Fastest Web Scraping Python

F

Top 7 Python Web Scraping Tools For Data Scientists

Top 7 Python Web Scraping Tools For Data Scientists

Data is an important asset in an organisation and web scraping allows efficient extraction of this asset from various web sources. Web scraping helps in converting unstructured data into a structured one which can be further used for extracting insights.
In this article, we list down the top seven web scraping frameworks in Python. Register for AWS Data & Analytics Conclave>>
(The list is in alphabetical order)
1| Beautiful Soup
Beautiful Soup is a Python library for pulling data out of HTML and XML files. It is mainly designed for projects like screen-scraping. This library provides simple methods and Pythonic idioms for navigating, searching, and modifying a parse tree. This tool automatically converts incoming documents to Unicode and outgoing documents to UTF-8.
Installation: If you’re using a recent version of Debian or Ubuntu Linux, you can install Beautiful Soup with the system package manager:
$ apt-get install python-bs4 (for Python 2)
$ apt-get install python3-bs4 (for Python 3)
2| LXML
The lxml is a Python tool for C libraries libxml2 and libxslt. It is recognised as one of the feature-rich and easy-to-use libraries for processing XML and HTML in Python language. It is unique in the case that it combines the speed and XML feature of these libraries with the simplicity of a native Python API and is mostly compatible but superior to the well-known ElementTree_API.
3| MechanicalSoup
MechanicalSoup is a Python library for automating interaction with websites. This library automatically stores and sends cookies, follows redirects and can follow links and submit forms. MechanicalSoup provides a similar API, built on Python giants Requests (for HTTP sessions) and BeautifulSoup (for document navigation). However, this tool became unmaintained for several years as it didn’t support Python 3.
4| Python Requests
Python Requests is the only Non-GMO HTTP library for Python language. It allows the user to send HTTP/1. 1 requests and there is no need to manually add query strings to your URLs, or to form-encode your POST data. There are a number of feature support such as browser-style SSL verification, automatic decompression, automatic content decoding, HTTP(S) proxy support and much more. Requests officially support Python 2. 7 & 3. 4–3. 7 and runs on PyPy.
5| Scrapy
Scrapy is an open-source and collaborative framework for extracting the data a user needs from websites. Written in Python language, Scrapy is a fast high-level web crawling & scraping framework for Python. It can be used for a wide range of purposes, from data mining to monitoring and automated testing. It is basically an application framework for writing web spiders that crawl web sites and extract data from them. Spiders are the classes that a user defines and Scrapy uses the Spiders to scrape information from a website (or a group of websites).
6| Selenium
Selenium Python is an open-source web-based automation tool which provides a simple API to write functional or acceptance tests using Selenium WebDriver. Selenium is basically a set of different software tools each with a different approach to supporting test automation. The entire suite of tools results in a rich set of testing functions specifically geared to the needs of testing of web applications of all types. With the help of Selenium Python API, a user can access all functionalities of Selenium WebDriver in an intuitive way. The currently supported Python versions are 2. 7, 3. 5 and above.
7| Urllib
The urllib is a Python package which can be used for opening URLs. It collects several modules for working with URLs such as quest for opening and reading URLs which are mostly HTTP, module defines the exception classes for exceptions raised by quest, module defines a standard interface to break Uniform Resource Locator (URL) strings up in components and botparser provides a single class, RobotFileParser, which answers questions about whether or not a particular user agent can fetch a URL on the Web site that published the file.
Join Our Discord Server. Be part of an engaging online community. Join Here.
Subscribe to our Newsletter
Get the latest updates and relevant offers by sharing your email.
Ambika ChoudhuryA Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.
5 Steps To Build a Faster Web Crawler | Better Programming

