2P4pr7/+Om5ubnTu3BmTycQrr7wiJzrXUIqtJPLOO+/g6elJs2bNSEtLY8qUKdJelxXN64ErJ4eTJ0/Gy8uLxx9/nO7du9O6dWs1I95TZeJ/XaPRMHDgQKBwhnTXrl2ZN28eeXl5nD9/XuV0riMjI4NXXnmFFStWAFf2mrFarUyePJmoqCh0Op3saXSNhQsX8vLLL5OZmYlWq3V+WH7zzTecPXuWzp07YzAYeO6552SdQGDXrl3OFQvgygfprFmzOHr0KC1btiQwMJDXX39d2guYO3cukydP5osvvuD48ePO73/11VecOnWKrl27ApS5wVJl4sqnOK1Wi91ux2g00qdPH1nFoJiJEyfSqlUrrFYr9erVc96X2L59O4GBgTz00EOA3OO5VkJCAt9++y2JiYno9Xrq1KmDw+EgNTWVRx99lAoVKuBwOMrch8PtKrqSnjBhAnv37iUoKIhKlSqh1+uxWCzEx8czevRoZ3t5eHioHVl1W7Zs4YsvvqB3795s377duXZbcnIybm5uPPLII3h7ezu3TyhLylzxAf6yl4oonEh69OhR3n77bX7//Xfi4+OdZ1TBwcE0b94cKF1LspeU0NBQkpOT6dWrF3FxcRw7doxly5bRv39/qlat+rf7H5UnRbtq7ty5k4MHD3LixAlsNhsBAQH4+PjQpEkTKleuLNtKFLNs2TLatm3LQw89RGZmJrNmzSIxMZEFCxYQEhJCu3btyuzxVWY79+XgviI/P5+MjAymTJkCwNixY5k6dSr79u0jMjLyqns8UniuZrPZ8Pb2Jjc3F09PTyZOnMiYMWPIy8sjMzOT4ODgMvnBcDeaN29OTEwMFouF2bNnc+TIEWJjY537G8l784qQkBCmTZuG1Wpl6tSpzJw5k8jISBISEli1ahVAmT2+yuSVj7iam5sbLVq0oFKlSlitVry8vDhx4gSLFy+mc+fO0v3xN7RarXPXSEVR8Pb2Zv369fTv358vvviChg0bEhgYqHZM1RX1NNjtdvR6Pe7u7kRHR9O2bVu2bdvGunXrAKhVq1aZ/TC9HUXtVb9+ffR6PXa7HU9PT0aMGIGHhweVKlViwYIFREVFldmlwMr0wqLi702ZMoUTJ04wZswYZ7ebKFwx4/Tp0zRq1Mj5vbNnz/LOO+9w9OhRXn31VTp27Mj+/ftp0qSJikldw8WLF/n888+xWCzUqVOHgQMH4uHh4VygFuCHH34gJSXFuRhreVbUXvn5+dSvX5/BgwdTUFDAk08+SXR0NE8//TRz5swhMTGRjz76SO24941c+ZRDRWddrVu3Jjk5GYPBQN26ddWO5RIURWHr1q089dRT+Pj40KBBA3Q6HV5eXhw6dIgmTZoQGxsLIFc8l40cOZLq1asTFBREQkICOp2O8PBw5+AfrVZLZGQkbdq0kW5drrRXcHAwe/bsAaBBgwbOpXO++uor8vLymDx5Mp6eniqnvX/kyqecu3jxYuldFfc+SUlJoVevXgQGBlKzZk0mTJhAWFgYZrPZOWdMbpoX2r9/P9OnT2fWrFlA4bp2GzZs4OOPP5b5Ytdxo/b69NNPnRO88/LycHd3L/PzE+XdU85J4fmr662YsXjx4qs+DKTwFAoMDMRsNhMfH4+iKLRo0YLjx4+Tl5cHwK+//nrVnJ/y7kbtdenSJTQaDWvXrsVkMpX5wgNSfIT4C19fX2rXro2XlxdvvPEG48ePZ9asWbzwwgtcvHhR7XguJSAggDFjxhAYGIhGo6F27doYDAb0ej1r167lhx9+ICQkRO2YLuNG7WUwGFi7di2zZ88uN925UnyEuIasmHHrNBoN7dq1o1q1akDhDpyhoaH8+eeffPvttzz77LNylVjMrbRXeRkNKEeFEH+jaJi1j48PX3zxxVUj4EShosmlAHq9HqPRyD//+U/atGlTptYiu1ekvQrJHUEhbkKn08kAg9swdOhQAMaMGaNyktKhvLaXjHYTQtxzxUcGipsrj+0lxUcIIUSJk34EIYQQJU6KjxBCiBInxUcIIUSJk+IjhBCixEnxEUIIUeKk+AghhChx/x/NXKJHCqQHMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(index2,RR_weeks,label=\"Delhi Recovery Rate\")\n",
    "plt.plot(index2,RR_weeksHR,label=\"Haryana Recovery Rate \")\n",
    "\n",
    "plt.title(\"Relation between Recovery Rate of Delhi & Haryana June weekly 2020 \")\n",
    "plt.ylabel(\"Percentage %\")\n",
    "plt.xticks(x,index2,rotation=55)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relation between Mortality Rate of Delhi & Haryana June weekly 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(index2,MR_weeks,label=\"Delhi Mortality Rate\")\n",
    "plt.plot(index2,MR_weeksHR,label=\"Haryana Mortality Rate \")\n",
    "\n",
    "plt.title(\"Relation between Mortality Rate of Delhi & Haryana June weekly 2020 \")\n",
    "plt.ylabel(\"Percentage %\")\n",
    "plt.xticks(x,index2,rotation=55)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *apart from this there are various other factor which affects this analysis. Like area & population of these two states "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset sources: https://www.covid19india.org & http://kaggle.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By Vigneshwaram"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}