feat(db): aggregate_marches pour les requêtes GROUP BY (#78)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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import polars as pl
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from src.db import aggregate_marches
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def test_aggregate_groupby_count_returns_named_columns():
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df = aggregate_marches(
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select_sql='"acheteur_departement_code", COUNT("uid") AS "uid__count"',
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group_by='"acheteur_departement_code"',
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)
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assert isinstance(df, pl.DataFrame)
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assert df.columns == ["acheteur_departement_code", "uid__count"]
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assert df["uid__count"].sum() > 0
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def test_aggregate_global_without_groupby_returns_one_row():
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df = aggregate_marches(select_sql='COUNT("uid") AS "uid__count"')
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assert df.height == 1
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assert df["uid__count"][0] > 0
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