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.

"Da molti, uno"

Metti in ordine le attività

Framework parte 2: merge one-to-many

Individuare le relazioni uno-a-molti

Join sulle colonne chiave

Verificare chiavi duplicate

Completare un merge one-to-many

Join basati sugli indici

Join sugli indici

Join di più tabelle

Ripasso della join uno-a-molti

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

Unire due o più insiemi di dati è necessario in quasi ogni analisi reale. L’hai già fatto con i fogli di calcolo usando VLOOKUP e funzioni simili. Puoi fare leva su questa esperienza mentre passi al mondo di Python? Certo! In questo corso imparerai tutto quello che serve per unire insiemi di dati con pandas, lo standard d’oro di Python per manipolare dati tabellari. Userai le funzioni di pandas per combinare dati della National Football League (NFL) in un contesto familiare, simile a un foglio di calcolo. Con queste competenze potrai sfruttare la potenza di pandas e integrare insiemi di dati più grandi e complessi in qualsiasi analisi.

Python for Spreadsheet Users

Scopri come unire efficacemente i dataset in formato tabellare con la libreria Python Pandas.

Join con pandas per chi usa i fogli di calcolo

Scopri come unire in modo efficace ed efficiente set di dati in formato tabellare usando la libreria Python Pandas.  

Join sugli indici

Join con pandas per chi usa i fogli di calcolo

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