covid-19-data-analysis-india.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COVID-19 DATA ANALYSIS IN INDIA\n",
    "Ok, \n",
    "so i am making my course project on the most important and the hot topic,i.e on Covid-19 in India.\n",
    "It contains daily updates on coronavirus cases.We will be able to understand the pandemic from a spatio-temporal perspective with the data ghathered.\n",
    "I have downloaded my data set from  https://www.kaggle.com/imdevskp/covid19-corona-virus-india-dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's begin by downloading the data, and listing the files within the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "project_name = \"covid-19-data-analysis-india\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install jovian --upgrade -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jovian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[jovian] Attempting to save notebook..\u001b[0m\n",
      "[jovian] Detected Kaggle notebook...\u001b[0m\n",
      "[jovian] Uploading notebook to https://jovian.ml/kanchanjeswani2000/covid-19-data-analysis-india\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "    require([\"base/js/namespace\"],function(Jupyter) {\n",
       "        var nbJson = JSON.stringify(Jupyter.notebook.toJSON());\n",
       "\n",
       "        console.log(\"[jovian] Extracted notebook JSON:\");\n",
       "        console.log(nbJson);\n",
       "\n",
       "        function jvnLog (data) {\n",
       "          console.log(\"Result from jovian.commit:\");\n",
       "          if (data.content.text) {\n",
       "              var result = JSON.parse(data.content.text.trim());\n",
       "              var msg = result['msg'];\n",
       "              var err = result['err'];\n",
       "              if (msg) {\n",
       "                  element.text(\"Committed successfully: \" + msg)\n",
       "              } else {\n",
       "                  alert(\"Notebook commit failed. Error: \" + (err || \"Unknown\"))\n",
       "              }\n",
       "          }\n",
       "          \n",
       "        };\n",
       "        \n",
       "        var pythonCode = `\n",
       "from contextlib import redirect_stdout, redirect_stderr\n",
       "from io import StringIO\n",
       "import json\n",
       " \n",
       "with open(\"covid-19-data-analysis-india.ipynb\", 'w') as f:\n",
       "    f.write(r\"\"\"${nbJson}\"\"\")\n",
       "\n",
       "jvn_update = StringIO()\n",
       "jvn_update_err = StringIO()\n",
       "with redirect_stdout(jvn_update), redirect_stderr(jvn_update_err):\n",
       "    from jovian import commit\n",
       "\n",
       "jvn_f_out = StringIO()\n",
       "jvn_f_err = StringIO()\n",
       "with redirect_stdout(jvn_f_out), redirect_stderr(jvn_f_err):\n",
       "    jvn_msg = jovian.commit(message=None, files=['../input/covid19-corona-virus-india-dataset/complete.cs'], outputs=[], environment='auto', privacy='auto', filename='covid-19-data-analysis-india.ipynb', project='covid-19-data-analysis-india', new_project=None)\n",
       "\n",
       "print(json.dumps({'msg': jvn_msg, 'err': jvn_f_err.getvalue(), 'update': jvn_update.getvalue()}))\n",
       "        `;\n",
       "\n",
       "        console.log(\"Invoking jovian.commit\")\n",
       "        // console.log(pythonCode)\n",
       "\n",
       "        Jupyter.notebook.kernel.execute(pythonCode, { iopub: { output: jvnLog }});\n",
       "    });"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jovian.commit(project=project_name, files=[\"../input/covid19-corona-virus-india-dataset/complete.cs\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation and Cleaning\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data= pd.read_csv('../input/covid19-corona-virus-india-dataset/complete.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Name of State / UT</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Total Confirmed cases</th>\n",
       "      <th>Death</th>\n",
       "      <th>Cured/Discharged/Migrated</th>\n",
       "      <th>New cases</th>\n",
       "      <th>New deaths</th>\n",
       "      <th>New recovered</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>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4687</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>18.1124</td>\n",
       "      <td>79.0193</td>\n",
       "      <td>73050.0</td>\n",
       "      <td>589</td>\n",
       "      <td>52103.0</td>\n",
       "      <td>2092</td>\n",
       "      <td>0</td>\n",
       "      <td>1289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4688</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Tripura</td>\n",
       "      <td>23.9408</td>\n",
       "      <td>91.9882</td>\n",
       "      <td>5725.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3793.0</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4689</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>26.8467</td>\n",
       "      <td>80.9462</td>\n",
       "      <td>104388.0</td>\n",
       "      <td>1857</td>\n",
       "      <td>60558.0</td>\n",
       "      <td>4078</td>\n",
       "      <td>0</td>\n",
       "      <td>3287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4690</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>30.0668</td>\n",
       "      <td>79.0193</td>\n",
       "      <td>8254.0</td>\n",
       "      <td>98</td>\n",
       "      <td>5233.0</td>\n",
       "      <td>246</td>\n",
       "      <td>0</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4691</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.8550</td>\n",
       "      <td>83800.0</td>\n",
       "      <td>1846</td>\n",
       "      <td>58962.0</td>\n",
       "      <td>2816</td>\n",
       "      <td>0</td>\n",
       "      <td>2078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4692 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date Name of State / UT  Latitude  Longitude  \\\n",
       "0     2020-01-30             Kerala   10.8505    76.2711   \n",
       "1     2020-01-31             Kerala   10.8505    76.2711   \n",
       "2     2020-02-01             Kerala   10.8505    76.2711   \n",
       "3     2020-02-02             Kerala   10.8505    76.2711   \n",
       "4     2020-02-03             Kerala   10.8505    76.2711   \n",
       "...          ...                ...       ...        ...   \n",
       "4687  2020-08-06          Telangana   18.1124    79.0193   \n",
       "4688  2020-08-06            Tripura   23.9408    91.9882   \n",
       "4689  2020-08-06      Uttar Pradesh   26.8467    80.9462   \n",
       "4690  2020-08-06        Uttarakhand   30.0668    79.0193   \n",
       "4691  2020-08-06        West Bengal   22.9868    87.8550   \n",
       "\n",
       "      Total Confirmed cases Death  Cured/Discharged/Migrated  New cases  \\\n",
       "0                       1.0     0                        0.0          0   \n",
       "1                       1.0     0                        0.0          0   \n",
       "2                       2.0     0                        0.0          1   \n",
       "3                       3.0     0                        0.0          1   \n",
       "4                       3.0     0                        0.0          0   \n",
       "...                     ...   ...                        ...        ...   \n",
       "4687                73050.0   589                    52103.0       2092   \n",
       "4688                 5725.0    31                     3793.0         97   \n",
       "4689               104388.0  1857                    60558.0       4078   \n",
       "4690                 8254.0    98                     5233.0        246   \n",
       "4691                83800.0  1846                    58962.0       2816   \n",
       "\n",
       "      New deaths  New recovered  \n",
       "0              0              0  \n",
       "1              0              0  \n",
       "2              0              0  \n",
       "3              0              0  \n",
       "4              0              0  \n",
       "...          ...            ...  \n",
       "4687           0           1289  \n",
       "4688           0             68  \n",
       "4689           0           3287  \n",
       "4690           0            386  \n",
       "4691           0           2078  \n",
       "\n",
       "[4692 rows x 10 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "So,our dataset has 4692 rows and 10 columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Name of State / UT', 'Latitude', 'Longitude',\n",
       "       'Total Confirmed cases', 'Death', 'Cured/Discharged/Migrated',\n",
       "       'New cases', 'New deaths', 'New recovered'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4692, 10)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4692 entries, 0 to 4691\n",
      "Data columns (total 10 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Date                       4692 non-null   object \n",
      " 1   Name of State / UT         4692 non-null   object \n",
      " 2   Latitude                   4692 non-null   float64\n",
      " 3   Longitude                  4692 non-null   float64\n",
      " 4   Total Confirmed cases      4692 non-null   float64\n",
      " 5   Death                      4692 non-null   object \n",
      " 6   Cured/Discharged/Migrated  4692 non-null   float64\n",
      " 7   New cases                  4692 non-null   int64  \n",
      " 8   New deaths                 4692 non-null   int64  \n",
      " 9   New recovered              4692 non-null   int64  \n",
      "dtypes: float64(4), int64(3), object(3)\n",
      "memory usage: 366.7+ KB\n"
     ]
    }
   ],
   "source": [
    "raw_data.info()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import jovian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[jovian] Attempting to save notebook..\u001b[0m\n",
      "[jovian] Detected Kaggle notebook...\u001b[0m\n",
      "[jovian] Uploading notebook to https://jovian.ml/kanchanjeswani2000/covid-19-data-analysis-india\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "    require([\"base/js/namespace\"],function(Jupyter) {\n",
       "        var nbJson = JSON.stringify(Jupyter.notebook.toJSON());\n",
       "\n",
       "        console.log(\"[jovian] Extracted notebook JSON:\");\n",
       "        console.log(nbJson);\n",
       "\n",
       "        function jvnLog (data) {\n",
       "          console.log(\"Result from jovian.commit:\");\n",
       "          if (data.content.text) {\n",
       "              var result = JSON.parse(data.content.text.trim());\n",
       "              var msg = result['msg'];\n",
       "              var err = result['err'];\n",
       "              if (msg) {\n",
       "                  element.text(\"Committed successfully: \" + msg)\n",
       "              } else {\n",
       "                  alert(\"Notebook commit failed. Error: \" + (err || \"Unknown\"))\n",
       "              }\n",
       "          }\n",
       "          \n",
       "        };\n",
       "        \n",
       "        var pythonCode = `\n",
       "from contextlib import redirect_stdout, redirect_stderr\n",
       "from io import StringIO\n",
       "import json\n",
       " \n",
       "with open(\"covid-19-data-analysis-india.ipynb\", 'w') as f:\n",
       "    f.write(r\"\"\"${nbJson}\"\"\")\n",
       "\n",
       "jvn_update = StringIO()\n",
       "jvn_update_err = StringIO()\n",
       "with redirect_stdout(jvn_update), redirect_stderr(jvn_update_err):\n",
       "    from jovian import commit\n",
       "\n",
       "jvn_f_out = StringIO()\n",
       "jvn_f_err = StringIO()\n",
       "with redirect_stdout(jvn_f_out), redirect_stderr(jvn_f_err):\n",
       "    jvn_msg = jovian.commit(message=None, files=['../input/covid19-corona-virus-india-dataset/complete.cs'], outputs=[], environment='auto', privacy='auto', filename='covid-19-data-analysis-india.ipynb', project='covid-19-data-analysis-india', new_project=None)\n",
       "\n",
       "print(json.dumps({'msg': jvn_msg, 'err': jvn_f_err.getvalue(), 'update': jvn_update.getvalue()}))\n",
       "        `;\n",
       "\n",
       "        console.log(\"Invoking jovian.commit\")\n",
       "        // console.log(pythonCode)\n",
       "\n",
       "        Jupyter.notebook.kernel.execute(pythonCode, { iopub: { output: jvnLog }});\n",
       "    });"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jovian.commit(project=project_name, files=[\"../input/covid19-corona-virus-india-dataset/complete.cs\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Analysis and Visualization\n",
    "\n",
    "Now,let's visualize our data by computing interesting statistics for numeric columns and relationship between columns using graphs.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's begin by importing`matplotlib.pyplot` and `seaborn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Name of State / UT</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Total Confirmed cases</th>\n",
       "      <th>Death</th>\n",
       "      <th>Cured/Discharged/Migrated</th>\n",
       "      <th>New cases</th>\n",
       "      <th>New deaths</th>\n",
       "      <th>New recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4632</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>22.2587</td>\n",
       "      <td>71.1924</td>\n",
       "      <td>65599.0</td>\n",
       "      <td>2533</td>\n",
       "      <td>48376.0</td>\n",
       "      <td>1014</td>\n",
       "      <td>0</td>\n",
       "      <td>898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4633</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>29.0588</td>\n",
       "      <td>76.0856</td>\n",
       "      <td>37796.0</td>\n",
       "      <td>448</td>\n",
       "      <td>31226.0</td>\n",
       "      <td>623</td>\n",
       "      <td>0</td>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4634</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>31.1048</td>\n",
       "      <td>77.1734</td>\n",
       "      <td>2879.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4635</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Jammu and Kashmir</td>\n",
       "      <td>33.7782</td>\n",
       "      <td>76.5762</td>\n",
       "      <td>22396.0</td>\n",
       "      <td>417</td>\n",
       "      <td>14856.0</td>\n",
       "      <td>390</td>\n",
       "      <td>0</td>\n",
       "      <td>824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4636</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>23.6102</td>\n",
       "      <td>85.2799</td>\n",
       "      <td>13940.0</td>\n",
       "      <td>128</td>\n",
       "      <td>5164.0</td>\n",
       "      <td>462</td>\n",
       "      <td>0</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4637</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>15.3173</td>\n",
       "      <td>75.7139</td>\n",
       "      <td>145830.0</td>\n",
       "      <td>2704</td>\n",
       "      <td>69272.0</td>\n",
       "      <td>6259</td>\n",
       "      <td>0</td>\n",
       "      <td>6772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4638</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>27956.0</td>\n",
       "      <td>87</td>\n",
       "      <td>16299.0</td>\n",
       "      <td>1083</td>\n",
       "      <td>0</td>\n",
       "      <td>1021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4639</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Ladakh</td>\n",
       "      <td>34.2996</td>\n",
       "      <td>78.2932</td>\n",
       "      <td>1534.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1127.0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4640</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>22.9734</td>\n",
       "      <td>78.6569</td>\n",
       "      <td>35082.0</td>\n",
       "      <td>912</td>\n",
       "      <td>25414.0</td>\n",
       "      <td>797</td>\n",
       "      <td>0</td>\n",
       "      <td>1315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4641</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>19.7515</td>\n",
       "      <td>75.7139</td>\n",
       "      <td>457956.0</td>\n",
       "      <td>16142</td>\n",
       "      <td>299356.0</td>\n",
       "      <td>7760</td>\n",
       "      <td>0</td>\n",
       "      <td>12326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4642</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Manipur</td>\n",
       "      <td>24.6637</td>\n",
       "      <td>93.9063</td>\n",
       "      <td>3018.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1814.0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4643</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>25.4670</td>\n",
       "      <td>91.3662</td>\n",
       "      <td>917.0</td>\n",
       "      <td>5</td>\n",
       "      <td>330.0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4644</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Mizoram</td>\n",
       "      <td>23.1645</td>\n",
       "      <td>92.9376</td>\n",
       "      <td>504.0</td>\n",
       "      <td>0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4645</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Nagaland</td>\n",
       "      <td>26.1584</td>\n",
       "      <td>94.5624</td>\n",
       "      <td>2405.0</td>\n",
       "      <td>5</td>\n",
       "      <td>659.0</td>\n",
       "      <td>276</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4646</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Odisha</td>\n",
       "      <td>20.9517</td>\n",
       "      <td>85.0985</td>\n",
       "      <td>37681.0</td>\n",
       "      <td>216</td>\n",
       "      <td>24483.0</td>\n",
       "      <td>1384</td>\n",
       "      <td>0</td>\n",
       "      <td>1409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4647</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Puducherry</td>\n",
       "      <td>11.9416</td>\n",
       "      <td>79.8083</td>\n",
       "      <td>4147.0</td>\n",
       "      <td>58</td>\n",
       "      <td>2537.0</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4648</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Punjab</td>\n",
       "      <td>31.1471</td>\n",
       "      <td>75.3412</td>\n",
       "      <td>19015.0</td>\n",
       "      <td>462</td>\n",
       "      <td>12491.0</td>\n",
       "      <td>488</td>\n",
       "      <td>0</td>\n",
       "      <td>609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4649</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>27.0238</td>\n",
       "      <td>74.2179</td>\n",
       "      <td>46679.0</td>\n",
       "      <td>732</td>\n",
       "      <td>32832.0</td>\n",
       "      <td>1704</td>\n",
       "      <td>0</td>\n",
       "      <td>1374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4650</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>27.5330</td>\n",
       "      <td>88.5122</td>\n",
       "      <td>783.0</td>\n",
       "      <td>1</td>\n",
       "      <td>299.0</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4651</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>11.1271</td>\n",
       "      <td>78.6569</td>\n",
       "      <td>268285.0</td>\n",
       "      <td>4349</td>\n",
       "      <td>208784.0</td>\n",
       "      <td>5063</td>\n",
       "      <td>0</td>\n",
       "      <td>6501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4652</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>18.1124</td>\n",
       "      <td>79.0193</td>\n",
       "      <td>70958.0</td>\n",
       "      <td>576</td>\n",
       "      <td>50814.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>0</td>\n",
       "      <td>1139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4653</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Tripura</td>\n",
       "      <td>23.9408</td>\n",
       "      <td>91.9882</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>30</td>\n",
       "      <td>3725.0</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4654</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>26.8467</td>\n",
       "      <td>80.9462</td>\n",
       "      <td>100310.0</td>\n",
       "      <td>1817</td>\n",
       "      <td>57271.0</td>\n",
       "      <td>2948</td>\n",
       "      <td>0</td>\n",
       "      <td>1878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4655</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>30.0668</td>\n",
       "      <td>79.0193</td>\n",
       "      <td>8008.0</td>\n",
       "      <td>95</td>\n",
       "      <td>4847.0</td>\n",
       "      <td>208</td>\n",
       "      <td>0</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4656</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.8550</td>\n",
       "      <td>80984.0</td>\n",
       "      <td>1785</td>\n",
       "      <td>56884.0</td>\n",
       "      <td>2752</td>\n",
       "      <td>0</td>\n",
       "      <td>2066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4657</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>11.7401</td>\n",
       "      <td>92.6586</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>14</td>\n",
       "      <td>326.0</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4658</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.7400</td>\n",
       "      <td>186461.0</td>\n",
       "      <td>1681</td>\n",
       "      <td>104354.0</td>\n",
       "      <td>10128</td>\n",
       "      <td>0</td>\n",
       "      <td>8729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4659</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>28.2180</td>\n",
       "      <td>94.7278</td>\n",
       "      <td>1855.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4660</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Assam</td>\n",
       "      <td>26.2006</td>\n",
       "      <td>92.9376</td>\n",
       "      <td>50445.0</td>\n",
       "      <td>121</td>\n",
       "      <td>35892.0</td>\n",
       "      <td>2284</td>\n",
       "      <td>0</td>\n",
       "      <td>1471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4661</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>25.0961</td>\n",
       "      <td>85.3131</td>\n",
       "      <td>64770.0</td>\n",
       "      <td>355</td>\n",
       "      <td>42414.0</td>\n",
       "      <td>2982</td>\n",
       "      <td>0</td>\n",
       "      <td>2066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4662</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>30.7333</td>\n",
       "      <td>76.7794</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>20</td>\n",
       "      <td>715.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4663</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>21.2787</td>\n",
       "      <td>81.8661</td>\n",
       "      <td>10407.0</td>\n",
       "      <td>71</td>\n",
       "      <td>7871.0</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4664</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Dadra and Nagar Haveli and Daman and Diu</td>\n",
       "      <td>20.1809</td>\n",
       "      <td>73.0169</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>2</td>\n",
       "      <td>960.0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4665</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.7041</td>\n",
       "      <td>77.1025</td>\n",
       "      <td>140232.0</td>\n",
       "      <td>4044</td>\n",
       "      <td>126116.0</td>\n",
       "      <td>1076</td>\n",
       "      <td>0</td>\n",
       "      <td>890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4666</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Goa</td>\n",
       "      <td>15.2993</td>\n",
       "      <td>74.1240</td>\n",
       "      <td>7423.0</td>\n",
       "      <td>64</td>\n",
       "      <td>5287.0</td>\n",
       "      <td>348</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4667</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>22.2587</td>\n",
       "      <td>71.1924</td>\n",
       "      <td>66669.0</td>\n",
       "      <td>2556</td>\n",
       "      <td>49433.0</td>\n",
       "      <td>1070</td>\n",
       "      <td>0</td>\n",
