pandas.DataFrame.value_counts#

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:

subsetlabel or list of labels, optional

Columns to use when counting unique combinations.

normalizebool, default False

Return proportions rather than frequencies.

sortbool, default True

Sort by frequencies when True. Sort by DataFrame column values when False.

ascendingbool, default False

Sort in ascending order.

dropnabool, default True

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

Added in version 1.3.0.

Returns:

Series

See also

Series.value_counts

Equivalent method on Series.

Notes

The returned Series will have a MultiIndex with one level per inputcolumn but an Index (non-multi) for a single label. By default, rowsthat contain any NA values are omitted from the result. By default,the resulting Series will be in descending order so that the firstelement 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_wingsfalcon         2          2dog            4          0cat            4          0ant            6          0

>>>df.value_counts()num_legs  num_wings4         0            22         2            16         0            1Name: count, dtype: int64

>>>df.value_counts(sort=False)num_legs  num_wings2         2            14         0            26         0            1Name: count, dtype: int64

>>>df.value_counts(ascending=True)num_legs  num_wings2         2            16         0            14         0            2Name: count, dtype: int64

>>>df.value_counts(normalize=True)num_legs  num_wings4         0            0.502         2            0.256         0            0.25Name: proportion, dtype: float64

Withdropna set toFalse 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_name0       John       Smith1       Anne        <NA>2       John        <NA>3       Beth      Louise

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

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

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