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pandas.DataFrame.value_counts — pandas 3.0.0.dev0+2231.g4f2aa4d2be documentation

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

Return a Series containing the frequency of each distinct row in the DataFrame.

Parameters:

subsetHashable or a sequence of the previous, optional

Columns to use when counting unique combinations.

normalizebool, default False

Return proportions rather than frequencies.

sortbool, default True

Sort by frequencies when True. Preserve the order of the data when False.

Changed in version 3.0.0: Prior to 3.0.0, sort=False would sort by the columns values.

ascendingbool, default False

Sort in ascending order.

dropnabool, default True

Do not include counts of rows that contain NA values.

Added in version 1.3.0.

Returns:

Series

Series containing the frequency of each distinct row in the DataFrame.

Notes

The returned Series will have a MultiIndex with one level per input column but an Index (non-multi) for a single label. By default, rows that contain any NA values are omitted from the result. By default, the resulting Series will be sorted by frequencies 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
2         2            1
6         0            1
Name: count, dtype: int64

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

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

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

With dropna set to False we can also count rows with NA values.

>>> 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

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

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

>>> df.value_counts("first_name")
first_name
John    2
Anne    1
Beth    1
Name: count, dtype: int64

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