cudf.Series.value_counts#

Series.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)#

Return a Series containing counts of unique values.

The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default.

Parameters
normalizebool, default False

If True then the object returned will contain the relative frequencies of the unique values.

sortbool, default True

Sort by frequencies.

ascendingbool, default False

Sort in ascending order.

binsint, optional

Rather than count values, group them into half-open bins, works with numeric data. This Parameter is not yet supported.

dropnabool, default True

Don’t include counts of NaN and None.

Returns
resultSeries containing counts of unique values.

See also

Series.count

Number of non-NA elements in a Series.

cudf.DataFrame.count

Number of non-NA elements in a DataFrame.

Examples

>>> import cudf
>>> sr = cudf.Series([1.0, 2.0, 2.0, 3.0, 3.0, 3.0, None])
>>> sr
0     1.0
1     2.0
2     2.0
3     3.0
4     3.0
5     3.0
6    <NA>
dtype: float64
>>> sr.value_counts()
3.0    3
2.0    2
1.0    1
dtype: int32

The order of the counts can be changed by passing ascending=True:

>>> sr.value_counts(ascending=True)
1.0    1
2.0    2
3.0    3
dtype: int32

With normalize set to True, returns the relative frequency by dividing all values by the sum of values.

>>> sr.value_counts(normalize=True)
3.0    0.500000
2.0    0.333333
1.0    0.166667
dtype: float32

To include NA value counts, pass dropna=False:

>>> sr = cudf.Series([1.0, 2.0, 2.0, 3.0, None, 3.0, 3.0, None])
>>> sr
0     1.0
1     2.0
2     2.0
3     3.0
4    <NA>
5     3.0
6     3.0
7    <NA>
dtype: float64
>>> sr.value_counts(dropna=False)
3.0     3
2.0     2
<NA>    2
1.0     1
dtype: int32