# pandas.Series.value_counts¶

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.

- param normalize
- boolean, default False
If True then the object returned will contain the relative frequencies of the unique values.

- param sort
- boolean, default True
Sort by frequencies.

- param ascending
- boolean, default False
Sort in ascending order.

- param bins
- integer, optional
Rather than count values, group them into half-open bins, a convenience for

`pd.cut`

, only works with numeric data.

- param dropna
- boolean, default True
Don’t include counts of NaN.

- return
Series

## Limitations¶

Parameters

`normalize`

and`bins`

are currently unsupported.Parameter

`dropna`

is unsupported for String Series.Elements with the same count might appear in result in a different order than in Pandas.

- This function may reveal slower performance than Pandas* on user system. Users should exercise a tradeoff
between staying in JIT-region with that function or going back to interpreter mode.

## Examples¶

```
import pandas as pd
import numpy as np
from numba import njit
@njit
def series_value_counts():
s = pd.Series([3, 1, 2, 3, 4, np.nan])
out_series = s.value_counts()
return out_series
print(series_value_counts())
```

```
$ python ./series/series_value_counts.py
3.0 2
4.0 1
2.0 1
1.0 1
dtype: int64
```

See also