Скачать или смотреть Plotting Different Data Frames on a Single Line Graph in Python

Learn how to efficiently plot multiple data frames on the same line graph using Python's Matplotlib and Pandas libraries.
---
This video is based on the question https://stackoverflow.com/q/76671727/ asked by the user 'Abhay Dodiya' ( https://stackoverflow.com/u/18288409/ ) and on the answer https://stackoverflow.com/a/76671928/ provided by the user 'MGoalabre' ( https://stackoverflow.com/u/9355511/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Plot Different Data Frame in Single Line Plot

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Plotting Different Data Frames on a Single Line Graph in Python

When working with data visualization in Python, especially using libraries like Matplotlib and Pandas, you may find yourself in a situation where you need to plot multiple data frames on a single graph. This can be particularly useful when you want to compare different datasets side by side. In this guide, we will explore how to efficiently plot multiple data frames and showcase their information on a single line graph.

The Initial Challenge

Imagine you have three data frames, each containing the same number of rows but different values. In this example:

L1: Static data frame with fixed values.

K1: First user-input data frame.

K2: Second user-input data frame.

Your goal is to visualize the data from all three frames on a single line plot for easy comparison. If you've attempted this using a loop but found that only some data frames are displayed, you're not alone. Let’s break down how to achieve your goal effectively.

Step-by-Step Solution

1. Import Required Libraries

To begin, ensure that you have both Pandas and Matplotlib installed and imported into your Python environment.

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

2. Create the Data Frames

Start by creating your data frames. Here's how you can set them up based on our example:

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

3. Setting Up the Plot

Instead of plotting each data frame in a loop, we can use the subplots method to set up a grid of plots. This method allows you to define the number of rows and columns of your plot, and it returns a figure object along with axes for each subplot.

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

4. Plot Each Data Frame

With axes set up, you can now plot each data frame into its own subplot using a straightforward approach. Here’s how to do it:

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

5. Labeling the Axes

It’s essential to label your axes properly for clarity. You can use the set_ylabel method for each subplot:

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

6. Aligning the Y-Axis

If you want the y-axis labels to align properly across all subplots, you can use the align_ylabels function from the figure object:

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

7. Display the Plot

Finally, to visualize your graph, simply use plt.show():

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

Complete Code

Here is the complete code that integrates all the steps we discussed:

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

Conclusion

Visualizing multiple data frames in a single line plot is straightforward when you use the right techniques with Matplotlib. By following the steps outlined above, you can create clear and informative plots that allow for easy comparison of different datasets. This approach not only enhances your data analysis but also helps in effectively communicating your findings.

Happy plotting!