# xarray.CFTimeIndex.value_counts
#### CFTimeIndex.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)[[source]](https://github.com/pydata/xarray/blob/main/xarray/../.pixi/envs/doc/lib/python3.14/site-packages/pandas/core/base.py#L1024-L1151)
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:**
* **normalize** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool), *default* [`False`](https://docs.python.org/3/builtins/constants.html#False)) – If True then the object returned will contain the relative
frequencies of the unique values.
* **sort** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool), *default* [`True`](https://docs.python.org/3/builtins/constants.html#True)) – Stable sort by frequencies when True. Preserve the order of the data
when False.
#### Versionchanged
Changed in version 3.0.0: Prior to 3.0.0, the sort was unstable.
* **ascending** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool), *default* [`False`](https://docs.python.org/3/builtins/constants.html#False)) – Sort in ascending order.
* **bins** ([`int`](https://docs.python.org/3/builtins/functions.html#int), *optional*) – Rather than count values, group them into half-open bins,
a convenience for `pd.cut`, only works with numeric data.
* **dropna** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool), *default* [`True`](https://docs.python.org/3/builtins/constants.html#True)) – Don’t include counts of NaN.
* **Returns:**
[`Series`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series) – Series containing counts of unique values.
#### SEE ALSO
`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
```pycon
>>> 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.
```pycon
>>> 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.
```pycon
>>> 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.
```pycon
>>> s.value_counts(dropna=False)
3.0 2
1.0 1
2.0 1
4.0 1
NaN 1
Name: count, dtype: int64
```
**Categorical Dtypes**
Rows with categorical type will be counted as one group
if they have same categories and order.
In the example below, even though `a`, `c`, and `d`
all have the same data types of `category`,
only `c` and `d` will be counted as one group
since `a` doesn’t have the same categories.
```pycon
>>> df = pd.DataFrame({"a": [1], "b": ["2"], "c": [3], "d": [3]})
>>> df = df.astype({"a": "category", "c": "category", "d": "category"})
>>> df
a b c d
0 1 2 3 3
```
```pycon
>>> df.dtypes
a category
b str
c category
d category
dtype: object
```
```pycon
>>> df.dtypes.value_counts()
category 2
category 1
str 1
Name: count, dtype: int64
```