bigframes.pandas.DataFrame.value_counts#

DataFrame.value_counts(subset: Hashable | Sequence[Hashable] = None, normalize: bool = False, sort: bool = True, ascending: bool = False, dropna: bool = True)[source]#

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

Examples:

>>> df = bpd.DataFrame({'num_legs': [2, 4, 4, 6, 7],
...                     'num_wings': [2, 0, 0, 0, pd.NA]},
...                    index=['falcon', 'dog', 'cat', 'ant', 'octopus'],
...                    dtype='Int64')
>>> df
         num_legs  num_wings
falcon          2          2
dog             4          0
cat             4          0
ant             6          0
octopus         7       <NA>

[5 rows x 2 columns]

value_counts sorts the result by counts in a descending order by default:

>>> df.value_counts()
num_legs  num_wings
4         0          2
2         2          1
6         0          1
Name: count, dtype: Int64

You can normalize the counts to return relative frequencies by setting normalize=True:

>>> df.value_counts(normalize=True)
num_legs  num_wings
4         0             0.5
2         2            0.25
6         0            0.25
Name: proportion, dtype: Float64

You can get the rows in the ascending order of the counts by setting ascending=True:

>>> df.value_counts(ascending=True)
num_legs  num_wings
2         2          1
6         0          1
4         0          2
Name: count, dtype: Int64

You can include the counts of the rows with NA values by setting dropna=False:

>>> df.value_counts(dropna=False)
num_legs  num_wings
4         0            2
2         2            1
6         0            1
7         <NA>         1
Name: count, dtype: Int64

Parameters:

• subset (label or list of labels, 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) Dont include counts of rows that contain NA values.

Returns:

Series containing counts of unique rows in the DataFrame

Return type:

bigframes.pandas.Series