pyspark.pandas.isna#

pyspark.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 NaN in object arrays).

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

Series.isna: Detect missing values in a Series.
Series.isnull: Detect missing values in a Series.
DataFrame.isna: Detect missing values in a DataFrame.
DataFrame.isnull: Detect missing values in a DataFrame.
Index.isna: Detect missing values in an Index.
Index.isnull: Detect missing values in an Index.

Examples

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

>>> ps.isna('dog')
False

>>> ps.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]])
>>> ps.isna(array)
array([[False,  True, False],
       [False, False,  True]])

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

>>> df = ps.DataFrame({'a': ['ant', 'bee', 'cat'], 'b': ['dog', None, 'fly']})
>>> df
     a     b
0  ant   dog
1  bee  None
2  cat   fly

>>> ps.isna(df)
       a      b
0  False  False
1  False   True
2  False  False

>>> ps.isnull(df.b)
0    False
1     True
2    False
Name: b, dtype: bool