Saçılım grafiği noktalarının boyutunu değiştirme

Bu egzersizde, her satırın bir araba modelini temsil ettiği ve arabanın üretim yılı, mil başına galon ("M.P.G.") değeri, motor gücü ("horsepower" olarak ölçülür) ve menşei ülke gibi bilgiler içeren Seaborn'un mpg veri kümesini inceleyeceğiz.

Bir arabanın motor gücü ("horsepower") ile yakıt verimliliği ("mpg") arasındaki ilişki nedir? Peki bu ilişki, arabadaki silindir sayısına ("cylinders") göre nasıl değişir? Hadi keşfedelim.

Daha esnek olduğu için scatterplot() yerine relplot() kullanmaya devam edelim.

Bu egzersiz

Seaborn ile Veri Görselleştirmeye Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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

# Create scatter plot of horsepower vs. mpg




# Show plot
plt.show()

Kodu Düzenle ve Çalıştır

Bu egzersiz

Seaborn ile Veri Görselleştirmeye Giriş

kursunun bir parçasıdır

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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: Introduction to Seaborn Exercise 2: Making a scatter plot with lists Exercise 3: Making a count plot with a list Exercise 4: Using pandas with Seaborn Exercise 5: "Tidy" vs. "untidy" data Exercise 6: Making a count plot with a DataFrame Exercise 7: Adding a third variable with hue Exercise 8: Hue and scatter plots Exercise 9: Hue and count plots

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: İlişkisel grafiklere ve alt grafiklere giriş Exercise 2: col ve row ile alt grafikler oluşturma Exercise 3: İki faktörlü alt grafikler oluşturma Exercise 4: Saçılım grafiklerini özelleştirme Exercise 5: Saçılım grafiği noktalarının boyutunu değiştirme

Geçerli Egzersiz

Exercise 6: Dağılım grafiği noktalarının stilini değiştirme Exercise 7: Çizgi grafiklere giriş Exercise 8: Çizgi grafiklerini yorumlama Exercise 9: Çizgi grafiklerle standart sapmayı görselleştirme Exercise 10: Çizgi grafiklerde alt grupları görselleştirme

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?