pandas.DataFrame.value_counts

DataFrame.value_counts(subset=None, normalize=False, sort=True, ascending=False)[source]

Return a Series containing counts of unique rows in the DataFrame.

New in version 1.1.0.

Parameters
subsetlist-like, optional

Columns to use when counting unique combinations.

normalizebool, default False

Return proportions rather than frequencies.

sortbool, default True

Sort by frequencies.

ascendingbool, default False

Sort in ascending order.

Returns
Series

See also

Series.value_counts

Equivalent method on Series.

Notes

The returned Series will have a MultiIndex with one level per input column. By default, rows that contain any NA values are omitted from the result. By default, the resulting Series will be in descending order so that the first element is the most frequently-occurring row.

Examples

>>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6],
...                    'num_wings': [2, 0, 0, 0]},
...                   index=['falcon', 'dog', 'cat', 'ant'])
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0
cat            4          0
ant            6          0
>>> df.value_counts()
num_legs  num_wings
4         0            2
6         0            1
2         2            1
dtype: int64
>>> df.value_counts(sort=False)
num_legs  num_wings
2         2            1
4         0            2
6         0            1
dtype: int64
>>> df.value_counts(ascending=True)
num_legs  num_wings
2         2            1
6         0            1
4         0            2
dtype: int64
>>> df.value_counts(normalize=True)
num_legs  num_wings
4         0            0.50
6         0            0.25
2         2            0.25
dtype: float64