Lerenn Lerenn - 8 days ago 5
Python Question

Pandas (Python) - Update column of a dataframe from another one with conditions and different columns

I had a problem and I found a solution but I feel it's the wrong way to do it. Maybe, there is a more 'canonical' way to do it.

I already had an answer for a really similar problem, but here I have not the same amount of rows in each dataframe. Sorry for the "double-post", but the first one is still valid so I think it's better to make a new one.

Problem

I have two dataframe that I would like to merge without having extra column and without erasing existing infos. Example :

Existing dataframe (df)

A A2 B
0 1 4 0
1 2 5 1
2 2 5 1


Dataframe to merge (df2)

A A2 B
0 1 4 2
1 3 5 2


I would like to update
df
with
df2
if columns 'A' and 'A2' corresponds.
The result would be :

A A2 B
0 1 4 2 <= Update value ONLY
1 2 5 1
2 2 5 1


Here is my solution, but I think it's not a really good one.

import pandas as pd

df = pd.DataFrame([[1,4,0],[2,5,1],[2,5,1]],columns=['A','A2','B'])

df2 = pd.DataFrame([[1,4,2],[3,5,2]],columns=['A','A2','B'])

df = df.merge(df2,on=['A', 'A2'],how='left')
df['B_y'].fillna(0, inplace=True)
df['B'] = df['B_x']+df['B_y']
df = df.drop(['B_x','B_y'], axis=1)
print(df)


I tried this solution :

rows = (df[['A','A2']] == df2[['A','A2']]).all(axis=1)
df.loc[rows,'B'] = df2.loc[rows,'B']


But I have this error because of the wrong number of rows :

ValueError: Can only compare identically-labeled DataFrame objects


Does anyone has a better way to do ?
Thanks !

Answer

I think you can use DataFrame.isin for check where are same rows in both DataFrames. Then create NaN by mask, which is filled by combine_first. Last cast to int:

mask = df[['A', 'A2']].isin(df2[['A', 'A2']]).all(1)
print (mask)
0     True
1    False
2    False
dtype: bool

df.B = df.B.mask(mask).combine_first(df2.B).astype(int)
print (df)
   A  A2  B
0  1   4  2
1  2   5  1
2  2   5  1