First_task_prodigy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7587aedf-8e40-4221-9dc4-c9914b797434",
   "metadata": {},
   "source": [
    "Here I am going to do data visualization of population growth of 15-64 age group of South Asian Countires using multiple bar diagram. For this I am importing pandas to read CSV files of data which I have taken from WorldBank Population Data  and matplotlib modules to make bar diagram of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b2655889-0962-4d77-be54-46aece27000b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "998c7ab0-5b63-4f26-b05d-2a0a43414e58",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"C:/Users/HP/Downloads/P_Data_Extract_From_World_Development_Indicators (1)/3987ad54-7b5a-4981-9b63-f10820910e11_Data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006b72ec-2393-400b-acae-612c335cfe41",
   "metadata": {},
   "source": [
    "I have read CSV files as there was large number of data in the file,so i dropped down some data  from original file using iloc which delete data of index given. I kept the data of population of age group of 15 to 64."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bfe9bb8d-8565-4962-b62a-0f132187e9a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.iloc[16:24] \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "802cd199-3024-4939-9dc9-6b798c0e8ea8",
   "metadata": {},
   "source": [
    "I also dropped some of the columns which was of no need for bar diagram and dropped some columns of year. Here axis 1 represents column, axis 0 represents rows by default .\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c030473-d22c-4c14-b309-8dfa5f51ec77",
   "metadata": {},
   "outputs": [],
   "source": [
    "population_data = data.drop(['Series Code','Series Name','Country Code','2018 [YR2018]','2022 [YR2022]'],axis = 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1879cf0-307a-4074-8b62-a70cffd63fa2",
   "metadata": {},
   "source": [
    "Population data which we want to visualize is seen below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94624ff1-e3bf-423a-a994-ea5cb4ea1ce7",
   "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>Country Name</th>\n",
       "      <th>2019 [YR2019]</th>\n",
       "      <th>2020 [YR2020]</th>\n",
       "      <th>2021 [YR2021]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>53.290894</td>\n",
       "      <td>53.775135</td>\n",
       "      <td>54.171399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>66.999306</td>\n",
       "      <td>67.384859</td>\n",
       "      <td>67.719904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>69.746723</td>\n",
       "      <td>70.441654</td>\n",
       "      <td>71.061041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>India</td>\n",
       "      <td>66.933943</td>\n",
       "      <td>67.215346</td>\n",
       "      <td>67.509944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maldives</td>\n",
       "      <td>73.855420</td>\n",
       "      <td>73.826584</td>\n",
       "      <td>73.737936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>63.218720</td>\n",
       "      <td>63.844319</td>\n",
       "      <td>64.559447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>58.306744</td>\n",
       "      <td>58.553442</td>\n",
       "      <td>58.838485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>65.622095</td>\n",
       "      <td>65.609808</td>\n",
       "      <td>65.628696</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country Name  2019 [YR2019]  2020 [YR2020]  2021 [YR2021]\n",
       "16  Afghanistan      53.290894      53.775135      54.171399\n",
       "17   Bangladesh      66.999306      67.384859      67.719904\n",
       "18       Bhutan      69.746723      70.441654      71.061041\n",
       "19        India      66.933943      67.215346      67.509944\n",
       "20     Maldives      73.855420      73.826584      73.737936\n",
       "21        Nepal      63.218720      63.844319      64.559447\n",
       "22     Pakistan      58.306744      58.553442      58.838485\n",
       "23    Sri Lanka      65.622095      65.609808      65.628696"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a48581ee-cbff-4d5f-bb75-a7c3399dd39c",
   "metadata": {},
   "source": [
    "I am setting index value as Country Name so that in bar diagram in X - axis Country Name is shown instead of index value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2fd73616-b98e-41b5-9d03-44b6361e22b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "population_data.set_index('Country Name',inplace = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b7e7761-e85f-402b-9acd-37efeb90e7b9",
   "metadata": {},
   "source": [
    "To plot bar daigram of above data from matlpotlib plot.bar is used and I have  given names of x axis ,y axis and title to the bar digram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee9ecb97-789b-4fd5-b7cc-d967478ec5f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x237d58ee830>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "population_data.plot.bar()\n",
    "plt.xlabel('Country Name')\n",
    "plt.ylabel('Percentage of Total Population')\n",
    "plt.title('Population of South Asian Countries of age 15-64')\n",
    "plt.legend(prop = {\"size\":7})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fb8fb3e-0fc2-49df-9420-bf1936bb955f",
   "metadata": {},
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}