What Time Does Marcus Theater Close?

26.07.2023 0 Comments

How long does a movie last at the theater?

Typically, a movie will premiere in theaters and stay for about three to four weeks. During this time, the film will experience its highest attendance as movie-goers rush to catch the latest blockbuster on the big screen.

How long is a movie preview?

The exact time when the actual movie starts can vary depending on the theater and the specific screening, but generally, movie theaters will show 15-20 minutes of trailers and advertisements before the feature presentation begins. This is often referred to as ‘pre-show’ content.

What is real 3D in theaters?

Technology – RealD 3D cinema technology is a that uses light to produce projection. The advantage of circular polarization over is that viewers are able to tilt their head and look about the theater naturally without seeing double or darkened images. However, as with other systems, any significant head tilt will result in incorrect and prevent the brain from correctly fusing the stereoscopic images.

The high-resolution, grade alternately projects right-eye frames and left-eye frames, switching between them 144 times per second. The projector is either a device or ‘s reflective LCOS (). A push-pull modulator called a is placed immediately in front of the projector lens to alternately polarize each frame.

It circularly polarizes the frames clockwise for the right eye and counter-clockwise for the left eye. The audience wears that have oppositely that ensures each eye sees only its designated frame. In RealD Cinema, each frame is projected three times to reduce flicker, a system called triple flash.

  • The source video is usually produced at 24 frames per second per eye (total 48 frames/s), which may result in subtle and stuttering on horizontal camera movements.
  • A is used to maintain the light polarization upon reflection and to reduce reflection loss to counter some of the significant light loss due to polarization filter absorption.

The result is a 3D picture that seems to extend behind and in front of the screen itself.

Can movies be 3 hours?

Stop Complaining, Some 3 Hour Movies Don’t Need to Be Cut Short I love a good three-hour movie. Existing as an adult is so busy with so many people, responsibilities, and worries always nipping at your heels. It’s often hard to stay focused in one place for too long.

  1. With a three-hour movie, though, I finally get to firmly root my feet in one spot for a prolonged period of time, especially if I’m watching it in a theater.
  2. That alone is a glorious experience while the way certain narratives just get extra absorbing when stretching on for so long is a similarly extraordinary thing to witness.

I may be ride-or-die for, but that’s not an opinion shared by everyone. In fact, in many cases, three-hour-plus movies have gotten a bad rap, with people turning down the opportunity to watch such a feature no matter what its plot or cast is. The very idea of sitting so long is an immediate turn-off.

  • This perception has become so widespread that it even crept into the 94th Academy Awards.
  • This was when Amy Schumer made a lengthy joke at the expense of The Power of the Dog mocking its runtime as too long despite the Jane Campion feature running for only 126 minutes, or just four minutes longer than Sonic the Hedgehog 2,

A lot of the derogatory commentary on longer movies can get pretty ridiculous. However, it’s valid for contrary takes on these kinds of narratives to exist, just as it’s valid to examine the various intrinsic values in letting movies go on for as long as they need to.

Do movies start 15 minutes after?

General Movie Info – Does the runtime shown for each movie include trailers? No. The listed runtime is the duration of the feature film. The feature film does not start at the published showtime. There are approximately 20 minutes of preshow material, including trailers, between the published showtime and the start of the feature film.

Was this information helpful? Where can I find MPAA movie ratings information? Was this information helpful? What does advertised showtime mean? The advertised showtime reflects the time that trailers, policy announcements and occasional public service announcements will begin. We consider movie trailers to be an integral part of the overall show.

The listed runtime for each feature film does not include approximately 20 minutes of this preshow material. Was this information helpful? What movies are playing right now at AMC? Was this information helpful? What movies are coming soon to AMC? AMC shows almost every major film release in its theatres as well as many independent releases.

  • For a view of what is coming soon, check out the Coming Soon section on this page to see what is available now.
  • Additionally, be sure to check out our AMC Artisan Films page, which brings a curated gallery of the finest movies to many of our theatres.
  • Was this information helpful? How can I find movie times at AMC? Was this information helpful? What are AMC Policies regarding Rated R titles? Guests under 17 must be accompanied by a guardian who is 21 or older for the duration of the show.

Please be prepared to show ID. Children ages 6 or under are not admitted to R-rated films after 6pm. More information on our policy regarding ratings can be found here, Was this information helpful?

How early should I get to the movies?

Arrive on time – Showing up on time for anything is just common courtesy for everyone around you—and the movie theater is no different. Luckily for people who are habitually late, most theaters list their showtimes for when the commercials and trailers begin—up to 20 minutes before the actual feature film starts. JohnArehart/Shutterstock

How many scenes in a 15 minute film?

End Your Short Film Strong – A memorable ending is a must for your short film. A very common way to accomplish this is to end with a plot twist or a punchline. What this turns out to be is totally dependent on what your story is. One strategy you could employ is to put yourself in the head of a casual viewer.

Ask: how does he or she expect this story to end? And then subvert that expectation. Also, when it comes to endings, feature films are more likely to tie up all loose ends. The audience is investing two hours of their time into the film, so they tend to like to see everything be resolved (unless this ambiguous ending is done particularly well).

Short movies don’t require such a lengthy investment, so they can get away with cliffhanger endings easier. In fact, there is a bit of an expectation that not everything will be resolved. Basically, while feature films are likely to have a fleshed-out falling action and resolution, short films often end at (or right after) the climax of the story.

