# Python & Matplotlib: Creating Box Plots, Scatter Plots, Heatmaps, & Pie Charts

Matplotlib 3.3.3    |    Intermediate
• 11 videos | 1h 28m 44s
• Includes Assessment
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Matplotlib can be used to create box-and-whisker plots to display statistics. These dense visualizations pack much information into a compact form, including the median, 25th and 75th percentiles, interquartile range, and outliers. In this course, you'll learn how to work with all aspects of box-and-whisker plots, such as the use of confidence-interval notches, mean markers, and fill color. You'll also build grouped box-and-whisker plots. Next, you'll create scatter plots and heatmaps, powerful tools in exploratory data analysis. You'll build standard scatter plots before customizing various aspects of their appearance. You'll then examine the ideal uses of scatter plots and correlation heatmaps. You'll move on to visualizing composition, first using pie charts, building charts that explode out specific slices. Lastly, you'll build treemaps to visualize data with multiple levels of hierarchy.

## WHAT YOU WILL LEARN

• discover the key concepts covered in this course
use Matplotlib to create box-and-whisker plots to display various statistics, such as the median, upper and lower quartiles and outliers
use Matplotlib to create filled box-and-whisker plots
use Matplotlib to visualize the relationship between two continuous variables using scatter plots
use Matplotlib to use correlation heatmaps to visually represent covariate relationships
use Matplotlib to create a heatmap that visualizes correlations and has labels for each correlation
• use Matplotlib to visualize how individual proportions add up to a whole using pie charts
use Matplotlib to create exploded pie charts and treemaps
illustrate how autocorrelation and cross-correlation can be used to identify recurring patterns in data through Matplotlib
use Matplotlib to visualize compositions over a period of time using area charts and changes over time using stem plots
summarize the key concepts covered in this course

## IN THIS COURSE

• 3.  Customizing Box-and-whisker Plots in Matplotlib
• 4.  Visualizing Relationships Using Scatter Plots
• 5.  Visualizing Correlations Using Matplotlib Heatmaps
• 6.  Creating Labeled Heatmaps in Matplotlib
• 7.  Visualizing Composition Using Matplotlib Pie Charts
• 8.  Creating Matplotlib Exploded Pie Charts and Treemaps
• 9.  Predicting with Auto-correlation & Cross-correlation
• 10.  Visualizing Data Using Stacked Plots and Stem Plots
• 11.  Course Summary

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