How To Plot Panel Data In Python

In this tutorial, you’ll learn how to plot panel data in Python using the popular libraries Pandas and Plotly. Panel data, also known as longitudinal data, consists of observational data collected over time for multiple subjects. By the end of this tutorial, you will be able to import, manipulate, and visualize the panel data effectively.

Step 1: Installing Required Libraries

To get started, you need to install Pandas and Plotly. You can install them using the following commands:

Step 2: Importing Required Libraries

Import the necessary libraries by executing the following code block:

Step 3: Loading Your Dataset

In this step, you will load a sample panel dataset into a Pandas DataFrame.

This dataset contains information on GDP and Unemployment rates for two countries (USA and Canada) from 2000 to 2002.

Step 4: Pivoting the Data

To plot the panel data, you need to transform the current dataset into a more appropriate format. This can be achieved using the pivot_table() function in Pandas.

This will reshape the dataset such that it has unique columns for GDP and Unemployment rates.

Step 5: Plotting the Data

In this step, you will use the Plotly library to create interactive line plots for the panel data.

5.1: Plotting GDP Over Time

5.2: Plotting Unemployment Rate Over Time


Full Code


You have successfully learned how to plot panel data in Python using Pandas and Plotly. With this knowledge, you can manipulate and visualize longitudinal data to perform powerful analyses and make compelling visualizations.