Standard-deviation choropleth (double gamme)

The StdMean method places its class breaks at standard-deviation steps either side of the mean, with a central class straddling it. It is a diverging classification, so it pairs with a double gamme (diverging) palette: one colour ramp below the mean and one above, meeting at a neutral centre. Only odd class counts make that symmetry work, so StdMean accepts 5 or 7 classes; any other count snaps to the nearest valid one with a warning.

reverse=True flips the double ramp (gamme inversee) without moving the neutral centre: handy when high values should read “cool” rather than “warm”.

Hover a department to read its value.

from pathlib import Path

import pandas as pd

from mappyng import Map, ChoroplethLayer
from mappyng.data import load_france_departments

# Metropolitan France only (two-character department codes).
deps = load_france_departments().to_crs(2154)
deps = deps[deps["COD_GEO"].str.len() == 2]
df = pd.read_csv(Path("data") / "cardio_mortalite_dep.csv", dtype={"code_dep": str})
deps = deps.merge(df, left_on="COD_GEO", right_on="code_dep", how="left")

m = Map(deps, width=820, height="auto", border_radius=0)

# StdMean with 7 classes and a diverging "double gamme" (RdBu). Use
# reverse=True to invert it: departments above the mean read blue, below
# the mean read red, with the neutral class kept in the centre.
m.add(ChoroplethLayer(
    deps,
    column="taux_mortalite",
    cmap="RdBu",
    method="StdMean",
    num_classes=7,
    reverse=True,
    stroke_width=0.2,
    legend={"title": "Deaths / 100,000 inhab. (std-dev classes)",
            "orientation": "horizontal", "decimals": 0,
            "nodata_label": "No data"},
))

m.title("Mortality from heart failure",
        subtitle="Standard-deviation classes around the mean")
m.source("Source: Sante publique France (ODISSE)")

# Hover tooltips. The gallery embeds an interactive SVG from this dict; the
# same call also yields a standalone HTML file.
TOOLTIP = {"columns": ["LIB_GEO", "taux_mortalite"],
           "aliases": {"LIB_GEO": "Department", "taux_mortalite": "Rate"}}
interactive = m.to_interactive(**TOOLTIP)
# interactive.save("stdmean.html")