The statistic above presents data on movie releases in North America between 1980 and 2017. In 2012, 668 movies were released, up from 602 a year earlier. The year 2017 was the most productive so far, with 724 movie releases.

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.

These datasets contain complete genre, cast, director, and producer information about 15,425 U.S.-produced movies released between 1894 and 2011.The initial movie year, title, and genre information was obtained by Wasserman et al. (Cross-evaluation of metrics to estimate the significance of creative works, PNAS, 2015) from IMDb.com That dataset was expanded by Moreira et al. (forthcoming, 2017) to include movie budget, gender composition, cast, director, and producer information.Assigning gender to individualsThe gender of actors is explicitly mentioned in their individual biographical pages, thus we are able to fully determine their gender. For producers and directors that do not also have acting credits, we use indirect methods to assign a gender. If present, we parse the individual's biographical text for gender-specific pronouns (he/his/him/himself, or she/her/hers/herself). If the number of (male-) female-specific pronouns exceeds that of (female-) male-specific ones, we assume the individual is a (male) female. If the previous attempt is inconclusive, we use the Python package gender-guesser (version 0.4.0) to "guess" the gender based on the first name of the individual. The output of gender-guesser is one of "female", "mostly female", "androgynous", "unknown", "mostly male", or "male". We only assign a gender if the guess is either "male" or "female". If we still have not been able to assign a gender, we try to find a photograph of the individual. If all attempts fail, we mark the individual's gender as "undetermined".actors.json - Contains the following information about 225,754 actors:_id - unique IMDb identifier of individual.name - individual's namegender - individual's genremovies_list - list of movie ids individual was cast in. Matches _id field in movies.json.directors.json - Contains the following information about 6,895 directors:_id - unique IMDb identifier of individual.name - individual's name movies_list - list of (year, movie_id, type) triplets. type is one of 'director', 'main_casting', or 'secondary_casting'. Remaining fields match year, _id, from movies.json gender - director gender: male, female, or undeterminedfirst_movie - year of first movie directed.last_movie - year of last movie directed.male_count - Number of male-specific pronouns (he/his/him/himself) from director's IMDb bio page.female_count - Number of female-specific pronouns (she/her/hers/herself) from director's IMDb bio page.actor_credits - True (False) if director has (does not have) "Actor" credits in IMDb filmography.actress_credits - True (False) if director has (does not have) "Actress" credits in IMDb filmography.movies.json - Contains the following information about 15,425 movies:_id - unique IMDb identifier of movie.adjusted_budget - movie budget, if present in IMDb, adjusted for 2014 inflation. Only present for about 36% of movies.all_actors - list of (gender, url, name) triplets for each actor in cast. Each triplet matches gender, _id, and name from movies.json, respectively. director - list of (name, url, type, gender) quadruplets for each director in the movie. type is one of 'director', 'main_casting', or 'secondary_casting'. Remaining fields match name, _id, and gender from directors.json, respectively.producer - list of (name, url, role, gender) quadruplets for each producer in the movie. role indicates specific producer role: producer, associate producer, executive producer, line producer, etc. Remaining fields match name, _id, and gender from producers.json.gender_percent - integer percent of female actors in movie.genre - list of movie genres.year - year when movie was released.title - title of movie.producers.json - Contains the following information about 25,557 producers:_id - unique IMDb identifier of individual.name - individual's name movies_list - list of (role, year, movie_id) triplets. role indicates specific producer role: producer, associate producer, executive producer, line producer, etc. Remaining fields match year, _id, from movies.json gender - producer gender: male, female, or undeterminedfirst_movie - year of first movie produced as any producer role.last_movie - year of last movie produced as any producer role.first_producer_movie - year of first movie produced as a "producer". Only present if individual has at least one credit as "producer".last_producer_movie - year of last movie produced as a "producer". Only present if individual has at least one credit as "producer".first_executive_movie - year of first movie produced as an "executive producer". Only present if individual has at least one credit as "executive producer".last_executive_movie - year of last movie produced as an "executive producer". Only present if individual has at least one credit as "executive producer".first_associate_movie - year of first movie produced as an "associate producer". Only present if individual has at least one credit as "associate producer".last_associate_movie - year of last movie produced as an "associate producer". Only present if individual has at least one credit as "associate producer".male_count - Number of male-specific pronouns (he/his/him/himself) from producer's IMDb bio page.female_count - Number of female-specific pronouns (she/her/hers/herself) from producer's IMDb bio page.actor_credits - True (False) if producer has (does not have) "Actor" credits in IMDb filmography.actress_credits - True (False) if producer has (does not have) "Actress" credits in IMDb filmography.

