cudf.DataFrame.value_counts#

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

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

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.

Returns:

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

>>> import cudf
>>> df = cudf.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().sort_index()
num_legs  num_wings
2         2            1
4         0            2
6         0            1
Name: count, dtype: int64