Tutorial: Web Scraping IMDB Data with Python to Predict Box Office Performance - Chris Giler (2023)

January 28, 2018 by Chris Giler

Part 1: Find and scrape the data with Selenium and Scrapy

Tutorial: Web Scraping IMDB Data with Python to Predict Box Office Performance - Chris Giler (1)

This is the first part of a multi-part series running on my second project in theMetis Data Science Boot Camp(Luther Project). In this first part, I will discuss the background and the methods used to scrape the data. Read on if you are interested in the general scraping process or any of the following technologies:

  • Python/Panda

  • Selenium

  • grated

You can find the full code for this project at myGitHub-page.

identify the problem

Tutorial: Web Scraping IMDB Data with Python to Predict Box Office Performance - Chris Giler (2)

For this project, I wanted to look at a specific feature to see if it could predict the performance of recently released movies. While many factors, such as critical acclaim, marketing, release season, and competition between films, can affect a film's performance in theaters, I wanted to try to answer one question:

?How much does the popularity of a movie's cast members affect opening weekend ticket sales??

In general, I expected to see a correlation between the opening weekend performance and the popularity of the cast. The hard part would be finding a way to quantify the "star power" aspect of a movie. To do this I had to find and scrape some data online!

Find and scrape the data

For this project I tried to stick to just one data source: IMDB. I knew that limiting my search to one site's database would give me a better chance of finding data to use together! For starters, I noticed that IMDB makes its data publicly available in adownloadable form-Perfect! In fact, this data is updated daily, so I knew it would be up to date! So I went ahead and downloaded all the data, which consisted of:

title.basics.tsv.gz – Contains the following title information:
  • tconst (string) - unique alphanumeric identifier of the title
  • titleType (string) - The type/format of the title (eg movie, short film, TV series, TV episode, video, etc.)
  • PrimarTitle (string): The most popular title/title used by filmmakers in promotional material at the time of release
  • originalTitle (string) - original title, in the original language
  • isAdult (boolean) - 0: non-adult title. 1: adult title.
  • StartYear (YYYY): Represents the year a title was released. In the case of television series, this is the year the series began.
  • endYear (YYYY) - End of the year television series. '\N' for all other title types
  • runtimeMinutes: main runtime of the title, in minutes
  • species (string array): contains up to three species related to the title
title.crew.tsv.gz – Contains director and writer information for all titles on IMDb. Fields include:
  • tconst(string)
  • directors (array nconsts) - directors of the given title
  • authors (nconsts array) – author of the given title
title.episode.tsv.gz – Contains information about the TV episode. Fields include:
  • tconst (string) - alphanumeric episode identifier
  • parentTconst(string) - alphanumeric identifier of the parent TV series
  • seasonNumber (integer) – season number the episode belongs to
  • EpisodeNumber (integer): The tconst episode number in the TV series.
title.principals.tsv.gz - contains the main cast/team of the titles
  • tconst(string)
  • mainCast(nconsts array): the most billed cast/crew of the title
title.ratings.tsv.gz – Contains IMDb rating and voting information for titles
  • tconst(string)
  • Average Rating: Weighted average of all individual user ratings
  • numVotes - number of votes the title received
name.basics.tsv.gz – Contains the following information about names:
  • nconst (string) - unique alphanumeric identifier of the name/person
  • PrimaryName (string): The name by which the person is most frequently referred to
  • year of birth – in YYYY format
  • year of death: in AAAA format if applicable; otherwise, '\N'
  • PrimarProfession (row of strings): the person's top 3 professions
  • knowForTitles (array tconsts) – titles the person is known by

Fearful! Now I have way more data than I need! Not that this is the worst, of course, but we have to filter a bit. To do this I decided to use Panda to import and clean the data.

Import and cleanup in Pandas

So the first thing that was done was to import all the tables into their own Panda DataFrames. Fair warning, this is quite a memory intensive process on your computer, so you may want to enter just one at a time if your computer starts to hang...

title_basics_df = pd.read_csv('Data/title.basics.tsv', sep='\t')title_cast_df = pd.read_csv('Data/title.principals.tsv', sep='\t')title_ratings_df = PD. read_csv('Datos/título.calificaciones.tsv', sep='\t')

Fearful! So we've imported our data, now it's time to merge these tables. However, it might make the process easier if I could first filter what I want from one of those tables. For this project I decided to filter my data by the following criteria:

  • The release year is between 2014 and 2017, inclusive.

  • The title type is "movie", to avoid downloading short films and TV shows.

  • The movie is not an "adult movie" whatever it is...

  • The length of the movie is at least 80 minutes (I only want to watch full-length movies, 2 minute shorts are not enough!)

