modin.pandas.DataFrame.all¶

DataFrame.all(axis=0, bool_only=False, skipna=True, **kwargs) → Self[source]¶

Return whether all elements are True, potentially over an axis.

Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or 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 True, 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

Series.all

Return True if all elements are True.

DataFrame.any

Return True if one (or more) elements are True.

Examples

Series

>>> pd.Series([True, True]).all()
True
>>> pd.Series([True, False]).all()
False
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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
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Default behaviour checks if values in each column all return True.

>>> df.all()
col1     True
col2    False
dtype: bool
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Specify axis='columns' to check if values in each row all return True.

>>> df.all(axis='columns')
0     True
1    False
dtype: bool
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Or axis=None for whether every value is True.

>>> df.all(axis=None)
False
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