Checking for NaN with math library in a Pandas Dataframe

Question

If I assign a value, 'some value' to one index of a dataframe, the other indexes for that column return NaN. I would later like to loop through the dataframe index and change the value from NaN. I am trying to check with the math.isnan(), but it wants a float as in input. What function can I use to perform this check?

import pandas as pd
import math
BabyDataSet = [['Bob', 968], ['Jessica', 155], ['Mary', 77], ['John', 578], ['Mel', 973]]
df = pd.DataFrame(data=BabyDataSet, columns=['Names', 'Births'])

df.ix[1, 'MyValue'] = 'some value'

for index, Name in df.iterrows():
    if math.isnan(df.ix[index, 'MyValue']):
        df.ix[1, 'MyValue'] = 'some other value'
print df

Desired Output:

     Names  Births     MyValue

0      Bob     968     some other value

1  Jessica     155     some value

2     Mary      77     some other value

3     John     578     some other value

4      Mel     973     some other value

Answer 1

Just use pandas own fillna():

df.fillna('some other value')

If you want to change only a specific column you can provide a dictionay:

df.fillna({'col_name': 'some other value'})

You can even change NaNs in different columns to different values:

df.fillna({'col_a': 'some other value', 'col_b': 'other value'})