  • Gender data is not empty, nor is it documentary

In a Pandas filter mask it looks like this:

máscara = ((title_basics_df['startYear'] >= 2014) & (title_basics_df['startYear'] <= 2017) & (title_basics_df['titleType'] == 'film') & (title_basics_df']=A 0) & (title_basics_df['runtimeMinutes'] > 80) & (title_basics_df['genres'] != '') & (title_basics_df['genres'] != 'Documentaire'))

I also had to clean up the data before applying these filters. Some columns weren't needed and some weren't formatted correctly (such as runtime years and minutes), so I created some helper functions to clean them up:

## Helperfunctiesdef clean_year(y): # Retourneer jaar als een geheel getal of "NaN" indien leeg importer numpy als np probeer: return int(y) behalve: return np.nandef clean_genre(y): # Retourneer allemeldehetre y = str( y) si y == '\\N': return '' return y.split(',')[0].strip()title_basics_df.drop('endYear', as=1, inplace=True )title_basics_df[' startYear'] = title_basics_df['startYear'].apply(clean_year)title_basics_df['runtimeMinutes'] = title_basics_df['runtimeMinutes'].apply(clean_year)title['gen_resics]df'title_genres_df .apply(clean_genre)title_basics_df.dropna( inplace=True, how='any', subset=['startYear', 'runtimeMinutes'])

So we just need to merge the data,

títulos = title_basics_df[masker].merge(title_cast_df, on='tconst')titles = títulos.merge(title_ratings_df, on='tconst')

That's why we narrowed our data set of over 4 million titles to just 17,546 titles that meet our criteria. This will obviously make the scraping process much faster. Let's complete this list of Title IDs so we don't have to do this cleanup every time:

# Save our data to a "pickle" file, which can be quickly loaded later in the scraping process. values, gherkin)

Collecting IMDB box office data with Scrapy

To pull the box office data from the IMDB website, I used the python librarygrated. Scrapy is a web data extraction framework and uses a "web spider" to crawl pages and retrieve the desired data. For my spider, I started by uploading the pickle file created in the previous section and used those header IDs to create a list of URLs to pull from. Fortunately, this was easy to do since IMDB uses a common format for each movie page:


where "tt0974015" is the title ID of the most recent (at the time of writing) "Justice League" movie, for example.

The code for most of my spider class is below.

import scrapyimport pickleclass IMDBSpider(scrapy.Spider): name = 'imdb_spider' # Use the scrape delay to avoid overloading the IMDB servers! custom_settings = { "DOWNLOAD_DELAY": 3, "CONCURRENT_REQUESTS_PER_DOMAIN": 3, "HTTPCACHE_ENABLED": True } # Load the list of title IDs from the pickle file # created in the previous section with open("../my_data.pkl ", 'rb') as a picklefile: links = list(pickle.load(picklefile)) # Create the list of urls to scrape (based on title id) start_urls = [ 'http://www.imdb.com/title / % s /' % l before l in links ] # Methods go here!

In the method...

Scrapy requires a spider object to have a parse method. This method basically tells the web scraper what to extract from the page and also does some basic data processing to ensure that the data being extracted a) actually exists and b) is in a format that can be used later.

The data is found using "xpaths". For example, suppose we want to extract the page title of a movie. If we inspect the page, we can find the HTML xpath of the title element.

Tutorial: Web Scraping IMDB Data with Python to Predict Box Office Performance - Chris Giler (3)

For this example, we know that the title of the movie is contained in the

header element and we also know an attribute of the element. We can define our xpath as follows:


So in the parsing code we would have the following line:

title = respons.xpath('//h1[@itemprop="name"]/text()').extract()[0].replace('\xa0','')

This code generates the embedded text for the first occurrence of a

tag with itemprop="name". It also makes some edits on the same line to remove extraneous characters from the text element ('\xa0').

We repeat a similar method to extract the following data points for each movie page:

  • Title ID (just pulled from the URL)

  • Title (i.e. "Justice League")

  • Publication date

  • MPAA Rating

  • Principal's name

  • study name

  • Box office data (if available)

    • Budget

    • Opening Weekend US Gross

    • Gross VS

    • Accumulated overall gross

  • metacritical score

I should also note that if the entry information is not included for the title of a removed movie, the movie will be ignored and not added to this database. Due to the magnitude of this problem (trying to predict weekend box office sales), we ignore movies where this information is not available to the public.

Below is the analysis method to scrape these web pages. for each page, if box office data is available, the tickets are extracted and stored in a Python dictionary. This dictionary is then returned to the object and stored in an external file.

