Learn Matplotlib from Beginner to Advanced

Discover the building blocks of a Matplotlib figure by using subplots, understanding figure, axis, and plot relationships, and learn how axis objects drive plotting on x and y axes.

Learn to add a title, x and y axis labels, and a legend to a Matplotlib plot using an OOP approach, with linear, quadratic, and cubic curves.

Compare the pyplot approach with the object oriented method to add a plot title, axis labels, and a legend on figures and axes.

Create multiple axes in a single figure using subplots. Arrange them in 1x2 or 2x2 layouts, adjust layout with tight layout, and set the figure size.

Adjust plot() parameters to customize matplotlib visuals with color, line style, and alpha. Use color names or hex codes, control linewidth, and choose solid or dotted lines.

Learn to create and customize scatter plots in Matplotlib by plotting data with subplots, adjusting marker size and color, and adding legends for multiple data series.

Learn how to improve matplotlib plot clarity by increasing the figure dpi using rcParams, doubling from 100 to 200, and applying higher resolution to labels and ticks.

Configure the plot typography by adjusting the title's font, size, color, and alignment, then tailor x and y axis labels with size and color, and add padding.

Add text to axes in matplotlib by specifying x and y coordinates. Customize alignment, rotation, color, font size and family, and apply a bounding box with style and colors.

Learn to add annotations in a matplotlib plot by marking a local max with text and an arrow, customize the arrow props, and adjust axis limits.

Learn how to set manual ticks in matplotlib, configuring x-axis tick values from zero to one hundred with a defined range, and contrast them with automatic ticks.

Learn to draw vertical and horizontal lines in Matplotlib by placing lines at 0.4 and 0.6 on subplots, and customizing style, color, and thickness with dotted and dashed options.

Explore plotting in different scales with matplotlib by creating a 2x2 grid of axes, setting a random seed for reproducibility, and applying linear, log, symlog, and logit scales.

Discover how to apply built-in matplotlib styles to customize plots, explore the available styles, and switch between options like Solaris light and classic to improve visualization.

Position legends using numeric or named locations, and adjust title and label fonts and colors. Create multiple legends with specific handles, anchor them outside the plot, and expand into columns.

Create various shapes in matplotlib using patches, including rectangles, ellipses, and polygons, by defining points or using built-in patch methods. Set the aspect ratio to equal to render accurate shapes.

Use the reconfiguration file to adjust matplotlib default properties via rcParams to control dpi, line width, colors, font size, and other plot attributes across all figures.

Create and customize bar diagrams in Matplotlib, including vertical and horizontal bars, groupings by category and gender, labeling, error bars, and on-bar value annotations.

Learn to create bar diagrams in matplotlib with multiple hatches, colors, and stacked bars, using figures, axes, and subplots, and add legends for clear comparison.

rotate the tick labels on a horizontal bar chart in matplotlib, adjust rotation angles (including 90° and 45°), set x-axis limits, and label axes as company names and revenue.

Create a currency formatter for the x-axis in matplotlib that converts values like 20,000 to 20 K and 1.2 million to 1.2 M, using dollars and set_major_formatter.

Enhance a bar chart by adding a mean revenue line, annotate data points with company indices, and adjust the line style for clear, readable visualizations.

Learn how to save a Matplotlib figure in multiple image formats, specify a save path, and adjust settings like bbox to prevent clipping.

Create and customize histograms in matplotlib, exploring bar, step, and stacked types, with bins, density options, colors and face color, and multiple data sets.

Explore creating and customizing pie charts with matplotlib, including labels, explode effects, start angles, clockwise ordering, percent display, colors, and layout adjustments.

Explore basic image manipulation in Matplotlib by loading images, inspecting color values (rgb) and alpha, applying color maps and color bars, and annotating with rectangles using patches.

Learn how to plot population data from an external csv file using matplotlib and pandas, with a side-by-side bar chart of 2010 and 2020 state populations.

Demonstrates how to enable a 3d projection, initialize a 3d figure, and plot 3d, scatter, and multi-color 3d bar diagrams with labeled x, y, and z axes.