Phase 1: pandas Mastery · ~30 minutes · Python · pandas

Joining and Merging

An inner merge doesn't fail when rows don't match. It just leaves, quietly, and the row count that's left over still looks like a perfectly reasonable number.

Hiring signal: Checks row counts before and after every merge, by habit, rather than trusting that a merge without an error means a merge without data loss

What you will learn

Merge two related DataFrames using a shared key column
Explain the difference between inner, left, right, and outer merges
Diagnose a silent row-count drop caused by an inner merge
Predict a merge's resulting row count given two tables and a merge type

Introduction

Type: Learn Languages: Python, pandas Prerequisites: Lesson 03 (Split-Apply-Combine) Time: ~30 minutes

Objective

Learning objectives

Merge two related DataFrames using a shared key column
Explain the difference between inner, left, right, and outer merges
Diagnose a silent row-count drop caused by an inner merge
Predict a merge's resulting row count given two tables and a merge type

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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