
Explore matplotlib, the popular open-source Python library for data visualization, its origins by John D. Hunter, current version 3.3.3, and its rich documentation, installation, and tutorials.
Learn to create simple graphs with matplotlib's plot function, importing numpy for data, using color and dot plots to visualize x and y values through practical examples.
Learn to customize line visuals in matplotlib by adjusting line styles, colors, and widths, including two plots on a single coordinate.
Learn how markers highlight peak points in plots, customize marker shapes, sizes, edge and face colors, and combine marker and line colors with alpha to illustrate areas under curves.
Learn to annotate line charts with xlabel, ylabel, title, and legend, using matplotlib to compare monthly profit across lines and highlight peaks with markers.
Plot data with matplotlib by defining x and y arrays, adding a title and axis labels, and enabling grid. Use markers and line style to identify points and improve readability.
Learn to create and interpret a histogram with matplotlib, focusing on frequency, density, and adjustable parameters to customize data visualization in Python.
Create and customize pie charts in matplotlib to visualize data with labels, legends, 3d effects; adjust start angle and explode slices; display percentages for JavaScript, Python, Java, C, and C++.
Learn to create box plots in Python with matplotlib to visualize median, mean, quartiles, and outliers. Import libraries, generate random data, and adjust figure size to interpret whiskers and descriptors.
Explore creating 3d plots in matplotlib, including surface and scatter plots, by combining X, Y, Z coordinates with cosine patterns and color variations for data visualization.
Learn to create subplots to plot multiple graphs in a grid of rows and columns, assign titles, labels, and legends, and set an overall subtitle for grouped visuals.
Learn to add and customize text in plots, including axis titles and labels, using the text function, colors, font size, equations with dollar signs, and annotations.
learn to make interactive visualizations in jupyter notebooks using ipywidgets and sliders, as you define an exponent function, plot it, and tweak parameters like maximum iterations.
The only way to truly learn how to use Matplotlib for Data Visualization with Python is by actually getting your hands dirty and trying out the features yourself. That’s where this course comes in!
The hour-long course starts off with an introduction to Matplotlib, including how to install and import it in Python. We will then move on to learn how you can create and customize basic 2D charts in order to best tell your story. Furthermore, you will also learn what subplots are and how you can create as well as customize them with the help of the Matplotlib library.
We will explore the full spectrum of interactive and explorable graphic representations including various plots such as Scatter, Line, Bar, Stacked Bar, Histogram, Pie, and much more. The course also walks you through the basics of creating a 3D plot in Matplotlib and how you can start plotting images using the Python visualization library.
And, once you are done with this course, you will be able to create almost any kind of plot that you need with Matplotlib and Python.
Why you should take this course?
Updated 2021 course content: All our course content is updated as per the latest version of the Matplotlib library.
Practical hands-on knowledge: This course is oriented to providing a step-by-step implementation guide for making amazing data visualization plots rather than just sticking to the theory.
Guided support: We are always there to guide you through the Q/As so feel free to ask us your queries