In 2024, a total of 569 movies were released in the United States and Canada, up from 506 in the previous year. Still, these figures are under the 792 titles released in 2019, before the COVID-19 outbreak. Will moviegoers return? The box office revenue in the U.S. and Canada more than tripled between 2020 and 2022, when it reached almost 7.4 billion U.S. dollars. The 2022 result still fell way behind the 11.3-billion-dollar annual revenue recorded just before the pandemic. But there are ways to attract newcomers to the moviegoing experience. During a mid-2022 survey conducted among members of the Generation Z – aged between 13 and 24 years – more than half of respondents mentioned movie offering as a leading motivation to go to the movies. About 40 percent of interviewees included the quality of the service and the physical comfort of the seats at the movie theater among their main incentives. Cinema circuits As the industry tries to reinvent itself for a post-pandemic scenario, the top movie theater chains in North America slowly bounce back. Their financial results improved since the coronavirus outbreak, but when or if they will see figures similar to those recorded before 2020 remains an open question. The leading circuit, AMC Theatres, reported a revenue of more than 2.5 billion dollars in 2021, over twice as much as in the previous year.

This dataset provides a detailed analysis of global box office performance from 2000 to 2024. It includes information on movies released during this period, covering key metrics such as release dates, genres, production budgets, worldwide gross, and more. The dataset aims to assist researchers, data scientists, and movie enthusiasts in exploring trends in the film industry, analyzing profitability, and understanding audience preferences over the years.

Key Features: 1. Timeframe: 2000–2024 2. Metrics: Revenue, production budget, profit margins, and more 3. Genres: Covers various genres to analyze trends in audience preferences 4. Insights: Ideal for trend analysis, profitability studies, and forecasting

This dataset is ideal for: - Machine learning projects such as predicting box office success - Exploratory data analysis (EDA) for trends in the movie industry - Research on the evolution of filmmaking economics

Note: All data is curated from publicly available sources.

In 2019, Avengers: Endgame overtook 2009's Avatar as the highest grossing film of all time at the international Box Office. The difference between Endgame and Avatar's totals was fewer than 100 million dollars by the end of 2019, yet Avatar re-took the top spot in 2021 due to theatrical re-releases in China; this gap is likely to grow in the coming years as Avatar will be shown again in theaters prior to the release of it's four sequels (the first of which was released in 2022). Before Avatar, the record had been held by 1997's Titanic (also directed by James Cameron). When adjusted for inflation, 1939's Gone With the Wind is generally cited as the most successful film of all time, with a total gross between three and four billion (including theatrical re-releases) - however, Gone With the Wind is estimated to have sold fewer tickets than Avatar, Star Wars, Titanic, and many Chinese releases. Recent developments The highest grossing film of 2020 was The Eight Hundred, which took over 460 million dollars worldwide; the first ever non-Hollywood production to feature on this list. The significant drop off in global revenues in 2020 was due to the Covid-19 pandemic, where lockdowns saw thousands of movie theaters close across the world. Varying restrictions per country saw Asian markets eventually overtake North American and European markets as the largest worldwide, and five of the ten highest grossing films in 2020 were either Chinese or Japanese productions. The pandemic also accelerated the trend of major releases coming to streaming platforms, and 2021 saw many of the previous year's postponements released simultaneously in theaters and online (often at a premium). It remains to be seen what the dominant method of big-budget releases will be in the coming years, as major studios such as Disney may look to draw consumers to their streaming platforms, however a strong domestic performance of Spider-Man: No Way Home in late-2021 shows optimism for the box office. Recurring figures Throughout the list, many of the same directors and actors appear in multiple films. Stephen Spielberg has directed more of these films than any other director, with six titles to his name. Cecil B. DeMille, a "founding father" of American Cinema, and Disney's Hamilton Luske, have each directed (or co-directed) five movies on this list. 2012's Frozen is the only film made by a woman director; Jennifer Lee, and until 2020, Mission: Impossible 2 was the only film made by a non-white director; John Woo. Looking at those in front of the camera, Harrison Ford, through his roles in the Star Wars and Indiana Jones films, has appeared in the highest number of films listed here; featuring in seven titles. His Star Wars co-star, Carrie Fisher, has appeared in five films listed here, more than any other actress. When looking at the companies behind the films featured on this list, we can see that Disney and Paramount Pictures (in all of their forms) have each produced and/or distributed 24 of the films on this list, at the time of their release. Disney dominates Since 1999, all but one of the highest grossing films were sequels or part of franchises. Although this is not a new trend in Hollywood, the box office pull of such "extended universes" has exploded in recent years, and these films dominate the annual lists; Disney in particular has been the most successful studio in this regard. After acquiring Marvel Entertainment in 2009 and Lucasfilm in 2012, in deals worth 4.24 billion and 4.05 billion dollars respectively, Disney built upon its position as the largest entertainment company in the world and has dominated the international box office over the last decade. With the continued success of the Marvel and Star Wars universes and the expansion of these products in series form, along with a number of planned live action remakes and Pixar titles, it is likely that Disney films will feature at the top of this list for years to come.

