Grafici a barre con percentuali

Continuiamo a esplorare le risposte a un sondaggio inviato ai giovani. La variabile "Interested in Math" è True se la persona ha dichiarato di essere interessata o molto interessata alla matematica, e False altrimenti. Qual è la percentuale di giovani che si dichiarano interessati alla matematica? E questa percentuale varia in base al genere? Usiamo un grafico a barre per scoprirlo.

Come promemoria, creeremo un grafico a barre usando la funzione catplot(), fornendo il nome della variabile categorica da mettere sull'asse x (x=____), il nome della variabile quantitativa da riassumere sull'asse y (y=____), il DataFrame di pandas da utilizzare (data=____) e il tipo di grafico categorico (kind="bar").

Seaborn è stato importato come sns e matplotlib.pyplot è stato importato come plt.

Questo esercizio fa parte del corso

Introduzione alla visualizzazione dei dati con Seaborn

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Istruzioni dell'esercizio

Usa il DataFrame survey_data e sns.catplot() per creare un grafico a barre con "Gender" sull'asse x e "Interested in Math" sull'asse y.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create a bar plot of interest in math, separated by gender



# Show plot
plt.show()

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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 plot e bar plot Exercise 2: Count plot Exercise 3: Grafici a barre con percentuali

Esercizio in corso

Exercise 4: Personalizzare i bar plot Exercise 5: Box plot Exercise 6: Crea e interpreta un box plot Exercise 7: Omettere i valori anomali Exercise 8: Regolare i baffi Exercise 9: Point plot Exercise 10: Personalizzare i point plot Exercise 11: Point plot con sottogruppi

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?