dask.dataframe.Index.any
dask.dataframe.Index.any¶
- Index.any(axis=None, skipna=True, split_every=False, out=None)¶
Return whether any element is True, potentially over an axis.
This docstring was copied from pandas.core.frame.DataFrame.any.
Some inconsistencies with the Dask version may exist.
Returns False unless there is 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. For Series this parameter is unused and defaults to 0.
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_onlybool, default False (Not supported in Dask)
Include only boolean columns. Not implemented for Series.
- skipnabool, 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.
- **kwargsany, 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([], dtype="float64").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)