Matplotlib Cheat Sheet

Updated 2026-04-27

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Matplotlib is a comprehensive, low-level plotting library for Python that creates static, animated, and interactive visualizations across 2D and 3D spaces. Originally designed to mimic MATLAB's plotting interface, it has become the foundational visualization library in the Python ecosystem, serving as the backend for many higher-level tools like Seaborn and Pandas plotting. Understanding both its pyplot interface (stateful, MATLAB-like) and its object-oriented API (explicit control via Figure and Axes) is essential — the latter offers better scalability for complex multi-subplot layouts and production code, while pyplot excels at quick exploratory analysis.

What This Cheat Sheet Covers

This topic spans 22 focused tables and 138 indexed concepts, 102 flashcards. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Core Plotting FunctionsTable 2: Figure and Axes CreationTable 3: Customization - Lines and MarkersTable 4: Axis Formatting and ScalingTable 5: Legends and AnnotationsTable 6: Colormaps and ColorbarsTable 7: Subplot Layout ManagementTable 8: Contour and Heatmap PlotsTable 9: 3D PlottingTable 10: Polar PlotsTable 11: Patches and ShapesTable 12: Text and Font PropertiesTable 13: Saving FiguresTable 14: AnimationTable 15: BackendsTable 16: Style Sheets and rcParamsTable 17: Advanced Axes FeaturesTable 18: Quiver and Vector Field PlotsTable 19: Statistical Plot TypesTable 20: Date PlottingTable 21: Event HandlingTable 22: Object-Oriented vs Pyplot Interface

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Table 1: Core Plotting Functions

The starting point for almost any visualization is picking the right chart type for your data. Line, scatter, and bar cover the everyday cases, while histograms, box plots, and violin plots reveal distributions, and specialized forms like stem, step, and broken_barh handle discrete signals and timeline data — each one a single call that turns arrays into a picture.

Function	Example	Description
plot	`plt.plot(x, y, 'r--')`	• Line plot connecting data points • accepts format strings for color, marker, and linestyle in compact notation.
scatter	`plt.scatter(x, y, s=50, c='blue')`	• Scatterplot for visualizing relationships • supports variable marker sizes and colors via arrays.
bar	`plt.bar(categories, values)`	• Vertical bar chart • use `barh()` for horizontal orientation.
hist	`plt.hist(data, bins=20)`	Histogram showing distribution of a single variable across binned ranges.
boxplot	`plt.boxplot([data1, data2])`	Box-and-whisker plot displaying quartiles and outliers for statistical distributions.
fill_between	`plt.fill_between(x, y1, y2)`	• Shaded region between two curves • commonly used for confidence intervals.
errorbar	`plt.errorbar(x, y, yerr=err)`	• Plot with error bars showing uncertainty • accepts symmetric or asymmetric errors.
stackplot	`plt.stackplot(x, y1, y2, y3)`	Stacked area chart showing cumulative totals of multiple series over time.
violinplot	`plt.violinplot(data)`	• Combination of boxplot and kernel density estimate • shows distribution shape and density.
pie	`plt.pie(sizes, labels=labels)`	• Pie chart for proportional data • supports explode parameter to separate slices.
stem	`plt.stem(x, y)`	• Vertical lines from baseline to data points • useful for discrete signal data.
step	`plt.step(x, y, where='mid')`	• Step plot connecting points with horizontal and vertical segments • `where` controls step placement.
hlines / vlines	`plt.hlines(y=[2, 4], xmin=0, xmax=5)`	• Horizontal/vertical lines at specific data-coordinate positions with defined extents • unlike `axhline`, lines don't span the full axis.
broken_barh	`ax.broken_barh([(10,50),(100,20)], (5,9))`	Horizontal sequence of rectangles — useful for Gantt charts and timeline/categorical interval plots.

Table 2: Figure and Axes Creation

Every plot lives inside a Figure (the canvas) holding one or more Axes (the actual plotting regions), and these functions are how you set that structure up. subplots is the everyday workhorse for a grid, but when you need irregular layouts — a wide panel above two narrow ones, or an inset zoomed view — subplot_mosaic, GridSpec, and inset_axes give you precise control over how the space is carved up.

