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Pandas Joins for Spreadsheet Users

Intermediate

Updated 12/2024

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

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PythonData Manipulation4 hours12 videos44 exercises3,700 XP3,877Statement 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

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

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

"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

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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