ajout: restructuration du code

pyproject.toml

@@ -1,7 +1,12 @@
 [project]
 name = "projet-millesima-s6"
 version = "0.1.0"
-dependencies = ["requests==2.32.5", "beautifulsoup4==4.14.3", "pandas==2.3.3", "tqdm==4.67.3"]
+dependencies = [
+    "requests==2.32.5",
+    "beautifulsoup4==4.14.3",
+    "pandas==2.3.3",
+    "tqdm==4.67.3",
+]
 
 [project.optional-dependencies]
 test = ["pytest==8.4.2", "requests-mock==1.12.1", "flake8==7.3.0"]

src/cleaning.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python3
+from pandas import DataFrame, to_numeric, get_dummies
+
+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}"})
+
+
+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 = get_dummies(appellations)
+
+    df_copy = df_copy.drop(columns=[column])
+
+    return df_copy.join(appellation_dummies)

src/main.py

@@ -0,0 +1,58 @@
+#!/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 *
+
+
+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}")

src/scraper.py

@@ -215,6 +215,7 @@ class _ScraperData:
         robinson = self.robinson()
         suckling = self.suckling()
         prix = self.prix()
+        prix = self.prix()
 
         return f"{appellation},{parker},{robinson},{suckling},{prix}"
 
@@ -383,8 +384,7 @@ class Scraper:
                 list[dict[str, Any]], data.get("products")
             )
 
-            if isinstance(products, list):
-                return products
+            return products
 
         except (JSONDecodeError, HTTPError):
             return None
@@ -460,12 +460,18 @@ class Scraper:
                         products_list, bar_format=custom_format
                     )
                     for product in pbar:
-                        keyword = product.get("seoKeyword", "Inconnu")[:40]
+                        keyword: str = cast(
+                            str, product.get("seoKeyword", "Inconnu")[:40]
+                        )
                         pbar.set_description(
                             f"Page: {page:<3} | Product: {keyword:<40}"
                         )
                         self._writevins(cache, product, f)
                     page += 1
+                    # va créer un fichier au début et l'override
+                    # tout les 5 pages au cas où SIGHUP ou autre 
+                    if page % 5 == 0 and not reset:
+                        savestate((page, cache))
         except (Exception, HTTPError, KeyboardInterrupt, JSONDecodeError):
             if not reset:
                 savestate((page, cache))

tests/test_cleaning.py

@@ -0,0 +1,64 @@
+import pandas as pd
+import pytest
+from pandas import DataFrame
+
+from cleaning import (
+    SCORE_COLS,
+    drop_empty_appellation,
+    mean_score,
+    fill_missing_scores,
+    encode_appellation,
+)
+
+
+@pytest.fixture
+def df_raw() -> DataFrame:
+    return pd.DataFrame({
+        "Appellation": ["Pauillac", "Pauillac ", "Margaux", None, "Pomerol", "Pomerol"],
+        "Robert":      ["95", None, "bad", 90, None, None],
+        "Robinson":    [None, "93", 18, None, None, None],
+        "Suckling":    [96, None, None, None, 91, None],
+        "Prix":        ["10.0", "11.0", "20.0", "30.0", "40.0", "50.0"],
+    })
+
+
+def test_drop_empty_appellation(df_raw: DataFrame):
+    out = drop_empty_appellation(df_raw)
+    assert out["Appellation"].isna().sum() == 0
+    assert len(out) == 5 
+
+
+def test_mean_score_zero_when_no_scores(df_raw: DataFrame):
+    out = drop_empty_appellation(df_raw)
+    m = mean_score(out, "Robert")
+    assert list(m.columns) == ["Appellation", "mean_Robert"]
+
+    # Pomerol n'a aucune note Robert => moyenne doit être 0
+    pomerol_mean = m.loc[m["Appellation"].str.strip() == "Pomerol", "mean_Robert"].iloc[0]
+    assert pomerol_mean == 0
+
+
+def test_fill_missing_scores(df_raw: DataFrame):
+    out = drop_empty_appellation(df_raw)
+    filled = fill_missing_scores(out)
+
+    # plus de NaN dans les colonnes de scores
+    for col in SCORE_COLS:
+        assert filled[col].isna().sum() == 0
+
+    assert filled.loc[1, "Robert"] == 95.0
+
+    # pas de colonnes temporaires mean_*
+    for col in SCORE_COLS:
+        assert f"mean_{col}" not in filled.columns
+
+
+def test_encode_appellation(df_raw: DataFrame):
+    out = drop_empty_appellation(df_raw)
+    filled = fill_missing_scores(out)
+    encoded = encode_appellation(filled)
+
+    # la colonne texte disparaît
+    assert "Appellation" not in encoded.columns
+    assert "Pauillac" in encoded.columns
+    assert encoded.loc[0, "Pauillac"] == 1