       "      <td>1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4668</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>29.0588</td>\n",
       "      <td>76.0856</td>\n",
       "      <td>38548.0</td>\n",
       "      <td>455</td>\n",
       "      <td>31960.0</td>\n",
       "      <td>752</td>\n",
       "      <td>0</td>\n",
       "      <td>734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4669</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>31.1048</td>\n",
       "      <td>77.1734</td>\n",
       "      <td>2916.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1762.0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4670</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Jammu and Kashmir</td>\n",
       "      <td>33.7782</td>\n",
       "      <td>76.5762</td>\n",
       "      <td>22955.0</td>\n",
       "      <td>426</td>\n",
       "      <td>15244.0</td>\n",
       "      <td>559</td>\n",
       "      <td>0</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4671</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>23.6102</td>\n",
       "      <td>85.2799</td>\n",
       "      <td>14888.0</td>\n",
       "      <td>136</td>\n",
       "      <td>5703.0</td>\n",
       "      <td>948</td>\n",
       "      <td>0</td>\n",
       "      <td>539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4672</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>15.3173</td>\n",
       "      <td>75.7139</td>\n",
       "      <td>151449.0</td>\n",
       "      <td>2804</td>\n",
       "      <td>74679.0</td>\n",
       "      <td>5619</td>\n",
       "      <td>0</td>\n",
       "      <td>5407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4673</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>29151.0</td>\n",
       "      <td>94</td>\n",
       "      <td>17533.0</td>\n",
       "      <td>1195</td>\n",
       "      <td>0</td>\n",
       "      <td>1234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4674</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Ladakh</td>\n",
       "      <td>34.2996</td>\n",
       "      <td>78.2932</td>\n",
       "      <td>1592.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1164.0</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4675</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>22.9734</td>\n",
       "      <td>78.6569</td>\n",
       "      <td>35734.0</td>\n",
       "      <td>929</td>\n",
       "      <td>26064.0</td>\n",
       "      <td>652</td>\n",
       "      <td>0</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4676</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>19.7515</td>\n",
       "      <td>75.7139</td>\n",
       "      <td>468265.0</td>\n",
       "      <td>16476</td>\n",
       "      <td>305521.0</td>\n",
       "      <td>10309</td>\n",
       "      <td>0</td>\n",
       "      <td>6165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4677</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Manipur</td>\n",
       "      <td>24.6637</td>\n",
       "      <td>93.9063</td>\n",
       "      <td>3093.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1862.0</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4678</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>25.4670</td>\n",
       "      <td>91.3662</td>\n",
       "      <td>929.0</td>\n",
       "      <td>5</td>\n",
       "      <td>330.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4679</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Mizoram</td>\n",
       "      <td>23.1645</td>\n",
       "      <td>92.9376</td>\n",
       "      <td>537.0</td>\n",
       "      <td>0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4680</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Nagaland</td>\n",
       "      <td>26.1584</td>\n",
       "      <td>94.5624</td>\n",
       "      <td>2498.0</td>\n",
       "      <td>6</td>\n",
       "      <td>685.0</td>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4681</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Odisha</td>\n",
       "      <td>20.9517</td>\n",
       "      <td>85.0985</td>\n",
       "      <td>39018.0</td>\n",
       "      <td>225</td>\n",
       "      <td>25738.0</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>1255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4682</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Puducherry</td>\n",
       "      <td>11.9416</td>\n",
       "      <td>79.8083</td>\n",
       "      <td>4433.0</td>\n",
       "      <td>65</td>\n",
       "      <td>2668.0</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4683</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Punjab</td>\n",
       "      <td>31.1471</td>\n",
       "      <td>75.3412</td>\n",
       "      <td>19856.0</td>\n",
       "      <td>491</td>\n",
       "      <td>12943.0</td>\n",
       "      <td>841</td>\n",
       "      <td>0</td>\n",
       "      <td>452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4684</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>27.0238</td>\n",
       "      <td>74.2179</td>\n",
       "      <td>47272.0</td>\n",
       "      <td>745</td>\n",
       "      <td>33849.0</td>\n",
       "      <td>593</td>\n",
       "      <td>0</td>\n",
       "      <td>1017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4685</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>27.5330</td>\n",
       "      <td>88.5122</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>303.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4686</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>11.1271</td>\n",
       "      <td>78.6569</td>\n",
       "      <td>273460.0</td>\n",
       "      <td>4461</td>\n",
       "      <td>214815.0</td>\n",
       "      <td>5175</td>\n",
       "      <td>0</td>\n",
       "      <td>6031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4687</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>18.1124</td>\n",
       "      <td>79.0193</td>\n",
       "      <td>73050.0</td>\n",
       "      <td>589</td>\n",
       "      <td>52103.0</td>\n",
       "      <td>2092</td>\n",
       "      <td>0</td>\n",
       "      <td>1289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4688</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Tripura</td>\n",
       "      <td>23.9408</td>\n",
       "      <td>91.9882</td>\n",
       "      <td>5725.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3793.0</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4689</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>26.8467</td>\n",
       "      <td>80.9462</td>\n",
       "      <td>104388.0</td>\n",
       "      <td>1857</td>\n",
       "      <td>60558.0</td>\n",
       "      <td>4078</td>\n",
       "      <td>0</td>\n",
       "      <td>3287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4690</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>30.0668</td>\n",
       "      <td>79.0193</td>\n",
       "      <td>8254.0</td>\n",
       "      <td>98</td>\n",
       "      <td>5233.0</td>\n",
       "      <td>246</td>\n",
       "      <td>0</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4691</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.8550</td>\n",
       "      <td>83800.0</td>\n",
       "      <td>1846</td>\n",
       "      <td>58962.0</td>\n",
       "      <td>2816</td>\n",
       "      <td>0</td>\n",
       "      <td>2078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date                        Name of State / UT  Latitude  \\\n",
       "4632  2020-08-05                                   Gujarat   22.2587   \n",
       "4633  2020-08-05                                   Haryana   29.0588   \n",
       "4634  2020-08-05                          Himachal Pradesh   31.1048   \n",
       "4635  2020-08-05                         Jammu and Kashmir   33.7782   \n",
       "4636  2020-08-05                                 Jharkhand   23.6102   \n",
       "4637  2020-08-05                                 Karnataka   15.3173   \n",
       "4638  2020-08-05                                    Kerala   10.8505   \n",
       "4639  2020-08-05                                    Ladakh   34.2996   \n",
       "4640  2020-08-05                            Madhya Pradesh   22.9734   \n",
       "4641  2020-08-05                               Maharashtra   19.7515   \n",
       "4642  2020-08-05                                   Manipur   24.6637   \n",
       "4643  2020-08-05                                 Meghalaya   25.4670   \n",
       "4644  2020-08-05                                   Mizoram   23.1645   \n",
       "4645  2020-08-05                                  Nagaland   26.1584   \n",
       "4646  2020-08-05                                    Odisha   20.9517   \n",
       "4647  2020-08-05                                Puducherry   11.9416   \n",
       "4648  2020-08-05                                    Punjab   31.1471   \n",
       "4649  2020-08-05                                 Rajasthan   27.0238   \n",
       "4650  2020-08-05                                    Sikkim   27.5330   \n",
       "4651  2020-08-05                                Tamil Nadu   11.1271   \n",
       "4652  2020-08-05                                 Telangana   18.1124   \n",
       "4653  2020-08-05                                   Tripura   23.9408   \n",
       "4654  2020-08-05                             Uttar Pradesh   26.8467   \n",
       "4655  2020-08-05                               Uttarakhand   30.0668   \n",
       "4656  2020-08-05                               West Bengal   22.9868   \n",
       "4657  2020-08-06               Andaman and Nicobar Islands   11.7401   \n",
       "4658  2020-08-06                            Andhra Pradesh   15.9129   \n",
       "4659  2020-08-06                         Arunachal Pradesh   28.2180   \n",
       "4660  2020-08-06                                     Assam   26.2006   \n",
       "4661  2020-08-06                                     Bihar   25.0961   \n",
       "4662  2020-08-06                                Chandigarh   30.7333   \n",
       "4663  2020-08-06                              Chhattisgarh   21.2787   \n",
       "4664  2020-08-06  Dadra and Nagar Haveli and Daman and Diu   20.1809   \n",
       "4665  2020-08-06                                     Delhi   28.7041   \n",
       "4666  2020-08-06                                       Goa   15.2993   \n",
       "4667  2020-08-06                                   Gujarat   22.2587   \n",
       "4668  2020-08-06                                   Haryana   29.0588   \n",
       "4669  2020-08-06                          Himachal Pradesh   31.1048   \n",
       "4670  2020-08-06                         Jammu and Kashmir   33.7782   \n",
       "4671  2020-08-06                                 Jharkhand   23.6102   \n",
       "4672  2020-08-06                                 Karnataka   15.3173   \n",
       "4673  2020-08-06                                    Kerala   10.8505   \n",
       "4674  2020-08-06                                    Ladakh   34.2996   \n",
       "4675  2020-08-06                            Madhya Pradesh   22.9734   \n",
       "4676  2020-08-06                               Maharashtra   19.7515   \n",
       "4677  2020-08-06                                   Manipur   24.6637   \n",
       "4678  2020-08-06                                 Meghalaya   25.4670   \n",
       "4679  2020-08-06                                   Mizoram   23.1645   \n",
       "4680  2020-08-06                                  Nagaland   26.1584   \n",
       "4681  2020-08-06                                    Odisha   20.9517   \n",
       "4682  2020-08-06                                Puducherry   11.9416   \n",
       "4683  2020-08-06                                    Punjab   31.1471   \n",
       "4684  2020-08-06                                 Rajasthan   27.0238   \n",
       "4685  2020-08-06                                    Sikkim   27.5330   \n",
       "4686  2020-08-06                                Tamil Nadu   11.1271   \n",
       "4687  2020-08-06                                 Telangana   18.1124   \n",
       "4688  2020-08-06                                   Tripura   23.9408   \n",
       "4689  2020-08-06                             Uttar Pradesh   26.8467   \n",
       "4690  2020-08-06                               Uttarakhand   30.0668   \n",
       "4691  2020-08-06                               West Bengal   22.9868   \n",
       "\n",
       "      Longitude  Total Confirmed cases  Death  Cured/Discharged/Migrated  \\\n",
       "4632    71.1924                65599.0   2533                    48376.0   \n",
       "4633    76.0856                37796.0    448                    31226.0   \n",
       "4634    77.1734                 2879.0     14                     1710.0   \n",
       "4635    76.5762                22396.0    417                    14856.0   \n",
       "4636    85.2799                13940.0    128                     5164.0   \n",
       "4637    75.7139               145830.0   2704                    69272.0   \n",
       "4638    76.2711                27956.0     87                    16299.0   \n",
       "4639    78.2932                 1534.0      7                     1127.0   \n",
       "4640    78.6569                35082.0    912                    25414.0   \n",
       "4641    75.7139               457956.0  16142                   299356.0   \n",
       "4642    93.9063                 3018.0      7                     1814.0   \n",
       "4643    91.3662                  917.0      5                      330.0   \n",
       "4644    92.9376                  504.0      0                      282.0   \n",
       "4645    94.5624                 2405.0      5                      659.0   \n",
       "4646    85.0985                37681.0    216                    24483.0   \n",
       "4647    79.8083                 4147.0     58                     2537.0   \n",
       "4648    75.3412                19015.0    462                    12491.0   \n",
       "4649    74.2179                46679.0    732                    32832.0   \n",
       "4650    88.5122                  783.0      1                      299.0   \n",
       "4651    78.6569               268285.0   4349                   208784.0   \n",
       "4652    79.0193                70958.0    576                    50814.0   \n",
       "4653    91.9882                 5628.0     30                     3725.0   \n",
       "4654    80.9462               100310.0   1817                    57271.0   \n",
       "4655    79.0193                 8008.0     95                     4847.0   \n",
       "4656    87.8550                80984.0   1785                    56884.0   \n",
       "4657    92.6586                 1027.0     14                      326.0   \n",
       "4658    79.7400               186461.0   1681                   104354.0   \n",
       "4659    94.7278                 1855.0      3                     1210.0   \n",
       "4660    92.9376                50445.0    121                    35892.0   \n",
       "4661    85.3131                64770.0    355                    42414.0   \n",
       "4662    76.7794                 1270.0     20                      715.0   \n",
       "4663    81.8661                10407.0     71                     7871.0   \n",
       "4664    73.0169                 1366.0      2                      960.0   \n",
       "4665    77.1025               140232.0   4044                   126116.0   \n",
       "4666    74.1240                 7423.0     64                     5287.0   \n",
       "4667    71.1924                66669.0   2556                    49433.0   \n",
       "4668    76.0856                38548.0    455                    31960.0   \n",
       "4669    77.1734                 2916.0     14                     1762.0   \n",
       "4670    76.5762                22955.0    426                    15244.0   \n",
       "4671    85.2799                14888.0    136                     5703.0   \n",
       "4672    75.7139               151449.0   2804                    74679.0   \n",
       "4673    76.2711                29151.0     94                    17533.0   \n",
       "4674    78.2932                 1592.0      7                     1164.0   \n",
       "4675    78.6569                35734.0    929                    26064.0   \n",
       "4676    75.7139               468265.0  16476                   305521.0   \n",
       "4677    93.9063                 3093.0      7                     1862.0   \n",
       "4678    91.3662                  929.0      5                      330.0   \n",
       "4679    92.9376                  537.0      0                      286.0   \n",
       "4680    94.5624                 2498.0      6                      685.0   \n",
       "4681    85.0985                39018.0    225                    25738.0   \n",
       "4682    79.8083                 4433.0     65                     2668.0   \n",
       "4683    75.3412                19856.0    491                    12943.0   \n",
       "4684    74.2179                47272.0    745                    33849.0   \n",
       "4685    88.5122                  800.0      1                      303.0   \n",
       "4686    78.6569               273460.0   4461                   214815.0   \n",
       "4687    79.0193                73050.0    589                    52103.0   \n",
       "4688    91.9882                 5725.0     31                     3793.0   \n",
       "4689    80.9462               104388.0   1857                    60558.0   \n",
       "4690    79.0193                 8254.0     98                     5233.0   \n",
       "4691    87.8550                83800.0   1846                    58962.0   \n",
       "\n",
       "      New cases  New deaths  New recovered  \n",
       "4632       1014           0            898  \n",
       "4633        623           0            756  \n",
       "4634         61           0             52  \n",
       "4635        390           0            824  \n",
       "4636        462           0            372  \n",
       "4637       6259           0           6772  \n",
       "4638       1083           0           1021  \n",
       "4639         49           0             18  \n",
       "4640        797           0           1315  \n",
       "4641       7760           0          12326  \n",
       "4642         98           0             48  \n",
       "4643         15           0             66  \n",
       "4644          3           0             16  \n",
       "4645        276           0              2  \n",
       "4646       1384           0           1409  \n",
       "4647        165           0            126  \n",
       "4648        488           0            609  \n",
       "4649       1704           0           1374  \n",
       "4650         95           0              2  \n",
       "4651       5063           0           6501  \n",
       "4652       2012           0           1139  \n",
       "4653        123           0             50  \n",
       "4654       2948           0           1878  \n",
       "4655        208           0            309  \n",
       "4656       2752           0           2066  \n",
       "4657         99           0             49  \n",
       "4658      10128           0           8729  \n",
       "4659         65           0            105  \n",
       "4660       2284           0           1471  \n",
       "4661       2982           0           2066  \n",
       "4662         64           0              0  \n",
       "4663        205           0            258  \n",
       "4664         41           0             41  \n",
       "4665       1076           0            890  \n",
       "4666        348           0            173  \n",
       "4667       1070           0           1057  \n",
       "4668        752           0            734  \n",
       "4669         37           0             52  \n",
       "4670        559           0            388  \n",
       "4671        948           0            539  \n",
       "4672       5619           0           5407  \n",
       "4673       1195           0           1234  \n",
       "4674         58           0             37  \n",
       "4675        652           0            650  \n",
       "4676      10309           0           6165  \n",
       "4677         75           0             48  \n",
       "4678         12           0              0  \n",
       "4679         33           0              4  \n",
       "4680         93           0             26  \n",
       "4681       1337           0           1255  \n",
       "4682        286           0            131  \n",
       "4683        841           0            452  \n",
       "4684        593           0           1017  \n",
       "4685         17           0              4  \n",
       "4686       5175           0           6031  \n",
       "4687       2092           0           1289  \n",
       "4688         97           0             68  \n",
       "4689       4078           0           3287  \n",
       "4690        246           0            386  \n",
       "4691       2816           0           2078  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[-60:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now ,let's see total number of active cases\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total no. of confirmed cases are 2230424.0\n"
     ]
    }
   ],
   "source": [
    "Total_confirmed = raw_data[-40:]['Total Confirmed cases'].sum()\n",
    "print(\"Total no. of confirmed cases are\",Total_confirmed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now ,let's see total number of deaths till now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total no. of deaths 589311857981846\n"
     ]
    }
   ],
   "source": [
    "Total_deaths = raw_data[-5:]['Death'].sum()\n",
    "print(\"Total no. of deaths\", Total_deaths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see which states are most affected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Name of State / UT</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Total Confirmed cases</th>\n",
       "      <th>Death</th>\n",
       "      <th>Cured/Discharged/Migrated</th>\n",
       "      <th>New cases</th>\n",
       "      <th>New deaths</th>\n",
       "      <th>New recovered</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4676</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>19.7515</td>\n",
       "      <td>75.7139</td>\n",
       "      <td>468265.0</td>\n",
       "      <td>16476</td>\n",
       "      <td>305521.0</td>\n",
       "      <td>10309</td>\n",
       "      <td>0</td>\n",
       "      <td>6165</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4641</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>19.7515</td>\n",
       "      <td>75.7139</td>\n",
       "      <td>457956.0</td>\n",
       "      <td>16142</td>\n",
       "      <td>299356.0</td>\n",
       "      <td>7760</td>\n",
       "      <td>0</td>\n",
       "      <td>12326</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4686</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>11.1271</td>\n",
       "      <td>78.6569</td>\n",
       "      <td>273460.0</td>\n",
       "      <td>4461</td>\n",
       "      <td>214815.0</td>\n",
       "      <td>5175</td>\n",
       "      <td>0</td>\n",
       "      <td>6031</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4651</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>11.1271</td>\n",
       "      <td>78.6569</td>\n",
       "      <td>268285.0</td>\n",
       "      <td>4349</td>\n",
       "      <td>208784.0</td>\n",
       "      <td>5063</td>\n",
       "      <td>0</td>\n",
       "      <td>6501</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4658</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.7400</td>\n",
       "      <td>186461.0</td>\n",
       "      <td>1681</td>\n",
       "      <td>104354.0</td>\n",
       "      <td>10128</td>\n",
       "      <td>0</td>\n",
       "      <td>8729</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4672</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>15.3173</td>\n",
       "      <td>75.7139</td>\n",
       "      <td>151449.0</td>\n",
