Hue dan count plot

Mari lanjut mengeksplorasi himpunan data siswa sekolah menengah dengan melihat variabel baru. Kolom "school" menunjukkan inisial sekolah yang diikuti siswa — "GP" atau "MS".

Pada latihan sebelumnya, kita membuat scatter plot dengan titik berwarna berdasarkan apakah siswa tinggal di area perkotaan atau pedesaan. Berapa banyak siswa yang tinggal di area perkotaan vs. pedesaan, dan apakah ini berbeda bergantung pada sekolah yang diikuti? Mari buat count plot dengan subkelompok untuk mencari tahu.

Latihan ini adalah bagian dari kursus

Pengantar Visualisasi Data dengan Seaborn

Petunjuk latihan

Lengkapi dictionary palette_colors untuk memetakan nilai lokasi "Rural" ke warna "green" dan nilai lokasi "Urban" ke warna "blue".
Buat count plot dengan "school" pada sumbu x menggunakan DataFrame student_data.
- Tambahkan subkelompok ke plot menggunakan variabel "location" dan gunakan dictionary palette_colors agar subkelompok lokasi berwarna hijau dan biru.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import Matplotlib and Seaborn
import matplotlib.pyplot as plt
import seaborn as sns

# Create a dictionary mapping subgroup values to colors
palette_colors = {____: "green", ____: "blue"}

# Create a count plot of school with location subgroups




# Display plot
plt.show()

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar Visualisasi Data dengan Seaborn

SkillTag.level.beginnerSkillTag.label

4.9+

Mulai Kursus Gratis

What is Seaborn, and when should you use it? In this chapter, you will find out! Plus, you will learn how to create scatter plots and count plots with both lists of data and pandas DataFrames. You will also be introduced to one of the big advantages of using Seaborn - the ability to easily add a third variable to your plots by using color to represent different subgroups.

Exercise 1: Pengenalan Seaborn Exercise 2: Membuat plot sebar dengan list Exercise 3: Membuat count plot dengan sebuah list Exercise 4: Menggunakan pandas dengan Seaborn Exercise 5: Data "rapi" vs. "tidak rapi"Exercise 6: Membuat count plot dengan DataFrame Exercise 7: Menambahkan variabel ketiga dengan hue Exercise 8: Hue dan scatter plot Exercise 9: Hue dan count plot

Latihan Saat Ini

In this chapter, you will create and customize plots that visualize the relationship between two quantitative variables. To do this, you will use scatter plots and line plots to explore how the level of air pollution in a city changes over the course of a day and how horsepower relates to fuel efficiency in cars. You will also see another big advantage of using Seaborn - the ability to easily create subplots in a single figure!

Exercise 1: Introduction to relational plots and subplots Exercise 2: Creating subplots with col and row Exercise 3: Creating two-factor subplots Exercise 4: Customizing scatter plots Exercise 5: Changing the size of scatter plot points Exercise 6: Changing the style of scatter plot points Exercise 7: Introduction to line plots Exercise 8: Interpreting line plots Exercise 9: Visualizing standard deviation with line plots Exercise 10: Plotting subgroups in line plots

Categorical variables are present in nearly every dataset, but they are especially prominent in survey data. In this chapter, you will learn how to create and customize categorical plots such as box plots, bar plots, count plots, and point plots. Along the way, you will explore survey data from young people about their interests, students about their study habits, and adult men about their feelings about masculinity.

Exercise 1: Count plots and bar plots Exercise 2: Count plots Exercise 3: Bar plots with percentages Exercise 4: Customizing bar plots Exercise 5: Box plots Exercise 6: Create and interpret a box plot Exercise 7: Omitting outliers Exercise 8: Adjusting the whiskers Exercise 9: Point plots Exercise 10: Customizing point plots Exercise 11: Point plots with subgroups

In this final chapter, you will learn how to add informative plot titles and axis labels, which are one of the most important parts of any data visualization! You will also learn how to customize the style of your visualizations in order to more quickly orient your audience to the key takeaways. Then, you will put everything you have learned together for the final exercises of the course!

Exercise 1: Changing plot style and color Exercise 2: Changing style and palette Exercise 3: Changing the scale Exercise 4: Using a custom palette Exercise 5: Adding titles and labels: Part 1 Exercise 6: FacetGrids vs. AxesSubplots Exercise 7: Adding a title to a FacetGrid object Exercise 8: Adding titles and labels: Part 2 Exercise 9: Adding a title and axis labels Exercise 10: Rotating x-tick labels Exercise 11: Putting it all together Exercise 12: Box plot with subgroups Exercise 13: Bar plot with subgroups and subplots Exercise 14: Well done! What's next?