Menggunakan fungsi pandas secara efektif

Anda sedang membuat aplikasi Python yang akan menghitung statistik ringkasan berdasarkan variabel yang dipilih pengguna. Himpunan data lengkap cukup besar. Untuk saat ini, Anda menyiapkan kode menggunakan sebagian himpunan data, yang telah dimuat sebagai adult. Saat Anda membuat proses yang dapat digunakan kembali, pastikan Anda memikirkan cara paling efisien untuk menyiapkan objek GroupBy.

Latihan ini adalah bagian dari kursus

Bekerja dengan Data Kategorikal di Python

Petunjuk latihan

Buat daftar nama untuk dua variabel yang dipilih pengguna: "Education" dan "Above/Below 50k".
Buat objek GroupBy, gb, dengan menggunakan user_list sebagai variabel pengelompokan.
Hitung mean dari "Hours/Week" pada tiap kelompok menggunakan pendekatan paling efisien seperti yang dibahas dalam video.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create a list of user-selected variables
user_list = ____

# Create a GroupBy object using this list
gb = ____

# Find the mean for the variable "Hours/Week" for each group - Be efficient!
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: Pengantar kursus Exercise 2: Kategorikal vs. numerik Exercise 3: Mengeksplorasi variabel target 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

Latihan Saat Ini

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