       "      <td>2804</td>\n",
       "      <td>74679.0</td>\n",
       "      <td>5619</td>\n",
       "      <td>0</td>\n",
       "      <td>5407</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4637</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>15.3173</td>\n",
       "      <td>75.7139</td>\n",
       "      <td>145830.0</td>\n",
       "      <td>2704</td>\n",
       "      <td>69272.0</td>\n",
       "      <td>6259</td>\n",
       "      <td>0</td>\n",
       "      <td>6772</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4665</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.7041</td>\n",
       "      <td>77.1025</td>\n",
       "      <td>140232.0</td>\n",
       "      <td>4044</td>\n",
       "      <td>126116.0</td>\n",
       "      <td>1076</td>\n",
       "      <td>0</td>\n",
       "      <td>890</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4689</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>26.8467</td>\n",
       "      <td>80.9462</td>\n",
       "      <td>104388.0</td>\n",
       "      <td>1857</td>\n",
       "      <td>60558.0</td>\n",
       "      <td>4078</td>\n",
       "      <td>0</td>\n",
       "      <td>3287</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4654</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>26.8467</td>\n",
       "      <td>80.9462</td>\n",
       "      <td>100310.0</td>\n",
       "      <td>1817</td>\n",
       "      <td>57271.0</td>\n",
       "      <td>2948</td>\n",
       "      <td>0</td>\n",
       "      <td>1878</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date Name of State / UT  Latitude  Longitude  \\\n",
       "4676 2020-08-06        Maharashtra   19.7515    75.7139   \n",
       "4641 2020-08-05        Maharashtra   19.7515    75.7139   \n",
       "4686 2020-08-06         Tamil Nadu   11.1271    78.6569   \n",
       "4651 2020-08-05         Tamil Nadu   11.1271    78.6569   \n",
       "4658 2020-08-06     Andhra Pradesh   15.9129    79.7400   \n",
       "4672 2020-08-06          Karnataka   15.3173    75.7139   \n",
       "4637 2020-08-05          Karnataka   15.3173    75.7139   \n",
       "4665 2020-08-06              Delhi   28.7041    77.1025   \n",
       "4689 2020-08-06      Uttar Pradesh   26.8467    80.9462   \n",
       "4654 2020-08-05      Uttar Pradesh   26.8467    80.9462   \n",
       "\n",
       "      Total Confirmed cases  Death  Cured/Discharged/Migrated  New cases  \\\n",
       "4676               468265.0  16476                   305521.0      10309   \n",
       "4641               457956.0  16142                   299356.0       7760   \n",
       "4686               273460.0   4461                   214815.0       5175   \n",
       "4651               268285.0   4349                   208784.0       5063   \n",
       "4658               186461.0   1681                   104354.0      10128   \n",
       "4672               151449.0   2804                    74679.0       5619   \n",
       "4637               145830.0   2704                    69272.0       6259   \n",
       "4665               140232.0   4044                   126116.0       1076   \n",
       "4689               104388.0   1857                    60558.0       4078   \n",
       "4654               100310.0   1817                    57271.0       2948   \n",
       "\n",
       "      New deaths  New recovered  year  month  day  weekday  \n",
       "4676           0           6165  2020      8    6        3  \n",
       "4641           0          12326  2020      8    5        2  \n",
       "4686           0           6031  2020      8    6        3  \n",
       "4651           0           6501  2020      8    5        2  \n",
       "4658           0           8729  2020      8    6        3  \n",
       "4672           0           5407  2020      8    6        3  \n",
       "4637           0           6772  2020      8    5        2  \n",
       "4665           0            890  2020      8    6        3  \n",
       "4689           0           3287  2020      8    6        3  \n",
       "4654           0           1878  2020      8    5        2  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[-60:].sort_values('Total Confirmed cases', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trend seen in different states\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4676                                 Maharashtra\n",
       "4686                                  Tamil Nadu\n",
       "4651                                  Tamil Nadu\n",
       "4658                              Andhra Pradesh\n",
       "4672                                   Karnataka\n",
       "4665                                       Delhi\n",
       "4689                               Uttar Pradesh\n",
       "4654                               Uttar Pradesh\n",
       "4691                                 West Bengal\n",
       "4656                                 West Bengal\n",
       "4687                                   Telangana\n",
       "4652                                   Telangana\n",
       "4667                                     Gujarat\n",
       "4661                                       Bihar\n",
       "4660                                       Assam\n",
       "4684                                   Rajasthan\n",
       "4649                                   Rajasthan\n",
       "4681                                      Odisha\n",
       "4668                                     Haryana\n",
       "4675                              Madhya Pradesh\n",
       "4673                                      Kerala\n",
       "4670                           Jammu and Kashmir\n",
       "4683                                      Punjab\n",
       "4648                                      Punjab\n",
       "4671                                   Jharkhand\n",
       "4663                                Chhattisgarh\n",
       "4690                                 Uttarakhand\n",
       "4655                                 Uttarakhand\n",
       "4666                                         Goa\n",
       "4688                                     Tripura\n",
       "4653                                     Tripura\n",
       "4682                                  Puducherry\n",
       "4647                                  Puducherry\n",
       "4677                                     Manipur\n",
       "4669                            Himachal Pradesh\n",
       "4680                                    Nagaland\n",
       "4659                           Arunachal Pradesh\n",
       "4674                                      Ladakh\n",
       "4664    Dadra and Nagar Haveli and Daman and Diu\n",
       "4662                                  Chandigarh\n",
       "4657                 Andaman and Nicobar Islands\n",
       "4678                                   Meghalaya\n",
       "4685                                      Sikkim\n",
       "4650                                      Sikkim\n",
       "4679                                     Mizoram\n",
       "Name: Name of State / UT, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trends = raw_data[-45:].sort_values('Total Confirmed cases', ascending=False)['Name of State / UT']\n",
    "trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4685           Sikkim\n",
       "4682       Puducherry\n",
       "4688          Tripura\n",
       "4690      Uttarakhand\n",
       "4683           Punjab\n",
       "4684        Rajasthan\n",
       "4687        Telangana\n",
       "4691      West Bengal\n",
       "4689    Uttar Pradesh\n",
       "4686       Tamil Nadu\n",
       "Name: Name of State / UT, dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trends = raw_data[-10:].sort_values('Total Confirmed cases', ascending=True)['Name of State / UT']\n",
    "trends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating separate dataframe for statewise data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Uttar Pradesh</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>2020-08-02</th>\n",
       "      <td>636.0</td>\n",
       "      <td>150209.0</td>\n",
       "      <td>1673.0</td>\n",
       "      <td>41726.0</td>\n",
       "      <td>54240.0</td>\n",
       "      <td>1079.0</td>\n",
       "      <td>9385.0</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>136716.0</td>\n",
       "      <td>6193.0</td>\n",
       "      <td>...</td>\n",
       "      <td>64786.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5233.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89048.0</td>\n",
       "      <td>7447.0</td>\n",
       "      <td>72777.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-03</th>\n",
       "      <td>734.0</td>\n",
       "      <td>158764.0</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>42904.0</td>\n",
       "      <td>57024.0</td>\n",
       "      <td>1117.0</td>\n",
       "      <td>9531.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>137677.0</td>\n",
       "      <td>6530.0</td>\n",
       "      <td>...</td>\n",
       "      <td>66677.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5374.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92921.0</td>\n",
       "      <td>7593.0</td>\n",
       "      <td>75516.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-04</th>\n",
       "      <td>830.0</td>\n",
       "      <td>166586.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>45275.0</td>\n",
       "      <td>59328.0</td>\n",
       "      <td>1159.0</td>\n",
       "      <td>9779.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>138482.0</td>\n",
       "      <td>6816.0</td>\n",
       "      <td>...</td>\n",
       "      <td>68946.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5505.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97362.0</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>78232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-05</th>\n",
       "      <td>928.0</td>\n",
       "      <td>176333.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>48161.0</td>\n",
       "      <td>61788.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>10202.0</td>\n",
       "      <td>1325.0</td>\n",
       "      <td>139156.0</td>\n",
       "      <td>7075.0</td>\n",
       "      <td>...</td>\n",
       "      <td>70958.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100310.0</td>\n",
       "      <td>8008.0</td>\n",
       "      <td>80984.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-06</th>\n",
       "      <td>1027.0</td>\n",
       "      <td>186461.0</td>\n",
       "      <td>1855.0</td>\n",
       "      <td>50445.0</td>\n",
       "      <td>64770.0</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>10407.0</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>140232.0</td>\n",
       "      <td>7423.0</td>\n",
       "      <td>...</td>\n",
       "      <td>73050.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5725.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104388.0</td>\n",
       "      <td>8254.0</td>\n",
       "      <td>83800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Name of State / UT  Andaman and Nicobar Islands  Andhra Pradesh  \\\n",
       "Date                                                              \n",
       "2020-01-30                                  NaN             NaN   \n",
       "2020-01-31                                  NaN             NaN   \n",
       "2020-02-01                                  NaN             NaN   \n",
       "2020-02-02                                  NaN             NaN   \n",
       "2020-02-03                                  NaN             NaN   \n",
       "...                                         ...             ...   \n",
       "2020-08-02                                636.0        150209.0   \n",
       "2020-08-03                                734.0        158764.0   \n",
       "2020-08-04                                830.0        166586.0   \n",
       "2020-08-05                                928.0        176333.0   \n",
       "2020-08-06                               1027.0        186461.0   \n",
       "\n",
       "Name of State / UT  Arunachal Pradesh    Assam    Bihar  Chandigarh  \\\n",
       "Date                                                                  \n",
       "2020-01-30                        NaN      NaN      NaN         NaN   \n",
       "2020-01-31                        NaN      NaN      NaN         NaN   \n",
       "2020-02-01                        NaN      NaN      NaN         NaN   \n",
       "2020-02-02                        NaN      NaN      NaN         NaN   \n",
       "2020-02-03                        NaN      NaN      NaN         NaN   \n",
       "...                               ...      ...      ...         ...   \n",
       "2020-08-02                     1673.0  41726.0  54240.0      1079.0   \n",
       "2020-08-03                     1698.0  42904.0  57024.0      1117.0   \n",
       "2020-08-04                     1758.0  45275.0  59328.0      1159.0   \n",
       "2020-08-05                     1790.0  48161.0  61788.0      1206.0   \n",
       "2020-08-06                     1855.0  50445.0  64770.0      1270.0   \n",
       "\n",
       "Name of State / UT  Chhattisgarh  Dadra and Nagar Haveli and Daman and Diu  \\\n",
       "Date                                                                         \n",
       "2020-01-30                   NaN                                       NaN   \n",
       "2020-01-31                   NaN                                       NaN   \n",
       "2020-02-01                   NaN                                       NaN   \n",
       "2020-02-02                   NaN                                       NaN   \n",
       "2020-02-03                   NaN                                       NaN   \n",
       "...                          ...                                       ...   \n",
       "2020-08-02                9385.0                                    1145.0   \n",
       "2020-08-03                9531.0                                    1184.0   \n",
       "2020-08-04                9779.0                                    1274.0   \n",
       "2020-08-05               10202.0                                    1325.0   \n",
       "2020-08-06               10407.0                                    1366.0   \n",
       "\n",
       "Name of State / UT     Delhi     Goa  ...  Telangana  Telangana***  Telengana  \\\n",
       "Date                                  ...                                       \n",
       "2020-01-30               NaN     NaN  ...        NaN           NaN        NaN   \n",
       "2020-01-31               NaN     NaN  ...        NaN           NaN        NaN   \n",
       "2020-02-01               NaN     NaN  ...        NaN           NaN        NaN   \n",
       "2020-02-02               NaN     NaN  ...        NaN           NaN        NaN   \n",
       "2020-02-03               NaN     NaN  ...        NaN           NaN        NaN   \n",
       "...                      ...     ...  ...        ...           ...        ...   \n",
       "2020-08-02          136716.0  6193.0  ...    64786.0           NaN        NaN   \n",
       "2020-08-03          137677.0  6530.0  ...    66677.0           NaN        NaN   \n",
       "2020-08-04          138482.0  6816.0  ...    68946.0           NaN        NaN   \n",
       "2020-08-05          139156.0  7075.0  ...    70958.0           NaN        NaN   \n",
       "2020-08-06          140232.0  7423.0  ...    73050.0           NaN        NaN   \n",
       "\n",
       "Name of State / UT  Tripura  Union Territory of Chandigarh  \\\n",
       "Date                                                         \n",
       "2020-01-30              NaN                            NaN   \n",
       "2020-01-31              NaN                            NaN   \n",
       "2020-02-01              NaN                            NaN   \n",
       "2020-02-02              NaN                            NaN   \n",
       "2020-02-03              NaN                            NaN   \n",
       "...                     ...                            ...   \n",
       "2020-08-02           5233.0                            NaN   \n",
       "2020-08-03           5374.0                            NaN   \n",
       "2020-08-04           5505.0                            NaN   \n",
       "2020-08-05           5628.0                            NaN   \n",
       "2020-08-06           5725.0                            NaN   \n",
       "\n",
       "Name of State / UT  Union Territory of Jammu and Kashmir  \\\n",
       "Date                                                       \n",
       "2020-01-30                                           NaN   \n",
       "2020-01-31                                           NaN   \n",
       "2020-02-01                                           NaN   \n",
       "2020-02-02                                           NaN   \n",
       "2020-02-03                                           NaN   \n",
       "...                                                  ...   \n",
       "2020-08-02                                           NaN   \n",
       "2020-08-03                                           NaN   \n",
       "2020-08-04                                           NaN   \n",
       "2020-08-05                                           NaN   \n",
       "2020-08-06                                           NaN   \n",
       "\n",
       "Name of State / UT  Union Territory of Ladakh  Uttar Pradesh  Uttarakhand  \\\n",
       "Date                                                                        \n",
       "2020-01-30                                NaN            NaN          NaN   \n",
       "2020-01-31                                NaN            NaN          NaN   \n",
       "2020-02-01                                NaN            NaN          NaN   \n",
       "2020-02-02                                NaN            NaN          NaN   \n",
       "2020-02-03                                NaN            NaN          NaN   \n",
       "...                                       ...            ...          ...   \n",
       "2020-08-02                                NaN        89048.0       7447.0   \n",
       "2020-08-03                                NaN        92921.0       7593.0   \n",
       "2020-08-04                                NaN        97362.0       7800.0   \n",
       "2020-08-05                                NaN       100310.0       8008.0   \n",
       "2020-08-06                                NaN       104388.0       8254.0   \n",
       "\n",
       "Name of State / UT  West Bengal  \n",
       "Date                             \n",
       "2020-01-30                  NaN  \n",
       "2020-01-31                  NaN  \n",
       "2020-02-01                  NaN  \n",
       "2020-02-02                  NaN  \n",
       "2020-02-03                  NaN  \n",
       "...                         ...  \n",
       "2020-08-02              72777.0  \n",
       "2020-08-03              75516.0  \n",
       "2020-08-04              78232.0  \n",
       "2020-08-05              80984.0  \n",
       "2020-08-06              83800.0  \n",
       "\n",
       "[186 rows x 40 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state = raw_data.pivot(index='Date', columns='Name of State / UT', values='Total Confirmed cases')\n",
    "state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Adding date time format additional columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2020-01-30\n",
       "1      2020-01-31\n",
       "2      2020-02-01\n",
       "3      2020-02-02\n",
       "4      2020-02-03\n",
       "          ...    \n",
       "4687   2020-08-06\n",
       "4688   2020-08-06\n",
       "4689   2020-08-06\n",
       "4690   2020-08-06\n",
       "4691   2020-08-06\n",
       "Name: Date, Length: 4692, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data['Date'] = pd.to_datetime(comp.Date)\n",
    "raw_data['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data['year'] = pd.DatetimeIndex(raw_data.Date).year\n",
    "raw_data['month'] = pd.DatetimeIndex(raw_data.Date).month\n",
    "raw_data['day'] = pd.DatetimeIndex(raw_data.Date).day\n",
    "raw_data['weekday'] = pd.DatetimeIndex(raw_data.Date).weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Name of State / UT</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Total Confirmed cases</th>\n",
       "      <th>Death</th>\n",
       "      <th>Cured/Discharged/Migrated</th>\n",
       "      <th>New cases</th>\n",
       "      <th>New deaths</th>\n",
       "      <th>New recovered</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</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>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4687</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>18.1124</td>\n",
       "      <td>79.0193</td>\n",
       "      <td>73050.0</td>\n",
       "      <td>589</td>\n",
       "      <td>52103.0</td>\n",
       "      <td>2092</td>\n",
       "      <td>0</td>\n",
       "      <td>1289</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4688</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Tripura</td>\n",
       "      <td>23.9408</td>\n",
       "      <td>91.9882</td>\n",
       "      <td>5725.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3793.0</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4689</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>26.8467</td>\n",
       "      <td>80.9462</td>\n",
       "      <td>104388.0</td>\n",
       "      <td>1857</td>\n",
       "      <td>60558.0</td>\n",
       "      <td>4078</td>\n",
       "      <td>0</td>\n",
       "      <td>3287</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4690</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>30.0668</td>\n",
       "      <td>79.0193</td>\n",
       "      <td>8254.0</td>\n",
       "      <td>98</td>\n",
       "      <td>5233.0</td>\n",
       "      <td>246</td>\n",
       "      <td>0</td>\n",
       "      <td>386</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4691</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.8550</td>\n",
       "      <td>83800.0</td>\n",
       "      <td>1846</td>\n",
       "      <td>58962.0</td>\n",
       "      <td>2816</td>\n",
       "      <td>0</td>\n",
       "      <td>2078</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4692 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date Name of State / UT  Latitude  Longitude  \\\n",
       "0    2020-01-30             Kerala   10.8505    76.2711   \n",
       "1    2020-01-31             Kerala   10.8505    76.2711   \n",
       "2    2020-02-01             Kerala   10.8505    76.2711   \n",
       "3    2020-02-02             Kerala   10.8505    76.2711   \n",
       "4    2020-02-03             Kerala   10.8505    76.2711   \n",
       "...         ...                ...       ...        ...   \n",
       "4687 2020-08-06          Telangana   18.1124    79.0193   \n",
       "4688 2020-08-06            Tripura   23.9408    91.9882   \n",
       "4689 2020-08-06      Uttar Pradesh   26.8467    80.9462   \n",
       "4690 2020-08-06        Uttarakhand   30.0668    79.0193   \n",
       "4691 2020-08-06        West Bengal   22.9868    87.8550   \n",
       "\n",
       "      Total Confirmed cases Death  Cured/Discharged/Migrated  New cases  \\\n",
       "0                       1.0     0                        0.0          0   \n",
       "1                       1.0     0                        0.0          0   \n",
       "2                       2.0     0                        0.0          1   \n",
       "3                       3.0     0                        0.0          1   \n",
       "4                       3.0     0                        0.0          0   \n",
       "...                     ...   ...                        ...        ...   \n",
       "4687                73050.0   589                    52103.0       2092   \n",
       "4688                 5725.0    31                     3793.0         97   \n",
       "4689               104388.0  1857                    60558.0       4078   \n",
       "4690                 8254.0    98                     5233.0        246   \n",
