Python code imdb
Here's our quick guide to the best family-friendly movies and shows you can stream right now. Watch now. After a military plane crash near a small American town, a giant man-eating snake sets off on a killing spree.
The locals must find a way to eliminate the snake, with the help of a scientist who knows about the snake and terminates it. After a genetically-altered python escapes, a scientist is enlisted to help kill it by releasing a giant boa constrictor that he owns. As a result of a drilling accident, a giant man-eating boa constrictor is released into a maximum security prison in Antarctica.
After witnessing his parents being killed by creatures on an island as a child, a young man is brought back to the island a few years later by his psychiatrist, only to be terrorized by the same creatures.
A group of college friends are attacked by a giant, man-eating crocodile while on spring break. When an unknown underwater object disables an American nuclear-powered submarine and attacks a submerged Arctic research complex, a scientific expedition flies to the North Pole to While attempting to find a research facility on an island, a group of activists discovers two giant creatures that have escaped the facility.
A bank, where Bruce Lee's daughter plays manager, is a Vietnamese mob front. When a group aborts their try,mobsters try to kill them. Much gun play,but robbers and bank manager, who has Vanessa, a television reporter covering a story of a farmer attacked by his chickens, discovers that this is not an isolated incident A man, his business partner, and his wife are enlisted to transport an unknown object from a Russian military base, only to discover that the object is a giant, genetically-altered python.
I was puzzled by the credits being mostly Russian names. So I say, cut it some slack. They certainly had the moves. And the CGI serpents are pretty dang good! I was impressed by a couple of sequences. In one, the snake's underside reflects the light of the flamethrower being used ineffectually against it.
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Sentiment Analysis with Python (Part 1)
But the problem is, each page for each year only show 50 movies, so after crawling the 50 movies, how can I move on to the next page? And after crawling each year, how can I move on to next year?
This is my code for the parsing url part so far, but it is only able to crawls 50 movies for a particular year. You can use CrawlSpiders to simplify your task. Finding and following the 'Next' link is done by the rules attribute. I agree with Padraic Cunningham that hard-coding values is not a great idea.
I figured out a very dumb way to solve this. Better solution would be very much appreciated! The code that Greg Sadetsky has provided needs some minor changes. Learn more. Asked 4 years, 1 month ago. Active 11 months ago.Keras Tutorial 10 - Sentiment Analysis on the IMDB Dataset
Viewed 4k times. Chiefscreation Chiefscreation 2 2 silver badges 10 10 bronze badges. Active Oldest Votes.Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general. To download the files, click on the links —. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below. Writing code in comment? Please use ide. This model is then used to predict items or ratings for items that user may have an interest in. Content-based filtering: Content-based filtering approaches uses a series of discrete characteristics of an item in order to recommend additional items with similar properties.
Check out all the movies and their respective IDs. Calculate mean rating of all movies. Calculate count rating of all movies. DataFrame data. Sorting values according to. Similar movies like starwars. Similar movies as of liarliar. Check out this Author's contributed articles. Load Comments. Calculate mean rating of all movies data. Calculate count rating of all movies data.If you are a movie buff, then you surely want to keep up with the upcoming movies, and their star cast.
But if you are a movie trivia buff then you will need to get access to some online resources which has all the information about movies, their cast, crew, plots and other finer details. Googling for movies conjures up images of the IMDB website. But it is always cluttered with movie details, reviews, and lots of other promos. You can search the IMDb website and get details about movies, their casts, and more granular information about the crew, schedules, reviews, and other meta-information like plot summary and release dates.
An API is a web-based interface that can retrieve information from the Internet. At RapidAPI, we are currently hosting quite a few APIs that are tailor-made for fetching information about movies, their casts, characters and more. Before we proceed further, make sure that you subscribe to the RapidAPI account. It does not provide detailed information about each movie. It offers separate distinct API endpoints for fetching information about the crew, star cast, plot, characters, and a host of other information.
This is adequate for building a quick and easy movie search app. Click on it and you can see the code snippet on the right to call this API. You also have the python code snippet to call the API. The next thing is to search for a movie. However, before calling the API, you must prompt the user to enter a search string for the Movie name. Here is the python code for prompting the user.
You need to make sure that the user enters at least 2 characters, otherwise, the API will go for a spin and retrieve a lot of movie names. So what is the first thing that you ask when someone tells you about a new movie?
The cast, plot, director? Now in this last and concluding step, you will add additional API calls to the python program to display information about the main persons involved in a movie. Here is how you will consume the data from these two endpoints to get the names of the cast.
The crew data is returned as a label along with its data. The label can represent a director or a writer of some other role. So, to retrieve this information, you have to create a dictionary and use the label as key and store all the names as values.
With this, you have a python based app that can search for movie names, and print out the essential information about a movie which best matches the search string. Here is the complete program with variables to store the information retrieved from API and some error handling to take care of corner cases.
Claps, please!! So how about doing some data analysis on movies. Apart from fetching general information about movies, this API can also fetch you some datasets about awards, reviews and filming locations.
You can get this data and do some cool analytics and visualization stuff which might give you some great insights.
How To Use IMDb API with Python To Power Your Movie Search App
For example, you can get the most popular locations for shooting or histogram of user ratings. The possibilities are endless and we hope this blog post has tickled your movie-obsessed brain cells to explore more. Shyam is the Founder of Radiostud. He's an entrepreneur, a technology evangelist, author, and mentor with a deep passion for nurturing ideas and building things around emerging and futuristic trends in Computing, Information Technology, and Telecommunications.
