Introduction – Create Choropleth World Map using Plotly
Plotly Express is a high-level data visualization library available in Python that allows for quick and easy creation of interactive visualizations for data exploration and storytelling. It’s built on top of Plotly, a popular data visualization library, and provides an intuitive syntax that lets you create complex graphics with few lines of code.
Plotly Express simplifies the process of plotting, making it easy for novice and experienced developers alike to visualize data. It makes it possible to create interactive visualizations, including scatter plots, line charts, area charts, box plots, and even maps, in just a few lines of code.
In this step-by-step tutorial, we will use Plotly Express and Python to create a choropleth world map.
What is a choropleth map?
A choropleth map is a type of map that uses shades of colors to represent different values in different geographical areas. It is a great way to visualize and compare data over an area like a region or a country by dividing it into different colors or zones. They are commonly used in demography to show population density, election results, education levels, and other quantities.
Prerequisites
- Python and Jupyter Notebook installed on your machine
- Pandas: For data preparation data for plotting
- Plotly: Data visualization library
Data Preparation
We will create a sample dataframe containing a few countries and their population. This sample data will be used to create the world map. However, feel free to use any other dataset of your choice. Just make sure that it should have a column with proper country names and a column containing a key metric like population, per capita income, GPD etc.
# import libraries
import pandas as pd
import plotly.express as px
# Create a dataframe with country names and populations
data = { 'Country': ['China', 'India', 'United States', 'Indonesia', 'Pakistan', 'Brazil', 'Nigeria', 'Bangladesh', 'Russia', 'Mexico'],
'Population': [1.4, 1.5, 0.3, 0.3, 0.2, 0.2, 0.2, 0.2, 0.1, 0.1] }
df = pd.DataFrame(data)
Create World Map using Plotly Express
The world map created with Plotly Express will be interactive when viewed in Jupyter Notebook.
custom_color_scale = px.colors.qualitative.Pastel[:len(df)]
# Create an interactive world map using Plotly Express
fig = px.choropleth(
df,
locations='Country',
locationmode='country names',
color='Population',
hover_name='Country',
hover_data=['Population'],
color_discrete_sequence=custom_color_scale,
title='Top 10 Most Populous Countries'
)
fig.show()
Output:
In summary, Plotly Express offers an intuitive and robust tool for creating interactive visualizations for data exploration and communication. Its usability in Python makes it a valuable addition to a developer’s toolkit for data analysis and visualization.
