Скачать или смотреть Creating Multi Plots in Jupyter Notebooks using pandas and matplotlib

Learn how to create multiple bar charts in a loop using `pandas` in Jupyter Notebooks without overwriting axes. Follow our step-by-step guide for effective data visualization!
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Creating Multi Plots in Jupyter Notebooks using pandas and matplotlib

If you've been working with data visualization in Python, you're likely familiar with pandas and the powerful plotting capabilities it offers, especially when integrated with libraries such as matplotlib and seaborn. One common challenge users face is generating multiple plots in a single cell of a Jupyter Notebook within a loop. In this guide, we'll break down the problem of creating multiple bar charts based on a DataFrame and walk you through the solution step-by-step.

Understanding the Problem

Imagine you have a DataFrame that holds boolean values for various places, and you want to visualize this data with bar charts. Here's a sample DataFrame (test_df):

[[See Video to Reveal this Text or Code Snippet]]

You might want to plot bar charts for both the 'Lehi' and 'Boise' columns. Here’s the code you initially used:

[[See Video to Reveal this Text or Code Snippet]]

However, running this in a loop results in only the bar chart for 'Boise' being displayed. Why is that? The issue arises because the loop overwrites the previous plotting axes each time it runs.

The Solution

To address this, we need to create new plotting axes for each iteration of the loop. This can be accomplished by explicitly calling plt.subplots() inside the loop, which helps to create separate figures for each plot.

Step-by-Step Instructions

Import Necessary Libraries: Ensure you've imported both pandas and matplotlib at the beginning of your notebook.

[[See Video to Reveal this Text or Code Snippet]]

Create Your DataFrame: Define your DataFrame test_df similar to the one shown above.

[[See Video to Reveal this Text or Code Snippet]]

Set Up Colors (Optional): If you want to add some color to your plots, define a list of colors.

[[See Video to Reveal this Text or Code Snippet]]

Create the Loop for Plotting: Now, set up your loop to create separate plots for each specified place.

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Code

plt.subplots(): Creates a new figure (fig) and axes (ax) for every iteration in the loop.

size(): Returns the counts of True and False values for the specified column.

plot(kind='bar', ax=ax): Plots the bar chart on the specified axes.

plt.show(): Displays the figure.

Conclusion

By following these steps, you can successfully generate multiple bar charts in a single Jupyter Notebook cell without them overwriting each other. This technique not only organizes your visualizations but also enhances the clarity of your data presentation.

Happy plotting, and may your data visualizations shine bright!