{"id":2520,"date":"2026-05-07T07:00:47","date_gmt":"2026-05-07T12:00:47","guid":{"rendered":"https:\/\/wollen.org\/blog\/?p=2520"},"modified":"2026-05-06T23:22:03","modified_gmt":"2026-05-07T04:22:03","slug":"celebrating-five-years-knock-on-wood","status":"publish","type":"post","link":"https:\/\/wollen.org\/blog\/2026\/05\/celebrating-five-years-knock-on-wood\/","title":{"rendered":"Celebrating five years (knock on wood)"},"content":{"rendered":"

I try to squeeze as much customization into my plots as I can, but the Matplotlib rabbit hole goes deep. Buried in the documentation<\/a> you’ll find bizarre relics seemingly from a different world.<\/p>\n

Need a discrete colorbar with variable-width log-scale patches and custom end caps? Matplotlib has you covered. How about a map of the night sky using the Aitoff projection? Oh, you need the Lambert projection instead? That’s fine too.<\/p>\n

I don’t pretend to know half of what’s there. I’m familiar enough to find what I need in the documentation when I need it, which is the important thing. And I appreciate<\/strong> that smart people are so dedicated to supporting those features. You never know what tools you’ll need tomorrow.<\/p>\n

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1. Paths.<\/h4>\n

To celebrate five years of writing blog posts (mostly for Claude at this point) let’s dig into something niche but also fun.<\/p>\n

Matplotlib lets you create custom markers. When calling scatter()<\/code> you can pass a list of tuples that define an arbitrary polygon. For example, here’s a goofy-looking trapezoid:<\/p>\n

ax.scatter(x=range(-2, 3),\r\n           y=range(-2, 3),\r\n           marker=[(-1, -1), (-1, 1), (1, 0.4), (1, -0.4), (-1, -1)],\r\n           s=300,\r\n           edgecolor=\"black\",\r\n           linewidth=1.0)<\/pre>\n
\"\"<\/a><\/p>\n
Or for a more scalable approach, pass a Path<\/a> object. Think of it like the path<\/em> your finger would trace around the edge of a shape. It’s very similar to an SVG file.<\/p>\n
A Path’s basic structure is composed of vertices<\/em> and codes<\/em>. Vertices are points in (x, y) space and codes tell Matplotlib how exactly it should travel between points. You could build a simple triangle like this:<\/p>\n
verts = [(0, 0), (1, 1), (2, 0), (0, 0)]\r\ncodes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY]<\/pre>\n
In this post I want to create a Path from plain text\u2014namely the W<\/strong> logo of this web site. That seems celebratory enough.<\/p>\n
Unsurprisingly if you’ve read the documentation, Matplotlib has a built-in API for this. There’s no need to tediously locate edges by hand or to jerry-rig your own Python solution. The process requires three imports and three lines of code.<\/p>\n
First, generate a Path using the aptly named TextToPath<\/code> method. It returns vertices and codes as discussed above.<\/p>\n
from matplotlib.textpath import TextToPath\r\nfrom matplotlib.font_manager import FontProperties\r\n\r\nverts, codes = TextToPath().get_text_path(FontProperties(family=\"Federant\", style=\"normal\"), \"W\")\r\n<\/pre>\n
Unfortunately it returns the Path of a bottom-left-aligned character, so markers will be drawn to the right and above where we want them. I don’t think there’s a good way to override this behavior on creation. If there is, please let me know!<\/p>\n
Fortunately it’s easy to translate the Path leftward and downward. Under the hood, verts<\/code> is just a NumPy array. Add a second NumPy array containing x and y offsets to center the marker. Matplotlib Transforms<\/a> would work as well if you’d prefer a built-in method.<\/p>\n
verts += np.array([-50.0, -34.0])<\/pre>\n
Then pass the instructions to Path<\/code>.<\/p>\n
from matplotlib.path import Path\r\n\r\nmarker_path = Path(verts, codes)<\/pre>\n
Now we can use little W<\/strong> markers instead of circles or the other default options. If you aren’t excited yet, you’re in the wrong place!<\/p>\n
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2. Prepare the data.<\/h4>\n
To use those markers we also need data. Since the traditional five-year anniversary gift is wood, let’s go with something exciting\u2014like lumber statistics.<\/p>\n
I scraped info about 584 wood species from wood-database.com<\/a>. The full dataset<\/a> includes 17 columns but I took the liberty of trimming it down for this post. We’ll deal with two variables: elastic modulus and hardness.<\/p>\n
These are material properties that, like much of the Matplotlib library, a normal person will never have to worry about. The variables happen to have a relationship that lends itself well to a scatter plot, which will allow us to show off custom markers.<\/p>\n
Start by reading the dataset and dropping rows with missing data. It’s a tab-separated file so pass an argument to delimiter<\/code>.<\/p>\n
import pandas as pd\r\n\r\ndf = pd.read_csv(\"wood_database_trimmed.tsv\", delimiter=\"\\t\")\r\n\r\ndf = df.dropna(subset=['janka_hardness', 'modulus_of_elasticity'])<\/pre>\n
We can jump straight to a linear regression. The details don’t really matter but conceptually, we’re modeling hardness as the independent variable and elastic modulus as dependent.<\/p>\n