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

Matplotlib 3.3.3    |    Intermediate
  • 11 videos | 1h 28m 44s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 18 users Rating 4.3 of 18 users (18)
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

  • 2m 15s
  • 10m 32s
  • Locked
    3.  Customizing Box-and-whisker Plots in Matplotlib
    11m 28s
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    4.  Visualizing Relationships Using Scatter Plots
    11m 3s
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    5.  Visualizing Correlations Using Matplotlib Heatmaps
    7m 57s
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    6.  Creating Labeled Heatmaps in Matplotlib
    4m 57s
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    7.  Visualizing Composition Using Matplotlib Pie Charts
    10m 4s
  • Locked
    8.  Creating Matplotlib Exploded Pie Charts and Treemaps
    7m
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    9.  Predicting with Auto-correlation & Cross-correlation
    7m 55s
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    10.  Visualizing Data Using Stacked Plots and Stem Plots
    12m 1s
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    11.  Course Summary
    3m 33s

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