Take Home Excercise #2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q. Register at www.kaggle.com / Download csv files / Store folder Dataset root Jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('covid-19.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Sno      Date     Time              State/UnionTerritory  \\\n",
      "0        1  30/01/20  6:00 PM                            Kerala   \n",
      "1        2  31/01/20  6:00 PM                            Kerala   \n",
      "2        3  01/02/20  6:00 PM                            Kerala   \n",
      "3        4  02/02/20  6:00 PM                            Kerala   \n",
      "4        5  03/02/20  6:00 PM                            Kerala   \n",
      "...    ...       ...      ...                               ...   \n",
      "3706  3707  03/07/20  8:00 AM                           Tripura   \n",
      "3707  3708  03/07/20  8:00 AM                       Uttarakhand   \n",
      "3708  3709  03/07/20  8:00 AM                     Uttar Pradesh   \n",
      "3709  3710  03/07/20  8:00 AM                       West Bengal   \n",
      "3710  3711  03/07/20  8:00 AM  Cases being reassigned to states   \n",
      "\n",
      "     ConfirmedIndianNational ConfirmedForeignNational  Cured  Deaths  \\\n",
      "0                          1                        0      0       0   \n",
      "1                          1                        0      0       0   \n",
      "2                          2                        0      0       0   \n",
      "3                          3                        0      0       0   \n",
      "4                          3                        0      0       0   \n",
      "...                      ...                      ...    ...     ...   \n",
      "3706                       -                        -   1146       1   \n",
      "3707                       -                        -   2405      42   \n",
      "3708                       -                        -  17221     735   \n",
      "3709                       -                        -  13037     699   \n",
      "3710                       -                        -      0       0   \n",
      "\n",
      "      Confirmed  \n",
      "0             1  \n",
      "1             1  \n",
      "2             2  \n",
      "3             3  \n",
      "4             3  \n",
      "...         ...  \n",
      "3706       1435  \n",
      "3707       2984  \n",
      "3708      24825  \n",
      "3709      19819  \n",
      "3710       6031  \n",
      "\n",
      "[3711 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Confirmed</th>\n",
       "      <th>Cured</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State/UnionTerritory</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Maharashtra</th>\n",
       "      <td>42897</td>\n",
       "      <td>19007</td>\n",
       "      <td>1766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <td>17530</td>\n",
       "      <td>9277</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>16156</td>\n",
       "      <td>8149</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gujarat</th>\n",
       "      <td>11132</td>\n",
       "      <td>6660</td>\n",
       "      <td>665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telangana</th>\n",
       "      <td>9550</td>\n",
       "      <td>4269</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cases being reassigned to states</th>\n",
       "      <td>6621</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <td>5492</td>\n",
       "      <td>3222</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madhya Pradesh</th>\n",
       "      <td>5216</td>\n",
       "      <td>3210</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajasthan</th>\n",
       "      <td>5173</td>\n",
       "      <td>3509</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bengal</th>\n",
       "      <td>4503</td>\n",
       "      <td>2281</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <td>3100</td>\n",
       "      <td>1515</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karnataka</th>\n",
       "      <td>2789</td>\n",
       "      <td>1421</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>2649</td>\n",
       "      <td>1621</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haryana</th>\n",
       "      <td>2560</td>\n",
       "      <td>1415</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jammu and Kashmir</th>\n",
       "      <td>1867</td>\n",
       "      <td>922</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assam</th>\n",
       "      <td>1682</td>\n",
       "      <td>912</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odisha</th>\n",
       "      <td>1558</td>\n",
       "      <td>1015</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Punjab</th>\n",
       "      <td>1548</td>\n",
       "      <td>1008</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telengana</th>\n",
       "      <td>1017</td>\n",
       "      <td>492</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kerala</th>\n",
       "      <td>808</td>\n",
       "      <td>426</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jharkhand</th>\n",
       "      <td>692</td>\n",
       "      <td>407</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttarakhand</th>\n",
       "      <td>650</td>\n",
       "      <td>374</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <td>592</td>\n",
       "      <td>348</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tripura</th>\n",
       "      <td>434</td>\n",
       "      <td>235</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himachal Pradesh</th>\n",
       "      <td>235</td>\n",
       "      <td>129</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goa</th>\n",
       "      <td>227</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manipur</th>\n",
       "      <td>202</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chandigarh</th>\n",
       "      <td>174</td>\n",
       "      <td>116</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ladakh</th>\n",
       "      <td>167</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nagaland</th>\n",
       "      <td>153</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dadra and Nagar Haveli and Daman and Diu</th>\n",
       "      <td>109</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puducherry</th>\n",
       "      <td>100</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unassigned</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sikkim</th>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mizoram</th>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andaman and Nicobar Islands</th>\n",
       "      <td>32</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meghalaya</th>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dadar Nagar Haveli</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daman &amp; Diu</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Confirmed  Cured  Deaths\n",