def parse(self, answer): # Extraheer de links naar de individualpagina festivalpagina's als 'Box Office' in answer.xpath('//h3[@class="subheading"]/text()').extract(): title_id = respons.url.split('/')[-2] título = respons.xpath('//h1[@itemprop="name"]/text()').extract()[0].replace( ' \xa0','') release = respons.xpath('//div[@class="subtext"]/a/text()').extract()[0].replace('\n',' " ) probeer: rating = answer.xpath('//meta[@itemprop="contentRating"]/@content').extract()[0] behalve: rating = '' probeer: director = answer.xpath('/ / span[@itemprop="σκηνοθέτης"]/a/span[@itemprop="name"]/text()').extract()[0] behalve: director = '' probeer: studio = answer.xpath(' / /span[@itemprop="creador"][@itemtype="http://schema.org/Organization"]/a/span[@itemprop="name"]/text()').extract()[ 0 ] comportarse: estudio = '' dinero = respuesta.xpath('//h3[@class="subheading"]')[0].xpath('volgende broer::div/text()').re( r' \$[0-9,]+') money_labels = answer.xpath('//h3[@class="subheading"]')[0].xpath('volgende-zuster::div/h4/text() ').extract() moneys = [i.replace(',','').replace('$','') for i in money] presupuesto = '' άνοιγμα = '' bruto = ''worldwide_bruto = ' ' probeer: για m, l σε zip(dinero, dinero_etiquetas[:len(dinero)]): if 'presupuesto' en l.inferior(): presupuesto = m elif 'apertura' en l.inferior(): άνοιγμα = m elif 'wereldwijd' in l.lower():worldwide_gross = m elif 'bruto' in l.lower(): bruto = m else: ga verder behalve: pass try: metacritic_score = response.xpath('//div[@ class ="titleReviewBarItem"]/a/div/span/text()').extract()[0] behalve: metacritic_score = '' απόδοση { 'title_id': title_id, 'title': title, 'release': release , 'director': regisseur, 'estudio': estudio, 'presupuesto': presupuesto, 'apertura': άνοιγμα, 'bruto': bruto, 'worldwide_gross':worldwide_gross, 'metacritic_score': metacritic_score, 'mpaa_rating': beoor

To run Scrapy's rotation, the following command is called on the command line

Rastreo scrapy imdb_spider -o 'import_data.json'

This command takes a while to run, but the spider will search all pages for movie titles stored in our pickle file and save the parsed results in json format (in the file called 'import_data.json').

[{"title_id": "tt0315642", "title": "Wazir", "release": "January 8, 2016 (VS)", "director": "Bejoy Nambiar", "studio": "Vinod Chopra Productions ", "budget": "", "opening": "586028", "gross": "586028", "worldwide_gross": "", "metacritic_score": "", "mpaa_rating": ""},{"title_id ": "tt0339736", "title": "The Evil Within", "release": "August 30, 2017 (Indonesia)", "director": "Andrew Getty", "studio": "Supernova LLC", " budget": " 6000000", "opening": "", "gross": "", "worldwide_gross": "", "metacritic_score": "", "mpaa_rating": "UNRATED"},{"title_id": "tt0365907", "title": "A walk among the tombstones", "launch": "September 19, 2014 (VS)", "regisseur": "Scott Frank", "studio": "1984 Private Defense Contractors ", "budget": "28000000", "opening": "12758780", "gross": "26307600", "worldwide_gross": "", "metacritic_score": "57", "mpaa_rating": "R"}, {"title_id": "tt03 " ", "title": "Jurassic World", "release": "June 12, 2015 (VS)", "regisseur": "Colin Trevorrow", "studio": "Universal Pictures ", "budget": "150000000", "opening": "208806270", "gross": "652270625", "worldwide_gross": "1670400637", "metacritic_score": "59", "mpaa_rating": "PG-13 "},{"title_id ": ", "title": "American Pastoral", "release": "October 21, 2016 (VS)", "regisseur": "Ewan McGregor", "studio": "Lakeshore Entertainment ", "budget": " ", " open ": "149038", "gross": "541457", "global gross": "", "meta_rating": "43", "mpaa_rating": "R"}, {"title_id": "tt0403935", "title" ": "Action Jackson", "release": "5 Dec 2014 (India)", "regisseur": "Prabhudheva", "studio": "Baba Films" , "budget": "", "opening": " 171795 ", "gross": "171795", "worldwide_gross": "", "metacritic_score": "", "mpaa_rating": "UNRATED"},{" title_id": "tt0420293", " title": "The Stanford Prison Experiment", "release": "July 17, 2015 (VS)", "regisseur": "Kyle Patrick Alvarez", "studio": "Coup d 'Etat Films", "budget": "", "opening": "37514", "gross": "643557", "worldwide_gross": "", "metacritic_score": "67", "mpaa_rating": "R" },{"title_id": "tt0435651 ", "title": " The Giver", "release": "15 Aug 2014 (VS)", "regisseur": "Phillip Noyce", "studio": "Tonik Productions", "budget": "25000000", "opening": "12305016", "gross": "45089048", "worldwide_gross": "", "metacritic_score": "47", "mpaa_rating": "PG-13 "},...

Fearful! After running this script, there are about 6,000 movie titles left for which box office data is available. It may still be more data than we really need for this project, but I think we can filter it down a bit more before we start scraping off the selenium...

Import and clean scrapy data

So we have the JSON file of all the data we collect! Let's load it into a Pandas DataFrame and see what we're working with.

