{

"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "962086b0-f623-442d-9004-78c1233d3a26",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f9cd430a-fba9-402b-b5c2-7dc87d3dc8bb",
"metadata": {},
"outputs": [],
"source": [
"df =
pd.read_csv(\"C:/Users/saswa/OneDrive/Desktop/Pinaki_WorldBank_Population/
Metadata_Country_API_SP.POP.TOTL_DS2_en_csv_v2_5871594.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7b739e6e-eef9-4273-8ee8-c9c4270c267b",
"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 Code</th>\n",
" <th>Region</th>\n",
" <th>IncomeGroup</th>\n",
" <th>SpecialNotes</th>\n",
" <th>TableName</th>\n",
" <th>Unnamed: 5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ABW</td>\n",
" <td>Latin America &amp; Caribbean</td>\n",
" <td>High income</td>\n",
" <td>NaN</td>\n",
" <td>Aruba</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AFE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>26 countries, stretching from the Red Sea in t...</td>\n",
" <td>Africa Eastern and Southern</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AFG</td>\n",
" <td>South Asia</td>\n",
" <td>Low income</td>\n",
" <td>The reporting period for national accounts dat...</td>\n",
" <td>Afghanistan</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AFW</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>22 countries, stretching from the westernmost ...</td>\n",
" <td>Africa Western and Central</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AGO</td>\n",
" <td>Sub-Saharan Africa</td>\n",
" <td>Lower middle income</td>\n",
" <td>The World Bank systematically assesses the app...</td>\n",
" <td>Angola</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>260</th>\n",
 " <td>XKX</td>\n",
" <td>Europe &amp; Central Asia</td>\n",
" <td>Upper middle income</td>\n",
" <td>NaN</td>\n",
" <td>Kosovo</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>261</th>\n",
" <td>YEM</td>\n",
" <td>Middle East &amp; North Africa</td>\n",
" <td>Low income</td>\n",
" <td>The World Bank systematically assesses the app...</td>\n",
" <td>Yemen, Rep.</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>262</th>\n",
" <td>ZAF</td>\n",
" <td>Sub-Saharan Africa</td>\n",
" <td>Upper middle income</td>\n",
" <td>Fiscal year end: March 31; reporting period fo...</td>\n",
" <td>South Africa</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>263</th>\n",
" <td>ZMB</td>\n",
" <td>Sub-Saharan Africa</td>\n",
" <td>Lower middle income</td>\n",
" <td>National accounts data were rebased to reflect...</td>\n",
" <td>Zambia</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>264</th>\n",
" <td>ZWE</td>\n",
" <td>Sub-Saharan Africa</td>\n",
" <td>Lower middle income</td>\n",
" <td>National Accounts data are reported in Zimbabw...</td>\n",
" <td>Zimbabwe</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>265 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Country Code Region IncomeGroup \\\n",
"0 ABW Latin America & Caribbean High income \n",
"1 AFE NaN NaN \n",
"2 AFG South Asia Low income \n",
"3 AFW NaN NaN \n",
"4 AGO Sub-Saharan Africa Lower middle income \n",
".. ... ... ... \n",
"260 XKX Europe & Central Asia Upper middle income \n",
"261 YEM Middle East & North Africa Low income \n",
"262 ZAF Sub-Saharan Africa Upper middle income \n",
 "263 ZMB Sub-Saharan Africa Lower middle income \n",
"264 ZWE Sub-Saharan Africa Lower middle income \n",
"\n",
" SpecialNotes \\\n",
"0 NaN \n",
"1 26 countries, stretching from the Red Sea in t... \n",
"2 The reporting period for national accounts dat... \n",
"3 22 countries, stretching from the westernmost ... \n",
"4 The World Bank systematically assesses the app... \n",
".. ... \n",
"260 NaN \n",
"261 The World Bank systematically assesses the app... \n",
"262 Fiscal year end: March 31; reporting period fo... \n",
"263 National accounts data were rebased to reflect... \n",
"264 National Accounts data are reported in Zimbabw... \n",
"\n",
" TableName Unnamed: 5 \n",
"0 Aruba NaN \n",
"1 Africa Eastern and Southern NaN \n",
"2 Afghanistan NaN \n",
"3 Africa Western and Central NaN \n",
"4 Angola NaN \n",
".. ... ... \n",
"260 Kosovo NaN \n",
"261 Yemen, Rep. NaN \n",
"262 South Africa NaN \n",
"263 Zambia NaN \n",
"264 Zimbabwe NaN \n",
"\n",
"[265 rows x 6 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "70eea6fc-bb5f-47c2-b5d5-a0b1865c310a",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gender_counts = df['Region'].value_counts()\n",
"bar_width = 0.9\n",
"x=range(len(gender_counts.index))\n",
"\n",
"\n",
"plt.bar(gender_counts.index,gender_counts.values)\n",
"plt.xlabel('Region')\n",
"plt.ylabel('Count')\n",
"plt.title('Distribution of Region')\n",
"\n",
"\n",
"plt.xticks(x,gender_counts.index,rotation=45)\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "68aa1d48-26a7-4dfd-af34-0cd29b32c24e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(265, 6)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "359fa12e-468e-4d55-a9d5-ee392f204a28",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 265 entries, 0 to 264\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Country Code 265 non-null object \n",
" 1 Region 217 non-null object \n",
" 2 IncomeGroup 216 non-null object \n",
" 3 SpecialNotes 127 non-null object \n",
" 4 TableName 265 non-null object \n",
" 5 Unnamed: 5 0 non-null float64\n",
"dtypes: float64(1), object(5)\n",
"memory usage: 12.6+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3b4b33d4-0a52-49cc-8735-1d348e7fb759",
"metadata": {},
"outputs": [
{
"data": {
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>0.0</td>\n",
" </tr>\n",
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 ],
"text/plain": [
" Unnamed: 5\n",
"count 0.0\n",
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"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a4cbfe12-2089-4f1e-9a87-a461265c6c04",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Country Code 0\n",
"Region 48\n",
"IncomeGroup 49\n",
"SpecialNotes 138\n",
"TableName 0\n",
"Unnamed: 5 265\n",
"dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
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],
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]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e40686e9-600f-4bdc-aab4-37a8ceddfe5d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
 "<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 265 entries, 0 to 264\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Country Code 265 non-null object \n",
" 1 Region 217 non-null object \n",
" 2 IncomeGroup 216 non-null object \n",
" 3 SpecialNotes 127 non-null object \n",
" 4 TableName 265 non-null object \n",
" 5 Unnamed: 5 0 non-null float64\n",
"dtypes: float64(1), object(5)\n",
"memory usage: 12.6+ KB\n"
]
}
],
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]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3803af4-c637-403e-be8e-d5617a1adb5a",
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