Analysis of corona .ipynb

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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sno</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>State/UnionTerritory</th>\n",
       "      <th>ConfirmedIndianNational</th>\n",
       "      <th>ConfirmedForeignNational</th>\n",
       "      <th>Cured</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>30/01/20</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>31/01/20</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>01/02/20</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>2</td>\n",
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       "      <td>4</td>\n",
       "      <td>02/02/20</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>03/02/20</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   Sno      Date     Time State/UnionTerritory ConfirmedIndianNational  \\\n",
       "0    1  30/01/20  6:00 PM               Kerala                       1   \n",
       "1    2  31/01/20  6:00 PM               Kerala                       1   \n",
       "2    3  01/02/20  6:00 PM               Kerala                       2   \n",
       "3    4  02/02/20  6:00 PM               Kerala                       3   \n",
       "4    5  03/02/20  6:00 PM               Kerala                       3   \n",
       "\n",
       "  ConfirmedForeignNational  Cured  Deaths  Confirmed  \n",
       "0                        0      0       0          1  \n",
       "1                        0      0       0          1  \n",
       "2                        0      0       0          2  \n",
       "3                        0      0       0          3  \n",
       "4                        0      0       0          3  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train=pd.read_csv(r'C:\\Users\\ADITYA\\Desktop\\covid_19_india.csv')\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "df2= df_train[df_train['Date']=='26/04/20']\n",
    "fig = px.bar(df2, x='State/UnionTerritory', color='State/UnionTerritory', y='Confirmed',height=700,title='Cases as of 26 April')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
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\"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Cases as of 26 April\"}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('5ca8f3ec-767b-4747-a786-325100de4ef2');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.pie(df_train[df_train['Date']=='26/04/20'], values='Confirmed', names='State/UnionTerritory',color_discrete_sequence=px.colors.sequential.RdBu,title='Cases as of 26 April')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2= df_train[df_train['Date']=='26/04/20']\n",
    "fig = px.bar(df2, x='State/UnionTerritory', y='Deaths', color='State/UnionTerritory',height=700,title='Deaths as of 26 April')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.line(df_train, x='Date', y='Confirmed',height=700,line_shape=\"spline\", render_mode=\"svg\",title='Increase of cases with date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "var outputEl = gd.closest('.output');\n",
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       "            </script>\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "df2= df_train[df_train['Date']=='26/04/20']\n",
    "fig = px.bar(df2, x='State/UnionTerritory', color='State/UnionTerritory', y='Cured',height=700,title='Cured as of 26 April')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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