# Load the scrapy results from the IMDB title # Read json fileimport jsonimport panda's as pd # Load scrapy json into my_datawith open('imdb_spider/import_20Jan18_5.json', 'r') as f: my_data = json.load(f)imdb. (my data)

Tutorial: Web Scraping IMDB Data with Python to Predict Box Office Performance - Chris Giler (4)

Looks like we need to clean up some more as some data is missing after running the scraper. Some movies don't release all of their box office information, which is great, but it doesn't make our job of predicting sales any easier. Some empty values ​​are not a problem, such as "gross" or "world_gross_global", since they are obviously not used to predict weekend sales. However, we need to remove the rows that are missing the following data:

  • Budget

  • Home [weekend box office mix]

  • MPAA Rating (remove if blank or if rating is ambiguous, such as "UNRATED", "UNRATED", or "TV-14")

We'll set up another filter mask as shown below, and then clear the DataFrame. The cleanup process also includes converting all data values ​​from strings to integers and date strings to date objects.

# Filter only useful data (ignore empty values ​​or rare ratings) imdb_mask = ((imdb_info['budget'] != '') & (imdb_info['opening'] != '') & (~imdb_info[' mpaa_rating ' ] .isin(['', 'UNRATED', 'UNRATED', 'TV-14'])))imdb_info = imdb_info[imdb_mask]imdb_info.filter('mpaa_rating NOT ON ["UNRATED", "UNRATED" ", "TV-14]')# Convert columns to usable data typesimdb_info['budget'] = imdb_info['budget'].apply(int)imdb_info['opening'] = imdb_info['opening'].apply( int) imdb_info[ 'launch'] = pd.to_datetime(imdb_info['launch'].apply(lambda x: x.split('(')[0].strip()))# Add new columns to plot(info financial in millions )imdb_info['budget_mil'] = imdb_info['budget']/1000000.imdb_info['opening_mil'] = imdb_info['opening']/1000000.# Rename the Title ID column to 'tconst ' for consistency with other array data [ ' tconst'] = imdb_info['title_id']imdb_info.drop('title_id', inplace=True, as=1)imdb_info.head()

Tutorial: Web Scraping IMDB Data with Python to Predict Box Office Performance - Chris Giler (5)

Now we have around 750 movies. This gives us a good dataset to work with (not too big to take the next step with Selenium, but enough data to build our model). Now we are going to merge this DataFrame with the list of movie titles, we need to have a list of unique cast members who were in those movies.

# Combinar con datos IMDB sin procesar usando open('my_data.pkl', 'rb') como picklefile: title = pickle.load(picklefile)titles_all = imdb_info.merge(titles, on='tconst')

Tutorial: Web Scraping IMDB Data with Python to Predict Box Office Performance - Chris Giler (6)

Now intitles_allDataFrame, we've linked a list of movie titles with their main cast and crew members (labeledmain cast). What we really want to extract here is a list of unique actors and crew members for all the movies of interest, but we want to associate those actors with each movie for subsequent merges and merges. We can achieve this simply by looking at the title IDs (constant)and related cast lists (main cast), and expand this DataFrame so that there is a single row for each title ID and name ID combination.

col_names = ['tconst', 'principalCast']expanded_data = []for idx, queue en titles_all[col_names].iterrows(): para nombre en queue['principalCast'].split(','): expand_data.append( [queue['tconst'], name.strip()])data_expanded = pd.DataFrame(data_expanded, columnas=['tconst', 'nconst'])

Tutorial: Web Scraping IMDB Data with Python to Predict Box Office Performance - Chris Giler (7)

We now have a list of all the actors we need more information on, as well as a breakdown of how these actors are connected to each movie! This simple data framework will prove extremely useful in the next few steps, where we will pull data from IMDB's STARmeter and aggregate that data in a way that will help us build our model.

Using Selenium to scrape IMDB-Pro

Once again, the main goal of this project is to find out how the popularity of a film's key cast members can be used to predict the hype surrounding a film's opening weekend (i.e., movie gross sales). tickets at the opening box office). To do this, we need to find a unit of measure to quantify the popularity of the cast. Lucky for us, IMDB already does this!

Tutorial: Web Scraping IMDB Data with Python to Predict Box Office Performance - Chris Giler (8)

Through an IMDB Pro account, we can track the popularity of a particular actor on IMDB, using the actor's STARmeter rating. This ranking, given weekly to each cast or crew member featured on IMDB, reflects how often a particular cast member appears in user searches. The IMDB site perfectly sums up the meaning of the STARmeter rating:

?Plain and simple, they represent what people care about, based not on small statistical samples, but on the actual behavior of millions of IMDb users. Unlike the AFI 100 or the Academy Awards, high scores on the STARmeter... don't necessarily mean something is "good." They signify that there is a high level of public awareness and/or interest in the... person...?

