pyspark.pandas.DataFrame.any¶
-
DataFrame.any(axis: Union[int, str] = 0, bool_only: Optional[bool] = None) → Series¶ Return whether any element is True.
Returns False unless there is at least one element within a series that is True or equivalent (e.g. non-zero or non-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.
- Returns
- Series
Examples
Create a dataframe from a dictionary.
>>> df = ps.DataFrame({ ... 'col1': [False, False, False], ... 'col2': [True, False, False], ... 'col3': [0, 0, 1], ... 'col4': [0, 1, 2], ... 'col5': [False, False, None], ... 'col6': [True, False, None]}, ... columns=['col1', 'col2', 'col3', 'col4', 'col5', 'col6'])
Default behaviour checks if column-wise values all return True.
>>> df.any() col1 False col2 True col3 True col4 True col5 False col6 True dtype: bool
Include only boolean columns when set bool_only=True.
>>> df.any(bool_only=True) col1 False col2 True dtype: bool
Returns empty Series when the DataFrame is empty. >>> df[[]].any() Series([], dtype: bool)