Python Statistical Plots: Visualizing & Analyzing Data Using Seaborn

Python 3.8
  • 17 Videos | 1h 54m 12s
  • Includes Assessment
  • Earns a Badge
The wealth of Python data visualization libraries makes it hard to decide the best choice for each use case. However, if you're looking for statistical plots that are easy to build and visually appealing, Seaborn is the obvious choice. You'll begin this course by using Seaborn to construct simple univariate histograms and use kernel density estimation, or KDE, to visualize the probability distribution of your data. You'll then work with bivariate histograms and KDE curves. Next, you'll use box plots to concisely represent the median and the inter-quartile range (IQR) and define outliers in data. You'll work with boxen plots, which are conceptually similar to box plots but employ percentile markers rather than whiskers. Finally, you'll use Violin plots to represent the entire probability density function, obtained via a KDE estimation, for your data.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    install the necessary Python modules to work with Seaborn
    create histograms for univariate data
    use the distplot() function for customizing histograms
    create figure-level and axis-level KDE curves
    implement bar charts, KDE curves, and rug plots
    represent bivariate visualizations with color coding and grouped charts
    create univariate KDE curves and cumulative distributions
    visualize bivariate histograms and KDE curves
  • customize joint plots using histograms, KDE curves, hexbin, and regression charts
    implement figure-level and axis-level scatter plots
    customize scatter plots with multiple variables and visualize categorical data
    use the catplot and boxplot functions to create box and whisker plots
    contrast box plots and boxen plots
    use the figure-level catplot() and axis-level violinplot()
    customize violin plots using hue and bandwidth
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 41s
    UP NEXT
  • Playable
    2. 
    Installing the Seaborn Module
    5m 6s
  • Locked
    3. 
    Visualizing Univariate Data
    7m 40s
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    4. 
    Representing Data Using Histograms
    5m 23s
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    5. 
    Creating KDE Curves
    6m 33s
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    6. 
    Creating Univariate Plots
    7m 13s
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    7. 
    Representing Data Using Bivariate Visualizations
    7m 10s
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    8. 
    Creating KDE Curves and Cumulative Distributions
    5m 16s
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    9. 
    Visualizing Data Using Bivariate Histograms
    7m 42s
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    10. 
    Understanding and Implementing Joint Plots
    6m 16s
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    11. 
    Understanding and Implementing Scatter Plots
    6m 47s
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    12. 
    Customizing Scatter Plots with Multiple Variables
    5m 48s
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    13. 
    Creating Box Plots
    7m 43s
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    14. 
    Understanding and Implementing Boxen Plots in Seaborn
    5m 14s
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    15. 
    Representing Data Using Violin Plots
    7m 47s
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    16. 
    Customizing Custom Violin Plots
    9m 45s
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    17. 
    Course Summary
    2m 37s

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