Movie Gross and Ratings

A Study of the Impact of Movies on Profitability and Popularity

By Yashwanth Sharaff [source]

About this dataset

This dataset of top20 movies offers insights on how the movie industry has evolved over two decades. With data on titles, MPAA ratings, budgets, grosses, release dates and genres this comprehensive dataset allows you to explore the film industry's most popular films and trace patterns in movie profits and ratings across time. Analyze how genre types have resonated with audiences, or take a closer look at the characteristics of movies that were highly rated by viewers. With more than three hundred movies featured in this dataset Movie Profits and Ratings acts as both an exploration into the history of film for novices looking for an introduction to popular films as well as a powerful tool for experienced data scientists interested in trend analysis of film industry data

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset is a great tool for analyzing the gross and ratings of movies released. With this data, we can learn more about the success of a movie. By exploring this dataset, we can answer questions such as which movies have been most profitable or what types of movies had the highest ratings.

Research Ideas

• Creating a tool that easily creates movie trailers without any manual editing and use a prediction algorithm to suggest the best trailer based on previously existing ones of similar genres and rating.
• Analyzing the data to detect trends in ratings and gross/budget over time, allowing businesses to adjust strategies accordingly.
• Developing an application that allows users to easily search for movies by genre, rating, runtime, budget and recommend movies based on their past choices or those with similar ratings from other users

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: Movies_gross_rating.csv | Column name | Description | |:-----------------|:-------------------------------------------------------------------------------| | Title | The title of the movie. (String) | | MPAA Rating | The Motion Picture Association of America (MPAA) rating of the movie. (String) | | Budget | The budget of the movie in US dollars. (Integer) | | Gross | The gross of the movie in US dollars. (Integer) | | Release Date | The date the movie was released. (Date) | | Genre | The genre of the movie. (String) | | Runtime | The length of the movie in minutes. (Integer) | | Rating Count | The number of ratings the movie has received. (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 Yashwanth Sharaff.

Movie Dataset

This dataset contains information about various movies, including details such as the year of release, title, genre, director, actors, plot, language, country, awards, ratings, and IMDb ID. It is designed for use in film analysis, recommendation systems, or as a resource for studying popular culture.

  Dataset Overview

Format: CSV Number of Records: 34 Number of Features: 13

  Features

Year: The year the movie was released. Title: The title of the… See the full description on the dataset page: https://huggingface.co/datasets/ZelonPrograms/MovieDataset.

According to the most recent data, U.S. streaming services released *** domestic-produced movies on their platforms in 2023. This marks a decline from the previous year, when over *** films were released on streaming platforms, such as Disney+ and Netflix.

Upcoming 2020 Hollywood Movies

Calendar, Production Companies, Cast and Crew

By Priyanka Dobhal [source]

About this dataset

This dataset provides a comprehensive list of the top upcoming Hollywood movies of 2021. With detailed information about each movie, including titles, production companies, cast and crew members, and sources for further reference, viewers can stay up to date on what's playing in theaters throughout the year. Discover beloved classics and modern-day blockbusters that will transport viewers to new worlds and stories for hours of entertainment!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

To use this dataset correctly here are the steps you should follow: - Read through the columns of the dataset to understand what is included in them such as “Month”, “Day”, “Title” and other columns to become familiar with the data. - Have an idea about which feature of Hollywood movies that you would like to explore further such as finding movies by a certain actors or directors or producers or release dates etcetera.
- Filter out columns needed and manipulate them according your requirements prior analysing so it will be easier to focus on valuable insights providing columns only that relates to your purpose of exploring according movie features chosen previously (ex; filter out casting director name column if isn’t related). - Analyse each row in dataset required carefully since different rows can provide important pieces of clues regarding movie features selected (ex; month column tend to tell us when a movie is usually released).

