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.

"Dari banyak, menjadi satu"

Urutkan tugas-tugasnya

Kerangka bagian 2: penggabungan one-to-many

Mengidentifikasi relasi one-to-many

Menggabungkan berdasarkan kolom kunci

Memeriksa kunci duplikat

Menyelesaikan penggabungan one-to-many

Join berbasis indeks

Menggabungkan berdasarkan indeks

Menggabungkan beberapa tabel

Meninjau join one-to-many

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

Menggabungkan dua atau lebih himpunan data diperlukan untuk hampir semua analisis dunia nyata. Anda sudah pernah melakukannya di spreadsheet menggunakan VLOOKUP dan fungsi terkait. Bisakah Anda membangun pengalaman ini saat beralih ke dunia Python? Bisa! Dalam kursus ini Anda akan mempelajari seluk-beluk menggabungkan himpunan data dengan pandas, standar emas Python untuk memanipulasi data tabular. Anda akan menerapkan fungsi pandas untuk menggabungkan data dari National Football League (NFL) dalam lingkungan spreadsheet yang familier. Dengan keterampilan ini, Anda akan mampu memaksimalkan kekuatan pandas dan mengintegrasikan himpunan data yang lebih besar dan kompleks ke dalam analisis apa pun.

Python for Spreadsheet Users

Pelajari cara menggabungkan dataset dalam format tabel dengan efektif menggunakan pustaka Python Pandas.

Menggabungkan Data dengan Pandas untuk Pengguna Spreadsheet

Pelajari cara menggabungkan dataset dalam format tabel secara efektif dan efisien menggunakan perpustakaan Python Pandas.  

Mengidentifikasi relasi one-to-many

Menggabungkan Data dengan Pandas untuk Pengguna Spreadsheet

Latihan interaktif praktis