Why do movies start so late?

The theatre receives cartridges that are put into a large machine. Then a manager or supervisor goes onto the computer and schedules movies for certain times. It’s usually 15-20 minutes of previews, which makes the theatre company money, then the movie starts. Most modern theatres are this way.

What movie is number 1?

All Time Worldwide Box Office

Rank Year Movie
1 2009 Avatar
2 2019 Avengers: Endgame
3 2022 Avatar: The Way of Water
4 1997 Titanic

What does 100 rotten tomatoes mean?

From Wikipedia, the free encyclopedia Rotten Tomatoes logo On the review aggregator website Rotten Tomatoes, a film has a rating of 100% if each professional review recorded by the website is assessed as positive rather than negative. The percentage is based on the film’s reviews aggregated by the website and assessed as positive or negative, and when all aggregated reviews are positive, the film has a 100% rating.

  1. Listed below are films with 100% ratings that have a critics’ consensus or have been reviewed by at least twenty film critics.
  2. Many of these films, particularly those with a high number of positive reviews, have achieved wide critical acclaim and are often considered among the best films ever made,
  3. 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 number 1 film of all time?

Gone with the Wind held the record of highest-grossing film for twenty-five years and, adjusted for inflation, has earned more than any other film. Films generate income from several revenue streams, including theatrical exhibition, home video, television broadcast rights, and merchandising,

However, theatrical box-office earnings are the primary metric for trade publications in assessing the success of a film, mostly because of the availability of the data compared to sales figures for home video and broadcast rights, but also because of historical practice. Included on the list are charts of the top box-office earners (ranked by both the nominal and real value of their revenue), a chart of high-grossing films by calendar year, a timeline showing the transition of the highest-grossing film record, and a chart of the highest-grossing film franchises and series.

All charts are ranked by international theatrical box-office performance where possible, excluding income derived from home video, broadcasting rights, and merchandise. Traditionally, war films, musicals, and historical dramas have been the most popular genres, but franchise films have been among the best performers of the 21st century.

There is strong interest in the superhero genre, with ten films in the Marvel Cinematic Universe featuring among the nominal top-earners. The most successful superhero film, Avengers: Endgame, is also the second-highest-grossing film on the nominal earnings chart, and there are four films in total based on the Avengers comic books charting in the top twenty.

Other Marvel Comics adaptations have also had success with the Spider-Man and X-Men properties, while films based on Batman and Superman from DC Comics have generally performed well. Star Wars is also represented in the nominal earnings chart with five films, while the Harry Potter, Jurassic Park and Pirates of the Caribbean franchises feature prominently.

  • Although the nominal earnings chart is dominated by films adapted from pre-existing properties and sequels, it is headed by Avatar, which is an original work.
  • Animated family films have performed consistently well, with Disney films enjoying lucrative re-releases prior to the home-video era.
  • Disney also enjoyed later success with films such as Frozen and Frozen II, Zootopia, and The Lion King (with its computer-animated remake as the highest-grossing animated film ), as well as its Pixar brand, of which Incredibles 2, Toy Story 3 and 4, and Finding Dory have been the best performers.
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Beyond Disney and Pixar animation, the Despicable Me, Shrek, and Ice Age series have met with the most success. While inflation has eroded the achievements of most films from the 1950s, 1960s, and 1970s, there are franchises originating from that period that are still active.

  • Besides the Star Wars and Superman franchises, James Bond and Godzilla films are still being released periodically; all four are among the highest-grossing franchises.
  • Some of the older films that held the record of highest-grossing film still have respectable grosses by today’s standards, but no longer compete numerically against today’s top-earners in an era of much higher individual ticket prices.

When those prices are adjusted for inflation, however, then Gone with the Wind —which was the highest-grossing film outright for twenty-five years—is still the highest-grossing film of all time. All grosses on the list are expressed in U.S. dollars at their nominal value, except where stated otherwise.

What is XD mean in movies?

What are the benefits of an XD theater? -What Viewers Should Know – Press Release Oct 10, 2022 An XD theater is a movie theater that uses a technology called extreme digital (XD) to project movies. This technology is a type of digital projection that uses a special projector to deliver a sharper image with more vibrant colors.

What is 4D in a movie?

4D film – Wikipedia 3D film with physical effects that occur in the theater 4D venue complete with motion-enhanced seating and multisensory olfactory technology 4D film is a presentation system combining with synchronized physical effects that occur in the theater.

Can I wear 3D glasses over my glasses?

Wear 3D Glass over Eyeglass – The most common and easiest way is to simply wear the 3D glasses over your eyeglasses. This sure is uncomfortable and can be heavy on your ears but the quickest tip to follow. There may even be special 3D glasses available in some theaters that can be worn over regular glasses. The frame size would be slightly bigger to accommodate your eyeglasses in it.

What movie is 7 hours long?

Cinematic films

Title Running time Year released
Melancholia 450 min (7 hr, 30 min) 2008
CzechMate: In Search of Jiří Menzel 448 min (7 hr, 28 min) 2018
Sátántangó 439 min (7 hr, 19 min) 1994
A Tale of Filipino Violence 434 min (7 hr, 14 min) 2022

How big is a 1 hour movie?

