hist2d

Gridded & matrix

Minimal call

import random
rng = random.Random(24)
xs = [rng.gauss(0, 1) for _ in range(3000)]
df = {"x": xs, "y": [x * 0.6 + rng.gauss(0, 0.8) for x in xs]}

c = pt.chart(df, aes(x="x", y="y"), xlabel="x", ylabel="y")
c.add_hist2d(bins=25)

Docstring

Rectangular 2-D histogram — point counts in a uniform x/y bin grid.

mpl `hist2d` / ggplot `geom_bin2d` / seaborn bivariate `histplot`. The
axis-aligned cousin of hexbin: same colormap-by-count encoding, but
rectangular bins under explicit binwidth control. Cells with no points
stay transparent (the `geom_bin2d` convention, and it keeps a sparse
scatter readable).

API: c.add_hist2d(aes(x="col", y="col"))

Binning (scalar applies to both axes; an (x, y) pair sets them apart):
  bins=30             bin count per axis; an explicit edge sequence, or an
                      (x_edges, y_edges) pair, pins the edges instead
  binwidth=           fixed bin width(s) instead of a count
  binrange=           (lo, hi) span to bin over, or ((xlo, xhi), (ylo, yhi));
                      points outside are dropped

Color:
  cmap='viridis'      colormap name for coloring cells by count
  vmin=0              colormap domain lower bound
  vmax=None           colormap domain upper bound (defaults to max count)