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

src/main.py

@@ -0,0 +1,64 @@
+#!/usr/bin/env python3
+
+from os import getcwd
+from os.path import normpath, join
+from sys import argv
+from pandas import read_csv, DataFrame
+
+from cleaning import (display_info,
+                      drop_empty_appellation,
+                      mean_robert,
+                      mean_robinson,
+                      mean_suckling,
+                      fill_missing_scores,
+                      encode_appellation)
+
+
+def load_csv(filename: str) -> DataFrame:
+    path: str = normpath(join(getcwd(), filename))
+    return read_csv(path)
+
+
+def save_csv(df: DataFrame, out_filename: str) -> None:
+    df.to_csv(out_filename, index=False)
+
+
+def main() -> None:
+    if len(argv) != 2:
+        raise ValueError(f"Usage: {argv[0]} <filename.csv>")
+
+    df = load_csv(argv[1])
+
+    display_info(df, "Avant le nettoyage")
+
+    df = drop_empty_appellation(df)
+    save_csv(df, "donnee_clean.csv")
+    display_info(df, "Après nettoyage d'appellations manquantes")
+    
+    #la moyenne des notes des vins pour chaque appellation
+    robert_means = mean_robert(df)
+    save_csv(robert_means, "mean_robert_by_appellation.csv")
+    display_info(robert_means, "Moyennes Robert par appellation")
+
+    robinson_means = mean_robinson(df)
+    save_csv(robinson_means, "mean_robinson_by_appellation.csv")
+    display_info(robinson_means, "Moyennes Robinson par appellation")
+  
+    suckling_means = mean_suckling(df)
+    save_csv(suckling_means, "mean_suckling_by_appellation.csv")
+    display_info(suckling_means, "Moyennes Suckling par appellation")
+    
+    df_missing_scores = fill_missing_scores(df)
+    save_csv(df_missing_scores, "donnee_filled.csv")
+    display_info(df_missing_scores, "Après remplissage des notes manquantes par la moyenne de l'appellation")
+     
+    df_ready = encode_appellation(df_missing_scores)
+    save_csv(df_ready, "donnee_ready.csv")
+    display_info(df_ready, "Après remplacer la colonne 'Appellation' par des colonnes indicatrices")
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except Exception as e:
+        print(f"ERREUR: {e}")