pandas.DataFrame.any¶
-
DataFrame.
any
(axis=0, bool_only=None, skipna=True, level=None, **kwargs)[source]¶ Return whether any element is True, potentially over an axis.
Returns False unless there at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).
Parameters: - axis : {0 or ‘index’, 1 or ‘columns’, None}, 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.
- 1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
- None : reduce all axes, return a scalar.
- bool_only : bool, default None
Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series.
- skipna : bool, default True
Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be False, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.
- level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
- **kwargs : any, default None
Additional keywords have no effect but might be accepted for compatibility with NumPy.
Returns: - Series or DataFrame
If level is specified, then, DataFrame is returned; otherwise, Series is returned.
See also
numpy.any
- Numpy version of this method.
Series.any
- Return whether any element is True.
Series.all
- Return whether all elements are True.
DataFrame.any
- Return whether any element is True over requested axis.
DataFrame.all
- Return whether all elements are True over requested axis.
Examples
Series
For Series input, the output is a scalar indicating whether any element is True.
>>> pd.Series([False, False]).any() False >>> pd.Series([True, False]).any() True >>> pd.Series([]).any() False >>> pd.Series([np.nan]).any() False >>> pd.Series([np.nan]).any(skipna=False) True
DataFrame
Whether each column contains at least one True element (the default).
>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]}) >>> df A B C 0 1 0 0 1 2 2 0
>>> df.any() A True B True C False dtype: bool
Aggregating over the columns.
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]}) >>> df A B 0 True 1 1 False 2
>>> df.any(axis='columns') 0 True 1 True dtype: bool
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]}) >>> df A B 0 True 1 1 False 0
>>> df.any(axis='columns') 0 True 1 False dtype: bool
Aggregating over the entire DataFrame with
axis=None
.>>> df.any(axis=None) True
any for an empty DataFrame is an empty Series.
>>> pd.DataFrame([]).any() Series([], dtype: bool)