       "4691                83800.0  1846                    58962.0       2816   \n",
       "\n",
       "      New deaths  New recovered  year  month  day  weekday  \n",
       "0              0              0  2020      1   30        3  \n",
       "1              0              0  2020      1   31        4  \n",
       "2              0              0  2020      2    1        5  \n",
       "3              0              0  2020      2    2        6  \n",
       "4              0              0  2020      2    3        0  \n",
       "...          ...            ...   ...    ...  ...      ...  \n",
       "4687           0           1289  2020      8    6        3  \n",
       "4688           0             68  2020      8    6        3  \n",
       "4689           0           3287  2020      8    6        3  \n",
       "4690           0            386  2020      8    6        3  \n",
       "4691           0           2078  2020      8    6        3  \n",
       "\n",
       "[4692 rows x 14 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Now let's plot and visualize the data statewise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Name of State / UT</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Total Confirmed cases</th>\n",
       "      <th>Death</th>\n",
       "      <th>Cured/Discharged/Migrated</th>\n",
       "      <th>New cases</th>\n",
       "      <th>New deaths</th>\n",
       "      <th>New recovered</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4676</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>19.7515</td>\n",
       "      <td>75.7139</td>\n",
       "      <td>468265.0</td>\n",
       "      <td>16476</td>\n",
       "      <td>305521.0</td>\n",
       "      <td>10309</td>\n",
       "      <td>0</td>\n",
       "      <td>6165</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4641</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>19.7515</td>\n",
       "      <td>75.7139</td>\n",
       "      <td>457956.0</td>\n",
       "      <td>16142</td>\n",
       "      <td>299356.0</td>\n",
       "      <td>7760</td>\n",
       "      <td>0</td>\n",
       "      <td>12326</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4686</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>11.1271</td>\n",
       "      <td>78.6569</td>\n",
       "      <td>273460.0</td>\n",
       "      <td>4461</td>\n",
       "      <td>214815.0</td>\n",
       "      <td>5175</td>\n",
       "      <td>0</td>\n",
       "      <td>6031</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4651</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>11.1271</td>\n",
       "      <td>78.6569</td>\n",
       "      <td>268285.0</td>\n",
       "      <td>4349</td>\n",
       "      <td>208784.0</td>\n",
       "      <td>5063</td>\n",
       "      <td>0</td>\n",
       "      <td>6501</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4658</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.7400</td>\n",
       "      <td>186461.0</td>\n",
       "      <td>1681</td>\n",
       "      <td>104354.0</td>\n",
       "      <td>10128</td>\n",
       "      <td>0</td>\n",
       "      <td>8729</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4672</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>15.3173</td>\n",
       "      <td>75.7139</td>\n",
       "      <td>151449.0</td>\n",
       "      <td>2804</td>\n",
       "      <td>74679.0</td>\n",
       "      <td>5619</td>\n",
       "      <td>0</td>\n",
       "      <td>5407</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4637</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>15.3173</td>\n",
       "      <td>75.7139</td>\n",
       "      <td>145830.0</td>\n",
       "      <td>2704</td>\n",
       "      <td>69272.0</td>\n",
       "      <td>6259</td>\n",
       "      <td>0</td>\n",
       "      <td>6772</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4665</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.7041</td>\n",
       "      <td>77.1025</td>\n",
       "      <td>140232.0</td>\n",
       "      <td>4044</td>\n",
       "      <td>126116.0</td>\n",
       "      <td>1076</td>\n",
       "      <td>0</td>\n",
       "      <td>890</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4689</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>26.8467</td>\n",
       "      <td>80.9462</td>\n",
       "      <td>104388.0</td>\n",
       "      <td>1857</td>\n",
       "      <td>60558.0</td>\n",
       "      <td>4078</td>\n",
       "      <td>0</td>\n",
       "      <td>3287</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4654</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>26.8467</td>\n",
       "      <td>80.9462</td>\n",
       "      <td>100310.0</td>\n",
       "      <td>1817</td>\n",
       "      <td>57271.0</td>\n",
       "      <td>2948</td>\n",
       "      <td>0</td>\n",
       "      <td>1878</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date Name of State / UT  Latitude  Longitude  \\\n",
       "4676 2020-08-06        Maharashtra   19.7515    75.7139   \n",
       "4641 2020-08-05        Maharashtra   19.7515    75.7139   \n",
       "4686 2020-08-06         Tamil Nadu   11.1271    78.6569   \n",
       "4651 2020-08-05         Tamil Nadu   11.1271    78.6569   \n",
       "4658 2020-08-06     Andhra Pradesh   15.9129    79.7400   \n",
       "4672 2020-08-06          Karnataka   15.3173    75.7139   \n",
       "4637 2020-08-05          Karnataka   15.3173    75.7139   \n",
       "4665 2020-08-06              Delhi   28.7041    77.1025   \n",
       "4689 2020-08-06      Uttar Pradesh   26.8467    80.9462   \n",
       "4654 2020-08-05      Uttar Pradesh   26.8467    80.9462   \n",
       "\n",
       "      Total Confirmed cases  Death  Cured/Discharged/Migrated  New cases  \\\n",
       "4676               468265.0  16476                   305521.0      10309   \n",
       "4641               457956.0  16142                   299356.0       7760   \n",
       "4686               273460.0   4461                   214815.0       5175   \n",
       "4651               268285.0   4349                   208784.0       5063   \n",
       "4658               186461.0   1681                   104354.0      10128   \n",
       "4672               151449.0   2804                    74679.0       5619   \n",
       "4637               145830.0   2704                    69272.0       6259   \n",
       "4665               140232.0   4044                   126116.0       1076   \n",
       "4689               104388.0   1857                    60558.0       4078   \n",
       "4654               100310.0   1817                    57271.0       2948   \n",
       "\n",
       "      New deaths  New recovered  year  month  day  weekday  \n",
       "4676           0           6165  2020      8    6        3  \n",
       "4641           0          12326  2020      8    5        2  \n",
       "4686           0           6031  2020      8    6        3  \n",
       "4651           0           6501  2020      8    5        2  \n",
       "4658           0           8729  2020      8    6        3  \n",
       "4672           0           5407  2020      8    6        3  \n",
       "4637           0           6772  2020      8    5        2  \n",
       "4665           0            890  2020      8    6        3  \n",
       "4689           0           3287  2020      8    6        3  \n",
       "4654           0           1878  2020      8    5        2  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[-60:].sort_values('Total Confirmed cases', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4676                                 Maharashtra\n",
       "4686                                  Tamil Nadu\n",
       "4651                                  Tamil Nadu\n",
       "4658                              Andhra Pradesh\n",
       "4672                                   Karnataka\n",
       "4665                                       Delhi\n",
       "4689                               Uttar Pradesh\n",
       "4654                               Uttar Pradesh\n",
       "4691                                 West Bengal\n",
       "4656                                 West Bengal\n",
       "4687                                   Telangana\n",
       "4652                                   Telangana\n",
       "4667                                     Gujarat\n",
       "4661                                       Bihar\n",
       "4660                                       Assam\n",
       "4684                                   Rajasthan\n",
       "4649                                   Rajasthan\n",
       "4681                                      Odisha\n",
       "4668                                     Haryana\n",
       "4675                              Madhya Pradesh\n",
       "4673                                      Kerala\n",
       "4670                           Jammu and Kashmir\n",
       "4683                                      Punjab\n",
       "4648                                      Punjab\n",
       "4671                                   Jharkhand\n",
       "4663                                Chhattisgarh\n",
       "4690                                 Uttarakhand\n",
       "4655                                 Uttarakhand\n",
       "4666                                         Goa\n",
       "4688                                     Tripura\n",
       "4653                                     Tripura\n",
       "4682                                  Puducherry\n",
       "4647                                  Puducherry\n",
       "4677                                     Manipur\n",
       "4669                            Himachal Pradesh\n",
       "4680                                    Nagaland\n",
       "4659                           Arunachal Pradesh\n",
       "4674                                      Ladakh\n",
       "4664    Dadra and Nagar Haveli and Daman and Diu\n",
       "4662                                  Chandigarh\n",
       "4657                 Andaman and Nicobar Islands\n",
       "4678                                   Meghalaya\n",
       "4685                                      Sikkim\n",
       "4650                                      Sikkim\n",
       "4679                                     Mizoram\n",
       "Name: Name of State / UT, dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trends = raw_data[-45:].sort_values('Total Confirmed cases', ascending=False)['Name of State / UT']\n",
    "trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "for i in trends:\n",
    "    plt.plot(state.index, state[i])\n",
    "\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Total Confirmed cases till now')\n",
    "\n",
    "plt.title('Trend of total Confirmed cases till now')\n",
    "plt.legend(trends);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                         0\n",
       "Name of State / UT           0\n",
       "Latitude                     0\n",
       "Longitude                    0\n",
       "Total Confirmed cases        0\n",
       "Death                        0\n",
       "Cured/Discharged/Migrated    0\n",
       "New cases                    0\n",
       "New deaths                   0\n",
       "New recovered                0\n",
       "year                         0\n",
       "month                        0\n",
       "day                          0\n",
       "weekday                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Name of State / UT</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Total Confirmed cases</th>\n",
       "      <th>Death</th>\n",
       "      <th>Cured/Discharged/Migrated</th>\n",
       "      <th>New cases</th>\n",
       "      <th>New deaths</th>\n",
       "      <th>New recovered</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>Punjab</td>\n",
       "      <td>31.1471</td>\n",
       "      <td>75.3412</td>\n",
       "      <td>772.0</td>\n",
       "      <td>20</td>\n",
       "      <td>112.0</td>\n",
       "      <td>292</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>22.2587</td>\n",
       "      <td>71.1924</td>\n",
       "      <td>3301.0</td>\n",
       "      <td>151</td>\n",
       "      <td>313.0</td>\n",
       "      <td>230</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4218</th>\n",
       "      <td>2020-07-24</td>\n",
       "      <td>Jammu and Kashmir</td>\n",
       "      <td>33.7782</td>\n",
       "      <td>76.5762</td>\n",
       "      <td>16429.0</td>\n",
       "      <td>282</td>\n",
       "      <td>8709.0</td>\n",
       "      <td>718</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>137.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2051</th>\n",
       "      <td>2020-05-18</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.8550</td>\n",
       "      <td>2677.0</td>\n",
       "      <td>238</td>\n",
       "      <td>959.0</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3352</th>\n",
       "      <td>2020-06-25</td>\n",
       "      <td>Ladakh</td>\n",
       "      <td>34.2996</td>\n",
       "      <td>78.2932</td>\n",
       "      <td>941.0</td>\n",
       "      <td>1</td>\n",
       "      <td>274.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>126</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4468</th>\n",
       "      <td>2020-07-31</td>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>25.4670</td>\n",
       "      <td>91.3662</td>\n",
       "      <td>803.0</td>\n",
       "      <td>5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3369</th>\n",
       "      <td>2020-06-25</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.8550</td>\n",
       "      <td>15173.0</td>\n",
       "      <td>591</td>\n",
       "      <td>9702.0</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>484</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4311</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Assam</td>\n",
       "      <td>26.2006</td>\n",
       "      <td>92.9376</td>\n",
       "      <td>32228.0</td>\n",
       "      <td>79</td>\n",
       "      <td>24040.0</td>\n",
       "      <td>1142</td>\n",
       "      <td>0</td>\n",
       "      <td>985</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>2020-05-16</td>\n",
       "      <td>Punjab</td>\n",
       "      <td>31.1471</td>\n",
       "      <td>75.3412</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>32</td>\n",
       "      <td>305.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date Name of State / UT  Latitude  Longitude  \\\n",
       "1551 2020-05-03             Punjab   31.1471    75.3412   \n",
       "1344 2020-04-27            Gujarat   22.2587    71.1924   \n",
       "4218 2020-07-24  Jammu and Kashmir   33.7782    76.5762   \n",
       "403  2020-03-27             Kerala   10.8505    76.2711   \n",
       "2051 2020-05-18        West Bengal   22.9868    87.8550   \n",
       "3352 2020-06-25             Ladakh   34.2996    78.2932   \n",
       "4468 2020-07-31          Meghalaya   25.4670    91.3662   \n",
       "3369 2020-06-25        West Bengal   22.9868    87.8550   \n",
       "4311 2020-07-27              Assam   26.2006    92.9376   \n",
       "1978 2020-05-16             Punjab   31.1471    75.3412   \n",
       "\n",
       "      Total Confirmed cases Death  Cured/Discharged/Migrated  New cases  \\\n",
       "1551                  772.0    20                      112.0        292   \n",
       "1344                 3301.0   151                      313.0        230   \n",
       "4218                16429.0   282                     8709.0        718   \n",
       "403                   137.0     0                       11.0         19   \n",
       "2051                 2677.0   238                      959.0        101   \n",
       "3352                  941.0     1                      274.0          9   \n",
       "4468                  803.0     5                      210.0         19   \n",
       "3369                15173.0   591                     9702.0        445   \n",
       "4311                32228.0    79                    24040.0       1142   \n",
       "1978                 1935.0    32                      305.0          0   \n",
       "\n",
       "      New deaths  New recovered  year  month  day  weekday  \n",
       "1551           0             22  2020      5    3        6  \n",
       "1344           0             31  2020      4   27        0  \n",
       "4218           0            102  2020      7   24        4  \n",
       "403            0              5  2020      3   27        4  \n",
       "2051           0             87  2020      5   18        0  \n",
       "3352           0            126  2020      6   25        3  \n",
       "4468           0              3  2020      7   31        4  \n",
       "3369           0            484  2020      6   25        3  \n",
       "4311           0            985  2020      7   27        0  \n",
       "1978           0             82  2020      5   16        5  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                         0\n",
       "Name of State / UT           0\n",
       "Latitude                     0\n",
       "Longitude                    0\n",
       "Total Confirmed cases        0\n",
       "Death                        0\n",
       "Cured/Discharged/Migrated    0\n",
       "New cases                    0\n",
       "New deaths                   0\n",
       "New recovered                0\n",
       "year                         0\n",
       "month                        0\n",
       "day                          0\n",
       "weekday                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          0\n",
       "1          0\n",
       "2          0\n",
       "3          0\n",
       "4          0\n",
       "        ... \n",
       "4687     589\n",
       "4688      31\n",
       "4689    1857\n",
       "4690      98\n",
       "4691    1846\n",
       "Name: Death, Length: 4692, dtype: object"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.Death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>month</th>\n",
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       "      <td>16.500000</td>\n",
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>10.933333</td>\n",
       "      <td>84.343750</td>\n",
       "      <td>234.285714</td>\n",
       "      <td>620.114286</td>\n",
       "      <td>1513.485714</td>\n",
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       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.833333</td>\n",
       "      <td>83.656250</td>\n",
       "      <td>255.542857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1487.142857</td>\n",
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       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
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       "      <td>277.828571</td>\n",
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       "      <td>1500.257143</td>\n",
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       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>89.606061</td>\n",
       "      <td>265.857143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1608.057143</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.387097</td>\n",
       "      <td>107.909091</td>\n",
       "      <td>273.085714</td>\n",
       "      <td>1317.000000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>24.935484</td>\n",
       "      <td>102.727273</td>\n",
       "      <td>307.171429</td>\n",
       "      <td>650.514286</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>17.451613</td>\n",
       "      <td>100.606061</td>\n",
       "      <td>257.742857</td>\n",
       "      <td>728.914286</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.870968</td>\n",
       "      <td>99.303030</td>\n",
       "      <td>273.171429</td>\n",
       "      <td>763.714286</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.387097</td>\n",
       "      <td>127.666667</td>\n",
       "      <td>294.057143</td>\n",
       "      <td>795.971429</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>29.322581</td>\n",
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       "      <td>324.657143</td>\n",
       "      <td>829.400000</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>25.677419</td>\n",
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       "      <td>839.057143</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>51.843750</td>\n",
       "      <td>112.787879</td>\n",
       "      <td>356.485714</td>\n",
       "      <td>819.342857</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>19.843750</td>\n",
       "      <td>120.212121</td>\n",
       "      <td>341.885714</td>\n",
       "      <td>859.542857</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>29.406250</td>\n",
       "      <td>113.333333</td>\n",
       "      <td>284.428571</td>\n",
       "      <td>940.971429</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.533333</td>\n",
       "      <td>45.468750</td>\n",
       "      <td>149.303030</td>\n",
       "      <td>296.714286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>16.968750</td>\n",
       "      <td>155.212121</td>\n",
       "      <td>355.742857</td>\n",
       "      <td>2027.485714</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.052632</td>\n",
       "      <td>41.687500</td>\n",
       "      <td>138.363636</td>\n",
       "      <td>381.771429</td>\n",
       "      <td>1084.323529</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>48.531250</td>\n",
       "      <td>161.484848</td>\n",
       "      <td>405.085714</td>\n",
       "      <td>1121.970588</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.590909</td>\n",
       "      <td>41.750000</td>\n",
       "      <td>160.666667</td>\n",
       "      <td>444.314286</td>\n",
       "      <td>1025.470588</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.260870</td>\n",
       "      <td>43.250000</td>\n",
       "      <td>177.909091</td>\n",
       "      <td>460.457143</td>\n",
       "      <td>1043.029412</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.173913</td>\n",
       "      <td>44.031250</td>\n",
       "      <td>193.181818</td>\n",
       "      <td>421.428571</td>\n",
       "      <td>1277.323529</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.541667</td>\n",
       "      <td>52.625000</td>\n",
       "      <td>186.088235</td>\n",
       "      <td>452.628571</td>\n",
       "      <td>1378.647059</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.440000</td>\n",
       "      <td>44.656250</td>\n",
       "      <td>196.264706</td>\n",
       "      <td>473.428571</td>\n",
       "      <td>1373.529412</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.259259</td>\n",
       "      <td>63.750000</td>\n",
       "      <td>177.257143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1846.371429</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>42.062500</td>\n",
       "      <td>152.685714</td>\n",
       "      <td>1037.657143</td>\n",
       "      <td>1399.705882</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.851852</td>\n",
       "      <td>48.218750</td>\n",
       "      <td>178.485714</td>\n",
       "      <td>574.000000</td>\n",
       "      <td>1429.828571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.629630</td>\n",
       "      <td>59.281250</td>\n",
       "      <td>203.571429</td>\n",
       "      <td>571.800000</td>\n",
       "      <td>1386.085714</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.370370</td>\n",
       "      <td>53.687500</td>\n",
       "      <td>216.971429</td>\n",
       "      <td>537.228571</td>\n",
       "      <td>1489.228571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>226.628571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1573.657143</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "month    1    2          3          4           5            6            7  \\\n",
       "day                                                                           \n",
       "1      NaN  1.0   0.000000  16.500000   53.187500   235.800000          NaN   \n",
