Series.all(axis=0, bool_only=None, skipna=True, level=None, **kwargs)
[source]
Return whether all elements are True, potentially over an axis.
Returns True if all elements within a series or along a Dataframe axis are non-zero, not-empty or not-False.
Parameters: |
axis : {0 or ‘index’, 1 or ‘columns’, None}, default 0 Indicate which axis or axes should be reduced.
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 scalar. 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. |
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Returns: |
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See also
pandas.Series.all
pandas.DataFrame.any
Series
>>> pd.Series([True, True]).all() True >>> pd.Series([True, False]).all() False
DataFrames
Create a dataframe from a dictionary.
>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]}) >>> df col1 col2 0 True True 1 True False
Default behaviour checks if column-wise values all return True.
>>> df.all() col1 True col2 False dtype: bool
Specify axis='columns'
to check if row-wise values all return True.
>>> df.all(axis='columns') 0 True 1 False dtype: bool
Or axis=None
for whether every value is True.
>>> df.all(axis=None) False
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Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.Series.all.html