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Python Advanced Data Operations

The examples combine generated School Management APIs into application-sized async workflows. Use model-aware Assist for names from a different model.

Search page with rows and two facets​

rows = await (
    Q.schools()
    .with_name_containing("Primary")
    .select_school_type_with(
        Q.school_types_minimal().select_code().select_name()
    )
    .select_platform_with(Q.platforms_minimal().select_name())
    .facet_by_school_type_as(
        "schoolTypes",
        Q.school_types_minimal()
        .with_code_in("PRIMARY", "SECONDARY")
        .select_code()
        .select_name()
        .count_schools_as("schoolCount"),
        include_all_facets=True,
    )
    .facet_by_platform_as(
        "platforms",
        Q.platforms_minimal().select_name().count_schools_as("schoolCount"),
        include_all_facets=False,
    )
    .order_by_id_descending()
    .limit(50)
    .comment("Search schools with dashboard facets")
    .purpose("Render the authorized operations dashboard")
    .execute_for_list(context)
)

type_buckets = rows.facet("schoolTypes")
platform_buckets = rows.facet("platforms")

The SchoolType Facet preserves allowed zero-count buckets; the Platform Facet returns matched-only buckets. Neither replaces the primary School list.

Deep graph and per-parent Top-N​

platforms = await (
    Q.platforms()
    .select_name()
    .select_school_list_with(
        Q.schools()
        .select_name()
        .select_school_type_with(
            Q.school_types_minimal().select_code().select_name()
        )
        .order_by_student_capacity_descending()
        .limit(3)
    )
    .comment("Load each platform and its three largest schools")
    .purpose("Render the authorized platform capacity review")
    .execute_for_list(context)
)

The nested .limit(3) means three Schools per Platform. The SQL provider uses its partition/window plan rather than loading every child and slicing in Python.

Grouped and relation analytics​

distribution = await (
    Q.schools()
    .group_by_school_type()
    .count_as("schoolCount")
    .sum_student_capacity_as("capacityTotal")
    .avg_student_capacity_as("capacityAverage")
    .comment("Aggregate school capacity by type")
    .purpose("Build the authorized capacity report")
    .execute_for_rows(context)
)

cards = await (
    Q.school_types_minimal()
    .select_code()
    .select_name()
    .count_schools_as("schoolCount")
    .sum_student_capacity_of_schools_as("capacityTotal", Q.schools())
    .comment("Calculate type-level school metrics")
    .purpose("Render the authorized type cards")
    .execute_for_list(context)
)

distribution contains dictionaries keyed by group fields and aggregate aliases. cards contains typed SchoolTypes decorated with relation metrics.

Audited read-modify-save​

school = await (
    Q.schools()
    .with_id_is(school_id)
    .select_platform_with(Q.platforms_minimal().select_name())
    .select_school_type_with(Q.school_types_minimal().select_code())
    .comment("Load the complete school for capacity approval")
    .purpose("Apply an authorized capacity revision")
    .execute_for_one(context)
)
if school is None:
    raise LookupError("school not found")

school.update_student_capacity(new_capacity)
school = await school.audit_as(
    "Approve revised student capacity"
).save(context)

Load all Checker/Fix inputs and keep the saved entity so the next mutation uses the authoritative optimistic version.