As of 2024, the top film-producing market on a global arena is the United states, with over **** thousand movies made in the country throughout the entire cinematic production period. The UK and France lead the film production in Europe, while China and India dominate the Asia-pacific region in that regard.

Global Movie Franchise Revenue and Budget Data

Tracks Lifetime Gross, Budgets, Ratings, and Release Dates

By Emma Culwell [source]

About this dataset

This dataset offers an extensive look at some of the most popular movie franchises in history, shedding light on their financial success and public reception. It includes data on the lifetime gross sales, budgets, ratings, and release dates of each featured movie. Furthermore, this dataset provides invaluable insights into how different elements such as ratings and runtime can affect the performance of a film at the box office. Whether you are an aspiring or established filmmaker looking for inspiration to craft your own successful blockbuster or simply a fan curious about these films’ inner workings, this dataset offers an unprecedented level of detail regarding many beloved franchises

More Datasets

For more datasets, click here.

Featured Notebooks

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How to use the dataset

This dataset provides comprehensive information on movie franchises released worldwide between 2000 and 2020. It includes data such as lifetime gross, budget, rating, runtime, release date and vote count/average. This dataset can be used to gain insights on the global movie industry trends over this time period.

The data can be explored in various ways to identify patterns of success or failure among movie franchises across countries, genres or decades. For example, you may want to examine the average budget for movies released each year or calculate the average number of votes received by movies of a particular genre. Additionally, you could use this dataset to compare different types of media (e.g., cable vs streaming) and understand how they impact box-office performance.

To get the most out of this data set it is essential that you first familiarize yourself with all the columns provided: Title: The title of the movie; Lifetime Gross: Total amount money earned by a franchise in all territories; Year: The year in which it was first made available publicly; Studio: The production company behind the production; Rating: Classification given by MPAA/BBFC; Runtime: Length in minutes/hours; Budget: Amount spent producing it ; Release Date : Date when publically announced Availability ; Vote Average : Average ratings based on user reviews ; Vote Count : Number people who rated franchise).
Once you have become comfortable with these variables then feel free to try out some larger analysis techniques such as predictive analytics (predicting future success based on existing trends) or clustering (grouping similar outcomes together). No matter which methods you decide to utilize it is important that you remember – always validate your assumptions! Good luck exploring!

Research Ideas

• A comparison of movie budget to box office returns, to identify over/underperforming movies.
• A study of the correlation between movie rating and viewership.
• An analysis of what types of movies tend to become franchise success stories (big budget, PG-13 rating, etc.)

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: MovieFranchises.csv | Column name | Description | |:-------------------|:------------------------------------------------------------------------| | Title | The title of the movie. (String) | | Lifetime Gross | The total amount of money the movie has made in its lifetime. (Integer) | | Year | The year the movie was released. (Integer) | | Studio | The studio that produced the movie. (String) | | Rating | The rating of the movie (e.g. PG-13, R, etc). (String) | | Runtime | The length of the movie in minutes. (Integer) | | Budget | The budget of the movie in USD. (Integer) | | ReleaseDate | The date the movie was released. (Date) | | VoteAvg | The average rating of the movie from users. (Float) | | VoteCount | The total number of votes the movie has received from users. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Emma Culwell.

Film Genre Statistics

Movie genre statistics and revenue data from 1995-2018

By Throwback Thursday [source]

About this dataset

This dataset contains genre statistics for movies released between 1995 and 2018. It provides information on various aspects of the movies, such as gross revenue, tickets sold, and inflation-adjusted figures. The dataset includes columns for genre, year of release, number of movies released in each genre and year, total gross revenue generated by movies in each genre and year, total number of tickets sold for movies in each genre and year, inflation-adjusted gross revenue that takes into account changes in the value of money over time, title of the highest-grossing movie in each genre and year, gross revenue generated by the highest-grossing movie in each genre and year, and inflation-adjusted gross revenue of the highest-grossing movie in each genre and year. This dataset offers insights into film industry trends over a span of more than two decades

How to use the dataset

Understanding the Columns

Before diving into the analysis, let's familiarize ourselves with the different columns in this dataset:

• Genre: This column represents the genre of each movie.
• Year: The year in which the movies were released.
• Movies Released: The number of movies released in a particular genre and year.
• Gross: The total gross revenue generated by movies in a specific genre and year.
• Tickets Sold: The total number of tickets sold for movies in a specific genre and year.
• Inflation-Adjusted Gross: The gross revenue adjusted for inflation, taking into account changes in the value of money over time.
• Top Movie: The title of the highest-grossing movie in a specific genre and year.
• Top Movie Gross (That Year): The gross revenue generated by the highest-grossing movie in a specific genre and year.
• Top Movie Inflation-Adjusted Gross (That Year): The inflation-adjusted gross revenue of the highest-grossing movie in a specific genre and year.