       "2      NaN  1.0   0.000000   4.678571   75.156250   211.057143  1084.942857   \n",
       "3      NaN  0.0   0.000000  10.933333   84.343750   234.285714   620.114286   \n",
       "4      NaN  0.0   0.000000  20.833333   83.656250   255.542857          NaN   \n",
       "5      NaN  0.0   0.333333  15.533333  121.875000   277.828571  1400.657143   \n",
       "6      NaN  0.0   0.166667  23.100000   89.606061   265.857143          NaN   \n",
       "7      NaN  0.0   0.000000  11.387097  107.909091   273.085714  1317.000000   \n",
       "8      NaN  0.0   0.625000  24.935484  102.727273   307.171429   650.514286   \n",
       "9      NaN  0.0   0.166667  17.451613  100.606061   257.742857   728.914286   \n",
       "10     NaN  0.0   1.000000  21.870968   99.303030   273.171429   763.714286   \n",
       "11     NaN  0.0   1.000000  33.387097  127.666667   294.057143   795.971429   \n",
       "12     NaN  0.0   0.384615  29.322581  109.212121   324.657143   829.400000   \n",
       "13     NaN  0.0   0.076923  25.677419  106.818182   336.828571   839.057143   \n",
       "14     NaN  0.0   0.692308  51.843750  112.787879   356.485714   819.342857   \n",
       "15     NaN  0.0   1.857143  19.843750  120.212121   341.885714   859.542857   \n",
       "16     NaN  0.0   0.133333  29.406250  113.333333   284.428571   940.971429   \n",
       "17     NaN  0.0   1.533333  45.468750  149.303030   296.714286     0.000000   \n",
       "18     NaN  0.0   0.705882  16.968750  155.212121   355.742857  2027.485714   \n",
       "19     NaN  0.0   1.052632  41.687500  138.363636   381.771429  1084.323529   \n",
       "20     NaN  0.0   1.000000  48.531250  161.484848   405.085714  1121.970588   \n",
       "21     NaN  0.0   3.590909  41.750000  160.666667   444.314286  1025.470588   \n",
       "22     NaN  0.0   3.260870  43.250000  177.909091   460.457143  1043.029412   \n",
       "23     NaN  0.0   3.173913  44.031250  193.181818   421.428571  1277.323529   \n",
       "24     NaN  0.0   3.541667  52.625000  186.088235   452.628571  1378.647059   \n",
       "25     NaN  0.0   3.440000  44.656250  196.264706   473.428571  1373.529412   \n",
       "26     NaN  0.0   3.259259  63.750000  177.257143          NaN  1846.371429   \n",
       "27     NaN  0.0   1.111111  42.062500  152.685714  1037.657143  1399.705882   \n",
       "28     NaN  0.0   6.851852  48.218750  178.485714   574.000000  1429.828571   \n",
       "29     NaN  0.0   2.629630  59.281250  203.571429   571.800000  1386.085714   \n",
       "30     0.0  NaN   3.370370  53.687500  216.971429   537.228571  1489.228571   \n",
       "31     0.0  NaN  10.666667        NaN  226.628571          NaN  1573.657143   \n",
       "\n",
       "month            8  \n",
       "day                 \n",
       "1      1631.942857  \n",
       "2      1563.857143  \n",
       "3      1513.485714  \n",
       "4      1487.142857  \n",
       "5      1500.257143  \n",
       "6      1608.057143  \n",
       "7              NaN  \n",
       "8              NaN  \n",
       "9              NaN  \n",
       "10             NaN  \n",
       "11             NaN  \n",
       "12             NaN  \n",
       "13             NaN  \n",
       "14             NaN  \n",
       "15             NaN  \n",
       "16             NaN  \n",
       "17             NaN  \n",
       "18             NaN  \n",
       "19             NaN  \n",
       "20             NaN  \n",
       "21             NaN  \n",
       "22             NaN  \n",
       "23             NaN  \n",
       "24             NaN  \n",
       "25             NaN  \n",
       "26             NaN  \n",
       "27             NaN  \n",
       "28             NaN  \n",
       "29             NaN  \n",
       "30             NaN  \n",
       "31             NaN  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_cases = raw_data.pivot_table(index='day', columns='month', values='New cases')\n",
    "new_cases\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"New cases\")\n",
    "sns.heatmap(new_cases, cmap='Blues');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>Name of State / UT</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Total Confirmed cases</th>\n",
       "      <th>Death</th>\n",
       "      <th>Cured/Discharged/Migrated</th>\n",
       "      <th>New cases</th>\n",
       "      <th>New deaths</th>\n",
       "      <th>New recovered</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4682</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Puducherry</td>\n",
       "      <td>11.9416</td>\n",
       "      <td>79.8083</td>\n",
       "      <td>4433.0</td>\n",
       "      <td>65</td>\n",
       "      <td>2668.0</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4683</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Punjab</td>\n",
       "      <td>31.1471</td>\n",
       "      <td>75.3412</td>\n",
       "      <td>19856.0</td>\n",
       "      <td>491</td>\n",
       "      <td>12943.0</td>\n",
       "      <td>841</td>\n",
       "      <td>0</td>\n",
       "      <td>452</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4684</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>27.0238</td>\n",
       "      <td>74.2179</td>\n",
       "      <td>47272.0</td>\n",
       "      <td>745</td>\n",
       "      <td>33849.0</td>\n",
       "      <td>593</td>\n",
       "      <td>0</td>\n",
       "      <td>1017</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4685</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>27.5330</td>\n",
       "      <td>88.5122</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>303.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4686</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>11.1271</td>\n",
       "      <td>78.6569</td>\n",
       "      <td>273460.0</td>\n",
       "      <td>4461</td>\n",
       "      <td>214815.0</td>\n",
       "      <td>5175</td>\n",
       "      <td>0</td>\n",
       "      <td>6031</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4687</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>18.1124</td>\n",
       "      <td>79.0193</td>\n",
       "      <td>73050.0</td>\n",
       "      <td>589</td>\n",
       "      <td>52103.0</td>\n",
       "      <td>2092</td>\n",
       "      <td>0</td>\n",
       "      <td>1289</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4688</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Tripura</td>\n",
       "      <td>23.9408</td>\n",
       "      <td>91.9882</td>\n",
       "      <td>5725.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3793.0</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4689</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>26.8467</td>\n",
       "      <td>80.9462</td>\n",
       "      <td>104388.0</td>\n",
       "      <td>1857</td>\n",
       "      <td>60558.0</td>\n",
       "      <td>4078</td>\n",
       "      <td>0</td>\n",
       "      <td>3287</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4690</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>30.0668</td>\n",
       "      <td>79.0193</td>\n",
       "      <td>8254.0</td>\n",
       "      <td>98</td>\n",
       "      <td>5233.0</td>\n",
       "      <td>246</td>\n",
       "      <td>0</td>\n",
       "      <td>386</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4691</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.8550</td>\n",
       "      <td>83800.0</td>\n",
       "      <td>1846</td>\n",
       "      <td>58962.0</td>\n",
       "      <td>2816</td>\n",
       "      <td>0</td>\n",
       "      <td>2078</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date Name of State / UT  Latitude  Longitude  \\\n",
       "4682 2020-08-06         Puducherry   11.9416    79.8083   \n",
       "4683 2020-08-06             Punjab   31.1471    75.3412   \n",
       "4684 2020-08-06          Rajasthan   27.0238    74.2179   \n",
       "4685 2020-08-06             Sikkim   27.5330    88.5122   \n",
       "4686 2020-08-06         Tamil Nadu   11.1271    78.6569   \n",
       "4687 2020-08-06          Telangana   18.1124    79.0193   \n",
       "4688 2020-08-06            Tripura   23.9408    91.9882   \n",
       "4689 2020-08-06      Uttar Pradesh   26.8467    80.9462   \n",
       "4690 2020-08-06        Uttarakhand   30.0668    79.0193   \n",
       "4691 2020-08-06        West Bengal   22.9868    87.8550   \n",
       "\n",
       "      Total Confirmed cases Death  Cured/Discharged/Migrated  New cases  \\\n",
       "4682                 4433.0    65                     2668.0        286   \n",
       "4683                19856.0   491                    12943.0        841   \n",
       "4684                47272.0   745                    33849.0        593   \n",
       "4685                  800.0     1                      303.0         17   \n",
       "4686               273460.0  4461                   214815.0       5175   \n",
       "4687                73050.0   589                    52103.0       2092   \n",
       "4688                 5725.0    31                     3793.0         97   \n",
       "4689               104388.0  1857                    60558.0       4078   \n",
       "4690                 8254.0    98                     5233.0        246   \n",
       "4691                83800.0  1846                    58962.0       2816   \n",
       "\n",
       "      New deaths  New recovered  year  month  day  weekday  \n",
       "4682           0            131  2020      8    6        3  \n",
       "4683           0            452  2020      8    6        3  \n",
       "4684           0           1017  2020      8    6        3  \n",
       "4685           0              4  2020      8    6        3  \n",
       "4686           0           6031  2020      8    6        3  \n",
       "4687           0           1289  2020      8    6        3  \n",
       "4688           0             68  2020      8    6        3  \n",
       "4689           0           3287  2020      8    6        3  \n",
       "4690           0            386  2020      8    6        3  \n",
       "4691           0           2078  2020      8    6        3  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"New cases\")\n",
    "sns.heatmap(new_cases, cmap='Reds');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>Name of State / UT</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Total Confirmed cases</th>\n",
       "      <th>Death</th>\n",
       "      <th>Cured/Discharged/Migrated</th>\n",
       "      <th>New cases</th>\n",
       "      <th>New deaths</th>\n",
       "      <th>New recovered</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4687</th>\n",
       "      <td>4687</td>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>18.1124</td>\n",
       "      <td>79.0193</td>\n",
       "      <td>73050.0</td>\n",
       "      <td>589</td>\n",
       "      <td>52103.0</td>\n",
       "      <td>2092</td>\n",
       "      <td>0</td>\n",
       "      <td>1289</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4688</th>\n",
       "      <td>4688</td>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Tripura</td>\n",
       "      <td>23.9408</td>\n",
       "      <td>91.9882</td>\n",
       "      <td>5725.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3793.0</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4689</th>\n",
       "      <td>4689</td>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>26.8467</td>\n",
       "      <td>80.9462</td>\n",
       "      <td>104388.0</td>\n",
       "      <td>1857</td>\n",
       "      <td>60558.0</td>\n",
       "      <td>4078</td>\n",
       "      <td>0</td>\n",
       "      <td>3287</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4690</th>\n",
       "      <td>4690</td>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>30.0668</td>\n",
       "      <td>79.0193</td>\n",
       "      <td>8254.0</td>\n",
       "      <td>98</td>\n",
       "      <td>5233.0</td>\n",
       "      <td>246</td>\n",
       "      <td>0</td>\n",
       "      <td>386</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4691</th>\n",
       "      <td>4691</td>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.8550</td>\n",
       "      <td>83800.0</td>\n",
       "      <td>1846</td>\n",
       "      <td>58962.0</td>\n",
       "      <td>2816</td>\n",
       "      <td>0</td>\n",
       "      <td>2078</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4692 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index       Date Name of State / UT  Latitude  Longitude  \\\n",
       "0         0 2020-01-30             Kerala   10.8505    76.2711   \n",
       "1         1 2020-01-31             Kerala   10.8505    76.2711   \n",
       "2         2 2020-02-01             Kerala   10.8505    76.2711   \n",
       "3         3 2020-02-02             Kerala   10.8505    76.2711   \n",
       "4         4 2020-02-03             Kerala   10.8505    76.2711   \n",
       "...     ...        ...                ...       ...        ...   \n",
       "4687   4687 2020-08-06          Telangana   18.1124    79.0193   \n",
       "4688   4688 2020-08-06            Tripura   23.9408    91.9882   \n",
       "4689   4689 2020-08-06      Uttar Pradesh   26.8467    80.9462   \n",
       "4690   4690 2020-08-06        Uttarakhand   30.0668    79.0193   \n",
       "4691   4691 2020-08-06        West Bengal   22.9868    87.8550   \n",
       "\n",
       "      Total Confirmed cases Death  Cured/Discharged/Migrated  New cases  \\\n",
       "0                       1.0     0                        0.0          0   \n",
       "1                       1.0     0                        0.0          0   \n",
       "2                       2.0     0                        0.0          1   \n",
       "3                       3.0     0                        0.0          1   \n",
       "4                       3.0     0                        0.0          0   \n",
       "...                     ...   ...                        ...        ...   \n",
       "4687                73050.0   589                    52103.0       2092   \n",
       "4688                 5725.0    31                     3793.0         97   \n",
       "4689               104388.0  1857                    60558.0       4078   \n",
       "4690                 8254.0    98                     5233.0        246   \n",
       "4691                83800.0  1846                    58962.0       2816   \n",
       "\n",
       "      New deaths  New recovered  year  month  day  weekday  \n",
       "0              0              0  2020      1   30        3  \n",
       "1              0              0  2020      1   31        4  \n",
       "2              0              0  2020      2    1        5  \n",
       "3              0              0  2020      2    2        6  \n",
       "4              0              0  2020      2    3        0  \n",
       "...          ...            ...   ...    ...  ...      ...  \n",
       "4687           0           1289  2020      8    6        3  \n",
       "4688           0             68  2020      8    6        3  \n",
       "4689           0           3287  2020      8    6        3  \n",
       "4690           0            386  2020      8    6        3  \n",
       "4691           0           2078  2020      8    6        3  \n",
       "\n",
       "[4692 rows x 15 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.reset_index(inplace=True)\n",
    " \n",
    "raw_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "Total_death=2000000000000000000000000000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jovian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[jovian] Attempting to save notebook..\u001b[0m\n",
      "[jovian] Detected Kaggle notebook...\u001b[0m\n",
      "[jovian] Uploading notebook to https://jovian.ml/kanchanjeswani2000/covid-19-data-analysis-india\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "    require([\"base/js/namespace\"],function(Jupyter) {\n",
       "        var nbJson = JSON.stringify(Jupyter.notebook.toJSON());\n",
       "\n",
       "        console.log(\"[jovian] Extracted notebook JSON:\");\n",
       "        console.log(nbJson);\n",
       "\n",
       "        function jvnLog (data) {\n",
       "          console.log(\"Result from jovian.commit:\");\n",
       "          if (data.content.text) {\n",
       "              var result = JSON.parse(data.content.text.trim());\n",
       "              var msg = result['msg'];\n",
       "              var err = result['err'];\n",
       "              if (msg) {\n",
       "                  element.text(\"Committed successfully: \" + msg)\n",
       "              } else {\n",
       "                  alert(\"Notebook commit failed. Error: \" + (err || \"Unknown\"))\n",
       "              }\n",
       "          }\n",
       "          \n",
       "        };\n",
       "        \n",
       "        var pythonCode = `\n",
       "from contextlib import redirect_stdout, redirect_stderr\n",
       "from io import StringIO\n",
       "import json\n",
       " \n",
       "with open(\"covid-19-data-analysis-india.ipynb\", 'w') as f:\n",
       "    f.write(r\"\"\"${nbJson}\"\"\")\n",
       "\n",
       "jvn_update = StringIO()\n",
       "jvn_update_err = StringIO()\n",
       "with redirect_stdout(jvn_update), redirect_stderr(jvn_update_err):\n",
       "    from jovian import commit\n",
       "\n",
       "jvn_f_out = StringIO()\n",
       "jvn_f_err = StringIO()\n",
       "with redirect_stdout(jvn_f_out), redirect_stderr(jvn_f_err):\n",
       "    jvn_msg = jovian.commit(message=None, files=['../input/covid19-corona-virus-india-dataset/complete.cs'], outputs=[], environment='auto', privacy='auto', filename='covid-19-data-analysis-india.ipynb', project='covid-19-data-analysis-india', new_project=None)\n",
       "\n",
       "print(json.dumps({'msg': jvn_msg, 'err': jvn_f_err.getvalue(), 'update': jvn_update.getvalue()}))\n",
       "        `;\n",
       "\n",
       "        console.log(\"Invoking jovian.commit\")\n",
       "        // console.log(pythonCode)\n",
       "\n",
       "        Jupyter.notebook.kernel.execute(pythonCode, { iopub: { output: jvnLog }});\n",
       "    });"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jovian.commit(project=project_name, files=[\"../input/covid19-corona-virus-india-dataset/complete.cs\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Asking and Answering Questions\n",
    "\n",
    "Here are some interesting questions about the dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Question 1: Weekdays having more cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "Week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.title(\"Weekday having most number of new cases\")\n",
    "\n",
    "data = raw_data.groupby('weekday')['New cases'].sum()\n",
    "pal = sns.color_palette(\"Purples_d\", len(data))\n",
    "rank = data.argsort().argsort()\n",
    "g = sns.barplot('weekday', 'New cases', palette=np.array(pal[::-1])[rank], data=raw_data)\n",
    "g.set_xticklabels(Week);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Question 2: Steepest increase in cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "state.index = state.index.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "steep_dict = {}\n",
    "for i in trends:\n",
    "    steep_dict[i] = (state.loc['2020-08-06'][i] - state.loc['2020-07-07'][i])/30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "steep_statedf = pd.DataFrame(steep_dict, index =['slope']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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>slope</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Maharashtra</th>\n",
       "      <td>8542.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <td>5548.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <td>5282.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karnataka</th>\n",
       "      <td>4204.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <td>2525.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bengal</th>\n",
       "      <td>2027.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>1754.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telangana</th>\n",
       "      <td>1577.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>1313.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assam</th>\n",
       "      <td>1276.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gujarat</th>\n",
       "      <td>996.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odisha</th>\n",
       "      <td>983.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajasthan</th>\n",
       "      <td>886.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kerala</th>\n",
       "      <td>784.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haryana</th>\n",
       "      <td>701.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madhya Pradesh</th>\n",
       "      <td>681.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jammu and Kashmir</th>\n",
       "      <td>476.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Punjab</th>\n",
       "      <td>445.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jharkhand</th>\n",
       "      <td>401.366667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <td>236.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goa</th>\n",
       "      <td>187.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttarakhand</th>\n",
       "      <td>169.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tripura</th>\n",
       "      <td>134.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puducherry</th>\n",
       "      <td>121.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nagaland</th>\n",
       "      <td>62.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himachal Pradesh</th>\n",
       "      <td>61.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manipur</th>\n",
       "      <td>56.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <td>52.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dadra and Nagar Haveli and Daman and Diu</th>\n",
       "      <td>35.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andaman and Nicobar Islands</th>\n",
       "      <td>29.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meghalaya</th>\n",
       "      <td>28.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chandigarh</th>\n",
       "      <td>26.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sikkim</th>\n",
       "      <td>22.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ladakh</th>\n",
       "      <td>19.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mizoram</th>\n",
       "      <td>11.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                slope\n",
       "Maharashtra                               8542.600000\n",
       "Andhra Pradesh                            5548.066667\n",
       "Tamil Nadu                                5282.733333\n",
       "Karnataka                                 4204.400000\n",
       "Uttar Pradesh                             2525.066667\n",
       "West Bengal                               2027.100000\n",
       "Bihar                                     1754.833333\n",
       "Telangana                                 1577.233333\n",
       "Delhi                                     1313.633333\n",
       "Assam                                     1276.166667\n",
       "Gujarat                                    996.566667\n",
       "Odisha                                     983.066667\n",
       "Rajasthan                                  886.133333\n",
       "Kerala                                     784.300000\n",
       "Haryana                                    701.466667\n",
       "Madhya Pradesh                             681.666667\n",
       "Jammu and Kashmir                          476.000000\n",
       "Punjab                                     445.500000\n",
       "Jharkhand                                  401.366667\n",
       "Chhattisgarh                               236.733333\n",
       "Goa                                        187.000000\n",
       "Uttarakhand                                169.766667\n",
       "Tripura                                    134.833333\n",
       "Puducherry                                 121.033333\n",
