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

Introduction to joining data

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

VLOOKUP-style 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.

"Uit velen één"

Zet de taken in de juiste volgorde

Kader deel 2: one-to-many merges

Relaties van één-op-veel identificeren

Joinen op sleutelkolommen

Controleren op dubbele sleutels

Een one-to-many-merge afronden

Joins op basis van index

Joinen op index

Meerdere tabellen joinen

Een one-to-many-join herhalen

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

Advanced joins

Games Plays

Players draft

Current players

Games

New weather data

Twee of meer gegevenssets samenvoegen is nodig voor vrijwel elke analyse in de echte wereld. Dat deed je eerder al in spreadsheets met VLOOKUP en aanverwante functies. Kun je voortbouwen op die ervaring wanneer je overstapt naar de wereld van Python? Zeker! In deze cursus leer je alles over het samenbrengen van gegevenssets met pandas, de gouden standaard van Python voor het bewerken van tabelgegevens. Je past pandas-functies toe om data uit de National Football League (NFL) te combineren in een vertrouwde spreadsheetomgeving. Met deze vaardigheden kun je de kracht van pandas benutten en grotere, complexere gegevenssets in elke analyse integreren.

Python for Spreadsheet Users

Leer effectief datasets samenvoegen in tabelvorm met de Python Pandas-bibliotheek.

Pandas-joins voor spreadsheetgebruikers

Leer hoe je op een effectieve en efficiënte manier datasets in tabelvorm kunt samenvoegen met behulp van de Python Pandas-bibliotheek.  

Relaties van één-op-veel identificeren

Pandas-joins voor spreadsheetgebruikers

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