Python Web Crawler Source Code
3 Python web scrapers and crawlers | Opensource.com
In a perfect world, all of the data you need would be cleanly presented in an open and well-documented format that you could easily download and use for whatever purpose you need.
In the real world, data is messy, rarely packaged how you need it, and often out-of-date.
Often, the information you need is trapped inside of a website. While some websites make an effort to present data in a clean, structured data format, many do not. Crawling, scraping, processing, and cleaning data is a necessary activity for a whole host of activities from mapping a website’s structure to collecting data that’s in a web-only format, or perhaps, locked away in a proprietary database.
Sooner or later, you’re going to find a need to do some crawling and scraping to get the data you need, and almost certainly you’re going to need to do a little coding to get it done right. How you do this is up to you, but I’ve found the Python community to be a great provider of tools, frameworks, and documentation for grabbing data off of websites.
Before we jump in, just a quick request: think before you do, and be nice. In the context of scraping, this can mean a lot of things. Don’t crawl websites just to duplicate them and present someone else’s work as your own (without permission, of course). Be aware of copyrights and licensing, and how each might apply to whatever you have scraped. Respect files. And don’t hit a website so frequently that the actual human visitors have trouble accessing the content.
With that caution stated, here are some great Python tools for crawling and scraping the web, and parsing out the data you need.
Pyspider
Let’s kick things off with pyspider, a web-crawler with a web-based user interface that makes it easy to keep track of multiple crawls. It’s an extensible option, with multiple backend databases and message queues supported, and several handy features baked in, from prioritization to the ability to retry failed pages, crawling pages by age, and others. Pyspider supports both Python 2 and 3, and for faster crawling, you can use it in a distributed format with multiple crawlers going at once.
Pyspyder’s basic usage is well documented including sample code snippets, and you can check out an online demo to get a sense of the user interface. Licensed under the Apache 2 license, pyspyder is still being actively developed on GitHub.
MechanicalSoup
MechanicalSoup is a crawling library built around the hugely-popular and incredibly versatile HTML parsing library Beautiful Soup. If your crawling needs are fairly simple, but require you to check a few boxes or enter some text and you don’t want to build your own crawler for this task, it’s a good option to consider.
MechanicalSoup is licensed under an MIT license. For more on how to use it, check out the example source file on the project’s GitHub page. Unfortunately, the project does not have robust documentation at this time
Scrapy
Scrapy is a scraping framework supported by an active community with which you can build your own scraping tool. In addition to scraping and parsing tools, it can easily export the data it collects in a number of formats like JSON or CSV and store the data on a backend of your choosing. It also has a number of built-in extensions for tasks like cookie handling, user-agent spoofing, restricting crawl depth, and others, as well as an API for easily building your own additions.
For an introduction to Scrapy, check out the online documentation or one of their many community resources, including an IRC channel, Subreddit, and a healthy following on their StackOverflow tag. Scrapy’s code base can be found on GitHub under a 3-clause BSD license.
If you’re not all that comfortable with coding, Portia provides a visual interface that makes it easier. A hosted version is available at
Others
Cola describes itself as a “high-level distributed crawling framework” that might meet your needs if you’re looking for a Python 2 approach, but note that it has not been updated in over two years.
Demiurge, which supports both Python 2 and Python 3, is another potential candidate to look at, although development on this project is relatively quiet as well.
Feedparser might be a helpful project to check out if the data you are trying to parse resides primarily in RSS or Atom feeds.
Lassie makes it easy to retrieve basic content like a description, title, keywords, or a list of images from a webpage.
RoboBrowser is another simple library for Python 2 or 3 with basic functionality, including button-clicking and form-filling. Though it hasn’t been updated in a while, it’s still a reasonable choice.
This is far from a comprehensive list, and of course, if you’re a master coder you may choose to take your own approach rather than use one of these frameworks. Or, perhaps, you’ve found a great alternative built for a different language. For example, Python coders would probably appreciate checking out the Python bindings for Selenium for sites that are trickier to crawl without using an actual web browser. If you’ve got a favorite tool for crawling and scraping, let us know in the comments below.
Scrapy, a fast high-level web crawling & scraping framework
Scrapy
Overview
Scrapy is a fast high-level web crawling and web scraping framework, used to
crawl websites and extract structured data from their pages. It can be used for
a wide range of purposes, from data mining to monitoring and automated testing.
Scrapy is maintained by Zyte (formerly Scrapinghub) and many other
contributors.
Check the Scrapy homepage at for more information,
including a list of features.
Requirements
Python 3. 6+
Works on Linux, Windows, macOS, BSD
Install
The quick way:
pip install scrapy
See the install section in the documentation at
for more details.
Documentation
Documentation is available online at and in the docs
directory.
Releases
You can check for the release notes.
Community (blog, twitter, mail list, IRC)
See for details.
Contributing
Code of Conduct
Please note that this project is released with a Contributor Code of Conduct
(see).
By participating in this project you agree to abide by its terms.
Please report unacceptable behavior to
Companies using Scrapy
See for a list.
Commercial Support
See for details.
