Скачать или смотреть How to Efficiently Change True Values to False in a Boolean Array Using Python

Learn how to manipulate boolean arrays in Python by selectively changing `True` values to `False`. This guide provides step-by-step instructions for using numpy efficiently.
---
This video is based on the question https://stackoverflow.com/q/64750289/ asked by the user 'f. c.' ( https://stackoverflow.com/u/1694518/ ) and on the answer https://stackoverflow.com/a/64750397/ provided by the user 'GZ0' ( https://stackoverflow.com/u/10157759/ ) 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: How to set certain True elements to False in a boolean array in python?

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 Efficiently Change True Values to False in a Boolean Array Using Python

Working with boolean arrays in Python can often lead to situations where you need to manipulate specific values based on certain conditions. One common problem arises when you have two boolean arrays, and you want to modify certain True values in one based on the entries of the other.

The Problem

You might find yourself with two arrays like this:

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

The boolean array a contains various True (1) and False (0) values, while the shorter boolean array b indicates which True values in a should be retained (when b is 1) and which should be set to False (when b is 0).

For example, if you identify the indices of the True values in a, using np.where(a)[0], you will get:

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

Given b, you want to retain the first two True values in a and flip the last one to False, so the final state of a will look like this:

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

The Solution

Step 1: Identify the Indices

You have already identified the indices of True values using numpy's np.where(a)[0]. This gives you a concise way of capturing where your True values are located in a.

Step 2: Modify Values Based on b

Now, to change the values in a using b, you can simply use boolean indexing. The operation is straightforward: you will apply the inverse of b to the indices stored in idx. Here's how you can do it:

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

Explanation of the Code:

~b is a numpy operation that returns the inverse boolean array (where True becomes False and vice versa).

idx[~b] gives you the indices of the True values in a that need to be set to False based on the positions of 0 in b.

This command effectively updates the specified indices in a to False.

Step 3: Result

After running the provided code, a will reflect the desired changes:

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

Conclusion

Manipulating boolean arrays in Python, especially with the help of numpy, allows for efficient and powerful data manipulation. Remember, the key steps in achieving the desired result are identifying the indices and using boolean indexing to modify the appropriate values. This technique can be adapted to a variety of scenarios, especially when working with larger arrays or needing more complex manipulations.

Now you can efficiently manage your boolean arrays in Python!