Examples
Series

In [1]:
import numpy as np
import pandas as pd
In [2]:
pd.Series([True, True]).all()
Out[2]:
True
In [3]:
pd.Series([False, True]).all()
Out[3]:
False
In [4]:
pd.Series([]).all()
Out[4]:
True
In [5]:
pd.Series([np.nan]).all()
Out[5]:
True
In [6]:
pd.Series([np.nan]).all(skipna=False)
Out[6]:
True

DataFrames
Create a dataframe from a dictionary.

In [7]:
df = pd.DataFrame({'c1': [True, True], 'c2': [True, False]})
df
Out[7]:
c1 c2
0 True True
1 True False

Default behaviour checks if column-wise values all return True.

In [8]:
df.all()
Out[8]:
c1     True
c2    False
dtype: bool

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

In [9]:
df.all(axis='columns')
Out[9]:
0     True
1    False
dtype: bool

Or axis=None for whether every value is True.

In [10]:
df.all(axis=None)
Out[10]:
False