Python Statistical Plots: Visualizing & Analyzing Data Using Seaborn
Python 3.8
| Intermediate
- 17 Videos | 1h 46m 42s
- 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
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discover the key concepts covered in this courseinstall the necessary Python modules to work with Seaborncreate histograms for univariate datause the distplot() function for customizing histogramscreate figure-level and axis-level KDE curvesimplement bar charts, KDE curves, and rug plotsrepresent bivariate visualizations with color coding and grouped chartscreate univariate KDE curves and cumulative distributionsvisualize bivariate histograms and KDE curves
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customize joint plots using histograms, KDE curves, hexbin, and regression chartsimplement figure-level and axis-level scatter plotscustomize scatter plots with multiple variables and visualize categorical datause the catplot and boxplot functions to create box and whisker plotscontrast box plots and boxen plotsuse the figure-level catplot() and axis-level violinplot()customize violin plots using hue and bandwidthsummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview2m 41sUP NEXT
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2.Installing the Seaborn Module5m 6s
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3.Visualizing Univariate Data7m 40s
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4.Representing Data Using Histograms5m 23s
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5.Creating KDE Curves6m 33s
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6.Creating Univariate Plots7m 13s
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7.Representing Data Using Bivariate Visualizations7m 10s
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8.Creating KDE Curves and Cumulative Distributions5m 16s
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9.Visualizing Data Using Bivariate Histograms7m 42s
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10.Understanding and Implementing Joint Plots6m 16s
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11.Understanding and Implementing Scatter Plots6m 47s
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12.Customizing Scatter Plots with Multiple Variables5m 48s
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13.Creating Box Plots7m 43s
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14.Understanding and Implementing Boxen Plots in Seaborn5m 14s
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15.Representing Data Using Violin Plots7m 47s
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16.Customizing Custom Violin Plots9m 45s
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17.Course Summary2m 37s
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