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Learn how to resolve the ValueError when plotting multiple DataFrames in Python. We'll guide you through the steps to visualize your data using Matplotlib and Pandas effectively.
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This video is based on the question https://stackoverflow.com/q/64788831/ asked by the user 'janicewww' ( https://stackoverflow.com/u/14403635/ ) and on the answer https://stackoverflow.com/a/64788881/ provided by the user 'Quang Hoang' ( https://stackoverflow.com/u/4238408/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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How to Plot Two DataFrames on One Graph in Python Using Matplotlib

Visualizing data is an essential step in data analysis, allowing you to comprehend patterns, trends, and insights. However, encountering errors can hinder your progress. In this post, we will address a common issue faced when attempting to plot two DataFrames on one graph using Python's Matplotlib library, specifically looking at a ValueError that can occur due to mismatched dimensions of the DataFrames.

The Problem: ValueError when Plotting

When plotting two DataFrames, you may encounter the following error message:

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

This error indicates that the x (index) and y (data points) do not have the same number of values, which is a requirement for plotting in Matplotlib. When dealing with time series data, this often happens if the two DataFrames cover different date ranges.

Understanding the DataFrames

In this case, we have two DataFrames:

df1 – contains net revenue over time for a company (e.g., ETSY).

df2 – contains stock price data for the same company sourced from Yahoo Finance.

Here’s how they are structured:

DataFrame df1

datesymbolnet_revenue2015-03-31ETSY585432015-06-30ETSY613652015-09-30ETSY65696.........DataFrame df2

dateHighLowOpenCloseVolumeAdj Close2015-04-1635.7428.2231.0030.001976330030.00.....................Identify the Issue

To plot both DataFrames on the same graph, ensure both have the same date range or index. If they don't, you'll have to plot each DataFrame against its own index correctly.

Solution: Plotting the DataFrames without Errors

Here are the steps to properly visualize your data while avoiding the ValueError:

Step 1: Use Separate Indices for Each DataFrame

Instead of plotting them directly against a common index, we can plot each DataFrame against its respective index. Here’s how you can do that:

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

Step 2: Using Pandas Plotting

Alternatively, you can use the Pandas built-in plotting functionality, which simplifies the process significantly:

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

Important Note on DateTime Conversion

Before plotting, ensure that your index is in the datetime format. You can do this using the following code:

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

Conclusion

Plotting data from multiple DataFrames on a single graph can provide valuable insights, but it requires careful handling when dimensions mismatch. By ensuring each DataFrame is referenced correctly against its own index, you can avoid common pitfalls like the ValueError. Use the provided solutions to visualize trends effectively!

Now you're ready to avoid those plot errors and bring clarity to your data visualizations! Happy plotting!