rug

Distributions

Minimal call

import random
rng = random.Random(9)
df = {"x": [rng.gauss(0, 1) for _ in range(150)]}

c = pt.chart(df, aes(x="x"), xlabel="value", ylabel="density")
c.add_density_1d(fill=True)
c.add_rug(color="#444444")

Docstring

Rug plot — short tick marks along an axis showing where each observation sits.

No-bin alternative (or companion) to a histogram. Pairs especially well
with `density_1d` to show both the smoothed estimate and the raw
observations.

  c.add_rug(aes(x="col"))                       # columns via aes (orientation="y" too)
  c.add_rug(aes(x="col", color="group"))        # ticks colored per group

Aesthetics:
  color=         bare → literal tick color; aes(color="col") → grouped ticks
  palette=       maps group levels → colors when color is mapped in aes

Other styling kwargs:
  orientation='x'  'y' draws ticks along the left axis instead
  length=0.04    tick length as a fraction of axis pixel extent
  alpha=0.6      tick opacity
  linewidth=0.8  tick stroke width
  rasterize=None None = auto-raster ticks to one <image> above the point
                 threshold; True/False forces it on/off