last update

projet_en_groupe/algorithme_netflix.py

@@ -23,20 +23,15 @@ def catalog(data_1):
     print(data_1.head(100))
 
     save_to_csv(data_1)
+    return
     # Be careful, you need to ask each time if they want to save the list to a .csv
 
 
 def movies(data_1): # register
 
     films = data_1[data_1['type'] == 'Movie']  # Filter the data to include only movies
-    movie_titles = films['title'].tolist()  # Extract movie titles
-
-    movie_df = pd.DataFrame({'Movie Titles': movie_titles})
-
-    # afficher que les 100 premiers
-
-    print(movie_df)  # Display movie titles
-    save_to_csv(movie_df)
+    print(films)  # Display movie titles
+    save_to_csv(films)
 
     return  # Be careful, you need to ask each time if they want to save the list to a .csv
 
@@ -44,13 +39,10 @@ def movies(data_1): # register
 def series(data_1):
 
     series = data_1[data_1['type'] == 'TV Show']  # Filter the data to include only series
-    series_titles = series['title'].tolist()  # Extract series titles
-
-    series_df = pd.DataFrame({'Movie Titles': series_titles})
 
-    print(series_df)  # Display series titles
+    print(series)  # Display series titles
 
-    save_to_csv(series_df)
+    save_to_csv(series)
 
     return  # Be careful, you need to ask each time if they want to save the list to a .csv
 
@@ -172,37 +164,42 @@ def genre(data_1):
 
 
 def duration(data_1):
+    # Filtrer par type de média
     filtered_data = filter_media_type(data_1)
 
+    # Obtenir la liste de tous les genres possibles
     genre_list = []
     for genres in data_1['listed_in'].dropna().str.split(', '):
         for genre in genres:
             if genre not in genre_list and genre != '':
                 genre_list.append(genre)
 
+    # Afficher la liste des genres possibles
     print("List of all possible genres:")
     genre_list.sort()
     print(genre_list)
 
+    # Demander à l'utilisateur de saisir le genre
     while True:
         type_input = input("Enter the type (romantic, action, drama, etc.) to display movies and/or series: ")
 
-        # Use FuzzyWuzzy to find the closest match
+        # Utiliser FuzzyWuzzy pour trouver la correspondance la plus proche
         matches = process.extractOne(type_input, genre_list)
 
-        if matches[1] >= 80:  # Adjust the similarity threshold as needed
+        if matches[1] >= 80:  # Ajuster le seuil de similarité si nécessaire
             type_input = matches[0]
             break
         else:
             closest_match = matches[0]
             print(f"Invalid genre. The closest match is: {closest_match}")
 
-    # Check if the entered genre is correct
+    # Vérifier si le genre saisi est correct
     if type_input in genre_list:
         print(f"You selected: {type_input}")
     else:
         print(f"You entered: {type_input}, which is not in the list.")
 
+    # Demander le type de tri
     print("What type of sorting do you want? ")
     print("1. Ascending")
     print("2. Descending")
@@ -215,20 +212,22 @@ def duration(data_1):
         else:
             print("Invalid sort order. Please enter 1 for ascending or 2 for descending.")
 
-    type_data = filtered_data[filtered_data['listed_in'].str.lower().str.contains(type_input.lower(), case=False, na=False)]
+    # Filtrer les données en fonction du genre saisi
+    type_data = filtered_data[filtered_data['listed_in'].str.lower().str.contains(type_input.lower(), case=False, na=False)].copy()
 
     if not type_data.empty:
         print(f"\nDisplaying data for {type_input} sorted in {'ascending' if sort_order == '1' else 'descending'} order:")
-        
-        type_data.loc[:, 'duration'] = type_data['duration'].str.extract('(\\d+)').astype(int)
 
