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

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

• 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

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.

Python & Matplotlib: Getting Started with Matplotlib for Data Visualization

Python & Matplotlib: Getting Started with Matplotlib for Data Visualization

• discover the key concepts covered in this course
• install Matplotlib and explore Matplotlib interactive back ends
• create various basic line charts visualizing random data using Matplotlib and pyplot
• import data from a CSV file using pandas and visualize it with a basic line chart
• customize various aspects of a line chart, such as the color of the line
• create a figure object with multiple axes objects and create line charts in the axes
• create a chart with two lines using two axes objects with the twinx() function
• create a histogram to visualize the frequency counts of data in bins using bars
• Create various special histograms, such as a histogram visualizing multiple columns
• Compare categorical data by category against continuous values using bar charts
• create drawn Lollipop charts to compare categorical data to continuous values
• create bar and lollipop charts that visualize multiple related variables in one chart
• summarize the key concepts covered in this course

Matplotlib is a Python plotting library used to create dynamic visualizations using pyplot, a state-based interface. You'll learn how to correctly install and use Matplotlib to build line charts, bar charts, and histograms in this course.

You'll create basic line charts out of randomly generated data. You'll learn how to use the plt.subplots() function, import data from a CSV file using pandas, and create and customize various line charts.

Additionally, you'll create figures holding more than one axes object, learn why and how to use the twinx() function, and create multiple lines in the same line chart with different y-axes for each line.

Moving on, you'll construct histograms that visualize multiple variables and approximate the cumulative probability density function. Lastly, you'll create some bar charts to represent categorical data.