Web scraping table multiple pages


  • Scrape Data from Multiple Web Pages with Power Query
  • Where We Left Off
  • Scraping Multiple Web Pages with For Loops (in bash) — Web Scraping Tutorial ep#2
  • Instant Data Scraping Extension
  • How to Scrape Multiple Pages of a Website Using Python?
  • Scrape Data from Multiple Web Pages with Power Query

    This makes sense because there are 50 movies on each page. Page1 is , page 2 is , page 3 is , and so on. Why is this important? This information will help us tell our loop how to go to the next page to scrape. So: Start at 1, stop at , and step by Stop at Why stop at ? The last page for movies would be at the URL number of This page has movies Step at We want the URL number to change by 50 each time the loop comes around — this parameter tells it to do that.

    You can change these parameters to any that you like. Please note that this will delay the time it takes to grab all the data we need from every page, so be patient. What does this mean? If you have any questions regarding how this code works, go to the first article to see what each line executes. Below is the code you can add to the bottom of your program to save your data to a CSV file: movies. Name it, and save it with a. Then, add the code to the end of your program: movies. Below are ways we can look up, manipulate, and change our data — for future reference.

    Missing data One of the most common problems in a dataset is missing data. Check missing data: We can easily check for missing data like this: print movies. Add default value for missing data: If you wanted to change your NaN values to something else specific, you can do so like this: movies. Be careful when changing your data, and always check to see what your data types are when making any alterations.

    Delete rows with missing data: Sometimes the best route to take when having a lot of missing data is to just remove them altogether. We can do this a couple of different ways: Dropping all rows with any NA values: movies.

    We can do so like this: drop all columns with any NA values: movies. I hope you enjoyed building a Python scraper. If you followed along, let me know how it went. Happy coding!

    Where We Left Off

    Where did we leave off? Scraping TED. Note: Why TED. As I always say, when you run a data science hobby project , you should always pick a topic that you are passionate about. My hobby is public speaking. But if you are excited about something else, after finishing these tutorial articles, feel free to find any project that you fancy!

    They will be downloaded to your server, extracted and cleaned — ready for data analysis. So in one sentence: you will scale up our little web scraping project! Bash For Loops — a 2-minute crash course Note: if you know how for loops work, just skip this and jump to the next headline.

    And since this is a repetitive task, your best shot is to write a loop. But this time, you will need a for loop. A for loop works simply. You have to define an iterable which can be a list or a series of numbers, for instance.

    It iterates through the numbers between 1 and and it prints them to the screen one by one. And how does it do that?

    It tells bash what you want to iterate through. In this specific case, it will be the numbers between 1 and Note: if you have worked with Python for loops before, you might recognize notable differences.

    Well, in every data language there are certain solutions for certain problems e. Different languages are created by different people… so they use different logic. You have to learn different grammars to speak different languages. But where can you find these? Obviously, these should be somewhere on TED.

    Chrome or Firefox. We want to see only English videos for now. And we want to sort the videos by the number of views most viewed first. Using these filters, the full link of the listing page changes a bit… Check the address bar of your browser.

    The unlucky thing is that TED. And then to page 3… And so on. And there are pages! If we can, we will be able to apply the same process for the remaining Extracting URLs from a listing page You have already learned curl from the previous tutorial. And now, you will have to use it again! There, you typed curl and the full URL. Here, you typed curl and the full URL between quotation marks!

    Why the quotation marks? Point is: when using curl, always put your URL between " quotation marks! Note: In fact, to stay consistent, I should have used quotation marks in my previous tutorial , too. But there because there were no special characters my code worked without them and I was just too lazy… Sorry about that, folks! And they are found in the html code itself. Great, those are the ones that we will need!

    You want to exclude these latter ones. When doing a web scraping project this happens all the time… There is no way around it, you have to do some classic data discovery. Lucky for us, it is a pretty clear pattern in this case. Note: It seems that TED. Many high-quality websites use a similar well-built hierarchy. For the great pleasure of web-scrapers like us. The unneeded red and blue parts are the same in all the lines. And the currently missing yellow and purple parts will be constant in the final URLs, too.

    Scraping Multiple Web Pages with For Loops (in bash) — Web Scraping Tutorial ep#2

    And then to page 3… And so on. And there are pages! If we can, we will be able to apply the same process for the remaining Extracting URLs from a listing page You have already learned curl from the previous tutorial. And now, you will have to use it again! There, you typed curl and the full URL.

    Here, you typed curl and the full URL between quotation marks! Why the quotation marks? Point is: when using curl, always put your URL between " quotation marks! Note: In fact, to stay consistent, I should have used quotation marks in my previous tutorialtoo. But there because there were no special characters my code worked without them and I was just too lazy… Sorry about that, folks!

    And they are found in the html code itself. Great, those are the ones that we will need! You want to exclude these latter ones. When doing a web scraping project this happens all the time… There is no way around it, you have to do some classic data discovery. Lucky for us, it is a pretty clear pattern in this case. This makes sense because there are 50 movies on each page. Page1 ispage 2 ispage 3 isand so on. Why is this important? This information will help us tell our loop how to go to the next page to scrape.

    Instant Data Scraping Extension

    So: Start at 1, stop atand step by Stop at Why stop at ? The last page for movies would be at the URL number of This page has movies Step at We want the URL number to change by 50 each time the loop comes around — this parameter tells it to do that. Create a BeautifulSoup class to parse the page. Extract and print the first forecast item. West northwest wind 12 to 17 mph decreasing to 6 to 11 mph after midnight.

    How to Scrape Multiple Pages of a Website Using Python?

    Winds could gust as high as 23 mph. There are four pieces of information we can extract: The name of the forecast item — in this case, Tonight. The description of the conditions — this is stored in the title property of img.

    A short description of the conditions — in this case, Mostly Clear. The temperature low — in this case, 49 degrees. Extracting all the information from the page Now that we know how to extract each individual piece of information, we can combine our knowledge with CSS selectors and list comprehensions to extract everything at once.

    North wind 3 to 5 mph. Light and variable wind becoming east southeast 5 to 8 mph after midnight. Southeast wind around 9 mph. Partly cloudy, with a low around South southeast wind 13 to 15 mph, with gusts as high as 20 mph. New precipitation amounts of less than a tenth of an inch possible.

    Cloudy, with a high near New precipitation amounts between a quarter and half of an inch possible.


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