Скачать или смотреть How to Efficiently Filter, Sort, and Get Indices` of Array Columns Using Numpy

Learn how to filter columns, find the minimum values, sort them, and get their indices in an array using Numpy. Follow these easy steps for an efficient solution!
---
This video is based on the question https://stackoverflow.com/q/73693999/ asked by the user 'nilyad65' ( https://stackoverflow.com/u/19976071/ ) and on the answer https://stackoverflow.com/a/73694115/ provided by the user 'mozway' ( https://stackoverflow.com/u/16343464/ ) 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: I want to filter the columns with condition ' 0' and den find minimum value along filtered columns and sort them and get index of column using numpy

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.
---
Unlocking the Power of Numpy: Filtering, Sorting, and Indexing Array Columns

When working with data in Python, particularly with Numpy, you may encounter situations where you need to filter array columns based on certain conditions. For instance, a common task is to filter out non-positive elements, find the minimum values in those filtered columns, sort the results, and retrieve the indices of the columns based on the sorted values. In this guide, we will walk through this process step by step, providing an example to illustrate each stage.

Step 1: Setting Up Your Array

Let’s start by defining our sample array. The structure of the array (arr_a) we'll be working with is as follows:

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

This array contains various floating-point and integer values. Our goal will be to process this data efficiently using Numpy.

Step 2: Filtering Columns with a Condition

Next, we will filter each column to include only the elements greater than zero. We can accomplish this by using a simple relational operation directly on our array:

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

This will yield a boolean array (m) indicating which elements satisfy our condition. The output will look like this:

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

Step 3: Finding Minimum Values Along Filtered Columns

Having filtered the columns, our next task is to find the minimum values along each column, ignoring the entries marked False. We can utilize the np.nanmin() function along with np.where() to achieve this:

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

As a result, we will obtain an array of minimum values for each column that satisfied our filter condition. The output is:

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

Step 4: Sorting the Minimum Values

Now that we have our minimum values, we can sort them in descending order. This can be done easily using Numpy's indexing capabilities:

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

The sorted output will be:

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

Step 5: Retrieving Indices of the Sorted Columns

Finally, we will extract the indices of the columns after sorting them. Using np.argsort() will yield the desired indices corresponding to the sorted values:

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

This results in the following indices:

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

Conclusion

In this guide, we’ve successfully demonstrated how to filter a multi-dimensional array, find minimum values along specific columns, sort those values, and retrieve their column indices using Numpy in Python.

Using the above steps, you can transform and analyze your datasets effectively while writing minimal and efficient code.

Happy coding!