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creating_dataset.py
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creating_dataset.py
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from bs4 import BeautifulSoup
import numpy as np
import pandas as pd
import time
from urllib.request import urlopen
#The API below helps in fetching data
from imdb import IMDb
import threading
import requests
from random import seed,random,randint
import re
import sys
#The array in which the information of movies will get appended
data = []
## PROXIES
#proxy = {"https": "185.56.209.114:51386"}
s = requests.Session()
#Incase a particular proxy gets blocked then just a different proxy needs to be added here.
s.proxies = {"http": "http://50.203.239.18:80"}
seed(1)
#The value in BoundedSemaphore decides the number of threads(windows) which can simultaneously run.
#The greater the number of threads, greater the speed but then you run the risk of being an aggressive crawler and will get banned accordingly.
lock = threading.BoundedSemaphore(25)
## GENERATING THE LISTS OF MOVIES
set_mov = {'None'}
movie_names=[]
wiki_url = "https://en.wikipedia.org/wiki/List_of_American_films_of_{0}"
#The range of for loop is from which year we want to feth movies.
for i in range(1990,2020):
content = s.get(wiki_url.format(i)).text
soup = BeautifulSoup(content,'lxml')
#Obtain the table which contains the names of the movies released in a particular year
tables = soup.find_all('table',class_='wikitable')
for item in tables:
rows = item.find_all('i')
for val in rows:
try:
text=val.string
if(text!=None):
movie_names.append(text)
except:
movie_names.append('Error occured')
#We remove the duplicate names.
movie_names = list(dict.fromkeys(movie_names))
print(len(movie_names))
#The driver code is written below the movie_page function
#The important function
def movie_page(num1, num2, movie_names):
#Create an imdb object using the API
imdb_obj = IMDb()
for i in range(num1, num2, 1):
flag_movie_url = 1
r_val = 0
time_val = 0
votes_val = 0
metascore = 0
moviename=movie_names[i]
prod_house=None
usa_revenue = None
world_revenue = None
budget = None
metacritic_critic=None
metacritic_user=None
director = None
writer = None
actors=""
genres=""
try:
#Attempt to acquire the lock which would allow the thread to fetch the data or else keeps the tread in wait stage
lock.acquire()
# This sleep call is used to prevent bombarding the Imdb server with a lot of calls to its website.
# Be careful that at times the sleep intervals need to be of higher values so that frequency of the requests can be deemed permissible by Imdb servers.
time.sleep(randint(6, 9))
#Using the Imdb API to fetch relevant data of movie url and using that to obtain the movie_object
rating_url = imdb_obj.get_imdbURL(imdb_obj.search_movie(movie_names[i])[0])
movie_object = imdb_obj.get_movie(imdb_obj.search_movie(movie_names[i])[0].movieID)
except:
flag_movie_url = 0
lock.release()
print('Failed to get movie URL.')
if (flag_movie_url == 1):
#The step below is necessary as typical execution fails to store the characters of names of people who are spanish, french, etc.
content = s.get(rating_url).content
soup = BeautifulSoup(content.decode('utf-8','ignore'),'lxml')
try:
#This fetches the Imdb rating.
