Choropleth Maps in Python

Choropleth maps are a great way to represent geographical data. I have done a basic implementation of two different data sets. I have used jupyter notebook to show the plots.

World Power Consumption 2014

First do Plotly imports

import plotly.graph_objs as go
from plotly.offline import init_notebook_mode,iplot
init_notebook_mode(connected=True)

Next step is to fetch the dataset, we’ll use Python pandas library to read the read the csv file

import pandas as pd
df = pd.read_csv('2014_World_Power_Consumption')

Next, we need to create data and layout variable which contains a dict

data = dict(type='choropleth',
locations = df['Country'],
locationmode = 'country names', z = df['Power Consumption KWH'],
text = df['Country'], colorbar = {'title':'Power Consumption KWH'},
colorscale = 'Viridis', reversescale = True)

Let’s make a layout

layout = dict(title='2014 World Power Consumption',
geo = dict(showframe=False,projection={'type':'Mercator'}))

Pass the data and layout and plot using iplot

choromap = go.Figure(data = [data],layout = layout)
iplot(choromap,validate=False)

The output will be be like below:

Check github for full code.

In next post I will try to make a choropleth for a different data set.

 References: 

https://www.udemy.com/python-for-data-science-and-machine-learning-bootcamp

      https://plot.ly/python/choropleth-maps/