This will give us a good yardstick to work with to quantify the popularity of a given movie's cast. Ideally, we could plot the current STARmeter range for each actor, but since we're working with movies released since 2014, that doesn't give us a good idea of ​​what the range was or around when each movie was. was released. This makes the scraping process more difficult, as we need to scrape not just a number, but also time series data from the last 5 years.

This data is available to us through any web page, but the actual values ​​are difficult to extract with a simple twist of Scrapy as we used before. The actual data is stored in a database filtered by the visualization itself. Here is an example of the SVG element of the form:

A few things to note about this

  • OfItems divide the interactive chart into blocks of 1~4 weeks.

  • The rectangles themselves do not inherently contain the data we need to extract, but instead contain relative coordinates within the

  • However, if you hover over each of these blocks, the text will appear in the first one.element (located in the firstarticle is found). This is where we will pull our data.

To accomplish this, we can develop a script in Selenium that essentially automates the process of opening each web page and pointing to each-element in of

Let's review the code.

def launch_selenium(names_list): # Inicie el navegador web y conéctese a IMDB Pro # Devuelve el objeto del controlador para usar en el proceso de raspado de selenium import webdriver from selenium.webdriver.common.keys import keys from selenium.webdriver.support import added_conditions if Import EC o import time import SENSIBLE as SENS # mv chrome driver from Descargas en aplicaciones chromedriver = "/Applications/chromedriver" os.environ["webdriver.chrome.driver"] = url chromedriver = 'https://pro-labs. imdb .com/name/' + lista_de_nombres[0] + '/' driver = webdriver.Chrome(chromedriver) driver.get(url) loginButton = driver.find_element_by_xpath('//a[@class="log_in"]' ) loginButton .click() time.sleep(.5) loginButton = driver.find_element_by_xpath('//input[@id="auth-lwa-button"]') loginButton.click() time.sleep(.5) nombre_usuario = controlador .find_element_by_id("ap_email") nombre_de_usuario.send_keys(SENS.nombre de usuario) password_form=driver.find_element_by_id('ap_password') password_form.send_keys(SENS.password) password_form.send_keys(Keys.RETURN)

Functionrelease_seleniumit is used to launch my selenium-powered web browser, log into my Amazon account (to access IMDB-Pro content), and navigate to the first web page I'm going to create, based on the cast list and workshop I'm I'm interested. In general, navigating the login menu involves following a similar pattern.

boton = conductor.find_element_by_xpath('

That is, we find the xpath of each element we want to select, give that element a name, and click on it. When we get to the username and password input form, we select these items in a similar way, but instead use the object's "send_keys" method to populate the text fields.

Once we are connected to the form, the next step is to extract the StarMETER time series data from each actor's page. This means hovering over each element and extracting a number that is updated within the SVG element of the visualization. The image below indicates which element to hover over and which element to select.

Tutorial: Web Scraping IMDB Data with Python to Predict Box Office Performance - Chris Giler (9)

The actual code for this is quite simple and consists of exploring the "rect" tags in the sort chart, clicking each item (to simulate a scroll), and extracting and parsing the "tspan" tag that contains the songs we are interested in. . . The code looks like this:

graph_div = driver.find_element_by_id('ranking_graph')ubicación = graph_div.find_elements_by_tag_name('rect')[1:]name = (driver.find_elements_by_class_name('display-name')[1] .find_element_by_text star_meter_data = []por ejemplo (1, len(localizar)+1): loc = graph_div.find_elements_by_tag_name('rect')[i] (driver.find_element_by_class_name('current_rank') .find_element_by.liks' ()) probeer: loc.click() behalve: time.sleep(0.5) g = graph_div.find_elements_by_tag_name('tspan')[-2:] fechas = g[0].text.split('-') start_date = datetime.datetime.strptime(dates[0].strip (), '%b %d, %Y') end_date = datetime.datetime.strptime(dates[1].strip(), '%b %d, %Y ') star_meter = int(g[1].text .split(':')[-1] .strip() .replace(',','')) star_meter_data.append([i, name_id, name, start_date , einddatum, ster_meter])

One thing to note is that before I click on each "rec" element, I click outside of the sort graph (second line of the for loop). Hovering over a "rec" element will occasionally display a tooltip to display movies released in the selected time period in which the actor appeared. Jumping directly from one "rect" element to the next sometimes caused the tooltip to be selected instead of the "rect" element, resulting in script execution errors. To explain this, we'll click the white space outside the grading graph to reset all the tooltips, then click the next "rect" element.

Finally, this function is executed for each cast or crew member of interest in our dataset and is stored in a Pandas DataFrame. This scraping process takes a while and I like some pickles, so this DataFrame is also saved to a pickle file after each actor is scraped. That way, I can pick up where I left off when I have to cancel surgery and give my laptop some much-needed rest.

And finally! We have now saved our StarMETER ranking in a bind. Stay tuned for Part 2 of this tutorial where I will go through the process of pre-processing this data to train a linear model that will be used to predict opening weekend box office grosses.

