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120 lines
3.8 KiB
Python
Executable File
120 lines
3.8 KiB
Python
Executable File
#!/usr/bin/env python3
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from os import getcwd
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from os.path import normpath, join
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from typing import cast
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from pandas import DataFrame, read_csv, to_numeric, get_dummies
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from sys import argv
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def path_filename(filename: str) -> str:
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return normpath(join(getcwd(), filename))
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class Cleaning:
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def __init__(self, filename) -> None:
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self._vins: DataFrame = read_csv(filename)
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# créer la liste de tout les scores
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self.SCORE_COLS: list[str] = [
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c for c in self._vins.columns if c not in ["Appellation", "Prix"]
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]
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# transforme tout les colonnes score en numérique
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for col in self.SCORE_COLS:
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self._vins[col] = to_numeric(self._vins[col], errors="coerce")
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def getVins(self) -> DataFrame:
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return self._vins.copy(deep=True)
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def __str__(self) -> str:
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"""
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Affiche un résumé du DataFrame
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- la taille
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- types des colonnes
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- valeurs manquantes
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- statistiques numériques
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"""
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return (
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f"Shape : {self._vins.shape[0]} lignes x {self._vins.shape[1]} colonnes\n\n"
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f"Types des colonnes :\n{self._vins.dtypes}\n\n"
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f"Valeurs manquantes :\n{self._vins.isna().sum()}\n\n"
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f"Statistiques numériques :\n{self._vins.describe().round(2)}\n\n"
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)
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def drop_empty_appellation(self) -> "Cleaning":
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self._vins = self._vins.dropna(subset=["Appellation"])
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return self
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def _mean_score(self, 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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means = self._vins.groupby("Appellation", as_index=False)[col].mean()
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means = means.rename(
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columns={col: f"mean_{col}"}
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) # pyright: ignore[reportCallIssue]
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return cast(DataFrame, means.fillna(0))
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def _mean_robert(self) -> DataFrame:
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return self._mean_score("Robert")
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def _mean_robinson(self) -> DataFrame:
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return self._mean_score("Robinson")
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def _mean_suckling(self) -> DataFrame:
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return self._mean_score("Suckling")
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def fill_missing_scores(self) -> "Cleaning":
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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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for element in self.SCORE_COLS:
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means = self._mean_score(element)
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self._vins = self._vins.merge(means, on="Appellation", how="left")
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mean_col = f"mean_{element}"
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self._vins[element] = self._vins[element].fillna(self._vins[mean_col])
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self._vins = self._vins.drop(columns=["mean_" + element])
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return self
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def encode_appellation(self, column: str = "Appellation") -> "Cleaning":
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"""
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Remplace la colonne 'Appellation' par des colonnes indicatrices
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"""
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appellations = self._vins[column].astype(str).str.strip()
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appellation_dummies = get_dummies(appellations, prefix="App")
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self._vins = self._vins.drop(columns=[column])
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self._vins = self._vins.join(appellation_dummies)
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return self
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def drop_empty_price(self) -> "Cleaning":
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self._vins = self._vins.dropna(subset=["Prix"])
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return self
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def main() -> None:
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if len(argv) != 2:
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raise ValueError(f"Usage: {argv[0]} <filename.csv>")
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filename = argv[1]
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cleaning: Cleaning = (
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Cleaning(filename)
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.drop_empty_appellation()
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.fill_missing_scores()
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.encode_appellation()
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.drop_empty_price()
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)
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cleaning.getVins().to_csv("clean.csv", index=False)
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if __name__ == "__main__":
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try:
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main()
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except Exception as e:
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print(f"ERREUR: {e}")
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