It already does that with conflicting titles even when following Kodi’s naming conventions. >How about: Parse the file name, let me choose the appropriate alternative, and Kodi can adjust the file name accordingly? I could choose from the same hits you can do when looking up the term on the site’s search feature: Now this might be specific to the scraper since I still scrape shows with scraper. I remember this happened only a few days ago when adding the Norwegian series Twin. When I scrape a show or movie with more than one hit, a dialogue pops up asking which one I mean. Please select one." No, it just take the most recent title and uses that, no questions asked. Kodi/the scraper doesn't return "Not found" or "The following entries match your file. sci_fi_df = pd.> but it most certainly IS Kodi's fault that the shows are listed improperly on my Kodi machine. I did a couple extra data cleaning steps here to finalize the data cleaning.Īfter you run the following cell, you should have a dataframe with the data you scraped. Pandas dataframes take as input arrays of data for each of their columns in key:value pairs. You can increase this for your own projects. Pages = np.arange(1, 9951, 50) # Last time I tried, I could only go to 10000 items because after that the URI has no discernable pattern to combat webcrawlers I just did 4 pages for demonstration purposes. # Note this takes about 40 min to run if np.arange is set to 9951 as the stopping point. Note that I use the sleep function to avoid being restricted by IMDB when it comes to cycling through their web pages too quickly. Things like this make exploratory data analysis and modeling easier. I removed parentheses from string data mentioning the year of the film for example. There are also some data cleaning steps I have added and documented in this code as well. It will pull all the columns mentioned above into arrays and populate them one movie at a time, one page at a time. You can run the following code which does the actual web scraping. Import seaborn as sns How to Do the Web Scraping If you want to get up and running quickly, you can use the Google Colab notebook. If you choose to run the code locally using something like a Jupyter Notebook you'll need to do that. Note: some of these packages need pip install package_name to be run to install them first. If you forget a package you can re-run just that cell. With that, let’s dive in! First things first, you should always import your packages as their own cell. You can use it out of the box with many of the packages already installed that are common in data science.īelow is an image of the Colab workspace and its layout: Introducing the Google Colab user interface It takes 40 min to scrape 200 webpages using the Google Colab Notebook.įor those of you who have not tried it before, Google Colab is a cloud-based Jupyter Notebook style Python development tool that lives in the Google app suite. Note: it will take longer the more pages you select. You can choose how many pages you want to scrape based on your data needs. The script pulls in movie titles, years, ratings (PG-13, R, and so on), genres, runtimes, reviews, and votes for each movie. Let’s get to the scraping script and get that running. Some websites frown upon the use of web scrapers, so use it wisely. My script uses the sleep function, for example, to slow down the pull requests intentionally, so as not to overload IMDB's servers. Through this, you will see what further data science projects are possible for you to try.ĭisclaimer: while web scraping is a great way of programmatically pulling data off of websites, please do so responsibly. I will conclude this article with a bit of exploratory data analysis (EDA). It can then write these data to a dataframe for further exploration. This article has a Python script you can use to scrape the data on sci-fi movies (or whatever genre you choose!) from the IMDB website. For that, web scraping is a good skill to have in your toolbox to pull data off your favorite website. Luckily, there are many free datasets available – but sometimes you want something more specific or bespoke. Have you ever struggled to find a dataset for your data science project? If you're like I am, the answer is yes.
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