Python Pandas - Replace NaN values using dict

Question

With the following DF:

     A  B
0    a  1
1    b  2
2  NaN  1
3  NaN  2

I would like to replace NaN values of A based on the numeric representation of B, to get:

I've built a dictionary of B/A values: {1 : 'a', 2: 'b'}

How can I apply the change to the NaN values?

nevemind, added to answer difference between using map and replace here, if in dict are all values like in B output is same (perfromnce the best test in real data). — jezrael
– jezrael, Commented Jun 9, 2020 at 8:12

jezrael · Accepted Answer · 2020-06-09 08:10:25Z

5

d = {1 : 'a', 2: 'b'}
df.A = df.A.fillna(df.B.map(d))
print (df)
   A  B
0  a  1
1  b  2
2  a  1
3  b  2

I suggest use map because replace is slowier and if no match in map is returned missing value (like original) not value of B:

df["A"] = df["A"].fillna(df["B"].replace({1 : 'a', 2: 'b'}))
print (df)
   A  B
0  a  1
1  b  2
2  a  1
3  3  3 <- changed last value to 3

d = {1 : 'a', 2: 'b'}
df.A = df.A.fillna(df.B.map(d))
print (df)
     A  B
0    a  1
1    b  2
2    a  1
3  NaN  3

answered Jun 9, 2020 at 8:02

jezrael

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2 Comments

Series.map is much faster than .replace and valid point that map doesn't replace values of A if there is no mapping. +1

@jezrael, can you please have a look here stackoverflow.com/questions/65090530/… ? thanks :)