Creating stubs and defining schemas

back/schemas.py

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+from pydantic import BaseModel
+
+
+class PeopleCreate(BaseModel):
+    name: str
+    age: int
+    gender: str
+    country: str
+
+
+class AverageAgeByCountry(BaseModel):
+    country: str
+    average_age: float
+
+
+class CountByCountry(BaseModel):
+    country: str
+    people_count: int
+
+
+class GenderRepartition(BaseModel):
+    female_proportion: float
+    male_proportion: float

main.py

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+from fastapi import FastAPI
+
+from typing import List
+
+from back.schemas import PeopleCreate, AverageAgeByCountry, CountByCountry, GenderRepartition
+
+app = FastAPI()
+
+
+@app.post("/people")
+async def create_people(people_list: PeopleCreate) -> dict:
+    return {
+        "message": "People list has been succesfully imported",
+        "records_created": 12
+    }
+
+
+@app.get("/average_age", response_model=List[AverageAgeByCountry])
+async def read_average_age(name: str):
+    return [
+        {'country': 'FR', 'average_age': 22.7},
+        {'country': 'BE', 'average_age': 14.7},
+        {'country': 'DE', 'average_age': 27.7},
+        {'country': 'IT', 'average_age': 32.7},
+    ]
+
+
+@app.get("/count_by_country", response_model=List[CountByCountry])
+async def read_count_by_country():
+    return [
+        {'country': 'FR', 'people_count': 2},
+        {'country': 'BE', 'people_count': 4},
+        {'country': 'DE', 'people_count': 3},
+        {'country': 'IT', 'people_count': 7},
+    ]
+
+
+@app.get("/count_by_gender/{country}", response_model=GenderRepartition)
+async def read_count_by_gender(country: str):
+    return {
+        "female_proportion": 51.2,
+        "male_proportion": 48.8
+    }

requirements.txt

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+fastapi==0.89.1