Gini index

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Tags: #worldbank #opendata #snippet #plotly
Author: Jeremy Ravenel​
Description: This notebook provides an analysis of the Gini index, a measure of income inequality, from the World Bank.
Input
Import library
import pandas as pd
from pandas_datareader import wb
import plotly.graph_objects as go
import plotly.express as px
Model
Get the association between the country and the ISO code
countries = wb.get_countries()
countries = countries[["name", "iso3c"]]
countries.columns = ["country", "iso3c"]
countries
Get gini index indicator per country
indicators = wb.download(indicator=["SI.POV.GINI"], country="all", start=1967, end=2018)
indicators.columns = ["GINI_INDEX"]
indicators
Merge previous tables
master_table = pd.merge(
    indicators.reset_index(), countries, left_on="country", right_on="country"
)
master_table = master_table.set_index(["country", "iso3c", "year"])
master_table
Pivot previous table and fill in undefined values with values from previous years
pivoted_table = pd.pivot_table(
    master_table, index=["country", "iso3c"], columns="year", values="GINI_INDEX"
)
pivoted_table = pivoted_table.ffill(axis=1)
pivoted_table
Show a map of gini index per country over the years (from 1969 to 2018)
pivoted_table = pd.pivot_table(
    master_table, index=["country", "iso3c"], columns="year", values="GINI_INDEX"
)
pivoted_table = pivoted_table.ffill(axis=1)
countries = list(pivoted_table.index.get_level_values(0))
locations = list(pivoted_table.index.get_level_values(1))
​
data = []
steps = []
i = 0
for year in pivoted_table.columns:
    data.append(
        dict(
            type="choropleth",
            name="",
            locations=locations,
            z=pivoted_table[year],
            hovertext=countries,
            colorscale=px.colors.sequential.Reds,
            visible=year == "2018",
        )
    )
​
    step = dict(
        method="restyle",
        args=["visible", [False] * len(pivoted_table.columns)],
        label=year,
    )
    step["args"][1][i] = True
    steps.append(step)
​
    i = i + 1
​
layout = go.Layout(
    title=dict(
        text="Evolution of the gini index from 1969 to 2018",
        x=0.5,
        font=dict(
            size=21,
        ),
    ),
    sliders=[dict(steps=steps, active=len(data) - 1)],
    annotations=[
        dict(text="Updated in 2018 from The World Bank", showarrow=False, x=1, y=-0.05)
    ],
    autosize=True,
    height=800,
)
​
fig = go.Figure(data, layout)
fig
Output
Export HTML
fig.write_html("file.html")
GDP per country and evolution
Worldometer
Last modified 1mo ago