Fonts in Matplotlib#
Matplotlib needs fonts to work with its text engine, some of which are shipped alongside the installation. The default font is DejaVu Sans which covers most European writing systems. However, users can configure the default fonts, and provide their own custom fonts. See Customizing text properties for details and Text with non-latin glyphs in particular for glyphs not supported by DejaVu Sans.
Matplotlib also provides an option to offload text rendering to a TeX engine
usetex=True), see Text rendering with LaTeX.
Fonts in PDF and PostScript#
Fonts have a long (and sometimes incompatible) history in computing, leading to different platforms supporting different types of fonts. In practice, Matplotlib supports three font specifications (in addition to pdf 'core fonts', which are explained later in the guide):
Type 1 (PDF)
Type 3 (PDF/PS)
One of the oldest types, introduced by Adobe
Similar to Type 1 in terms of introduction
Newer than previous types, used commonly today, introduced by Apple
Restricted subset of PostScript, charstrings are in bytecode
Full PostScript language, allows embedding arbitrary code (in theory, even render fractals when rasterizing!)
Include a virtual machine that can execute code!
These fonts support font hinting
Do not support font hinting
Hinting supported (virtual machine processes the "hints")
Non-subsetted through Matplotlib
Subsetted via external module ttconv
Subsetted via external module fontTools
Adobe disabled support for authoring with Type 1 fonts in January 2023.
Other font specifications which Matplotlib supports:
Type 42 fonts (PS):
OpenType is a new standard for digital type fonts, developed jointly by Adobe and Microsoft
Generally contain a much larger character set!
Limited support with Matplotlib
The PDF and PostScript formats support embedding fonts in files, allowing the display program to correctly render the text, independent of what fonts are installed on the viewer's computer and without the need to pre-rasterize the text. This ensures that if the output is zoomed or resized the text does not become pixelated. However, embedding full fonts in the file can lead to large output files, particularly with fonts with many glyphs such as those that support CJK (Chinese/Japanese/Korean).
The solution to this problem is to subset the fonts used in the document and only embed the glyphs actually used. This gets both vector text and small files sizes. Computing the subset of the font required and writing the new (reduced) font are both complex problem and thus Matplotlib relies on fontTools and a vendored fork of ttconv.
Currently Type 3, Type 42, and TrueType fonts are subsetted. Type 1 fonts are not.
In addition to the ability to embed fonts, as part of the PostScript and PDF specification there are 14 Core Fonts that compliant viewers must ensure are available. If you restrict your document to only these fonts you do not have to embed any font information in the document but still get vector text.
This is especially helpful to generate really lightweight documents:
# trigger core fonts for PDF backend
plt.rcParams["pdf.use14corefonts"] = True
# trigger core fonts for PS backend
plt.rcParams["ps.useafm"] = True
chars = "AFM ftw!"
fig, ax = plt.subplots()
ax.text(0.5, 0.5, chars)
Fonts in SVG#
Text can output to SVG in two ways controlled by
as a path (
'path') in the SVG
as string in the SVG with font styling on the element (
When saving via
'path' Matplotlib will compute the path of the glyphs used
as vector paths and write those to the output. The advantage of doing so is
that the SVG will look the same on all computers independent of what fonts are
installed. However the text will not be editable after the fact.
In contrast, saving with
'none' will result in smaller files and the
text will appear directly in the markup. However, the appearance may vary
based on the SVG viewer and what fonts are available.
Fonts in Agg#
To output text to raster formats via Agg, Matplotlib relies on FreeType. Because the exact rendering of the glyphs changes between FreeType versions we pin to a specific version for our image comparison tests.
How Matplotlib selects fonts#
Internally, using a font in Matplotlib is a three step process:
FontPropertiesobject is created (explicitly or implicitly)
the Python proxy for the font object is used by the backend code to render the text -- the exact details depend on the backend via
The algorithm to select the "best" font is a modified version of the algorithm specified by the CSS1 Specifications which is used by web browsers. This algorithm takes into account the font family name (e.g. "Arial", "Noto Sans CJK", "Hack", ...), the size, style, and weight. In addition to family names that map directly to fonts there are five "generic font family names" (serif, monospace, fantasy, cursive, and sans-serif) that will internally be mapped to any one of a set of fonts.
Currently the public API for doing step 2 is
FontManager.findfont (and that
method on the global
FontManager instance is aliased at the module level as
font_manager.findfont), which will only find a single font and return the absolute
path to the font on the filesystem.
There is no font that covers the entire Unicode space thus it is possible for the
users to require a mix of glyphs that can not be satisfied from a single font.
While it has been possible to use multiple fonts within a Figure, on distinct
Text instances, it was not previous possible to use multiple fonts in the
Text instance (as a web browser does). As of Matplotlib 3.6 the Agg,
SVG, PDF, and PS backends will "fallback" through multiple fonts in a single
fig, ax = plt.subplots()
.5, .5, "There are 几个汉字 in between!",
family=['DejaVu Sans', 'WenQuanYi Zen Hei'],
Internally this is implemented by setting The "font family" on
FontProperties objects to a list of font families. A (currently)
private API extracts a list of paths to all of the fonts found and then
constructs a single
ft2font.FT2Font object that is aware of all of the fonts.
Each glyph of the string is rendered using the first font in the list that
contains that glyph.
A majority of this work was done by Aitik Gupta supported by Google Summer of Code 2021.