who.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "\n",
    "Data documentation: https://tidyr.tidyverse.org/reference/who.html\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import sqlite3\n",
    "\n",
    "df = pd.read_csv('who.csv')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 575,
   "source": [
    "df = df.drop(columns=['iso2', 'iso3'])\n",
    "df.info()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7240 entries, 0 to 7239\n",
      "Data columns (total 58 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   country       7240 non-null   object \n",
      " 1   year          7240 non-null   int64  \n",
      " 2   new_sp_m014   3173 non-null   float64\n",
      " 3   new_sp_m1524  3209 non-null   float64\n",
      " 4   new_sp_m2534  3206 non-null   float64\n",
      " 5   new_sp_m3544  3219 non-null   float64\n",
      " 6   new_sp_m4554  3223 non-null   float64\n",
      " 7   new_sp_m5564  3218 non-null   float64\n",
      " 8   new_sp_m65    3209 non-null   float64\n",
      " 9   new_sp_f014   3174 non-null   float64\n",
      " 10  new_sp_f1524  3194 non-null   float64\n",
      " 11  new_sp_f2534  3200 non-null   float64\n",
      " 12  new_sp_f3544  3199 non-null   float64\n",
      " 13  new_sp_f4554  3204 non-null   float64\n",
      " 14  new_sp_f5564  3195 non-null   float64\n",
      " 15  new_sp_f65    3197 non-null   float64\n",
      " 16  new_sn_m014   1045 non-null   float64\n",
      " 17  new_sn_m1524  1030 non-null   float64\n",
      " 18  new_sn_m2534  1022 non-null   float64\n",
      " 19  new_sn_m3544  1025 non-null   float64\n",
      " 20  new_sn_m4554  1027 non-null   float64\n",
      " 21  new_sn_m5564  1021 non-null   float64\n",
      " 22  new_sn_m65    1020 non-null   float64\n",
      " 23  new_sn_f014   1040 non-null   float64\n",
      " 24  new_sn_f1524  1022 non-null   float64\n",
      " 25  new_sn_f2534  1016 non-null   float64\n",
      " 26  new_sn_f3544  1020 non-null   float64\n",
      " 27  new_sn_f4554  1018 non-null   float64\n",
      " 28  new_sn_f5564  1017 non-null   float64\n",
      " 29  new_sn_f65    1019 non-null   float64\n",
      " 30  new_ep_m014   1038 non-null   float64\n",
      " 31  new_ep_m1524  1026 non-null   float64\n",
      " 32  new_ep_m2534  1020 non-null   float64\n",
      " 33  new_ep_m3544  1024 non-null   float64\n",
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      " 35  new_ep_m5564  1015 non-null   float64\n",
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      " 37  new_ep_f014   1032 non-null   float64\n",
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      " 42  new_ep_f5564  1017 non-null   float64\n",
      " 43  new_ep_f65    1014 non-null   float64\n",
      " 44  newrel_m014   190 non-null    float64\n",
      " 45  newrel_m1524  182 non-null    float64\n",
      " 46  newrel_m2534  183 non-null    float64\n",
      " 47  newrel_m3544  184 non-null    float64\n",
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      " 49  newrel_m5564  185 non-null    float64\n",
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      " 51  newrel_f014   190 non-null    float64\n",
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      " 56  newrel_f5564  183 non-null    float64\n",
      " 57  newrel_f65    185 non-null    float64\n",
      "dtypes: float64(56), int64(1), object(1)\n",
      "memory usage: 3.2+ MB\n"
     ]
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "source": [
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>diagnosis_method</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1998</td>\n",
       "      <td>30.0</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1999</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2000</td>\n",
       "      <td>52.0</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2001</td>\n",
       "      <td>129.0</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2002</td>\n",
       "      <td>90.0</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  year  value diagnosis_method Gender   Age\n",
       "18  Afghanistan  1998   30.0  PulmonarySmear+   Male  0-14\n",
       "19  Afghanistan  1999    8.0  PulmonarySmear+   Male  0-14\n",
       "20  Afghanistan  2000   52.0  PulmonarySmear+   Male  0-14\n",
       "21  Afghanistan  2001  129.0  PulmonarySmear+   Male  0-14\n",
       "22  Afghanistan  2002   90.0  PulmonarySmear+   Male  0-14"
      ]
     },
     "metadata": {},
     "execution_count": 601
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Question 1. We need to know what's included in this data.\n",
    "- How many countries are present?  \n",
    "- What's the timespan of the data?  \n",
    "- Does each country have a row for every year present?  \n",
    "- Which countries are missing years?  \n",
    "- How many rows have at least 1 non-null values across the columns 3-57?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### How many countries are present?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 577,
   "source": [
    "print('Number of countries present:', len(df.country.unique()))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Number of countries present: 219\n"
     ]
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### What's the timespan of the data?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "source": [
    "\n",
    "print('Timespan of the data:',df.year.max()-df.year.min(),'years')\n",
    "    "
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Timespan of the data: 33 years\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Does each country have a row for every year present?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "source": [
    "#Total Number of countries \n",
    "len(df.year.unique())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "metadata": {},
