Performing Inner Joins in Python 1

Performing Inner Joins in Python


Joins are used to combine data from two or more tables in a relational database. An inner join returns the rows in both tables where there is a match in the specified join condition. In Python, performing an inner join is relatively simple using the pandas library.

Step 1: Importing the Required Libraries

The first step is to import the necessary libraries for working with tables and data frames. The pandas library is essential for this task: Dive deeper into the topic and uncover extra information in this specially selected external resource. joins in pandas, examine fresh information and viewpoints on the topic discussed in the piece.

import pandas as pd

Step 2: Loading the Data

Next, load the data into Python using the read_csv() function. For this example, we will use two CSV files – customers.csv and orders.csv – that are related by a common column, CustomerID:

customers = pd.read_csv(‘customers.csv’)

orders = pd.read_csv(‘orders.csv’)

Step 3: Performing the Inner Join

Finally, we can perform the inner join using the merge() function in pandas. The syntax is as follows:

merged_data = pd.merge(customers, orders, on=’CustomerID’)

This will return a new data frame that contains only the rows where there is a match between the CustomerID column in both tables.

Step 4: Examining the Results

We can now examine the results of our inner join by printing the first few rows of the merged data:


This will display the first five rows of the merged data frame.

Additional Options

The merge() function in pandas allows for several additional options, including specifying the type of join (inner, left, right, or outer), renaming columns, and handling duplicate keys. For more information on these options, consult the pandas documentation.


Performing an inner join in Python using the pandas library is a straightforward process that involves importing the necessary libraries, loading the data, performing the join, and examining the results. Once the data has been merged using a common key, it can be analyzed and manipulated further to extract additional insights. We’re always striving to provide a complete learning experience. Visit this handpicked external website and uncover more details about the subject. python join list!

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