density_1d

Distributions

Minimal call

import random
rng = random.Random(10)
df1 = {"x": [rng.gauss(0, 1) for _ in range(300)]}
df2 = {"x": [rng.gauss(1.2, 1.3) for _ in range(300)]}

c = pt.chart(xlabel="value", ylabel="density")
c.add_density_1d(df1, aes(x="x"), fill=True)
c.add_density_1d(df2, aes(x="x"), fill=True)

Docstring

1-D Gaussian KDE curve — the bin-free alternative to histogram.

Where `hist` answers "how many in each bin?", `density_1d` answers
"what's the smoothed distribution shape?" — bin-free, scaled so the
area integrates to 1 so you can overlay multiple groups fairly.

  c.add_density_1d(aes(x="col"))                      # columns via aes
  c.add_density_1d(aes(x="col", color="group"))       # one curve per group

Aesthetics:
  color=         bare → literal line color; aes(color="col") → grouped curves
  fill=False     True shades the area under each curve in the line color
  palette=       maps group levels → colors when color is mapped in aes

Other styling kwargs:
  bw=None        bandwidth override; defaults to Silverman's rule
  n_grid=200     KDE evaluation grid resolution
  alpha=0.25     fill opacity (used only when fill=True)
  linewidth=1.6  curve stroke width
  label=None     legend label (single-series only)