Common Video Sizes

Common Name Pixel Size File Size for 1 hour of video
720p 1280 x 720 800 – 900MB
1080p 1920 x 1080 1.2 – 1.4GB
2K 2048 x 1080 2.8 – 3GB
4K 3840 x 2160 20 – 22GB

What is the 20 minute movie rule?

If you’re waiting for film critic Marshall Fine’s review of Ramin Bahrani’s ” At Any Price,” you’re going to be waiting for a long time because it’s not coming. Fine saw the movie at the Toronto Film Festival last fall and walked out — when it violated what he calls ” The 20-Minute Rule :” “It basically says that a movie that hasn’t hooked me in the first 20 minutes probably isn’t going to.

  1. I tend to apply it most forcefully when I’m watching films at festivals or when I’m sorting through DVD (or online) screeners at home.
  2. If nothing’s happening after 20 minutes, sorry, I’m out.” “At this particular point in our cinematic history,” Fine adds, “there isn’t sufficient time to watch all the movies that come my way,” so they’ve got 20 minutes to grab him before he pulls the ripcord.

In the case of “At Any Price,” which current has a B- average from 33 critics in our Criticwire Network, Fine says he sampled (despite not drinking the Kool-Aid over Bahrani’s previous work, “Goodbye Solo”) and didn’t care for it. “After 20 minutes of the kind of obvious melodrama that Bahrani seemed to be dishing up,” he writes, “I’d had enough and walked out.

  1. You’ll undoubtedly read rapturous reviews of this film when it opens Friday; large grains of salt are encouraged.” Back when I was in college, I had to take a class on cultural appreciation.
  2. I don’t remember the exact title of the course, but it was a small seminar of about fifteen people and each week we attended a different kind of performance and wrote about it.

One week we went to the opera, the next the symphony, the next a musical, the last a film. The class was an absolute gimme. There was absolutely no way to fail — except one, by violating the professor’s one rule. “To review something, you first have to watch something,” he told us. Granted, Fine does say in his post that you “can’t really review a movie you haven’t seen all the way through” — although the paragraph describing and dismissing “At Any Price” amounts to about 125 words, which is the length of a capsule review in many print publications these days (I suppose that’s where his use of the word ” really ” between “can’t” and “review” comes in).

So you should probably take his mini-non-review with large grains of salt as well. But let’s consider the larger issue: The 20-Minute Rule as it relates to film viewership, not just film criticism. Is 20 minutes enough time to consider a movie fully? When this topic came up, Roger Ebert often cited ” Brotman’s Law,” named after Chicago movie exhibitor Oscar Brotman, which declared that “If nothing has happened by the end of the first reel, nothing is going to happen.” A reel of film is 1,000 feet, about ten minutes when projected, but most movies are projected two reels at a time, which means “the first reel” is about 20 minutes — hence, another variation on The 20-Minute Rule.

As a critic or as a paying customer, I have never in my life walked out of a movie in a theater. If I’m there for work, it’s my job to endure the whole thing no matter how bad it gets. And if I paid my money, I want my money’s worth — even if my money’s worth is of time-wasting horror.

That said, I’d be lying if I pretended that Netflix, Hulu and other streaming services haven’t made me much quicker to bail on a bad movie at home. Back when you used to have to go to the video store to rent stuff, if you picked out a stinker, you were kind of stuck with it. If you turned it off, you’d wasted your money for nothing (and, as we’ve established, I’m getting my money’s worth come hell or high Uwe Boll movie).

But on Netflix I don’t even abide by The 20-Minute Rule; I’ve turned things off after five minutes if there’s nothing to catch my attention. With literally thousands of titles at your fingertips at all time, why subject yourself to something terrible? Where I get a little uncomfortable is the idea of making the 20-Minute Rule a hard-and-fast rule — as if you’re sitting there watching a movie with a mental stopwatch, thinking to you yourself “Nope, not digging this, how much time? Eight minutes, okay, I’ll try a few more scenes.

Eh, that line was kinda funny, how good was it? Good enough to keep going? How much time now? Eleven minutes. All right, almost there.” I can’t imagine too many things more distracting than putting an arbitrary time limit on every single movie you watch and then monitoring it carefully. Focusing on a movie’s runtime means you’re not focusing on the movie.

At that point it becomes The 20-Minute Self-Fulfilling Prophecy. Note that Brotman’s Law only states that if nothing happens after the first reel, nothing is going to happen. It doesn’t stipulate whether the viewer should give up or leave, or set an alarm to let them know when those 20 minutes have elapsed.

Some movies do take longer to get started and pay off than others; I imagine if we instituted a rigid 20-Minute Rule in every movie theater in the world, nobody would have seen all of “Meek’s Cutoff” or “Le Quattro Volte,” to name two recent examples. And those were both superb films, worth seeing at any price — of money or time.

Read more of ” The 20-Minute Rule,”

Do movie theaters have cameras?

FAQs: Movie Theater Surveillance –

Is there night vision cameras in movie theaters?

Yes, many movie theaters utilize cameras with night vision capabilities. This feature allows them to monitor the environment even when the theater lights are dimmed for the movie screening.

Do Theatres have cameras inside?

Absolutely. Theaters commonly place cameras in various locations such as the lobbies, hallways, and occasionally, the screening rooms themselves for security and safety purposes.

Do movie cinemas have cameras?

