pandas.Series.any¶
-
Series.
any
(axis=0, bool_only=None, skipna=True, level=None, **kwargs)[source]¶ Return whether any element is True over requested axis.
Unlike
DataFrame.all()
, this performs an or operation. If any of the values along the specified axis is True, this will return True.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.
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result will be NA.
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.
bool_only : boolean, default None
Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series.
**kwargs : any, default None
Additional keywords have no effect but might be accepted for compatibility with NumPy.
Returns: - any : scalar or Series (if level specified)
See also
pandas.DataFrame.all
- Return whether all elements are True.
Examples
Series
For Series input, the output is a scalar indicating whether any element is True.
>>> pd.Series([True, False]).any() 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)