#MovieDataScraping | Explore Tumblr posts and blogs

iwebscrapingblogs · 4 years ago

Text

How Web Scraping Is Used To Extract Movie Details From YIFY Movies?

Web Scraping

Web scraping is a technique for extracting information (or simply data) from websites using a range of tools and techniques.

There are numerous websites that contain different kinds of data that can be extremely beneficial in terms of day-to-day necessities, scientific work, industry use, businesses, and so on.

Stock prices, product information, sports statistics, weather reports, movie ratings, and so on.

YIFY Movies:

YIFY Movies Is A Website That Provides Free Movie Torrent Connections And Has A Large Database Of Movies And Documentaries.

For Our Project, We Would Like To Extract Movie Information (Such As Title, Year, Genre, Rating, Movie Link, Synopsis, And Number Of Times Downloaded).

Tools

YIFY Movies is a website that provides free movie torrent download links and has a massive database of movies and television shows.

For our project, we'd like to extract movie information (such as title, year, genre, rating, movie link, synopsis, and number of downloads).

Outline

Here is a summary of the steps that you will need to follow:

Using The Queries, Download The Webpage.

Beautiful Soup Is Used To Parse The HTML Source Code.

<Tags> Usually Contains Information For Movie Title, Year, Genre, Rating, Movie-Url, Synopsis, And Number Of Downloads Are Being Searched.

Scrape Information From Multiple Pages (In Our Case, 20) And Publish It Into Python Lists And Dictionaries.

Save The Features Extracted As A CSV File.

By the end of our project, we would have a CSV file in the following format:

Movie,Year,Genre,Ratings,Url,Synopsis,Downloaded Whale Hunting,1984,Drama,6.5 / 10,https://yts.rs/movie/whale-hunting-1984," A disillusioned student meets a eccentric beggar and a mute prostitute he falls in love with. Together, without money, they cross South Korea to help the girl go home. "," Downloaded 101 times Sep 27, 2021 at 09:08 PM ........

Download the Webpage using Requests

The requests library will be used to install the website and start creating a bs4 doc' object. Pip can be used to install the library.

def get_doc(url): """Download a web page and return a beautiful soup doc""" # Download the page response = requests.get(url) # Check if download was successful if response.status_code != 200: raise Exception('Failed to load page {}'.format(url)) # Create a bs4 doc doc = BeautifulSoup(response.text, 'html.parser') return doc doc = get_doc(site_url) doc.find('title') <title>Search and Browse YIFY Movies Torrent Downloads - YTS</title>

The get_doc() function will develop a BeautifulSoup doc using the doc.find(‘title’) function we get the title tag from the html source code.

Searching <tags> Containing Movie Data

We'll look at the <tags> in the source code to find and get the following information:

Movie

Year

Genre

Rating

URL

Synopsis

Downloads

Extracting the Movie Titles from Web Page

Before we continue, right-click on the web page where you will see the movie's title and inspect. As seen in the image, the <a> tag contains the title of the movie. This will apply to all of the movies on the page.

def get_movie_titles(doc): # get all the <a> tags with a unique class movie_title_tags = doc.find_all('a', class_ ='text--bold palewhite title') # create an empty list movie_titles = [] for tag in movie_title_tags: # for 'title' in each <tag7gt; append it to the list movie_titles.append(tag.text) # return list return movie_titles The get_movie_titles() function successfully returns a list of movie tiltes.

Extract Movie Years from the Web Page

def get_movie_years(doc): # get all the <sapn7gt; tags with a unique class movie_year_tags = doc.find_all('span', class_ = 'text--gray year') # create an empty list movie_years =[] for tag in movie_year_tags: # for year in each <tag7gt; append it to the list. movie_years.append(tag.text) return movie_years

The get_movie_years() sucessfully returns a list of movie years

Extract Movie Genres from Web Page

def get_movie_genres(doc): # get all the <h4> tags with unique a class genre_tags = doc.find_all('h4', class_ = 'genre') # create an empty list movie_genres = [] for tag in genre_tags: # for genre in each <tag> append it to the list. movie_genres.append(tag.text) return movie_genres

The get_movie_genres() function successfully retunrs a list of movie genres.

Extract Movie Ratings from Web Page

def get_movie_ratings(doc): # get all the <h4> tags with a unique class rating_tags= doc.find_all('h4', class_ = 'rating') # create an empty list movie_ratings = [] for tag in rating_tags: # for rating in each append it to the list. movie_ratings.append(tag.text) return movie_ratings

The get_movie_ratings() function successfuly returns a list of movie ratings.