     "execution_count": 579
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "source": [
    "every_country= (df.groupby('country')['year'].count())==34\n",
    "print('Each country have a row for every year present :',all(every_country))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Each country have a row for every year present : False\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Which countries are missing years?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "source": [
    "print('Countries which are missing years :')\n",
    "for i,j in every_country.iteritems():\n",
    "    if j == False:\n",
    "        print('-',i)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Countries which are missing years :\n",
      "- Bonaire, Saint Eustatius and Saba\n",
      "- Curacao\n",
      "- Montenegro\n",
      "- Netherlands Antilles\n",
      "- Serbia\n",
      "- Serbia & Montenegro\n",
      "- Sint Maarten (Dutch part)\n",
      "- South Sudan\n",
      "- Timor-Leste\n"
     ]
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### How many rows have at least 1 non-null values across the columns 3-57?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "source": [
    "new_df =df.drop(['country', 'year'], axis=1)\n",
    "new_df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "<p>5 rows × 56 columns</p>\n",
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       "   new_sp_m014  new_sp_m1524  new_sp_m2534  new_sp_m3544  new_sp_m4554  \\\n",
       "0          NaN           NaN           NaN           NaN           NaN   \n",
       "1          NaN           NaN           NaN           NaN           NaN   \n",
       "2          NaN           NaN           NaN           NaN           NaN   \n",
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       "\n",
       "   new_sp_m5564  new_sp_m65  new_sp_f014  new_sp_f1524  new_sp_f2534  ...  \\\n",
       "0           NaN         NaN          NaN           NaN           NaN  ...   \n",
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       "\n",
       "   newrel_m4554  newrel_m5564  newrel_m65  newrel_f014  newrel_f1524  \\\n",
       "0           NaN           NaN         NaN          NaN           NaN   \n",
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       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 582
    }
   ],
   "metadata": {
    "scrolled": true
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  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "source": [
    "new_df[new_df.notnull().any(1)].head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "      <th>newrel_m5564</th>\n",
       "      <th>newrel_m65</th>\n",
       "      <th>newrel_f014</th>\n",
       "      <th>newrel_f1524</th>\n",
       "      <th>newrel_f2534</th>\n",
       "      <th>newrel_f3544</th>\n",
       "      <th>newrel_f4554</th>\n",
       "      <th>newrel_f5564</th>\n",
       "      <th>newrel_f65</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>36.0</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>18</th>\n",
       "      <td>30.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>419.0</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>19</th>\n",
       "      <td>8.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>160.0</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>20</th>\n",
       "      <td>52.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>565.0</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>21</th>\n",
       "      <td>129.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>799.0</td>\n",
       "      <td>888.0</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    new_sp_m014  new_sp_m1524  new_sp_m2534  new_sp_m3544  new_sp_m4554  \\\n",
       "17          0.0          10.0           6.0           3.0           5.0   \n",
       "18         30.0         129.0         128.0          90.0          89.0   \n",
       "19          8.0          55.0          55.0          47.0          34.0   \n",
       "20         52.0         228.0         183.0         149.0         129.0   \n",
       "21        129.0         379.0         349.0         274.0         204.0   \n",
       "\n",
       "    new_sp_m5564  new_sp_m65  new_sp_f014  new_sp_f1524  new_sp_f2534  ...  \\\n",
       "17           2.0         0.0          5.0          38.0          36.0  ...   \n",
       "18          64.0        41.0         45.0         350.0         419.0  ...   \n",
       "19          21.0         8.0         25.0         139.0         160.0  ...   \n",
       "20          94.0        80.0         93.0         414.0         565.0  ...   \n",
       "21         139.0       103.0        146.0         799.0         888.0  ...   \n",
       "\n",
       "    newrel_m4554  newrel_m5564  newrel_m65  newrel_f014  newrel_f1524  \\\n",
       "17           NaN           NaN         NaN          NaN           NaN   \n",
       "18           NaN           NaN         NaN          NaN           NaN   \n",
       "19           NaN           NaN         NaN          NaN           NaN   \n",
       "20           NaN           NaN         NaN          NaN           NaN   \n",
       "21           NaN           NaN         NaN          NaN           NaN   \n",
       "\n",
       "    newrel_f2534  newrel_f3544  newrel_f4554  newrel_f5564  newrel_f65  \n",
       "17           NaN           NaN           NaN           NaN         NaN  \n",
       "18           NaN           NaN           NaN           NaN         NaN  \n",
       "19           NaN           NaN           NaN           NaN         NaN  \n",
       "20           NaN           NaN           NaN           NaN         NaN  \n",
       "21           NaN           NaN           NaN           NaN         NaN  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 602
