Unleashing the Power of Matplotlib for Multiple Plots

Name

Matt Popovic

Published on 5/3/2023

Open SourceMatplotlibPyGWalker

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Data visualization is a crucial aspect of data analysis, and Matplotlib is a widely-used Python library that enables users to create high-quality graphs, charts, and figures. In this article, we will discuss how to create multiple plots on the same figure using Matplotlib and introduce you to an open-source alternative, PyGWalker, which simplifies data visualization within Python Pandas.

Crafting Multiple Plots with Matplotlib

Matplotlib offers an intuitive way to create multiple plots within a single figure. This enables users to compare data easily and efficiently. Here are the steps to create multiple plots on the same figure using Matplotlib:

1. Import Matplotlib library and required modules:

import matplotlib.pyplot as plt

2. Create multiple plots on a single figure using the subplot() function:

plt.subplot(rows, columns, plot_number)

3. Customize plot appearance and add data to each plot.

4. Display the final figure using plt.show().

PyGWalker: A Powerful Open-Source Alternative to Matplotlib

PyGWalker is an open-source data analysis and data visualization package that offers a lightweight and easy-to-use interface, making it an excellent alternative to Matplotlib. To get started with PyGWalker, you can run it in Google Colab, Binder, or Kaggle. Check out PyGWalker's GitHub page for more information and read the Towards Data Science article for an in-depth overview.

Integrating PyGWalker into Your Jupyter Notebook Workflow

PyGWalker simplifies the data analysis and data visualization process within your Jupyter Notebook workflow. Here's how to integrate PyGWalker into your project:

1. Import PyGWalker and Pandas libraries:

import pandas as pd
import pygwalker as pyg

2. Call up Graphic Walker with a loaded DataFrame:

df = pd.read_csv('./bike_sharing_dc.csv', parse_dates=['date'])
gwalker = pyg.walk(df)

Create various types of plots with your Pandas DataFrame without requiring a graphical user interface:

Now that you have PygWalker set up, you can use it for powerful data visualization. Change the mark type to create different types of charts, such as a line chart:

Compare different measures by creating a concat view with multiple measures in rows/columns:

Create a facet view with multiple subviews divided by a dimension value:

View your data frame in a table and configure analytic types and semantic types:

Learn more about PyGWalker on PyGWalker Documentation page.

Conclusion

While Matplotlib is a powerful library for creating multiple plots on a single figure, PyGWalker offers a user-friendly alternative that simplifies data visualization within Python Pandas. By using either Matplotlib or PyGWalker, you can create visually appealing plots that effectively communicate insights from your data, allowing you to make informed decisions and drive your projects forward.