replacing null values in a Pandas Dataframe using applymap

Question

I've got an "Age" column, but sometimes NaN values are displayed. I know I can use "fillna" for this purposes but I've tried to define my own function (and learning to do this way) and use applymap to dataframe

no success so far.

Age
69
49
NaN
54
NaN

I've tried

   def get_rid_of_nulls(value):
     if value == np.nan:
        return 'Is Null value'
     else:
        return value

with this not working either

 if value == None
   if value isnull
   if value == np.na
   if value ==''
   if value == NaN
   if value == 'NaN'

None of the comparisons seems to work. I'm wrong for sure but I'm stuck and I'm very stubborn to use fillna

thanks

Answer 1

As there is "replacing" in your title, and you mentioned fillna but not the replace() method, you can also obtain the same result doing something like that :

df.Age.replace(np.NaN, 'Is Null value', inplace=True)

# Or, depending on your needs:
df['Age'] = df.Age.replace(np.NaN, 'Is Null value')

# Or without `replace` :
df['Age'] = df.Age.apply(lambda x: x if not pd.isnull(x) else 'Is Null value')

Answer 2

You can use pd.isnull():

In [4]:
def get_rid_of_nulls(value):
    if pd.isnull(value):
        return 'Is Null value'
    else:
        return value

df['Age'].apply(get_rid_of_nulls)

Out[4]:
0               69
1               49
2    Is Null value
3               54
4    Is Null value
Name: Age, dtype: object

Similarly you can use the property that NaN does not equal itself:

In [5]:
def get_rid_of_nulls(value):
    if value != value:
        return 'Is Null value'
    else:
        return value

df['Age'].apply(get_rid_of_nulls)

Out[5]:
0               69
1               49
2    Is Null value
3               54
4    Is Null value
Name: Age, dtype: object