Extract Movie URLs from the Web Page

def get_movie_urls(doc): # get all the <a> tags with a unique class movie_url_tags = doc.find_all('a', class_ ='text--bold palewhite title') # create an empty list movie_urls = [] # the base url for the website base_url = 'https://yts.rs' for tag in movie_url_tags: # for url in each tag, append it to the list after adding the base_url with url from each tag movie_urls.append(base_url + tag['href']) return movie_urls The get_movie_urls() function successfully returns a list of movie urls.

Similarily, we define functions get_synopsis and get_downloaded to get a list of movie synopsis and number of downloads information.

Extract Movie Synopsis from Web Page

tags inside the first

tag is extracted using .text feature synopsis = p_tags[0].text # the synopsis is appended to the list synopses synopses.append(synopsis) return synopses

The get_synopsis() function gets a list of synopsis for every movie of a web page and returns it.

Extract the Movie Downloads from the Web Page

def get_downloaded(doc): # create an empty list downloadeds = [] # get all the movie urls on page urls = get_movie_urls(doc) for url in urls: # for each url(page) create a beautiful soup doc object movie_doc = get_doc(url) # get all the <div> tags with unique class div_tag = movie_doc.find_all('div', class_ = 'synopsis col-sm-10 col-md-13 col-lg-12') # get all the <p> tags inside the first <div> tag p_tags = div_tag[0].find_all('p') # get all the <em> tags inside the second <p> tag em_tag = p_tags[1].find_all('em') # extarct the text from the <em> tag using .text download = em_tag[0].text # using reular expressions to strip of alphabets from the text using .compile() regex = re.compile('[^0-9]') downloaded = regex.sub('',download) # append the integer to the list downloadeds downloadeds.append(downloaded) return downloadeds

The get_downloaded() function retrieves and returns a list of download counts for each movie on a web page. To match and extract our string, we used the re (regular expression operations) functions.

Extract Movie Details for a URL (page)

def scrap_page(url): # get beautiful soup doc object for url doc = get_doc(url) # create 7 empty lists for each field movies,years,genres,ratings,urls,synopses,downloadeds=[],[],[],[],[],[],[] # get list of movie titles movies = get_movie_titles(doc) # get list of years years = get_movie_years(doc) # get list of genres genres = get_movie_genres(doc) # get list of ratings ratings = get_movie_ratings(doc) # get list of urls urls = get_movie_urls(doc) # get list of synopsis synopses = get_synopsis(doc) # get list of downloads downloadeds = get_downloaded(doc) return movies,years,genres,ratings,urls,synopses,downloadeds

The scrap_page() function effectively reverts a list of movies, years, genres, ratings, urls, synopses, and downloads for a new website whose url is passed as an argument to the scrape_page(url)' function.

Extract Movie Details for the Entire Website

def website_scrap(): # create 7 empty list for each field to append the corrsponding field list being returned all_movies,all_years,all_genres,all_ratings,all_urls,all_synopses,all_downloadeds = [],[],[],[],[],[],[] for i in range(1,21): url = 'https://yts.rs/browse-movies?page={}'.format(i) # get lists of movie filed details and append them to the final list movies,years,genres,ratings,urls,synopses,downloadeds = scrap_page(url) all_movies += movies all_years += years all_genres += genres all_ratings += ratings all_urls += urls all_synopses += synopses all_downloadeds += downloadeds # create a dictionary from the final list attained for each 'key' as movie detail movies_dict = { 'Movie': all_movies, 'Year': all_years, 'Genre': all_genres, 'Rating': all_ratings, 'Url': all_urls, 'Synopsis': all_synopses, 'Downloads': all_downloadeds }

The above website_scrap() function is the primary function from which all other defined functions are executed.

It collects a list of details (movies, years, genres, ratings, urls, synopses, and downloadeds) from various pages and adds them to a relating larger list (all_movies, all_years, all_genres, all_ratings, all_urls, all_synopses, and all_downloads). Finally, a vocabulary movie_dict is created, with the larger lists serving as 'values' for the dictionary'Keys'.

