diff --git a/projet_en_groupe/algorithme_netflix.py b/projet_en_groupe/algorithme_netflix.py
index c4392c52cad60928c0da9bcd0ed149fb5d895b5e..051bb9a30c73ed94cd7160fad72a13385e36fb56 100644
--- a/projet_en_groupe/algorithme_netflix.py
+++ b/projet_en_groupe/algorithme_netflix.py
@@ -273,80 +273,73 @@ def specific_genre_actor(data_1):
save_to_csv(actor_type_data)
else:
print(f"No movies or series found for the actor {actor_input} and type {type_input}.")
+ return
-def most_rated(data_1):
- filtered_data = filter_media_type(data_1)
+# rating
- # Check if the 'rating' column exists in the dataset
- if 'rating' not in filtered_data.columns:
- print("The dataset does not contain a 'rating' column.")
- return
+# these are variables that needs to be registered in general not in a local function
+notes = data_2.drop('show_id', axis = 1)
+mean_type = notes.mean(axis = 1) * 100
+data_2['appreciation (%)'] = mean_type
- # Sort the data by the 'rating' column in descending order
- sorted_data = filtered_data.sort_values(by='rating', ascending=False)
+def most_rated(data_1, data_2) :
- # Display the top-rated movies and/or series
- if not sorted_data.empty:
- print("Top-rated movies and/or series:")
- print(sorted_data[['title', 'type', 'rating']].head(10)) # Display the top 10
- save_to_csv(sorted_data)
- else:
- print("No rated movies or series found.")
+ filtered_data = filter_media_type(data_1)
+ link_between = pd.merge(filtered_data,data_2, on='show_id')
-def most_rated_year(data_1):
- filtered_data = filter_media_type(data_1)
+ link_between_sorted = link_between.sort_values(by='appreciation (%)', ascending=False)
- # Check if the 'rating' and 'release_year' columns exist in the dataset
- if 'rating' not in filtered_data.columns or 'release_year' not in filtered_data.columns:
- print("The dataset does not contain the necessary columns.")
- return
+ print("Films et séries les mieux notés :")
+ print(link_between_sorted[['show_id', 'title', 'type', 'appreciation (%)']])
+ save_to_csv(link_between_sorted)
+ return
- year_input = input("Enter the year to show the most rated movies and/or series: ")
+
+def most_rated_year(data_1, data_2):
+ # Display all available unique release years
+ available_years = data_1['release_year'].unique()
+ print("Available years: ", available_years)
+
+ # Ask the user to enter a release year
+ year = input("Enter a release year: ")
try:
- year_input = int(year_input)
+ # Convert the year to an integer
+ year = int(year)
except ValueError:
- print("Invalid input. Please enter a valid year.")
+ print("Please enter a valid year.")
return
- # Filter the data for the specified year
- year_data = filtered_data[filtered_data['release_year'] == year_input]
-
- if not year_data.empty:
- # Sort the data by the 'rating' column in descending order
- sorted_data = year_data.sort_values(by='rating', ascending=False)
+ # Filter the data based on the release year
+ filtered_data = filter_media_type(data_1[data_1['release_year'] == year])
- # Display the top-rated movies and/or series in the specified year
- print(f"\nTop-rated movies and/or series in {year_input}:")
- print(sorted_data[['title', 'type', 'rating']].head(10)) # Display the top 10
- save_to_csv(sorted_data)
- else:
- print(f"No rated movies or series found for the year {year_input}.")
-
-
-def most_rated_recent(data_1):
- filtered_data = filter_media_type(data_1)
+ # Merge the DataFrames on the 'show_id' key
+ link_between = pd.merge(filtered_data, data_2, on='show_id')
- # Check if the 'rating' and 'date_added' columns exist in the dataset
- if 'rating' not in filtered_data.columns or 'date_added' not in filtered_data.columns:
- print("The dataset does not contain the necessary columns.")
- return
+ # Sort the DataFrame by the 'appreciation' column (in descending order)
+ link_between_sorted = link_between.sort_values(by='appreciation (%)', ascending=False)
- # Sort the data by the 'rating' column in descending order and 'date_added' in descending order
- sorted_data = filtered_data.sort_values(by=['rating', 'date_added'], descending=[False, True])
+ print(f"Top-rated shows for the year {year}:")
+ print(link_between_sorted[['show_id', 'title', 'type', 'release_year', 'appreciation (%)']])
+ save_to_csv(link_between_sorted)
+ return
- # Display the most rated and recent movies and/or series
- if not sorted_data.empty:
- print("Most rated and recent movies and/or series:")
- print(sorted_data[['title', 'type', 'rating', 'date_added']].head(10)) # Display the top 10
- save_to_csv(sorted_data)
- else:
- print("No rated and recent movies or series found.")
+def most_rated_recent(data_1, data_2):
+ # Merge the DataFrames on the 'show_id' key
+ merged_data = pd.merge(data_1, data_2, on='show_id')
+ # Sort the DataFrame by the 'appreciation' column (in descending order) and 'release_year' (in descending order)
+ sorted_data = merged_data.sort_values(by=['release_year', 'appreciation (%)'], ascending=[False, False])
+ # Display the most rated and recent shows
+ top_20_data = sorted_data.head(20)
+ print("Top 20 most rated and recent shows:")
+ print(top_20_data[['show_id', 'title', 'type', 'release_year', 'appreciation (%)']])
+ save_to_csv(top_20_data)
+ return
# Example usage
@@ -360,6 +353,7 @@ def parental_code(data_1):
code_list.append(code)
print("Here are the parental codes: ")
print(code_list)
+ save_to_csv(code_list)
@@ -440,9 +434,7 @@ def basic_statistics(data_1):
country_counts = data_1['country'].str.split(', ').explode().value_counts()
print("\nCountries that produced movies/series, sorted from most to least productive:")
print(country_counts)
- save_to_csv(pd.DataFrame({'Country Count' : [country_counts], 'Movies Count': [movies_count], 'Series Count': [series_count]}), 'counts.csv')
-
-
+ return
# attention il faut demander à chaque fois, s'il désire enregistrer la liste sur un .csv
@@ -545,11 +537,11 @@ def action() :
elif command == "11" :
specific_genre_actor(data_1)
elif command == "12" :
- most_rated(data_1)
+ most_rated(data_1, data_2)
elif command == "13" :
- most_rated_year(data_1)
+ most_rated_year(data_1, data_2)
elif command == "14" :
- most_rated_recent(data_1)
+ most_rated_recent(data_1, data_2)
elif command == "15" :
parental_code(data_1)
elif command == "16" :