GDP per capita and growth

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Tags: #worldbank #opendata
Author: Jeremy Ravenel​
Objective : allows to visualize a map of GDP per capita and the GDP growth of all the countries in the world. Click on the country on the map or select it to see the details info.
Data :
GDP per capita (current US$)
GDP growh (annual %)
Source: World Bank national accounts data.
Input
Import library
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import pandas as pd
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import numpy as np
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import plotly.graph_objects as go
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from pandas_datareader import wb
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from naas_drivers import plotly
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Model
Get data from World Bank
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indicators = wb.download(indicator=['NY.GDP.PCAP.CD', 'NY.GDP.PCAP.KD.ZG'], country='all', start=2013, end=2021)
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​
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indicators = indicators.reset_index()
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indicators = indicators[['country', 'year', 'NY.GDP.PCAP.CD', 'NY.GDP.PCAP.KD.ZG']]
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indicators.columns = ['country', 'year', 'GDP_PER_CAPITAL', 'GDP_GROWTH_PER_CAPITAL']
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​
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indicators = indicators.fillna(0)
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​
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countries = wb.get_countries()
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countries = countries[['name', 'region', 'iso3c']]
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​
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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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​
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master_table = master_table.drop(columns=['name'])
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​
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master_table = master_table.dropna()
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​
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# Création de l'ensemble final
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xls_formatted = pd.DataFrame(columns=['COUNTRY', 'YEAR', 'GDP_PER_CAPITAL', 'GDP_GROWTH_PER_CAPITAL', 'REGION', 'ISO3C'])
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​
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for index, line in master_table.iterrows():
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  xls_formatted = xls_formatted.append(
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    {
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        'COUNTRY': line['country'],
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        'YEAR': line['year'],
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        'GDP_PER_CAPITAL': line['GDP_PER_CAPITAL'],
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        'GDP_GROWTH_PER_CAPITAL': line['GDP_GROWTH_PER_CAPITAL'],
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        'REGION': line['region'],
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        'ISO3C': line['iso3c'],
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    }, ignore_index=True
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  )
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​
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master_table = xls_formatted
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​
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master_table
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Choose the year to display
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year = "2019"
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Create mapchart
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master_year_table = master_table[master_table['YEAR'] == year]
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​
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GDP_GROWTH_PER_CAPITAL = "GDP growth per capita"
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GDP_PER_CAPITAL = "GDP per capita"
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​
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fig = go.Figure()
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​
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fig.layout = go.Layout(
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    #width=500,
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    #height=300,
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    annotations=[
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        go.layout.Annotation(
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            showarrow=False,
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            text='Source: World Bank',
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            xanchor='right',
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            x=1,
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            yanchor='top',
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            y=-0.05
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        )])
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​
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fig.add_trace(go.Choropleth(
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    locations=master_year_table['ISO3C'],
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    z = master_year_table['GDP_PER_CAPITAL'],
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    colorscale = [(0,"#053D8B"),(0.25,"#1164B0"),(0.5,"#298BC8"),(0.75,"#3FB7DB"), (1,"#5CD5E8")],
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    colorbar_title = "Value",
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    customdata = master_year_table['COUNTRY'],
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    hovertemplate = '<b>%{customdata}: %{z:,.0f}</b><extra></extra>'
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))
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​
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fig.add_trace(go.Choropleth(
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    locations=master_year_table['ISO3C'],
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    visible= False,
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    z = master_year_table['GDP_GROWTH_PER_CAPITAL'],
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    colorscale = [(0,"#053D8B"),(0.25,"#1164B0"),(0.5,"#298BC8"),(0.75,"#3FB7DB"), (1,"#5CD5E8")],
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    colorbar_title = "Growth ",
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    customdata = master_year_table['COUNTRY'],
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    hovertemplate = '<b>%{customdata}: %{z:0.2f}%</b><extra></extra>'
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))
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​
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fig.update_layout(
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    autosize=True,
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    width= 900,
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    #height= 900,
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    title=f"GDP per capital in {year}",
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    title_x=0.5,
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    title_y=0.95,
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    updatemenus=[
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        dict(
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            type = "buttons",
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            active=0,
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            direction = "left",
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            buttons=list([
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                dict(
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                    args=[{"visible": [True, False]}, {"title": f"{GDP_PER_CAPITAL} in {year}"}],
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                    label=GDP_PER_CAPITAL,
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                    method="update"
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                ),
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                dict(
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                    args=[{"visible": [False, True]}, {"title": f"{GDP_GROWTH_PER_CAPITAL} in {year}"}],
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                    label=GDP_GROWTH_PER_CAPITAL,
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                    method="update"
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                )
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            ]),
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            showactive=True,
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            x=0.32,
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            xanchor="left",
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            y=1.2,
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            yanchor="top"
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        ),
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    ]
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)
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​
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fig.show()
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Output
Export chart
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plotly.export(fig, "GDP.png", css=None)
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plotly.export(fig, "GDP.html", css=None)
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Input

Model

Output