pandas.isna — pandas 2.2.3 documentation (original) (raw)

pandas.isna(obj)[source]#

Detect missing values for an array-like object.

This function takes a scalar or array-like object and indicates whether values are missing (NaN in numeric arrays, None or NaNin object arrays, NaT in datetimelike).

Parameters:

objscalar or array-like

Object to check for null or missing values.

Returns:

bool or array-like of bool

For scalar input, returns a scalar boolean. For array input, returns an array of boolean indicating whether each corresponding element is missing.

Examples

Scalar arguments (including strings) result in a scalar boolean.

ndarrays result in an ndarray of booleans.

array = np.array([[1, np.nan, 3], [4, 5, np.nan]]) array array([[ 1., nan, 3.], [ 4., 5., nan]]) pd.isna(array) array([[False, True, False], [False, False, True]])

For indexes, an ndarray of booleans is returned.

index = pd.DatetimeIndex(["2017-07-05", "2017-07-06", None, ... "2017-07-08"]) index DatetimeIndex(['2017-07-05', '2017-07-06', 'NaT', '2017-07-08'], dtype='datetime64[ns]', freq=None) pd.isna(index) array([False, False, True, False])

For Series and DataFrame, the same type is returned, containing booleans.

df = pd.DataFrame([['ant', 'bee', 'cat'], ['dog', None, 'fly']]) df 0 1 2 0 ant bee cat 1 dog None fly pd.isna(df) 0 1 2 0 False False False 1 False True False

pd.isna(df[1]) 0 False 1 True Name: 1, dtype: bool