How to Efficiently Bin a Pandas Column and Count Values in Each Bin?

Binning a Column with Pandas

In data analysis, it is often useful to bin data into categories to simplify its representation and analysis. This is a common technique when working with numeric data, such as when dealing with percentages.

Suppose we have a data frame column named "percentage" containing numeric values, as shown below:

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

To bin this column and get the value counts for each bin, we can use the pd.cut function. Here are two ways to achieve this:

Using pd.cut with value_counts:

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

Using np.searchsorted and groupby:

df['binned'] = np.searchsorted(bins, df['percentage'].values)
print(df.groupby(df['binned']).size())

Both methods will return the following output:

percentage
(0, 1]       0
(1, 5]       0
(5, 10]      0
(10, 25]     0
(25, 50]     3
(50, 100]    1
dtype: int64

This output indicates that there are no values in the bins (0, 1], (1, 5], (5, 10], and (10, 25]. Three values fall in the bin (25, 50], and one value falls in the bin (50, 100].

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