5 Steps To Build a Faster Web Crawler | Better Programming

Make your Python scraper up to 100 times fasterPhoto from Web Developer Ninja on raping a large amount of data requires you to have a very fast web scraper. If you want to scrape 10 million items and your scraper gets 50 items per minute, you’ll be waiting for 130 days for that scraper to finish. That’s way too long! This guide provides a structured approach to building a super-fast web let me take you from this:Scraper with average rate lower than 50 items/minTo this:Scraper with average rate greater than 2, 000 items/minIf you’re scraping in Python and want to go fast, there is only one library to use: Scrapy. This is a fantastic web scraping framework if you’re going to do any substantial scraping. BeautifulSoup, Requests, and Selenium are just too slow for large projects. If you aren’t familiar with Scrapy, I would recommend learning it and then revisiting this article later are now two things we need to do before we start scraping:Change your user agent. Your user agent tells servers who is accessing their website. By default, Scrapy tells servers that a bot is crawling their site. If you don’t change this setting, you are going to get banned in minutes. To change it, google “User agents” and set one of them equal to the USER_AGENT variable in It is also possible to rotate your user agent. However, I’ve found this unnecessary. If you want to do this, just google how — I believe the setup is relatively and set up proxies for your crawler. When choosing proxies, you should consider the pricing structure of the proxies. Do you pay per GB of bandwidth? Do you pay per proxy? Do you pay per thread? For large projects, I always pay per thread and use StormProxies. For smaller projects, I’d recommend SmartProxy. They charge by GB of bandwidth and provide unlimited threads. Next, you want to set up your proxies, which can be done by creating a new middleware in the file as shown below. This will set the proxies for all spiders in your project. You then need to add the middleware to your file. Alternatively, you can set proxies for each spider individually. There is a short video on how to do this below:Middleware for proxies (all spiders)Setting proxies for each spider individuallyWork smarter, not section is about the three scraping techniques that are going to make a huge difference to your and conquerIf you are using a single large spider, split it into many smaller ones. You do this so that you can make use of Scrapyd (more details in Step 4). Scrapyd allows you to run many spiders simultaneously (with Scrapy, you can only run one spider at a time). Each smaller spider will crawl part of what the large spider crawled. These mini-spiders should not overlap in the content they crawl, as this will waste time. If you split one spider into ten smaller ones, your scraping process is going to be around ten times faster (provided there are no other bottlenecks — see Step 5). Minimize the number of requests sentSending requests and waiting for responses is the slowest part of using a scraper. If you can reduce the number of requests sent, your scraper will be much faster. For example, if you are scraping prices and titles from an e-commerce site, then you don’t need to visit each item’s page. You can get all the data you need from the results page. If you have 30 items per page, then using this technique will make your scraper 30 times faster (it only has to send one request now instead of 30). Always be on the lookout for ways to reduce your number of requests. Below is a list of things you can try. If you can think of any others, please leave a ways to reduce requests:Increase the number of results on the results page (e. g. from ten to 100) filters before scraping (e. price filters) a general spider — not a items to the database in batchesAnother cause of a slow scraper is that people tend to scrape their data and then immediately add that data to their database. This is slow for two reasons. Firstly, processing in batches is always going to be faster than adding item by item. Secondly, with batching, you can make use of the many tools Python has to offer for batch uploading to databases. For example, the pandas library can be used to put your data into a dataframe and then upload that data to a SQL database. That is much faster! If you are interested in learning more, I highly recommend you read this article on batch uploading to SQL NCURRENT_REQUESTS:“The maximum number of concurrent (i. e. simultaneous) requests that will be performed by the Scrapy downloader. ” — Scrapy’s documentationThis is the number of simultaneous requests that your spider will send. You will want to experiment a little with different values and see which gives you the best scrape rate. A good place to start is 50. If you get a lot of timeout errors, then you have set this too high. Reduce by 10% and try again. 2. DOWNLOAD_TIMEOUT:“The amount of time (in secs) that the downloader will wait before timing out. ” — Scrapy’s docsThis is how long the spider will wait for the response after sending a request before retrying. Set this too low and you will get endless timeout errors. Set this too high and your spider will be waiting around instead of retrying a request, wasting time and slowing you down. Start at 100 seconds and experiment to find the optimal value. 3. DOWNLOAD_DELAY:“The amount of time (in secs) that the downloader should wait before downloading consecutive pages from the same website. ” — Scrapy’s docsThis is how long your spider will wait between downloading responses. For maximum speed, set this to zero. If you get response codes 400, 403, or 502 consistently, then you are scraping too fast. Increase the download delay slightly and try again (a good starting point is 0. 5). According to its documentation, Scrapyd is an application for deploying and running Scrapy rapyd allows you to run multiple spiders simultaneously. This will enable us to improve the overall speed of the scraping process significantly. If you want spider deployment that’s free and easy to set up, use Scrapyd. The Scrapy Cluster docs include a number of alternatives, but I would still recommend nefits of using Scrapyd:If your project contains one or more large spiders, split them up (as mentioned in Step 2 of this guide) spiders that don’t need to be split up can be run as they setup of Scrapyd can appear intimidating if you only read the docs, but the video below makes it very easy to understand and implement:Note: It is possible to run all the spiders in a project with a single command. It takes a small amount of work to set up, but for projects with ten or more spiders, I’d recommend doing it. Learn how on Stack have followed this guide and your scraper is running at a respectable speed. There is now one final step to take your crawler from respectable to lightning-fast: Dealing with bottlenecks! Photo from is a bottleneck? It is the limiting factor for the speed of your process. If addressed, it will give your process a significant speed boost up to the next aling with bottlenecks is an iterative process that goes like this:You have a bottleneck slowing down your find out what the bottleneck address the bottleneck, and your process becomes have a new is the process you are going to be repeating and repeating (and repeating) until you’ve squeezed every last bit of speed from your is a table containing some common scraping of common scraping bottlenecks and solutions (by the author)Well done. You’ve learned the ins and outs of building a rapid web scraper in Python. I hope you found this article useful and would love to hear any ideas you have. What projects are you working on? What do you like about coding/scraping? What’s your highest ever items/min rate? Thanks for reading. As always, if you have any questions, just leave a comment.
Scrapy VS Beautiful Soup: A Comparison Of Web Crawling Tools