And as always, feel free to do so.keep in touchIf you have any questions or suggestions about my process!


Does IMDb allow web scraping? ›

You can scrape IMDb with both search URLs and links to specific titles. If you want to make your search URL as specific as possible, we recommend using IMDb's Advanced Title Search. Simply choose the desired filters and generate your link by clicking Search at the bottom of the page.

How do I extract a movie review from IMDb? ›

Problem Statement
  1. Step 1 a. Install Selenium and Scrapy. ...
  2. Step 1 b. Download chrome driver. ...
  3. Step 2: Import libraries. Let's import all the relevant libraries. ...
  4. Step 3: Selenium Test. ...
  5. Step 4: Extract the review count. ...
  6. Step 5: Load all reviews. ...
  7. Step 6: Extract Review from HTML. ...
  8. Step 7: Putting it all together.
Jul 13, 2022

Which Python library would you use to automate the scraping of a website which has information across multiple pages? ›

One useful package for web scraping that you can find in Python's standard library is urllib , which contains tools for working with URLs. In particular, the urllib.request module contains a function called urlopen() that you can use to open a URL within a program.

How to extract data using web scraping with Python? ›

To extract data using web scraping with python, you need to follow these basic steps:
  1. Find the URL that you want to scrape.
  2. Inspecting the Page.
  3. Find the data you want to extract.
  4. Write the code.
  5. Run the code and extract the data.
  6. Store the data in the required format.
Mar 14, 2023

Is there a free IMDb API? ›

Is the IMDb API free? Yes, the IMDb API has a free version that provides a limit of 1,000 API requests per day. If you need more usage, you can subscribe to the paid pricing plans.

What programming language does IMDb use? ›

JavaScript is a lightweight, object-oriented, cross-platform scripting language, often used within web pages. jQuery is a JavaScript library that simplifies HTML document traversing, event handling, animating and Ajax interaction.

Can you download IMDb data? ›

The dataset files can be accessed and downloaded from https://datasets.imdbws.com/. The data is refreshed daily.

How do I export IMDb to CSV? ›

Click the three-dot menu ⋮. It's three vertical dots right above the list (to the right of the list's name). A menu will expand. Click Export on the menu.

Which movies have maximum views ratings in Python? ›

So, according to this dataset, Joker (2019) got the highest number of 10 ratings from viewers. This is how you can analyze movie ratings using Python as a data science beginner.

What is the fastest Python web scraping library? ›

Scrapy is the most efficient web scraping framework on this list, in terms of speed, efficiency, and features. It comes with selectors that let you select data from an HTML document using XPath or CSS elements. An added advantage is the speed at which Scrapy sends requests and extracts the data.

Which Python library is best for web scraping? ›

  • 7 Best Python Libraries For Web Scraping. Here are the seven most popular Python libraries for web scraping that every data professional must be familiar with.
  • BeautifulSoup. ...
  • Scrapy. ...
  • Selenium. ...
  • Requests. ...
  • Urllib3. ...
  • Lxml. ...
  • MechanicalSoup.
Apr 24, 2023

What is the best web scraping tool for Python? ›

Top 7 Python Web Scraping Libraries & Tools in 2023
  • Beautiful Soup.
  • Requests.
  • Scrapy.
  • Selenium.
  • Playwright.
  • Lxml.
  • Urllib3.
  • MechanicalSoup.
Feb 24, 2023

Is web scraping in Python hard? ›

The most common way to scrape it using a programming language, such as Python. This path can be a little tricky, especially for those who are not used to coding, as you'll have to deal with different scenarios that will demand different approaches, and techniques that will take even more time to get familiarized with.

Is Python web scraping legal? ›

Scraping for personal purposes is usually OK, even if it is copyrighted information, as it could fall under the fair use provision of the intellectual property legislation. However, sharing data for which you don't hold the right to share is illegal.

What is the difference between web scraping and ETL? ›

Web scraping is the automated process of retrieving data from the internet. ETL stands for extract, transform, load, and is a widely used industry acronym representing the process of taking data from one place, changing it up a little, and storing it in another place.

Which database does IMDb use? ›

We do not distribute this software. Our entire website and nearly all of our internal-use tools are created with open source software such as Apache, and the usual collection of GNU and Linux utilities. The software that runs the database itself was completely developed in-house.

Is there an API for IMDb? ›

The IMDb API is powered by AWS Data Exchange, bringing the entertainment information you need in a convenient GraphQL-based API to help you utilize the world's most authoritative source of Movie, TV/OTT, Box Office data and more! To request a free trial, please visit IMDb's AWS Marketplace.

How to create a IMDb API? ›

Get the API key
  1. Log in to your TMDB account.
  2. Click on your name icon at the top right corner and then click “Settings” to go to the settings page.
  3. Click “API” to go to the API creation page.
  4. Click “click here” under the Request an API Key section.
  5. Select “Developer” as the type of your API.