Indeed, they do. Cameras in cinemas serve various purposes, from deterring illegal activities like movie piracy to ensuring overall audience safety and orderliness.

Can movie theater employees see you?

In a sense, yes. While employees are not typically monitoring live footage, they can access security camera feeds if necessary. This is particularly true in situations requiring evidence of incidents or disruptive behavior.

Do cinemas have CCTV in the screen?

Generally, cinemas do have CCTV cameras, but these are not often pointed directly at the screen. They’re typically aimed towards the audience to monitor behavior and prevent potential piracy or disruptive incidents.

Do movie theaters have listening devices?

While movie theaters primarily rely on visual surveillance, it is unlikely for them to have listening devices. These could raise significant privacy concerns and are typically not necessary for the theater’s security purposes.

Do movie theaters have night vision cameras?

Yes, some theaters use night vision cameras to monitor the activities when the light are turned dim to facilitate the show.

Is there a CCTV in Cinema?

CCTV cameras are used for general surveillance however, they are usually not directly aimed at the screen.

Are there cameras in AMC movies theaters?

Yes, AMC as well as other top chains have cameras in movie theaters.

What is the 10 minute rule movie?

Games are like movies – or so some would have us believe. With their ever-increasing budgets and graphical realism, they’re certainly heading down that path. In movies, there’s something that’s colloquially known as the “10 minute rule”. The idea is that, after 10 minutes, the viewer will generally have a good idea of whether they’ll enjoy the rest of the movie or not.

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In games, which are exponential in terms of cost and time for the audience when compared to movies, where does the 10 minute rule lie? The reason it’s called the 10 minute rule is because that’s when the hero usually receives their first inkling of the events that are about to unfold: 10 minutes in.

In The Princess Bride, the 10-minute mark falls conveniently on the point where Princess Buttercup is about to be eaten by a Shrieking Eel. If done correctly, this moment will convince the audience to keep watching, in order to find out what happens next. Simple and to the point. Enough said. Spinning a tale to hook players was where cinematics came in – they told the player the story, linked the gameplay to something the player could relate to, understand, or wonder about. Far apart from the gameplay, they gave the player a reason to start playing.

They acted as the first intellectual or emotional half of the 10 minute rule, with the gameplay acting as the second, tactile half. This meant that both facets had to combine to create a certain level of interest. Most gamers can agree that if, after playing through the opening cinematic and tutorial, you still aren’t hooked, you’ve got a problem.

And, more importantly, as games are becoming more expensive to both produce and procure, the publisher has a problem, too. Australian price for the Fable III Limited Edition So it’s important to convince the player that they have spent their money wisely. And because games are so much more expensive for the player in terms of time and money than movies are, it has to do so quickly.

Can a movie be 80 minutes?

What is a feature film? – A feature film is a film that typically has a run time between 80 minutes and 180 minutes long. This distinction, however, can depend on who you ask. The Screen Actors Guild defines a feature as a minimum of 80 minutes whereas The Academy defines a feature as a minimum of 40 minutes.

Are most movies 2 hours long?

After reading the master screenwriter Syd Field’s book the Foundation of Screenwriting, it makes sense why a typical movie is about 2-hour (120 mins) long. You see, traditionally, when writing a movie script, a story is divided into three parts: Act I, Act II, Act III. Act I is 30 mins. Act II is 60 mins.

How long is the longest theater movie?

What is the longest Hollywood movie ever made? – Cleopatra remains the longest Hollywood movie ever made, with a runtime of four hours and eight minutes (248 minutes). This is closely followed by Gone with the Wind, which comes in just 10 minutes behind. Below you’ll find the top five longest Hollywood movies ever made, excluding director’s cuts, ranked in order of runtime:

Cleopatra: 248 minutes (4 hours 8 minutes) Gone with the Wind: 238 minutes (3 hours 58 minutes) Once Upon a Time in America: 229 minutes (3 hours 49 minutes) The Ten Commandments: 220 minutes (3 hours 40 minutes) Lawrence of Arabia: 216 minutes (3 hours 36 minutes)

How long are most movies in hours?

Average length of the top 10 highest-grossing movies in the United States and Canada from 1980 to 2021 (in minutes) –

Year Average length in minutes

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Already have an account? Login Source More information Region Canada, United States Survey time period 1980, 1990, 2000, 2010, 2020, and 2021 Supplementary notes Figures calculated by Statista based on the data provided by the sources. The date of release is the date of access.

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How many hours is the average movie?

Crunching data from IMDb.com – If you like to watch movies and I mean a lot of movies, there is a chance that you noticed that movies are getting longer and longer nowadays. When was the last time you went to the cinema and watched blockbuster which was shorter than 120 minutes? More and more movies (thank you Marvel for encouraging this trend!) have also scenes after subtitles, so you wait patiently all the way till the end, even if your bladder is killing you for some time already.

These are the times when you could think “Gosh, movies are getting ridiculously long lately”. Are they? I asked myself the same question. I discussed the matter with some fellow movie lovers. They had similar feelings. That wasn’t enough for me. I decided to use my data analysis skills to investigate this issue.

In this article you can read what I have found out. There is no better place to look for data about movies than IMDb.com, It’s the biggest movie website in the world. Developed since 1990 (sic!), its database includes around 5.3 million titles and 9.3 million personalities.

