Physical geography

Each covered profile acts as a compact physical-geography reference: land and water area, coastline, elevation, named highest and lowest points, source-listed major rivers and lakes, a plain-language climate summary, and represented Köppen-Geiger climate classes. Everything works offline.

240

elevation-extreme profiles

375

source-listed river and lake records

241

Köppen-Geiger profiles

A country in four lines

Brazil at a glance

8,358,140 km² land · 157,630 km² water · 7,491 km coastline · highest point Pico da Neblina · dominant represented class Aw

>>> from pyworldatlas import Atlas
>>> with Atlas() as atlas:
...     brazil = atlas.country("Brazil")
...     print(f"{brazil.flag} {brazil.name}")
...     print(f"Land {brazil.land_area_km2:,.0f} km² | Water {brazil.water_area_km2:,.0f} km²")
...     print(brazil.highest_point.name, f"{brazil.highest_point.elevation_m:,.0f} m")
...     print(brazil.climate.summary)
🇧🇷 Brazil
Land 8,358,140 km² | Water 157,630 km²
Pico da Neblina 2,994 m
mostly tropical, but temperate in south

Area and coastline

Country.area_km2 is total area. land_area_km2 and water_area_km2 are its source components when available, while water_percent is calculated from water divided by total area. coastline_km is the source-reported coastline length.

>>> with Atlas() as atlas:
...     japan = atlas.country("Japan")
...     print(japan.area_km2, japan.land_area_km2, japan.water_area_km2)
...     print(round(japan.water_percent, 2), japan.coastline_km)
...     print(japan.is_coastal, japan.is_landlocked)
377915.0 364485.0 13430.0
3.55 29751.0
True False

The boolean conveniences remain None when coastline data is unavailable. They never treat a missing coastline as zero. A coastline value of zero means the source describes the profile as landlocked; it is not a measurement of international boundaries.

Elevation and named extremes

The highest and lowest points are ElevationPoint objects. Elevations use metres relative to sea level, so a negative lowest point is below sea level.

>>> with Atlas() as atlas:
...     japan = atlas.country("Japan")
...     print(japan.mean_elevation_m)
...     print(japan.highest_point.name, japan.highest_point.elevation_m)
...     print(japan.lowest_point.name, japan.lowest_point.elevation_m)
438.0
Mount Fuji 3776.0
Hachiro-gata -4.0

source_label preserves the compact source wording. is_approximate is true only when the source explicitly marked its measurement as approximate. This release does not publish a separate “major mountains” inventory: a named highest point is not automatically labelled as a mountain.

Rivers and lakes

Rivers and lakes are immutable typed records. Search helpers make shared features easy to discover:

>>> with Atlas() as atlas:
...     print([country.name for country in atlas.countries_with_river("Amazon")])
...     print([country.name for country in atlas.countries_with_lake("Geneva")])
['Brazil', 'Peru']
['France', 'Switzerland']

The feature tuples are source-listed major features, not exhaustive inventories. An empty tuple means the source did not list a feature for that profile. It does not mean that no river or lake exists.

For a shared river, River.length_km is the full source-reported river length, not the portion inside one country. Likewise, Lake.area_km2 is the full lake area rather than the area inside one profile. The original compact wording remains available in source_label.

Climate profiles

Country.climate combines two deliberately separate views:

• summary is a short source-provided description.

• koppen_geiger_zones contains classes derived from the 1991–2020 global Köppen-Geiger raster.

>>> with Atlas() as atlas:
...     brazil = atlas.country("Brazil")
...     print(brazil.climate.reference_period)
...     print(brazil.climate.zone_codes[:4])
...     zone = brazil.climate.dominant_zone
...     print(zone.code, zone.name, round(zone.share_percent, 2))
1991-2020
('Aw', 'Am', 'Af', 'BSh')
Aw Tropical, savannah 46.33

Zone shares are latitude-area-weighted estimates from a 0.1-degree raster and pinned map-unit polygons. Classes below the documented 0.1% extraction threshold are omitted. They are useful for broad educational comparison, not site-level climate determination.

>>> with Atlas() as atlas:
...     print(len(atlas.countries_in_climate_zone("Af")))
...     print(atlas.climate_zone_codes()[-2:])
73
('ET', 'EF')

Physical filters and rankings

Physical filters compose with the existing region and metadata filters:

atlas.countries(continent="Europe", coastal=False)
atlas.countries(koppen_geiger_code="Af")
atlas.countries(has_rivers=True, has_lakes=True)

Physical ranking metrics include land_area, water_area, water_percent, coastline, mean_elevation, highest_elevation, lowest_elevation, river_count, lake_count, and climate_zone_count.

>>> with Atlas() as atlas:
...     rows = atlas.rank("coastline", limit=5)
...     print([(row.country.name, row.value) for row in rows])
[('Canada', 202080.0), ('Indonesia', 54716.0), ('Greenland', 44087.0), ('Russia', 37653.0), ('Philippines', 36289.0)]

Rankings describe the bundled source fields; they do not judge countries. Missing values are excluded and ties use the country name for deterministic ordering. Feature-count rankings count source-listed records, not every feature on the ground.

Learning prompts

The deterministic flashcard API now supports climate_zones, coastlines, highest_points, rivers, and lakes. These are structured prompts and answers that can be used in a notebook, command-line lesson, or application without adding game state to the package.

Executable example

Want to rotate the terrain itself? The optional Interactive 3D maps guide shows how to open elevation, climate, rivers, and the capital in one interactive view.

"""Explore physical geography without a network connection."""

from pyworldatlas import Atlas


with Atlas() as atlas:
    brazil = atlas.country("Brazil")
    print(
        brazil.flag,
        brazil.name,
        f"{brazil.land_area_km2:,.0f} km² of land",
        f"{brazil.water_area_km2:,.0f} km² of water",
    )
    print(
        "Highest point:",
        brazil.highest_point.name,
        f"({brazil.highest_point.elevation_m:,.0f} m)",
    )
    print("Source-listed rivers:", ", ".join(river.name for river in brazil.rivers))
    print("Climate classes:", ", ".join(brazil.climate.zone_codes))

    print(
        "Amazon profiles:",
        ", ".join(country.name for country in atlas.countries_with_river("Amazon")),
    )
    print(
        "Lake Geneva profiles:",
        ", ".join(country.name for country in atlas.countries_with_lake("Geneva")),
    )

    print("Longest sourced coastlines:")
    for row in atlas.rank("coastline", limit=5):
        print(f"{row.position}. {row.country.name}: {row.value:,.0f} {row.unit}")