In this lesson, we'll learn how to merge pandas DataFrames.

The DataFrames We Will Be Using In This Lesson

In this lesson, we will be using the following two pandas DataFrames:

import pandas as pd

leftDataFrame = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],

                     'A': ['A0', 'A1', 'A2', 'A3'],

                     'B': ['B0', 'B1', 'B2', 'B3']})

   

rightDataFrame = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],

                          'C': ['C0', 'C1', 'C2', 'C3'],

                          'D': ['D0', 'D1', 'D2', 'D3']})    

The columns A, B, C, and D have real data in them, while the column key has a key that is common among both DataFrames. To merge two DataFrames means to connect them along one column that they both have in common.

How To Merge Pandas DataFrames

You can merge two pandas DataFrames along a common column using the merge columns. For anyone that is familiar with the SQL programming language, this is very similar to performing an inner join in SQL.

Do not worry if you are unfamiliar with SQL, because merge syntax is actually very straightforward. It looks like this:

pd.merge(leftDataFrame, rightDataFrame, how='inner', on='key')

Let's break down the four arguments we passed into the merge method:

1. leftDataFrame: This is the DataFrame that we'd like to merge on the left.
2. rightDataFrame: This is the DataFrame that we'd like to merge on the right.
3. how=inner: This is the type of merge that the operation is performing. There are multiple types of merges, but we will only be covering inner merges in this course.
4. on='key': This is the column that you'd like to perform the merge on. Since key was the only column in common between the two DataFrames, it was the only option that we could use to perform the merge.

Moving On

This concludes our brief discussion of how to perform merges using pandas DataFrames. We'll work through some practice problems before learning about joins, which are similar to merges but have slightly different syntax and functionality.