GitHub LinkedIn. Your email address will not be published. Register Now. Search this website. Leave a Reply Your email address will not be published. About Team Jobs Contact Us.O ften after a few introductory courses in Python, beginners wonder how to write a cool Python program which demonstrates somewhat advanced capabilities of the language such as web scraping or database manipulation.
In this article, I will show how to use simple Python libraries and built-in capabilities to scrape the web for movie information and store them in a local SQLite database, which can later be queried for data analytics with movie info.
Think of this as a project to build your own mini IMDB database! This type of data engineering task — gathering from web and building a database connection — is often the first step in a data analytics project. Before you do any cool predictive modeling, you need to master this step. This step is often messy and unstructured i.
So, you have to extract the data from web, examine its structure and build your code to flawlessly crawl through it. Specifically, this demo will show the usage of following features. Brief descriptions of these are given below. The gateway from Python to web is done through urllib module. It offers a very simple interface, in the form of the urlopen function. This is capable of fetching URLs using a variety of different protocols. It also offers a slightly more complex interface for handling common situations — like basic authentication, cookies, proxies and so on.
These are provided by objects called handlers and openers. Web scraping is often done by API services hosted by external websites. Think of them as repository or remote database which you can query by sending search string from your own little program.
Because it is a free service, they have a restriction of requests per day. Note, you have to register on their website and get your own API key for making request from your Python program.
It is easy for humans to read and write. It is easy for machines to parse and generate. These properties make JSON an ideal data-interchange language. The json library can parse JSON pages from strings or files. It is an extremely useful module and very simple to learn.
This module is likely to be used in any Python based web data analytics program as the majority of webpages nowadays use JSON as primary object type while returning data. This module provides a portable way of using operating system dependent functionality.
If you just want to read or write a file see openif you want to manipulate paths, see the os. For creating temporary files and directories see the tempfile module, and for high-level file and directory handling see the shutil module.
In this demo, we will use OS module methods for checking existing directory and manipulate files to save some data. Some applications can use SQLite for internal data storage.
The flow of the program is shown below. Please note that the boiler plate code is available in my Github repository. The basic idea is to send request to external API with a movie title that is entered by the user. The program then tries to download the data and if successful, prints it out.
Just for example, the JSON file looks like following. If the program finds a link to an image file for the poster of the movie, it asks the user if s he wants to download it.Recommender systems are among the most popular applications of data science today. They are used to predict the "rating" or "preference" that a user would give to an item. Almost every major tech company has applied them in some form or the other: Amazon uses it to suggest products to customers, YouTube uses it to decide which video to play next on autoplay, and Facebook uses it to recommend pages to like and people to follow.
What's more, for some companies -think Netflix and Spotify- the business model and its success revolves around the potency of their recommendations.
In this tutorial, you will see how to build a basic model of simple as well as content-based recommender systems. While these models will be nowhere close to the industry standard in terms of complexity, quality or accuracy, it will help you to get started with building more complex models that produce even better results. As described in the previous section, simple recommenders are basic systems that recommends the top items based on a certain metric or score.
Before you perform any of the above steps, load your movies metadata dataset into a pandas DataFrame:. One of the most basic metrics you can think of is the rating. However, using this metric has a few caveats. For one, it does not take into consideration the popularity of a movie. Therefore, a movie with a rating of 9 from 10 voters will be considered 'better' than a movie with a rating of 8.
As the number of voters increase, the rating of a movie regularizes and approaches towards a value that is reflective of the movie's quality. It is more difficult to discern the quality of a movie with extremely few voters. Taking these shortcomings into consideration, it is necessary that you come up with a weighted rating that takes into account the average rating and the number of votes it has garnered.
Such a system will make sure that a movie with a 9 rating fromvoters gets a far higher score than a YouTube Web Series with the same rating but a few hundred voters. Mathematically, it is represented as follows:. It is also possible to directly calculate C from this data. What you need to determine is an appropriate value for mthe minimum votes required to be listed in the chart. There is no right value for m. You can view it as a preliminary negative filter that ignores movies which have less than a certain number of votes.
The selectivity of your filter is up to your discretion. In this case, you will use the 90th percentile as your cutoff. As the percentile decreases, the number of movies considered increases. Feel free to play with this value and observe the changes in your final chart.
Next, let's calculate the number of votes, mreceived by a movie in the 90th percentile. The pandas library makes this task extremely trivial using the. You use the. You see that there are movies which qualify to be in this list.
Now, you need to calculate your metric for each qualified movie. Finally, let's sort the DataFrame based on the score feature and output the title, vote count, vote average and weighted rating or score of the top 15 movies.
In this section, you will try to build a system that recommends movies that are similar to a particular movie. More specifically, you will compute pairwise similarity scores for all movies based on their plot descriptions and recommend movies based on that similarity score. The plot description is available to you as the overview feature in your metadata dataset.
Let's inspect the plots of a few movies:. In its current form, it is not possible to compute the similarity between any two overviews. To do this, you need to compute the word vectors of each overview or document, as it will be called from now on. This will give you a matrix where each column represents a word in the overview vocabulary all the words that appear in at least one document and each column represents a movie, as before.
In its essence, the TF-IDF score is the frequency of a word occurring in a document, down-weighted by the number of documents in which it occurs. This is done to reduce the importance of words that occur frequently in plot overviews and therefore, their significance in computing the final similarity score.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This package implements a Python interface to IMDb plain text data files. This will result in files imdb. For search, movies.
The module includes examples of a simple program example. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Python interface to IMDb plain-text data files.
Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. At this time, the API should not be considered stable.
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Reload to refresh your session. You signed out in another tab or window. Allow search performance hack to be enabled at runtime. Jan 4, Initial commit. Apr 13, Update readme to reflect partial database support. Jun 6,