Python scraping download files

To extract the whole raw text and parse URLs by using regular expressions. After running the code, you will get the output with links:. First, we need to get the text version of our PDF file: The next step is to parse the URLs from the text by running the following module. The output will be the following:. It enables the content extraction, PDF documents splitting into pages,documents merging, cropping, and page transforming. It supports both encrypted and unencrypted documents.

The alternative to manual scraping is building an in-house PDF scraper. You have to turn off JavaScript in browser and check if you can get files in browser without JavaScript. And then you have to see how it works and do the same in code. But sometimes it may need Selenium to control real browser which can run JavaScript.

I have to click year and then it loads different URL with. You have to use beautifulsoup to find these urls and load them with requests and then you should search. Your webpage only contains the folders that, as a human you have to click in order to get the files.

What simplifies your case is that both folders and files have the class attribute DocumentBrowserNameLink. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. Scraping and downloading excel files using python from url Ask Question.

Asked 9 months ago. Active 9 months ago. Viewed times. First question, so take it easy on me. I'm running it through anaconda navigator, navigating to the directory with the script and then running it using the below: python file-scraper. Throughout this entire web scraping tutorial, Python 3. Specifically, we used 3. If you have already installed Python but did not mark the checkbox, just rerun the installation and select modify. One of the Python advantages is a large selection of libraries for web scraping.

These web scraping libraries are part of thousands of Python projects in existence — on PyPI alone, there are over , projects today. Notably, there are several types of Python web scraping libraries from which you can choose:. However, standard Python HTTP libraries are difficult to use and, for effectiveness, require bulky lines of code, further compounding an already problematic issue. Unlike other HTTP libraries, the Requests library simplifies the process of making such requests by reducing the lines of code, in effect making the code easier to understand and debug without impacting its effectiveness.

The library can be installed from within the terminal using the pip command:. If there is a need for a form to be posted, it can be done easily using the post method.

The form data can sent as a dictionary as follows:. But this library has a limitation in that it does not parse the extracted HTML data, i. Also, it cannot be used to scrape websites that are written using purely JavaScript. Beautiful Soup is a Python library that works with a parser to extract data from HTML and can turn even invalid markup into a parse tree. For this reason, it is mostly used alongside the Python Requests Library.

The following example demonstrates the use of the html. Due to its simple ways of navigating, searching and modifying the parse tree, Beautiful Soup is ideal even for beginners and usually saves developers hours of work. For example, to print all the blog titles from this page, the findAll method can be used.

This information can be supplied to the findAll method as follows:. BeautifulSoup also makes it easy to work with CSS selectors. The following is the same example, but uses CSS selectors:. While broken-HTML parsing is one of the main features of this library, it also offers numerous functions, including the fact that it can detect page encoding further increasing the accuracy of the data extracted from the HTML file.

What is more, it can be easily configured, with just a few lines of code, to extract any custom publicly available data or to identify specific data types. Our Beautiful Soup tutorial contains more on this and other configurations, as well as how this library works.

Additionally, lxml is ideal when extracting data from large datasets. However, unlike Beautiful Soup, this library is impacted by poorly designed HTML, making its parsing capabilities impeded. This library contains a module html to work with HTML.

However, the lxml library needs the HTML string first. Once the HTML is available, the tree can be built using the fromstring method as follows:. This tree object can now be queried using XPath. Continuing the example discussed in the previous section, to get the title of the blogs, the XPath would be as follows:. This XPath can be given to the tree. This will return all the elements matching this XPath. Notice the text function in the XPath.

This will extract the text within the h2 elements. Suppose you are looking to learn how to use this library and integrate it into your web scraping efforts or even gain more knowledge on top of your existing expertise.

In that case, our detailed lxml tutorial is an excellent place to start. As stated, some websites are written using JavaScript, a language that allows developers to populate fields and menus dynamically.

This creates a problem for Python libraries that can only extract data from static web pages. In fact, as stated, the Requests library is not an option when it comes to JavaScript. This is where Selenium web scraping comes in and thrives. This Python web library is an open-source browser automation tool web driver that allows you to automate processes such as logging into a social media platform. Selenium is widely used for the execution of test cases or test scripts on web applications.

Its strength during web scraping derives from its ability to initiate rendering web pages, just like any browser, by running JavaScript — standard web crawlers cannot run this programming language.

Yet, it is now extensively used by developers. After installation, the appropriate class for the browser can be imported.