# 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, only works with numeric data.

dropnabool, default True

Don’t include counts of NaN and None.

Returns:
resultSeries containing counts of unique values.

`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
Name: count, dtype: int64
```

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
Name: count, dtype: int64
```

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
Name: proportion, dtype: float64
```

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
Name: count, dtype: int64
```
```>>> s = cudf.Series([3, 1, 2, 3, 4, np.nan])
>>> s.value_counts(bins=3)
(2.0, 3.0]      2
(0.996, 2.0]    2
(3.0, 4.0]      1
Name: count, dtype: int64
```