  1. Release year
  2. Runtime
  3. Number of votes (to filter out niche movies)

Unfortunately, we need to download two datasets and join them later. In title.basics.tsv.gz there is a lot of data about every movie, TV show and episode in the database. In title.ratings.tsv.gz there is info about number of votes and average rating of items from the database. Let’s start writing our movies_data.py script with crunching this huge amount of data and preparing it for further investigation. import pandas as pd # Download data from IMDB website # Data description https://www.imdb.com/interfaces/ movies = pd.read_csv(‘https://datasets.imdbws.com/title.basics.tsv.gz’, compression=’gzip’, sep=’\t’) print(‘”title.basics.tsv.gz” downloaded’) ratings = pd.read_csv(‘https://datasets.imdbws.com/title.ratings.tsv.gz’, compression=’gzip’, sep=’\t’) print(‘”title.ratings.tsv.gz” downloaded’) print(movies.shape) print(ratings.shape) >>>”title.basics.tsv.gz” downloaded >>>”title.ratings.tsv.gz” downloaded >>>(5504894, 9) >>>(900802, 3) So far it looks good. We have two datasets. One has 900k rows and 3 columns, the other has 5.5 million entries and 11 columns. Both datasets have tconst variable, which is unique id for every title. We can merge existing data on this column. # Merge data on ‘tconst’, which is unique id for any title in IMDB database. movies = pd.merge(movies, ratings, on=’tconst’) print(movies.shape) >>>(900802, 11) In total there are 900k unique titles. Now we can investigate our data further. There is a column called titleType, which indicates if the title is a movie, TV show, episode, short etc. print(movies.unique()) >>> There are 11 types of titles. We are interested only in movies, so we will leave in our dataset rows marked as movie and tvMovie, We could argue if we should consider TV movies, but in the final dataset they contribute for only 5% of all titles and they don’t change the results, I checked that. movies = movies.isin()] print(movies.shape) >>>(271427, 11) The number of titles dropped to 271k. Another thing we should consider are possible genres of the movies. We need to be sure that we consider only feature movies, not documentaries etc. We can check the genres column. genres = movies.unique() len(genres) >>>1313 There are 1313 unique genres! IMDb has a weird way to build database, because there is only one column for the genre. If a movie is drama, comedy and fantasy at once, it will be written as Comedy,Drama,Fantasy. After looking through the array, I could find for example:

  • Documentary,News,Sport
  • Biography,Drama,History
  • Documentary,War
  • Animation,Musical,Sci-Fi
  • Crime,Documentary,Sport

This column is a mess. Since first draft of this article to publishing it 4 new genres were added. Fortunately, we don’t need to deal with this. We only want to filter out documentaries. Pandas has a great tool to filter rows containing some string. movies = movies.str.contains(‘Documentary’) == False] Finally we have only the movies we need in our data.

  1. startYear — represents the release year of a title
  2. runtimeMinutes — primary runtime of the title, in minutes
  3. numVotes — number of votes the title has received