Analyzing Data

To make use of this dataset effectively, here are some potential analyses you can perform:

• Find popular genres: You can determine which genres are popular by looking at columns like Movies Released or Tickets Sold. Analyzing these numbers will give you insights into what types of movies attract more audiences.

• Measure financial success: Explore columns like Gross, Inflation Adjusted Gross, or Top Movie Gross (That Year) to compare the financial success of different genres. This will allow you to identify genres that generate higher revenue.

• Understand movie trends: By analyzing the dataset over different years, you can observe trends in movie releases and gross revenue for specific genres. This information is crucial for understanding how movie preferences change over time.

• Identify highest-grossing movies: The column Top Movie gives you the title of the highest-grossing movie in each genre and year. You can use this information to analyze the success of specific movies within their respective genres.

Data Visualization

To enhance your analysis, consider using data visualization techniques

Research Ideas

• Predicting the popularity and success of movies in different genres: By analyzing the data on tickets sold and gross revenue, we can identify trends and patterns in movie genres that attract more audiences and generate higher revenue. This information can be useful for filmmakers, production studios, and investors to make informed decisions about which genres to focus on for future movie releases.
• Comparing the performance of movies over time: With the inclusion of inflation-adjusted figures, this dataset allows us to compare the box office success of movies across different years. We can analyze how movies in specific genres have performed over time in terms of gross revenue and adjust these figures for inflation to get a better understanding of their true financial success.
• Analyzing the impact of genre popularity on ticket sales: By examining the relationship between genre popularity (measured by tickets sold) and total gross revenue, we can gain insights into audience preferences and behavior. This information is valuable for marketing strategies, as it helps determine which movie genres are most likely to attract a larger audience base and generate higher ticket sales

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns...

Between 1995 and 2024, adventure was the highest-grossing movie genre at the so-called North American box office, which consists of Canada and the United States (including the unincorporated territories of Guam and Puerto Rico). Adventure films recorded a box office revenue of more than 67 billion U.S. dollars within that period. Action movies followed with more than 60 billion dollars in revenue. Cinema: releases versus revenue Titles with adventure and action elements are also on the top on a global scale. "Avatar" (2009) and "Avengers: Endgame" (2019) are the most commercially successful movies of all time, each grossing around 2.8 billion dollars worldwide. In terms of production, however, historically, the North American market has plenty of dramas, comedies, and documentaries. These were the top three genres of movie released in the U.S. and Canada between 1995 and the beginning of 2023. Will the cinema market fully recover? The film industry in the U.S. faced unparalleled challenges after the COVID-19 pandemic led to preventive lockdowns across the globe. It remains to be seen if the demand for movies on the big screen will reach the same levels recorded before the outbreak. The number of movie tickets sold in the U.S. and Canada increased by more than 60 percent between 2021 and 2022, when it surpassed 813 million. Still, the latter figure amounted to less than 70 percent of the nearly 1.23 billion tickets sold in 2019.

In 2021, the global box office revenue amounted to approximately **** billion U.S. dollars, out of which more than half – **** billion dollars – came from the Asia Pacific region. In Europe, the Middle East, and Africa – collectively known as EMEA – the box office revenue added up to **** billion dollars or almost one-fourth of the total. Despite the increase when compared to 2020, the 2021 figures remained distant from the ****-billion-dollar global revenue recorded in 2019, before the COVID-19 outbreak. What is box office data used for? The term box office revenue refers to the total revenue generated through movie ticket sales. It is primarily used to measure and compare the commercial success of a film. Ticket sales may account for a large portion of the film industry’s total revenue – especially before the pandemic. They are also the main source of income for movie theaters. Leading box office markets The United States and Canada – known as the North American movie market – were the leading box office market worldwide for several decades. But China, alongside other Asian markets, has also begun to make its mark on the global movie industry in recent years. Bollywood movies, in particular, are gaining popularity outside of India. While the Indian film industry released far more movies than China and the U.S. until the coronavirus outbreak, its box office revenues remained comparatively small.

Complete dataset of “Film Circulation on the International Film Festival Network and the Impact on Global Film Culture”

A peer-reviewed data paper for this dataset is in review to be published in NECSUS_European Journal of Media Studies - an open access journal aiming at enhancing data transparency and reusability, and will be available from https://necsus-ejms.org/ and https://mediarep.org

Please cite this when using the dataset.

