Modificare lo stile dei punti in uno scatter plot

Continuiamo a esplorare l'insieme di dati mpg di Seaborn osservando la relazione tra la rapidità di accelerazione di un'auto ("acceleration") e la sua efficienza nei consumi ("mpg"). Queste caratteristiche variano in base al paese di origine ("origin")?

Nota che la variabile "acceleration" rappresenta il tempo (in secondi) necessario per passare da 0 a 60 miglia all'ora. Valori più alti indicano un'accelerazione più lenta.

Questo esercizio fa parte del corso

Introduzione alla visualizzazione dei dati con Seaborn

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Istruzioni dell'esercizio

Usa relplot() e il DataFrame mpg per creare uno scatter plot con "acceleration" sull'asse x e "mpg" sull'asse y. Varia lo stile e il colore dei punti del grafico in base al paese di origine ("origin").

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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()

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Introduzione alla visualizzazione dei dati con Seaborn

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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: Introduzione ai grafici relazionali e ai subplot Exercise 2: Creare subplot con col e row Exercise 3: Creare sottotrame a due fattori Exercise 4: Personalizzare gli scatter plot Exercise 5: Modificare la dimensione dei punti in uno scatter plot Exercise 6: Modificare lo stile dei punti in uno scatter plot

Esercizio in corso

Exercise 7: Introduzione ai grafici a linee Exercise 8: Interpretare i grafici a linee Exercise 9: Visualizzare la deviazione standard con i grafici a linee Exercise 10: Rappresentare sottogruppi nei grafici a linee

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?