How Can I Bin Numeric Data in a Pandas DataFrame and Count Values within Each Bin?

Binning a Column with pandas to Obtain Value Counts

When dealing with numeric data in a pandas data frame, it can be useful to bin the data into specific ranges for analysis. This process is known as binning.

To bin a column in pandas, you can use the following steps:

1. Define the bin ranges using the bins parameter.
2. Create a new column in the data frame using the cut function.
3. Use value counts or groupby to obtain the count of values within each bin.

Example:

Consider the following data frame with a numeric column named 'percentage':

To bin the 'percentage' column into the following bins:

You can use the cut function as follows:

This will create a new column called 'binned' in the data frame that contains the bin labels.

To obtain the value counts within each bin, you can use the value_counts method:

Output:

Alternatively, you can use groupby and aggregate the size:

Output:

This provides you with the count of values within each bin.

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