Detailed description of the dataset:

1 Film Dataset: Festival Programs

The Film Dataset consists a data scheme image file, a codebook and two dataset tables in csv format.

The codebook (csv file “1_codebook_film-dataset_festival-program”) offers a detailed description of all variables within the Film Dataset. Along with the definition of variables it lists explanations for the units of measurement, data sources, coding and information on missing data.

The csv file “1_film-dataset_festival-program_long” comprises a dataset of all films and the festivals, festival sections, and the year of the festival edition that they were sampled from. The dataset is structured in the long format, i.e. the same film can appear in several rows when it appeared in more than one sample festival. However, films are identifiable via their unique ID.

The csv file “1_film-dataset_festival-program_wide” consists of the dataset listing only unique films (n=9,348). The dataset is in the wide format, i.e. each row corresponds to a unique film, identifiable via its unique ID. For easy analysis, and since the overlap is only six percent, in this dataset the variable sample festival (fest) corresponds to the first sample festival where the film appeared. For instance, if a film was first shown at Berlinale (in February) and then at Frameline (in June of the same year), the sample festival will list “Berlinale”. This file includes information on unique and IMDb IDs, the film title, production year, length, categorization in length, production countries, regional attribution, director names, genre attribution, the festival, festival section and festival edition the film was sampled from, and information whether there is festival run information available through the IMDb data.

2 Survey Dataset

The Survey Dataset consists of a data scheme image file, a codebook and two dataset tables in csv format.

The codebook “2_codebook_survey-dataset” includes coding information for both survey datasets. It lists the definition of the variables or survey questions (corresponding to Samoilova/Loist 2019), units of measurement, data source, variable type, range and coding, and information on missing data.

The csv file “2_survey-dataset_long-festivals_shared-consent” consists of a subset (n=161) of the original survey dataset (n=454), where respondents provided festival run data for films (n=206) and gave consent to share their data for research purposes. This dataset consists of the festival data in a long format, so that each row corresponds to the festival appearance of a film.

The csv file “2_survey-dataset_wide-no-festivals_shared-consent” consists of a subset (n=372) of the original dataset (n=454) of survey responses corresponding to sample films. It includes data only for those films for which respondents provided consent to share their data for research purposes. This dataset is shown in wide format of the survey data, i.e. information for each response corresponding to a film is listed in one row. This includes data on film IDs, film title, survey questions regarding completeness and availability of provided information, information on number of festival screenings, screening fees, budgets, marketing costs, market screenings, and distribution. As the file name suggests, no data on festival screenings is included in the wide format dataset.

3 IMDb & Scripts

The IMDb dataset consists of a data scheme image file, one codebook and eight datasets, all in csv format. It also includes the R scripts that we used for scraping and matching.

The codebook “3_codebook_imdb-dataset” includes information for all IMDb datasets. This includes ID information and their data source, coding and value ranges, and information on missing data.

The csv file “3_imdb-dataset_aka-titles_long” contains film title data in different languages scraped from IMDb in a long format, i.e. each row corresponds to a title in a given language.

The csv file “3_imdb-dataset_awards_long” contains film award data in a long format, i.e. each row corresponds to an award of a given film.

The csv file “3_imdb-dataset_companies_long” contains data on production and distribution companies of films. The dataset is in a long format, so that each row corresponds to a particular company of a particular film.

The csv file “3_imdb-dataset_crew_long” contains data on names and roles of crew members in a long format, i.e. each row corresponds to each crew member. The file also contains binary gender assigned to directors based on their first names using the GenderizeR application.

The csv file “3_imdb-dataset_festival-runs_long” contains festival run data scraped from IMDb in a long format, i.e. each row corresponds to the festival appearance of a given film. The dataset does not include each film screening, but the first screening of a film at a festival within a given year. The data includes festival runs up to 2019.

The csv file “3_imdb-dataset_general-info_wide” contains general information about films such as genre as defined by IMDb, languages in which a film was shown, ratings, and budget. The dataset is in wide format, so that each row corresponds to a unique film.

The csv file “3_imdb-dataset_release-info_long” contains data about non-festival release (e.g., theatrical, digital, tv, dvd/blueray). The dataset is in a long format, so that each row corresponds to a particular release of a particular film.

The csv file “3_imdb-dataset_websites_long” contains data on available websites (official websites, miscellaneous, photos, video clips). The dataset is in a long format, so that each row corresponds to a website of a particular film.

The dataset includes 8 text files containing the script for webscraping. They were written using the R-3.6.3 version for Windows.

The R script “r_1_unite_data” demonstrates the structure of the dataset, that we use in the following steps to identify, scrape, and match the film data.

