Pandas: Replace missing dataframe values / conditional calculation: fillna

Question

I want to calculate a pandas dataframe, but some rows contain missing values. For those missing values, i want to use a diffent algorithm. Lets say:

• If column B contains a value, then substract A from B
• If column B does not contain a value, then subtract A from C

import pandas as pd
df = pd.DataFrame({'a':[1,2,3,4], 'b':[1,1,None,1],'c':[2,2,2,2]})
df['calc'] = df['b']-df['a']

results in:

print(df)
   a    b  c  calc
0  1  1.0  2   0.0
1  2  1.0  2  -1.0
2  3  NaN  2   NaN
3  4  1.0  2  -3.0

Approach 1: fill the NaN rows using .where:

df['calc'].where(df['b'].isnull()) = df['c']-df['a']

which results in SyntaxError: cannot assign to function call.

Approach 2: fill the NaN rows using .iterrows():

for index, row in df.iterrows():
    i = df['calc'].iloc[index]

    if pd.isnull(row['b']):
        i = row['c']-row['a']
        print(i)
    else:
        i = row['b']-row['a']
        print(i)

is executed without errors and calculation is correct, these i values are printed to the console:

0.0
-1.0
-1.0
-3.0

but the values are not written into df['calc'], the datafram remains as is:

print(df['calc'])
0    0.0
1   -1.0
2    NaN
3   -3.0

What is the correct way of overwriting the NaN values?

Answer 1

Finally, I stumbled over .fillna:

df['calc'] = df['calc'].fillna( df['c']-df['a'] )

gets the job done! Can anyone explain what is wrong with above two approaches...?