CODEWITHSUNDEEP
pythonpandas
Laura
Laura

Reputation: 1282

Add specific column values based on other Dataframe

I have this first dataFrame

df1:

A         B       C    D
Car               0
Bike              0
Train             0
Plane             0
Other_1  Plane    2
Other_2  Plane    3
Other 3  Plane    4

and this other one:

df2:

A         B       
Car       4 %        
Bike      5 %        
Train     6 %        
Plane     7 %

So I want to get this combination: 

df1:

A         B       C    D
Car               0    4 %
Bike              0    5 %
Train             0    6 %
Plane             0    7 %
Other_1  Plane    2    2
Other_2  Plane    3    3
Other 3  Plane    4    4

Which is the best way to do this?

Upvotes: 2

Views: 49

Answers (2)

piRSquared
piRSquared

Reputation: 294218

map

df1.assign(D=df1.A.map(dict(zip(df2.A, df2.B))))

         A      B  C    D
0      Car    NaN  0  4 %
1     Bike    NaN  0  5 %
2    Train    NaN  0  6 %
3    Plane    NaN  0  7 %
4  Other_1  Plane  2  NaN
5  Other_2  Plane  3  NaN
6  Other_3  Plane  4  NaN

Upvotes: 1

Scott Boston
Scott Boston

Reputation: 153460

If df and df2 are identically indexed, then you can use:

df['D'] = df2['B'].combine_first(df['C'])

Output:

         A      B  C    D
0      Car    NaN  0  4 %
1     Bike    NaN  0  5 %
2    Train    NaN  0  6 %
3    Plane    NaN  0  7 %
4  Other_1  Plane  2    2
5  Other_2  Plane  3    3
6  Other_3  Plane  4    4

If not identically index, then you can use merge on column A:

df_out = df.merge(df2, on ='A', how='left', suffixes=('','y'))
df_out.assign(D = df_out.By.fillna(df_out.C)).drop('By', axis=1)

or use @piRSquared improved one-liner:

df.drop('D',1).merge(df2.rename(columns={'B':'D'}), how='left',on ='A')

Output:

         A      B  C    D
0      Car    NaN  0  4 %
1     Bike    NaN  0  5 %
2    Train    NaN  0  6 %
3    Plane    NaN  0  7 %
4  Other_1  Plane  2    2
5  Other_2  Plane  3    3
6  Other_3  Plane  4    4

Upvotes: 3

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