rating_value = soup.find('div', class_='ratingValue').find('span')
r_val = float(rating_value.string)
except:
r_val = None
#Find the number of metacritic critics and users who rated the movie using BS4 and RegEx
try:
metacritic_ratingdivs = soup.find('div', class_="titleReviewBarItem titleReviewbarItemBorder").find_all('a')
metacritic_ratingdiv_count=0;
for meta_div in metacritic_ratingdivs:
if(metacritic_ratingdiv_count==0):
metacritic_critic = int(re.findall(r"\d{5}|\d{4}|\d{3}|\d{2}|\d{1}", meta_div.string.replace(',', ''))[0])
if(metacritic_ratingdiv_count==1):
metacritic_user = int(re.findall(r"\d{5}|\d{4}|\d{3}|\d{2}|\d{1}", meta_div.string.replace(',', ''))[0])
metacritic_ratingdiv_count+=1
except:
pass
# Find duration of movie using the API
try:
time_val = int(movie_object['runtimes'][0])
except:
time_val = None
#Find the director, writer, star actors of the movie using BS4
try:
divs = soup.find_all('div', class_="credit_summary_item")
count = 0
for div in divs:
if (count == 0):
try:
director = div.find('a').string
except:
pass
if (count == 1):
try:
writer = div.find('a').string
except:
pass
if (count == 2):
try:
list_of_a_tags = div.find_all('a')
for a in list_of_a_tags:
if (a.string == "See full cast & crew"):
break
actors += a.string + ","
except:
pass
count += 1
try:
actors = actors[0:len(actors) - 1]
except:
actors=None
except:
actors=None
#Find the genres of the movie using BS4
try:
genrediv = soup.find('div', class_='subtext')
list_of_a_tags = genrediv.find_all('a')
for a in list_of_a_tags:
if (re.findall(r"\d{4}", a.string)):
break
genres += a.string + ','
try:
genres = genres[0:len(genres) - 1]
except:
genres=None
except:
genres=None
# Find the votes of movie using BS4
try:
votes_val = int(soup.find('span',class_='small').string.replace(',',''))
except:
votes_val = None
# Find the metascore of the movie using BS4
try:
metascore = float(soup.find('div', class_="titleReviewBarItem").find('span').string)
except:
metascore = None
# Find the budget, Production House, revenue of movie in USA and over the world using BS4 and RegEx
try:
divs = soup.find('div', id='titleDetails').find_all('div', class_='txt-block')
h4_count = 0
for div in divs:
if (h4_count> 12):
break
if (div.find('h4', class_='inline').string == "Budget:"):
budget_array = re.findall('\d{3}|\d{2}|\d{1}', div.text)
budget = ''
for i in budget_array:
budget += i
if (div.find('h4', class_='inline').string == "Gross USA:"):
usa_revenue_array = re.findall('\d{3}|\d{2}|\d{1}', div.text)
usa_revenue = ''
for i in usa_revenue_array:
usa_revenue += i
if (div.find('h4', class_='inline').string == "Cumulative Worldwide Gross:"):
world_revenue_array = re.findall('\d{3}|\d{2}|\d{1}', div.text)
world_revenue = ''
for i in world_revenue_array:
world_revenue += i
if (div.find('h4', class_='inline').string == "Production Co:"):
prod_house = div.find('a').string.strip()
h4_count += 1
except:
pass
# Find the year in which movie was released using BS4
try:
year = int(soup.find('div', class_='title_wrapper').find('span', id='titleYear').find('a').text)
except:
year = None
#We add the information using append method which is thread safe.
data.append([year, moviename, time_val,director,writer,actors,genres,prod_house, budget, usa_revenue, world_revenue, votes_val, r_val,metacritic_critic,metacritic_user, metascore])
#So that a waiting thread can execute
lock.release()
print(len(data))
thread_array = []
#Used to allocate the number of movies to each thread. The value of denominator can be adjusted as needed.
step = round(len(movie_names) / 50, ndigits=None)
for i in range(0, len(movie_names), step):
if (i != 0):
i += 1
if (i + step < len(movie_names)):
t = threading.Thread(target=movie_page, args=(i, i + step, movie_names,))
else:
t = threading.Thread(target=movie_page, args=(i, i + len(movie_names) % step - 1, movie_names,))
thread_array.append(t)
for i in thread_array:
i.start()
for i in thread_array:
i.join()
df = pd.DataFrame(data=data, columns=['Year Released','Movie Name','Runtime (Minutes)','Director','Writer','Cast','Genres','Production House','Budget','USA Revenue','Worldwide Revenue','IMDB Votes','IMDB Rating','Metacritic Critics','Metacritic Users','Metascore'])
print(df.info())
df.to_csv(r"1990_2019_movies.csv")