Check for None in pandas dataframe

Question

I would like to find where None is found in the dataframe.

pd.DataFrame([None,np.nan]).isnull()
OUT: 
      0
0  True
1  True

isnull() finds both numpy Nan and None values.

I only want the None values and not numpy Nan. Is there an easier way to do that without looping through the dataframe?

Edit: After reading the comments, I realized that in my dataframe in my work also include strings, so the None were not coerced to numpy Nan. So the answer given by Pisdom works.

Answer 1

If you want to get True/False for each line, you can use the following code. Here is an example as a result for the following DataFrame:

df = pd.DataFrame([[None, 3], ["", np.nan]])

df
#      0      1
#0  None    3.0
#1          NaN

How to check None

Available: .isnull()

>>> df[0].isnull()
0     True
1    False
Name: 0, dtype: bool

Available: .apply == or is None

>>> df[0].apply(lambda x: x == None)
0     True
1    False
Name: 0, dtype: bool

>>> df[0].apply(lambda x: x is None)
0     True
1    False
Name: 0, dtype: bool

Available: .values == None

>>> df[0].values == None
array([ True, False])

Unavailable: is or ==

>>> df[0] is None
False

>>> df[0] == None
0    False
1    False
Name: 0, dtype: bool

Unavailable: .values is None

>>> df[0].values is None
False

How to check np.nan

Available: .isnull()

>>> df[1].isnull()
0    False
1     True
Name: 1, dtype: bool

Available: np.isnan

>>> np.isnan(df[1])
0    False
1     True
Name: 1, dtype: bool

>>> np.isnan(df[1].values)
array([False,  True])

>>> df[1].apply(lambda x: np.isnan(x))
0    False
1     True
Name: 1, dtype: bool

Unavailable: is or == np.nan

>>> df[1] is np.nan
False

>>> df[1] == np.nan
0    False
1    False
Name: 1, dtype: bool

>>> df[1].values is np.nan
False

>>> df[1].values == np.nan
array([False, False])

>>> df[1].apply(lambda x: x is np.nan)
0    False
1    False
Name: 1, dtype: bool

>>> df[1].apply(lambda x: x == np.nan)
0    False
1    False
Name: 1, dtype: bool