Box plot con sottogruppi

In questo esercizio analizziamo un insieme di dati con le risposte a un sondaggio rivolto a giovani. Una delle domande era: "Ti interessa avere animali domestici?" Esploriamo se la distribuzione delle età di chi ha risposto "sì" tende a essere più alta o più bassa rispetto a chi ha risposto "no", controllando per genere.

Questo esercizio fa parte del corso

Introduzione alla visualizzazione dei dati con Seaborn

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Istruzioni dell'esercizio

Imposta la palette di colori su "Blues".
Aggiungi sottogruppi per colorare i box plot in base a "Interested in Pets".
Imposta il titolo dell'oggetto FacetGrid g su "Age of Those Interested in Pets vs. Not".
Fai visualizzare il grafico usando una funzione di Matplotlib.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Set palette to "Blues"
____

# Adjust to add subgroups based on "Interested in Pets"
g = sns.catplot(x="Gender",
                y="Age", data=survey_data, 
                kind="box", hue=____)

# Set title to "Age of Those Interested in Pets vs. Not"
____

# Show plot
plt.____

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Introduzione alla visualizzazione dei dati con Seaborn

InicianteNível de habilidade

4.9+

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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: 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: Modificare stile e colori del grafico Exercise 2: Modificare stile e palette Exercise 3: Cambiare la scala Exercise 4: Usare una palette personalizzata Exercise 5: Aggiungere titoli ed etichette: Parte 1 Exercise 6: FacetGrid vs. AxesSubplot Exercise 7: Aggiungere un titolo a un oggetto FacetGrid Exercise 8: Aggiungere titoli ed etichette: Parte 2 Exercise 9: Aggiungere un titolo e le etichette degli assi Exercise 10: Ruotare le etichette dei tick sull'asse x Exercise 11: Mettere tutto insieme Exercise 12: Box plot con sottogruppi

Esercizio in corso

Exercise 13: Bar plot con sottogruppi e sottoplot Exercise 14: Ben fatto! E adesso?