Menghapus kategori

Sebelum mengadopsi anjing, orang tua mungkin ingin tahu apakah anjing baru menyukai anak-anak atau tidak. Saat meninjau himpunan data anjing yang dapat diadopsi, dogs, Anda melihat bahwa frekuensi respons untuk Series kategorikal "likes_children" terlihat seperti ini:

maybe     1718
yes       1172
no          47

Pemilik data ingin mengonversi semua respons "maybe" menjadi "no", karena tidak aman membiarkan sebuah keluarga mengadopsi anjing jika anjing tersebut tidak menyukai anak-anak. Kode untuk mengonversi semua "maybe" menjadi "no" disediakan pada Langkah 1. Namun, opsi "maybe" masih tetap ada sebagai sebuah kategori.

Latihan ini adalah bagian dari kursus

Bekerja dengan Data Kategorikal di Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Set "maybe" to be "no"
dogs.loc[dogs["likes_children"] == "maybe", "likes_children"] = "no"

# Print out categories
print(____)

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: Course introduction Exercise 2: Categorical vs. numerical Exercise 3: Exploring a target variable Exercise 4: Ordinal categorical variables Exercise 5: Categorical data in pandas Exercise 6: Setting dtypes and saving memory Exercise 7: Creating a categorical pandas Series Exercise 8: Setting dtype when reading data Exercise 9: Grouping data by category in pandas Exercise 10: Create lots of groups Exercise 11: Setting up a .groupby() statement Exercise 12: Using pandas functions effectively

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: Mengatur variabel kategori Exercise 2: Menetapkan kategori Exercise 3: Menambahkan kategori Exercise 4: Menghapus kategori

Latihan Saat Ini

Exercise 5: Memperbarui kategori Exercise 6: Pemeriksaan pengetahuan: Menggabungkan kategori Exercise 7: Mengganti nama kategori Exercise 8: Menggabungkan kategori Exercise 9: Mengurutkan ulang kategori Exercise 10: Mengurutkan ulang kategori dalam sebuah Series Exercise 11: Menggunakan .groupby() setelah mengubah urutan Exercise 12: Membersihkan dan mengakses data Exercise 13: Membersihkan variabel Exercise 14: Mengakses dan memfilter 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