movies = movies] In the end we need to change data type of those columns to numeric and drop rows with missing values. for column in movies.columns.values.tolist(): movies = pd.to_numeric(movies, errors=’coerce’) movies = movies.dropna() print(movies.shape) >>>(197552, 3) After this step our number of movies dropped to 197.5k. Before we continue with further analysis, it is good to check descriptive statistics of our dataset to determine if everything looks all right. print(movies.describe()) >>>startYear runtimeMinutes numVotes >>>count 197552.000000 197552.000000 1.975520e+05 >>>mean 1988.940932 94.929492 3.643819e+03 >>>std 24.758088 29.967162 3.173653e+04 >>>min 1894.000000 1.000000 5.000000e+00 >>>25% 1973.000000 83.000000 1.700000e+01 >>>50% 1996.000000 92.000000 6.500000e+01 >>>75% 2010.000000 103.000000 3.390000e+02 >>>max 2019.000000 5760.000000 2.029673e+06 We can notice that at least one movie is only 1 minute long, which doesn’t look right. There are probably some mistakes in the database. According to the Academy of Motion Picture Arts and Sciences, an original film needs to be 40 minutes or less to qualify as a short film, whereas a feature film is more than 40 minutes. That’s a great rule to drop movies which are too short. movies = movies > 40] What’s more important, we are only interested in popular movies. There are thousands of movies in IMDb database which have only a few dozen votes. They can skew our results. Let’s say a popular movie is the one with more than 1000 ratings. We drop all movies which don’t apply to this rule (good bye thousands of TV movies and garage productions!). movies = movies >= 1000] print(movies.describe()) >>>startYear runtimeMinutes numVotes >>>count 27951.000000 27951.000000 2.795100e+04 >>>mean 1995.441165 104.993167 2.494047e+04 >>>std 21.236780 22.305108 8.118090e+04 >>>min 1911.000000 43.000000 1.000000e+03 >>>25% 1986.000000 91.000000 1.679000e+03 >>>50% 2003.000000 100.000000 3.440000e+03 >>>75% 2011.000000 114.000000 1.195000e+04 >>>max 2018.000000 450.000000 2.029673e+06 In our final dataset there are 27,951 movies. The shortest one is 43 minutes long and the longest is 450 minutes long (the price of Iron Bladder goes to anyone who can watch it without bathroom break!). The oldest movie(s) is(are) from 1911. On average every movie in our dataset have almost 25k votes, but the standard deviation is 81k, which probably means that the distribution is skewed right and the mean is overvalued by minority of movies with huge amount of votes (there is at least one movie with over 2 million ratings!). Median looks closer to reality, 50% of movies have 3,440 votes or less. Now we can save our data to CSV and move to a new script. This one takes a long time to execute. Python needs to download in total over 100MB data and process it few times. If we start over with a new script and smaller dataset, our workflow will be much faster. movies.to_csv(‘movies.csv’, index=False) print(‘Success!’) >>>Success! New dataset has the size of 515 KB, less than 1% of the original ones! That’s how you get rid of irrelevant data! Let’s create a new script called movies.py. import pandas as pd, \ matplotlib.pyplot as plt, \ matplotlib.patches as mpatches, \ matplotlib.lines as mlines, \ seaborn as sns movies = pd.read_csv(‘movies.csv’) We should start with thinking about the first year of our studies. Cinematography in the beginning of XX century was still in its infancy. There were not many movies created back then and most of them were just short presentations of new technology and experiments. Let’s make a histogram with a number of titles in our dataset from these early years of movie history. plt.hist(movies < 1940]) plt.title('Movies count') plt.xlabel('Year of release') plt.ylabel('Number of movies') plt.show() Ok, somewhere around early 1930's number of movies starts to increase. Let's take a closer look at the years before 1930: plt.hist(movies < 1930]) plt.title('Movies count') plt.xlabel('Year of release') plt.ylabel('Number of movies') plt.show() These are just histograms with default numbers of bins (10), we just use it to quickly visualize our data. If we want to know the exact number of movies in our dataset, we should instead look at the table (or create histogram with more bins). print(movies < 1940].value_counts().sort_index()) >>>1911.0 1 >>>1913.0 3 >>>1914.0 4 >>>1915.0 3 >>>1916.0 2 >>>1917.0 2 >>>1918.0 5 >>>1919.0 10 >>>1920.0 9 >>>1921.0 9 >>>1922.0 9 >>>1923.0 10 >>>1924.0 15 >>>1925.0 18 >>>1926.0 15 >>>1927.0 22 >>>1928.0 27 >>>1929.0 22 >>>1930.0 22 >>>1931.0 49 >>>1932.0 48 >>>1933.0 51 >>>1934.0 42 >>>1935.0 52 >>>1936.0 68 >>>1937.0 52 >>>1938.0 45 >>>1939.0 73 There is only 1 movie from 1911 in our dataset and 1924 is the first year when the number of titles is higher than 10. This is not enough data to create reliable results. We need to decide what year we should start with. I decided to use the same rule of thumb as with popular approach to normal distribution. According to that, minimum sample size to create it is 30. Now we can calculate starting year of our data. start_year = 0 # This will be starting year of the data. # Create data frame with year as first column and movie count as second. movies_per_year = movies.value_counts().sort_index() # The year is an index, we need it as a column. movies_per_year_df = pd.DataFrame( ) for i in range(0, len(movies_per_year_df)): year = movies_per_year_df.iloc movie_count = movies_per_year_df.iloc # Check if in a given year there were more than 30 movies. if movie_count > 30: movies_per_year_df = movies_per_year_df.iloc # Drop years before current one in the loop # Check whether the rest of years have movie count above 30, if not, the loop continues. # If every year left has movie count above 30, the loop breaks and we have the answer. if sum(movies_per_year_df < 30) == 0: start_year = year break print(start_year) >>>Name: startYear, dtype: int64 >>>1931.0 Our