Write a program to join these two dataframes on their indexes, but keep in mind that

Q. Consider the two data-frames namely emp and dept given below:

DataFrame emp :-

    emp_id emp_name   hire date  dept_id
0       1     Rina  2001-05-01      4.0
1       2     Mina  2002-07-15      1.0
2       3     Tina  2005-10-18      5.0
3       4     Dina  2007-01-03      3.0
4       5     Nina  2008-06-24      NaN

DataFrame dept:-

    dept_id         dept_name
0        1    Administration
1        2  Customer Service
2        3           Finance
3        4   Human Resources
4        5             Sales

Write a program to join these two data frames on their indexes, but keep in mind that both data-frames have a common column name which, if not handled, leads to error.

Answer :-

To resolve common column name in join(), we can use lsuffix and rsuffix arguments

import pandas as pd

y = [ {'emp_id': 1, 'emp_name': 'Rina', 'hire date': '2001-05-01', 'dept_id': 4.0},
      {'emp_id': 2, 'emp_name': 'Mina', 'hire date': '2002-07-15', 'dept_id': 1.0},
      {'emp_id': 3, 'emp_name': 'Tina', 'hire date': '2005-10-18', 'dept_id': 5.0},
      {'emp_id': 4, 'emp_name': 'Dina', 'hire date': '2007-01-03', 'dept_id': 3.0},
      {'emp_id': 5, 'emp_name': 'Nina', 'hire date': '2008-06-24', 'dept_id': np.NaN}]

x = [ {'dept_id': 1, 'dept_name': 'Administration'},
      {'dept_id': 2, 'dept_name': 'Customer Service'},
      {'dept_id': 3, 'dept_name': 'Finance'},
      {'dept_id': 4, 'dept_name': 'Human Resources'},
      {'dept_id': 5, 'dept_name': 'Sales'}]

emp = pd.DataFrame (x)
dept = pd.DataFrame (y)

ndf = emp.join(dept, lsuffix = '_emp', rsuffix = '_dept')
print("Joined dataframe is : -\n")
print (ndf)

Output :-

Joined dataframe is : -

   dept_id_emp         dept_name  emp_id emp_name   hire date  dept_id_dept
0            1    Administration       1     Rina  2001-05-01           4.0
1            2  Customer Service       2     Mina  2002-07-15           1.0
2            3           Finance       3     Tina  2005-10-18           5.0
3            4   Human Resources       4     Dina  2007-01-03           3.0
4            5             Sales       5     Nina  2008-06-24           NaN
>>>