5 Once all analysis has been done feel free utilize visuals so we can draw significance relationships more efficiently between different categorical/numerical variables using charts & graphs etcetera .

6 Finally make sure that collected information relate directly towards problem statement given by conducting thorough validations from obtained results from above steps giving reliable & correct available insights related feature chosen initially making sense in context subjective scenario at hand

Research Ideas

• Creating a timeline view of the up-coming Hollywood movie releases and their associated cast, crew and production company data.
• Using production company data to analyze what genres, actors, and directors are popular this year.
• Utilizing the cast and crew data to display the most experienced actor or filmmaker within each movie

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: WIki_Movies.csv | Column name | Description | |:-----------------------|:-------------------------------------------------------------------------------| | Month | The month in which the movie is scheduled to be released. (String) | | Day | The day of the month in which the movie is scheduled to be released. (Integer) | | Title | The title of the movie. (String) | | Production company | The production company responsible for the movie. (String) | | Cast and crew | The names of the cast and crew involved in the movie. (String) | | Ref | The source from which the data was collected. (String) |

File: Hollywood Movies - 2020.csv | Column name | Description | |:-----------------------|:-----------------------------------------------------------------------| | Title | The title of the movie. (String) | | Production company | The production company responsible for the movie. (String) | | Cast and crew | The names of the cast and crew involved in the movie. (String) | | Opening | The date the movie is scheduled to be released in the US. (Date) | | Opening2 | The date the movie is scheduled to be released internationally. (Date) | | Ref. | The source from which the data was collected. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Priyanka Dobhal.

This dataset includes a csv of a list of more than 17K movies scraped from Wikipedia's list of American films tables for the years 1950-2020 (see https://en.wikipedia.org/wiki/Lists_of_American_films).

Analysis of ‘Hollywood Theatrical Market Synopsis 1995 to 2021’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/johnharshith/hollywood-theatrical-market-synopsis-1995-to-2021 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

https://images7.alphacoders.com/116/thumb-350-1165584.jpg" alt="Hollywood Films">

Context

This Dataset contains the data of market analysis built on The Numbers unique categorization system, which uses 6 different criteria to identify a movie. All movies released since 1995 are categorized according to the following attributes: Creative type (factual, contemporary fiction, fantasy etc.), Source (book, play, original screenplay etc.), Genre (drama, horror, documentary etc.), MPAA rating, Production method (live action, digital animation etc.) and Distributor. In order to provide a fair comparison between movies released in different years, all rankings are based on ticket sales, which are calculated using average ticket prices announced by the MPAA in their annual state of the industry report.

Content

The Dataset contains various files illustrating statistics such as annual ticket sales, highest grossers each year since 1995, top grossing creative types, top grossing distributors, top grossing genres, top grossing MPAA ratings, top grossing sources, top grossing production methods and the number of wide releases each year by various distributors.

Acknowledgements

The data was obtained from The Numbers website. Their theatrical market pages are based on the domestic theatrical market performance only. The domestic market is defined as the North American movie region (consisting of the United States, Canada, Puerto Rico and Guam). This data can be found from the website https://www.the-numbers.com/market/ with detailed analysis.

Inspiration

2020 and 2021 have been rough years for the movie industry, and being a huge movie fanatic inspired me to share a dataset showing the exponential growth of box office collections as well as ticket sales over time (and the decline after 2020 due to the Covid-19 pandemic) indirectly indicating the quality of modern day films. This Dataset can also be used to study the genres which attract audience the most and encourage one to create an amazing genre specific plot in order to take one step closer to becoming the next most successful director!

