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.

Introduction to Seaborn

Making a scatter plot with lists

Making a count plot with a list

Using pandas with Seaborn

"Tidy" vs. "untidy" data

Making a count plot with a DataFrame

Adding a third variable with hue

Hue and scatter plots

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!

Introduction to relational plots and subplots

Creating subplots with col and row

Creating two-factor subplots

Customizing scatter plots

Changing the size of scatter plot points

Changing the style of scatter plot points

Introduction to line plots

Interpreting line plots

Visualizing standard deviation with line plots

Plotting subgroups in line plots

Visualizing Two Quantitative Variables

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.

Count plots and bar plots

Count plots

Bar plots with percentages

Customizing bar plots

Box plots

Create and interpret a box plot

Omitting outliers

Adjusting the whiskers

Point plots

Customizing point plots

Point plots with subgroups

Visualizing a Categorical and a Quantitative Variable

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!

Changing plot style and color

Changing style and palette

Changing the scale

Using a custom palette

Adding titles and labels: Part 1

FacetGrids vs. AxesSubplots

Adding a title to a FacetGrid object

Adding titles and labels: Part 2

Adding a title and axis labels

Rotating x-tick labels

Putting it all together

Box plot with subgroups

Bar plot with subgroups and subplots

Well done! What's next?

Customizing Seaborn Plots

Countries

Mileage per gallon

Students

Survey responses

Course Glossary

Seaborn is a powerful Python library that makes it easy to create informative and attractive data visualizations. This 4-hour course provides an introduction to how you can use Seaborn to create a variety of plots, including scatter plots, count plots, bar plots, and box plots, and how you can customize your visualizations.
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You’ll explore this library and create Seaborn plots based on a variety of real-world data sets, including exploring how air pollution in a city changes through the day and looking at what young people like to do in their free time. This data will give you the opportunity to find out about Seaborn’s advantages first hand, including how you can easily create subplots in a single figure and how to automatically calculate confidence intervals.
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By the end of this course, you’ll be able to use Seaborn in various situations to explore your data and effectively communicate the results of your data analysis to others. These skills are highly sought-after for data analysts, data scientists, and any other job that may involve creating data visualizations. If you’d like to continue your learning, this course is part of several tracks, including the Data Visualization track, where you can add more libraries and techniques to your skillset.

The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.
The course glossary can be found on the right in the resources section.
To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

<h2>Create Your Own Seaborn Plots </h2>
  Seaborn is a powerful Python library that makes it easy to create informative
  and attractive data visualizations. This 4-hour course provides an
  introduction to how you can use Seaborn to create a variety of plots,
  including scatter plots, count plots, bar plots, and box plots, and how you
  can customize your visualizations.
  <br><br>
  <h2>Turn Real Datasets into Custom Seaborn Visualizations </h2>
  You’ll explore this library and create your Seaborn plots based on a variety
  of real-world data sets, including exploring how air pollution in a city
  changes through the day and looking at what young people like to do in their
  free time. This data will give you the opportunity to find out about Seaborn’s
  advantages first hand, including how you can easily create subplots in a
  single figure and how to automatically calculate confidence intervals.
  <br><br>
  <h2>Improve Your Data Communication Skills </h2>
  By the end of this course, you’ll be able to use Seaborn in various situations
  to explore your data and effectively communicate the results of your data
  analysis to others. These skills are highly sought-after for data analysts,
  data scientists, and any other job that may involve creating data
  visualizations. If you’d like to continue your learning, this course is part
  of several tracks, including the Data Visualization track, where you can add
  more libraries and techniques to your skillset.

Introduction to Python

Learn how to use the Python library, Seaborn, to create data visualizations, including scatter plots, box plots, and bar plots, in just four hours. 

Introduction to Data Visualization with Seaborn

Learn how to create informative and attractive visualizations in Python using the Seaborn library.

Associate Data Scientist  in Python

Data Analyst  in Python

Data Visualization in Python

Python Data Fundamentals

DataCamp

Questions about quantitative variables

Visualizing subgroups

Introducing relplot()

scatterplot() vs. relplot()

Subplots in columns

Subplots in rows

Subplots in rows and columns

Subgroups for days of the week

Wrapping columns

Ordering columns

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_15192/subtitles/course_15192_700c6b97617f551c8363aedec2e4a670.vtt

Visualizing Two Quantitative Variables - Introduction to relational plots and subplots

Introduction to relational plots and subplots

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