Index.value_counts(sort=None, ascending=False, dropna=True, normalize=False, split_every=None, split_out=_NoDefault.no_default)

Return a Series containing counts of unique values.

This docstring was copied from pandas.core.series.Series.value_counts.

Some inconsistencies with the Dask version may exist.

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 when True. Preserve the order of the data when False.

ascendingbool, default False

Sort in ascending order.

binsint, optional (Not supported in Dask)

Rather than count values, group them into half-open bins, a convenience for `pd.cut`, only works with numeric data.

dropnabool, default True

Don’t include counts of NaN.

Returns
Series

`Series.count`

Number of non-NA elements in a Series.

`DataFrame.count`

Number of non-NA elements in a DataFrame.

`DataFrame.value_counts`

Equivalent method on DataFrames.

Examples

```>>> index = pd.Index([3, 1, 2, 3, 4, np.nan])
>>> index.value_counts()
3.0    2
1.0    1
2.0    1
4.0    1
Name: count, dtype: int64
```

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

```>>> s = pd.Series([3, 1, 2, 3, 4, np.nan])
>>> s.value_counts(normalize=True)
3.0    0.4
1.0    0.2
2.0    0.2
4.0    0.2
Name: proportion, dtype: float64
```

bins

Bins can be useful for going from a continuous variable to a categorical variable; instead of counting unique apparitions of values, divide the index in the specified number of half-open bins.

```>>> s.value_counts(bins=3)
(0.996, 2.0]    2
(2.0, 3.0]      2
(3.0, 4.0]      1
Name: count, dtype: int64
```

dropna

With dropna set to False we can also see NaN index values.

```>>> s.value_counts(dropna=False)
3.0    2
1.0    1
2.0    1
4.0    1
NaN    1
Name: count, dtype: int64
```