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.

Tipi di join

Join uno-a-uno

Join uno-a-molti

Join molti-a-molti

Uno sguardo più da vicino alle join uno-a-uno

Ordina le attività

Rimettere in ordine il framework.

Replicare VLOOKUP

Unione su due o più chiavi

Combinare i dati comuni con gli inner join

Merge in stile orientato agli oggetti

Inner join di base

Gestire nomi diversi

Affermazione

Concatena le righe

Left merge

Inner merge

Scegliere il metodo di join corretto

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.

"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

One-to-many 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

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.  

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