--- Original source retains full ownership of the source dataset ---

Author: Víctor Yeste. Universitat Politècnica de Valencia.This work is an exploratory, quantitative, and not experimental study with an inductive inference type and a longitudinal follow-up. It analyzes movie data and tweets published by users using the official Twitter hashtags of movie premieres the week before, the same week, and the week after each release date.The scope of the study is the collection of movies released in February 2022 in the USA, and the object of the study includes them and the tweets that refer to the film in the 3 closest weeks to their premiere dates. The tweets recollected were classified by the week they were published, so they are classified by a time dimension called timepoint. The week before the release date has been designated as timepoint 1, the week of the release date is timepoint 2, and the week immediately afterward is timepoint 3. Another dimension that has been considered is if the movie has domestic production or not, which means that if one of the countries of origin is the United States, the movie is designated as domestic.The chosen variables are organized in two data tables, one for the movies and one for the collected tweets.Variables related to the movies:id: Internal id of the moviename: Title of the moviehashtag: Official hashtag of the moviecountries: List of countries of the movie, separated by a semicolonmpaa: Film ratings system by the Motion Picture Association of America. It is a completely voluntary rating system and ratings have no legal standing. The currently rating systems include G (general audiences), PG (parental guidance suggested), PG-13 (parents strongly cautioned), R (restricted, under 17 requires accompanying parent or adult guardian) and NC-17 (no one 17 and under admitted)(Film Ratings - Motion Picture Association, n.d.)genres: List of genres of the movie, e.g., Action or Thriller, separated by a semicolonrelease_date: Release date of the movie in a format YYYY-MM-DDopening_grosses: Amount of USA dollars that the movie obtained on the opening date (the first week after the release date)opening_theaters: Amount of USA theaters that released the movie on the opening date (the first week after the release date)rating_avg: Average rating of the movieVariables related to the tweets:id: Internal id of the tweetstatus_id: Twitter id of the tweetmovie_id: Internal id of the movietimepoint: Week number related to the movie premiere that the tweet was published on. “1” is the week before the movie release, “2” is the week after the movie release” and “3” is the second week after the movie release.author_id: Twitter id of the author of the tweetcreated_at: Date and time of the tweet, with format “YYYY-MM-DD HH:MM:SS”quote_count: Number of the tweet’s quotesreply_count: Number of the tweet’s repliesretweet_count: Number of the tweet’s retweetslike_count: Number of the tweet’s likessentiment: Sentiment analysis of the tweet’s content with a range from -1 (negative) to 1 (positive)This dataset has contributed to the elaboration of the book chapters:Yeste, Víctor; Calduch-Losa, Ángeles (2022). Genre classification of movie releases in the USA: Exploring data with Twitter hashtags. In Narrativas emergentes para la comunicación digital (pp. 1012-1044). Dykinson, S. L.Yeste, Víctor; Calduch-Losa, Ángeles (2022). Exploratory Twitter hashtag analysis of movie premieres in the USA. In Desafíos audiovisuales de la tecnología y los contenidos en la cultura digital (pp. 169-187). McGraw-Hill Interamericana de España S.L.Yeste, Víctor; Calduch-Losa, Ángeles (2022). ANOVA to study movie premieres in the USA and online conversation on Twitter. The case of rating average using data from official Twitter hashtags. In El mapa y la brújula. Navegando por las metodologías de investigación en comunicación (pp. 151-168). Editorial Fragua.

Wikipedia Movie Plots with Images.

30,000+ movies plot descriptions and images. Plot summary descriptions of movies scrapped from Wikipedia. Dataset is subset of this dataset.

  Content

The dataset contains descriptions of 34,886 movies from around the world. Column descriptions are listed below: Release Year - Year in which the movie was released Title - Movie title Origin/Ethnicity - Origin of movie (i.e. American, Bollywood, Tamil, etc.) Director - Director(s) Genre -… See the full description on the dataset page: https://huggingface.co/datasets/Coder-Dragon/wikipedia-movies.

This is a dataset comprising the technical data and the data from user reviews and score corresponding to the top 250 IMDb movies at April 13th, 2021. The fields are as follows:

Name: Name of the film.

Score: Mean score of the users in IMDb.

Genre1: Most important genre of the film (IMDb classifies each film with up to 3 main genres).

Genre2: 2nd most important genre of the film (IMDb classifies each film with up to 3 main genres).

Genre3: 3rd most important genre of the film (IMDb classifies each film with up to 3 main genres).

Duration: Duration of the film in minutes.