dataset will start from the year 1931. Of course, we could take a quick peek at the table above to determine it, but the goal was to practice loops and conditions to automate the process in case of more complicated data. movies = movies >= 1931] print(movies.describe()) >>>startYear runtimeMinutes numVotes >>>count 27743.000000 27743.000000 2.774300e+04 >>>mean 1995.971380 105.048156 2.507714e+04 >>>std 20.407283 22.103663 8.145749e+04 >>>min 1931.000000 43.000000 1.000000e+03 >>>25% 1986.000000 91.000000 1.684000e+03 >>>50% 2003.000000 100.000000 3.459000e+03 >>>75% 2011.000000 114.000000 1.205400e+04 >>>max 2018.000000 450.000000 2.029673e+06 Our final dataset consists of 27,743 titles. What’s interesting, the median release year is 2003, which means that 50% of all movies in our dataset were released in 2003 or later. It means that people mostly watch and rate new movies. Median for runtime is 100 minutes and the mean is 105 minutes, which looks right. Let’s plot distribution of runtimes. We limited it to 40–200 minutes range to improve readability. There are not many titles longer than 200 minutes and 40 minutes is lower band of our data. Every bin corresponds to 10 minute range. plt.hist(movies, range=(40, 200), bins=16, ec=’black’) plt.title(‘Movies length’) plt.xlabel(‘Minutes’) plt.ylabel(‘Number of movies’) plt.show() The most popular runtime is 90–100 minutes. Vast majority of movies is 80–120 minutes long. This is consistent with our movie-watching intuition. Let’s find an average movie runtime by year. We group dataset by year and get descriptive statistics of every subset. statistics_grouped = movies.groupby(movies).describe() We can create a plot of this data. Besides average movie runtime we can also create confidence interval based on the standard deviation. We will use simple formulas for that: avg_runtime_by_year = statistics_grouped # Mean avg_runtime_lower_band = statistics_grouped – statistics_grouped # Lower band of data created using standard deviation. avg_runtime_upper_band = statistics_grouped + statistics_grouped # Upper band of data. Let’s create the plot: fig, ax1 = plt.subplots(figsize=(10, 5)) ax1.plot(avg_runtime_by_year, color=”blue”) ax1.plot(avg_runtime_lower_band, color=”aqua”) ax1.plot(avg_runtime_upper_band, color=”aqua”) ax1.fill_between(statistics_grouped.index, avg_runtime_lower_band, avg_runtime_upper_band, facecolor=’aqua’) # Fill space between bands to create confidence interval. ax1.set_title(‘Movies runtime by year’) ax1.set_ylabel(‘Minutes’) ax1.set_xlabel(‘Release year’) ax1.set_xlim(1931, 2018) legend_sd = mpatches.Patch(color=’aqua’, label=’Mean +/- standard deviation’) # Used mpatches to create rectangular for a legend. legend_line = mlines.Line2D(,, color=’blue’, label=’Mean runtime’) ax1.legend(handles=) # Nice legend with rectangular and line. plt.show()“` Looks like our intuitive thinking about movies getting longer was wrong. It’s true that in the first decades of cinema movies were shorter, they were on average 90 minutes long in early 1930s and reached 100–110 minutes in mid-‘50s. Since then there is no trend in our data. Also the confidence interval is fairly consistent with 80–130 minutes runtime. However, it looks like 2018 can be a beginning of new uptrend, because it’s one of two years in movie history when the average runtime was longer than 110 minutes. It’s too soon to speculate, especially because 2018 didn’t end yet and the number of movies getting at least 1000 votes will increase faster than for other years even in early 2019 (there are 597 titles in our dataset from 2018 and 955 from 2017). We can wonder what part of our dataset was considered when creating confidence interval. It’s easy to check that. We need to find a number of movies longer (shorter) than lower (upper) band of the confidence interval and divide it by the number of all movies from given year. percentage_of_included_movies = for year in statistics_grouped.index: movies_from_year = movies == year] avg_runtime_low = avg_runtime_lower_band avg_runtime_up = avg_runtime_upper_band movies_included = movies_from_year > avg_runtime_low] < avg_runtime_up] percentage_of_included_movies.append(len(movies_included)/len(movies_from_year)) Now we can add new column to our statistics_grouped data frame: statistics_grouped = percentage_of_included_movies print(statistics_grouped.describe()) >>>count 88.000000 >>>mean 0.782741 >>>std 0.058665 >>>min 0.619718 >>>25% 0.745369 >>>50% 0.786273 >>>75% 0.817378 >>>max 0.928571 >>>Name: included_movies_perc, dtype: float64 On average 78% of movies from every year fit into the confidence interval. We can add extra line to our previous plot showing this proportion by year. # Main plot fig, ax1 = plt.subplots(figsize=(10, 5)) ax1.plot(avg_runtime_by_year, color=”blue”) ax1.plot(avg_runtime_lower_band, color=”aqua”) ax1.plot(avg_runtime_upper_band, color=”aqua”) ax1.fill_between(statistics_grouped.index, avg_runtime_lower_band, avg_runtime_upper_band, facecolor=’aqua’) ax1.set_title(‘Movies runtime by year’) ax1.set_ylabel(‘Minutes’) ax1.set_xlabel(‘Release year’) ax1.set_xlim(1931, 2018) # Plot with proportions ax2 = ax1.twinx() ax2.plot(statistics_grouped, color=’olive’) ax2.set_ylabel(‘Proportion’) plt.axhline(y=0.70, color=’red’, linestyle=’dashed’) # Add line at 0.70 legend_sd = mpatches.Patch(color=’aqua’, label=’Mean +/- standard deviation’) legend_line = mlines.Line2D(,, color=’blue’, label=’Mean runtime’) legend_line_2 = mlines.Line2D(,, color=’olive’, label=’Proportion included in CI’) dashed_line = mlines.Line2D(,, color=’red’, label=’Proportion = 0.7′, linestyle=’dashed’) ax1.legend(handles=) plt.show() The plot looks a little messy, but the message is clear. Since late ’40s our confidence interval contained more than 70% of titles every year. Let’s create another plot, this time with median and interquartile range and check the results. We are mostly interested in the confidence interval, which