Introduction to Seaborn

1. Introduction to Seaborn

Hello! Welcome to this introductory course on Seaborn! My name is Erin Case, and I'll be your instructor.

2. What is Seaborn?

So what is Seaborn? Seaborn is a powerful Python library for creating data visualizations. It was developed in order to make it easy to create the most common types of plots. The plot shown here can be created with just a few lines of Seaborn code.

3. Why is Seaborn useful?

This is a picture of a typical data analysis workflow. Data visualization is often a huge component of both the data exploration phase and the communication of results, so Seaborn will be very useful there.

4. Advantages of Seaborn

There are several tools that can be used for data visualization, but Seaborn offers several advantages. First, Seaborn's main purpose is to make data visualization easy. It was built to automatically handle a lot of complexity behind the scenes. Second, Seaborn works extremely well with pandas data structures. pandas is a Python library that is widely used for data analysis. Finally, it's built on top of Matplotlib, which is another Python visualization library. Matplotlib is extremely flexible. Seaborn allows you to take advantage of this flexibility when you need it, while avoiding the complexity that Matplotlib's flexibility can introduce.

5. Getting started

To get started, we'll need to import the Seaborn library. The line "import seaborn as sns" will import Seaborn as the conventionally used alias "sns". Why "sns"? The Seaborn library was apparently named after a character named Samuel Norman Seaborn from the television show "The West Wing" - thus, the standard alias is the character's initials ("sns"). We also need to import Matplotlib, which is the library that Seaborn is built on top of. We do this by typing "import matplotlib.pyplot as plt". "plt" is the alias that most people use to refer to Matplotlib, so we'll use that here as well.

6. Example 1: Scatter plot

Let's now dive into an example to illustrate how easily you can create visualizations using Seaborn. Here, we have data for 10 people consisting of lists of their heights in inches and their weights in pounds. Do taller people tend to weigh more? You can visualize this using a type of plot known as a scatter plot, which you'll learn more about later in the course. Use "sns dot scatterplot()" to call the scatterplot function from the Seaborn library. Then, specify what to put on the x-axis and y-axis. Finally, call the "plt dot show()" function from Matplotlib to show the scatterplot. This plot shows us that taller people tend to have a higher weight.

7. Example 2: Create a count plot

How many of our observations of heights and weights came from males vs. females? You can use another type of plot - the count plot - to investigate this. Count plots take in a categorical list and return bars that represent the number of list entries per category. Use the "countplot()" function and provide the list of every person's gender. This count plot shows that out of the 10 observations we had in our height and weight scatter plot, 6 were male and 4 were female.

8. Course Preview

Now, those were a couple of simple examples. Throughout this course, you'll learn to make more complex visualizations such as those pictured here. More importantly, you'll learn when to use each type of visualization in order to most effectively extract and communicate insights using data.

9. Let's practice!

I'm excited to dive into Seaborn with you throughout this course. For now, let's practice what you've just learned!

This exercise is part of the course

Introduction to Data Visualization with Seaborn

BeginnerSkill Level

4.8+

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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

Current Exercise

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 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?