Create a Pandas DataFrame using the Dictionary movies_dict

movies_df = pd.DataFrame(movies_dict, index = None) return movies_df

NoMovieYearGenreRatingUrlSynopsisDownloads

0The Rise of the Synths2019DocumentaryMusic7.1 / 10https://yts.rs/movie/the-rise-of-the-synths-2019Raise your hand if you haven't seen or heard0

1Vengeance1968Western6.2 / 10https://yts.rs/movie/vengeance-1968A man tracks down the five outlaws who murder1111

2King1978BiographyDrama8 / 10https://yts.rs/movie/king-1978The story of Dr. Martin Luther King Jr., stre...1212

3Once Upon a Time at Christmas2017Horror3.4 / 10https://yts.rs/movie/once-upon-a-time-at-chris...When a serial-killer couple dressed as Santa1515

4The Rolling Stones: Sweet Summer Sun - Hyde Pa...2013DocumentaryMusic8.1 / 10https://yts.rs/movie/the-rolling-stones-sweet-...The Rolling Stones historic and triumphant re...1313

................................

396Potato Salad2015ComedyHorror1.3 / 10https://yts.rs/movie/potato-salad-2015When a deadly zombie virus infects a school i...3232

39715 Years and One Day2013Drama5.9 / 10https://yts.rs/movie/15-years-and-one-day-2013Margo is struggling to deal with her son, Jon...3838

398Elisa's Day2021CrimeDrama5.4 / 10https://yts.rs/movie/elisas-day-2021It's a tragic case of history repeating as tw...5151

Converting and Saving the DataFrame data type (above output) to a csv file

movies_df.to_csv('movies_data.csv') # Converts the Dataframe object 'movies_df' to a csv file and saves it in .csv format

When Opened in Notepad, the Contents of the.csv File Would Look like This.

Conclusion

So Far, And Here Is What We've Accomplished With Our Project:

Using Requests.Get() And The Web Page's URL, I Downloaded A Web Page.

BeautifulSoup Was Used To Parse The HTML Source Script Of The Site And Create An Item Doc Of Category Beautiful Soup.

A Function Was Defined To Generate A Doc Object For Each URL Page.

Defined Functions To Extract Movie Details From Each Page, Such As Movies, Years, Genres, Ratings, URLs, Synopses, And Downloaded.

Python Lists And Dictionaries Were Created From The Extracted Data.

You Can Create A Pandas Data Frame Project To Display The Extracted Information In Tabular Form.

The Data Frame Was Converted And Saved As A.Csv File.

If you are looking to scrape the movie details from YIFY Movies, contact iWeb Scraping today.

https://www.iwebscraping.com/how-web-scraping-is-used-to-extract-movie-details-from-yify-movies.php

#ExtractMovieDetails #MovieDataScraping #ScrapeTVShowsfromYTS

1 note · View note

iwebdatascrape · 2 years ago

Text

How To Gain Cinematic Insights By Scraping Movie Data Using API

How To Gain Cinematic Insights By Scraping Movie Data Using API?

An Application Programming Interface (API) dedicated to movies, commonly known as a Movie API, encompasses a meticulously crafted ensemble of protocols and tools. Its primary objective is to empower developers by furnishing them with the means to effortlessly acquire diverse data pertaining to movies, TV shows, actors, directors, and associated elements. This reservoir of data is sourced from a centralized database, sparing developers the need to engage in manual data collection or upkeep. Movie APIs find widespread utility across applications, websites, and services that revolve around showcasing, presenting, or engaging with content related to the cinematic world. By enabling developers to navigate this extensive reservoir, Movie API data scraping fosters dynamic access to an expansive repository of movie-related information, all achieved through a structured and programmable interface.

List Of Data Fields

The following list of data fields is available from scraping movie data.

Title

Release Year

Rating

Release Date

Runtime

Genre(s)

Director(s)

Writer(s)

Actor(s)

Plot Summary

Language(s)

Country/Countries

Awards Won

Movie posters (URL of film image)

Ratings received

Metascore

IMDb Rating

Key Aspects of Movie API

Data Retrieval: Scraping movie data using API offers endpoints for developers to request specific data. It encompasses a broad range of information, including intricate details about movies, TV shows, actors, genres, release dates, ratings, plot summaries, posters, trailers, and more. Developers can access highly detailed and up-to-date cinema-related content by interacting with these endpoints.

Integration: Integrating a Movie API into applications or websites involves seamlessly incorporating its functionalities. It is achievable by making HTTP requests to the API's designated endpoints. These requests retrieve data in diverse formats such as JSON or XML. This integration enables developers to dynamically access and incorporate movie-related information into their platforms, enriching the user experience.

