# Introduction to Seaborn

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

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Seaborn

Seaborn barplot

Seaborn function plots means by default

Barplot error bars

Estimator argument in barplot

Seaborn hue

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.

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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…

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