Gini index

​​
​​
Tags: #worldbank #opendata
Author: Jeremy Ravenel​
Objective
The Gini index measures the equality per country. The closest to 0 the index is, the most equal the country is. The world average Gini index is at 38.
Data
GINI INDEX
Source
World Bank, Development Research Group.
Pitch
https://drive.google.com/file/d/10PXCX0Czck8QJwhinVEoKV3MZGvlAVDC/view
Input
Import library
1
import pandas as pd
2
from pandas_datareader import wb
3
import plotly.graph_objects as go
4
import plotly.express as px 
Copied!
Model
Get the association between the country and the ISO code
1
countries = wb.get_countries()
2
countries = countries[['name', 'iso3c']]
3
countries.columns = ['country', 'iso3c']
4
countries
Copied!
Get gini index indicator per country
1
indicators = wb.download(indicator=['SI.POV.GINI'], country='all', start=1967, end=2018)
2
indicators.columns = ['GINI_INDEX']
3
indicators
Copied!
Merge previous tables
1
master_table = pd.merge(indicators.reset_index(), countries, left_on='country', right_on='country')
2
master_table = master_table.set_index(['country', 'iso3c', 'year'])
3
master_table
Copied!
Pivot previous table and fill in undefined values with values from previous years
1
pivoted_table = pd.pivot_table(master_table, index=['country', 'iso3c'], columns='year', values='GINI_INDEX')
2
pivoted_table = pivoted_table.ffill(axis=1)
3
pivoted_table
Copied!
Show a map of gini index per country over the years (from 1969 to 2018)
1
pivoted_table = pd.pivot_table(master_table, index=['country', 'iso3c'], columns='year', values='GINI_INDEX')
2
pivoted_table = pivoted_table.ffill(axis=1)
3
countries = list(pivoted_table.index.get_level_values(0))
4
locations = list(pivoted_table.index.get_level_values(1))
5
​
6
data = []
7
steps = []
8
i = 0
9
for year in pivoted_table.columns:
10
  data.append(dict(
11
    type='choropleth',
12
    name='',
13
    locations=locations,
14
    z=pivoted_table[year],
15
    hovertext=countries,
16
    colorscale=px.colors.sequential.Reds,
17
    visible=year=='2018'
18
  ))
19
  
20
  step = dict(
21
    method='restyle',
22
    args=['visible', [False] * len(pivoted_table.columns)],
23
    label=year)
24
  step['args'][1][i] = True
25
  steps.append(step)
26
​
27
  i = i + 1
28
​
29
layout = go.Layout(
30
  title=dict(
31
    text='Evolution of the gini index from 1969 to 2018', 
32
    x=0.5,
33
    font=dict(
34
      size=21,
35
    )
36
  ),
37
  sliders=[dict(steps=steps, active=len(data) - 1)],
38
  annotations=[dict(
39
    text='Updated in 2018 from The World Bank',
40
    showarrow=False,
41
    x=1,
42
    y=-0.05
43
  )],
44
  autosize=True,
45
  height=800
46
)
47
​
48
fig = go.Figure(data, layout)
49
fig
Copied!
Output
Export HTML
1
fig.write_html("file.html")
Copied!

WorldBank

GDP per capita and growth

Last modified 2mo ago

Copy link

Edit on GitHub

Contents

Input

Model

Output