values in the Subjects column are taken from both the DataFrames.

import pandas

dict1 = {"Subjects": ["Physics", "Chemistry", "Mathematics"], "Grades": ["A", "C", "B"], "Marks": [95, 72, 88]}
df1 = pandas.DataFrame(dict1)
print("df1: \n", df1)

dict2 = {"Subjects": ["Biology", "Chemistry", "Literature"], "Marks": [85, 72, 68], "Remarks": ["Very Good", "Good", "Fair"]}
df2 = pandas.DataFrame(dict2)
print("df2: \n", df2)

df5 = pandas.merge(df1, df2, how="right", on="Subjects")
print("After right join: \n", df5)

Here also, the how parameter indicates we are performing one right join operation. And the merge operation is performed on the Subjects column as per the “on” parameter.

So, all the rows from df2 will be taken in df5. And only the rows that have common values in the Subjects column will be taken from both the DataFrames.

So, the output of the above program will be:

df1: 
       Subjects Grades  Marks
0      Physics      A     95
1    Chemistry      C     72
2  Mathematics      B     88
df2: 
      Subjects  Marks    Remarks
0     Biology     85  Very Good
1   Chemistry     72       Good
2  Literature     68       Fair
After right join: 
      Subjects Grades  Marks_x  Marks_y    Remarks
0     Biology    NaN      NaN       85  Very Good
1   Chemistry      C     72.0       72       Good
2  Literature    NaN      NaN       68       Fair

Merging DataFrames using an outer join

In the case of the outer join operation, all the rows from both the DataFrames will be taken in the output …