05- covid-19.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a7f795d-c80b-42ee-8508-6bdcd2aaeb9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "08e87a28-7c33-49e4-a9d5-ac4ec42ecb6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</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>0</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</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>1</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</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>2</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        Date Country/Region  Confirmed  Deaths  Recovered  \\\n",
       "0           0  2020-01-22    Afghanistan          0       0        0.0   \n",
       "1           1  2020-01-22        Albania          0       0        0.0   \n",
       "2           2  2020-01-22        Algeria          0       0        0.0   \n",
       "3           3  2020-01-22        Andorra          0       0        0.0   \n",
       "4           4  2020-01-22         Angola          0       0        0.0   \n",
       "\n",
       "   Active  New cases  New deaths  New recovered  \n",
       "0     0.0          0           0              0  \n",
       "1     0.0          0           0              0  \n",
       "2     0.0          0           0              0  \n",
       "3     0.0          0           0              0  \n",
       "4     0.0          0           0              0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame = pd.read_csv(\"COVID-19-time-series-clean-complete.csv\")\n",
    "data_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d09e4f-d95c-47c4-8167-2b685f875949",
   "metadata": {},
   "source": [
    "#### YearMonth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "55af3ac3-95a1-41a6-be6f-898224e8b922",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>New cases</th>\n",
       "      <th>New deaths</th>\n",
       "      <th>New recovered</th>\n",
       "      <th>YearMonth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       Date Country/Region  Confirmed  Deaths  Recovered  Active  \\\n",
       "0           0 2020-01-22    Afghanistan          0       0        0.0     0.0   \n",
       "1           1 2020-01-22        Albania          0       0        0.0     0.0   \n",
       "2           2 2020-01-22        Algeria          0       0        0.0     0.0   \n",
       "3           3 2020-01-22        Andorra          0       0        0.0     0.0   \n",
       "4           4 2020-01-22         Angola          0       0        0.0     0.0   \n",
       "\n",
       "   New cases  New deaths  New recovered  YearMonth  \n",
       "0          0           0              0     202001  \n",
       "1          0           0              0     202001  \n",
       "2          0           0              0     202001  \n",
       "3          0           0              0     202001  \n",
       "4          0           0              0     202001  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame['Date'] = pd.to_datetime(data_frame['Date'])\n",
    "data_frame['YearMonth'] = data_frame['Date'].map(lambda date : 100 * date.year + date.month)\n",
    "data_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1a0b465-adf7-4d3f-8ad5-5dab0fd45e23",
   "metadata": {},
   "source": [
    "#### April month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b8359556-ed01-4f76-a092-2042bd572f92",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>New cases</th>\n",
       "      <th>New deaths</th>\n",
       "      <th>New recovered</th>\n",
       "      <th>YearMonth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13510</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>197</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13511</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>Albania</td>\n",
       "      <td>259</td>\n",
       "      <td>15</td>\n",
       "      <td>67.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>202004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13512</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>847</td>\n",
       "      <td>58</td>\n",
       "      <td>61.0</td>\n",
       "      <td>728.0</td>\n",
       "      <td>131</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>202004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13513</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>390</td>\n",
       "      <td>14</td>\n",
       "      <td>10.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>202004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13514</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>Angola</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13515</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13516</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>1054</td>\n",
       "      <td>28</td>\n",
       "      <td>248.0</td>\n",
       "      <td>778.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>202004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13517</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>571</td>\n",
       "      <td>4</td>\n",
       "      <td>31.0</td>\n",
       "      <td>536.0</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>202004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0       Date       Country/Region  Confirmed  Deaths  Recovered  \\\n",
       "0       13510 2020-04-01          Afghanistan        197       4        5.0   \n",
       "1       13511 2020-04-01              Albania        259      15       67.0   \n",
       "2       13512 2020-04-01              Algeria        847      58       61.0   \n",
       "3       13513 2020-04-01              Andorra        390      14       10.0   \n",
       "4       13514 2020-04-01               Angola          8       2        1.0   \n",
       "5       13515 2020-04-01  Antigua and Barbuda          7       0        0.0   \n",
       "6       13516 2020-04-01            Argentina       1054      28      248.0   \n",
       "7       13517 2020-04-01              Armenia        571       4       31.0   \n",
       "\n",
       "   Active  New cases  New deaths  New recovered  YearMonth  \n",