The R script “r_2_scrape_matches” reads in the dataset with the film characteristics described in the “r_1_unite_data” and uses various R packages to create a search URL for each film from the core dataset on the IMDb website. The script attempts to match each film from the core dataset to IMDb records by first conducting an advanced search based on the movie title and year, and then potentially using an alternative title and a basic search if no matches are found in the advanced search. The script scrapes the title, release year, directors, running time, genre, and IMDb film URL from the first page of the suggested records from the IMDb website. The script then defines a loop that matches (including matching scores) each film in the core dataset with suggested films on the IMDb search page. Matching was done using data on directors, production year (+/- one year), and title, a fuzzy matching approach with two methods: “cosine” and “osa.” where the cosine similarity is used to match titles with a high degree of similarity, and the OSA algorithm is used to match titles that may have typos or minor variations.

The script “r_3_matching” creates a dataset with the matches for a manual check. Each pair of films (original film from the core dataset and the suggested match from the IMDb website was categorized in the following five categories: a) 100% match: perfect match on title, year, and director; b) likely good match; c) maybe match; d) unlikely match; and e) no match). The script also checks for possible doubles in the dataset and identifies them for a manual check.

The script “r_4_scraping_functions” creates a function for scraping the data from the identified matches (based on the scripts described above and manually checked). These functions are used for scraping the data in the next script.

The script “r_5a_extracting_info_sample” uses the function defined in the “r_4_scraping_functions”, in order to scrape the IMDb data for the identified matches. This script does that for the first 100 films, to check, if everything works. Scraping for the entire dataset took a few hours. Therefore, a test with a subsample of 100 films is advisable.

The script “r_5b_extracting_info_all” extracts the data for the entire dataset of the identified matches.

The script “r_5c_extracting_info_skipped” checks the films with missing data (where data was not scraped) and tried to extract data one more time to make sure that the errors were not caused by disruptions in the internet connection or other technical issues.

The script “r_check_logs” is used for troubleshooting and tracking the progress of all of the R scripts used. It gives information on the amount of missing values and errors.

4 Festival Library Dataset

The Festival Library Dataset consists of a data scheme image file, one codebook and one dataset, all in csv format.

The codebook (csv file “4_codebook_festival-library_dataset”) offers a detailed description of all variables within the Library Dataset. It lists the definition of variables, such as location and festival name, and festival categories,

Dataset Description: Movie Box Office Earnings by Release Group

Overview This dataset contains box office earnings for various movies, categorized by their release group. The data includes details on domestic and foreign earnings, along with their respective percentages of total earnings. Each entry represents a specific movie, providing a comprehensive view of its financial performance both in domestic and international markets.

Use Cases

This dataset can be used for various analyses, such as:

• Box Office Trends: Analyzing trends in domestic and international box office earnings over the years.
• Market Analysis: Understanding how different markets contribute to a movie's overall financial success.
• Comparative Studies: Comparing the performance of different movies or franchises across various regions. ## Potential Applications
• Predictive Modeling: Predicting box office performance for upcoming movie releases.
• Revenue Optimization: Identifying key markets for targeted marketing and distribution strategies.
• Historical Analysis: Studying how the movie industry's focus on domestic vs. international markets has evolved over time.

🎬 Top Movies Ultimate Dataset

📌 Dataset Overview

This dataset provides a comprehensive collection of financial and performance metrics for 6,500+ movies, scraped from The Numbers. It includes key details such as production budget, box office revenue (domestic & international), estimated DVD/Blu-ray sales, release dates, ratings, and more.

Designed for film industry analysis, revenue forecasting, and data-driven insights, this dataset offers a deep dive into Hollywood's box office performance.

🏆 Source

The data was scraped from The Numbers, a well-known website for movie financial data. The dataset covers thousands of movies, including major blockbusters, indie films, and international releases.

📊 Data Fields

Here’s a breakdown of the columns available in this dataset:

🎥 General Movie Information

• ID – Unique identifier for each movie
• Movie Name – Title of the movie
• Release Date – Date when the movie was released in theaters
• MPAA Rating – Age classification (e.g., PG, R, etc.)
• Running Time (minutes) – Duration of the movie
• Franchise – The movie's franchise (if applicable)
• Keywords – Keywords describing the movie’s themes

💰 Financial Performance

• Production Budget (USD) – Estimated cost to produce the movie
• Domestic Gross (USD) – Total earnings in the US & Canada
• Worldwide Gross (USD) – Total earnings across all regions
• Domestic Box Office (USD) – Domestic Gross
• International Box Office (USD) – Revenue from international markets
• Worldwide Box Office (USD) – Total worldwide earnings
• Infl. Adj. Dom. BO (USD) – Domestic revenue adjusted for inflation
• Opening Weekend (USD) – Revenue generated during the opening weekend
• Legs – A measure of how well a movie performed after its opening weekend (domestic box office/biggest weekend))