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Number of rows have at least 1 non-null values across the columns 3-57 : 3484"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Question 2: Wide data isn't usually helpful.\n",
    "Convert this to a long format with the following columns:  \n",
    "1. country  \n",
    "2. year  \n",
    "3. diagnosis method: mapping should be: \n",
    "\n",
    "  {'newsp':'Pulmonary Smear (+)'\n",
    "  ,'newsn':'Pulmonary Smear (-)'\n",
    "  ,'newep':'Extrapulmonary'\n",
    "  ,'newrel':'Relapse'}   \n",
    "  \n",
    "\n",
    "4. gender: male or female.   \n",
    "5. age: lower_age - higher_age, e.g., 0-14  \n",
    "6. number of cases  "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "source": [
    "# Code\n",
    "df=pd.melt(df, id_vars=['country','year'])"
   ],
   "outputs": [],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "source": [
    "df['variable'] = df.variable.str.replace('new_sp', 'PulmonarySmear+')\n",
    "df['variable'] = df.variable.str.replace('new_sn', 'PulmonarySmear-')\n",
    "df['variable'] = df.variable.str.replace('new_ep', 'Extrapulmonary')\n",
    "df['variable'] = df.variable.str.replace('newrel', 'Relapse')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "source": [
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1980</td>\n",
       "      <td>PulmonarySmear+_m014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1981</td>\n",
       "      <td>PulmonarySmear+_m014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>PulmonarySmear+_m014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1983</td>\n",
       "      <td>PulmonarySmear+_m014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1984</td>\n",
       "      <td>PulmonarySmear+_m014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year              variable  value\n",
       "0  Afghanistan  1980  PulmonarySmear+_m014    NaN\n",
       "1  Afghanistan  1981  PulmonarySmear+_m014    NaN\n",
       "2  Afghanistan  1982  PulmonarySmear+_m014    NaN\n",
       "3  Afghanistan  1983  PulmonarySmear+_m014    NaN\n",
       "4  Afghanistan  1984  PulmonarySmear+_m014    NaN"
      ]
     },
     "metadata": {},
     "execution_count": 586
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "source": [
    "df[['diagnosis_method','variable']] = df.variable.str.split(\"_\",expand=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "source": [
    "df['variable'] = df.variable.str.replace('m', 'Male_')\n",
    "df['variable'] = df.variable.str.replace('f', 'Female_')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "source": [
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>diagnosis_method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1980</td>\n",
       "      <td>Male_014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1981</td>\n",
       "      <td>Male_014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>Male_014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1983</td>\n",
       "      <td>Male_014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1984</td>\n",
       "      <td>Male_014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  variable  value diagnosis_method\n",
       "0  Afghanistan  1980  Male_014    NaN  PulmonarySmear+\n",
       "1  Afghanistan  1981  Male_014    NaN  PulmonarySmear+\n",
       "2  Afghanistan  1982  Male_014    NaN  PulmonarySmear+\n",
       "3  Afghanistan  1983  Male_014    NaN  PulmonarySmear+\n",
       "4  Afghanistan  1984  Male_014    NaN  PulmonarySmear+"
      ]
     },
     "metadata": {},
     "execution_count": 589
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "source": [
    "df[['Gender','Age']] = df.variable.str.split(\"_\",expand=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "source": [
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>diagnosis_method</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1980</td>\n",
       "      <td>Male_014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1981</td>\n",
       "      <td>Male_014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>Male_014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1983</td>\n",
       "      <td>Male_014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1984</td>\n",
       "      <td>Male_014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  variable  value diagnosis_method Gender  Age\n",
       "0  Afghanistan  1980  Male_014    NaN  PulmonarySmear+   Male  014\n",
       "1  Afghanistan  1981  Male_014    NaN  PulmonarySmear+   Male  014\n",
       "2  Afghanistan  1982  Male_014    NaN  PulmonarySmear+   Male  014\n",
       "3  Afghanistan  1983  Male_014    NaN  PulmonarySmear+   Male  014\n",
       "4  Afghanistan  1984  Male_014    NaN  PulmonarySmear+   Male  014"
      ]
     },
     "metadata": {},
     "execution_count": 591
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "source": [
    "df['Age'] = df.Age.str.replace('014', '0-14')\n",
    "df['Age'] = df.Age.str.replace('1524', '15-24')\n",
    "df['Age'] = df.Age.str.replace('2534', '25-34')\n",
    "df['Age'] = df.Age.str.replace('3544', '35-44')\n",
    "df['Age'] = df.Age.str.replace('4554', '45-54')\n",
    "df['Age'] = df.Age.str.replace('5564', '55-64')"
   ],
   "outputs": [],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 593,
   "source": [
    "df=df.drop(['variable'],axis=1)\n",
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
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       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1983</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  value diagnosis_method Gender   Age\n",
       "0  Afghanistan  1980    NaN  PulmonarySmear+   Male  0-14\n",
       "1  Afghanistan  1981    NaN  PulmonarySmear+   Male  0-14\n",