Release: Release date of the film (formatted as 13 April 2021).

Rating: Age rating of the film in the US.

Country: Filming country.

Language: Main language.

Sound: Sound technology (usually there are more than just one, separated by |).

Color: Color o B/W.

Ratio: Aspect ratio of the film (format X : 1).

Budget: Budget of the film in the country's own currency (formatted $100,000,000 or GBP100,000,000, with ISO code if the currency is not the US dollar).

Gross: Worldwide gross of the film in US dollars (format $100,000,000).

BadReviews: Number of user reviews with a score between 1 and 4.

NeutralReviews: Number of user reviews with a score between 5 and 7.

GoodReviews: Number of user reviews with a score between 8 and 10.

The data is shared with a license CC BY-NC-SA 4.0.

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.

This table contains 56 series, with data for years 2006 - 2011 (not all combinations necessarily have data for all years), and was last released on 2015-07-28. This table contains data described by the following dimensions (Not all combinations are available): Geography (14 items: Canada; Prince Edward Island; Nova Scotia; Newfoundland and Labrador ...), North American Industry Classification System (NAICS) (1 items: Motion picture and video production ...), Summary statistics (4 items: Operating revenue; Operating profit margin; Salaries; wages and benefits; Operating expenses ...).

Movie Data Scraper

  ⚠️ SOME DATA IS MESSED UP ⚠️

some of the colums aren't properly registered with huggingface, so be careful with using this data.

  Overview

This Project Scraped movies from 1990-2003 (due to api limitations) and has popular movies.

  Data Structure

The resulting Data file will show the following columns:

Title: The title of the movie. Year: The release year of the movie. Genre: The genre(s) of the movie. Director: The director of the… See the full description on the dataset page: https://huggingface.co/datasets/ZelonPrograms/PopularMovieDataset.

We are sharing a dataset that contains a collection of tweets generated as reactions of the release of 50 different movies. The dataset can be used for gaining useful insights regarding the conversation that is generated around a particular movie. It is particularly suitable for conducting sentiment analysis and other NLP techniques. The dataset contains approximately 2.5 million tweets with their related meta data and cover 50 movies. For each movie, its IMDb rating is included. The movies are the 25 releases with the highest number of votes during 2020 and 2021. The collected tweets represent the reactions of the twitter community during the first week of the release date in US of that particular movie. The tweets per movie ranged from 1.000 to approximately 200.000 tweets with an average of 50.000 per release. We used The Internet Archive Wayback Machine in order to retrieve the IMDb movie rating after one week of the US release date. The tweets and related metadata have been collected using the Tweet Downloader tool. Contact at Tilburg University: Francesco Lelli

Dataset Card for "imdb"

  Dataset Summary

Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.

  Supported Tasks and Leaderboards

More Information Needed

  Languages

More Information Needed

  Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/imdb.

Pixar Movies Dataset

A comprehensive dataset of Pixar movies, including details on their release dates, directors, cast, box office performance, and ratings. This dataset is gathered from official sources, including Pixar, Rotten Tomatoes, and IMDb. For more information, visit Pixar.

  How the Data is Compiled

All information in this dataset has been collected from public sources, including official information from Pixar, Rotten Tomatoes, and IMDb. Cells are each… See the full description on the dataset page: https://huggingface.co/datasets/RummageLabs/pixar_movies.

sample_mflix.embedded_movies

This data set contains details on movies with genres of Western, Action, or Fantasy. Each document contains a single movie, and information such as its title, release year, and cast. In addition, documents in this collection include a plot_embedding field that contains embeddings created using OpenAI's text-embedding-ada-002 embedding model that you can use with the Atlas Search vector search feature.

  Overview

This dataset offers a… See the full description on the dataset page: https://huggingface.co/datasets/MongoDB/embedded_movies.

This table contains 21 series, with data for years 2007 - 2011 (not all combinations necessarily have data for all years), and was last released on 2015-07-27. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), North American Industry Classification System (NAICS) (1 items: Motion picture and video distribution ...), Industry expenditures (21 items: Total operating expenses; Commissions paid to non-employees; Professional and business services fees; Salaries; wages and benefits ...).