📀 Home Video & Digital Sales

• Est. Domestic DVD Sales (USD) – Estimated DVD sales revenue
• Est. Domestic Blu-ray Sales (USD) – Estimated Blu-ray sales revenue
• Total Est. Domestic Video Sales (USD) – Combined video sales revenue

🎭 Production & Genre Details

• Source – Whether the movie is an original screenplay, adaptation, etc.
• Genre – Primary genre (e.g., Action, Comedy, Drama)
• Production Method – Live-action or animated
• Creative Type – Storytelling category (e.g., Fantasy, Superhero, Historical)
• Production/Financing Companies – Studios involved in production
• Production Countries – Countries where the movie was produced
• Languages – Languages spoken in the movie

🎟️ Theatrical & Digital Releases

• Domestic Releases – Number of domestic releases
• International Releases – Number of international releases
• Theater Counts – Number of theaters the movie was released in
• Domestic Share Percentage – Domestic box office share of total revenue
• Video Release Date – Date when the movie was released for home viewing

🔗 Additional Information

• Movie URL – Direct link to the movie’s page on The Numbers

🔥 Potential Uses

This dataset can be used for:
✅ Box Office Predictions – Predicting movie revenue based on historical data
✅ Market Trends Analysis – Analyzing trends in production budgets and earnings
✅ Movie Comparisons – Comparing performance across genres, franchises, and studios
✅ Financial Modeling – Creating models for investment in films
✅ Exploratory Data Analysis (EDA) – Discovering insights into movie performance

🛠 Data Cleaning

The dataset has been processed to remove duplicates, standardize currency values, and handle missing data where applicable. Numeric values have been formatted consistently, and categorical fields have been standardized.

⚠️ Limitations

• Dynamic Data – Box office earnings, streaming revenue, and rankings may change over time.
• Missing Values – Some movies have incomplete financial data, especially older or lesser-known titles.
• Inflation Adjustments – Only domestic box office earnings have inflation-adjusted values but using it you can calculate the inflation and get other inflation-adjusted earnings if you want.

📌 Note to Users

1. "Gross" vs. "Box Office" Columns:

   • Columns like Domestic Gross (USD) and Domestic Box Office (USD) (and their worldwide counterparts) contain identical values. This reflects source conventions (The Numbers), where terms are used interchangeably.
   • Tip: Use either column for analysis. Future updates may consolidate these.

2. Worldwide Gross (US...

Throughout 2024, movie theaters in the United States and Canada managed to sell around 760.5 million tickets, which is less than the 2023 figure of about 940 million tickets. Furthermore, the 2024 figure falls short of the nearly 1.23 billion movie tickets sold in 2019, before the COVID-19 pandemic hit the world. Cinema's slow recovery after the coronavirus The pandemic also impacted cinemas' finances. The revenue at the so-called North American box office – consisting of Canada and the U.S. – stood just below 8.6 billion dollars in 2024. That value remained well under the figure recorded in 2019. The box office revenue in Canada alone struggled even more, adding up to 600 million Canadian dollars in 2023, barely more than half the value reported four years earlier. Newer audiences and how to attract them The coronavirus outbreak has not changed the fact that teenagers watched the highest number of movies per year in the U.S. The 12-17 age group watched, on average, 2.5 feature films at a movie theater in 2021, or five times more than moviegoers aged 60 and above. According to a 2022 survey, the availability of films is what most motivated Gen Zers to attend movie theaters.

As of November 2025, the most expensive production of all time was the seventh episode of the Star Wars movies, "Star Wars: The Force Awakens," directed by J.J. Abrams. The movie was produced and financed by Lucasfilm and Bad Robot and cost approximately 533.2 million U.S. dollars to make. The movies "Avatar: The Way of Water," "Avengers: Endgame," and "Mission: Impossible—The Final Reckoning" each cost around 400 million U.S. dollars to produce. The (not so) hidden cost of a movie A high budget suggests that the studio behind the film believes the movie will be so profitable it will far surpass its pricey costs. But the figure covers only production-related costs. The largest film production companies often invest big sums of money in marketing to promote their new releases. The advertising expense of the Walt Disney Company, for instance, reached 6.1 billion U.S. dollars in 2024. Go big film budget or go home Similarly, Sony's annual advertising costs amounted to billions of dollars in the past few years. The Japanese holding company runs Columbia, one of the leading studios both in the United States and worldwide. Publicizing a big-budget movie may pay off. Many of the titles in this ranking are also among the world's highest-grossing films of all time, including "Avatar" (2009), "Avengers: Endgame" (2019), and "Titanic" (1997).

This dataset offers a wide-ranging collection of movie information, featuring essential details like movie titles, URLs to IMDb pages, release years, genres, and ratings. Additional data include synopses, director names, leading actors, and links to IMDb images. Compiled from IMDb, this dataset is perfect for anyone interested in movie analytics, trend analysis, or creating data-driven applications related to the film industry.