       "2  Afghanistan  1982    NaN  PulmonarySmear+   Male  0-14\n",
       "3  Afghanistan  1983    NaN  PulmonarySmear+   Male  0-14\n",
       "4  Afghanistan  1984    NaN  PulmonarySmear+   Male  0-14"
      ]
     },
     "metadata": {},
     "execution_count": 593
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Question 3: Create a graph that shows when countries started to report TB cases."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "source": [
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>diagnosis_method</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1983</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  value diagnosis_method Gender   Age\n",
       "0  Afghanistan  1980    NaN  PulmonarySmear+   Male  0-14\n",
       "1  Afghanistan  1981    NaN  PulmonarySmear+   Male  0-14\n",
       "2  Afghanistan  1982    NaN  PulmonarySmear+   Male  0-14\n",
       "3  Afghanistan  1983    NaN  PulmonarySmear+   Male  0-14\n",
       "4  Afghanistan  1984    NaN  PulmonarySmear+   Male  0-14"
      ]
     },
     "metadata": {},
     "execution_count": 594
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "source": [
    "df.value.replace(0, np.nan, inplace=True)\n",
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>diagnosis_method</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1983</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  value diagnosis_method Gender   Age\n",
       "0  Afghanistan  1980    NaN  PulmonarySmear+   Male  0-14\n",
       "1  Afghanistan  1981    NaN  PulmonarySmear+   Male  0-14\n",
       "2  Afghanistan  1982    NaN  PulmonarySmear+   Male  0-14\n",
       "3  Afghanistan  1983    NaN  PulmonarySmear+   Male  0-14\n",
       "4  Afghanistan  1984    NaN  PulmonarySmear+   Male  0-14"
      ]
     },
     "metadata": {},
     "execution_count": 595
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "source": [
    "df = df.dropna()\n",
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>diagnosis_method</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1998</td>\n",
       "      <td>30.0</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1999</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2000</td>\n",
       "      <td>52.0</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2001</td>\n",
       "      <td>129.0</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2002</td>\n",
       "      <td>90.0</td>\n",
       "      <td>PulmonarySmear+</td>\n",
       "      <td>Male</td>\n",
       "      <td>0-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  year  value diagnosis_method Gender   Age\n",
       "18  Afghanistan  1998   30.0  PulmonarySmear+   Male  0-14\n",
       "19  Afghanistan  1999    8.0  PulmonarySmear+   Male  0-14\n",
       "20  Afghanistan  2000   52.0  PulmonarySmear+   Male  0-14\n",
       "21  Afghanistan  2001  129.0  PulmonarySmear+   Male  0-14\n",
       "22  Afghanistan  2002   90.0  PulmonarySmear+   Male  0-14"
      ]
     },
     "metadata": {},
     "execution_count": 596
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "source": [
    "reported_year_df = df[['country','year']].copy()\n",
    "reported_year_df=reported_year_df.drop_duplicates('country', keep='first')\n",
    "\n",
    "reported_year_df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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",
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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>country</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            country  year\n",
       "18      Afghanistan  1998\n",
       "52          Albania  1998\n",
       "85          Algeria  1997\n",
       "119  American Samoa  1997\n",
       "185          Angola  1995"
      ]
     },
     "metadata": {},
     "execution_count": 597
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 598,
   "source": [
    "reported_year_df.set_index('country')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Curacao</th>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>British Virgin Islands</th>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monaco</th>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anguilla</th>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Marino</th>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>217 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        year\n",
       "country                     \n",
       "Afghanistan             1998\n",
       "Albania                 1998\n",
       "Algeria                 1997\n",
       "American Samoa          1997\n",
       "Angola                  1995\n",
       "...                      ...\n",
       "Curacao                 2010\n",
       "British Virgin Islands  2004\n",
       "Monaco                  1999\n",
       "Anguilla                2010\n",
       "San Marino              2000\n",
       "\n",
       "[217 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 598
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 599,
   "source": [
    "# Increasing the size of output window\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>div.output_scroll { height: 100em; }</style>\"))\n",
    "\n"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style>div.output_scroll { height: 100em; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "source": [
    "fig= plt.figure(figsize=(10,40))\n",
    "plt.scatter(reported_year_df['year'],reported_year_df['country'])\n",
    "plt.title(\"First case reported year for each country\", fontsize =17)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Countries\")\n",
    "plt.grid()\n",
    "plt.show()\n"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x2880 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}