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Course

Pandas Joins for Spreadsheet Users

IntermediateSkill Level

4.7+

Updated 04/2026

Learn how to effectively and efficiently join datasets in tabular format using the Python Pandas library.

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PythonData Manipulation4 hr12 videos44 Exercises3,700 XP4,447Statement of Accomplishment

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Course Description

Joining two or more datasets is necessary for almost any real-world analysis. You’ve done it before with spreadsheets using VLOOKUP and related functions. Can you build on this experience as you transition to the world of Python? Yes! In this course you will learn the ins and outs of bringing datasets together with pandas, Python’s gold standard for manipulating tabular data. You’ll apply pandas functions to combine data from the National Football League (NFL) framed in a familiar spreadsheet environment. Armed with these skills you will be able to harness the power of pandas and integrate larger, more complex datasets into any analysis.

Prerequisites

Python for Spreadsheet Users

1

Introduction to joining data

In this chapter, we'll build a foundation for using pandas to join data. You'll learn about the types of joins and how pandas can improve your effectiveness and productivity.

Joining data: a real-world necessity

The need for joining data

Working with split data

Working with complementary data

Concatenation

Concatenating rows

Concatenating rows with duplicated indexes

Concatenating columns

Power and flexibility

Advantages of pandas joins

Simple coding for complex merges

2

VLOOKUP-style joins

In this chapter, you'll learn how to use pandas for joining data in a way similar to using VLOOKUP formulas in a spreadsheet. You'll learn about three types of joins and then focus on the first type, one-to-one joins.

Types of joins

One-to-one joins

One-to-many joins

Many-to-many joins

A closer look at one-to-one joins

Unscrambling the framework.

Replicating VLOOKUP

Merging on two or more keys

Combining common data with inner joins

Object-oriented merges

Basic inner joins

Dealing with different names

Choosing the correct join method

3

One-to-many joins

In this chapter, we'll focus on one-to-many relationships. You'll practice identifying the relationship of key columns and joining data frames by column. You'll also learn how to join two or more data frames based on their indices.

"Out of many, one"

Framework part 2: one-to-many merges

Identifying one-to-many relationships

Joining on key columns

Checking for duplicate keys

Completing a one-to-many merge

Index-based joins

Joining on index

Joining multiple tables

Reviewing the one-to-many join

4

Advanced joins

In the final chapter, you'll learn advanced joining techniques to use when faced with challenging data. You'll be presented with a challenge of your own in the form of a case study that tests your skills.

Joining data in real life

Mixing indexes and columns

Suffixes and indicators

Working with time data

Combining time series

Matching to the nearest time

Recap and case study

Case study challenge - part 1

Case study challenge - part 2

Case study challenge - part 3

Pandas Joins for Spreadsheet Users

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FAQs

Is this course designed for people transitioning from spreadsheets to Python?

Yes. It is built specifically for spreadsheet users who know VLOOKUP and want to learn the equivalent data-joining techniques in pandas using familiar concepts.

What dataset is used throughout the course?

You work with National Football League data, applying pandas join functions to combine NFL datasets while learning one-to-one, one-to-many, and advanced joining techniques.

What types of joins does this course cover?

You learn one-to-one joins, one-to-many joins, index-based joins, and advanced joining techniques for handling challenging real-world data scenarios.

Do I need prior Python experience for this course?

You need basic Python knowledge along with Google Sheets experience. The prerequisites include Python for Spreadsheet Users and several Google Sheets courses.

How does this course compare joins in pandas to VLOOKUP?

Chapter 2 explicitly frames pandas joins as a VLOOKUP equivalent, helping you map familiar spreadsheet operations to their pandas counterparts for a smoother transition.

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