Hue e count plot

Continuiamo a esplorare il nostro insieme di dati sugli studenti della scuola secondaria guardando una nuova variabile. La colonna "school" indica le iniziali della scuola frequentata dallo studente - "GP" o "MS".

Nell'esercizio precedente, abbiamo creato uno scatter plot in cui i punti erano colorati in base al fatto che lo studente vivesse in un'area urbana o rurale. Quanti studenti vivono in aree urbane rispetto a quelle rurali, e questo varia in base alla scuola frequentata? Creiamo un count plot con sottogruppi per scoprirlo.

Questo esercizio fa parte del corso

Introduzione alla visualizzazione dei dati con Seaborn

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Istruzioni dell'esercizio

Compila il dizionario palette_colors per mappare il valore di "location" "Rural" al colore "green" e il valore "Urban" al colore "blue".
Crea un count plot con "school" sull'asse x usando il DataFrame student_data.
- Aggiungi i sottogruppi al grafico usando la variabile "location" e usa il dizionario palette_colors per rendere i sottogruppi di location verdi e blu.

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 dictionary mapping subgroup values to colors
palette_colors = {____: "green", ____: "blue"}

# Create a count plot of school with location subgroups




# Display plot
plt.show()

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: Introduzione a Seaborn Exercise 2: Creare uno scatter plot con liste Exercise 3: Creare un count plot da una lista Exercise 4: Usare pandas con Seaborn Exercise 5: Dati "tidy" vs. "untidy"Exercise 6: Creare un count plot con un DataFrame Exercise 7: Aggiungere una terza variabile con hue Exercise 8: Hue e scatter plot Exercise 9: Hue e count plot

Esercizio in corso

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: 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?