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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "716d419a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Country Name</th>\n",
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" <th>Country Code</th>\n",
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" <th>Year</th>\n",
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" <th>Value</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Arab World</td>\n",
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" <td>ARB</td>\n",
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" <td>1968</td>\n",
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" <td>2.576068e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Arab World</td>\n",
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" <td>ARB</td>\n",
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" <td>1969</td>\n",
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" <td>2.843420e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Arab World</td>\n",
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" <td>ARB</td>\n",
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" <td>1970</td>\n",
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" <td>3.138550e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Arab World</td>\n",
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" <td>ARB</td>\n",
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" <td>1971</td>\n",
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" <td>3.642691e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Arab World</td>\n",
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" <td>ARB</td>\n",
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" <td>1972</td>\n",
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" <td>4.331606e+10</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Country Name Country Code Year Value\n",
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"0 Arab World ARB 1968 2.576068e+10\n",
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"1 Arab World ARB 1969 2.843420e+10\n",
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"2 Arab World ARB 1970 3.138550e+10\n",
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"3 Arab World ARB 1971 3.642691e+10\n",
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"4 Arab World ARB 1972 4.331606e+10"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"df = pd.read_csv('gdp.csv')\n",
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "158f5de4",
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"metadata": {},
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"source": [
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"## 1) How many unique Countries Data we're having in the dataset?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "deb06683",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "71c4c9b6",
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"metadata": {},
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"source": [
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"## 2) How many years of Data we're having for India"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "679f2edb",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "b7023ae4",
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"metadata": {},
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"source": [
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"## 3) Whats the world GDP in 1969?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "41f5ba6a",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "c7f00c6a",
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"metadata": {},
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"source": [
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"## 4) Whats the % of GDP growth of India in Year 1994 (*as compare to 1993)?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8770deb7",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "9c848bca",
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"metadata": {},
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"source": [
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"# Tasks\n",
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"\n",
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"### 1) Find GDP Growth of India from 1960 - 2016\n",
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"### 2) Create a new columns named as GDP which shows the GDP growth of each country for that year\n",
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"### 3) Create a graph of GDP Growth of India from 1960 - 2016"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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