Function	Example	Description
subplots	`fig, ax = plt.subplots(2, 3)`	• Creates figure and grid of subplots in one call • returns Figure and Axes array.

The rest of this section is part of the full cheat sheet

Table 3: Customization - Lines and Markers

These are the properties that turn a default line into a deliberate one — color, dash pattern, width, marker shape and size, and transparency. The marker-specific controls (face color, edge color, edge width) let you style the points independently of the connecting line, and most have terse two-letter aliases like lw and mfc you'll see everywhere in real code.

Property	Example	Description
color	`plt.plot(x, y, color='#FF5733')`	Sets line/marker color via named colors, hex codes, or RGB tuples.

The rest of this section is part of the full cheat sheet

Table 4: Axis Formatting and Scaling

Once the data is drawn, the axes themselves often need work — setting limits and labels, switching to a log scale for wide-ranging values, controlling exactly where ticks fall, and formatting their labels as percentages or scientific notation. These methods are mostly the object-oriented ax.set_* form, which scales better than the pyplot equivalents once you have multiple subplots in play.

Method	Example	Description
set_xlim / set_ylim	`ax.set_xlim(0, 10)`	• Sets axis range explicitly • pyplot equivalent is `plt.xlim()`.

The rest of this section is part of the full cheat sheet

Table 1: Core Plotting Functions

Function	Example	Description
plot	`plt.plot(x, y, 'r--')`	• Line plot connecting data points • accepts format strings for color, marker, and linestyle in compact notation.
scatter	`plt.scatter(x, y, s=50, c='blue')`	• Scatterplot for visualizing relationships • supports variable marker sizes and colors via arrays.
bar	`plt.bar(categories, values)`	• Vertical bar chart • use `barh()` for horizontal orientation.
hist	`plt.hist(data, bins=20)`	Histogram showing distribution of a single variable across binned ranges.
boxplot	`plt.boxplot([data1, data2])`	Box-and-whisker plot displaying quartiles and outliers for statistical distributions.
fill_between	`plt.fill_between(x, y1, y2)`	• Shaded region between two curves • commonly used for confidence intervals.
errorbar	`plt.errorbar(x, y, yerr=err)`	• Plot with error bars showing uncertainty • accepts symmetric or asymmetric errors.
stackplot	`plt.stackplot(x, y1, y2, y3)`	Stacked area chart showing cumulative totals of multiple series over time.
violinplot	`plt.violinplot(data)`	• Combination of boxplot and kernel density estimate • shows distribution shape and density.
pie	`plt.pie(sizes, labels=labels)`	• Pie chart for proportional data • supports explode parameter to separate slices.
stem	`plt.stem(x, y)`	• Vertical lines from baseline to data points • useful for discrete signal data.
step	`plt.step(x, y, where='mid')`	• Step plot connecting points with horizontal and vertical segments • `where` controls step placement.
hlines / vlines	`plt.hlines(y=[2, 4], xmin=0, xmax=5)`	• Horizontal/vertical lines at specific data-coordinate positions with defined extents • unlike `axhline`, lines don't span the full axis.
broken_barh	`ax.broken_barh([(10,50),(100,20)], (5,9))`	Horizontal sequence of rectangles — useful for Gantt charts and timeline/categorical interval plots.

Table 2: Figure and Axes Creation

Function	Example	Description
subplots	`fig, ax = plt.subplots(2, 3)`	• Creates figure and grid of subplots in one call • returns Figure and Axes array.

Table 3: Customization - Lines and Markers

Property	Example	Description
color	`plt.plot(x, y, color='#FF5733')`	Sets line/marker color via named colors, hex codes, or RGB tuples.

Table 4: Axis Formatting and Scaling

Method	Example	Description
set_xlim / set_ylim	`ax.set_xlim(0, 10)`	• Sets axis range explicitly • pyplot equivalent is `plt.xlim()`.