pandas.isnull#
- pandas.isnull(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
orNaN
in 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.
See also
notna
Boolean inverse of pandas.isna.
Series.isna
Detect missing values in a Series.
DataFrame.isna
Detect missing values in a DataFrame.
Index.isna
Detect missing values in an Index.
Examples
Scalar arguments (including strings) result in a scalar boolean.
>>> pd.isna('dog') False
>>> pd.isna(pd.NA) True
>>> pd.isna(np.nan) True
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