any(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs)
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).
Indicate which axis or axes should be reduced.
Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series.
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.
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.
Additional keywords have no effect but might be accepted for compatibility with NumPy.
If level is specified, then, Series is returned; otherwise, scalar is returned.
Return whether any element is True, potentially over an axis.
DataFrame.all
Return whether all elements are True over requested axis.
DataFrame.any
Return whether any element is True over requested axis.
Series.all
Return whether all elements are True.
Series.any
Return whether any element is True.
numpy.any
Numpy version of this method.
Series
For Series input, the output is a scalar indicating whether any element is True.
This example is valid syntax, but we were not able to check execution>>> pd.Series([False, False]).any() FalseThis example is valid syntax, but we were not able to check execution
>>> pd.Series([True, False]).any() TrueThis example is valid syntax, but we were not able to check execution
>>> pd.Series([], dtype="float64").any() FalseThis example is valid syntax, but we were not able to check execution
>>> pd.Series([np.nan]).any() FalseThis example is valid syntax, but we were not able to check execution
>>> pd.Series([np.nan]).any(skipna=False) True
DataFrame
Whether each column contains at least one True element (the default).
This example is valid syntax, but we were not able to check execution>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})This example is valid syntax, but we were not able to check execution
... 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.
This example is valid syntax, but we were not able to check execution>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})This example is valid syntax, but we were not able to check execution
... df A B 0 True 1 1 False 2
>>> df.any(axis='columns') 0 True 1 True dtype: boolThis example is valid syntax, but we were not able to check execution
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})This example is valid syntax, but we were not able to check execution
... 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)See :
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