7/30/2023 0 Comments Imdb raw data set![]() The easiest way out of the Python command line interface (indicated by the >) is by entering exit(). The Python install can be tested by entering python on a command line. Step 0: PreparationsĪll the tools mentioned above should be installed and tested. So if anyone is planning to use it with MySQL or SQLite in the background please feel free to extend my code accordingly. While I extended the code with topics like ratings, business data, filming locations, and biographical data, I did all of this exclusively for PostgreSQL as a backend. While Ameer had mostly the reusability in mind when writing his code, I need to extract the data for a work project. Ameer did a great job in making his script very modular and well commented, while at the same time keeping it flexible and extensible. Sounds straightforward enough, but isn’t as it turned out. The script reads the source files, parses them into a more usable format, and then saves the results into a database. I didn’t start from scratch but built upon the great work done by Ameer Ayoub which is available on his GitHub repository for download. That said, it’s not necessary to understand every single step in detail to be able to use the script and get the data. If you (just as myself) think there should be an IMDb API, please feel free to participate in this survey! The Workflow Introduction Since I’m in Germany, I used the mirror at the FU Berlin – thanks to all the people providing IMDb mirror sites! Well hidden under the headline “Alternative Interfaces” it is possible to download “Plain Text Data Files”. That would be too easy and also defy the existence of this blog post. Therefore it’s a lot easier to just download the complete IMDb in one go! Unfortunately, the data model underlying the IMDb is not just available for download. Since the IMDb changes so quickly, inconsistent data would probably be the outcome. Of course, one could just start to crawl and download all that data, but this would not only be against their terms of usage but would also probably be awfully slow. According to their own statistics, the IMDb currently contains data on 4,431,127 productions and 8,047,620 people. The data stems from what is most likely the most famous and complete source for anything and everything about movies, cinema, and TV: the Internet Movie Database (IMDb). Users of the free Public Edition will need to export their data as a CSV file. And of course, we need Tableau Desktop, to visualize the data! In my case I save the data in a PostgreSQL database to connect to this, I am using the Professional Edition of Tableau Desktop.(Hint: Don’t forget to set the “flavor” on the left to “Python”…) It probably won’t be necessary for simply using the scripts, but since they make heavy use of regular expressions I used a lot.For having a look into the rather massive source data files I also recommend Sublime. Alternatively, you could resort to a true Python IDE such as Spyder. Personally, I like Notepad++, but any given editor should be fine. I developed and tested the scripts using PostgreSQL 9.5. The default setup of the scripts assumes a local installation, but a remote database will also work. The main script writes the output data directly into a PostgreSQL database, so you will need access to that.On Linux machines I recommend gzip or gunzip, for Windows 7zip is a good free option – commercial WinZip can also handle them. For the following to work, we assume Python 2.7 is on your machine – I haven’t tested the scripts on Python 3. The collection of data and main extraction of the usable information is happening using a number of Python scripts.But with this walk-through, everybody should be able to build their own dataset! The Tools I decided to produce this write-up of how to extract the data from the Internet Movie Database (IMDb), as copyright reasons make it impossible to provide the ready-made data. Originally written for attendees of the Tableau Cinema Tour, it might be equally helpful for people entering IronViz "Silver Screen" hence we are re-publishing it here. This is an abridged version of Konstantin’s original blog post from his personal website. Konstantin Greger is Associate Sales Consultant at Tableau. Reference Materials Toggle sub-navigationĭr.Teams and Organizations Toggle sub-navigation. ![]() Plans and Pricing Toggle sub-navigation.
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