Let's start by understanding the concept of wide and long formats and the advantages of using each of them. You’ll then learn how to pivot data from long to a wide format, and get summary statistics from a large DataFrame.

Wide and long data formats

The long and the wide

Flipping players

Reshaping using pivot method

Dribbling the pivot method

Offensive or defensive player?

Replay that last move!

Pivot tables

Reviewing the moves

Exploring the big match

The tallest and the heaviest

Introduction to Data Reshaping

Master the technique of reshaping DataFrames from wide to long format. In this chapter, you'll learn how to use the melting method and wide to long function before discovering how to handle string columns by concatenating or splitting them.

Reshaping with melt

Gothic times

Rating is not everything

How is Frankenstein, Dorian Gray?

Wide to long function

The golden age

Decrypting the code

Time to read, Katniss!

Working with string columns

Did you say dystopia?

What's your rating, Harry?

Elementary, dear Watson!

Converting Between Wide and Long Format

In this chapter, you’ll level-up your data manipulation skills using multi-level indexing. You'll learn how to reshape DataFrames by rearranging levels of the row indexes to the column axis, or vice versa. You'll also gain the skills you need to handle missing data generated in the stacking and unstacking processes.

Stacking DataFrames

Stack the calls!

Phone directory index

Text me!

Unstacking DataFrames

International caller

Call another time

Organizing your voicemail

Working with multiple levels

Swap your SIM card

Two many calls

Handling missing data

A missed phone call

Don't drop the stack

Stacking and Unstacking DataFrames

You'll finish by learning how to combine the reshaping process with grouping to produce quick data manipulations. Lastly, you'll discover how to transform list-like columns and handle complex nested data, such as nested JSON files.

Membentuk ulang dan menggabungkan data

Kurangi makanan cepat saji, ya!

Hanya naik

Analisis berdasarkan grup

Transformasi kolom mirip daftar

Gabungkan semuanya

Meledakkan bounds

Split klasik

Membaca data bertingkat ke dalam DataFrame

Film bertingkat (nested)

Sebuah film yang kompleks

Menangani kolom data bertingkat

Membuka sarang data burung

Jangan buang burungnya

Bentuk ulang terakhir

Advanced Reshaping

Customer churn data

Books data

FIFA players data

Obesity data

Sering kali data berformat mudah dibaca manusia, tetapi kurang cocok untuk analisis data. Di sinilah pandas membantu—ini adalah alat yang kuat untuk mengubah bentuk DataFrame ke berbagai format. Dalam kursus ini, Anda akan mengembangkan keterampilan sebagai data scientist dan analis saat mempelajari cara menangani kolom string dan data bertingkat (nested) dalam sebuah DataFrame. Anda akan bekerja dengan data dunia nyata, termasuk penilaian pemain FIFA, ulasan buku, dan data analisis churn, sambil mempelajari cara mengubah DataFrame dari format lebar ke panjang, melakukan stack dan unstack pada baris serta kolom, dan mendapatkan statistik deskriptif dari DataFrame multi-indeks.

Data Manipulation with pandas

Ubah DataFrame dari format lebar ke panjang, tumpuk dan bongkar baris dan kolom, kelola multi-indeks.

Mengubah Bentuk Data dengan pandas

Ubah format DataFrame dari format lebar ke format panjang, susun dan urutkan baris dan kolom, serta olah DataFrame dengan indeks ganda.

Membaca data bertingkat ke dalam DataFrame

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