       "Nagaland                                    62.433333\n",
       "Himachal Pradesh                            61.300000\n",
       "Manipur                                     56.766667\n",
       "Arunachal Pradesh                           52.833333\n",
       "Dadra and Nagar Haveli and Daman and Diu    35.633333\n",
       "Andaman and Nicobar Islands                 29.533333\n",
       "Meghalaya                                   28.300000\n",
       "Chandigarh                                  26.033333\n",
       "Sikkim                                      22.500000\n",
       "Ladakh                                      19.566667\n",
       "Mizoram                                     11.333333"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steep_statedf.sort_values(by = 'slope', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "for i in steep_statedf.index[:8]:\n",
    "    plt.plot(state.index, state[i])\n",
    "\n",
    "plt.xlabel('weekday')\n",
    "plt.ylabel('Total Confirmed cases till  now')\n",
    "\n",
    "plt.title('Trend of states')\n",
    "plt.legend(trends);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question 3: Analyses of different states in the total cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "sortd = raw_data[-35:].sort_values('Total Confirmed cases', \\\n",
    "                            ascending=False)['Total Confirmed cases']\n",
    "\n",
    "values = sortd[:15].reset_index(drop = True)\n",
    "values[15] = sortd[15:].sum()\n",
    "\n",
    "label = trends[:15].reset_index(drop = True)\n",
    "label[15] = 'Other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DNjfah9s0zZHAaOBxKWWyaZqPAAXAmaZpDjVNc+vtmQ8DrjBNc7BpmpXA4w31DQE+Ap7YlW+UDiStOdgqi/M/s/lrrymaeKcjWJ4f7XraDJsrhWAoHO0yWp3KWj+3PPdr0vr86nM8vuD3REYxTUUBnxAZ0ZxD5A16qywio41lwCQgE5CNHv/UNE3LNM1qYCWREQjACCnlrw3bPQMM3e6Y7zT62A28JaVcCswgMsIZ0vDYT8A/pZQ3Al1M02y8GuZjANM0NwKVQNe/eI3fmaZZ1ujz46WUsxrqu20H9e2QDiStSeVv2uAsL9j8i91TMabkw/vtuhddyzJcyfiDlh4h7QGvP8S9r85wz19VcojHF/wNaNeEu58IPAgsM02z8UV3/wCmAoMbRil5RKbVtmp8hXMYsEspncBnwDjTNAcBxwEJ2x2vrtHHjwJFwLCGEcucRsc4HbibyHTez1LKxgs8/ufYf/H6th1PSpkDPAuc11DfZdu9pj+lA0lrMjVVlcmuBMdCe3XhgWX/esSuArpbQEuzuVPwBHQe7alQWDHh/Xmu/87c1N/rDy0EejTFfk3TXA/cAzy03UPpwEbTNJWU8mh2rZPE1vNNW2+xcd1Onp8ObDFNM9Swcu5Q2LbgoadpmnNM03ycyC3hh+3C8WuAtL94PBUIAEVSSgO4Zhf2CehFDVoTqSgtaWcjvEAUml3Kv3/Vpi92jQ4jMZlan6X/0NwLSsE/v13uLK3yZF984oD5iU77kcDCvd2vaZqv7+DLdwKvNCxcWNLw3872UyOlvB+YK6XcDHy/k00eBt6TUl4ArAN+bfi6DZgopUwHLCIBd+cuvJQXiEzzeYgsati+vqVSyk+B5UTOSf1C5BzTTgml9F9T2t7ZtHZ1h4yMtCWh1bOyqn56V78ZRlHm0ZcrM2mYePjtOdEuJS4cNLizunns8PpEp/0IYG6064l3+s1D2yvzf/u1U1pqytLgqt90GMUAIzldVdfpPnZN5belheLJ9+Yl+wKhKeiuDs1Ov4Foe+z7zz/qJAcMWmWtn9u+eur7+ncpBtjcaVTU6EBqSnNXFPP8xwuTfYHQL0C/aNcTz/SbiLZHPnvn9XaDRx40P5xgT0uQ+wns+lYzscDmThHlVd5olxF3pi8uEP/4fEmaLxCazu9Lr7UmpgNJ222vPD4+bd/RR8xf68nveP1/7mW9v5qO1zyvcO7Syk6tGdlcyaJUB1Kz+GneFvH2N8szfP7Qb/z1NTnaHtKBpO2Wm+6/xX3QmFMXF6nqbq8u+MAWCAd5dNpLwqwrpuM1LyjD2ZTXE2q7y5bgpqhcd/puLt/P3Gh8PHl1ptcf+hXI2OkG2m7RgaTtstFnHe244KzzfybV0fWFuRMNq2Fpd9gKM2HGq2J51Wayrn1BGYnJUa60jRIGwuFE33qieX3+0xr75Dmbsz2+4I+AK9r1xBMdSNouyR6Raxt35U0fdOrScfgzs9+whbbr2B1WFk/PfEMsKltLh2tfUEZSepQqbbuMBDcqHCIU0teANbc3vl6asGh16QCPL/gFTd/7rs3SgaTtVPaIXOP/rr318dEHH3b6EzP+YfeFdryKSynFC7P/KeYULafD1c8qe2r7Fq60bTNcKVghfduJlqAUTHh/vmtjYc1hXn/oTWCPb7mg/U4HkrZT55923pWXjb1k3NOz3rCVeir+8rkKxStz3xPT8xaSdeXTyp7esYWq1GyuZEK603eLCYUtxr8xy11W5T0rEArfFu164oEOJO0vXTTu8lG3XXfzs+8u+dxmlq3b5e3eXPCxmLJxFlmXT8DerkszVqhtZbhTCAZ1p++W5PWHeOCNmUmhkPV34JBo19Pa6UDS/tThZx3T6+6/3fHprKKFzl83zd7tKYl3F38uvl/3q8q69HGcHXKao0StEVtiCj7d6bvFlVZ6efK9eS6fP/Q1f3InVW3X6EDSdkiOGpw+/vb7vqmz+zI+XPb1Hp+0/XjZN+Kb1VNU+4sfwdl5VxoZa3vKcCVT71f6XEYUzF9VwjfT1qd4fMGv0Ysc9pgOJO1/ZI/ITbj/5ns+6NG7Z98X5rxt39sGvJ+v+E58uvw71f6C8SR07b/zDbQ9YrhTqfFZOpCi5IP/rnRsLKzZxxcIPRrtWlorHUjaH2SPyDXOHXPWPScfc+KxT/z25yvqdte/V08WHyz5WrUbey+JubpHZXOwJaVb1fXBaJfRZlkKHp04xx0Ihm8ATox2Pa2RDiTtD7p36X7K7dffeuvbi/5lK60v3/kGu2HSul/ExIWfqsyz78DVe0ST7lsDw52G7vQdXdV1AR5+e47bFwh9BORGu57WRgeStk32iNxBD93xwIQ1NZucv22Z3yzH+GnDb+KNeR+RefqtJPUb2SzHaKtsSamiokbfpTfaVm6s4IP/rnJ7fMHv+N9bi2t/QQeSBkD2iNz0q86//Ikh+wzJeWvhx816J+Fpm+fw8px3ST/lRpIGjWrOQ7UpNlcqZbqxakz46pd1tmXrynM8vtDL0a6lNdGBpJE9ItcYMmDwTbdcM+7o52e/bfeGmv+v7Fl5C3h+5tukn3g1KUOPavbjtQU2V5IoqdB97GLFUx/Md3t8wfMspc6Pdi2thQ4kDafTOXrCA0/c+P3aqbY15Rta7LjzCpbw1IzXST32MlL3PaHFjhuvDKeLIh1IMcPrD/H3N2e5g8Hwa0B2tOtpDXQgtXHZI3K73j/u7scdqQlpX676b4v/PiwuWsET0/5B8hEXkHbgKS19+PhhsyMMG+XV+hxSLNlYWMM309Y7673BN6JdS2ugA6kNyx6Rm3D0oUc+cOaYM4a9MPttx9bbSbS0ZSUmj/76Eu7Dzib90LOjUkNrZ3MlY4X0ku9Y9PEPpsMbCI0GTop2LbFOB1Ib5rA7Tr//tnvO/HDpVzttmtrcVpWt5aGpz+MaOYaMw/WU++6KdPrWgRSLAiGLZz9a4Pb6Q28D+mZhf0EHUhuVPSJ30LgrbrjRcpL084aZMXF1/9qKjYz/+VkS9j2ejKMuiXY5rYrNlUIwFNZ97GLUkjVlzF1RlOz1hx6Ldi2xLC4CSUo5W0q5SEq5QkoZavh4kZTyn02w7wellOc0fDxeSvnUnzxvqpSyREqZvN3XdnuYLqXcKKUctOdV/7XsEbnJ3bK73nT5+ZeNeH3hRw5F7LyPbajcwn0/PYVj6BFkHn917BQW4wxXMgHdWDWmvfblUpdS6nJgeLRriVXNer1JSzFN8wAAKWUuMM80zaFNuO/7d+PpHuBW4O9NdfxmMuaxex45YtrmObZNVXnRruV/bKku4N4pE3joyNtEO7tTlf/7xZgYwcUymysFb1DfKTaW1dQHeP2rpYlXnTr4A3eiYxCg7xWynbgIpB2RUtqB/wDtiNz3fg5wtWmaASnlJcBYoArYB8gHbgQmAH2AucAFpmkqKeVEIiH30i4c9jHgQSnly6Zplm1Xz1jgb4Cz4Uu3maY5peGxQ4FXAC8wi0Z3n5RSKiDFNM26HX2+u7JH5OYeN/qY84cNHpozbtL4mO1KXFBbzN2Tn+ThI28T7U67RZV/+YwOpb9guFKo052+Y96UuVvEcSNzu/Xqkn6Tw248G+16Yk1cTNn9iTAw1jTNfYFBRFrCX9bo8f2AW0zT7EckCD4kElIDgMHAkXtwzHzgXeCeHTw2CTjQNM1hwLnAOwBSygTgY+BG0zT3B6YD3ffg2DuVPSLXluB0XvL3/3vg4HcWf2r3BmN7iXBxXSl3//gEVu5A0f6su/R01F8w3GmqxhvWgdQKPPfRwiTLsh4GukW7llgTz4FkALdJKRcBS4AjgMZTeTNM09w6X7UQmG6aZrVpmiFgMbCnN+95DBgrpdw+VHoBk6SUy4F/AZ2klJ0ACXhM05wKYJrmJ0D1Hh57Zw76v+tuO6FG1bmbq1ddUyv1VHDX5CcIdOkp2p93vw6lP2G406ipC0S7DG0X5JfW8fnPax31vuDbNJoN0eI7kMYSuaXwoaZpDiYyJZbY6PHGw4PwDj7fo+lM0zQrgBeB8ds99BHwimmaA4mc1Aw11LOzX8gwDT8nKWXiTp77p7JH5Kb1yul5+YVnnT/0zQUfO/Z0P9FQ4a3irslP4O3QRWRd8JAOpR2wJaWpytrYHvFqv/t0yhqHxxscCRwf7VpiSTwHUjpQZppmrZQyjUhAtZRngWOJjIoa17O1L8/l/N4FeBXgklIeBiClPBNIa7TdOiLTi7B3r+G08bffd9BPG2YY+bVFe7Gb6Kj21XD35CeozcgUWZc+rkNpOzZ3mijTXRpajVDY4s2vlyXV+4LPoEdJ28RzIL0LpDRMkX0KTGupA5umWU9k6q5roy+PA76SUk4ncp+U8obn+oHzgJellHOAfYHNjba7GXhNSjkNyNqTerJH5PYeOnDImAOG7d/jy1WTYnYhw87U+uu4Z8oEKt1uOlzxlIrvX9/dY3MlC93pu3WZuayQ6lp/F+DkaNcSK8Te3p5ai23ZI3JtwPgv3v507AZRkPvlypbvV9fUXI5Exo++WWWFBSVv3iawQtEuKepybn2XW1+Zy7r85jr9qDWHAwd1Ztx5w9YkJTokxNAFgVHS6t+ctJ0afsj+B+/Tv4/s/t2an+Pi5+0N+rj/52dEgQjS8epnFUbcXr2wywxnIoXl9dEuQ9tNs5YVUlXj7wzozsLoQIpr2SNyHcC594y7c+gXK/9r84fi5/bW/pCfB6c+JzYF6+h47QsWdufON4pTwpkISuHx6ZFia/T2t8uTPb7gU+j3Y/0NiHMHjjrw0D7du3XvPHn9tLg7cRoIB3nk1xfFWm+56HjNCwrnHi9CbNVsurFqqzZneRHl1b6OwGnRriXadCDFqewRuYnAWXfcePvgr1dNsgfj9DxL0Arx+PRXxKq6Qjpe86IyEpOiXVKLM1zJhEOhNn/+oTX757+XJ3t8wQm08ffkNv3i49zBB+93UPce3XO7Tl4/Pe5GR42FrTATZrwmllZuoMM1LyjDnRrtklqUzZVCIKTborVmc1cWU1rlzQLOiHYt0aQDKQ41jI5OvfOm2wd9bf5gC4TjfzrHUhbPznpLzCsx6XD1c8pIzox2SS3GcKXgD+hO361do1FSq700Y2/pQIpPI2Wvvh1kz7458T46akwpxctz3hEzC5fS4apnlD1tjy7banVsrhQ8utN3qzd/VQklFZ52wFnRriVadCDFmewRuQnA6TdfdVPOtE1zhC+OVtbtCoXitXkfiF+3zCfriqewZ3SKdknNznAlU+tT+t9yHJj4nxXJ9d7gQ7TR7g36lzj+7OtKdKUefvDooZPW/dJmh/5vL/yX+HHDbyrr8idxZMV3U2XDna5qPPG5aKWtWWCWEAiGOwMjol1LNOhAiiPZI3IFcOK1F12VtakqTxTUFke7pKh6f8mX4j9rpqqsSx7F2alntMtpNkZSuqqua1sj4XilFPxnxoZEjy94Q7RriQYdSPGlN9D5zDGnj/h+3dS2e6VoI58s/1Z8ueoH1f7CB3F26RvtcpqFLSmNCt1YNW78OGezzW4zzgaSo11LS9OBFF+OOvygUckpKakZ8wuWRruWmPHlykniX8u/Ve3Pf4DE7gOjXU6Ts7lSRHmNDqR4UVHjY/mG8jBwdrRraWk6kOJE9ojcTGC/6y+7tt8P636xWUqvumrsP6t/Eu8t+kJlnns3rp5Dd75BK2JzJYnSSt3pO578e9r65DpP4OZo19HSdCDFj5Edszo6hg4Y0m/Kht/0z3UHflw/Tby94BOVceb/4e6z3843aCVsCW6KKnRj1Xgyf1UJCnoC8Tek/wv6jSsONDRRPe6Wq27Knl+wVNX4a6NdUsyaunGmeG3eB2ScfjNJAw6Odjl7TxgIh5MS3ek7rliW4r8zNzp8/tB10a6lJelAig+DgOTRh4weMnXTzFZ1e/JomLF5Li/Neof0k68nefDoaJezV4xENyocIqRnaOPOpFmbHEKIi4A20zVYB1Ir17DU+/ihA4eI9JS0tOUlq6NdUqswO38hz818i7QTriJl2NHRLmePRTp962uQ4lFxhYd1+VUKODXatbQUHUitX2egz6XnXNx1Vt4C9GKGXTe/YCkTpr9GyjGXkrr/SdEuZ48YrhRCQd3pO179e9r6lDOn+7kAACAASURBVDpP4JZo19FSdCC1fkMA68D9Dhg6M2+BvnXqblpSvJLHf32Z5NFjSTvo9GiXs9saOn3rQIpTs5YVYRhiENAj2rW0BB1IrVjDdN2o4YOGWekpaWkrStdEu6RWaUXpGh759QXch5xBxmHnRruc3WK4kvHpxqpxKxS2mL28CODEaNfSEnQgtW6dgY4Xn31hDz1dt3fMsvU8OPU5Eg48iYzDL4h2ObvM5kqh3q/aZCPOtmLWskJXbX2gdf2ltId0ILVu26brftPTdXttXcUmHvjpGZz7HkvG0Ze3imkww5VCjc/SgRTHFq0uJTHBti/gjnYtzU0HUivVeLouLSU1baWermsSm6ryuG/KUziGjBKZJ14b86FkJGVY1fXxfwPGtszjC7GxsMYPjI52Lc1NB1Lr1RnocPHZF/aYtWWhnq5rQnk1hdwz+UmMfgeIdmP+FtOhZCSloTt9x78ZiwuSvf7QadGuo7npQGq9hgBq+NChA+cVLtHTdU2ssK6Euyc/ieo9RLQ/4/aYDSWbO1VUVOs+dvFu7spiAziFOL9xnw6kVmjrdF1qckpt1w5dslaWrY12SXGppL6MuyY/Qah7P9H+nLtjMpRs7hRKq3QgxbvNRbUEQ+EkQEa7luakA6l16gR0OPW4MRlbqgpC/jZ2m/KWVO6p5K4fH8ffKVdknT8+5kLJlpgkSio80S5DawGzlxUZlqXievm3DqTWqScgDj3gkB5LSlbp3nXNrNJXzd2Tn6C+XUeRddEjMRVKhtNFcYUeIbUFs5YXJdZ7g+dEu47mpAOpdRoG1Pfv118uL12tf4YtoNpfy92Tn6Q6NU1kXfpEbISSzY4wbFTom/O1CUvWlJKYYBsCpES7luai38xamewRuTZgYGZ6hqdLVuf2q8vWR7ukNqMuUM+9UyZQ4Uqkw5VPW9H+5xNprBqIag1ay/EFwqzZUuUDjox2Lc1FB1Lr0xlwnnLsmE6bKvOC/rB+Q2pJnqCX+396WhTbhOhw9bMKI3oLHA1XCuGQbqzalsxcWpji9Yfi9jySDqTWpycgDtn/oJ5LS1Y5o11MW+QN+Rj/8zMiX/npcPVzUQslmyuFUCgclWNr0bE2r0qEQtbIaNfRXHQgtT5DAU8/2a/vstI1cX1NQizzhwM8+MvzYlOwho7XvaiwJ7R4DYYrmUDQ0iOkNmRDfjWJCfbegC3atTQHHUitSMP5owFpKal12e07t1tdrs8fRVMwHOSRX18Sa+pL6Hjti8pwtmyrMZsrBU9Ad+hoS+p9Ieq9gSDQO9q1NAcdSK1LF8Bx2IGHZhbXlgaDYd3DLNpCVognpv9DrKjJI+va55WRmNxixzZcKdTpTt9tzrr8aovIStu4owOpdekBGPvuM7zThsrN+mcXI8LK4unfXheLy9eTdc3zynCntchxDXeqqvWFdSC1Mas2ViQHguF9o11Hc9Bvaq3LAKC+b+++XTbU5OkLYmOIpSyen/22mFu8kg5XP6eMlHbNfkwjKV3pxqptz7q8asMXCB8c7Tqagw6k1qU3UJfbPbd7faA+2rVo21FK8Y+574kZBYvocNXTyp7esVmPZ0tKo7JGB1Jbsy6/mgSHbSBx2GhVd4luJbJH5LqBDCHE5uyMDpnXpJ/LNUPPw+OtU2XBWjbVFYkVJatZULiMGn9ttMttsxSKN+Z/JILhkDr88icpnXg3ofL8ZjmWzZ0qyqv1z7qtqajxYVnKAWQDzfPLFSU6kFqPToAa3G9QasjnsxZcdJnhzMzEndNduHO6M6BPb2v/HkeJa4afJ8LBAHW+eqsoWGWsr8pjWckqlhatJGCFov0a2oyJiz4VgXBAHXvp46LsnXsIlm5u8mPYXMmirKqkyferxb6NhTX+/j0yh6IDSYuGJE+4v1D0Gdi1F3WbN1sAgYoKAhUVVC1cBFunXw0DV+dOuHO6G0k5ORzYt4911IBzDcfIVAJej6oJ1Kl8f4WxpmIji4qWs7Z8Y/ReVJz7cOnXIhAOqpMveUSUvz+eQOG6Jt2/LSFJN1Zto1ZurEjqm5Mx3GaI/0S7lqakA6mVaF8V7qAEof6dcrr7Nm3+8wUNloU3vwBvfgHlv82ChqAynE5c3bqKpJwc0aFHrurRpzenHTha2BIS8HnqVEWwTm32FBurytaxoGApJfXlLfTK4ttnK74TgXBQnXnBg6L8o4fw561qsn0bzgQKyvS5xLZoXX6Vvd4bPDQ1Kb6atehAaiVsis4oFo0YNiIjaK7e7ZOZViBA/br11K9bD41OhtpTknHn5Ah39+6iZ++e1j69DhQXDzpNKCuMx1uvSoM1bKgtEMtLTBYWrsAT1Pfe2V3fmD+KQDigxo69T1T863F8m5bu9T6F0wVK4Qvoadi2aGNhDTZDDIx2HU1NB1IrcEinbAF0A8oz22VmeErLmmzfodo6apYtp2bZcmi06jIhK2vb+akhfftYB/c8SSTsd7EI+X3U+utVYaCSdZWbxdLiVSwrNbEs3THgr/x37S8iZIXVRefcKSo/fxrvugV7tT+bK4VwSF8Y3VaVV/twOozMaNfR1HQgtQ6pQAIQSknPSKssa7pA+jP+0lL8paVUzpsPDUEl7HZc2Z1x53QXabm5HNqntzp+6P7CnpyM31OvqgN1Ks9fZqwu28DCwmVsqo6r8617bfL66SIQDnLlGbdR+fXzeMzZe7wvw5W8tdN33C391Xau3hvEEMIBuIG4mbbQgdQ6pAMWQFJGelKgPDrnd1QohGfzFjybt8C0GdDwZmhzJeLq1k0k5eSI7J65qm+fgZw96lghDAOvt16VB2vZVF8kVpauZUHBUip91VGpPxb8umk2wXCI6079G+LbV6hfPn2P9mNzpRAM6k7fbVmdN+hLS07oCGyIdi1NRQdS65AKiNSMDIfd6bQHq2uiXc8fhL0+6lavoW71Gmj0F7sjPR13TneRlNMd2buXGtFrFFcOPVuEg0Hq/fVWcbDa2FCdx9Jik8VFKwi0kXs7zcybT0iFuOmk6xB2J3WLf9rtfRiuFPyRTt96hNRGVdcFQmnJCZ3QgaS1sDTA6DNoYKqvoiIItIqlNcGqKqqrqqhevAS2vnEaBokdOuDO7W64c3LUvn16q9H9zjCcB15O0OdVNf56VeAvF2srN4vFRStYXbYei/g7PzU3fzHP/PYGNx93JYbdSc38/+7W9jZ3CvW603ebVl7tFd07pXSKdh1NSQdS69AeCOX07p3mKytv3e9CloWvqAhfUREVs+YIGoJKOBy4u3YR7pwc0a5Hrsrp01uN2f8QYUtMxOetV5WBOrXFW2qYZWtZULCMwrrWf0HowsLlTJj+KrcfeQ3YndTM/maXtzUSk6n169ZfbVlZlddB5IL5uKEDqXXoBPg7d++eFSgtjcs3IRUMUr9hI/UbNsLUX7ZNQ9mSkkiKrPYTOT17WgN77ysuGDBGKKXwNixL31RXKFaUrmZBwTJqW1mPv6XFq3hs2kvcNep6DIeTqumf7dJ2RlKaqq0P6Om6NqysypsYtlS2zYifXwMdSK1DFuBPy8x0h6qq4vJOkX8mXF9PzYqV1KxYCY2WpTvbt8PdPbIsfWCf3tYBPY8V1464QIQDfmp9dVZRoNpYV7WZZcWrWFpiEorhtkkrS9fy0C8vcO+omxB2J5VTP9zpNkZShqqs0oHUllXU+oXPH8pNcsVP438dSK1DFlDndrsTVG1dmwqkPxMoKydQVk7VgoXQuG1SdmfcOTlGck53Du7bRx2zz/nCkZJCwOuJLEv3lUXaJhUuZ31l0/eX21Nryjcw/udneWD0ODJsDiqnvPOXz7clpYnKzb4Wqk6LRZU1PsKW1S3adTQlHUgx7pBO2XYgGahwu12ucEnzX4PUalkW3rx8vHn5lM/4DRrOTxkJCbi7dRXunBzRqWeu6t1HcubBRwrhcODz1quKQK3a5Ck2VpVFlqWXeSqjUv6Gys088PPTjD/8FjLtTlUx6Y0/HQHZXMmUV+vfhbasstaPQHSOdh1NSQdS7EvY+oHb5XaFvXFzDVyLsfx+6tauo27tOpjSqG1Sauq281N9evVSQ3sfzGWDzxBWOEy9r16VBKvZUJMvlpesZlHhcryh5h+RbKrK594pE3jwiFtFO4dTlX/78g5DyZaYLEoqtzR7PVrsqqz1Ybcb7aNdR1PSgRT7Emi4KDYxMTEx7NXdnZtKqKaG6qXLqF66DLYuSxeChKwsknK7C1dODsP79LIO7T3GSNj/EkI+H7X+OqsgWCnWVW4Si4tWsbJkTZMvS8+vKeKeyU/y0JG3ifb2m1XZV8/+TyjZEt0Ul7euBRxa06qu9eN0GGnRrqMp6UCKfduuOUpITEis9erzBs1KKfwlJfhLSmDOPGjcNqlLF9w53Yz0HrlqVJ8+1gnDDzLsSW78nnpVFahVeb4ywyxfz8KCZWypKdyrMorqSrl78pM8fOTtot2Zd6jyz574PZSEgbA7KK3Uo+W2LBi2sBmGjcgfUyra9TQFHUixb9uUnTPRlaBHSNGhQiE8mzbh2bQJfp2+7fopm9uNu3s34c7pLrr07Klk7yGcd/gJAiHweutVWbCGTXVFYmXpGuYXLqPat+tdNkrry7n7xyd4+MjbRftz71VlHz8cOSeWmIQKhQi17ivStL2kFFhKKUMIA4iLPlI6kGJfo0BKcOpAii1hj4faVSa1q0xo1MbHmZmxbVl6/z69rf16HimuGn6eCAcC1G93N98lRSv+9G6+5d5K7pr8eCSUzn+Qsg/ux+ZKwdKdvjVAWcrCJhzoQNJaiJOt3QyEIaxQXPzexb1ARSWBikqqFi2GRsvSEzt1JCknx3Dn5nBA717qiAFnC+fINIJeb8PdfMuNNRUbWVy0ktXl6wGo8tVw9+QneeiIW2l/8aOqdvI7IhQK6z52GpbCssXR+3jcvJA4tm2EhLKUiKOrstscy8JXUIivoJDymbNg67J0pxNXQ9uk9j1zVW6fPpx6wGhhS4zczbcyWKc2eUqMXzbN5uieh4rM028loANJAyxLKeLofTxuXkgc2zZCUkophH4PijdWIED9+g3Ur98AP293N9/ItJ/o2auntU/fYUZyUgahYAibFTI+Hn+EsuLiVLa2exRCiMhP3go4IH5aNehAin3b3nKUUipy/lJrC0K1dXi25JG53764BwwSCVntCYfDCAGGYSMh0RA+bz3zp/6Hkry4uQOBtmsEwGlX3hGE+GmHrwMp9jUOJNBTdnHPmdWermecRtLwfXFnZlCQV8W03wrEPiMMuua2JxgMgrBhECbBlcJBx51Fcd4GNeuHz0XRprXRLl9rWYo4WdAAOpBaA9XwX2SEZOgRUjxK7NKFbmeejnvIUJWYmiI2ry9Xc37OE+ay+fi8QS65fn9S08AKhrHb7QTCChtgCQsbNjrn9BWnXHYbZUV5zP7hc/LWrYz2S9JaghACPULSWtDvIyTLsvQ5pPiR1LsXXU47FfegwcqZ5BbrzRLr1+/WGWtXlhDwhxtuaAhX3jwSh81DWUEJSb3SsTkNnCiCIYVDhQgZAlsoiMJG5+69OPHCm6iqKFWzf/hcbFy1OMqvUmtOIhJIsdvKfjfpQIp9fzyHpEdIrVra4MF0PuVk5ZL9sCckCHN5kVr+xSqx3iwlFLL+8MM17AZX3zJShf0V4tv3XubCW58kUObBbwVwJSYgDIFhT8RhWQRR2EIhLMsAYScpraM46qyr8NbXMOuHz1m3fH7kSkotbhiGDSPyhhA3FyfuUiBJKTOAQuBV0zTH7cmBpJTjgWTTNG9r/PGe7Gsnx7gOKAASgRnAtaZpBvZyvxuBk0zTXLYH244GnjJNc989PPy2d5GgPxC0JSbu4W60aMk88AA6nniCcvXsLYTNxorFBWrFR0uNjWvLsSy1wyGv3Wlw7a0HU19dIL595zmOPvsqPHnVkGynvLSUrMwEEpI7UlbjJckhcKggYWcCyu9FBEKoYFiFE5JEWZnBqFMuUQefcI6Y9cMXrFkyG2XFzQxPm+ZMTCQcDvvshhE3f2ns6p/b5wMzgfOklM6dPbmpSCkNKeXuzlG9a5rmUGAIMAC4Zgf7bU33FNr2y+bzer02tzuatWi7KOvwUQx8/DE14qMP6X7j31hb7ebjifOZcN8kvv10qbF+dRnWn6zZTnTZueH/DqWqdAPf/PMZHAmJ5PYdQsW0zThSnJSURG7f7vH7WFZZR9AShCxBcONylN1OUbgWZ2KCqJs7k+656WxaX83ShVUcdPxYdckdTzNg38MwbK3pn4C2I84EN5YViquGhrs6ZXcZcDtwFzAG+Ay2jUgkkAb0BNYBZ5mm6ZFSpgFvEQmFzUApUNxon12klN/tYLvxQG8i9wDqBRwmpbwHGEXkmpwy4DLTNDf9VcGmafqllNMiZcrRwHPANGA/4GEpZSrwN35vXnqbaZpTGl7XocArRIbCs2h0AaKUUjbsq33Dts+ZpvlPKaUbeAcYCAQjJZhnN2xml1K+BowkEjDnmqa5q2edrYZt8Hq93sQkHUgxyW6n0zFH0/7II1Vi167C7wuxZEE+K/87h8K8atjFi1iTU5xcdctBFGxYzo+fvI6yLI464wpVv7FShL1B7A4HZWVlGEYPgp4i6gOJBC2L6UtKOHa//tQu+IGs4UfzW/5iDj7oQDZ/+IHqdNDB9DyoB5O+Xi6cDjsHHXmGGnncmWLOlK/Virm/irBuQ9QqORMTsSyrLtp1NKWdBpKUcgiQCfwEdCISTp81esq+RN7kq4FJREZTbwD3AzWmaQ6QUrYHFgCf7MJ2AIcBw03TLGuo4fGt03tSyiuAJ4Bzd1J3GnAM8FLDlwYTmb67seHxdsBHpmmqhpCZAnSVUiYAHwPnm6Y5VUp5NrB1GzvwYcNjq6SUKcA8KeVMoD+QYZrmgIbnZjQqZyBwqWmaVzeE670Nr3dX+Ld+UO/xeJKSknZxM625GYkJdD7pRDIPG2W5Oncyaqp9auH8PFZ+MYPS4jrYzU4K6ZlurvjbAWxYOZ+fv5wISpGUlkG3XgPE5vcWkSLbU15eTiAQQBg26spXMajDYUzeUsYRAzoxbWkJB/YYinfWv9nvgJN4b9k3nH/uOaJs6i+q+NtvOfrqa1R5uY93/zFfdMvNYNSxJ3Pg0acx7+dv1bLZP4tgwL/TGrXY4Ux0oyxr17v1tgK7MkK6nMg0mJJSfgG8KKXsYppmfsPjk0zTrAKQUs4mMqoBOJyGN3LTNMsatm3sz7YD+G5rGDU4Xkp5PZFR085qvkhKeRSRkcW3wNtEAm6NaZozGz2vF/CRlLILkRFNJyllJ6AD4DFNc2pD7Z9IKV9v2KYvkeD5OJJhQKS1T39gMdBPSvkyMBX4T6NjmaZpLmz4eBZw8k5eQ2Pb7jdRU1NTl52SolvGRJE9OYns004lbeRBlqtDllFRWqdmz88Xqz5YTWW5Z49/Llkdk7nkhv1ZNX8aM777eNvXjz7rKmrXlKlghVe4Rudi5m0kGAwiDBvlBfMY2Ps4ps5aw7E5WcxeXsywnn1RQuCf/wNnDT+Rx357jZsPvli4c7qz5LrrRc9rr1ZX33oo039aq156fLqQAzty5InHqv2OGCMWTPteLfltsgj44uYceVxzJiQCtJ1AajhfNBbwSSkvaviyA7gYeLTh88Y36AkDroaPd/aP88+2A9g2DJVS5gDPAvuZprlBSnkQkVHKn3l3+8USDeGx/dD2I+BW0zS/klIagIfIQoi/qlsAZQ3nqP6HlLI/cCRwPPColHJww0Pbv9bdWd247c/WqooKjy0nJ0TkZ6C1EGdmJl1OP5WU/Q9QrswMUVRQrWbMzjdWLV1GbY1vr/84yO6WxgVX78viGZPU3Clfb9tfWruOZHfvw6aJCyJfy3BQuKKQYDCIYdgIBWrxBQL0SEtiWkElFxzXj/vensezN55E0QfjSXIlqXEjLxf3TnmacfterIY8O0GsuP9BUfDlV+x/5x0M3a8bX36wkJcem2706pfFsWMOZ8SoE1k840e1aPok4fPE1WxQ3HEmuhFCVEa7jqa0szfGU4FVpmkesvULUsqRwLv8Hkh/ZgpwKTCjYXrsNODTPagxFQgARQ3B8T+LFPZQOrC138rl/N7EdBXgklIeZprmr1LKM4mcIwMwAY+U8kLTNN8DkFL2I7KqLxWoaAi4Hxq+ltkEdXppCMmqsjKPfZ999BKpFpDYuSNdzziDpKHDVWJ6qsjbWKF+npovVi9fgKc+0GQj1Jxe7Tj3smHMnfKVWjR90h/2e9RZV6qaVSUqVO03AJxJiRQWFuJwOBAisigh7C2lb2YS/15TyOjRA8lMTeTrGVs4+YzbyXv1RpGZkKQeGD1O3Dn5cXHewDHqsKefEOaTT7PositE7mWXcMHVx7Fkfr6a/O0q8cqTM0T3npkcf9pBasjBR4vlc35RC379Tnhqq5vq5WpNKCk1HbvDGVc9o3a2yu5S4IPGX2iY9jKklIftZNuHgAwp5QrgPeCHPSnQNM2lRIJsOZHzWE31AxgHfCWlnA7kAuUNx/MD5wEvSynnEDnXtbnhsRCR6bZzpZRLpJTLiSx+cBI5RzVTSrkYmAM8ZppmQRPU6aHh51RWXFzvSE9vgl1qO5LUM5e+t93M0H/+U+3z4otUdxmofpi0QTw9/kfefXW2WDRnC576vbqC4A/6DOjAeZcPY8Z3H7N9GLXr1I2O2bmicsaWSBh1TAKlqKmpIRQKbQuk+opVDGqfqhQwp7iaC4/vxz+/XUG5z1AZR11K+RdPCUfxJvXQEbfy3tIvxD+Xf4m84zayTz1FbXx7IotvuJE+XWzcdM/hyEEd2by+gtee/s2Y+PIcOuSM4MLbnmT0qRer5LSm+NtKa0qpGVn+eAskofTFcjHtkE7Zgshij4KBI0akPvfeu9ctuPQKPWXXRFIG9KfLqaeoxH4DcLoSxJoVxWrpwkKxziwhFGy+wejAYdmcfNZAfv5qImsWzfqfx8+54e/KXmyj7KcNAqDdobnUdgrz2Wef4Xa7ufrqK1k4+S6ciRn0O/hObvxhEUrBM0cM5qG3ZlNUXs9bd4yi5Mun8K5bSNbFj6rK5GTunTJBdE3rzP0jr6dm/kK19oWXhQoG6XzSCXQ5/wI2b6xU//50qairicwUZ3VM5sSzBqnOXVLF2mVz1byfvhHV5SXN9n3Rdt2Yy26t6d5n0KXA9ufnWy192X+Mm15UoIBawLFm2bIaZ0qKXdh1g429kTFiOP3/fr8a9v77Sj7wAFuMjnz1r2XiiXsn8dl7C4W5rKhZw2jEgd0Zc9ZAJn/y+g7DqFO3XrTr0EVUzsr7/Q60XZJVXl4eQGSVXUPX94CvkkAoSLdUNxawqLSKC46Tqrzax1vfr6HDKX/DcCVT+s7dItMf5L5Rf1ObqvK54Ye/YwyW7DPhMeXISKfw2+9YcPkVtAuXi+vvPJwRI7uDgNLiOia+NEv8Y8J0ElN6c+5ND3Lc2OtUZofsZvv+aLsmJb29ArZEu46mpAOpdagCnAG/3/JUV3kSOmRFu55Wp/1hhzDg0YfViA8/JPfW21lXn8qn7y0ST973A998vESsM0uxws0/WzBydE+OHtOX7z54kfUrFuzwOUecfrmqWlCgwp7frw8y0hwUFRUBNEzZGWxdfxPyltM3MxmAj1fk06trhujVNY1/T1vPhhIv7U+6QQGUvHm76CwSuOuQ61R9yMv1P4wXG5xehj7/DEm9emJ5PKy8937WTXiKw4/uxZXjDiGrY2S/VRUe3nt1rnjxsV9Rtm6cdf39nHjxOJWVndNs3yvtryWlpDnRgaRFQRGRFYDUlJVXJ3bsGOVyWgHDoOOxRzNwwpNq348/IvuKa1hZ4uCDN+fy1AM/8P0Xy8WmdeW05B3uRh/bl1HH9OTfE59hy5rlO3xO114DSM/MEpVz838/p2RAgitRbA0kAKWsbaMkb9UaBraLBIcvbLGivIbzj5EK4J7X5+LsNlAkDToMsCh581bRw9VO3DrySiWE4KHpL4r/Fs5h0KMP0e6QgwConDuXhZdcgrFuqbp83CGMPq4vNnvkWHU1fj56a554/uFf8AWyOP2quzjl8v9Tnbo3vmpDa242ux27w+kA4mr+VM/9tA55wP4AZWVlZckdO+r5kh0wnE46nXg8maNHW67O2UZ9nZ8l8/PEim9mUlJYC1G8fuvoMf0Zvn9nvnrzyb+8md7hp1yiKubmY/lC22pN6pGJ1+fF5/v96gGlLIRhR4XDlBfMo/f+hyCItPR4f0Uejx82QHTtkExeSR3Pfrqc2865Ct/mFYRryih5/Wb6X/uCuH7/i9RLs98R7y/+grUVG7jxhutw98hly/sfgWWx9pnnRFLPbxh+551qnxFdxVcfLWLz+goAPPUBPn1ngXAm2jnh9EGMufQ2yovz1KwfPhf561c117dRa5CclkkoGKh02lxxtepWB1LrUErDm2lRUWHpgI4dLPToFgDD7abLKSeTfvAhytWxg6iq8Kh58/PFyo+mUVFWH+3yADj5nMGq/6B24vPXHqWiOP9Pn5fbbwjJqRli4/w/3mQvqXcmBQUFf/iZRwLJBmHw1RURtiyyU1zk13qpC4RYU1mnzj26L099sEBMX1zAUft2Uf1PvUUUvXsPKuCl9I1bGXH1c+KyYWertxb+S8zaspAtVYU8fOw4knJzWT3hGSyfj/r161l81VWi+4XnM/byk1mxpJAfvlmJzxuZTgz4Qnz14SJhtxscd/ogccIFN1FTWaJmTvpcbF69tHm+oRpp7ToSDof/sn1aa6Tf1FqHShpuwlWwaXOls3OnNt18zJ6WRu6lF7PPq69a+707EfvBR6vf5leIl5+Yyj8m/Cpm/LROxEoYnXnRUCX7p4tPX3n4L8MIYPTJF6mKWZuVFfjjDUDtHVxWXl7eH/+tKoVh/P73ZNBXqbaeRwJ4f3meOHBQZ9EhI3K9+cP/nCtIzyZl/5MUOzI02wAAIABJREFUgOWpoeyt/+PQ7vuK8waNUQD5tUVcPel+/D06qiHPPkVChw7b9rf5vQ9YfO115GYp68a7D2fAkM5/KCcUsvj2kyVMuH8K69dZ4phzrlFjb36EngOGo+/h1fTadeyC3eGYG+06mpoOpNahioYR0gbTrHRld97J0+OPs0MWPa+7miFvvqFGvPUG4X0OUr9MLzSef3gKbz43Q8yZtpGaKt/Od9SCxl4xQnXLcYtPXn6Q6vLiv3xu3yEH4nKniOqFRf/z7m2kOP9w/ggit7Pfei0SQKB6nRjYLnnbCbFyX4BNNfWceURvAEIWPPjuYjJHnSsc7btFvlZTRtnEuzmu9yhxijxGAQTCAcZNfkQsDhaoIc89RerAAb8fo6KC5eNuMfInvs1JZw7i/Kv2V6npf7wdimXBpK+W8+QDP4kVS+v5f/bOO6zJ64vj3xuSsPcSRWQIF1AEtygOHHVrq3XUOuoeVeusta2j1tqhVltrXa11VGvVam211r0HKi4QeRmC7BX2CCHJ/f2R0B9qgCxUNJ/n8VGT9733Zr3nPfec8z3dhkxgYxZ8De/A9srmpgb0gWOjJiUCobHqrJh6jMEg1Q8qDRK5delytpmLi+B1SP02bdwY3nNnI3D7dtbyx40obRLITp16TL797DR2bLxObl9PQkmx/gpV9cm499sxBycjcuDHFSguyK31+JA+I5noShJj0idDAjxjPoTGQlLZcqKSyhhSJaK0W/Cxt3ziiv9rVCq6t3GDjaVChOTBIxHO3c1gTkMXAspzpaJUiH5dhqH+fUgvz87/nbvu+k/k97gT8Fv6CZx793pi7swTp3Bn/HjYlKZjxofd0K6zO3vG1siBs/9wWLP0HAm/LkJI/9Fs3KI18GsdAh7P0PpCVxwbNpFCIRbwSvHqX9VeAS5npFWENGiYD0BYmJdXXiwSFZk1bmxdkvBKFWkDACx8vNHorTdh6t+cCc1MSRyXKb9wNI4XF52FCons5b/F5gGTPwhmAn4ZOfjjVxCX1r516N+2K4RCU1J4P+OZ5yyoPQryC5hM9uRrV3QPNvrvsZKCJBAAzubGyCxRFLWmF4uRXixmb3X1JL8cVXQ7+e73e6TNp92YTdeRJP/crwAAScYjiH5fhTEjP4FYWo5LSTcAAH9Fn0JCbhIWvTcZZp4eSNz6M5hMsZ0oF5cjevkKYh0UiC5z56Jlezf25547JFORPPIEl8/E4fKZONIuxB2deg1nwX2GkRtn/sTDW5cgk74y3befH4TAytbRDK+gQTJ4SPWHFABmAJCenJJu7unxgpejP6yDAuG39FPWcvdu5rtyJdKMG7G/DkaRb5acwIEdt3kP76ej4qm4yssIj8/D9PmdGI8Vkj82f6GWMQKAjr3eZjmXEsFU1EGZu9siOeXZUhPGGHjkSU9DIi6UV40jAcBv0amkX0dPmJv+X9zjk23hxLpNXxi7/qdYj/KkKOQd+hZT2oxC20aB/z0ekcVh9umVMA9uy5p9sYLxLZ8cv+DuPdwZ9x5hkTcxfnYn9Bzgy/h81ZeVG5cTsW7FBXL6WAJadn6TjV+8HkEhvRlf8Nx6fr4SWNk6QCaTFuIVU/oGDAapPhELRfsNxMfFJZl5erz8V+gasOvYAf4rV6DV3j3wWvQREiS2+GPvPbL60xP4c+89EvswCzJp/clo5Qt5mLGwE8pLs8jhbV+holy9eFZgSG8YQUCKHmSrfJ7YG8vT0tKe8Qyf3rIDgIqiRzx/e8sn3rRH+SUQlZWzgSH/v4FJyizCoUtJcBqyAET4/xhQWVw48o9twuwO49HC2e+/x3PL8jH15BKSYydA0HfrYNrY9Zl1xv/wI4mctwABftZs1seh8PB2qPY1372RjO++uEiO/RGNZu37YvzH69E6dAATGptWe46B/2Pn3AgyqfSVzK03Wr58+YtegwE12L5mrSmA9gAK7Ryd+B169fTPPnWm/my58nhw6h4K9+lTWZOJE4lF63aIThCzs//GkH//fIDYqCySn1uG+iitaGLCx/SFnZCfnYhjO9dDkw6sg8bMY7kXk4gkW7U3ZdvZjVy7dg1lZU/2KGrbphUKcx4Qifj/3QdkMinc3duREwlPxptyyiTk3faeOHr5EaRKL+xebA66t23CrBs0RFnMzf8MXkV2ElBahNBuExGVHQdRqWJ8xhjOJF4ljrYNWPuhY0lp4mOI09KfmEdaWIjMo0eJUGiE9u/0gLOLJUt8JCLVyTBlZxTjxuUkkpVRgnZd27L2PfsTvsCYiTKSiLTitU4krRHvwPZyVy+/gzwjI60Eq19mDB5S/eG/X3/YuXMZlk3cBOC95B8fnw+Xgf3RfN1a1nbfXjiOHo+IZGDnpuv4dvlpnDgSRVIS85QN2usnFpZCTP+wE7KSo3F89wbIZerHRNp2HwQiJaQoWrV3xLc1AY/HQ27us0kRjLFnPKQiEQehEQ92Jk9ugUXmFKGovIL17vCkzM/iLTeJuW8wMfNu8+Q4d06i5NJBfNxlJjxsGz/x3NbwveSniIPwWTgPjYa+pfKTS/79AO5OmQpXSwlmLe6OgFY113HHRmVh49eXefu230ETv04Yt2gtOvUbycwsrGo873XF1dO3mC8QXH7R66gL6s8dtoEcKJr7GWWmporLiovLTF0amJel6qPDhf7gmRij4aCBsOnchZk1cCYF+WXsdngqHh64gpwszdt6v8xY25pi8pwOSHgYjvOHd0Aj5XxC0KpTX+ScSqjWIFv6OiI7O1tlh2C5XM54xOiZx8vFRXIfOwve9bQnjdhfjzLJiJ4Ux64kQipTeCy5hWJsOxqDKQNnIXnzLMhL/x+SKLh2GDwTMyztNgefnlmN1ML/J1ycTbiKxPwULB86E+Zenixu/QYilzyZ7SgtKMCDBQuJY7eu6Dt5Clp1cGNH9t0n+bml1b4liXEibF5zlTRys0a/oW0wpn0oHt6+zG6fP0bUyVR8XXBq5G4MIEybcymlxlD0snsTik7ZZQA+U/ZxCwLgw3Hc/irHMwCWHMc9l26NL/kttoFKLmekyQEkQhlHykhKSrPw8Xmha6qEb2mJJmNHI2DTj/I2v+6Gcbe+LOxOPtm0+gJ+/PoCuXQ6jiiN0SuDg7MFpswNRszdKzh36BfNjBGAjr2HQV4mR3GsqNpjTF2tUKnw/TRyFR4SAEiLH/P8qtQjVXIjPQ8Vchnr3ubJ+M8/VxMRn1nKHAfOeuacvHN7UHH/Avus+3w4mT8ZE3qUl4QZJ5cT4u+FgDVfMaGdrcp1Zp+/gDvjx8M89zGmLeyC4FBPRng135OkJhVg27pr5Kfvr8G2QQDenfclug+dyKxsDaLCNg7OYIru1+m1HqyaHwG4AmjGcZwvgDEAflD2twsCMFw/K/0/lFK1HR+DQapfcFAapIi792IsWzR/YRvtQnt7eE6dhBZbt7LWv/wM1roLu3Q1i7fhi7PY+u1lcv1iAvJzy2ofqB7i4mqNCbPaI+L6KVw+9pvG5/N4PAS064Gc87Wk7dsI5Onp6Sqv3kzOFNJBT5GXcQ9+DlYqzznxOJu829sXvKcMwidbbhJBQ19i0SL0mXNyT/xEWOxt9nmPBbA1tX7iuWJJKWacXE4S+MUI+u5bWDRVLbAql0jArVxFuM8+Q6dOrpi+sAtcXK1VHluV7PQi/LIhjGxecxlmNj54Z85KvDFyGrN1fP0KwytxaeIDuVym1XYdpbQJgBEApnMcJwYAjuMiAXwB4DMAKwD0pJTepZR+X+XU2ZTSm5TSR5TSoVXGa08pPUcpDVf+6a983J1SmkMpXa5sgDpJ3TUaDFL9IhGAEQBcOH48wbpFi+c6uUmjRmg6eyYCf/6ZtdyyCWVNW7Oz55LJuhWn8cuGayT82mMUF5U/1zU9b9w87TB2eluEn/uL3Tj9p1ZjhAx8F7LCclaakFfjcUIzY156uuobYblcrtIg5Wc9gIWQDyvhszel55JywOPzWEjgkzEdsUSKNb9HwKH3RPCtn/VCREe+I0YpcWxl94WwNLZ45vnPL/9A/k6+guZfrIBDl5BqX09hZBTuvDeeSG5dwbgZwej9pj8TCGsvks0TlWLXphvkh68ugidsguEzl6HfmNlyB5fGtZ77qtHY27/UxNT8Xy1PDwAQx3Hc0/uf15XPLQVwmuO4II7jZld5vpDjuLZQeFPfAwCl1AbAZgCjOI5rDWAAgC3KxwHAHsBDjuNCOI7brO4CDQapfvHf/s3ty1dERMCXmTSo21YU5l6e8FkwD0E7drAW361DfgNf+Ynjj8iaZafw65Yb5N7NFJSVvh4ZUd5+Thg1qRWuHv8ddy79q1UsjM8Xwj+oM3LOJ9Z4vqmrNaRSKUpKVGffyeUMPKJqJ0SOMnGx3NvuWcMBAOdTRGR0H79n5OWuRWYgPCaXOQ5ZwECevSzk7F9FTHIz2YrQeTATPJuevS/yL3x7ayc8Z0yD27gxrCb9uoQt2xDxwRz4eZph1sehaOrrVO2xVSkqEGPvT7fId19cgETmTIZO/QSDJixgzo091Tr/VaCRp58cwCUtT9c2frtP+fd1AA0ppSYAOgLwAHCcUnoXwHEooqFNlceKAex/eqDaMCQ11C+yoOgea8wYK38cE5NoHdDcV5xRs06aplg1bwaXwYOYma8f+MbGJCYqg0UejiaPuGxIpfLX8iamWZALBg5vjvNHdiLmzjWtx+k6eAwkolJWllxQ48XB3McO6enpKhMaAEBWzZYdAMhKknm+9o4sPCP/mXOPxWege2MH1ta/Abnx4ElliFU7b5E9S7sz6/aDWMH1P585N3v3EuI4YTVb2m0Olp1dS8plTyYy3Ey9iwWF6VjVay7MPdxZzNdriKxM9batOD0dETPeJw3fGoyhY0biUUwO++ePSKKOFFRpsQT7dyhaX/QfGoDBExciJz2JXT/xB0lLjKn1/PqKhbUdjE1MAeChlkNEAGhKKbV7ykvqAOB+DedVbu/JKKWAwm4QAPc5juvy9MGUUncAJRzHaZw/+1peXOorynbmdwHYAsC923diLAJb6MU9sWvXFn4rlrNWe35l3p8uQTJzYod+iyDfLDmBQ7/eJTEPMiGtR4Wq+qRlBzcMGt4cpw9s08kY8YUm8GnRAaILNXtHACB0sWApKSnVHsfkcqIqqQEA8jMj0ayaOBIAXMvII2P7+j5zsZDKgeU77hDbzsOJ0El1J9js7YuIk5yHj7vMYgIV86cXZWLqiaWktLE9C1y/BrV58GmHj+DuxMloICzGzMWhCGr3bNFtdUjEUhzec4esWX4WGZnGpP/YORg+cxlz826u9hj1iSY+AZBJK05DqfyvKRzHJQI4AGCT0ssBpbQ5gE+giCEVAqg9uKfgKgBvSul/gUdKaVtKqU5ZtAaDVP+IACAEgHN/H02waRGg9RfAsVtX+H+5irX+bS+afDAXcUUW+H3HHbJ6yQn8vf8+71FMznNp6/0y06GrB3oP8sHxvT/g0YNwncbqMXQ8E6cXMXHas3pvT0MsBagufgQAMpmcVCdSmpt5FzYmQpgJVD9/KDoVDjZmpEXTZ9UUHibm4VR4ukKA1Uj1lmDWtvnETWiJBZ2mMiMV23sSmQTzzqzi3S5LYoHfroZ1QM0GQlpcjKhFi0nCd9+hVz8fNmFWR2bnYF7jOU+cL5Hj6P4IrF5+DokJjLwxcjp7Z85K5uHX8pVqfeEV0KbI2NT8gI7DTAeQBiCKUhoN4FcAH3AcdwHAGQDmlNJ7TyU1PAPHcXkABgFYpjz+IYDl0LGsg2iarmrgxRLSoKE9gNUAkgDg3wcRC2OWrTArS1adHvwEfD4avNETDj16MBPXxqRcLEXE7VT28H46SUsuqNuF10