Mengeksplorasi variabel target

Anda diminta membangun model Machine Learning untuk memprediksi apakah seseorang berpenghasilan lebih dari $50.000 per tahun atau tidak. Untuk memahami variabel target, Above/Below 50k, Anda memutuskan untuk mengeksplorasi variabel tersebut lebih rinci.

Paket Python pandas akan digunakan sepanjang kursus ini dan akan dimuat sebagai pd. Himpunan data pendapatan sensus dewasa, adult, juga telah dimuat sebelumnya untuk Anda.

Latihan ini adalah bagian dari kursus

Bekerja dengan Data Kategorikal di Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Explore the Above/Below 50k variable
print(adult[____].____)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Bekerja dengan Data Kategorikal di Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

Almost every dataset contains categorical information—and often it’s an unexplored goldmine of information. In this chapter, you’ll learn how pandas handles categorical columns using the data type category. You’ll also discover how to group data by categories to unearth great summary statistics.

Exercise 1: Pengantar kursus Exercise 2: Kategorikal vs. numerik Exercise 3: Mengeksplorasi variabel target

Latihan Saat Ini

Exercise 4: Variabel kategorikal ordinal Exercise 5: Data kategorikal di pandas Exercise 6: Mengatur dtype dan menghemat memori Exercise 7: Membuat Series kategorikal pandas Exercise 8: Menetapkan dtype saat membaca data Exercise 9: Mengelompokkan data berdasarkan kategori di pandas Exercise 10: Buat banyak kelompok Exercise 11: Menyusun pernyataan .groupby()Exercise 12: Menggunakan fungsi pandas secara efektif

Now it’s time to learn how to set, add, and remove categories from a Series. You’ll also explore how to update, rename, collapse, and reorder categories, before applying your new skills to clean and access other data within your DataFrame.

Exercise 1: Setting category variables Exercise 2: Setting categories Exercise 3: Adding categories Exercise 4: Removing categories Exercise 5: Updating categories Exercise 6: Collapsing categories knowledge check Exercise 7: Renaming categories Exercise 8: Collapsing categories Exercise 9: Reordering categories Exercise 10: Reordering categories in a Series Exercise 11: Using .groupby() after reordering Exercise 12: Cleaning and accessing data Exercise 13: Cleaning variables Exercise 14: Accessing and filtering data

In this chapter, you’ll use the seaborn Python library to create informative visualizations using categorical data—including categorical plots (cat-plot), box plots, bar plots, point plots, and count plots. You’ll then learn how to visualize categorical columns and split data across categorical columns to visualize summary statistics of numerical columns.

Exercise 1: Introduction to categorical plots using Seaborn Exercise 2: Boxplot understanding Exercise 3: Creating a box plot Exercise 4: Seaborn bar plots Exercise 5: Creating a bar plot Exercise 6: Ordering categories Exercise 7: Bar plot using hue Exercise 8: Point and count plots Exercise 9: Creating a point plot Exercise 10: Creating a count plot Exercise 11: Review catplot() types Exercise 12: Additional catplot() options Exercise 13: One visualization per group Exercise 14: Updating categorical plots

Lastly, you’ll learn how to overcome the common pitfalls of using categorical data. You’ll also grow your data encoding skills as you are introduced to label encoding and one-hot encoding—perfect for helping you prepare your data for use in machine learning algorithms.

Exercise 1: Categorical pitfalls Exercise 2: Memory usage knowledge check Exercise 3: Overcoming pitfalls: string issues Exercise 4: Overcoming pitfalls: using NumPy arrays Exercise 5: Label encoding Exercise 6: Create a label encoding and map Exercise 7: Using saved mappings Exercise 8: Creating a Boolean encoding Exercise 9: One-hot encoding Exercise 10: One-hot knowledge check Exercise 11: One-hot encoding specific columns Exercise 12: Wrap-up video