will now contain 50% movies.25% of the shortest titles and 25% of the longest ones will be outside the blue area. # Data avg_runtime_by_year = statistics_grouped avg_runtime_lower_band = statistics_grouped avg_runtime_upper_band = statistics_grouped # Plot fig, ax1 = plt.subplots(figsize=(10, 5)) ax1.plot(avg_runtime_by_year, color=”blue”) ax1.plot(avg_runtime_lower_band, color=”aqua”) ax1.plot(avg_runtime_upper_band, color=”aqua”) ax1.fill_between(statistics_grouped.index, avg_runtime_lower_band, avg_runtime_upper_band, facecolor=’aqua’) ax1.set_title(‘Movies runtime by year’) ax1.set_ylabel(‘Minutes’) ax1.set_xlabel(‘Release year’) ax1.set_xlim(1931, 2018) legend_sd = mpatches.Patch(color=’aqua’, label=’Interquartile range’) legend_line = mlines.Line2D(,, color=’blue’, label=’Median runtime’) ax1.legend(handles=) plt.show() Here we also cannot see any clear pattern. However, the jump in recent 2–3 years is quite high. Still, it doesn’t mean this is the start of new trend, but we should check that sentiment in the future. We can also notice, that the median is on average lower than the mean. It fluctuates around 100 minutes, about 5 minutes shorter than the mean. It makes sense, because mean is affected by a small percentage of long movies and median is just the central value from every year. OK, so now we know that movies in general are not getting longer. Maybe our intuition wasn’t wrong and it only happens with the most popular movies, the biggest blockbusters. We can create few more plots and every time consider smaller sample of most popular movies from every year. Let’s take a look only at movies since 1960, so we can have a closer look at data most interesting for us. Maybe if we take only 50 most popular movies from every year, there will be some trend visible. movies_since_1960 = movies >= 1960] Because we want to check few different values, we can create a function to return statistics about n most popular movies from every year. We can use it later. def top_n_movies(data, n): top_n_movies_per_year = data.groupby(‘startYear’).head(n) stats = top_n_movies_per_year.groupby( top_n_movies_per_year).describe() return stats Now we can get the data needed and create our plot. statistics_grouped_50 = top_n_movies(movies_since_1960, 50) # Data avg_runtime_by_year = statistics_grouped_50 avg_runtime_lower_band = statistics_grouped_50 – statistics_grouped_50 avg_runtime_upper_band = statistics_grouped_50 + statistics_grouped_50 # Plot fig, ax1 = plt.subplots(figsize=(10, 5)) ax1.plot(avg_runtime_by_year, color=”blue”) ax1.plot(avg_runtime_lower_band, color=”aqua”) ax1.plot(avg_runtime_upper_band, color=”aqua”) ax1.fill_between(statistics_grouped_50.index, avg_runtime_lower_band, avg_runtime_upper_band, facecolor=’aqua’) ax1.set_title(‘Runtime of 50 most popular movies by year’) ax1.set_ylabel(‘Minutes’) ax1.set_xlabel(‘Release year’) ax1.set_xlim(1960, 2018) legend_sd = mpatches.Patch(color=’aqua’, label=’Mean +/- standard deviation’) legend_line = mlines.Line2D(,, color=’blue’, label=’Mean runtime’) ax1.legend(handles=) plt.show() There is still no visible trend. What’s more, when we consider smaller amount of more popular movies, even the peak from 2017–2018 disappears. What if we take a look at 30 most popular movies? Or 10? We can create new plot with means for different values. This time we will drop confidence intervals. Our top_n_movies function will be useful to do that. mean_10 = top_n_movies(movies_since_1960, 10) mean_30 = top_n_movies(movies_since_1960, 30) mean_50 = top_n_movies(movies_since_1960, 50) mean_100 = top_n_movies(movies_since_1960, 100) mean_all = top_n_movies(movies_since_1960, len(movies_since_1960)) # Chart fig, ax = plt.subplots(figsize=(10, 5)) ax.plot(mean_10, color=’black’) ax.plot(mean_30, color=’blue’) ax.plot(mean_50, color=’red’) ax.plot(mean_100, color=’green’) ax.plot(mean_all, color=’purple’) ax.set_title(‘Movies runtime by year’) ax.set_ylabel(‘Minutes’) ax.set_xlabel(‘Release year’) ax.set_xlim(1960, 2018) ax.legend(labels=) plt.show() No matter what number of most popular movies we take, there is no sign of trend. When we consider less movies from every year, there is more volatility on the chart, which is in pair with our statistical intuition — smaller sample leads to higher volatility. To be sure that more popular movies are not longer, let’s create a table with mean from all n-most popular movies of the year — mean of means. total_mean = pd.Series() mean_list = index_list = for i in range(0, 5): mean_n = pd.Series(.mean()], index=]) total_mean = total_mean.append(mean_n) print(total_mean) >>>top_10 103.716949 >>>top_30 106.461017 >>>top_50 106.330508 >>>top_100 106.327119 >>>all 105.893473 >>>dtype: float64 The difference between mean runtimes are marginal, they oscillate around 106 minutes with one exception of top 10 movies from every year, where the average is 103,7 minutes. As we said earlier, the sample here is small and volatile, so it doesn’t mean that most popular movies are in fact shorter than average. We looked at the movie runtimes year after year. Let’s create one last plot. This time we will generalize and create a new dataset with the decade movies were released instead of a year. Thanks to that we will have smaller number of groups and we can create a boxplots for them. movies_by_decade = movies.copy() movies_by_decade = ((movies_by_decade // 10) * 10).astype(‘int64′) sns.boxplot(x=”startYear”, y=”runtimeMinutes”, data=movies_by_decade, color=’lightskyblue’, showfliers=False) plt.ylim(40,180) plt.title(‘Movies runtime by decade’) plt.xlabel(‘Decade’) plt.ylabel(‘Minutes’) plt.show() There is a big jump between 1930’s and 1940’s, then a smaller one after 1950’s and since then the differences are marginal. In conclusion, our intuition was wrong. There is no trend in the movies runtime. The differences are too small to be noticed. We can say that for the last 60 years movies on average have the same length.