pyspark.pandas.Series.all

Series.all(axis: Union[int, str] = 0, skipna: bool = True) → bool

Return whether all elements are True.

Returns True unless there at least one element within a series that is False or equivalent (e.g. zero or empty)

Parameters

axis{0 or ‘index’}, default 0

Indicate which axis or axes should be reduced.

• 0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.

skipnaboolean, default True

Exclude NA values, such as None or numpy.NaN. If an entire row/column is NA values and skipna is True, then the result will be True, as for an empty row/column. If skipna is False, numpy.NaNs are treated as True because these are not equal to zero, Nones are treated as False.

Examples

>>> ps.Series([True, True]).all()
True

>>> ps.Series([True, False]).all()
False

>>> ps.Series([0, 1]).all()
False

>>> ps.Series([1, 2, 3]).all()
True

>>> ps.Series([True, True, None]).all()
True

>>> ps.Series([True, True, None]).all(skipna=False)
False

>>> ps.Series([True, False, None]).all()
False

>>> ps.Series([]).all()
True

>>> ps.Series([np.nan]).all()
True

>>> ps.Series([np.nan]).all(skipna=False)
True

>>> ps.Series([None]).all()
True

>>> ps.Series([None]).all(skipna=False)
False

>>> df = ps.Series([True, False, None]).rename("a").to_frame()
>>> df.set_index("a").index.all()
False

pyspark.pandas.Series.abs pyspark.pandas.Series.any