Bar plots with percentages

Let's continue exploring the responses to a survey sent out to young people. The variable "Interested in Math" is True if the person reported being interested or very interested in mathematics, and False otherwise. What percentage of young people report being interested in math, and does this vary based on gender? Let's use a bar plot to find out.

As a reminder, we'll create a bar plot using the catplot() function, providing the name of categorical variable to put on the x-axis (x=____), the name of the quantitative variable to summarize on the y-axis (y=____), the pandas DataFrame to use (data=____), and the type of categorical plot (kind="bar").

Seaborn has been imported as sns and matplotlib.pyplot has been imported as plt.

Deze oefening maakt deel uit van de cursus

Introduction to Data Visualization with Seaborn

Cursus bekijken

Oefeninstructies

Use the survey_data DataFrame and sns.catplot() to create a bar plot with "Gender" on the x-axis and "Interested in Math" on the y-axis.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

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



# Show plot
plt.show()

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introduction to Data Visualization with 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: 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

Huidige oefening

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?