modin.pandas.Series.any¶

Series.any(axis=0, bool_only=None, skipna=True, **kwargs)[source]¶

Return whether any element are 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. 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_only (bool, default False) – Include only boolean columns. 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.

• **kwargs (any, default None) – Additional keywords have no effect but might be accepted for compatibility with NumPy.

Return type:

Series

Notes

• Snowpark pandas currently only supports this function on DataFrames/Series with integer or boolean columns.

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

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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

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>>> df.any()
A     True
B     True
C    False
dtype: bool

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Aggregating over the columns.

>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
>>> df
       A  B
0   True  1
1  False  2

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>>> df.any(axis='columns')
0    True
1    True
dtype: bool

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>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
>>> df
       A  B
0   True  1
1  False  0

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>>> df.any(axis='columns')
0     True
1    False
dtype: bool

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Aggregating over the entire DataFrame with axis=None.

>>> df.any(axis=None)
True

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any for an empty DataFrame is an empty Series.

>>> pd.DataFrame([]).any()
Series([], dtype: bool)

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