De stijl van punten in een spreidingsplot aanpassen

Laten we Seaborn’s mpg-gegevensset verder verkennen door te kijken naar de relatie tussen hoe snel een auto kan accelereren ("acceleration") en het brandstofverbruik ("mpg"). Verschillen deze eigenschappen per land van herkomst ("origin")?

Let op: de variabele "acceleration" is de tijd in seconden om van 0 naar 60 mijl per uur te accelereren. Hogere waarden betekenen tragere acceleratie.

Deze oefening maakt deel uit van de cursus

Introductie tot datavisualisatie met Seaborn

Cursus bekijken

Oefeninstructies

Gebruik relplot() en de DataFrame mpg om een spreidingsplot te maken met "acceleration" op de x-as en "mpg" op de y-as. Varieer de stijl en kleur van de plotpunten op basis van het land van herkomst ("origin").

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 scatter plot of acceleration vs. mpg




# Show 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: 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: Introductie tot relationele plots en subplotten Exercise 2: Subplots maken met col en row Exercise 3: Subplots met twee factoren maken Exercise 4: Spreidingsplots aanpassen Exercise 5: De grootte van punten in een scatterplot aanpassen Exercise 6: De stijl van punten in een spreidingsplot aanpassen

Huidige oefening

Exercise 7: Introductie tot lijngrafieken Exercise 8: Lijnplots interpreteren Exercise 9: Standaarddeviatie visualiseren met lijndiagrammen Exercise 10: Subgroepen plotten in lijndiagrammen

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?