How to replace missing entries of a column in one data frame with entries from other data frame in python?

Question

There are two data frames df1 and df2. Two columns in df1 are A and B. There are missing values in B. For the missing values in B in df1, there are entries in df2 and its columns are A and B (The records in df2 are the ones which are missing in B of df1 only). I want to replace the missing values of B in df1 with corresponding entry of B from df2.

EDIT:

df1 = pd.DataFrame({'A': [1,2,3,4,5,6,7,8], 'b': [101,123,np.nan,678,np.nan,672,np.nan,786], 'C': ['ABC', 'DER', 'ERC','DFE','HJI','JKL','SDH',np.Nan]})

df2 = pd.DataFrame({'A': [3,7], 'B': [563,785]})

Desired O/P:

op = pd.DataFrame({'A': [1,2,3,4,5,6,7,8], 'b': [101,123,563,678,np.nan,672,785,786], 'C': ['ABC', 'DER', 'ERC','DFE','HJI','JKL','SDH',np.Nan]})

Answer 1

Use, pd.merge to left merge the dataframes df1 & df2 on column A , then using Series.fillna fill the missing values in column b from the values in column B :

df = pd.merge(df1, df2, on='A', how='left')
df['b'] = df['b'].fillna(df.pop('B'))

Result:

# print(df)
   A      b    C
0  1  101.0  ABC
1  2  123.0  DER
2  3  563.0  ERC
3  4  678.0  DFE
4  5    NaN  HJI
5  6  672.0  JKL
6  7  785.0  SDH
7  8  786.0  NaN