I have a two data frames df and df1 have same id columns but different column names in both data frames and I want update values in second dataframe

Question

I want to update df1 as per updated df1, if df1 have nans replace with values if df have values matched with ID column on both data frames.

My Expected Output is in the second image nan replaces with values I have provided sample below

ID QD QP QE

101 4 6 4

102 5 8 5

103 7 6 6

104 8 3 5

105 4 2 5

Answer 1

If your ID columns is sorted and these two columns are one-to-one correspondence, you can use

df1[df1.isnull()] = df.values

print(df1)

    ID   QD   QP   QE
0  101  4.0  6.0  4.0
1  102  5.0  8.0  5.0
2  103  7.0  6.0  6.0
3  104  8.0  3.0  5.0
4  105  4.0  2.0  5.0

If not, you'd better set the ID column as index and choose one among fillna method, combine_first method and update method to update column according to index.

df1 = df1.set_index('ID')

# fillna
df1 = df1.fillna(df.set_index('ID').set_axis(df1.columns, axis=1)).reset_index()

# combine_first, if df is bigger than your original df1,
# the additional rows and columns are added
df1 = df1.combine_first(df.set_index('ID').set_axis(df1.columns, axis=1)).reset_index()

# update method will modify data inplace,
# you need to do reset index in separate step
df1.update(df.set_index('ID').set_axis(df1.columns, axis=1))
df1.reset_index()

print(df1)


    ID   QD   QP   QE
0  101  4.0  6.0  4.0
1  102  5.0  8.0  5.0
2  103  7.0  6.0  6.0
3  104  8.0  3.0  5.0
4  105  4.0  2.0  5.0