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Showing content from https://pandas.pydata.org/docs/dev/development/../reference/api/pandas.Index.isna.html below:

pandas.Index.isna — pandas 3.0.0.dev0+2231.g4f2aa4d2be documentation

final Index.isna()[source]#

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.

Examples

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

>>> 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.

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

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

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

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