Which of the following can be used to merge two data frames by common columns?concatenate()bind()join()merge()

The function that can be used to merge two data frames by common columns is merge(). This function is used in pandas, a library in Python, to combine two or more data frames on the basis of common columns (also known as 'keys').

Here is a step by step guide on how to use it:

1. Import the pandas library:

import pandas as pd

2. Create the data frames that you want to merge. For example:

df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3'],
                    'key': ['K0', 'K1', 'K0', 'K1']})

df2 = pd.DataFrame({'C': ['C0', 'C1'],
                    'D': ['D0', 'D1']},
                   index=['K0', 'K1'])

3. Use the merge() function to merge the data frames. In this case, we are merging on the 'key' column:

result = pd.merge(df1, df2, left_on='key', right_index=True)

4. Print the result:

print(result)

This will give you a merged data frame where the rows from the two original data frames are combined based on the values in the 'key' column.