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Learn how to effectively use the `pandas.cut()` function in Python to categorize values in an array, and customize categories with your own labels.
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How to Use pandas.cut() to Categorize Values in an Array with Custom Labels

Working with large datasets often requires categorizing numerical data into bins or segments to facilitate analysis. In Python's pandas library, this can be seamlessly achieved using the pandas.cut() function. This guide will guide you on how to use pandas.cut() to categorize values in an array, complete with custom labels.

What is pandas.cut()?

The pandas.cut() function is utilized for segmenting and sorting data values into bins. This function is particularly useful when you need to convert continuous numerical data into discrete intervals. The result is a good way to understand the distribution and ranges of your data.

Basic Usage of pandas.cut()

Below is a simple example that demonstrates how to use pandas.cut():

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

In the example above, the data array is divided into bins defined by the bins list. Each element in the data array is assigned to a bin.

Custom Labels

To make the output more interpretable, you can assign custom labels to each bin. Here's how you can do it:

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

In this example, custom labels 'Very Low', 'Low', 'Medium', and 'High' are assigned to the bins. This makes the output more understandable.

Important Parameters

bins: The number of bins or the edges of the bins.

labels: The labels for the returned bins. If you use this parameter, it has to be of the same length as the resulting bins.

Conclusion

The pandas.cut() function is a powerful tool for converting continuous data into categorical data, facilitating better segmentation and analysis. Whether you aim to categorize large datasets or simply want to define custom labels for your bins, pandas.cut() offers a flexible and efficient solution.

Exploring further, you can integrate pandas.cut() with dataframes to categorize columns within a larger dataset, making it a versatile function for various data manipulation tasks.

Happy coding!