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DataFrame.any(axis=None, bool_only=None, skipna=None, 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.


axis : int, default 0

Select the axis which can be 0 for indices and 1 for columns.

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 Series.

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.


any : Series or DataFrame (if level specified)

See also

Return whether all elements are True.



For Series input, the output is a scalar indicating whether any element is True.

>>> pd.Series([True, False]).any()


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

any for an empty DataFrame is an empty Series.

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