-        if sort_order == '1':
-            type_data_sorted = type_data.sort_values(by=['type', 'duration'], ascending=[True, True])
-        elif sort_order == '2':
-            type_data_sorted = type_data.sort_values(by=['type', 'duration'], ascending=[True, False])
-        
+        # Extraire les valeurs numériques de la colonne 'duration'
+        type_data['duration'] = type_data['duration'].str.extract('(\\d+)').astype(float)
+
+        # Trier les données en fonction du type et de la durée
+        type_data_sorted = type_data.sort_values(by=['type', 'duration'], ascending=[True, sort_order == '1'])
+
+        # Convertir les valeurs de durée en texte formaté
         type_data_sorted['duration'] = type_data_sorted.apply(
-            lambda row: f"{row['duration']} min" if row['type'].lower() == 'movie' else f"{row['duration']} Season", axis=1
+            lambda row: f"{int(row['duration'])} min" if row['type'].lower() == 'movie' else f"{int(row['duration'])} Season",
+            axis=1
         )
 
         print(type_data_sorted)
@@ -273,14 +272,32 @@ def director(data_1):
     else:
         print(f"You entered: {director_input}, which is not in the list.")
 
+    # Menu for sorting order
+    while True:
+        print("Select sorting order:")
+        print("1. Ascending")
+        print("2. Descending")
+        sort_order_input = input("Enter the number of your choice: ")
+
+        if sort_order_input == '1':
+            sort_order_bool = True
+            break
+        elif sort_order_input == '2':
+            sort_order_bool = False
+            break
+        else:
+            print("Invalid input. Please enter '1' for Ascending or '2' for Descending.")
+
+    order_text = 'ascending' if sort_order_bool else 'descending'
+
     director_data = filtered_data[filtered_data['director'].str.lower().str.contains(director_input.lower(), case=False, na=False)]
 
     if not director_data.empty:
-        print(f"\nDisplaying data for movies and/or series directed by {director_input} sorted by release year in ascending order:")
+        print(f"\nDisplaying data for movies and/or series directed by {director_input} sorted by release year in {order_text} order:")
 
-        director_data_sorted = director_data.sort_values(by='release_year', ascending=True)
+        director_data_sorted = director_data.sort_values(by='release_year', ascending=sort_order_bool)
         print(director_data_sorted)
-        save_to_csv(director_data_sorted)
+        save_to_csv(director_data_sorted)  # Uncomment this line if you want to save to CSV
     else:
         print(f"No person found with the name {director_input}.")
 
@@ -560,6 +577,7 @@ def most_rated_recent(data_1, data_2):
 
 def parental_code(data_1):
     valid_codes = set(['PG-13', 'TV-MA', 'PG', 'TV-14', 'TV-PG', 'TV-Y', 'TV-Y7', 'R', 'TV-G', 'G', 'NC-17', 'NR', 'TV-Y7-FV', 'UR'])
+    # there is a problem in the csv (values ​​which should not be there). So we sorted
 
     # Filter out entries that are not valid parental codes
     filtered_data = data_1[data_1['rating'].isin(valid_codes)]
@@ -650,8 +668,6 @@ def filter_media_type(data):
             print("Invalid choice. Please enter a valid number.")
 
 
-
-
 # Example usage
 def basic_statistics(data_1):
     # Check if the 'type' and 'country' columns exist in the dataset
@@ -872,7 +888,7 @@ def action() :
   print("Here are the different options available:")
   print("1.  View the entire catalog")
   print("2.  View all movies in the catalog")
-  print("3.  View all series")
+  print("3.  View all series in the catalog")
   print("4.  View all series, movies or both by year")
   print("5.  View all series, movies or both by country")
   print("6.  View all series, movies or both by type")
@@ -927,7 +943,7 @@ def action() :
     basic_statistics(data_1)
   elif command == "18" :
     recommandation_algorithm()
-  elif command == "STOP" :
+  elif command.upper() == "STOP" :
     return False
 
 

projet_personnel/algorithme_gestion_etudiants.py

@@ -19,7 +19,7 @@ all_course_MA2 = ("Advanced English 2", "Español avanzado 1", "Responsabilité
 
 
 
-file_path = '/coding-project/Data_Base.xlsx'
+file_path = '/Users/adrien/vscodeworkspace/coding-project/Data_Base.xlsx'
 data = pd.read_excel(file_path)
 
 excel_file_path = 'export_names.xlsx'