Explore the figure class as the top-level container for all plot elements, instantiate figures with width, height, and dpi, and configure axes with titles and labels.

Learn to control grid visibility and styling in Matplotlib using the axis grid function, displaying major and minor grid lines, and customizing color, line style, and width for each axis.

Learn to plot bar charts in Matplotlib to visualize categorical data with bar height representing values, using vertical or horizontal bars. Compare discrete values across multiple series.

Learn to build a histogram that shows the distribution of numerical and continuous data using matplotlib's hist function, with bins, counts, range, density, and cumulative options.

Create a pie chart in matplotlib to display a single data series as proportional wedges with labeled percentages, using area values, labels, and colors to show each group's contribution.

Explore how to create scatter plots with Matplotlib, plotting x and y data and differentiating a third variable by color or marker size, using examples like boys and girls grades.

Visualize velocity vectors as arrows using a quiver plot, mapping x and y coordinates to arrow locations and u and v components to arrow directions with color options in matplotlib.

Plot a 3d contour of a three-dimensional sinusoidal function using a 2d regular grid to compute z from x and y, visualizing the surface with a 2d mesh in matplotlib.

discover how a 3d surface plot visualizes the relationship between dependent and independent variables, with filled polygons and color maps that reveal topology, as a companion to the contour plot.

Explore how to display and customize text in plots with the Plot Lib library, controlling fonts, color, position, axis labels, titles, subtitles, and Unicode text, including math expressions.