Key Concepts

Review core concepts you need to learn to master this subject

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 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
  1. 1
    Introduction to Seaborn
    In 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…
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  2. 2
    Using Pandas For Seaborn
    Throughout 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…
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  3. 3
    Plotting Bars with Seaborn
    Take 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…
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  4. 4
    Understanding Aggregates
    Seaborn 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 …
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  5. 5
    Plotting Aggregates
    Recall 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…
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  6. 6
    Modifying Error Bars
    By 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…
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  7. 7
    Calculating Different Aggregates
    In 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…
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  8. 8
    Aggregating by Multiple Columns
    Sometimes 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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  9. 9
    Review
    In this lesson you learned how to extend Matplotlib with Seaborn to create meaningful visualizations from data in DataFrames. You’ve also learned how Seaborn creates aggregated charts and how to c…
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What you'll create

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How you'll master it

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

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