Menggunakan pemetaan yang disimpan

Anda menggunakan subset dari sebuah himpunan data dan diminta membuat visualisasi yang merangkum keluarannya. Dalam kondisi saat ini, Anda hanya melihat angka! Untungnya, Anda sudah membuat dan menyimpan kamus (color_map, fuel_map, dan transmission_map) yang akan memetakan kolom-kolom ini kembali ke nama kategorikal aslinya. Himpunan data used_cars_updated telah dimuat sebelumnya. Pratinjau himpunan data ditampilkan. Periksa konsol untuk melihat tipe data kolom.

       engine_fuel  color  transmission  price_usd
0                3      8             0   10900.00
1                3      1             0    5000.00
2                3      7             0    2800.00

Latihan ini adalah bagian dari kursus

Bekerja dengan Data Kategorikal di Python

Petunjuk latihan

Perbarui kolom "color" kembali ke nilai aslinya menggunakan kamus color_map.
Perbarui kolom "engine_fuel" kembali ke nilai aslinya menggunakan kamus fuel_map.
Perbarui kolom "transmission" kembali ke nilai aslinya menggunakan kamus transmission_map.
Gunakan .info() pada himpunan data untuk melihat apakah dtypes telah berubah.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Update the color column using the color_map
used_cars_updated["color"] = ____
# Update the engine fuel column using the fuel_map
used_cars_updated["engine_fuel"] = ____
# Update the transmission column using the transmission_map
used_cars_updated["transmission"] = ____

# Print the info statement
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: 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: Kendala pada Data Kategori Exercise 2: Pemeriksaan pengetahuan penggunaan memori Exercise 3: Mengatasi jebakan: masalah string Exercise 4: Mengatasi jebakan: menggunakan array NumPy Exercise 5: Label encoding Exercise 6: Buat label encoding dan peta Exercise 7: Menggunakan pemetaan yang disimpan

Latihan Saat Ini

Exercise 8: Membuat encoding Boolean Exercise 9: One-hot encoding Exercise 10: Cek pengetahuan one-hot Exercise 11: One-hot encoding pada kolom tertentu Exercise 12: Video penutup