Membuat count plot

Saat membuat analisis cepat atas hitungan frekuensi, Anda telah menggunakan .value_counts(). Ini cara yang sangat baik untuk melihat jumlah dan memahami kategori apa saja yang ada dalam data. Namun, mengirimkan tabel frekuensi kepada klien atau rekan kerja tidak selalu merupakan ide yang baik. Pada latihan ini, Anda akan memvisualisasikan jumlah ulasan berdasarkan "Score". Meskipun "Score" sebelumnya digunakan sebagai variabel numerik, variabel ini dapat diperlakukan sebagai variabel kategorikal karena memiliki lima nilai unik yang diurutkan dari terburuk hingga terbaik. Himpunan data reviews telah dimuat sebelumnya.

Latihan ini merupakan bagian dari kursus

Bekerja dengan Data Kategorikal di Python

Instruksi latihan

Gunakan fungsi catplot() untuk menampilkan frekuensi hitungan menggunakan himpunan data reviews.
Hitung frekuensi untuk variabel "Score" pada sumbu x.
Saat menghitung frekuensi, warnai batang menggunakan kolom "Traveler type".

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

sns.set(font_scale=1.4)
sns.set_style("darkgrid")

# Create a catplot that will count the frequency of "Score" across "Traveler type"
sns.catplot(
  ____
)
plt.show()

Edit dan Jalankan Kode

Latihan ini merupakan 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: Pendahuluan ke plot kategorikal menggunakan Seaborn Exercise 2: Memahami boxplot Exercise 3: Membuat box plot Exercise 4: Plot batang Seaborn Exercise 5: Membuat bar plot Exercise 6: Mengurutkan kategori Exercise 7: Plot batang menggunakan hue Exercise 8: Plot titik dan plot hitung Exercise 9: Membuat point plot Exercise 10: Membuat count plot

Latihan Saat Ini

Exercise 11: Meninjau jenis-jenis catplot()Exercise 12: Opsi tambahan catplot()Exercise 13: Satu visualisasi per grup Exercise 14: Memperbarui plot kategorikal

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