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‎Lesson06/.ipynb_checkpoints/Activity06-checkpoint.ipynb

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‎Lesson06/.ipynb_checkpoints/Example01-checkpoint.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Our world in data 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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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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": 2,
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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>HPI Rank</th>\n",
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" <th>Country</th>\n",
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" <th>Region</th>\n",
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" <th>Life Expectancy (years)</th>\n",
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" <th>Wellbeing (0-10)</th>\n",
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" <th>Inequality of outcomes</th>\n",
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" <th>Ecological Footprint (gha/capita)</th>\n",
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" <th>Happy Planet Index</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>1</td>\n",
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" <td>Costa Rica</td>\n",
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" <td>Americas</td>\n",
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" <td>79.1</td>\n",
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" <td>7.3</td>\n",
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" <td>15%</td>\n",
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" <td>2.8</td>\n",
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" <td>44.7</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>2</td>\n",
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" <td>Mexico</td>\n",
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" <td>Americas</td>\n",
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" <td>76.4</td>\n",
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" <td>7.3</td>\n",
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" <td>19%</td>\n",
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" <td>2.9</td>\n",
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" <td>40.7</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>3</td>\n",
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" <td>Colombia</td>\n",
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" <td>Americas</td>\n",
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" <td>73.7</td>\n",
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" <td>6.4</td>\n",
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" <td>24%</td>\n",
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" <td>1.9</td>\n",
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" <td>40.7</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>4</td>\n",
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" <td>Vanuatu</td>\n",
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" <td>Asia Pacific</td>\n",
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" <td>71.3</td>\n",
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" <td>6.5</td>\n",
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" <td>22%</td>\n",
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" <td>1.9</td>\n",
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" <td>40.6</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>5</td>\n",
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" <td>Vietnam</td>\n",
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" <td>Asia Pacific</td>\n",
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" <td>75.5</td>\n",
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" <td>5.5</td>\n",
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" <td>19%</td>\n",
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" <td>1.7</td>\n",
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" <td>40.3</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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" HPI Rank Country Region Life Expectancy (years) \\\n",
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"0 1 Costa Rica Americas 79.1 \n",
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"1 2 Mexico Americas 76.4 \n",
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"2 3 Colombia Americas 73.7 \n",
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"3 4 Vanuatu Asia Pacific 71.3 \n",
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"4 5 Vietnam Asia Pacific 75.5 \n",
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"\n",
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" Wellbeing (0-10) Inequality of outcomes Ecological Footprint (gha/capita) \\\n",
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"0 7.3 15% 2.8 \n",
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"1 7.3 19% 2.9 \n",
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"2 6.4 24% 1.9 \n",
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"3 6.5 22% 1.9 \n",
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"4 5.5 19% 1.7 \n",
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"\n",
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" Happy Planet Index \n",
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"0 44.7 \n",
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"1 40.7 \n",
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"2 40.7 \n",
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"3 40.6 \n",
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"4 40.3 "
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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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"#Download the data from Github repo \n",
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"hpi_url = \"https://raw.githubusercontent.com/TrainingByPackt/Interactive-Data-Visualization-with-Python/master/datasets/hpi_data_countries.tsv\"\n",
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"\n",
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"# Once downloaded, read it into a dataframe using pandas\n",
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"hpi_df = pd.read_csv(hpi_url, sep='\\t')\n",
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"hpi_df.head()\n"
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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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"metadata": {},
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"outputs": [],
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"source": []
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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": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎Lesson06/.ipynb_checkpoints/Example02-checkpoint.ipynb

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