pyspark.pandas.Index.all¶
-
Index.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