What Default Units Does Plt Subplots Figsize Assume?

2025-09-04 05:21:59 199
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3 Answers

Graham
Graham
2025-09-05 04:57:51
Funny little detail that trips people up: matplotlib's figsize is measured in inches. I say this like someone chatting over coffee with a sketchpad of plots — figsize=(6,4) means 6 inches wide and 4 inches tall, not pixels, not centimeters. The reason that matters is DPI (dots per inch) — matplotlib uses the figure's DPI to convert those inches into pixels. By default, modern matplotlib sets figsize to (6.4, 4.8) inches and dpi to 100, so a default figure ends up being 640×480 pixels when rendered or saved (6.4*100 by 4.8*100).

In practice I often treat figsize like the physical size of a poster: if I need a poster for a talk or a high-res image for a paper, I pick bigger inches and/or bump dpi when saving. For example, figsize=(8,6) with dpi=200 gives 1600×1200 pixels. You can set dpi in plt.figure(..., dpi=...) or override it at save time with savefig(..., dpi=300). If you want to inspect or change a created figure you can use fig.get_size_inches() and fig.set_size_inches(w,h).

Tiny pro tip from my late-night tinkering: if you prefer metric, multiply inches by 2.54 to get centimeters. When embedding in notebooks some backends or frontends scale images visually, so pixel counts might feel off — but mathematically, figsize is always inches and the DPI does the conversion. I find thinking about inches helps when preparing figures for print or slides, and it makes resizing less mysterious.
Alex
Alex
2025-09-09 00:21:04
If you prefer a slower, careful take: figsize in plt.subplots is specified in inches. I tend to sketch layouts on paper first, so treating figsize as a physical measurement makes layout decisions intuitive. The library itself has defaults — pylab.rcParams['figure.figsize'] typically defaults to (6.4, 4.8) — and the conversion to screen pixels goes through DPI, defaulting to 100. So figsize and DPI together determine the pixel dimensions: width_pixels = width_inches * dpi, height_pixels = height_inches * dpi.

This matters in a couple of practical situations: when saving images for publications you often need a specific pixel dimension or resolution (PPI/DPI). You can either increase inches, increase dpi in savefig, or both. Also, when you embed plots into a GUI or a report, remember that some viewers may resample or scale the image, which is separate from the raw inch-to-pixel math. I often set rcParams['figure.dpi'] or pass dpi explicitly to savefig to guarantee final image quality. If you ever want exact control, set figsize in inches and then calculate pixels with your chosen dpi — it's predictable and reproducible, which I appreciate when polishing figures for a chapter or a post.
Charlotte
Charlotte
2025-09-09 05:20:28
Here’s the short practical rundown I keep in my head: figsize units are inches. I use that like a rule of thumb when I’m whipping up quick charts or prepping export-ready graphics. The number in figsize is multiplied by the figure DPI to yield pixel dimensions, so a figsize of (6.4, 4.8) with dpi 100 gives 640x480 pixels. If you need a larger image, either increase the inches or bump the dpi when saving (savefig(..., dpi=300)).

A couple of quick utilities I use: fig.get_size_inches() to check current size, fig.set_size_inches(w,h) to change it programmatically, and remember that rcParams holds defaults if you want project-wide consistency. Thinking in inches feels odd at first but it’s actually handy — it mirrors thinking about print layouts and slide design, and keeps pixel math simple.
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Oh, matplotlib sizing is one of those little puzzles I tinker with whenever a figure looks either cramped or ridiculously spacious. Figsize in plt.subplots is simply the canvas size in inches — a tuple like (width, height). That number doesn't directly set the gap between axes in absolute terms, but it strongly affects how those gaps look because it changes the total real estate each subplot gets. Practically, spacing is controlled by a few things: wspace/hspace (fractions of average axis size), fig.subplots_adjust(left, right, top, bottom, wspace, hspace) (normalized coordinates), and auto-layout helpers like tight_layout() and constrained_layout=True. For instance, wspace is a fraction of the average axis width; if you make figsize bigger, that same fraction becomes a larger physical distance (more inches/pixels), so subplots appear further apart. DPI multiplies inches to pixels, so a (6,4) figsize at 100 DPI is 600x400 pixels — larger DPI increases resolution but not the inch spacing. I like practical snippets: fig, axs = plt.subplots(2,2, figsize=(8,6), gridspec_kw={'wspace':0.25,'hspace':0.35}); or fig.subplots_adjust(wspace=0.2, hspace=0.3). If labels or legends overlap, try fig.set_constrained_layout(True) or fig.tight_layout(). Also consider gridspec_kw with width_ratios/height_ratios or using GridSpec directly for fine control. Bottom line: figsize sets the stage; subplots_adjust, wspace/hspace, and layout engines direct the actors. Play with the DPI and constrained_layout until everything breathes the way you want — I often tweak it when saving figures for papers versus slides.
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