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

pandas.DataFrame.update pandas.DataFrame.var