Pandas DataFrame Join Method

Introduction

In this lab, we will learn how to use the join() method in the Python Pandas library. The join() method is used to join the columns of another DataFrame to an existing DataFrame. It can join the columns based on the index or on a key column.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Import the required libraries and create the DataFrames

    import pandas as pd

    ## Create the first DataFrame
    df_1 = pd.DataFrame({"A":[0,1], "B":[3,4]})
    print("The first DataFrame:")
    print(df_1)

    ## Create the second DataFrame
    df_2 = pd.DataFrame({"C":[0,1], "D":[3,4]})
    print("The second DataFrame:")
    print(df_2)

Join the two DataFrames using the join() method

    print("Joined DataFrame:")
    joined_df = df_1.join(df_2)
    print(joined_df)

Join the two DataFrames using the lsuffix and rsuffix parameters

    print("Joined DataFrame with suffixes:")
    joined_df_suffix = df_1.join(df_2, lsuffix='_first', rsuffix='_second')
    print(joined_df_suffix)

Join the two DataFrames using a specific column as the key

    print("Joined DataFrame using A as the key:")
    joined_df_key = df_1.set_index('A').join(df_2.set_index('A'))
    print(joined_df_key)

Summary

In this lab, we learned how to use the join() method in the Python Pandas library. We saw how to join two DataFrames based on the index or a key column, and how to use suffixes to differentiate the overlapping columns. The join() method is a useful tool for combining and merging DataFrames in Pandas.