Scrapy VS Beautiful Soup: A Comparison Of Web Crawling Tools

One of the most critical assets for data-driven organisations is the kind of tools used by their data science professionals. Web crawler and other such web scraping tools are few of those tools that are used to gain meaningful insights. Web scraping allows efficient extraction of data from several web services and helps in converting raw and unstructured data into a structured whole.
There are several tools available for web scraping, such as lxml, BeautifulSoup, MechanicalSoup, Scrapy, Python Requests and others. Among these, Scrapy and Beautiful Soup are popular among developers. Follow us on Google News
In this article, we will compare these two web scraping tools, and try to understand the differences between them. Before diving deep into the tools, let us first understand what these tools are.
Scrapy
Scrapy is an open-source and collaborative framework for extracting the data you need from websites in a fast and simple manner. This tool can be used for extracting data using APIs. It can also be used as a general-purpose web crawler. Thus, Scrapy is an application framework, which can be used for writing web spiders that crawl websites and extract data from them.
The framework provides a built-in mechanism for extracting data – known as selectors – and can be used for data mining, automated testing, etc. Scrapy is supported under Python 3. 5+ under CPython and PyPy starting with PyPy 5. 9.
Features of Scrapy:
Scrapy provides built-in support for selecting and extracting data from HTML/XML sources using extended CSS selectors and XPath expressionsAn interactive shell console for trying out the CSS and XPath expressions to scrape dataBuilt-in support for generating feed exports in multiple formats (JSON, CSV, XML) and storing them in multiple backends (FTP, S3, local filesystem)
Scraping With Scrapy
Using pip
If you just want to install scrapy globally in your system, you can install scrapy library using the python package ‘pip’. Open your terminal or command prompt and type the following command.
pip install scrapy
Using Conda
If you want scrapy to be in your conda environment just type in and execute the following command in your terminal
conda install -c conda-forge scrapy
The scrapy shell: It allows to scrape web pages interactively using the command line.
To open scrapy shell type scrapy shell.
Scraping with Scrapy Shell
Follow the steps below to start scraping:
1. Open the html file in a web browser and copy the url.
2. Now in the scrapy shell type and execute the following command:
fetch(“url–”)
Replace url– with the url of the html file or any webpage and the fetch command will download the page locally to your system.
You will get a similar message in your console
[] DEBUG: Crawled (200)
3. Viewing the response
The fetch object will store whatever page or information it fetched into a response object. To view the response object simply type in and enter the following command.
view(response)
The console will return a True and the webpage that was downloaded with fetch() will open up in your default browser.
4. Now that all the data you need is available locally. You just need to know what data you need.
5. Scraping the data: Coming back to the console, all the elements need to be printed behind the webpage that was fetched earlier. Enter the following command:
print()
Click here to get the detailed web scraping.
Beautiful Soup
Beautiful Soup is one of the most popular Python libraries which helps in parsing HTML or XML documents into a tree structure to find and extract data. This tool features a simple, Pythonic interface and automatic encoding conversion to make it easy to work with website data.
This library provides simple methods and Pythonic idioms for navigating, searching, and modifying a parse tree, and automatically converts incoming documents to Unicode and outgoing documents to UTF-8.
Features of Beautiful Soup:
