# Introduction to Seaborn

Use Seaborn, a Python data visualization library, to create bar charts for statistical analysis.

Start## Key Concepts

Review core concepts you need to learn to master this subject

Seaborn

Estimator argument in barplot

Seaborn barplot

Barplot error bars

Seaborn hue

Seaborn function plots means by default

Box and Whisker Plots in Seaborn

Seaborn Package

Seaborn

Seaborn

Seaborn is a Python data visualization library that builds off the functionalities of Matplotlib and integrates nicely with Pandas DataFrames. It provides a high-level interface to draw statistical graphs, and makes it easier to create complex visualizations.

Learn Seaborn Introduction

Lesson 1 of 2

- 1In this lesson, you’ll learn how to use Seaborn to create bar charts for statistical analysis. Seaborn is a Python data visualization library that provides simple code to create elegant visualizat…
- 2Throughout this lesson, you’ll use Seaborn to visualize a Pandas DataFrame. DataFrames contain data structured into rows and columns. DataFrames look similar to other data tables you may be famil…
- 3Take a look at the file called
**results.csv**. You’ll plot that data soon, but before you plot it, take a minute to understand the context behind that data, which is based on a hypothetical situat… - 4Seaborn can also calculate
*aggregate statistics*for large datasets. To understand why this is helpful, we must first understand what an*aggregate*is. An aggregate statistic, or aggregate, is … - 5Recall our gradebook from the previous exercise: |student|assignment_name|grade| |-|-|-| |Amy|Assignment 1|75| |Amy|Assignment 2|82| |Bob|Assignment 1|99| |Bob|Assignment 2| 90| |Chris|Assignm…
- 6By default, Seaborn will place
*error bars*on each bar when you use the barplot() function. Error bars are the small lines that extend above and below the top of each bar. Errors bars visually in… - 7In most cases, we’ll want to plot the mean of our data, but sometimes, we’ll want something different: * If our data has many outliers, we may want to plot the
*median*. * If our data is categorica… - 8Sometimes we’ll want to aggregate our data by multiple columns to visualize nested categorical variables. For example, consider our hospital survey data. The mean satisfaction seems to depend on…

## What you'll create

Portfolio projects that showcase your new skills

## How you'll master it

Stress-test your knowledge with quizzes that help commit syntax to memory