Matplotlib 3 10 9

Matplotlib 3.10.9

Overview

Matplotlib is the foundational Python plotting library for creating static, animated, and interactive visualizations. It produces publication-quality figures in a wide variety of formats and supports all major operating systems. Built on NumPy arrays, it provides both a high-level pyplot interface for quick scripting and a lower-level object-oriented API for fine-grained control.

Matplotlib 3.10.9 is a micro release of the v3.10.x series. Key changes from 3.10.8:

• Security hardening: Removed eval usage in cyclers — eliminates potential code injection via crafted cycler specifications.
• Security hardening: Removed shell escapes in LaTeX and PS calls — prevents command injection through malicious LaTeX or PostScript content.
• Various minor bug and documentation fixes.

Constrained layout remains the default, with improved color handling (alpha tuples), enhanced animation writers (PillowWriter, FFMpegWriter), and refined subplot mosaic layouts. Works seamlessly with Jupyter notebooks, IPython, PyQt/PySide, Tkinter, wxPython, and headless environments.

When to Use

• Generating 2D plots: line charts, scatter plots, bar charts, histograms, box plots, violin plots
• Creating gridded data visualizations: heatmaps (imshow), contour plots, pcolormesh
• Building statistical visualizations: ECDFs, hexbin, error bars, stackplots
• Producing 3D plots: surfaces, wireframes, scatter3d, bar3d
• Saving figures to PNG, SVG, PDF, PS, or PGF formats
• Customizing plot appearance through rcParams, style sheets, and colormaps
• Creating animations with FuncAnimation or ArtistAnimation
• Embedding Matplotlib in GUI applications (Qt, Tk, wx) or web servers
• Working with date/time data, custom tick locators/formatters
• Building complex multi-axis layouts with subplot_mosaic or GridSpec

Core Concepts

The Object Hierarchy

Everything visible in a Matplotlib figure is an Artist. The hierarchy is:

• Figure — the top-level container (window, canvas, or output file). Created via plt.figure() or plt.subplots().
• Axes — a plotting area within a Figure. Each Axes has its own x/y axes (Axis objects), data region, title, labels, and legend. Most plotting methods live here.
• Axis — the tick-generating objects that control scale, limits, locators, and formatters.
• Artist — everything visible: Line2D, Text, Rectangle, Patch, Collection, Image, etc. Artists are drawn when the Figure is rendered.

Two API Styles

Object-oriented (OO) style — recommended for most use cases:

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots(figsize=(6, 4), layout='constrained')
ax.plot([1, 2, 3, 4], [1, 4, 2, 3])
ax.set_xlabel('x label')
ax.set_ylabel('y label')
ax.set_title('Simple Plot')
plt.show()

Pyplot (state-machine) style — convenient for quick scripts:

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [1, 4, 2, 3])
plt.xlabel('x label')
plt.ylabel('y label')
plt.title('Simple Plot')
plt.show()

The OO style is preferred because it avoids global state, supports multiple figures/axes cleanly, and makes customization explicit.

Creating Figures and Axes

# Empty figure, no axes
fig = plt.figure()

# Figure with a single Axes
fig, ax = plt.subplots()

# 2x2 grid of Axes
fig, axs = plt.subplots(2, 2)

# Complex layout with subplot_mosaic
fig, axs = plt.subplot_mosaic([['left', 'top'], ['left', 'bottom']])

The layout='constrained' parameter (default in 3.10) automatically adjusts spacing to prevent label overlap. Use fig.subplots_adjust() for manual control or tight_layout() for the legacy approach.

Saving Figures

fig.savefig('plot.png')           # PNG (raster)
fig.savefig('plot.svg')           # SVG (vector)
fig.savefig('plot.pdf')           # PDF (vector)
fig.savefig('plot.eps')           # Encapsulated PostScript
fig.savefig('plot.png', dpi=300)  # High-resolution PNG

Key savefig parameters: dpi, bbox_inches='tight', pad_inches, facecolor, format.

Usage Examples

Line Plots with Multiple Series

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 2, 100)
fig, ax = plt.subplots(figsize=(6, 4), layout='constrained')
ax.plot(x, x, label='linear')
ax.plot(x, x**2, label='quadratic')
ax.plot(x, x**3, label='cubic')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Multiple Lines')
ax.legend()
plt.show()

Scatter Plot with Data Keyword

np.random.seed(42)
data = {'a': np.arange(50), 'c': np.random.randint(0, 50, 50)}
data['b'] = data['a'] + 10 * np.random.randn(50)
data['d'] = np.abs(data['a']) * 100

fig, ax = plt.subplots(figsize=(5, 3), layout='constrained')
ax.scatter('a', 'b', c='c', s='d', data=data)
ax.set_xlabel('entry a')
ax.set_ylabel('entry b')

Bar Chart with Error Bars

categories = ['A', 'B', 'C', 'D']
values = [30, 45, 25, 60]
errors = [3, 5, 2, 7]

fig, ax = plt.subplots(layout='constrained')
ax.bar(categories, values, yerr=errors, capsize=5, color='steelblue')
ax.set_ylabel('Value')
ax.set_title('Bar Chart with Error Bars')

Subplot Mosaic Layout

import matplotlib.pyplot as plt

grid = [
    ['top_left', 'top_right'],
    ['bottom_left', 'top_right'],
]
fig, axs = plt.subplot_mosaic(grid, figsize=(8, 6), layout='constrained')
for name, ax in axs.items():
    ax.text(0.5, 0.5, name.replace('_', '\n'), ha='center', va='center', fontsize=14)

Advanced Topics

Figures and Backends: Output backends (Agg, QtAgg, GTK4Agg, SVG, PDF, PS), interactive vs non-interactive, backend selection, Jupyter integration → Figures and Backends

Axes, Subplots, and Layouts: GridSpec, subplot_mosaic, constrained layout, tight_layout, twin axes, shared axes, inset axes, colorbar placement → Axes and Layouts

Plot Types Reference: Complete catalog of plotting methods — line, scatter, bar, histogram, contour, imshow, pcolormesh, pie, boxplot, violin, quiver, streamplot, hexbin, ECDF, stairs → Plot Types

Colors and Colormaps: Color specification formats (RGB, hex, named, xkcd, Tableau), colormaps (sequential, diverging, qualitative, cyclic), normalization, alpha blending, custom colorbars → Colors and Colormaps

Text, Annotations, and Math: Text placement, annotations with arrows, mathematical expressions (LaTeX-like syntax), font management, text properties, mathtext rendering → Text and Annotations

Customization and rcParams: Runtime rc settings, style sheets (built-in and custom), matplotlibrc configuration, rc_context for temporary changes, cycler for property cycles → Customization

Transformations: Data coordinates, display coordinates, axes coordinates, blended transforms, compound transforms, offsetbox annotations → Transformations

Animations: FuncAnimation, ArtistAnimation, blitting for performance, saving to MP4/GIF via PillowWriter and FFMpegWriter → Animations

3D Plotting: mplot3d toolkit, 3D line/scatter/surface/wireframe plots, volumetric data (voxels), tri-surface, contour3D → 3D Plotting

API Reference Summary: pyplot module functions, Axes methods, Artist base class, Figure methods, Axis locators and formatters, patches, collections → API Reference