This Python library provides a few simple methods, as well as Pythonic idioms for navigating, searching, and modifying a parse treeThe library automatically converts incoming and outgoing documents to Unicode and UTF-8, respectivelyThis library sits on top of popular Python parsers like lxml and html5lib, allowing you to try out different parsing strategies or trade speed for flexibility
Scraping With Beautifulsoup
Installing Beautiful Soup 4
Beautiful Soup library can be installed using PIP with a very simple command. It is available on almost all platforms. Here is a way to install it using Jupyter Notebook.
This library can be imported with the following code and assign it to an object.
Getting Started
We will be using this basic, and default, HTML doc to parse the data using Beautiful Soup.
The following code will expand HTML into its hierarchy:
Exploring The Parse Tree
To navigate through the tree, we can use the following commands:
Beautiful Soup has many attributes which can be accessed and edited. This extracted parsed data can be saved onto a text file.
Click here to know more about web scraping with BeautifulSoup.
Scrapy VS Beautiful Soup
Structure
Scrapy is an open-source framework, whereas Beautiful Soup is a Python library designed for quick turnaround projects like screen-scraping. A framework inverts the control of the program and informs the developer what they need. Whereas in the case of a library, the developer calls the library where and when they need it.
Performance
Due to the built-in support for generating feed exports in multiple formats, as well as selecting and extracting data from various sources, the performance of Scrapy can be said to be faster than Beautiful Soup. Working with Beautiful Soup can speed up with the help of Multithreading process.
Extensibility
Beautiful Soup works best when working on smaller projects. On the other hand, Scrapy may be the better choice for larger projects with more complexities, as this framework can add custom functionalities and can develop pipelines with flexibility and speed.
Beginner-Friendly
For a beginner who is trying hands-on web scraping for the first time, Beautiful Soup is the best choice to start with. Scrapy can be used for scraping, but it is comparatively more complex than the former.
Community
The developer’s community of Scrapy is stronger and vast compared to that of Beautiful Soup. Also, developers can use Beautiful Soup for parsing HTML responses in Scrapy callbacks by feeding the response’s body into a BeautifulSoup object and extracting whatever data they need from it.
Join Our Discord Server. Be part of an engaging online community. Join Here.
Subscribe to our Newsletter
Get the latest updates and relevant offers by sharing your email.
Ambika ChoudhuryA Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.

Frequently Asked Questions about fastest web scraping python

What is the fastest way to scrape a website in Python?

Setup. If you’re scraping in Python and want to go fast, there is only one library to use: Scrapy. This is a fantastic web scraping framework if you’re going to do any substantial scraping. BeautifulSoup, Requests, and Selenium are just too slow for large projects.Aug 29, 2020

Is Scrapy faster than BeautifulSoup?

Due to the built-in support for generating feed exports in multiple formats, as well as selecting and extracting data from various sources, the performance of Scrapy can be said to be faster than Beautiful Soup. Working with Beautiful Soup can speed up with the help of Multithreading process.Apr 8, 2020

Which IDE is best for web scraping?

PyCharm. In industries most of the professional developers use PyCharm and it has been considered the best IDE for python developers. … Spyder. Spyder is another good open-source and cross-platform IDE written in Python. … Eclipse PyDev. … IDLE. … Wing. … Emacs. … Visual Studio Code. … Sublime Text:More items…•Feb 7, 2020

About the author

proxyreview

If you 're a SEO / IM geek like us then you'll love our updates and our website. Follow us for the latest news in the world of web automation tools & proxy servers!

By proxyreview

Recent Posts

Useful Tools