all(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs)

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.

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 True, 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 scalar.

**kwargs : any, default None

Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

scalar or Series

If level is specified, then, Series is returned; otherwise, scalar is returned.

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

See Also

DataFrame.any

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

Series.all

Return True if all elements are True.

Examples

Series

This example is valid syntax, but we were not able to check execution

>>> pd.Series([True, True]).all()
True

This example is valid syntax, but we were not able to check execution

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

This example is valid syntax, but we were not able to check execution

>>> pd.Series([], dtype="float64").all()
True

This example is valid syntax, but we were not able to check execution

>>> pd.Series([np.nan]).all()
True

This example is valid syntax, but we were not able to check execution

>>> pd.Series([np.nan]).all(skipna=False)
True

DataFrames

Create a dataframe from a dictionary.

This example is valid syntax, but we were not able to check execution

>>> 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.

This example is valid syntax, but we were not able to check execution

>>> df.all()
col1     True
col2    False
dtype: bool

Specify axis='columns' to check if row-wise values all return True.

This example is valid syntax, but we were not able to check execution

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

Or axis=None for whether every value is True.

This example is valid syntax, but we were not able to check execution

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

See :

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File: /pandas/core/generic.py#10888
type: <class 'function'>
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