ajout : aout des tests test_cleaning.py

test_cleaning.py

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+import pandas as pd
+import pytest
+from pandas import DataFrame
+
+from cleaning import (
+    SCORE_COLS,
+    drop_empty_appellation,
+    mean_score,
+    fill_missing_scores,
+    encode_appellation,
+)
+
+
+@pytest.fixture
+def df_raw() -> DataFrame:
+    return pd.DataFrame({
+        "Appellation": ["Pauillac", "Pauillac ", "Margaux", None, "Pomerol", "Pomerol"],
+        "Robert":      ["95", None, "bad", 90, None, None],
+        "Robinson":    [None, "93", 18, None, None, None],
+        "Suckling":    [96, None, None, None, 91, None],
+        "Prix":        ["10.0", "11.0", "20.0", "30.0", "40.0", "50.0"],
+    })
+
+
+def test_drop_empty_appellation(df_raw: DataFrame):
+    out = drop_empty_appellation(df_raw)
+    assert out["Appellation"].isna().sum() == 0
+    assert len(out) == 5 
+
+
+def test_mean_score_zero_when_no_scores(df_raw: DataFrame):
+    out = drop_empty_appellation(df_raw)
+    m = mean_score(out, "Robert")
+    assert list(m.columns) == ["Appellation", "mean_Robert"]
+
+    # Pomerol n'a aucune note Robert => moyenne doit être 0
+    pomerol_mean = m.loc[m["Appellation"].str.strip() == "Pomerol", "mean_Robert"].iloc[0]
+    assert pomerol_mean == 0
+
+
+def test_fill_missing_scores(df_raw: DataFrame):
+    out = drop_empty_appellation(df_raw)
+    filled = fill_missing_scores(out)
+
+    # plus de NaN dans les colonnes de scores
+    for col in SCORE_COLS:
+        assert filled[col].isna().sum() == 0
+
+    assert filled.loc[1, "Robert"] == 95.0
+
+    # pas de colonnes temporaires mean_*
+    for col in SCORE_COLS:
+        assert f"mean_{col}" not in filled.columns
+
+
+def test_encode_appellation(df_raw: DataFrame):
+    out = drop_empty_appellation(df_raw)
+    filled = fill_missing_scores(out)
+    encoded = encode_appellation(filled)
+
+    # la colonne texte disparaît
+    assert "Appellation" not in encoded.columns
+    assert "Pauillac" in encoded.columns
+    assert encoded.loc[0, "Pauillac"] == 1