pandas.notnull#

pandas.notnull(obj)[source]#

Detect non-missing values for an array-like object.

This function takes a scalar or array-like object and indicateswhether values are valid (not missing, which isNaN in numericarrays,None orNaN in object arrays,NaT in datetimelike).

Parameters:

objarray-like or object value

Object to check fornot null ornon-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 valid.

See also

isna

Boolean inverse of pandas.notna.

Series.notna

Detect valid values in a Series.

DataFrame.notna

Detect valid values in a DataFrame.

Index.notna

Detect valid values in an Index.

Examples

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

>>>pd.notna("dog")True

>>>pd.notna(pd.NA)False

>>>pd.notna(np.nan)False

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.notna(array)array([[ True, False,  True],       [ True,  True, False]])

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[us]', freq=None)>>>pd.notna(index)array([ True,  True, False,  True])

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  NaN  fly>>>pd.notna(df)      0      1     20  True   True  True1  True  False  True

>>>pd.notna(df[1])0     True1    FalseName: 1, dtype: bool