Скачать или смотреть How to Plot Custom Confidence Intervals in Python with Matplotlib

Learn how to visualize your precalculated means and confidence intervals using Matplotlib in Python. This guide provides an in-depth solution for plotting error bars in a bar chart format.
---
This video is based on the question https://stackoverflow.com/q/63158411/ asked by the user 'Richard Summers' ( https://stackoverflow.com/u/5904412/ ) and on the answer https://stackoverflow.com/a/63159101/ provided by the user 'Richard Summers' ( https://stackoverflow.com/u/5904412/ ) 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: Python - How to plot from precalculated means and confidence intervals

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.
---
How to Plot Custom Confidence Intervals in Python with Matplotlib

Visualizing data effectively is a critical component of data analysis. One common requirement is plotting means along with their confidence intervals. In this post, we will explore how to plot custom confidence intervals using Matplotlib in Python, particularly when you already have the means and confidence intervals precalculated.

The Problem

Imagine you have a dataset containing the average values (means) for different years along with lower and upper confidence intervals (CIs). You want to create a bar chart where each bar represents the mean and the error bars extend to the upper and lower CIs.

Here's a simple DataFrame structure you might be using:

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

Now, let's say you attempt to plot your data using Matplotlib and you encounter an error message:

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

This means there is an issue with how the error bars are being specified. Let's dive into the solution!

The Solution

1. Import Necessary Libraries

Start by importing the libraries required for your analysis. You'll need Pandas for data manipulation and Matplotlib for plotting:

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

2. Prepare Your Data

Make sure your confidence interval values are in a format that can be interpreted correctly by Matplotlib. You can convert the DataFrame columns directly to NumPy arrays for easy handling:

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

3. Create the Error Bar Array

The yerr parameter in the errorbar() function accepts the errors in a specific format. You need to provide the difference between the mean and the lower CI, as well as the upper CI:

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

4. Plot Your Data

Now, utilize the plt.errorbar() function to create the bar chart with error bars:

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

5. Finishing Touches

To enhance your visualization, feel free to add titles, labels, or even gridlines for clarity:

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

Conclusion

Visualizing means with confidence intervals provides valuable insights into your data's reliability. By following the steps outlined above, you can successfully plot a bar chart with custom error bars in Python using Matplotlib.

If you encounter issues, make sure you are managing your data types correctly and specifying the error bars as demonstrated. Happy plotting!