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92 lines
2.4 KiB
Python
92 lines
2.4 KiB
Python
#!/usr/bin/env python3
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from pandas import DataFrame, to_numeric
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import pandas as pd
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SCORE_COLS = ["Robert", "Robinson", "Suckling"]
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def display_info(df: DataFrame) -> None:
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df.describe()
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print(df.info())
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print("\nNombre de valeurs manquantes par colonne :")
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print(df.isna().sum())
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def drop_empty_appellation(df: DataFrame) -> DataFrame:
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return df.dropna(subset=["Appellation"])
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def mean_score(df: DataFrame, col: str) -> DataFrame:
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"""
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Calcule la moyenne d'une colonne de score par appellation.
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- Convertit les valeurs en numériques, en remplaçant les non-convertibles par NaN
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- Calcule la moyenne par appellation
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- Remplace les NaN résultants par 0
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"""
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tmp = df[["Appellation", col]].copy()
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tmp[col] = to_numeric(tmp[col], errors="coerce")
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# moyenne par appellation
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means = tmp.groupby("Appellation", as_index=False)[col].mean()
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means[col] = means[col].fillna(0)
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means = means.rename(columns={col: f"mean_{col}"})
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return means
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def mean_robert(df: DataFrame) -> DataFrame:
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return mean_score(df, "Robert")
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def mean_robinson(df: DataFrame) -> DataFrame:
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return mean_score(df, "Robinson")
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def mean_suckling(df: DataFrame) -> DataFrame:
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return mean_score(df, "Suckling")
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def fill_missing_scores(df: DataFrame) -> DataFrame:
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"""
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Remplacer les notes manquantes par la moyenne
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des vins de la même appellation.
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"""
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df_copy = df.copy()
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df_copy["Appellation"] = df_copy["Appellation"].astype(str).str.strip()
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for score in SCORE_COLS:
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df_copy[score] = to_numeric(df_copy[score], errors="coerce")
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temp_cols: list[str] = []
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for score in SCORE_COLS:
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mean_df = mean_score(df_copy, score)
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mean_name = f"mean_{score}"
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temp_cols.append(mean_name)
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df_copy = df_copy.merge(mean_df, on="Appellation", how="left")
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df_copy[score] = df_copy[score].fillna(df_copy[mean_name])
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df_copy = df_copy.drop(columns=temp_cols)
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return df_copy
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def encode_appellation(df: DataFrame, column: str = "Appellation") -> DataFrame:
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"""
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Remplace la colonne 'Appellation' par des colonnes indicatrices
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"""
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df_copy = df.copy()
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appellations = df_copy[column].astype(str).str.strip()
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appellation_dummies = pd.get_dummies(appellations)
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df_copy = df_copy.drop(columns=[column])
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return df_copy.join(appellation_dummies)
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