pandas.Series.all

Series.all(*, axis=0, bool_only=False, skipna=True, **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. 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_onlybool, default False

Include only boolean columns. Not implemented for Series.

skipnabool, 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.

**kwargsany, default None

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

Returns:

Series or scalar

If axis=None, then a scalar boolean is returned. Otherwise a Series is returned with index matching the index argument.

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([], dtype="float64").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 values in each column all return True.

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

Specify axis='columns' to check if values in each row 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