       "State/UnionTerritory                                              \n",
       "Maharashtra                                   42897  19007    1766\n",
       "Tamil Nadu                                    17530   9277     190\n",
       "Delhi                                         16156   8149     484\n",
       "Gujarat                                       11132   6660     665\n",
       "Telangana                                      9550   4269     216\n",
       "Cases being reassigned to states               6621      0       0\n",
       "Uttar Pradesh                                  5492   3222     151\n",
       "Madhya Pradesh                                 5216   3210     234\n",
       "Rajasthan                                      5173   3509     117\n",
       "West Bengal                                    4503   2281     213\n",
       "Andhra Pradesh                                 3100   1515      47\n",
       "Karnataka                                      2789   1421      46\n",
       "Bihar                                          2649   1621      17\n",
       "Haryana                                        2560   1415      35\n",
       "Jammu and Kashmir                              1867    922      23\n",
       "Assam                                          1682    912       3\n",
       "Odisha                                         1558   1015       5\n",
       "Punjab                                         1548   1008      35\n",
       "Telengana                                      1017    492      29\n",
       "Kerala                                          808    426       5\n",
       "Jharkhand                                       692    407       4\n",
       "Uttarakhand                                     650    374       7\n",
       "Chhattisgarh                                    592    348       2\n",
       "Tripura                                         434    235       0\n",
       "Himachal Pradesh                                235    129       3\n",
       "Goa                                             227     69       0\n",
       "Manipur                                         202     68       0\n",
       "Chandigarh                                      174    116       2\n",
       "Ladakh                                          167     65       0\n",
       "Nagaland                                        153     59       0\n",
       "Dadra and Nagar Haveli and Daman and Diu        109     32       0\n",
       "Puducherry                                      100     39       1\n",
       "Unassigned                                       53      0       0\n",
       "Sikkim                                           42     13       0\n",
       "Arunachal Pradesh                                37      7       0\n",
       "Mizoram                                          33      6       0\n",
       "Andaman and Nicobar Islands                      32     24       0\n",
       "Meghalaya                                        24     14       0\n",
       "Dadar Nagar Haveli                                5      0       0\n",
       "Daman & Diu                                       2      0       0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot =pd.pivot_table(data,index=\"State/UnionTerritory\",values = [\"Confirmed\",\"Cured\",\"Deaths\"]).sort_values(by=\"Confirmed\",ascending=False).astype(int)\n",
    "df_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pivot.to_excel(\"Covid19_infected.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pivot.plot(kind='bar')\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q.Tkinter Pandemic Analyser "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkinter import *\n",
    "import tkinter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "root = Tk()\n",
    "root.title(\"Pandemic Analytics Engine\")\n",
    "root.geometry(\"608x275\")\n",
    "def calculation():\n",
    "\n",
    "    total_ppl = entry_2.get()\n",
    "    int_total_ppl = int(total_ppl)\n",
    "\n",
    "    infected = entry_3.get()\n",
    "    int_infected = int(infected)\n",
    "\n",
    "    deceased = entry_4.get()\n",
    "    int_deceased = int(deceased)\n",
    "\n",
    "    ifr = (int_deceased / int_infected) * 100\n",
    "    cmr = (int_deceased / int_total_ppl)\n",
    "    \n",
    "    \n",
    "    \n",
    "    #root = Tk()\n",
    "    #root.title(\"Report\")\n",
    "\n",
    "    label_1 = Label(root, font=(\"helvetica\", 22, \"bold\"), text=\"Result\")\n",
    "    label_1.grid(row=7, columnspan=1)\n",
    "\n",
    "    label_2 = Label(root, font=(\"courier\", 12, \"bold\"), text=\"IFR : \")\n",
    "    label_2.grid(row=9, column=1)\n",
    "    label_3 = Label(root, text=ifr)\n",
    "    label_3.grid(row=9, column=2)\n",
    "\n",
    "    label_4 = Label(root, font=(\"courier\", 12, \"bold\"), text=\"CMR : \")\n",
    "    label_4.grid(row=10, column=1)\n",
    "    label_5 = Label(root, text=cmr)\n",
    "    label_5.grid(row=10, column=2)\n",
    "\n",
    "options = [\"Maharashtra\",\"Tamil Nadu\",\"Delhi\",\"Gujarat\",\"Uttar Pradesh\",\"West Bengal\",\"Rajasthan\",\"Telengana\",\"Karnataka\",\"Andhra Pradesh\",\"Haryana\",\"Madhya Pradesh\",\"Bihar\",\"Assam\",\"Jammu and Kashmir\",\"Odisha\",\"Punjab\",\"Kerala\",\"Chhattisgarh\",\"Uttarakhand\",\"Jharkhand\",\"Goa\",\"Tripura\",\"Manipur\",\"Himachal Pradesh\",\"Ladakh\",\"Puducherry\",\"Nagaland\",\"Chandigarh\",\"Dadra and Nagar Haveli\",\"Arunachal Pradesh\",\"Mizoram\",\"Andaman and Nicobar\",\"Sikkim\",\"Meghalaya\",\"Lakshadweep\"]\n",
    "\n",
    "select = StringVar() \n",
    "select.set(options[0])\n",
    "\n",
    "label_1 = Label(root,font=(\"helvetica\", 22, \"bold\"), text=\"Pandemic Analytics Engine\")\n",
    "label_1.grid(row=1,columnspan = 1)\n",
    "\n",
    "label_2 = Label(root, text=\"Enter State \")\n",
    "label_2.grid(row=2)\n",
    "drop = OptionMenu(root,select,*options)\n",
    "drop.grid(row=2,column=2)\n",
    "\n",
    "label_3 = Label(root, text=\"Total number of ppl\")\n",
    "label_3.grid(row=3)\n",
    "entry_2 = Entry(root)\n",
    "entry_2.grid(row=3, column=2)\n",
    "\n",
    "label_4 = Label(root, text=\"Infected\")\n",
    "label_4.grid(row=4)\n",
    "entry_3 = Entry(root)\n",
    "entry_3.grid(row=4, column=2)\n",
    "\n",
    "label_5 = Label(root, text=\"Deceased\")\n",
    "label_5.grid(row=5)\n",
    "entry_4 = Entry(root)\n",
    "entry_4.grid(row=5, column=2)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "button1 = Button(root, text=\"Calculate IFR & CMR \",command=calculation)\n",
    "button1.grid(column=2)\n",
    "root.mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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