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48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
#!/usr/bin/env python3
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from pandas import DataFrame, to_numeric
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def display_info(df: DataFrame) -> None:
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print(df.all())
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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") |