Uitschieters weglaten

Laten we nu de gegevensset student_data gebruiken om de verdeling van eindcijfers ("G3") te vergelijken tussen studenten die thuis internettoegang hebben en studenten die dat niet hebben. Hiervoor gebruiken we de variabele "internet", een binaire (ja/nee) indicator of de student thuis internet heeft.

Omdat internet op het platteland mogelijk minder toegankelijk is, voegen we subgroepen toe op basis van waar de student woont. Hiervoor kunnen we de variabele "location" gebruiken, een indicator of een student in een stedelijk ("Urban") of landelijk ("Rural") gebied woont.

Seaborn is al geïmporteerd als sns en matplotlib.pyplot is geïmporteerd als plt. Ter herinnering: je kunt uitschieters in boxplots weglaten door showfliers=False in te stellen.

Deze oefening maakt deel uit van de cursus

Introductie tot datavisualisatie met Seaborn

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Oefeninstructies

Gebruik sns.catplot() om een boxplot te maken met de DataFrame student_data, met "internet" op de x-as en "G3" op de y-as.
Voeg subgroepen toe zodat elke boxplot is ingekleurd op basis van "location".
Geef de uitschieters niet weer.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Create a box plot with subgroups and omit the outliers






# Show plot
plt.show()

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot datavisualisatie met Seaborn

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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 en staafdiagrammen Exercise 2: Countplots Exercise 3: Staafdiagrammen met percentages Exercise 4: Staafdiagrammen aanpassen Exercise 5: Boxplots Exercise 6: Maak en interpreteer een boxplot Exercise 7: Uitschieters weglaten

Huidige oefening

Exercise 8: De snorharen aanpassen Exercise 9: Puntplots Exercise 10: Puntplots aanpassen Exercise 11: Puntplots met subgroepen

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?