       "0   188.0         22           0              0     202004  \n",
       "1   177.0         16           0             15     202004  \n",
       "2   728.0        131          14             15     202004  \n",
       "3   366.0         14           2              0     202004  \n",
       "4     5.0          1           0              0     202004  \n",
       "5     7.0          0           0              0     202004  \n",
       "6   778.0          0           1              8     202004  \n",
       "7   536.0         39           1              1     202004  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "april_month_data = data_frame.query(\"YearMonth == 202004\").reset_index(drop=True)\n",
    "april_month_data.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c65bfd5a-1ee1-4134-b8a5-2527346c0cc1",
   "metadata": {},
   "source": [
    "#### total_Confirmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "da37c71a-1fd1-4ba8-9cf5-52d7e6a24c6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>27074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>15632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>69497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>19106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>77576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>36511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country/Region  Confirmed\n",
       "0          Afghanistan      27074\n",
       "1              Albania      15632\n",
       "2              Algeria      69497\n",
       "3              Andorra      19106\n",
       "4               Angola        601\n",
       "5  Antigua and Barbuda        617\n",
       "6            Argentina      77576\n",
       "7              Armenia      36511"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_Confirmed = april_month_data.groupby('Country/Region')['Confirmed'].sum().reset_index()\n",
    "total_Confirmed.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6741096b-db94-47a9-9543-8fc5c74e11f2",
   "metadata": {},
   "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>Country/Region</th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>US</td>\n",
       "      <td>19960815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Spain</td>\n",
       "      <td>5236777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Italy</td>\n",
       "      <td>4928524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Germany</td>\n",
       "      <td>3942925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>France</td>\n",
       "      <td>3456319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>3328344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>China</td>\n",
       "      <td>2500143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Iran</td>\n",
       "      <td>2255837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country/Region  Confirmed\n",
       "179              US   19960815\n",
       "162           Spain    5236777\n",
       "84            Italy    4928524\n",
       "65          Germany    3942925\n",
       "61           France    3456319\n",
       "183  United Kingdom    3328344\n",
       "36            China    2500143\n",
       "80             Iran    2255837"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_total_Confirmed = total_Confirmed.sort_values(by=['Confirmed'], ascending=False)\n",
    "sorted_total_Confirmed.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b12a99-8566-4791-9f0f-317c5c0d1581",
   "metadata": {},
   "source": [
    "### death rate, number of patients in Iran"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "df457557-4927-4e14-9231-86704adc2ac3",
   "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>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>New cases</th>\n",
       "      <th>New deaths</th>\n",
       "      <th>New recovered</th>\n",
       "      <th>YearMonth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>80</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Iran</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>273</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Iran</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>466</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Iran</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>659</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>Iran</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>852</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>Iran</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0       Date Country/Region  Confirmed  Deaths  Recovered  \\\n",
       "80           80 2020-01-22           Iran          0       0        0.0   \n",
       "273         273 2020-01-23           Iran          0       0        0.0   \n",
       "466         466 2020-01-24           Iran          0       0        0.0   \n",
       "659         659 2020-01-25           Iran          0       0        0.0   \n",
       "852         852 2020-01-26           Iran          0       0        0.0   \n",
       "\n",
       "     Active  New cases  New deaths  New recovered  YearMonth  \n",
       "80      0.0          0           0              0     202001  \n",
       "273     0.0          0           0              0     202001  \n",
       "466     0.0          0           0              0     202001  \n",
       "659     0.0          0           0              0     202001  \n",
       "852     0.0          0           0              0     202001  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iran = data_frame.loc[(data_frame['Country/Region'] == 'Iran')]\n",
    "iran.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d049bf9-87ca-47ee-b492-4b1c5e4bd2af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='days'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iran_covid19_datafreme = pd.DataFrame({'days' : iran['Date'], 'New deaths' : iran['New deaths'], 'New cases' : iran['New cases']})\n",
    "# plt.figure(figsize = (24, 8))\n",
    "iran_covid19_datafreme.plot.area(x='days', stacked=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8add45f1-702d-423d-99c9-c21538d35ba6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}