Lab2

lab2/lab2.txt

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+Title,Popularity,Score,Last_day_in_cinema
+Pan Samochodzik i templariusze,1050.4,6,2023-09-06 00:00:00
+Knights of the Zodiac,1336.6,6,2023-06-22 00:00:00
+Warhorse One,1879.4,6,2023-08-25 00:00:00
+The Little Mermaid,2078.2,6,2023-07-13 00:00:00
+The Flash,5930.1,6,2023-08-08 00:00:00
+The Out-Laws,840.4,6,2023-09-01 00:00:00
+Bird Box Barcelona,985.9,6,2023-09-08 00:00:00
+Elemental,1106.1,7,2023-08-09 00:00:00
+John Wick: Chapter 4,1130.0,7,2023-05-17 00:00:00
+The Super Mario Bros. Movie,1134.7,7,2023-05-31 00:00:00
+Fast X,2017.6,7,2023-07-12 00:00:00
+�����: ˳���� ����,2211.9,7,2023-04-27 00:00:00
+"Ruby Gillman, Teenage Kraken",2776.7,7,2023-08-23 00:00:00
+Transformers: Rise of the Beasts,5458.2,7,2023-08-01 00:00:00
+Barbie,6058.2,7,2023-09-13 00:00:00
+The Darkest Minds,792.4,7,2018-09-19 00:00:00
+Spider-Man: Across the Spider-Verse,1134.0,8,2023-07-26 00:00:00
+Sound of Freedom,1310.6,8,2023-08-28 00:00:00
+Oppenheimer,1449.3,8,2023-09-13 00:00:00
+Guardians of the Galaxy Vol. 3,2446.6,8,2023-06-28 00:00:00

lab2/main.py

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+import collections
+import requests
+import csv
+from datetime import datetime, timedelta
+from copy import deepcopy
+
+
+class Films:
+    def __init__(self, numb_page):
+        self.header = {
+            'accept': 'application/json',
+            'Authorization': 'Bearer eyJhbGciOiJIUzI1NiJ9.eyJhdWQiOiIzMTI3NGFmYTRlNTUyMjRjYzRlN2Q0NmNlMTNkOTZjOSIsInN1YiI6IjVkNmZhMWZmNzdjMDFmMDAxMDU5NzQ4OSIsInNjb3BlcyI6WyJhcGlfcmVhZCJdLCJ2ZXJzaW9uIjoxfQ.lbpgyXlOXwrbY0mUmP-zQpNAMCw_h-oaudAJB6Cn5c8'
+        }
+        self.page = numb_page
+        self.films = []
+        self.genres = []
+        self.fetch_data()
+        self.fetch_genre()
+        self.fieldnames = ['Title', 'Popularity', 'Score', 'Last_day_in_cinema']
+
+    def fetch_data(self):
+        for i in range(1, self.page + 1):
+            respons = requests.get(
+                f'https://api.themoviedb.org/3/discover/movie?include_adult=false&include_video=false&sort_by=popularity.desc&page={i}',
+                headers=self.header)
+            self.films.extend(respons.json()['results'])
+
+    def fetch_genre(self):
+        genres_resp = requests.get('https://api.themoviedb.org/3/genre/movie/list?language=en', headers=self.header)
+        self.genres = genres_resp.json()
+
+    def give_all_data(self):
+        return self.films
+
+    def give_data_with_index_3_19(self):
+        return self.films[3:19:4]
+
+    def the_most_popular(self):
+        return max(self.films, key=lambda x: x['popularity'])['original_title']
+
+    def finde_name_from_discription(self, word='planet'):
+        return [f['original_title'] for f in self.films if word in f['overview']]
+
+    def collection_of_genres(self):
+        return frozenset(n for f in self.films for n in f['genre_ids'])
+
+    def delete_film_with_genre(self, id_genre_del):
+        return [f for f in self.films if int(id_genre_del) not in f['genre_ids']]
+
+    def popular_genres(self):
+        lst_for_count = [ids for f in self.films for ids in f['genre_ids']]
+        cnt = collections.Counter(lst_for_count).most_common(3)
+        x = {i['id']: i['name'] for i in self.genres}
+        most_genre = [(x[i[0]], i[1]) for i in cnt]
+        return most_genre
+
+    def group_films_by_genres(self):
+        return {(f['original_title'], k['original_title']) for idx, f in enumerate(self.films) for k in self.films[idx:] if
+                        set(f['genre_ids']) & set(k['genre_ids'])}
+
+    @staticmethod
+    def change_22_for_map(x):
+        x['genre_ids'][0] = 22
+        return x
+
+    def copy_data_with_22(self):
+        return list(map(self.change_22_for_map, deepcopy(self.films)))
+
+    @staticmethod
+    def make_dict(f):
+        title = f['original_title']
+        popular = format(f['popularity'], '.1f')
+        score = int(f['vote_average'])
+        realase = f['release_date']
+        day = datetime.strptime(realase, '%Y-%m-%d') + timedelta(weeks=8)
+        return {'Title': title, 'Popularity': popular, 'Score': score, 'Last_day_in_cinema': day}
+
+    def make_collection(self):
+        result_lst_dic = [self.make_dict(f) for f in self.films]
+        sorted_list = sorted(result_lst_dic, key=lambda x: (x['Score'], x['Popularity']))
+        return sorted_list
+
+
+    def csv_file_maker(self, coll_dict):
+        with open('lab2.txt', mode='w') as csv_file:
+            writer = csv.DictWriter(csv_file, fieldnames=self.fieldnames)
+            writer.writeheader()
+            writer.writerows(coll_dict)
+
+
+
+print('Write number of page')
+page = int(input())
+answer = Films(page)
+
+# 2 answer
+print(f'2.	Give a user all data {answer.give_all_data()}')
+# 3 answers
+print(f'3.	All data about movies with indexes from 3 till 19 with step 4 {answer.give_data_with_index_3_19()}')
+# 4 answer
+print(f'4.	Name of the most popular title {answer.the_most_popular()}')
+# 5 answer
+word = input()
+print(f'5.	Names of titles which has in description key words which a user put as parameters {answer.finde_name_from_discription(word)}')
+# 6 answer
+print(answer.collection_of_genres())
+# 7 answer
+id_genre = input()
+print(f'7.	Delete all movies with user provided genre{answer.delete_film_with_genre(id_genre)}')
+# 8 answer
+print(f'8.	Names of most popular genres with numbers of time they appear in the data {answer.popular_genres()}')
+# 9 answer
+print(f'9.	Collection of film titles grouped in pairs by common genres{answer.group_films_by_genres()}')
+# 10 answer
+print(
+    f'10.	Return initial data and copy of initial data where first id in list of film genres was replaced with 22 {answer.copy_data_with_22()}')
+# 11 answer
+dict_coll = answer.make_collection()
+# 12 answer
+answer.csv_file_maker(dict_coll)
+# See PyCharm help at https://www.jetbrains.com/help/pycharm/
+# print(type(data))