How Can I Effectively Bin a Pandas Column Using Pandas.cut and NumPy.searchsorted?

Binning a Pandas Column

Binning involves dividing a continuous data column into discrete intervals to analyze data distribution. To bin a column with numeric values using Pandas, we can explore various methods.

Pandas.cut Method

Pandas provides the cut function to perform binning. It takes the series to be binned and a list of bin edges as arguments. By default, it returns a categorical column with bin labels. For example:

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

NumPy.searchsorted Method

NumPy's searchsorted function can also be used for binning. It returns the index of the bin where each value in the series falls. The resulting values can then be used to create a binned category:

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

Calculating Value Counts

Once the binned column is created, we can calculate value counts to determine the number of observations in each bin. This can be achieved using either value_counts or groupby and aggregate size:

s = pd.cut(df['percentage'], bins=bins).value_counts()

s = df.groupby(pd.cut(df['percentage'], bins=bins)).size()

By using these techniques, we can effectively bin numeric data columns in Pandas to gain insights into their distribution.

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