Richest countries top10

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Tags: #worldbank #opendata
Author: Jeremy Ravenel​
Goal
Top 10 richest regions & countries in GDP per capita (wealth created per habitant) and in GDP current (total wealth created by the country)
Data
GDP CURRENT & GDP PER CAPITA by countries, agregated by region
Sources
World Bank national accounts data
OECD National Accounts data files
Notes
The top 10 for GDP current is including the G8, should the European Union be included in this ranking, it would come up 2nd biggest economy after the USA.
In the top 10 for GDP per capita, the ranking include smaller countries, only the USA remains in this ranking from the GDP current ranking.
Pitch
https://drive.google.com/file/d/1wGo9aI6mXS_2AbmnNlLoSJ9bGxkXmDhq/view
Input
Import libraries
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import pandas as pd
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from pandas_datareader import wb
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import plotly.graph_objects as go
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Model
Get the association between the country and the region
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pd.options.display.float_format = '{: .0f}'.format
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countries = wb.get_countries()
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countries = countries[['name', 'region']]
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countries
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Get indicators
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indicators = wb.download(indicator=['NY.GDP.PCAP.CD', 'NY.GDP.MKTP.CD'], country='all', start=2018, end=2018)
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indicators = indicators.reset_index()
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indicators = indicators[['country', 'NY.GDP.PCAP.CD', 'NY.GDP.MKTP.CD']]
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indicators.columns = ['country', 'GDP_PER_CAPITA', 'CURRENT_GDP']
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indicators
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Format a master table
1.Associate countries with regions
2.Clean up the data
3.Group rows by columns
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master_table = pd.merge(indicators, countries, left_on='country', right_on='name')
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​
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master_table = master_table[master_table['region'] != 'Aggregates']
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master_table = master_table[(master_table['GDP_PER_CAPITA'] > 0) | (master_table['CURRENT_GDP'] > 0)]
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master_table = master_table.fillna(0)
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​
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master_table = pd.melt(master_table, id_vars=['region', 'country'], value_vars=['GDP_PER_CAPITA', 'CURRENT_GDP'], var_name='INDICATOR', value_name='VALUE')
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master_table = master_table.set_index(['region', 'country', 'INDICATOR'])
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master_table = master_table.sort_index()
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​
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master_table
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Output
Visualize data with a chart
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table = master_table.reset_index()
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gdp_per_capita_per_region = table[table['INDICATOR'] == 'GDP_PER_CAPITA'][['region', 'VALUE']].groupby('region').mean().sort_values('VALUE', ascending=False)
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current_gdp_per_region = table[table['INDICATOR'] == 'CURRENT_GDP'][['region', 'VALUE']].groupby('region').mean().sort_values('VALUE', ascending=False)
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gdp_per_capita_per_country = table[table['INDICATOR'] == 'GDP_PER_CAPITA'][['country', 'VALUE']].sort_values('VALUE', ascending=False).head(10)
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current_gdp_per_country = table[table['INDICATOR'] == 'CURRENT_GDP'][['country', 'VALUE']].sort_values('VALUE', ascending=False).head(10)
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​
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data = [
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  go.Bar(x=gdp_per_capita_per_region.index, y=gdp_per_capita_per_region['VALUE'], text=gdp_per_capita_per_region['VALUE'], textposition='outside'),
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  go.Bar(x=current_gdp_per_region.index, y=current_gdp_per_region['VALUE'], text=current_gdp_per_region['VALUE'], textposition='outside', visible=False),
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  go.Bar(x=gdp_per_capita_per_country['country'], y=gdp_per_capita_per_country['VALUE'], text=gdp_per_capita_per_country['VALUE'], textposition='outside', visible=False),
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  go.Bar(x=current_gdp_per_country['country'], y=current_gdp_per_country['VALUE'], text=current_gdp_per_country['VALUE'], textposition='outside', visible=False),
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]
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​
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layout = go.Layout(
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  title='Top 10 richest regions & countries',
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  margin = dict(t = 60, b = 150),
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  updatemenus=list([
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    dict(showactive=True, type="buttons", active=0, buttons=[
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      {'label': 'GDP / Capita per region', 'method': 'update', 'args': [{'visible': [True, False, False, False]}]},
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      {'label': 'Current GDP per region', 'method': 'update', 'args': [{'visible': [False, True, False, False]}]},
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      {'label': 'GDP / Capita per country', 'method': 'update', 'args': [{'visible': [False, False, True, False]}]},
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      {'label': 'Current GDP per country', 'method': 'update', 'args': [{'visible': [False, False, False, True]}]}
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    ])
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  ]),
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  annotations=[dict(
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    text = 'Updated in 2018 from The World Bank',
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    showarrow = False,
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    xref = 'paper', x = 1,
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    yref = 'paper', y = -0.4)]
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)
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​
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go.Figure(data, layout)
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GDP per country and evolution

World population and density

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Contents

Input

Model

Output