pandas.isnull#

pandas.isnull(obj)[source]#

Detect missing values for an array-like object.

This function takes a scalar or array-like object and indicateswhether values are missing (NaN in numeric arrays,None orNaNin 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 eachcorresponding 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]])>>>arrayarray([[ 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"])>>>indexDatetimeIndex(['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    20  ant   bee  cat1  dog  None  fly>>>pd.isna(df)       0      1      20  False  False  False1  False   True  False

>>>pd.isna(df[1])0    False1     TrueName: 1, dtype: bool