.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "gallery/plot_02_choropleth.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_gallery_plot_02_choropleth.py: Choropleth ========== A choropleth maps a *relative* quantity (here a standardised mortality rate) to colour value. Data: mortality from heart failure, rate per 100,000 inhabitants, 2020-2022 average (Sante publique France, ODISSE). The legend swatches have rounded corners (``swatch_shape="rounded"``). Hover a department to read its value. .. GENERATED FROM PYTHON SOURCE LINES 12-71 .. raw:: html .. code-block:: Python from pathlib import Path import pandas as pd import mappyng as mp from mappyng.data import load_france_departments, load_france_regions, load_europe europe = load_europe().to_crs(2154) regions = load_france_regions().to_crs(2154) deps = load_france_departments().to_crs(2154) 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") # Mainland frame (Lambert-93) and overseas cartouches (declared in 3857). BBOX_METRO = [90000, 6040000, 1280000, 7150000] OVERSEAS = { "Guadeloupe": [-6890454, 1780151, -6799589, 1874186], "Martinique": [-6826862, 1609607, -6759494, 1685367], "French Guiana": [-6136680, 235261, -5707791, 671780], "Reunion": [6136383, -2448160, 6226316, -2366992], "Mayotte": [5006226, -1459183, 5050208, -1416184], } insets = mp.Insets( [mp.Inset(name, bbox=bbox, crs=3857) for name, bbox in OVERSEAS.items()], spacing=15, title_size=7, border_radius=6, box_shadow=True, ) m = ( mp.Map(europe, bbox=BBOX_METRO, width=900, height=1000, padding=25, facecolor="#B9D9EB", border_radius=10, box_shadow={"dx": 3, "dy": 3, "blur": 6, "opacity": 0.2}) + insets # Lighter water along the coasts of the European countries. + mp.OceanGradient(europe) # Meridians and parallels, under the land. + mp.Graticule(step=2, stroke="#ffffff", opacity=0.9, stroke_width=0.7) # European countries as background, with France lifted by a drop shadow. + mp.Basemap() + mp.Vector(regions, stroke="#4d4d4d", stroke_width=0.1) + mp.DropShadow(europe, query="code_pays_iso3 == 'FRA'") + mp.Choropleth( deps, column="taux_mortalite", cmap="Reds", method="Quantiles", num_classes=5, stroke_width=0.15, legend=mp.Legend("Deaths / 100,000 inhabitants", orientation="horizontal", decimals=0, nodata_label="No data", swatch_shape="rounded"), ) + mp.Vector(regions, stroke="#333333", stroke_width=0.4, position="top") + mp.Title("Mortality from heart failure", subtitle="Standardised rate, 2020-2022 average") # Scale bar over the sea, in the Gulf of Lion. + mp.ScaleBar(length=100000, label="100 km", position=(0.55, 0.855), opacity=0.8) + mp.Source("Source: Sante publique France (ODISSE)") # Hover tooltips of the interactive export (m.render("map.html")). + mp.Tooltip(["LIB_GEO", "taux_mortalite"], aliases={"LIB_GEO": "Department", "taux_mortalite": "Rate"}) ) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.626 seconds) .. _sphx_glr_download_gallery_plot_02_choropleth.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_02_choropleth.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_02_choropleth.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_02_choropleth.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_