Metadata for the IMDb Movies Dataset

Certainly! Here's the updated description for the "About Dataset" section with the information about the data being scraped using Scrapy:

About the Dataset

• Title: IMDb Movies Dataset
• Subtitle: Comprehensive Movie Data: Titles, Ratings, and More
• Description: This dataset contains detailed information about movies, meticulously compiled using the Scrapy web scraping framework. It features titles, IMDb page links, images, release years, genres, ratings, and additional movie details. Each entry includes synopses, directors, and main actors, making it an invaluable resource for movie analytics and applications in the film industry.
• Keywords: Movies, Film, IMDb, Ratings, Directors, Actors, Analytics, Cinema, Data Science, Web Scraping, Scrapy
• Data Source: IMDb (Internet Movie Database)
• Date of Data Retrieval: Specify the exact date you compiled the data.
• Data Coverage: Movie details from various years, primarily focusing on recent releases.
• Geographical Coverage: Global (Movies from various countries and in multiple languages)
• Temporal Coverage: The dataset focuses on movies released in recent years (e.g., 2020-2022).
• Data Format: CSV
• Dataset Size: 54 MB
• Number of Rows: 40k
• Number of Columns: 20
• Columns and Descriptions:
  • Title: Name of the movie
  • Title_URL: IMDb URL of the movie
  • Image: Link to the movie’s poster image
  • listeritemimage_URL: Alternative IMDb URL for the movie
  • listeritemindex: Index number of the movie in the dataset
  • Year: Year of movie release
  • certificate: Movie certification (rating by age suitability)
  • Time: Runtime of the movie
  • genre: Genre(s) of the movie
  • Rating: IMDb rating of the movie
  • Score: Metascore of the movie
  • Synopsis: Brief description of the movie plot
  • Content: Detailed content including director and key actors
  • Director: Director(s) of the movie
  • Content4, Content6, Content8, Content10: Names of key actors in the movie
  • Votes: Number of votes the movie has received on IMDb
  • Gross: Gross earnings of the movie (if available)
• License: Specify the license (e.g., CC BY-SA, GPL, etc.) or state if the data is proprietary.
• Creator/Contributor: Subashanan Nair
• Version: V1
• Update Frequency: Sporadically.

A Movies Dataset is a comprehensive collection of data related to films, often used for analysis, research, and building recommendation systems. This dataset typically includes a wide array of information about movies, providing detailed insights into various aspects of the film industry. Here’s

# ID: A unique identifier assigned to each movie in the dataset. This ID helps distinguish individual films and is useful when linking the movie to other associated datasets or information such as cast, crew, or reviews. # Title:

The official title of the movie. This field includes the name under which the film was released, and it might include original titles for international films.

# Overview: A brief summary or synopsis of the movie. This field provides an overview of the film's storyline, giving a concise description of the plot, themes, or major events without giving away major spoilers. Release Date:

The official date when the movie was first released to the public. This can be either a theatrical release date or a digital/streaming release date, depending on the distribution strategy.

# Popularity: A numerical score that represents the movie's overall popularity. This metric is typically based on factors such as viewership, audience interaction, and social media activity. It can fluctuate over time as new data becomes available.

# Vote Average: The average rating of the movie, calculated based on the votes or ratings submitted by users. This value represents how well the movie has been received by the audience, usually on a scale of 1 to 10.

# Vote Count: The total number of user votes or ratings submitted for the movie. This field provides insight into the level of audience engagement, showing how many people have rated the film. These columns collectively offer essential information for analyzing and comparing movies based on their release, audience reception, and popularity.

Description

The dataset is a thorough compilation of data about movies that includes both qualitative and quantitative characteristics. Every record reflects a distinct film and offers information about its identity, genre, release date, country of production, financial performance, audience reaction, and major players.

Important points to note are:

Identifiers and Metadata:Unique movie IDs, titles, genres, and release details.

Financial Indicators:Early sales performance, domestic (U.S.) and international box office performance, and budget.

Audience and Critical Reaction:Rotten Tomatoes scores, IMDb ratings, and the number of votes that go along with them.

Contributors:Details about the director and the main actor.

This dataset works well with:

Exploratory Data Analysis (EDA):Finding trends in the popularity, profitability, and creation of films.

Predictive Modeling:Forecasting financial success, ratings, or audience engagement.

Comparative Studies: Examining patterns in various genres, nations, and years of publication.

Recommendation systems use votes and ratings to sugest films.

This dataset offers a solid basis for data analysis, visualization, and machine learning applications in the film industry by providing a well-balanced combination of descriptive, financial, and evaluative information.