Usage: Movie data scraping services are pivotal in elevating user experiences across various applications. Whether it's movie discovery platforms, review websites, streaming services, or entertainment blogs, these APIs facilitate the display of real-time and relevant information to users. By leveraging Movie APIs, developers can ensure their users can access the latest insights and updates about movies and related content.

Popular Movie APIs: Several prominent APIs are meant for movie-related data, each catering to specific needs:

The Movie Database (TMDb) API: This API boasts an expansive repository of movie and TV show data. It encompasses intricate information about the cast, crew, images, and more, enriching applications with comprehensive details.

OMDb API: Providing access to a vast collection of movie and TV show data, the OMDb API is instrumental in acquiring metadata, ratings, and concise plot summaries.

IMDb API: Offering access to IMDb's movie and TV show data (primarily for non-commercial use), this API grants insights into one of the most renowned databases in the industry.

Trakt API: Focused on tracking user interactions with movies and TV shows, the Trakt API facilitates personalized recommendations based on viewing history.

Authentication and Rate Limiting: Authentication is often a prerequisite for utilizing certain movie APIs. Developers typically need to acquire an API key, a credential used to verify requests and monitor usage. Additionally, rate limiting ensures fair utilization of resources. It restricts the number of requests a developer can make within a specific time frame, preventing undue strain on the API's infrastructure.

Documentation: Comprehensive documentation is a hallmark of a well-designed API. It provides developers with clear guidelines on effectively employing the API's functionalities. It includes exhaustive information about available endpoints, request parameters, acceptable response formats, authentication methodologies, and usage limits.

Why Scrape Movie Data?

Script Your Movie Concierge: Imagine having your AI-powered movie concierge that recommends films based on your mood, preferences, and watching history. By scraping movie data, you can curate a personalized movie recommendation engine that suggests hidden gems, all-time classics, or recent releases tailored to your taste.

Predict Box Office Hits: Combine historical movie data with advanced analytics to predict potential box office successes. Develop algorithms that analyze factors like genre, director, cast, release timing, and social media buzz to forecast the next blockbuster hits before they even hit theaters.

Explore Cinematic Trends: With access to comprehensive movie data, you can delve into the ebb and flow of cinematic trends over time. Unearth how genres, themes, and visual styles have evolved, allowing you to create articles, infographics, or interactive visualizations that take your audience through cinematic history.

Enhance Virtual Film Festivals: Organize virtual film festivals with a twist! Web scrape movie data to curate a lineup of films around specific themes, like "underdog stories" or "mind-bending plots." Add trivia, behind-the-scenes insights, and audience polls to create an immersive online film festival experience.

Create an Ultimate Movie Database: Imagine building the ultimate online repository of movie knowledge. Scrape data from multiple APIs to create a comprehensive database that covers not only mainstream films but also indie, international, and cult classics. It will help users explore trivia, actor biographies, filming locations, and more.

Reimagine IMDb Rankings: Challenge traditional movie ranking systems by incorporating additional criteria like social media sentiment, critical reviews, and audience engagement metrics. Use scraped data to craft an alternative movie ranking that reflects a more diverse and dynamic perspective.

Cinematic Word Clouds: Generate captivating word clouds using movie scripts. Visualize the frequently used words in specific genres, directorial styles, or periods. This unique approach could lead to intriguing insights into the language of storytelling.

Source to Find an API to Access IMDb Data

Numerous alternatives to IMDb, such as TMDb and OMDb, along with open movie database app scraping APIs, provide direct access to IMDb's extensive repository. These APIs and web services are invaluable tools for creating an ideal website, mobile app, or software solution. They enable you to harness IMDb's vast dataset to craft platforms that excel in functionality, offering your users enriched insights and seamless experiences.

Conclusion: Scraping movie data using APIs opens movie enthusiasts, developers, and businesses opportunities. The wealth of information available through platforms like TMDb, OMDb, and others empowers us to create innovative applications, websites, and services that cater to diverse needs. These APIs offer a dynamic playground for creativity and insight, from personalized movie recommendations to predicting cinematic trends. By seamlessly integrating with these APIs, we can transform raw data into engaging user experiences, driving informed decision-making and enhancing how we interact with movies.

knowmore: https://www.iwebdatascraping.com/insights-by-scraping-movie-data-using-api.php

#ScrapeIMDbforMovieData #extractmoviedata #Moviedatascrapingservices #scrapingmoviedata #MovieAPIdatascraping #ScrapingMovieDataUsingAPI #MovieAPI #MovieDataScraper

0 notes