Please help me in Pandas, i cant find good solution Tried map, assign, merge, join, set_index. Maybe just i am too tired :)
df:
m_num A B
0 1 0 9
1 1 1 8
2 2 2 7
3 2 3 6
4 3 4 5
5 3 5 4
df1:
m_num C
0 2 99
1 2 88
df_final:
m_num A B C
0 1 0 9 NaN
1 1 1 8 NaN
2 2 2 7 99
3 2 3 6 88
4 3 4 5 NaN
5 3 5 4 NaN
Try:
df2 = df[df['m_num'].isin(df1['m_num'])].reset_index(drop=True)
df2 = pd.merge(df2,df1,on=[df1.index,'m_num']).drop('key_0',axis=1)
df2 = pd.merge(df,df2,on=['m_num','A','B'],how='left')
print(df2)
Prints:
m_num A B C
0 1 0 9 NaN
1 1 1 8 NaN
2 2 2 7 99.0
3 2 3 6 88.0
4 3 4 5 NaN
5 3 5 4 NaN
Explanation:
There may be better solutions out there but this was my thought process. The problem is slightly tricky in the sense that because 'm_num' is the only common key and it and it has repeating values.
So first I created a dataframe matching df and df1 here so that I can use the index as another key for the subsequent merge.
df2 = df[df['m_num'].isin(df1['m_num'])].reset_index(drop=True)
This prints:
m_num A B
0 2 2 7
1 2 3 6
As you can see above, now we have the index 0 and 1 in addition to the m_num as key which we can use to match with df1.
df2 = pd.merge(df2,df1,on=[df1.index,'m_num']).drop('key_0',axis=1)
This prints:
m_num A B C
0 2 2 7 99
1 2 3 6 88
Then tie the above resultant dataframe to the original df and do a left join to get the output.
df2 = pd.merge(df,df2,on=['m_num','A','B'],how='left')