Merge branch 'jalon2_Chahrazad' of https://github.com/guezoloic/millesima_projetS6 into jalon2-loic

src/cleaning.py

@@ -0,0 +1,106 @@
+#!/usr/bin/env python3
+from pandas import DataFrame, to_numeric
+import pandas as pd
+
+SCORE_COLS = ["Robert", "Robinson", "Suckling"]
+
+
+def display_info(df: DataFrame, name: str = "DataFrame") -> None:
+    """
+    Affiche un résumé du DataFrame
+        -la taille
+        -types des colonnes
+        -valeurs manquantes
+        -statistiques numériques
+    """
+    print(f"\n===== {name} =====")
+
+    print(f"Shape : {df.shape[0]} lignes × {df.shape[1]} colonnes")
+
+    print("\nTypes des colonnes :")
+    print(df.dtypes)
+
+    print("\nValeurs manquantes :")
+    print(df.isna().sum())
+
+    print("\nStatistiques numériques :")
+    print(df.describe().round(2))
+
+
+def drop_empty_appellation(df: DataFrame) -> DataFrame:
+
+    return df.dropna(subset=["Appellation"])
+
+
+def mean_score(df: DataFrame, col: str) -> DataFrame:
+    """
+    Calcule la moyenne d'une colonne de score par appellation.
+        - Convertit les valeurs en numériques, en remplaçant les non-convertibles par NaN
+        - Calcule la moyenne par appellation
+        - Remplace les NaN résultants par 0 
+
+    """
+    tmp = df[["Appellation", col]].copy()
+
+    tmp[col] = to_numeric(tmp[col], errors="coerce")
+
+    # moyenne par appellation
+    means = tmp.groupby("Appellation", as_index=False)[col].mean()
+    
+    means[col] = means[col].fillna(0)
+    
+    means = means.rename(columns={col: f"mean_{col}"})
+    
+    return means
+
+
+def mean_robert(df: DataFrame) -> DataFrame:
+    return mean_score(df, "Robert")
+
+
+def mean_robinson(df: DataFrame) -> DataFrame:
+    return mean_score(df, "Robinson")
+
+
+def mean_suckling(df: DataFrame) -> DataFrame:
+    return mean_score(df, "Suckling")
+
+
+def fill_missing_scores(df: DataFrame) -> DataFrame:
+    """
+    Remplacer les notes manquantes par la moyenne
+    des vins de la même appellation.
+    """
+    df_copy = df.copy()
+    df_copy["Appellation"] = df_copy["Appellation"].astype(str).str.strip()
+
+    for score in SCORE_COLS:
+        df_copy[score] = to_numeric(df_copy[score], errors="coerce")
+
+    temp_cols: list[str] = []
+
+    for score in SCORE_COLS:
+        mean_df = mean_score(df_copy, score)
+        mean_name = f"mean_{score}"
+        temp_cols.append(mean_name)
+
+        df_copy = df_copy.merge(mean_df, on="Appellation", how="left")
+        df_copy[score] = df_copy[score].fillna(df_copy[mean_name])
+
+    df_copy = df_copy.drop(columns=temp_cols)
+    return df_copy
+
+
+def encode_appellation(df: DataFrame, column: str = "Appellation") -> DataFrame:
+    """
+    Remplace la colonne 'Appellation' par des colonnes indicatrices
+    """
+    df_copy = df.copy()
+    
+    appellations = df_copy[column].astype(str).str.strip()
+
+    appellation_dummies = pd.get_dummies(appellations)
+
+    df_copy = df_copy.drop(columns=[column])
+
+    return df_copy.join(appellation_dummies)