pyspark.pandas.DataFrame.all¶
-
DataFrame.all(axis: Union[int, str] = 0, bool_only: Optional[bool] = None, skipna: bool = True) → Series[source]¶ Return whether all elements are True.
Returns True unless there is 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.
- bool_onlybool, default None
Include only boolean columns. If None, will attempt to use everything, then use only boolean data.
- 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.
- Returns
- Series
Examples
Create a dataframe from a dictionary.
>>> df = ps.DataFrame({ ... 'col1': [True, True, True], ... 'col2': [True, False, False], ... 'col3': [0, 0, 0], ... 'col4': [1, 2, 3], ... 'col5': [True, True, None], ... 'col6': [True, False, None]}, ... columns=['col1', 'col2', 'col3', 'col4', 'col5', 'col6'])
Default behavior checks if column-wise values all return True.
>>> df.all() col1 True col2 False col3 False col4 True col5 True col6 False dtype: bool
Include NA values when set skipna=False.
>>> df[['col5', 'col6']].all(skipna=False) col5 False col6 False dtype: bool
Include only boolean columns when set bool_only=True.
>>> df.all(bool_only=True) col1 True col2 False dtype: bool