What is the difference between IMDb and IMDbPro? ›

IMDbPro includes all the data from IMDb plus features specifically designed for entertainment industry professionals, including the world's biggest stars, the most influential decision-makers, and the leading entertainment companies and brands.

What is IMDb called now? ›

IMDb TV will be renamed Amazon Freevee

The new name will take effect on April 27, the company said in a news release. The retailer said the streaming service will also expand its original programming by 70% in 2022, with spinoffs of shows such as “Bosh: Legacy” and other series. It will also add more original movies.

Has anything got a 10 on IMDb? ›

10 'Interstellar' (2014)

Can I use IMDb as a source? ›

The IMDb should be used only as a tertiary source for hard data on released films. Citing the Internet Movie Database (IMDb) on Wikipedia raises questions if such references do not follow the important points given in the reliable sources guideline.

How many movies are in the IMDb database? ›

Feature film629,807
Short film862,336
TV series235,708
TV episode7,147,915
8 more rows

What is the new IMDb format? ›

The latest version of the site not only introduces stylistic changes like round profile pictures of cast and crew, an emphasis on image and video content like trailers, but also the addition of more advertisements. Studios can now promote their new and upcoming releases on pages for other films and shows.

How do I export IMDb to excel? ›

Export your lists
  1. On IMDb, go to “Your Lists” from the user menu (top right)
  2. Click the title of the list to export (under All Lists)
  3. Click the three vertical dots at top right, then select “Export”
  4. <List Title>.csv will be saved to your Downloads folder.
  5. Repeat these steps for each list.

How do I import data from IMDb? ›

Steps to import IMDb data to Google Sheets.
  1. Install the Amigo Data add-on.
  2. Get the IMDb API key.
  3. Choose data endpoint.
  4. Import the data to Google Sheets.
Oct 28, 2022

What is the difference between Letterboxd and IMDb? ›

Letterboxd movie reviews resemble quirky Twitter threads, while IMDB ratings read more like Amazon product reviews. Letterboxd users majored in film or wished they majored in film, while the average IMDB user is pretty much the average human.

What movie has 100% audience score? ›

A number of these films also appear on the AFI's 100 Years...100 Movies lists, but there are many others and several entries with dozens of positive reviews, which are considered surprising to some experts. To date, Leave No Trace holds the site's record, with a rating of 100% and 252 positive reviews.

What is the most used language in movies? ›

Across those 8,798 movies studied, 81.4% featured English as one of their main languages. Other popular languages included French (featured in 12% of movies), Spanish (8.6%), German, (5.2%) and Hindi (4.9%).

Is Python used for film industry? ›

Python is a programming language designed to be very easy to read and write. It's incredibly popular in the feature film industry as well as other groups, like mathematics, science and machine learning. You can learn more about Python on the official website.

Which language is best at Webscraping? ›

Best Programming Languages for Effective Web Scraping
  1. Python. If you asked developers focused on web scraping what their language of choice is, most would likely answer Python, and for a good reason. ...
  2. JavaScript. JavaScript, without Node. ...
  3. Ruby. Speaking of simplicity, it'd be difficult to ignore Ruby. ...
  4. PHP. ...
  5. C++ ...
  6. Java.
Mar 31, 2023

Which is the fastest web scraping language? ›

Fastest Web Scraping: Go and Node.

Go and Node. js are two programming languages built with performance in mind. Both have a non-blocking nature, which makes them fast and scalable. Plus, they can perform asynchronous tasks thanks to the async/await built-in instructions.

What is the easiest language to web scrape? ›

Python web scraping is the go-to choice for many programmers building a web scraping tool. Python is the most popular programming language today, primarily due to its simplicity and ability to handle virtually any process related to data extraction.

Is web scraping better in R or Python? ›

Junior developers who require basic web scraping, data processing, and scalability prefer Python. Is R easier than Python? Both R and Python programming languages are easy to learn. However, Python has a better learning curve due to syntactic sugar, i.e., simple keyword-based syntax.

How long does it take to learn web scraping with Python? ›

Depending on your Python knowledge, and how much time you're allocating to learn this skill, it could take anywhere from two days to two years.

Is web scraping easier in Python or R? ›

So who wins the web scraping battle, Python or R? If you're looking for an easy-to-read programming language with a vast collection of libraries, then go for Python. Keep in mind though, there is no iOS or Android support for it. On the other hand, if you need a more data-specific language, then R may be your best bet.

What are the two libraries you would need to scrape website data on Python? ›

Python offers a variety of libraries that one can use to scrape the web, libraires such as Scrapy, Beautiful Soup, Requests, Urllib, and Selenium.

Which is better for web scraping Python BeautifulSoup or selenium? ›

Selenium is a web browser automation tool that can interact with web pages like a human user, whereas BeautifulSoup is a library for parsing HTML and XML documents. This means Selenium has more functionality since it can automate browser actions such as clicking buttons, filling out forms and navigating between pages.