O6vuGD4G5uOLpjHXTdCjIxM8f4ReuRui8C5Vm1aNzxefCc1R4bN25ERTWKBcOHDYORJBrpj06pfN47ZDnbFZVB7mWp/lzHNneDg4ywD3+4rPICsvOTbozEXEDemZ2qLzB8YzjP2MDu5yVi/bWfCaummGqATw+86zcACTt3s8x/1Ii78fnwmT+X2bRpQy6fjWdXzsYRjW+KeECPvr5o1d6FlYtLyPUTfyA+8qbGqfkvEzwjPqYs2yjhC4QNAVRfK1DPMUgH1TO2r1krBtAFimw7aftu3RxtTEyci6M5lT92nlCIhm8Ogvu0afIm740jRl5+uPcgH6f+fkjO/hONhNgcUlT4amfGaUOvgX5o26kRjvy8GhlJ8TqP1/ud95mgxBgF4c/q0j2NhY89WAMhbt26Ve0xvn5+4CMfRblxKp+3dfBBOc8SD3KKVM4XlVOAES3cyL3YHIgKntXduxmdQ4YMCSXlydGQFqpQkpDLUHbvLPHq8i6crZwRnhahcp4YUQKicx9hQP/xxLRBA5Z/+w6pUR9KLofoylVScO8eAkb0QesQL5KWlI9CFWusFgYkxObg6rlEYmxihuCeIaxFxx6kvKwEuZlp9dIwuXr5wtO/dTRfIFz7otdSlxgMUj1jwoL52L5mrRMACqDY0sZG1r5fP5p9+v+6dnwLc7gOextNpk2Tu40bQ2QuHuz2vRzy758PyMWTsXgcn0tKX9I+Qi8DA4Y1Zy1aOZPD275CTlqSzuOZWdmg24DRJPNvjsjKag/52bV3RXJhBouLi6vWeFFKITQqYUWiGJXH8PnGpIGLP84n5ah8Xs6AxtZmLLCxLc7ffjZWVVQqATHio13vN1B45xSgQhKJSStQGnmZ+HYbBysTK9zLfKhyruzSXJxLCkPfkKFw7hCM3Os3nlF2eBqJKBcZR/4i5vbWCB4ZCisbE/b4US6RyTQLnzx+lItr5xMJY3wE9+zEWnZ+g1SUiyHKSAFj9ScmGtjpDUnDJt6bCY934UWvpS4xxJDqJw8ACADg6G/7Eszd3IzMmjSBx8TxaLFli7z1jl/A69CdXb6Zw/vhy3PYvOYSuXruEckTVS/bYkDB0NGBzLe5HTm46XOIMtTYBlWDXm9PYcXxIiZR8/3nOZqoVPiuikwmU6nUUIko/TaczE2IsVH1P/FfI5OIv6c9cWtgqfL5305yyCiUye37TK7WpZAX5yLnl0Wkh0cwGerfr9rj8sQFmHZyKcm0JgjasA5mburVECVu34F7M2fCx1WA2Z+EgjbXrswh7FIC1q24QM79+xitur3Fxi9eh8COvepN6wuvZq0kPCOjoy96HXWNwUOqh2xfs7YYQB8AxRUSibz/iOHUc/gwqzyBPbtxPY137GAEblxKJGlJBZCUGxqgqcs7k1ozVzczcnDTShTmqhY81RQrW0eE9B1BMv7miFys3mdh06kxbty4QaqrQQKApk2bwtxEygqyo6pRcpDCulFnllgoJtmlqr2RCjlDUxtz5tPAClfuqTaAVyIyyLDBnUiFKAUVolSV48jFJZDE3UarHpNJuayCxeYmqF4TYzj7+Bqxs3JgHYa9R8qSkqGOFqOspBRZx48TUl6GdqPegJuHLUuMFxFJueYdWNJTChB28TEpyJegfff2aNe9HwHAcjKSiSbCuM8TGwdnBHZ6o8zIiD8X9VqKuHYMBqkeMmHBfMn2NWt9ATgBKLN2cJBY2Xv4/LrlplFGaiEqKup1q6QXwrgZ7Zi9PY8c3LQSJYU1KyhowoCxcxlLl6IoMkut7CMjCyHs2jYi586frzHW4eHhAUtzsIKsyGrHtXX0J6XEjEWLiqs9Jja3iIxu7UEu3ElFiYrtRLFEhvxSKboO7Ivi+2fBKlTHG2UlBZA8foB2PaeQ/LIilpifXO2cdzIekJzyArzx1iSAEFYUpXqr72mKY2KR8c9xNA5pg06D2xBxmYSlp9Rcz1Ud2RlFuHEpiWRnlipbXwwgRkYClpORTDRpH/I8aNGxl9TFzWuXEZ//yntIhi27+stVKU/gXsq36Lp522/+jdxsjUzNBLWfZeBJeMCkOcHMwqKCHPjxc5QW6S/b0Na5EZxdPUjuleovzk9jSR0gys1lcnnN8Q2pVAoer+bPu0j0EM0da27hkFcuRUJBCRvR06faCU9cf4yYtGLmMOiDGu/Oy1M55B34BhNaDSfBrq1qnPdC4nV8fGEtnN8cCPrRQsYTqrd1Ji8txcNPl5D41WvQvZcXJs8JYY7OqlUp1CHmQSY2fn2Z9/svd+HerAve+2gtOvYdzkzNVW9jvgj823QWC4xNdrzodTwPDAapniI2MospEtrZivkWxfli+YlH8UnxtHmDF72segWPB0yb34kZkULyx6YvIC7VryJ5r6GTWUFkJpNqoO9n2sSGVafwXZXaYkgAIEq7iYaWpoRfi7r2r1EppEtLV56dlUm1xyzddoPwGzQlFkE9ajRKZQn3kP/X95jRfixaujSrcd7E/BRMP7mcyH3d0WLtN0xob1fj8VXJu3kTt8e9R3jxEZg4JwTd+vgwo2pap6tDQmwONq+5QnZvuYWGnu3Z2A/XoMvA0czcyqb2k+sQx4ZNIDQxKwFw44Uu5DlhMEj1lFupcVkyI+EpuZEgDoRXcObM+TsBrV0MqXNqwhfyMGNRZ1ZRlkUOb/0KknL9KpM7NnKHQwNXkndNfe8IAGArYLUlNACVHlLNBkkqKYZYImEe1jUXmWaWlCO1qJQNDW1arbERS+T4+rcIOPSaQPg2NScWlERfR8HxnzA3eDL8Hb1rPLa0ohQzT35GYkmeQjHcp+bjn0AuR9y360nUosVoFWiPmR91g5un+kZNFSmJedi67ipv+/fXYNcoEGMWfI3QIeOZpW31LdnrEtqqo4RnZLQdr3jsqBKDQarfXIRS6mPn9t84Zxcr2NqbveAlvfyYmPDx/ochKBQ9Jn9tXwNphf7teM+hk1j+3TQm01B4VmhevcJ3VaRSKQiPX6vhkpVlwcfOvNaL2W/RKaR3hybEsoZt35tRmQiLzmFO1QiwVqX4/lkUn9+LjzrPgJedahmiqqy6uokcSbqI5p8vh0O3rrUeX5WSR49wb/IUUnjmX4ya1BaDRrZgJqa6bV9nphdh+/fXyea1l2Fh54tRc75ArxFTmY3D89uFIITAt2UnKZ8v2P3cJn3BGAxS/SZS+TcpKSmVhYffux/YplH9Ka54AZhZVLYc5/DPru8hk+o/s6qhO4WtfQOSF5aqkXckdDYHGFBYWFjrsQoPqfbWDSW5HKktjgQAiQVlyCopZ4O7eNVovL7aHU6k5o6w7qi6fXlVCm8cRdm1v7Ck2wdobF1zG3MA2B95FGtu/gLPaZPRZMI4Bp5ml6ek3Xtwb/oMeDgwNuvjUPgH6t43KTe7FDt/vEE2fn0RfFN3jJi1HH1Hz2L2DdTX3NOWhh6+IARp0F5Mtd5hMEj1mOj4sBwAMVCKre7Zvf9mULvGsldIvkuvWNuaYvrCjkiOvYuT+zZBLq+bbMTub41neeEpTN0070osfRyRkZmh1taM0kOq9ThR6k00sTZX0ez8WQ7EpJFBXbyIqXH148rlwPJf7hDbjkOI0Nmj1jHzLv2O8tun8VnoPLhY1N5mIjztPuad/RI23bvAf/kSZmSmmccvEeUics48XuqO7RjwdnOMntqOWdlUHxtTl8J8MfZsVbS+kLIGeHv6pxg4fh5zcq39PdCWZm27lAmMTbbW2QQvIQaDVP85DcASAM6fvZxVXFxc4EUNrZ6fxt7RHFPmBiP23jWc/WN7ncnHNPFpAUsbe5J/s/Y40NMIG1mw5ORktY6tqKioNYYEABJxHiRSKWtsVfuF/aGoCAViibxvR/ca3xwuKQ/Hb6bBaehCEKPat8byTu+A9OF1tqLHfNibqW51XpXM4mxM+3cJKWpozYK+WwsTF809ncwTp3Bn/HhYl2Rgxofd0K6zO9PHjVppsQS//3KbrPvsHIpKbfHmpA/x1uSPmEsTDWJfamBiZg7PZq14PJ7RL3od+CXHYJDqP5EAyqFUbjh79kJYYLtGL1chxQumQSNrTPygAyLDzuDy0b11Ole3QWNZ7vVkJpdo7n3xrAXIyMhQ67IplUpBSO1bdgAgE4vgU03Dvqc5HJfBG9bDmwhqyVjbfCgCRXJjuU33MWpZ9txjPxKSGMVWdl8Ia5PatxAlcikWnP2Kd7MkgQV++w2sAzXvjiwXlyN6+Wck9stV6NLNHVPmd2HOLvpJ5xaLpTi0+y5Zs/wssnJMyYD35uLtGUuYq5e/Xsb3a91ZLpPJ/gaQo5cB6wkGg1TPiY4PE0OR3OAIANu27Irwok7EzLx+SKLUNW4ethg3oy3Czx9lYacO1elcTQPawMzcmhTcSdf8XpwHGJuakIyMjNqPhcJDIjx1NuKA0rwY0tzBUi3DcTszH2UVMtazbe3SPou33ORZBfUgJk3U6z+U88dqIshKkq/oPh/mQvW24jbc2EF2Rx+F78eL0GBAf63c2vw793Bn3DjCHtzC+Nmd0HOAL+PrkCJeFalEjr9/v4/Vy84hJYlH+rz7PkbOXsHcfQO1H5QQBHXuU2ZsYrpGL4usRxgM0qvBVSg9pMyMrPLI+w8fturg9tonN3j5OmLU5Na4dmI/7lz8p84ja537vstEVx8zVqH5W2/uYYcycRnEYvVUrZUGSa1jRWm34GlrofaO1T+JWeSd3r4wqqV+KV1Uir2nE+D01jwQY/UMTM7eFTyLwny2vNtcZsI3Vuuc47HnsOLKRri+O4J4zZ7JCF+7RtfxGzaSyPkL0cLPms38OBQe3vpL5ZZL5fjnUCS+WXoWMdES0uPtyezd+V/Cq3kbjXsyNfEJAJ8vSMVrUntUFYNBejV4DEXTLUsA2Lr5l0vtOjeR61IoWN/xD3TBsLGBuHBkNyKvn6vz+fxah8DY2IwU3lNvy+1pzJvaIS0tTW1LVlFRAZ6aW3bi4gzI5XI0sjRV6/jLKSKAgHVp2ajWY/efiUFavlRu33eq2t5L9o7FxL5Cxj7tOpsJ1IhBAUB0ThxmnVoB4zYBaP7VF4xvVfu2nyrKkpJwb+o0Xv6xIxj+XmsMeTfICZ+OAAAgAElEQVSImZrrUeFEDpw++hCrl54j98ML0WXwODZ24TegLYNB1MwaDArpXWxsavY1XpPao6q8vlesV4jo+DAG4CQAOwC4eOFadnpaZlpAq9pTbV9Fgto1xuARzXH6wE/g7lx9LnN27DWciS4/BtOywy7fyVSekpKi9u9R4SGp//OtEOcxdeNIAHAmJYeM7uOr1s394i03eGZerYm5bwe1x8/6+UNeQ54pFoVMZ0Zqenr54kJM/3cZSbeQo+WGdTBzr72+qTqSfz+Au1OmwtVSglmLu6MufisXTsZg7bLzJOxyJoL7vMPeW7QW/m27gKeyE68CS1sHuDRpSgDs0/uC6gEGg/TqcAtABZTtzffu2X8+uJuHRLeGwvWPDl090OdNiuN7N+rcclxdWnTsCb6RkBRGZmk9Bs9CqHb8CFAapFqKU6tSXhBPmjlYqG0tTzzKgokJn3VoXnuGW2GJBBv/jIZD//dhZKGm1I5ciqxt84iXmQPmBk9iRM1tLTnkWHJxHTmVGY6Ar1fBrkN79eZTgbSgAA8WLCRJmzeh72B/jJvRgdnY6b+w/Nr5BHz72QVy/lQS2nQfysYv/hYtgnsyI/6znlnLkN7ljLGfALyWvWIMBukVITo+rATACSgUwLFv76EEOasoaUprr/14Vej6hje69fbCsZ3rkRQT8dzmbRf6JhNdfKzoeqcFPGM+hCZCkpWlvkGTSCSAmlt2gCKO5G1nqdHtyeW0XDKmr69aL+r0zSREJxcyx8Fz1H8TpBJkb5lHmtk2wYy2YxnR4O5p592D2HTnN3jPnQXXkcN12trKPn8Bd8aPh3nuY0xb0AXBoZ6M1BI/04bwq0lY//lF8u+ROLToNIBN+Hg9WnbuywRCRZ2UiZkF/Np0YQKh8Td6n7yeYDBIrxYXoPhMeYwxHDl87HxwqPtroW/Xc4AvOnR1w5GfVyM1gXtu87bu1h88mREpeqi9d2RB7VGQX8BkMvVTxTX1kEoKkkAAOJurl0gAAEdi0mFrZYKWPurVtS396SbhOXrCstUbahsIuaQU2Vvnk7YN/DGhlWaG5VLSDSw+vxZOA/uCfryI8YzVf23PrkMCbuUqwq1YgU6dXDF9YRe4uFprPV5NRISn4vtVl3hH9kXBr+0bbPzib9Gm+yDWpvsgGWOyA1DEg19LDAbpFUKp3HANgDMA/PjDz5H2juayho3r5of1stD/7easVXsXHNryJTKTHz3XuVt3HsBEFxN1Cj+bu9siOUW9gthKFO0pGKCBVyERF8g1iSPJAdzKKiCj1fSSJFI5vtp7n9j3GEf4tuoXs8pLC5Dz00LSuXEbMrL5II3eyccFqZh+YhmReTdG4NpvIHTQLXOuMPIB7rw3nkhuXcG4GcHo/aY/EwjV90Q1IToyAz98dZl3YNc9eAeGsubtuvGExqYr6mSyeoLBIL16nARgDABicbn86N/Hz3Xt4/3KFsoOGR3E/ALsyMFNKyHK0Oyirisd3hgKJpaTYk632kVib1xry3JVMMbUkg+qRFKYwPO3t9QoJ/33h8lwdbIkfu7qqWiHR2fhalQ2cxpauwBrVaSFOcjZ8TH6enclg3x7aWSUSqVizDq1gjxEDgv6bi0sfakmp6skYcs2RHwwB36eZpj1cSia+tbd1nc8l4N74RlMKsNhAHF1NlE9wGCQXj2SAEQDcACA1V//cNvR2Vzi5qGbLP/LyMiJrZm7pzk5sHEF8nPUTwjQCzweAjv0guh8os5D8S2EPE0SGv6DydUSWK0kN/02qL2lRr95qRy4n1PI3u1N1TYSq/fcJhUm9rDpNFQjwyIVpUL063K87d+X9PLsrLHP+fXVLeRwwjk0+2wpHLuH6pwyLU5PR8SM90n2H/sxdEwQho1rxcwt9F9wLjQ2QqfQphITE8Eneh+8nmEwSK8YyhTwvwFYAEBZaZnsj4NHTof2836lYkljZ7Rjzs5G5MDGFSjKFz33+Tv3HwlZcQUreZSr0zh8WxPweDzk5mo+DgNTuzgWAIpyYyE04sHORLOL6p6oZEKb2BGPhurV/sjlwJKfw4l18JtE6OKl0VyS9Hjk7vsSY4KGkBC3thqdCwAHo/7BN2E/w2PKBOI+eYLGiuGqSDt8BHcnTkYDYTFmLg5FUDv9Kn237eQuY4ydhOJG8rXGYJBeTaIAxELpJX337Zb7FlYC8SshusoDJn4QzKwspOTAj5+jpCj/+S+Bz0ezVl0hOp+ocyqWpa8jsrOztbqbZ3I5CNFMtaBcXKRRHAkAxFI5ovOK2Kg31PeS4lMLcOxaCpyHLATha2YAxUkPkHd4Paa2fRdtGmquYXcnIxJzz6yCVZeOaLZiGTMy1z2VW1pcjKhFi0nC99+hVz8fNmFWR2bnUHPjQ3UwNRMgpKe3xMRUsEjnwV4BDAbpFUTpJR2EQrmBSCQS+W97D54M7Vu/vSQeD5g2rxMT8IrIwc0r9d5yXF26DhyNilwxK32suzE0dbWCOi3LVaNZDAkApMWPeX726tcjVbIrMpm0pE6koQYX4W1/PUB+hYDZ9hin8XxlsTeRf3QTPgiegABnX01PR1aJCFNPLCUFDSwR9N23MG2kn8JX0ZVruP3eeGKcFoMp8zujcy9vpqakoEo69/KWgOF3AM8vNfQlxmCQXl1iAERAKbq66YftUTy+vNg34Pl1vNQnfD4P0z8MYRXibHJ421eQiPXbclztdQiF8A3siJwLuntHAAAbgTw9XQsxViiSGjSJIQFAXsY9+DlYaTxfkUSK+PwSNqKXj0bG5aMtN4hlQDdi6qG5p1Py4BKKTu/Cwk7TQB08NT5fKpdi4dmvyPXCONZi7TewaRmk8RiqB5Yi5uvVJPrTJWjX1hnvL+oG1yZqFgRXwdrWFK07NJEZm/AX62dh9R+DQXpFUXpJhwCYASAymYxt/3n38R79aYUud3QvAqEJHzMWhbDivGTy1/bVqJCUv7C1dH9rPMSZJUycWntXV3UQmqnXslwVjMmZph5SftYDWAj5sBJqLlC6+0Ey6dSiIXHQoOFdVl4Zdp2Mh+Obc8Ez0XyLqzD8X5RcPoiPu8yCh23tCuSq+PHWLrIz6gjoRwvhMnig3vThirgY3J0wkZRePovRU9uj39DmTFhDc8On6TXQrwwE3wJ4zhk5Ly8Gg/QKEx0f9ghAOJR1Sbt+2Rcnys1JbRdScwO2lwkzCyHe/7ATctJiyLGd39VJy3F1EZqYwcu/DUR68o5MXa0hlUpRUlKi1flMztTuifR/5CgTF2scRwKAnDIJkgvL2NvdvTX6/hw6H4dkUTmz7zddq+9dwdVDEN88jqXd5qCRpXYe/om4C1h+eQMajXibNJ07W2vFcFUkbt+BezNnwcdVgNmfhII2d671HFd3WzT1dSoTCIy+1NtCXgEMBunV508o6pJ4APDF56uPhvRoKquL9FV9Y2VjgukLOiIl7j5O7P2xzlqOq0vPtyeyspQCVp6pn9iVuY8d0tPTtb45UNQhaV60KStJ5vlqEUcCgD0Pk0nPtm7EWsPvz8ebbxATj0Bi7t9Jm2mRd+5XVERcYp91nw9Hc3utxogRPcL7p1ZAEOSPgG++ZAJr/RWMS7KyETFrNsnYuwdvjgzEOxPbMAurapQjCNB/aPNSgdDoAwBa3Y1QSm0ppWJK6Xodlv3SYTBIrzjR8WHJUEgKuQBA2LVw0Y2wW+E9BtAX52qogb2jOabOC0ZcZBjO/PFznbUcVxdTC2u4+wQS0cXHetvvFLpYsJSUFK3Hk2sRQwKA/MxI+GsRRwKAlCIxMovF8sFdvDT6QIrFUmw49BAO/abByFK7mrjcf7cSFneHrey+ALam2hmTwvIizDixnKSYShD0/TqYe3hoNU51pB89htsTJsJelov3PwpF645u7GkxjcA2rszGziyBEKJL++J3oVBleYdS+vLfXaqJwSC9HvwJhRKMMQB8+vEXZz19HCpe1mLZ/1qO3ziHS3/9CrxgYwQAvd6exIoTcpkkR38izMRSAG3jRwDAmFzjLDsAyM28C1sTIcwE2kni7ONSeQNDPImZiWZznwtPwYPEAub45lymieRRVURH1hOj1Hj2efcFsBRql3YthxzLLn1HTqTfQMBXK2HXMVircaodv7QUDz9dQuJXr0H3Xl6YPCeEOTortkhNzQXoPbhZubGJYCwUv0ltmQBgJRSJS4MAgFI6mFIaQSm9SymNpJR2Uz6+jFIarXz8DqXURvn4HkrpLeU5hymltsrHu1FK71FKtymfu00pbUYp3U8pjaKUnqCU6p7zrgKDQXoNiI4PywdwAEADAMjJFkl27th7tN/bzSp4daBqrAuu7oqW47cvHGVhJ/940csBAFha28PV04/kXtKfdwQ+D8amxiQzM1PrIZicMU3rkAAAcinKysuYt63mcSQAiM0rQW5Zubx/Jw+N7xSWbb9FePZusGzTR+u7jJz9q4hpbhb7rPt8mArUT7B4mt33D+GHO3vgPft9NH73HaZpZ9fayLt5E7fHvUd48RGYOCcE3fr4oM+bzSQ8HtkO4La241JKA6HofXYWwC9QGCcAWAFgBsdxQQACAdxWGpkFAFoqH+8CoHLP+QOO49pwHBcA4AGAqrVQ/gA2Kp+7BkUngXkcx/kDkAF4R9v118RrZ5AopYmU0ubKf5sprf0vlNK6UVD8/7w2lNIPNTieUUq1u2Ko5gKATAA2APDjhp+jioryMzp09XhpWp17+Tpi9JTWuH7iAG5fqPuW4+rSc/gUFMXmsIo89dqLq4NFUzsUFxezigrtZQblWsaQAEBWmgZqp10cCQAOxWXwhoZ6E2MNvSypVI6Vu+8R+9DRRGBfe0fa6sjevYRYl5WxZV3nMGMj7XesriTdwofnV8Oh3xvw/eQjxjPR3sCpRC5H3LfrSdSixWjT2pnRZg1kQmP+RzqOOhHALo7jKjNpO1BKG0FhoNZSShcC8OM4rhBAIRQ1Tr9SSicDsOA4rnK7fiylNJxSGgFgFICqefEcx3F3lf++DeAux3GVBXPhAJrq+BpU8toZpEoopdZQCJFGA5jAcZxaEXMdDJcNALUNkr6Jjg+rALADgC0AwhjD4kWf/dEx1FNWuZ3wIvFr0QDDxgbhwl+/IuL62Re9nP+wcXSBS2Mv5F5O0quBtPCy06EgVoFcLgdPiy07ACjMjiTNHC21nvteVgFKKqTyXu3dNDZq92JzcDEiizkNWcCgpUEFgOyfFxEnGOHjLjMZX8v3AQBSCtMx7cQyIvFsiMB1q2HsqH9Fk7KUFPDkFWKhMX8EgCJtx1HGi0YBmEApTQTwEIAAwDiO4+ZCYawkAA5QSicrr2sdAHwPwBVAOKW0BaW0M4DpAPoovaBPAVS1xlXvvmQq/q+/NMUq1Mmg9QAnALsB/M1x3FJAsZ8KgEIRZ4mDwkjlKfdh1wO4BKAtgJWU0h8A7ALQC4pkgTUcx/2gHGcNgK5QdG7NUY7zGMBGADaU0rsASjmO60gpnQ9gJBSfgxjA9Cp3JVCOxwOwForttvcAfFHN+OrAAbgCoA2AtPv3HhQcPHDkxOB33ui9/furArmWDeZ0JahdY/R9yxenD/yE+MhbL2QN1dHr7cmsMCoL0sJyvRoknqOJPO1Omk43hHKt0r4ViNJvI4AOJsZGPJTLtHOSjyZk8kb2ovj3WiKkGrZuX7v3DtmzJJTZhAxn+Rd/0/K9lSNr63ziNn0DW9hpKvv68iYiZ9q9FrFUjA9Of04WdpjCgtavJQ9XrkLRQ/1Jy7mOekfKNzU7AYXOpC68CSCa47iQygcopcEAdlFK/+A4LgJAhHJ3pS2ldB8UXtEFABeUxzaHwigWABBRSo3x/22/F8rr6iHtRxVjpKSm/dQAAHs5juvAcdxR5WNmHMcFA+gG4Ksq22tfcRzXluO4QAC/Afha+fj7API5jgviOK6j8rFdymNbAlgCYPNT6zRRrlUGYBTHceU1jF8rymLZA8rxzADgy5XrwsvEhekdQz1fyNZdu84e6PuWL/7d++NLZ4wcXNzg6OJGcq8m63370Mhcs5blqpDL5Vpv2cmlYpSVi5mXrfax6WupuZAxxrq20k5s9JOfwol1+wHEuKG31muAXIrMrXOIj2VDzG4/XqOus6pYfX0rOfjoFJotXwKnXj30codm4d0ULn3eKOObm03Rw3DjAeyp+gDHcdeguJZvViYz3IXiZvlrANYA/qSU3qeURkJRhHsIwHEA8VDsEB2HDjEtffK6ekj/ABhBKd3EcVxlmtNYSum7UHge5lBI71QSq/zQq7IPADiOS6SU5kHhDkcD6EspfR8Kte3a3t/WlNKPoQhQygH4PPX8vwD2cRy3pspjmoz/DNHxYXm+Xu1/ATATQAJjjH24YOkfu/dumRkblcXLTNd6N0FjuvTyRsfQJji6cz1SH718Qsc9h05iBfczICuR6NUgGVkIIRDwSU6Obn2U5HJGtN2yAwBZaQahdhYsKqdI69d3MjmbvNvHF+duJWvcwT0xvRBHriZj4NAFSNk8G6xCSwWOinJkb51LgqZ/zya3HsW2hu/R6fM6/PAEHuUlY+GEicTcy5MlbP2ZQK7d/RrP2Bg+C+eLjYyFkwBk67IuAOA4rm81j9ckq96+msdHVDPWeSh2USr/vwOK7f7K/y+veZXa87p6SN9A8Qafp5S6qLGfqqoS8pk9VUppEwDrALzDcVxzKNxglVFS5V7wQQBzlMf2gTItuwrnAPSpTLHUZPxauAngBpS1SQ8iowv3/fbH8cHvtHhuskI9+vsiuJuy5fhLaIwauDWFnVNDkndd/96RJXWAKDeXybW8yFUi08FDAoBi0UM0d1SvpUR1nEnMhkBgxIIDtBMv/eXvKOSKecyu53idvBG5uBhZ2xaQjq6BGBM4RGfP5l5GFOaeWQWLTu3Q/IsVjG+hXZy1ycTxUoGlxd+Ex9uv65peB15XgwSO474EsBPAeQDu0M9+qhUUAcUMZexnWpXnCgGYUUorb2lNoPBwKtuczlAx3mcATgH4l1JqVcv4aqPcuvsVgBQKbxCrv9pwp7A4P7XrG951LofQf2hz1rqDCw5vff4tx9Wlx5CJLC88lcnK9F8/bNrEhuma0AAAchkj2saQAECUdgsNLU0JX8fU/0tpIjKmn5/W5y/afJNYNOtMTD11Ez+VF+ciZ/si0tOjExni11dno5RdKsK0E8uIyN5YoRjuqllWoF27tnDs3CmPb2Y2Sde1vC68tgYJADiOWwVFcsJyAHnQcT9VGVA8AEUM6iyAhCrP5UKx9xtBKb2qTMlcCuAmpfQiqpEQ4Tjua+WYpwGkVje+pihrk7ZDoXNHGGOYM3PRwaB2jSR12a75rVGBzK+FHTm4eSVy0p9vy3F1aezdDFa2DiT/ZmrduIu2AqZNy/KnUXhI2m/ZSSXFEEskzMNatxrHv2IzYGUuZK21/N6ICsT45XgcnAbPAc9Ut4xPaV4mRLs+xZu+vUhfb927xkrlUnx0fjW5kh/NWqz5GratW6l1ntDeDl6zZ5YLzMzehOJm1IAakBctyWLgxeHr1Z5A4WW1gsLYYeiwgW6LP5k/5qf1V/iF+fqruwGAERNasUaNTckfm1ehKE+3+EldMnb+aiaJLkbede1lfWqiyex22LlzJwoLdbtODRkyBCZIRFrsP1qP0bTtLHYuywjH4jN1eq0j/VzhIRSyD769oPU462d3ZM7iBGQd/Frn993Y1Rf2o5Zg++397HziNb18jj29QjCh+VAk79vP0g4fqX5MHg/NvvxcYunhsdLI2Phzfcz9uvBae0ivO8qtu70ASqHIxsEfB/5OOn3q3IW3x7bUazxpzPS2rIGLQNFy/CU2Rp7+rWFuaUPyw7XrUVQbQmdzgEFnYwQAMpkMPB5fpzvKklyOBOgYRwKA/Q9T4GxvTpp5aid8CgAfb71BhG7NiXmzzjqvpzwlGnkHV2NiqxGkg6t6Xk1tnI6/jKWXv4PLsCHEe/4cRgQClce5jhwuN23Y6LaRsfEXepn4NcJgkF5zouPDCqCokbKFosAOn3y08kqpuCC1Rz+ql3jSxA86MGsrmaLleOHzbzmuCV36v8tyryUxVlE3oTRLH0dkZGboJcVeJpOB8AQ6GSRR6k24WZsTXe895ADuZudjdB9frddTKpZi/cEoOPSdCiNL7Q1bJWWP7iL/rw14v/1YtHRppvN4ABAnSsTMk5/BKICyFqu/ZALbJxvz2bRqiYYD+xcbW1m+CQ216pQqMpWac9FKLTkBpXQapXSu8pj3KKUH9fJiXkIMBskAouPDYqCod3IFAJlMxt6ftmC/f5BzuTq9XaqDxwOmLejIBEYl5OCmlRCXPL+Ucm2gQcEwNbUgBXcz6izVUNjIgiUn6ydzTyqVgsfj6zSWRJwHiUwKNyszndezLyoVXq42xMtV+7YOl+6m4t6jPOb01jytBVirUhJ9DQUnfsbc4Mnwc9Sh3qkKhZJiTD+5jPdYKGZB36+DRVNFxrWJiwu8580pF5iZ9YNCpksb3lZqzjVT/hnCcdxmjuPW6WXxAKokVr10GAySgUpOALgDoBEAJCelln3+2eq9A4YFVGgjLVTZclxaLiKHt375wlqOa0KnPiNZzpXHYNK6qxHmWQuQkaEfg6fwkHTbsgMAWZmIeWvRsO9pxDI5okSFbNQbVKc1fb79JoGtK6za9ddLgLv47hkUn/8NH3WeAS+7JvoYEgDw2eXvecdTr6P5Fyvg1LMH6JLFFUTAnwuFGoqumCj/5FFKlysVYCqxopT+Til9QCm9QiltAACU0gBK6SWlOncUpXRO5QmU0h2U0g2U0n8BhOlhfXWCwSAZAABEx4fJoMi6K4ZSgPXoXydS9+z+/eg7k9pUmGnQkO2/luP5KeTIz9+80Jbj6tK8fSiEfBNSFKG9+nat8ABjUxOdFRoqkUqlOmXZVVKaF0MCHCz1cvH/NSqFtPB2JK5O2hs4qRz4fNddYtf1HSJw0E4F4mkKb/wN8fW/saTrB2hsrV3NlCr23D+M72/vhufkCeCZm58UmJhs0nHIg0qlhQwACRzHnVRxTFsACziOawYgCsAs5eOJAHpyHNcKQDsAUyilVfPxgwEM5TiutY5rrDMMBsnAf0THhxUC+AGKBAchAHy75sf7N27cChsxvnWFEb/2r4uZeWXL8VhybOf6F9pyXBM69BjKci4lgmmoyaYJ5u62KBOXQSzWT/aiIqlBdWBdE0Rpt+Bha6GX5gvFEini8orZyF4+Or2REfEinLubwZyGLgT0YHQBIO/iPkjunMZnofPQwEJ/Aqpe1m7yQknZfTMbmyF6GK5yy84RgElVL6cKVziOq6yZuA6gUqXBDMDPSvXuKwAaQtGGopKDHMdp1aH2eWEwSAaeIDo+LB6K2ixXKL8fc2d/fDa/IPvRoJEtarQuVjYmmL6wE1LiI3Bi70bIZS+25bi6tOzcF0YwIkUPsup0HnNve6SlpeltP1AZQ9J5HHFxBmRyORpZmuphVcDuB8mkQ/OGxMlWt/HW/36PlBlZMpuuI/V2l5B7egdk0WHs8+4LYG9qq/N43dyDWTf34DxbK9vuUBSt6wWO48QAjkKhSfc01Slvr4LCs2qp1Lq8gdoVZ14qDAbJgCrOQ6Gj1wRQJDlMHD/zoK0DP7fLG94qL6h2jmaYOq8j4iOv48zBn154y3FNaNN1IMu5kAjU8ZL5TqbylJQUvf3mKioqQPSUmy8V5zEfPcSRACBXXIHHhSVsWA9vnd/RT7aFE+s2fYlxI6qPpQEAREc3EvI4in3eYwGsjbVvwdHMiWJMiyESU6FpCACR3haI/1T+u+JJTc3asAGQzHGcVNnzTff8+eeMwSAZeAZlfdJ+KJIcXAGgsKBIOnXSnF1B7RqWBbRq9MSFxrmhFSZ9EIwHN8/h4kvSclxd2vV8E0QCUhxd97VRPAvdFb6roq8YEgCIC+JJMwftG/Y9za9RKSS0tRuxsXxanlEzkjKLcOhSEpyGLgDRoTvs0+T8sZoYZ6fIV/RYAHOh5hmGHrZumBc8qQIy1k8gEOhTjLEyhhQJxfV5hQbnrgQwmVJ6Ewo9zot6XNdzwaDUYKBafL3amwFYDMAeQBYAhHTp4Lh+w1cT/94XYRz7MAuu7rZ4d3Ir3L5wjIWfP/bSdHlVCx4PUz/5Edkn41ESl1u3Uxnz4T6jLTZs2ACZnrYyAwICEBIcwB5e+1bn993M2g3uraZj9qn7+lgaAOCTDj7szr10bD8apfP6ti7szMwy7jLRsY16vYl2HP8VyzU1xZKza4hYql7yTQMLR6wInS8Vl4jHNHB03qfP9bzuGDwkA9USHR9WCuA7KERYbQDg8sXr2cs+WbV70DstKjr1aIrRU1oj7OQfqHfGCECnPsMgL5WyujZGAGBB7VGQX8D0ZYwA5ZYd0Y+HVFqQBALA2Vw3j6Yqv0Wnkr6dPIi5qe6JFx9tuUnM/YJ5Zk31myCW/ctHxF4qx6ddZjGBUe3rtDO1waedZ0vzcvM+Mhgj/WMwSAZqJDo+LAeKjrkWUCqDHzt6MnXV59/82SnUDfevncH9a2de6Bq1gcfjIaBtd+ScT3guhtTc3RbJKfoVk1UkNehP30kiLpDrK44EAI/ySyAqLWcDQzx03obJLRTjp2MxcBw0Gzwz3aWOqpL100LS0MgMi0KmMyNS/SXRytgSS7t8IC3ML1zv4eq+Vq+LMADAYJAMqEF0fNgjKIySIwATQGr758FffT9fvizMv02XCseG+is2fF50GTgaFfnlrDTx+UgZEXtjuT4UvquiTGrQ23iSwgSev72lXquCD8akkbe6NSUmQt3XeexKIuIzS5nDgJn6jTPIpcjaNo94mTlibvAkRlQkwJsLzbCk82xpSUHxjqZungv1Or+B/zAYJANqER0fFglgIyBzNyJF3QkksX8d+v3fbVt+PDRg/DypfYPGL3qJasPnC+HbMgSi5+QdAQDfQsjTZ/LzaUgAACAASURBVEIDULllpz+DlJt+G9TeUq/XhMicIhSVV8h7d3DXixH5ZMtNInT1IxYB3fQx3P+RSpC9ZS5pZueBGW3HPrFWc4EZPgmZKS0vKjvs3aSpPtqQG6gGg0EyoDbR8WE3eSg9SFD+mBA5BwA/bd4YvXP7T4cHTZgvtXPSXwV8XdLtrXGQZJeyspTn06aGb2sCIyMj5ObqN1albw+pKDcWQiMe7EzUV+VQh78eZfCG9/QhfCPdLzdiiRRrf4+EQ59J4Fvrr7gVAOSSUmRvnUfaNvDH+JbDGQBYCs2xtOscqaSw/JiPu/cI1HlxwOuNwSAZ0Iio+KiDhLB1UKSDCwBg04b1UXt27/pr8KQPpU6uHi92gbUgNDGBd/N2zy12BACW1BFZWVl6v5ApPCT9/oTLxUVyaq+/OBIA3EjPh0QuY93b6EcG6GpEOsJjcpnjW/MZ9Pz65aUFyPn5Q9LNrR15L2gYW951njQvU3TY38v3LWhpjJSK3csppZxSf+4upfQgpdS/lvNWUEpHaPVCqh8ziFI6XJ9j6hODQTKgDWeh6H7bGMoq8Q3rVkds3fzjwYHvzatoQlu80MXVRPchE1lZWiErz3h+Reumja2gj5blTyORSPRukKTFj3l+9vqrR6rk38fZZFRvX/B0bJdeyaqdtwizagCrdgP1vlZpQTbyD61BqGdHkpWW+feAMW/q6hn9AqAFgPZK/bmWAH4HUGPfd47jlnIc97smE6mh5B0E4KU1SC+tDLmBl5eI2CQW4O12EorvzwgASQCk27du4rIyM3d9uvzz0df+3S+MDr/8UqWCm5hZwIMGkZTfIp7vxDYCefrddL3f/NWFh5SXcQ++1E/vn9uFpBz0d3diIYENycU7qTqPJ5UDy3fcIV9NHYGyR3dQkZ2kh1UqEDg0hk3/mbKwG9e2j5o9fmpaeKLWxohS6g3gLQCuHMflAwDHcQzAAeXzOwDc4jjuh6f//9S/e0BR+GoCxe/uC47j9inPOQ/gKoD2AMSU0sEAjkFRP2gKhYTQVACWUBTaWimLby9yHDdb29dWFxg8JANaERGbxAD8A8WdnhsAYwA4euRQygczpmxr2+Ot0tahA+uuj4MW9Bw2mZU+zmeS7OerLyk0M+alp6frfVx9x5AAID/rASyEfFgb6/9e9XxqLhndxw96UXEF8DAxD6fC05nz0A8BI/2s17ihNxxGLZedvRm2RldjpKQlgFiO4/J0HOc2gBCO41oC6AlgDaW0qhhfcwC9OY7rD4W+3SiO49ooHzcCMIHjOBGApQBOcxwX9LIZI8BgkAzoQERsEouITToGYAsAFyjrlMKuXRG99+7wzZ4B7fO6vfWelPBe/NfM3MoWbl7NiOjS4+fqtZm6WkMqlaKkpG6MIGNyvWbaAXKUiYvl3rb6jSMBwD/xGTA3E7C2/g30NuYPB++REmLGbLuO0nnrztSrJeyHLZbtP7z/6/49+32kB2P0DJRSf2UMKYZS+p0GpzpCISsUCUXvMjsAVQX+9nIcVyl+zAOwQOkF3QfQHYqtupeeF3+lMFDviYhNugJgDRRqDjYAEBcbUzxy6KCtZnYuaX1Hz6rgC/SbuaUpvYZNQXGciFXkPt9GgeY+dkhPT6+zzCzG5Hr3kmQlyTzfOogjAcC1jDwytq/2bc5VsXjrLWLVujcxaVxjSKZGLNsOkNv0e19y8u8/p4wbO+UTPS7vDgBvSqkNAHAcF6VsL/E9FG1epHjyOlydYN8mKESPA5Tnp6B6Je9RAEIAdOY4LgDAjzWM+1JhMEgG9EJEbFIkgC+g2B5wBICc7GzJyKGDdhWWVcQMnrSowsRce2VlXbC2d0LDJt4QXX6+3hEACF0sWEpKSt3Ny5jeBFYryc+MgL+DVZ2s+VB0KhxszEiLpg56GzM1uxj7zyXCacgCEKGGLS94fNj2my7jNe9ZvGvLhn6D3hm3XW8LA8BxXCyAIwC2UUqr9nY3V/4dD0XDPVBKXQCEVjOUDYBEjuMYpbQXgKY1TGsDIIfjuCLlnKOqPFcIhSF8KTEYJAN6IyI2KRGKwGsxFM3BUFZaKhs78u2DD6Njbg6d9sn/2jvz+Kiq8/+/7ySTPZAFZAmbS3iACFQEXKKIyCIquOFW1LrytdrWpdUvtV9btK0/bdW2tlZba10rftUKLsj2BUWkQJDNEOEQkJCQkEBC9mSSzMz9/XHuwBATwjKTBDjv12temTv33nPuTGbuc85znufzNHVErtL462bYVZv32N7K9q9cayW6Ccf6UYDQu+xgX8lGkmOiiHOHtl0AP7BubyU3XxraWdLbixQl1T47ddLdh92uK64L3W/6pbfMnVr09GMPZ/7Xw78MlwbWbcAWYI0T9v0lcDZ6lvR3oI+IbHS2m5cXD7yfmeh1o5XANLQrrjXeABJFJAcdPLE8aN8SIF5ENorI88f2tkKPibIzhJTs3PySoen9nkSXVT4NyLdtm/vvm7H4oUce3XvjjJ9ftuyjN93bvs5ql+tJ7ZFGj94DrJ3z17ZLfwcR6SI6NtoqKQlfWXTbtnGF2GWH30tdQ52dnpxgbdxTGdq2gdnf5PPsxUOtgf2S2Zp/rGv9B5j5Upb16qNjiVOjqNu65pDHRvc6ndRrHvGuzVq19tlZM6/64D+bQiujEYRSqhF4zHm0RGuzolNw6iwppRYD6a20P7bZdiU68KGlYyuB89u86A7CzJAMISc7N78SeBbYCJyK8z177ndPbpj58IP/PHfS9TUXTpnudUWEfgTenPHTZtiV2cW2tyZkxTwPm4QzUqipqbGbmprC1ocdBpcdgL+2CEkJzzpSkx82lVbb0ydJSNuvqGnkbx8ruk/5MRHxrXul4oddbHe74X+877/z1pu/vH/G+HAao6NFRBaiw7TndfS1tCfGIBnCQnZufj3wAtpFMAAnLHzp4oXF1111+Quxyb3yr57x86aErilhu4ZT+pxGao80q3xVGNdwDkH86SlhSYgNxrb9dqiDGgCq9uZYGd3Dt+b3Vk6+NeS0VKtfz9D2sWDlTnKLauxuU3/yHWNnRbhJvvS/vNHnXFv/zKyfz3z1T7+bsWRzUacs662UmqSUulApVd3R19KeGINkCBvZufle4E3gZbT7IQWgeHeR5/qrr3hrzdr1X1z/o1lNpw0ZEZb+x19zp12xrsj21YVvhnIoIrrHhFzhuzm2bYesJlIwZcXrOCU+xooOgf5cS9R5/ah91fZNE0M7SwJ47OUsK7JnupUw/JL9bbu796PH7b/zFltdds+89/arln4y57klm4u8h2rH0P4Yg2QIK06u0nLg10AjkAbg8/nsXzzy0JeP//LR1zOvmF47Zuot3ojIYy/kFqDP6YNJSj3FKl9T2GFqERHxoS1Z3hJhWUMC/F4P9Q0e+/Tk+LYPPkreyimwRg3uYfVIOfIS4ofC0+jn97M30W3iHVZkUg8SR15mnzL9Ce/cuXOWPHj7jWNeen/B4iWbi4xIaifEGCRDu+BE4D0ObEKvK0UBfPrxh4XXTp38Z1dc8vbr7vtVU0qPtJD0d/HU2+x9awptv6djBsERCVG43ZFWaWlpWPvReUjhiU3y1RVb4VpHAqho8LKjsta+YfzAkPex+pti1m/bZ/eZ8Qd8g8ZW/s+P73rmlT8+fcOCjXl5oe7LEDqMQTK0G9m5+dXAX4C30coOyQAlxbsbvn/dVe/Mmzdv/tV3z2wadclV/ojIo7/J9h80nISuKVbl2vC6yw5FonSjbN8+2+8Pr3qSdtmFJzikpmwzZ3YPbXXW5ryVU2CNOauPldIltHmbF41IY2C/ZN+qFctzZ1x3+W3rVi5/bMnmoqMKGRSR1Y66wjci4nWebxCRV1s4dqyIfHXs7+DkxIR9G9qV7Nx8H7BwaHq/rcAP0Yrhu2zbtp956jfrP/lwzrYn/t/TU2+8/zf9P5/zmrvw2y1H3MfYK261960qwN/o6zCDFNs/yd66Ky/s/fj9tm2FsIx5MGVFX5FxxmTL7bJo8odnolRS10hhdZ197cVn8PKHm475fSQlRHPvNUP8A7pH1f5+5n1zVyxZ8PiSzUXbj6VNpdQ5ACIyAC12elzI8ByPGINk6BCyc/N3DE3vNwuYDlwIFAN1WzbnVF9/1RX/umPGD+WuGT+8smDbJvd/Pv3fSE/d4QVDnTFsNHHxiVbJhiM3ZKHESnbbRd8Uhd0DodeQwvMz9jbW4GlstE9Nire27gtfMNrsLbusn52bzjuLFdXHEIBy0Yg07pqa4V3w8Yd7H3/uieeaaiv+umRzUV0IL3U/InIZ8Au0JE8j8KBSalWzYyJpQXVbKdUoIrehFRTK0QKoFcC1SqliEYlCexLGAnuADUBPpdQ0EQlIAcU7ff9dKfVHp7/XAA8wED3QWwn8wFF3+D5wP46rHPiZUipcicBHjXHZnWSISJ6IbHEytbeJyIci0mainIi8JiI/cp7PEpFnWjnuHhF58HCuJTs3vw74B/rHF48u+ucC+OffX1RTJ1/yx/Kapg03Pfhbr5x13mG9vwsnf98uW5Fv200dKzTujg+Pwndz/P7Qa9kF4/PsYWAY15EA8irr2VPbYF855vSj6mdAry48fd/5jdPG9Kl88OFfznngsecu21Ce8GwYjdHp6CTXyUqps4G7gHdbOLRF1e2g/aPQhiED+AadTA66VEQ/YAg6wXVk0Dl5wHil1AhgNDBDRIJF/M4ELgMy0GoQgQTZhcC5jlr4jcDrR/7Ow48xSCcn05RSw5VSZ6C/mJ+KyDmhaFgp9ZJS6g+He7wThZeFlkb5EuiPs7ZUundv4z13/WDerx6d+eqwCy8vm3rnw01dU3u02taQUWOIjoq1qr4u7tA6TFE94sGGqqrwl0j3+8MT9h2gtkxZZ3YLvwbhu1uLrKljTrdij6DsRXxMJDOuzPA9ec+5TYvmf7J+zMRrH52/eNndxduzNhRvzwqnEZ0EnA584Shq/wuIFJHmX862VLdXKKUKnOernDZBKze8qZTyKqU8wOygc+KAV0QkG1iBlugaHrR/rlLK46hDrAtq83RgoSMn9L9ATxEJnex6iDAuu5McpdQHIjIa/cOZjhZIvQg9tc8GfqiUaslfkyYin6LlgbYD1yml6kRkFpCglPrZkVyHE/Dw2tD0fv8B7kQbpiKgaeH8T4o+W7Lor7+Y9Ztzpt372MUbv1wYseHLhS5v08HqC+eNv84uW77Tsn0dG9GbOLA7xSXFftphwOf3+8MS9h2grHANgwZcYkVYEM6PdUtZNZWeRv/k8wdYH3y27ZADCpfLYsKofvbNk8X31bqNeeMu/+kXBbt2vwisDbMhCmABC5RStzbf0Wy2Eqy6XS0ij6LdaQE8Qc99HLgfW7ReofZJtHv7NqWUV0QWcbCSd2ttzgZ+qpSaKyIuoI5OqABuZkgG0IKOGcAjQKVSarSzcFsE/LyVc0aif3CDATd6LeiYyc7N34p2h/wb6An0AGhsbPT/6tFHVt55600vdOl1+vabf/p005BRF9mBm/HwzElEWm6rKmdPKC7jmIhKS7ALCgraZZbm94dHOihAo6ecRq+Xfl1CmyvUEnO2FbuuG5duuSNbvy2dNbA7f35oTNOEEcl77/3JzAU33/GTXxfs2v1g8fasr9rJGAEsAi4VkYzACyIyqoXjDqW6fSg+A24WkUgRiUFXZQ5us8AxRmei118PhyRgh/P8ThzllM6GmSEZQI/IAKaiyxtPc7aj0Xp0LbEwUJJZRFZzwDVwzGTn5jcCnwxN77cWuAVtLEuAuuyNGypvuGbK25dePqX3fT958LLvXXjpKasX/ds9euxUu2xZnkWYosGOBFdXN8XF7eM29Pv9VrjCvgN4PaV2ekqCtaMyLEsy+1lXUsG09F72+FF9rfkrdx60r1/PRO64YnBT3+6xjc89/7es19/+4HPbtt8s3p61o5XmwoZSKldEbka7zmLR3oQVQHNF1zeAKx03WSFadftw6mO8hHbD5QAFwFq0qw60mv6bTv/bgS8O87IfAOaKSCGwDEe0tbNhDJIB9OJqIGH1XqXU0sM4p7lr4AgL0bRNdm7+7qHp/Z4BzgVuRkcr7Qa8C+Z9XLTw00/+cdll11xx/89+OiQqPjbW3+gL9SUcOS6Ijo0Ju0JDAJ/ftsI5QwKoL99qDe12lr1ox56wG9lP80qsmyYNYtHqfHx+mwG9unDThPSmM09L8c9+78ON0559cW29x/MvYFXx9qx2/YcrpfKAbs7zReiZUvNjPscJQmhDdfs14LWWtp0ovAecmVU08BG6jARKqfXowIWW2ryttW2l1JtoGa8AoSxCGDKMQTrJEZEr0flAlwITgIdEZKVSql5EEoE+SqnNHXV92bn5fuA/Q9P7bQImApPRZXV2Y1txyxcui1uzdM1fp9/9g77X3HDtxK7npsVVrNwVVfdt6MoaHAnxA5Kp99Tj8XjaPjgE+H3hXUMCKCv8ijPOudA61MJGqPhy1z6mnNbTvnGiWKf2Smga1D/Z/94Hn6y//fa/ba2srJ4LLCzenhXeqVrn4P8cYxQD/B9BxutExhikk5P3RaQBHWr9DXCZUmqViKwFZqELifnR95/HgQ4zSAGyc/OrgPeHpvf7HJgCjIl2xaZHELnL9tk1b7302ua3X35zy/QZtw6+5oZrJyRl9omrXlsSVaP20p5BDvHpqRQVFbVLQAOAz+8PWx5SAE9tCT6/n7TEWHZVh7cE/ODURBr8fv+l5/b1vfv+h2umT//7jpqa2uXAnOLtWXvD2nknIpCMe7Jh2XbH+9wNhiNlaHq/tISI5PtjXPG9LMuqQScQ2gCuiAhr2i3XnzF12lVjunXv1rNqY7GramOJqz1Uv3vfMtS/Ometa/369WHvC+Dqq68mziqwC3M/Cas77YxzH7Y/LWiwlu4MvU2IsGBkr2Qm9E3xxmDXvvX2++v+8rc3dtXXe9YBH3bEOpGhYzAGyXBcMy4jsz9wJTACqCfIMAGMvvDc7tPvvCVz0NAhGTW5ZVSvL45s2FMbtuvpc+/Z9py5c6z2SIoFmDp1Kl2i9ti71NywGqS+g6+hOGqQ/ee1O0LWT2yki8w+qfaE/t28e0tLK5557sX6eZ8u/czv968DPirenpV3tG07gTbR6ICDgeg1UoD1Sqnbmx07FnjGSWA1dCDGZWc4rlmas2In8Py4jMwBwBVow+RHR+U1ZS1ftTdr+aq5vfumLbztvjtHZl59wXneioaImg17omq3lYXUneeKjiQqOsras6f9Qs+9Xi+uWLfNgUjJsFBW9BXpI0Ydcx8WMLhbIpm9k5qGnpLk2rJ9x7c/fWzWN0tXrCr3erz5fr//leLtWfnH2o/Rnzs+MQbJcEKwNGdFHvCXcRmZPYAx6OimKHR4a01RQWH9kzOfWB4dE73ixjtvHjxx8sTz+o8/rUe12kttTmmkZ/exF+ZMkFQqKyptn6/9RF19Pl9Y85AC1FXmYwE946Mprm044vN7xEdzfu9k//lpKb6a2tqqpcu+WP/Am7P3FBaX1KGrCi8t3LgirJbc6M91foxBMpxQLM1ZUQK8Ny4jcx46nP0KtOpDLVDW4Gnwv/7CKzmvv/BKzsCMQV2uu/WG4aMvPWdUpCsyunZzaWStKnM1lh1dEFf8gGS27ypo+8AQ4vV628UgATR6Kv3pKQmuwzVIyTFuzu6ZZI/u0bWpW1yUnbV+48YfPf9s7vKsr5rQ/495wIqdq5eFXWMpSH9uklKqyklqnY/WjAsmoD9XJiIWWlrrDnRuEOjv1DClVIGIvIzWn/sFB+vPRQKfA4H69Xlo/bkGEUkAskRkYVD06pnoAZQfWO88X4zWn5vtGCdBG+4+ofpMOiPGIBlOSJbmrKgDlo3LyPwSrSYxGX2z8AN7Ac/WnC1Vv/3vx5dblrV87KXjek+aetmwodOGDbM9voi6LWXuuh3l1hGtN6VG+Yuywq/wHYzX68XlCr+KAkBj1Q5XRupp/uUFZa2+x6RoNyN6Jtmje3Rp7JUY68reslW9+s9/b37no3k1DY2NFrANHca8YefqZUc+1Tp6gvXnAq8dSn9uMloMNRktsxOguf7cBOf5fv05wCsiszmgohAHvCgiw9Hfv4D+XMAgzXU06xCRgP7cYufvbBFJA5pw9OeUUu2T5NYBGINkOKFZmrPCh17Q3jQuI7M3OmnxEuAU9I98r23b3s/mLyn6bP6SIldExMKJUyb1vWTyxKEDrxCJjo6Krd1Rbnt2VLrrdlZwqAq07oRoV3slxAZoL5cdwL7d65Bhww8yRi4LBnSNZ3Bqgj00NaGxV2Ksa9PW3K1vvTE3++25H9d4GhoigUq0Efpq5+plHXUzNfpzxwHGIBlOGpbmrCgCPnLceelAJnAO+nfgAcr8Pp9vwdxP8xfM/TQfmHfmWcOSL7l8whlnnX3WkP4Tz+7rKa3xevKq3HU7yl0NJTX7b0GRyTFERESwb9++dn1PeoYU2S5rVtX7cnG7XJzZvQs94qIZkhLXkJ7aJbKiuqZ6S27u1rfmrf72nY8+qaqr97gBL1oj8Utg287VyzpaRmMR8CsRyVBK5YDWn1NKNZf7aUl/7nAqwAb0595Ff59uQGtBBtr8upn+3NuH0eZxoT8XSoxBMpx0OLOmLcCWcRmZ/wIEbZxGoF02XmAf4Nm0/uvyTeu/XgOsiYuPixg/ZVK/88dcIDJp0KD4LvEJNbsr/E1Fde7orjHs3bM37NFuzQn3GpIrIoq4Ln2J79rPjulyaqPf9kfdOri3Z8u2bbmfzVuy9f5F/5f/7c6COPTN0ovWXVsFqHZ2yR0Soz93fGDykAwGh3EZmXHochrD0MXPuji7atARVQdV/euZ1is2/azBE4aNHJ445oIxSV1Tk1Lq6+t9JSUldklJibu0tNQqLy+nsrISvz88BQOHDx/OeaMH21tW/fGYDWFkVCIx8d2IjutOTHxvb1zSAF9cQnf3rsKdnk1bdxRt3bFr/eIvVhR+k7vNQn82NtrFtA4dHaZ2rl7WPppJnRARSWyuP6eU+kdHX9fxhDFIBkMLjMvItNDlLwaijdMg9OzHB1SjjZS/irrJHhpzbMiPiIiwRo4cmTJ69OheQ4YM6ZuWltYrNTU1JSEhIa6qqqpp37599r59+9wVFRWu8vJyampqqKmpwettfV2qLTIyMhiTeZa9eeUzbRoky4rAHZNE1P5Hsu2O7d4UE3+KHZvQLdLv8/kqK8oq9+4t2ZOXl1e47quVBSuWLykprfWeVU7XZC/ub5ymtqNnALlAwc7Vy8IvgXEcEJSMG9Cfe8AJcjAcJsYgGQyHwbiMzBj07GkQOky3v41NBbXnN+L9Eu3ia1HoLSEhIXLEiBEpgwcPTj311FNT+/Tp06tbt27dEhMT4xMSEmK9Xq9dW1vrq6urs+vr66mrq4tobGx0eb1el8/nw+v17n8Eti3LwuVykZaWxtCMgRRtX4grIoqIiCgsV5TfckX5rYgof0RkrB0Vk0R0bFJEpDsmwlNfXV9TXVldUVFRsWfPnr0F+Xml27dtKduUva5sd9GuevQNtQvaTWUDts92Fe2ja1EdcZ8DeSfzLMgQXoxBOoEQERtIDK7wKiKlwEilVJ6IPAC8rZTa4+wbC0Q5UvrH2vcAdEjvJvQ6TC262uyGY2x3FkdRgTbo/DzgCqXUpjYOPSLGZWRGAWkV1H6vgaZU9DpUKvomHoi4qnMeHlqJwLIsi969e8eeeuqpCT179ozr3r17fEpKSlxCQkJ0dHS02+12u6Ojo6OioqKi3G53lFsTWVW5LzU2NqY8MtLdEB8fG1tWWlLq8TQ0eDRNHk99o6e+rqm6uspTVJhftePb3KqdedtrvN6mwHXEoI1OHDq82e9cdy1acHcTOo+m6ItV2xpbuHSDIeSYoIaTiwfQroRARvxYIIEW6rq0hYhEKKWaR05VBCRaROR+4J/oQIHg8yJPBDfG0pwVjegIqP3CnxkZGfHoCrepQC90Qm4fdAa+7TwCYdMNQKNt202FhYWNhYWFpRxBdYf4mKYpURH+1S4XLakbRKAX7QPuoyh0Pk1XZ78LrTaQB+xER4OVAaVAxRertoV9lCoivwFSlVI/dLavAD4GzgyKgvsEmKOUeuUI204CZiilftfK/gEcGDyB/pxeVEo9fzTv5VgIkjbq1t59d0aMQTpJEJFfoBPy3hcRD/AD4B7AJSLjgXeAZzi0bMqN6KTSIegw1EPNfhaj8y8Cs5RXgHHAt861zEa7hmKAeUqpR5xjuzrHDgHynf5KnH1RwG+Bi9A32Wz0LKxGRGYAD6Jv9C7geqXUFudarney6nuhRTT/cuSfYNvk5OTUAt86j/1kZGS4gRT059oFbRhOQRd7S3ZeS+RAhJ7d7O93sG28WKRwQFYmYOgsdH5VOXqGU+I8ytH5QJVAeSeY9XwO/Dlo+yJ0mPhYIEdEItD5QD85iraTgEeAFg2SQ/DgqTuwQ0TePZGTTo8HjEE6SVBK/VZE7gamBdxXIvISQe4wRyrlULIpFwDDlVLbD6PL69AyKAF6KaUudvqJAaY4hsQNLBSRS5VSC4BfAlVKqSEi0g0dwfWu08YjQKVSarTTztPAz9HSLb9Hj64LnCin4Kp1cUqp85zR6CYReS3YrRlucnJymjhgGFokIyPDQo/UIwG38zey2baFdq35vT6XOzbKX4vWZGsK+tv0xapt4QnpCy0rgFNFpIdSqgRtkJ5AD5ReAM5Cfw++FZFeaOPVDz1Qmq2UetJJFv0LeqDTANQopTKd85NEZANQp5Q6v41rSUR/fvUArfXn7MtDh4ZPoNkAR0QuRGvW2ei8pKuAy5VSm0TkGQ4MpEqBO5RSB9dpNxiDdJJwuC6YtmRTvmzDGAVuAhZ6lnBb0L43gp5HAL8XkfOdY3sC3wMWoCVYfgyglCoVkQ+CzpsKdBGRac52NLDReb4UeFVEPkTPuIJnKe847eWJSDnaA+zDuwAABjhJREFUjbaFTkROTo7NwRn7JzROReI1wFgR+RQtPDof+INzyFj0TR30d+fXSqkvnFnyEufcUrTu2yCllF9Ekp3j76Nthe/AdzUSnST9307J8Vb7U0otdvZ/Z4CDHgzMBm5SSi0Xkas5eHb3VNDA7y7gabTHwRCEMUgnFnvRbqEa2K9c3NV5/XBoSzalrVlFxSFuAsHnPoQ2ducopTwi8ncOSKIcKnzZAu5VSi1tYd81aOHLccBnInKPUmq+s681aRZDx/IZ2vBUoQc7PhHJdYRPxwL/FpF453n3IA26RLQ+4evowc0rIrIU+OQI+g522fUGVorIl2h9udb6CxiklgY4UUC9Umq5s2+OiFQE9TdZRO5Dr9ma718rmA/mxGIxWnX4UWd7BrBKKRWY5VRxYGE7sJ0WtH20silHShKw2zFGaegCey86+5YAtwMrRCQVuBp4z9n3EfCQiKx0RtiJ6JtBLtBfKZWFVlI+He3ymY+hM/M52r1WiVYiAK1iMA49MPoRetZuA6OUUt/JdwoyXpcAT4vIiObHtIVSqkhEVqFn5+pQ/Tm0NMBpVctORPqjZ36jlFI7HM/A4UgHnXS0qzKxIew8AAwQka8dd8Rk4Jag/c+j3VobRGQIMAcY6WzPRLsqEh3ZlPfQsinh4HkgU0TWow1RcI2XXwPJIvIN8CYHRwA+hXbRrRGRr9E6aYPRo+TXRCRbRDaifft/C9O1G0LHf4ABwLVo4wTaIP0YPYPJU0pVo7+HMwMniUhfEenpBCPEOmuPM9GG7TT0QCvO8RC0iTOwORvYeqj+2mhmCxAvIpnOOVeiB16gg1YagWJn3euew7mukxGTh2QICyYnqsXz8whDTtSx0sEh2KvQAreBtcBotFGZrZS6zTmmJ3qGcaZzTDU62CYOeJkDwR8L0UXs/E5U5QVAefOghhbCvmOA94F/oD0CZ7bUn1JqS/P/YfC2iFyEDrKoQ69p3o6eFRWIyJ+AKejI0WXoInwDTNj3wRiXnaGjMDlRnYfP6bgQ7BsJCkAIhGATNENxQrFvauX8s1t6USl1d2sXpJTKo4V7n2McDtmfUmrAIbbXKaWGOm1dDExHC7SilLofXf01wK+CrsUYIwdjkAztjpicqLDnRB0hJgQ7NCHY14rIg+j/uQcdcXc8hOB3GswakqHdUUr9Fq0OME0p9T2l1EZ0rtMbzvZTHCglPRLtOolAu2kCXADMUkqdfRiuuBZzopRSd6JVvKcopc5Gh56PFJFLneP250QBN6NvYgH250Q5o/sidE4U6Jyoic7ro9DGLECcUuo89OzjKdElrTsUpVQ9ugzDWGc9JRCCHYiYHMvBIdjPO7lgZ6OjxyagSy+MB4YopYajS8eDDsGucP6vrRmjJGcdcxM6yu2JZiHYLfUX4Dufp+g8tNnoiMxh6BlgcKnyp5RSo5zrnI0OwT5mlFKvKaWGK6WGOu2vCEW7JxNmhmRob0xOFJ0yJ8qEYBs6HPPPMIQLkxN1fOVEfY4JwTZ0MMZlZwgXgZyoAIeTExW83VJOVDhoKScqQCAniqCcqACBnKhYZ3+iiAx2DO9pSqksx/W4CL0G09kxIdiGDqezjM4MJx4PAH9y8oX86LLOLeVE1aGNzRzgFsfN9g46P+loSkkfKc8D7zk5UQV8Nyfqn05OVB7fzYmahc6J8qNH5I+j3YOvOeHOgfc9k06OY5BXA2lKqSLn5TXoxOn3gg6dDvxBRLKd7YNCsB3DE4leg1rlhGD/C8gWke+EYDsE3KugZ6fvKKU+bKO/VkVQlVINIvJ94CXn+7UUHYxS6YRgv4cuNR4IwR7T9idkaA9MHpLBYDjhEKecuPP8YvQa1wAT9da5MTMkg8FwImJCsI9DzAzJYDAYDJ0CE9RgMBgMhk6BMUgGg8Fg6BQYg2QwGAyGToExSAaDwWDoFBiDZDAYDIZOgTFIBoPBYOgUGINkMBgMhk6BMUgGg8Fg6BQYg2QwGAyGToExSAaDwWDoFBiDZDAYDIZOgTFIBoPBYOgUGINkMBgMhk6BMUgGg8Fg6BQYg2QwGAyGToExSAaDwWDoFBiDZDAYDIZOgTFIBoPBYOgUGINkMBgMhk6BMUgGg8Fg6BQYg2QwGAyGTsH/BwQZqvFy8PvNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.pie(values, labels= label, shadow=True);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Question 4:States with lowest number of active cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "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>Name of State / UT</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Total Confirmed cases</th>\n",
       "      <th>Death</th>\n",
       "      <th>Cured/Discharged/Migrated</th>\n",
       "      <th>New cases</th>\n",
       "      <th>New deaths</th>\n",
       "      <th>New recovered</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4679</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Mizoram</td>\n",
       "      <td>23.1645</td>\n",
       "      <td>92.9376</td>\n",
       "      <td>537.0</td>\n",
       "      <td>0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4650</th>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>27.5330</td>\n",
       "      <td>88.5122</td>\n",
       "      <td>783.0</td>\n",
       "      <td>1</td>\n",
       "      <td>299.0</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4685</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>27.5330</td>\n",
       "      <td>88.5122</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>303.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4678</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>25.4670</td>\n",
       "      <td>91.3662</td>\n",
       "      <td>929.0</td>\n",
       "      <td>5</td>\n",
       "      <td>330.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4657</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>11.7401</td>\n",
       "      <td>92.6586</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>14</td>\n",
       "      <td>326.0</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4662</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>30.7333</td>\n",
       "      <td>76.7794</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>20</td>\n",
       "      <td>715.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4664</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Dadra and Nagar Haveli and Daman and Diu</td>\n",
       "      <td>20.1809</td>\n",
       "      <td>73.0169</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>2</td>\n",
       "      <td>960.0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4674</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Ladakh</td>\n",
       "      <td>34.2996</td>\n",
       "      <td>78.2932</td>\n",
       "      <td>1592.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1164.0</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4659</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>28.2180</td>\n",
       "      <td>94.7278</td>\n",
       "      <td>1855.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4680</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>Nagaland</td>\n",
       "      <td>26.1584</td>\n",
       "      <td>94.5624</td>\n",
       "      <td>2498.0</td>\n",
       "      <td>6</td>\n",
       "      <td>685.0</td>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                        Name of State / UT  Latitude  \\\n",
       "4679 2020-08-06                                   Mizoram   23.1645   \n",
       "4650 2020-08-05                                    Sikkim   27.5330   \n",
       "4685 2020-08-06                                    Sikkim   27.5330   \n",
       "4678 2020-08-06                                 Meghalaya   25.4670   \n",
       "4657 2020-08-06               Andaman and Nicobar Islands   11.7401   \n",
       "4662 2020-08-06                                Chandigarh   30.7333   \n",
       "4664 2020-08-06  Dadra and Nagar Haveli and Daman and Diu   20.1809   \n",
       "4674 2020-08-06                                    Ladakh   34.2996   \n",
       "4659 2020-08-06                         Arunachal Pradesh   28.2180   \n",
       "4680 2020-08-06                                  Nagaland   26.1584   \n",
       "\n",
       "      Longitude  Total Confirmed cases Death  Cured/Discharged/Migrated  \\\n",
       "4679    92.9376                  537.0     0                      286.0   \n",
       "4650    88.5122                  783.0     1                      299.0   \n",
       "4685    88.5122                  800.0     1                      303.0   \n",
       "4678    91.3662                  929.0     5                      330.0   \n",
       "4657    92.6586                 1027.0    14                      326.0   \n",
       "4662    76.7794                 1270.0    20                      715.0   \n",
       "4664    73.0169                 1366.0     2                      960.0   \n",
       "4674    78.2932                 1592.0     7                     1164.0   \n",
       "4659    94.7278                 1855.0     3                     1210.0   \n",
       "4680    94.5624                 2498.0     6                      685.0   \n",
       "\n",
       "      New cases  New deaths  New recovered  year  month  day  weekday  \n",
       "4679         33           0              4  2020      8    6        3  \n",
       "4650         95           0              2  2020      8    5        2  \n",
       "4685         17           0              4  2020      8    6        3  \n",
       "4678         12           0              0  2020      8    6        3  \n",
       "4657         99           0             49  2020      8    6        3  \n",
       "4662         64           0              0  2020      8    6        3  \n",
       "4664         41           0             41  2020      8    6        3  \n",
       "4674         58           0             37  2020      8    6        3  \n",
       "4659         65           0            105  2020      8    6        3  \n",
       "4680         93           0             26  2020      8    6        3  "
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[-45:].sort_values('Total Confirmed cases', ascending=True).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Question 5:States with highest number of active cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "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>index</th>\n",
       "      <th>Name of State / UT</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Total Confirmed cases</th>\n",
       "      <th>Cured/Discharged/Migrated</th>\n",
       "      <th>New cases</th>\n",
       "      <th>New deaths</th>\n",
       "      <th>New recovered</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4676</th>\n",
       "      <td>4676</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>19.7515</td>\n",
       "      <td>75.7139</td>\n",
       "      <td>468265.0</td>\n",
       "      <td>305521.0</td>\n",
       "      <td>10309</td>\n",
       "      <td>0</td>\n",
       "      <td>6165</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4686</th>\n",
       "      <td>4686</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>11.1271</td>\n",
       "      <td>78.6569</td>\n",
       "      <td>273460.0</td>\n",
       "      <td>214815.0</td>\n",
       "      <td>5175</td>\n",
       "      <td>0</td>\n",
       "      <td>6031</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4651</th>\n",
       "      <td>4651</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>11.1271</td>\n",
       "      <td>78.6569</td>\n",
       "      <td>268285.0</td>\n",
       "      <td>208784.0</td>\n",
       "      <td>5063</td>\n",
       "      <td>0</td>\n",
       "      <td>6501</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4658</th>\n",
       "      <td>4658</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.7400</td>\n",
       "      <td>186461.0</td>\n",
       "      <td>104354.0</td>\n",
       "      <td>10128</td>\n",
       "      <td>0</td>\n",
       "      <td>8729</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4672</th>\n",
       "      <td>4672</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>15.3173</td>\n",
       "      <td>75.7139</td>\n",
       "      <td>151449.0</td>\n",
       "      <td>74679.0</td>\n",
       "      <td>5619</td>\n",
       "      <td>0</td>\n",
       "      <td>5407</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4665</th>\n",
       "      <td>4665</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.7041</td>\n",
       "      <td>77.1025</td>\n",
       "      <td>140232.0</td>\n",
       "      <td>126116.0</td>\n",
       "      <td>1076</td>\n",
       "      <td>0</td>\n",
       "      <td>890</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4689</th>\n",
       "      <td>4689</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>26.8467</td>\n",
       "      <td>80.9462</td>\n",
       "      <td>104388.0</td>\n",
       "      <td>60558.0</td>\n",
       "      <td>4078</td>\n",
       "      <td>0</td>\n",
       "      <td>3287</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4654</th>\n",
       "      <td>4654</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>26.8467</td>\n",
       "      <td>80.9462</td>\n",
       "      <td>100310.0</td>\n",
       "      <td>57271.0</td>\n",
       "      <td>2948</td>\n",
       "      <td>0</td>\n",
       "      <td>1878</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4691</th>\n",
       "      <td>4691</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.8550</td>\n",
       "      <td>83800.0</td>\n",
       "      <td>58962.0</td>\n",
       "      <td>2816</td>\n",
       "      <td>0</td>\n",
       "      <td>2078</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4656</th>\n",
       "      <td>4656</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.8550</td>\n",
       "      <td>80984.0</td>\n",
       "      <td>56884.0</td>\n",
       "      <td>2752</td>\n",
       "      <td>0</td>\n",
       "      <td>2066</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index Name of State / UT  Latitude  Longitude  Total Confirmed cases  \\\n",
       "4676   4676        Maharashtra   19.7515    75.7139               468265.0   \n",
       "4686   4686         Tamil Nadu   11.1271    78.6569               273460.0   \n",
       "4651   4651         Tamil Nadu   11.1271    78.6569               268285.0   \n",
       "4658   4658     Andhra Pradesh   15.9129    79.7400               186461.0   \n",
       "4672   4672          Karnataka   15.3173    75.7139               151449.0   \n",
       "4665   4665              Delhi   28.7041    77.1025               140232.0   \n",
       "4689   4689      Uttar Pradesh   26.8467    80.9462               104388.0   \n",
       "4654   4654      Uttar Pradesh   26.8467    80.9462               100310.0   \n",
       "4691   4691        West Bengal   22.9868    87.8550                83800.0   \n",
       "4656   4656        West Bengal   22.9868    87.8550                80984.0   \n",
       "\n",
       "      Cured/Discharged/Migrated  New cases  New deaths  New recovered  year  \\\n",
       "4676                   305521.0      10309           0           6165  2020   \n",
       "4686                   214815.0       5175           0           6031  2020   \n",
       "4651                   208784.0       5063           0           6501  2020   \n",
       "4658                   104354.0      10128           0           8729  2020   \n",
       "4672                    74679.0       5619           0           5407  2020   \n",
       "4665                   126116.0       1076           0            890  2020   \n",
       "4689                    60558.0       4078           0           3287  2020   \n",
       "4654                    57271.0       2948           0           1878  2020   \n",
       "4691                    58962.0       2816           0           2078  2020   \n",
       "4656                    56884.0       2752           0           2066  2020   \n",
       "\n",
       "      month  day  weekday  \n",
       "4676      8    6        3  \n",
       "4686      8    6        3  \n",
       "4651      8    5        2  \n",
       "4658      8    6        3  \n",
       "4672      8    6        3  \n",
       "4665      8    6        3  \n",
       "4689      8    6        3  \n",
       "4654      8    5        2  \n",
       "4691      8    6        3  \n",
       "4656      8    5        2  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[-45:].sort_values('Total Confirmed cases', ascending=False).head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Question 6:Death Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Death rate of the cases in the country is 7.266992954650331e+21\n"
     ]
    }
   ],
   "source": [
    "Death_rate = Total_death/Total_confirmed\n",
    "print(\"Death rate of the cases in the country is\", Death_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us save and upload our work to Jovian before continuing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inferences and Conclusion\n",
    "\n",
    "* COVID cases in some states are increasing drastically like Maharashtra, Tmil Nadu, Andra Pradesh.\n",
    "* Saturdays and Sundays have highest number of new cases.\n",
    "* States having steepest curve are Maharashtra, Tamil Nadu, Andhra Pradesh, Karnataka, Delhi, UP and so on.\n",
    "* While doing this project i have realized that there is lot more to learn, i am excited to move forward in this journey of becoming a Data Analyst/Scientist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jovian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[jovian] Attempting to save notebook..\u001b[0m\n",
      "[jovian] Detected Kaggle notebook...\u001b[0m\n",
      "[jovian] Uploading notebook to https://jovian.ml/kanchanjeswani2000/covid-19-data-analysis-india\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "    require([\"base/js/namespace\"],function(Jupyter) {\n",
       "        var nbJson = JSON.stringify(Jupyter.notebook.toJSON());\n",
       "\n",
       "        console.log(\"[jovian] Extracted notebook JSON:\");\n",
       "        console.log(nbJson);\n",
       "\n",
       "        function jvnLog (data) {\n",
       "          console.log(\"Result from jovian.commit:\");\n",
       "          if (data.content.text) {\n",
       "              var result = JSON.parse(data.content.text.trim());\n",
       "              var msg = result['msg'];\n",
       "              var err = result['err'];\n",
       "              if (msg) {\n",
       "                  element.text(\"Committed successfully: \" + msg)\n",
       "              } else {\n",
       "                  alert(\"Notebook commit failed. Error: \" + (err || \"Unknown\"))\n",
       "              }\n",
       "          }\n",
       "          \n",
       "        };\n",
       "        \n",
       "        var pythonCode = `\n",
       "from contextlib import redirect_stdout, redirect_stderr\n",
       "from io import StringIO\n",
       "import json\n",
       " \n",
       "with open(\"covid-19-data-analysis-india.ipynb\", 'w') as f:\n",
       "    f.write(r\"\"\"${nbJson}\"\"\")\n",
       "\n",
       "jvn_update = StringIO()\n",
       "jvn_update_err = StringIO()\n",
       "with redirect_stdout(jvn_update), redirect_stderr(jvn_update_err):\n",
       "    from jovian import commit\n",
       "\n",
       "jvn_f_out = StringIO()\n",
       "jvn_f_err = StringIO()\n",
       "with redirect_stdout(jvn_f_out), redirect_stderr(jvn_f_err):\n",
       "    jvn_msg = jovian.commit(message=None, files=['../input/covid19-corona-virus-india-dataset/complete.cs'], outputs=[], environment='auto', privacy='auto', filename='covid-19-data-analysis-india.ipynb', project='covid-19-data-analysis-india', new_project=None)\n",
       "\n",
       "print(json.dumps({'msg': jvn_msg, 'err': jvn_f_err.getvalue(), 'update': jvn_update.getvalue()}))\n",
       "        `;\n",
       "\n",
       "        console.log(\"Invoking jovian.commit\")\n",
       "        // console.log(pythonCode)\n",
       "\n",
       "        Jupyter.notebook.kernel.execute(pythonCode, { iopub: { output: jvnLog }});\n",
       "    });"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jovian.commit(project=project_name, files=[\"../input/covid19-corona-virus-india-dataset/complete.cs\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References and Future Work\n",
    "Numerical computing with Numpy: https://jovian.ml/aakashns/python-numerical-computing-with-numpy\n",
    "\n",
    "Analyzing tabular data with Pandas: https://jovian.ml/aakashns/python-pandas-data-analysis\n",
    "\n",
    "Matplotlib & Seaborn tutorial: https://jovian.ml/aakashns/python-matplotlib-data-visualization\n",
    "\n",
    "Pandas user guide: https://pandas.pydata.org/docs/user_guide/index.html\n",
    "\n",
    "Matplotlib user guide: https://matplotlib.org/3.3.1/users/index.html\n",
    "\n",
    "Seaborn user guide & tutorial: https://seaborn.pydata.org/tutorial.html|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jovian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[jovian] Attempting to save notebook..\u001b[0m\n",
      "[jovian] Detected Kaggle notebook...\u001b[0m\n",
      "[jovian] Uploading notebook to https://jovian.ml/kanchanjeswani2000/covid-19-data-analysis-india\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "    require([\"base/js/namespace\"],function(Jupyter) {\n",
       "        var nbJson = JSON.stringify(Jupyter.notebook.toJSON());\n",
       "\n",
       "        console.log(\"[jovian] Extracted notebook JSON:\");\n",
       "        console.log(nbJson);\n",
       "\n",
       "        function jvnLog (data) {\n",
       "          console.log(\"Result from jovian.commit:\");\n",
       "          if (data.content.text) {\n",
       "              var result = JSON.parse(data.content.text.trim());\n",
       "              var msg = result['msg'];\n",
       "              var err = result['err'];\n",
       "              if (msg) {\n",
       "                  element.text(\"Committed successfully: \" + msg)\n",
       "              } else {\n",
       "                  alert(\"Notebook commit failed. Error: \" + (err || \"Unknown\"))\n",
       "              }\n",
       "          }\n",
       "          \n",
       "        };\n",
       "        \n",
       "        var pythonCode = `\n",
       "from contextlib import redirect_stdout, redirect_stderr\n",
       "from io import StringIO\n",
       "import json\n",
       " \n",
       "with open(\"covid-19-data-analysis-india.ipynb\", 'w') as f:\n",
       "    f.write(r\"\"\"${nbJson}\"\"\")\n",
       "\n",
       "jvn_update = StringIO()\n",
       "jvn_update_err = StringIO()\n",
       "with redirect_stdout(jvn_update), redirect_stderr(jvn_update_err):\n",
       "    from jovian import commit\n",
       "\n",
       "jvn_f_out = StringIO()\n",
       "jvn_f_err = StringIO()\n",
       "with redirect_stdout(jvn_f_out), redirect_stderr(jvn_f_err):\n",
       "    jvn_msg = jovian.commit(message=None, files=['../input/covid19-corona-virus-india-dataset/complete.cs'], outputs=[], environment='auto', privacy='auto', filename='covid-19-data-analysis-india.ipynb', project='covid-19-data-analysis-india', new_project=None)\n",
       "\n",
       "print(json.dumps({'msg': jvn_msg, 'err': jvn_f_err.getvalue(), 'update': jvn_update.getvalue()}))\n",
       "        `;\n",
       "\n",
       "        console.log(\"Invoking jovian.commit\")\n",
       "        // console.log(pythonCode)\n",
       "\n",
       "        Jupyter.notebook.kernel.execute(pythonCode, { iopub: { output: jvnLog }});\n",
       "    });"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jovian.commit(project=project_name, files=[\"../input/covid19-corona-virus-india-dataset/complete.cs\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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