Hue ve count plot'lar

Ortaokul öğrencilerinden oluşan veri kümemizi yeni bir değişkene bakarak keşfetmeye devam edelim. "school" sütunu, öğrencinin hangi okula gittiğini baş harflerle gösterir — "GP" veya "MS".

Son egzersizde, veri noktalarını öğrencinin kentsel mi kırsal mı bir bölgede yaşadığına göre renklendirdiğimiz bir saçılım grafiği oluşturmuştuk. Kaç öğrenci kentsel, kaçı kırsal bölgede yaşıyor ve bu, öğrencinin gittiği okula göre değişiyor mu? Bunu anlamak için alt gruplu bir count plot oluşturalım.

Bu egzersiz

Seaborn ile Veri Görselleştirmeye Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

palette_colors sözlüğünü doldurarak "Rural" konum değerini "green" rengine ve "Urban" konum değerini "blue" rengine eşle.
student_data DataFrame'ini kullanarak x ekseninde "school" olacak şekilde bir count plot oluştur.
- "location" değişkenini kullanarak grafiğe alt gruplar ekle ve konum alt gruplarının yeşil ve mavi olması için palette_colors sözlüğünü kullan.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# 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()

Kodu Düzenle ve Çalıştır

Bu egzersiz

Seaborn ile Veri Görselleştirmeye Giriş

kursunun bir parçasıdır

InicianteNível de habilidade

4.9+

Kursa Ücretsiz Başlayın

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: Seaborn’a Giriş Exercise 2: Listelerle saçılım grafiği oluşturma Exercise 3: Bir listeyle count plot oluşturma Exercise 4: Seaborn ile pandas kullanma Exercise 5: "Düzenli" ve "düzensiz" veri Exercise 6: Bir DataFrame ile count plot oluşturma Exercise 7: hue ile üçüncü bir değişken ekleme Exercise 8: Hue ve saçılım grafikleri Exercise 9: Hue ve count plot'lar

Geçerli Egzersiz

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?