Mengubah gaya titik pada scatter plot

Mari lanjut mengeksplorasi himpunan data mpg dari Seaborn dengan melihat hubungan antara seberapa cepat sebuah mobil berakselerasi ("acceleration") dan efisiensi bahan bakarnya ("mpg"). Apakah karakteristik ini berbeda menurut negara asal ("origin")?

Perhatikan bahwa variabel "acceleration" adalah waktu untuk berakselerasi dari 0 hingga 60 mil per jam, dalam detik. Nilai yang lebih tinggi menunjukkan akselerasi yang lebih lambat.

Latihan ini adalah bagian dari kursus

Pengantar Visualisasi Data dengan Seaborn

Petunjuk latihan

Gunakan relplot() dan DataFrame mpg untuk membuat scatter plot dengan "acceleration" pada sumbu-x dan "mpg" pada sumbu-y. Ubah gaya dan warna titik plot berdasarkan negara asal ("origin").

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 scatter plot of acceleration vs. mpg




# Show 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: 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: Pengantar plot relasional dan subplot Exercise 2: Membuat subplot dengan col dan row Exercise 3: Membuat subplot dua faktor Exercise 4: Menyesuaikan scatter plot Exercise 5: Mengubah ukuran titik pada scatter plot Exercise 6: Mengubah gaya titik pada scatter plot

Latihan Saat Ini

Exercise 7: Pendahuluan ke plot garis Exercise 8: Menafsirkan plot garis Exercise 9: Memvisualisasikan simpangan baku dengan plot garis Exercise 10: Memplot subkelompok pada plot garis

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?