Hue en count plots

We gaan verder met het verkennen van onze gegevensset van middelbare scholieren door naar een nieuwe variabele te kijken. De kolom "school" geeft de initialen aan van de school die de leerling bezocht — "GP" of "MS".

In de vorige oefening maakten we een scatter plot waarbij de punten waren ingekleurd op basis van of de leerling in een stedelijk (urban) of landelijk (rural) gebied woonde. Hoeveel leerlingen wonen er in stedelijke versus landelijke gebieden, en verschilt dit per school? Laten we een count plot met subgroepen maken om dat uit te zoeken.

Deze oefening maakt deel uit van de cursus

Introductie tot datavisualisatie met Seaborn

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Oefeninstructies

Vul de dictionary palette_colors in om de "Rural"-locatiewaarde te koppelen aan de kleur "green" en de "Urban"-locatiewaarde aan de kleur "blue".
Maak een count plot met "school" op de x-as met behulp van de DataFrame student_data.
- Voeg subgroepen toe aan de plot met de variabele "location" en gebruik de dictionary palette_colors om de locatie-subgroepen groen en blauw te maken.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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()

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot datavisualisatie met Seaborn

SkillTag.level.beginnerSkillTag.label

4.9+

Begin de cursus 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: Introductie tot Seaborn Exercise 2: Een spreidingsdiagram maken met lijsten Exercise 3: Een count-plot maken met een lijst Exercise 4: pandas gebruiken met Seaborn Exercise 5: "Tidy" vs. "untidy" data Exercise 6: Een count plot maken met een DataFrame Exercise 7: Een derde variabele toevoegen met hue Exercise 8: Hue en scatterplots Exercise 9: Hue en count plots

Huidige oefening

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?