Box Office Performance of Movies with Female Leads

An Exploration of Gender in Film

By Carolee Mitchell [source]

About this dataset

This dataset explores the impact of gender on box office success. It looks at data from films released between 1970 and 2013, including their budgets, domestic and international gross earnings, and their pass/fail status on the Bechdel Test - a benchmark measuring gender biases in movies.

The dataset gives us an opportunity to investigate how men and women are portrayed in films over time, explore gendered earning gaps in Hollywood, answer questions about movie trends through the decades such as whether or not films with strong female roles outgross those without them when adjusted for inflation. We can also uncover correlations between movie ratings on IMDB and whether or not they pass the Bechdel Test as well as look at patterns based on MPAA ratings classification systems around film content that could influence box office performance. So dive into this set to discover what stories gender representation tells us about film success!

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How to use the dataset

This dataset contains information about the box office performance of over 2000 movies released between 1970 and 2013. The data includes budget, gross domestic and international earnings, IMDB ratings, Bechdel Test pass/fail status, and release period. This dataset is a great resource for anyone interested in exploring the impact of gender on box office performance or studying trends within the movie industry over time.

Here is how to use this dataset: - Start by exploring the data yourself to get a sense of what is in here. Look for any questions that come up or interesting patterns you notice. - Take a look at different columns in the dataset such as budget, gross domestic/international earnings, IMDB rating, etc., to get an idea of how these factors might be related to each other and if there are any correlations between them. - Filter out any data entries that don’t fit your needs - remember that this dataset contains movies released between 1970 and 2013 so make sure you adjust your filters accordingly if you need more recent films! - Once you are happy with your selection criteria then do some additional digging by sorting through other relevant columns like “period code” or “decade code” which can give you insight into larger trends within the movie industry over time (such as when certain genres became popular). This can also help you focus your results on an even more specific selection of films than before! - Now it’s time to start making conclusions from all this data - look for relationships between different variables like genders roles being portrayed in films and its impact to overall financial success (budget vs money made). Also try comparing statistics from before/after particular periods where large leaps have been made regarding female representation on screen (1990s feminism for example). With all these pieces together it should become clear what type of stories these numbers are telling us about gender roles throughout Hollywood history!

Research Ideas

• Analyzing the impact of female-led movies on box office success over time.
• Comparing the average budget, domestic, and international gross of movies that pass the Bechdel Test versus those that do not.
• Examining how budget and other film industry factors (IMDb ratings, MPAA rating) are related to box office performance for movies with different levels of gender representation

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: movies.csv | Column name | Description | |:-------------------|:-----------------------------------------------------------...

Throughout 2024, cinemas across the United Kingdom sold approximately ****** million tickets, up from ****** million movie tickets a year earlier. Yet the 2024 figure is still under the number of admissions recorded in 2019: in the last year before the COVID-19 outbreak, movie theaters in the UK sold more than *** million tickets. Ticket price & box office revenue Despite the decrease in the number of movie tickets sold since the coronavirus spread, the average price of a cinema admission in the UK reached **** British pounds in 2024. But this was not enough to push the British box office revenue even further. In 2024, the figure stood at about *** million pounds, less than the ****-billion-pound box office revenue recorded in 2019. Success abroad According to another source, the United Kingdom/Ireland region is among the top five box office markets worldwide, with a revenue of approximately **** billion U.S. dollars in 2024. Yet the impact of British cinema knows no boundaries. That same year, the global box office revenue of films made in the UK added up to *** billion dollars, almost the double of the *** billion dollars amassed in the previous year.

As of January 2025, the four biggest movie flops of all time were Disney productions. "Turning Red" (2022), "Jungle Cruise" (2021), "Mars Needs Moms" (2011), and "Mulan" (2020) recorded losses of more than *** million U.S. dollars at the global box office. Film releases and bad timing Out of the ** movies listed, ***** were released in or after March 2020, a month remembered for the worldwide outbreak of COVID-19. These films went big with the budget but went literally home as streaming became a safer bet for movie premieres amidst the pandemic. The coronavirus impacted movie ticket sales worldwide. In 2021, the box office revenue in the United States and Canada amounted to less than ** percent of the value reported in 2019, before the outbreak. The largest cinema market on the planet seems to have experienced a swifter recovery. China saw its box office revenue add up to **** billion yuan in 2021, or almost ** percent of the 2019 figure. Can Disney afford flopping at the box office? Although the media Leviathan had also struggled during the core months of the COVID-19 outbreak, the Walt Disney Company's revenue started to bounce back and continues to amount to dozens of billions of dollars. In the meantime, the corporation invested even more in publicizing its new releases. Disney's advertising spending stood at *** billion dollars in 2021 – a value almost *** times higher than the ad expense recorded in 2019. The expansion indicates that the company could manage eventual failures at the box office while profiting from its blockbusters and non-film-related divisions.