pandas.DataFrame.any — pandas 0.24.0rc1 documentation (original) (raw)
DataFrame. any(axis=0, bool_only=None, skipna=True, level=None, **kwargs)[source]¶
Return whether any element is 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. 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 None Include only boolean columns. If None, will attempt to use everything, then use only boolean data. 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. level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series. **kwargs : any, default None Additional keywords have no effect but might be accepted for compatibility with NumPy. |
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| Returns: | Series or DataFrame If level is specified, then, DataFrame is returned; otherwise, Series is returned. |
See also
Numpy version of this method.
Return whether any element is True.
Return whether all elements are True.
Return whether any element is True over requested axis.
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([]).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)