# xarray.CFTimeIndex.isna

#### CFTimeIndex.isna()[[source]](https://github.com/pydata/xarray/blob/main/xarray/../.pixi/envs/doc/lib/python3.14/site-packages/pandas/core/indexes/base.py#L2639-L2693)

Detect missing values.

Return a boolean same-sized object indicating if the values are NA.
NA values, such as `None`, `numpy.NaN` or `pd.NaT`, get
mapped to `True` values.
Everything else get mapped to `False` values. Characters such as
empty strings ‘’ or `numpy.inf` are not considered NA values.

* **Returns:**
  `numpy.ndarray[bool]` – A boolean array of whether my values are NA.

#### SEE ALSO
`Index.notna`
: Boolean inverse of isna.

`Index.dropna`
: Omit entries with missing values.

[`isna`](#xarray.CFTimeIndex.isna)
: Top-level isna.

`Series.isna`
: Detect missing values in Series object.

### Examples

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Show which entries in a pandas.Index are NA. The result is an
array.

```pycon
>>> idx = pd.Index([5.2, 6.0, np.nan])
>>> idx
Index([5.2, 6.0, nan], dtype='float64')
>>> idx.isna()
array([False, False,  True])
```

Empty strings are not considered NA values. None is considered an NA
value.

```pycon
>>> idx = pd.Index(["black", "", "red", None])
>>> idx
Index(['black', '', 'red', nan], dtype='str')
>>> idx.isna()
array([False, False, False,  True])
```

For datetimes, NaT (Not a Time) is considered as an NA value.

```pycon
>>> idx = pd.DatetimeIndex(
...     [pd.Timestamp("1940-04-25"), pd.Timestamp(""), None, pd.NaT]
... )
>>> idx
DatetimeIndex(['1940-04-25', 'NaT', 'NaT', 'NaT'],
              dtype='datetime64[us]', freq=None)
>>> idx.isna()
array([False,  True,  True,  True])
```

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