[FIXED] Check for None in pandas dataframe

Issue

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.

Solution

You could use applymap with a lambda to check if an element is None as follows, (constructed a different example, as in your original one, None is coerced to np.nan because the data type is float, you will need an object type column to hold None as is, or as commented by @Evert, None and NaN are indistinguishable in numeric type columns):

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

df
#      0      1
#0  None    3.0
#1          NaN

df.applymap(lambda x: x is None)

#       0       1
#0   True   False
#1  False   False

Answered By - Psidom