value_counts(self, subset: 'Sequence[Hashable] | None' = None, normalize: 'bool' = False, sort: 'bool' = True, ascending: 'bool' = False, dropna: 'bool' = True)

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.

Parameters

subset : list-like, optional

Columns to use when counting unique combinations.

normalize : bool, default False

Return proportions rather than frequencies.

sort : bool, default True

Sort by frequencies.

ascending : bool, default False

Sort in ascending order.

dropna : bool, default True

Don’t include counts of rows that contain NA values.

versionadded

Returns

Series

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

See Also

Series.value_counts

Equivalent method on Series.

Examples

This example is valid syntax, but we were not able to check execution

>>> 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

This example is valid syntax, but we were not able to check execution

>>> df.value_counts()
num_legs  num_wings
4         0            2
2         2            1
6         0            1
dtype: int64

This example is valid syntax, but we were not able to check execution

>>> df.value_counts(sort=False)
num_legs  num_wings
2         2            1
4         0            2
6         0            1
dtype: int64

This example is valid syntax, but we were not able to check execution

>>> df.value_counts(ascending=True)
num_legs  num_wings
2         2            1
6         0            1
4         0            2
dtype: int64

This example is valid syntax, but we were not able to check execution

>>> df.value_counts(normalize=True)
num_legs  num_wings
4         0            0.50
2         2            0.25
6         0            0.25
dtype: float64

With dropna set to :None:None:`False` we can also count rows with NA values.

This example is valid syntax, but we were not able to check execution

>>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'],
...                    'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']})
... df
  first_name middle_name
0       John       Smith
1       Anne        <NA>
2       John        <NA>
3       Beth      Louise

This example is valid syntax, but we were not able to check execution

>>> df.value_counts()
first_name  middle_name
Beth        Louise         1
John        Smith          1
dtype: int64

This example is valid syntax, but we were not able to check execution

>>> df.value_counts(dropna=False)
first_name  middle_name
Anne        NaN            1
Beth        Louise         1
John        Smith          1
            NaN            1
dtype: int64

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.frame.DataFrame.drop_duplicates pandas.core.frame.DataFrame.count pandas.core.groupby.generic.DataFrameGroupBy.value_counts pandas.core.base.IndexOpsMixin.value_counts

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