What is the difference between web scraping and API in Python? ›

Web scraping involves gathering specific information from multiple websites and organizing it into a structured format for users. On the other hand, APIs allow seamless access to the data of an application or any software, but the owner determines the availability and limitations of this data.

Can web scraping make money? ›

Web scraping is a fun and accessible way to make money online. With Python and a web scraping framework, you can extract valuable data from websites and use it to your advantage. Whether you're looking to start a side hustle or just earn some extra cash, web scraping is definitely worth exploring.

How much HTML do I need to know for web scraping? ›

It's not hard to understand, but before you can start web scraping, you need to first master HTML. To extract the right pieces of information, you need to right-click “inspect.” You'll find a very long HTML code that seems infinite. Don't worry. You don't need to know HTML deeply to be able to extract the data.

How long does it take to learn to web scrape? ›

Firstly, it's important to understand that learning web scraping is a continuous process that requires consistent practice. Depending on your level of experience with programming and web development, it can take anywhere from a few weeks to several months to become proficient in web scraping.

Can you get IP banned for web scraping? ›

Website owners can detect and block your web scrapers by checking the IP address in their server log files. Often there are automated rules, for example if you make over 100 requests per 1 hour your IP will be blocked.

Is web scraping legal in US? ›

Web scraping is completely legal if you scrape data publicly available on the internet. But some kinds of data are protected by international regulations, so be careful scraping personal data, intellectual property, or confidential data.

Which websites allow web scraping? ›

eBay. E-commerce websites are always the most popular websites for web scraping and eBay is definitely one of them. We have many users running their own businesses on eBay and getting data from eBay is an important way to keep track of their competitors and follow the market trend.

Is web scraping easy or hard? ›

Web Scraping is the process of extracting data from a website. Learning Web Scraping could be as easy as following a tutorial on how libraries like Beautiful Soup or Selenium work; however, you should know some concepts to understand better what these scraping tools do and come up with effective ways to tackle a task.

What is the difference between data scraping and web scraping? ›

Web scraping is when you take any publicly available online data and import the found information into any local file on your computer. The main difference here to data scraping is that web scraping definition requires the internet to be conducted. It is also often done through a Python scraper.

Is web scraping worth learning? ›

Without web scraping knowledge, it would very difficult to amass large amounts of data that can be used for analysis, visualization and prediction.

Are IMDb images public domain? ›

Most of the photos and videos on our site are licensed to us for our own use only. We do not have the authority to sublicense them to others.

Can I use IMDb data? ›

The data can only be used for personal and non-commercial use and must not be altered/republished/resold/repurposed to create any kind of online/offline database of movie information (except for individual personal use).

Is web scraping easy to learn? ›

Web Scraping is the process of extracting data from a website. Learning Web Scraping could be as easy as following a tutorial on how libraries like Beautiful Soup or Selenium work; however, you should know some concepts to understand better what these scraping tools do and come up with effective ways to tackle a task.

Which browser is best for scraping? ›

Some of the most popular headless browsers for web scraping are Puppeteer, Selenium, Playwright, Pyppeteer, and Splash. Each has its own advantages and drawbacks; for example Puppeteer is fast and powerful but complex and resource-intensive, while Splash is easy to use but limited and slow.

What is the best alternative to IMDb? ›

IMDb alternatives
  • Reelgood. 31 reviews. 15 alternatives. ...
  • Letterboxd. 16 reviews. 34 alternatives. ...
  • JustWatch. 1 review. 14 alternatives. ...
  • Flixfindr. Leave a review. 10 alternatives. ...
  • Cathod TV. Leave a review. 4 alternatives. ...
  • FandangoNOW. Leave a review. 13 alternatives. ...
  • FXNOW.

Are FBI images public domain? ›

As a work of the U.S. federal government, the image is in the public domain in the United States. This image shows a flag, a coat of arms, a seal or some other official insignia. The use of such symbols is restricted in many countries.

Is it illegal to sell public domain images? ›

Content in the public domain isn't just legal to download for free. It's also legal to sell.

Is it legal to sell images that are public domain? ›

If an image is in the public domain, anyone can use the given work without permission or paying a fee and in any way they want, including making any modifications, creating derivative works, or using it for commercial purposes and making profit.

How much does it cost to use IMDb? ›

IMDb.com is free for non-commercial use. The site and mobile apps are supported by revenues from our advertisers.

How big is the IMDb database? ›

Since 1998, it has been owned and operated by IMDb.com, Inc., a subsidiary of Amazon. As of March 2022, the database contained some 10.1 million titles (including television episodes) and 11.5 million person records.

How is data stored in IMDb? ›

IMDBs work by keeping all data in RAM. That is the medium in which data is stored in RAM versus disks or SSDs. IMDBs essentially replace the disk-accessing component of disk-based databases with RAM accesses. In some IMDBs, a disk-based component remains intact, but RAM is the primary storage medium.


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