Counting Values with value_counts()

value_counts() quickly shows the distribution of values in a column.

df['status'].value_counts()

Returns counts for each unique value, sorted descending.

Get percentages instead:

df['status'].value_counts(normalize=True)

Include NaN in the count:

df['status'].value_counts(dropna=False)

Bin numeric data:

df['age'].value_counts(bins=5)

This is your go-to method for exploring categorical columns and understanding data distributions.

I use value_counts() constantly throughout my Pandas course.