pandas.Series.all

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

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.

• 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 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.

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

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