How Can I Bin a Pandas Column with Custom Bins and Get Value Counts?

Binning a pandas Column with Customized Bins and Value Counts

When working with numerical data, it is often useful to group values into bins to detect patterns or trends. This process, known as binning, can be easily performed using the pandas library.

To bin a column, you can use the pandas.cut function. Here's how it works in the example you provided:

bins = [0, 1, 5, 10, 25, 50, 100]
df['binned'] = pd.cut(df['percentage'], bins)

This code creates a new column called binned in your DataFrame. Each value in this column represents the bin to which the corresponding numeric value in the percentage column belongs. The bins parameter specifies the boundaries of the bins.

To visualize the distribution of values across the bins, you can use the value_counts function:

df['binned'].value_counts()

This code will return the number of occurrences of each bin, effectively providing the value counts for the bins.

For example, if you have the following data:

df['percentage'].head()
46.5
44.2
100.0
42.12

And you use the following bins:

bins = [0, 1, 5, 10, 25, 50, 100]

The output of df['binned'].value_counts() would be:

(25, 50]     3
(50, 100]    1

This means that three values fall within the bin (25, 50], and one value falls within the bin (50, 100].

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