Visualising Electricity Access Over Space and Time

How is the world changing over the years!

Nipun Batra


June 26, 2018

In this post, I’ll explore electricity access, i.e. globally what fraction of people have access to electricity. Beyond the goal of finding the electricity access, this post will also serve to illustrate how the coolness coefficient of the Python visualisation ecosystem!

I’ll be using data from World Bank for electricity access. See the image below for the corresponding page.

Downloading World Bank data

Now, a Python package called wbdata provides a fairly easy way to access World Bank data. I’d be using it to get data in Pandas DataFrame.

%matplotlib inline
import pandas as pd
import wbdata
import matplotlib.pyplot as plt
import datetime
data_date = (datetime.datetime(1990, 1, 1), datetime.datetime(2016, 1, 1))
df_elec = wbdata.get_data("EG.ELC.ACCS.ZS", pandas=True, data_date=data_date)
country     date
Arab World  2016    88.768654
            2015    88.517967
            2014    88.076774
            2013    88.389705
            2012    87.288244
Name: value, dtype: float64

Downloading Geodata and Reading Using GeoPandas

I’d now be downloading shapefile data for different countries. This will help us to spatially plot the data for the different countries.

--2018-06-26 15:52:50--
Resolving (
Connecting to (||:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 5077755 (4.8M) [application/x-zip-compressed]
Saving to: ‘’

ne_10m_admin_0_coun 100%[===================>]   4.84M   246KB/s    in 22s     

2018-06-26 15:53:12 (228 KB/s) - ‘’ saved [5077755/5077755]

Extracting shapefile

import zipfile
zip_ref = zipfile.ZipFile('', 'r')
import geopandas as gpd
gdf = gpd.read_file('ne_10m_admin_0_countries_lakes.shp')[['ADM0_A3', 'geometry']]
ADM0_A3 geometry
0 IDN (POLYGON ((117.7036079039552 4.163414542001791...
1 MYS (POLYGON ((117.7036079039552 4.163414542001791...
2 CHL (POLYGON ((-69.51008875199994 -17.506588197999...
3 BOL POLYGON ((-69.51008875199994 -17.5065881979999...
4 PER (POLYGON ((-69.51008875199994 -17.506588197999...

Visualising electricity access in 2016

Getting electricity access data for 2016

df_2016 = df_elec.unstack()[['2016']].dropna()
date 2016
Afghanistan 84.137138
Albania 100.000000
Algeria 99.439568
Andorra 100.000000
Angola 40.520607

In order to visualise electricity access data over the map, we would have to join the GeoPandas object gdf and df_elec

Joining gdf and df_2016

Now, gdf uses alpha_3 codes for country names like AFG, etc., whereas df_2016 uses country names. We will thus use pycountry package to get code names corresponding to countries in df_2016 as shown in this StackOverflow post.

import pycountry
countries = {}
for country in pycountry.countries:
    countries[] = country.alpha_3
codes = [countries.get(country, 'Unknown code') for country in df_2016.index]
df_2016['Code'] = codes
date 2016 Code
Afghanistan 84.137138 AFG
Albania 100.000000 ALB
Algeria 99.439568 DZA
Andorra 100.000000 AND
Angola 40.520607 AGO

Now, we can join the two data sources

merged_df_2016 = gpd.GeoDataFrame(pd.merge(gdf, df_2016, left_on='ADM0_A3', right_on='Code'))
ADM0_A3 geometry 2016 Code
0 IDN (POLYGON ((117.7036079039552 4.163414542001791... 97.620000 IDN
1 MYS (POLYGON ((117.7036079039552 4.163414542001791... 100.000000 MYS
2 CHL (POLYGON ((-69.51008875199994 -17.506588197999... 100.000000 CHL
3 PER (POLYGON ((-69.51008875199994 -17.506588197999... 94.851746 PER
4 ARG (POLYGON ((-68.4486097329999 -52.3466170159999... 100.000000 ARG

Finally plotting!

# Example borrowed from
figsize = (16, 5)
ax = merged_df_2016.plot(column='2016', cmap=cmap, figsize=figsize,legend=True)
title = 'Electricity Access(% of population) in {}'.format('2016')
gdf[~gdf.ADM0_A3.isin(merged_df_2016.ADM0_A3)].plot(ax=ax, color='#fffafa', hatch='///')
ax.set_title(title, fontdict={'fontsize': 15}, loc='left')

Creating animation for access across time

!mkdir -p elec_access
def save_png_year(year, path="elec_access"):
    df_year = df_elec.unstack()[['{}'.format(year)]].dropna()
    codes = [countries.get(country, 'Unknown code') for country in df_year.index]
    df_year['Code'] = codes
    merged_df_year = gpd.GeoDataFrame(pd.merge(gdf, df_year, left_on='ADM0_A3', right_on='Code'))
    figsize = (16, 5)
    ax = merged_df_year.plot(column='{}'.format(year), cmap=cmap, figsize=figsize,legend=True,vmin=0.0, vmax=100.0)
    title = 'Electricity Access(% of population) in {}'.format(year)
    gdf[~gdf.ADM0_A3.isin(merged_df_year.ADM0_A3)].plot(ax=ax, color='#fffafa', hatch='///')
    ax.set_title(title, fontdict={'fontsize': 15}, loc='left')
    plt.savefig('{}/{}.png'.format(path, year), dpi=300)
for year in range(1990, 2017):
# Borrowed from
def create_gifv(input_files, output_base_name, fps):
    import imageio
    output_extensions = ["gif"]
    input_filenames = ['elec_access/{}.png'.format(year) for year in range(1990, 2017)]

    poster_writer = imageio.get_writer("{}.png".format(output_base_name), mode='i')
    video_writers = [
        imageio.get_writer("{}.{}".format(output_base_name, ext), mode='I', fps=fps)
        for ext in output_extensions]

    is_first = True
    for filename in input_filenames:
        img = imageio.imread(filename)

        for writer in video_writers:
        if is_first:

        is_first = False

    for writer in video_writers + [poster_writer]:
create_gifv("elec_access/*.png", "electricity_access", 4)

Across Africa and SE Asia, one can clearly see a gradual improvement in access! Hope you had fun reading this post :)