How to Build a Web Crawler in Python from Scratch – Datahut …
How often have you wanted a piece of information and have turned to Google for a quick answer? Every information that we need in our daily lives can be obtained from the internet. This is what makes web data extraction one of the most powerful tools for businesses. Web scraping and crawling are incredibly effective tools to capture specific information from a website for further analytics and processing. If you’re a newbie, through this blog, we aim to help you build a web crawler in python for your own customized first, let us cover the basics of a web scraper or a web mystifying the terms ‘Web Scraper’ and ‘Web Crawler’A web scraper is a systematic, well-defined process of extracting specific data about a topic. For instance, if you need to extract the prices of products from an e-commerce website, you can design a custom scraper to pull this information from the correct source. A web crawler, also known as a ‘spider’ has a more generic approach! You can define a web crawler as a bot that systematically scans the Internet for indexing and pulling content/information. It follows internal links on web pages. In general, a “crawler” navigates web pages on its own, at times even without a clearly defined end, it is more like an exploratory search of the content on the Web. Search engines such as Google, Bing, and others often employ web crawlers to extract content for a URL or for other links, get URLs of these links and other ever, it is important to note that web scraping and crawling are not mutually exclusive activities. While web crawling creates a copy of the content, web scraping extracts specific data for analysis, or to create something new. However, in order to scrape data from the web, you would first have to conduct some sort of web crawling to index and find the information you need. On the other hand, data crawling also involves a certain degree of scraping, like saving all the keywords, the images and the URLs of the web Read: How Popular Price Comparison Websites Grab DataTypes of Web CrawlersA web crawler is nothing but a few lines of code. This program or code works as an Internet bot. The task is to index the contents of a website on the internet. Now we know that most web pages are made and described using HTML structures and keywords. Thus, if you can specify a category of the content you need, for instance, a particular HTML tag category, the crawler can look for that particular attribute and scan all pieces of information matching that attribute. You can write this code in any computer language to scrape any information or data from the internet automatically. You can use this bot and even customize the same for multiple pages that allow web crawling. You just need to adhere to the legality of the are multiple types of web crawlers. These categories are defined by the application scenarios of the web crawlers. Let us go through each of them and cover them in some detail. 1. General Purpose Web CrawlerA general-purpose Web crawler, as the name suggests, gathers as many pages as it can from a particular set of URLs to crawl large-scale data and information. You require a high internet speed and large storage space are required for running a general-purpose web crawler. Primarily, it is built to scrape massive data for search engines and web service providers. 2. Focused Web CrawlerA Focused Web Crawler is characterized by a focused search criterion or a topic. It selectively crawls pages related to pre-defined topics. Hence, while a general-purpose web crawler would search and index all the pages and URLs on a site, the focused crawler only needs to crawl the pages related to the pre-defined topics, for instance, the product information on an e-commerce website. Thus, you can run this crawler with smaller storage space and slower internet speed. Most search engines, such as Google, Yahoo, and Baidu use this kind of web crawler. 3. Incremental Web CrawlerImagine you have been crawling a particular page regularly and want to search, index and update your existing information repository with the newly updated information on the site. Would you crawl the entire site every time you want to update the information? That sounds unwanted extra cost of computation, time and memory on your machine. The alternative is to use an incremental web incremental web crawler crawls only newly generated information in web pages. They only look for updated information and do not re-download the information that has not changed, or the previously crawled information. Thus it can effectively save crawling time and storage space. 4. Deep Web CrawlerMost of the pages on the internet can be divided into Surface Web and Deep Web (also called Invisible Web Pages or Hidden Web). You can index a surface page with the help of a traditional search engine. It is basically a static page that can be reached using a pages in the Deep Web contain content that cannot be obtained through static links. It is hidden behind the search form. In other words, you cannot simply search for these pages on the web. Users cannot see it without submitting some certain keywords. For instance, some pages are visible to users only after they are registered. Deep web crawler helps us crawl the information from these invisible web read: Scraping Nasdaq news using pythonWhen do you need a web crawler? From the above sections, we can infer that a web crawler can imitate the human actions to search the web and pull your content from the same. Using a web crawler, you can search for all the possible content you need. You might need to build a web crawler in one of these two scenarios:1. Replicating the action of a Search Engine- Search ActionMost search engines or the general search function on any portal sites use focused web crawlers for their underlying operations. It helps the search engine locate the web pages that are most relevant to the searched-topics. Here, the crawler visits web sites and reads their pages and other information to create entries for a search engine index. Post that, you can index the data as in the search replicate the search function as in the case of a search engine, a web crawler helps:Provide users with relevant and valid contentCreate a copy of all the visited pages for further processing2. Aggregating Data for further actions- Content MonitoringYou can also use a web crawler for content monitoring. You can then use it to aggregate datasets for research, business and other operational purposes. Some obvious use-cases are:Collect information about customers, marketing data, campaigns and use this data to make more effective marketing llect relevant subject information from the web and use it for research and academic information on macro-economic factors and market trends to make effective operational decisions for a company. Use a web crawler to extract data on real-time changes and competitor can you build a Web Crawler from scratch? There are a lot of open-source and paid subscriptions of competitive web crawlers in the market. You can also write the code in any programming language. Python is one such widely used language. Let us look at a few examples ing a Web Crawler using PythonPython is a computationally efficient language that is often employed to build web scrapers and crawlers. The library, commonly used to perform this action is the ‘scrapy’ package in Python. Let us look at a basic code for the scrapy
class spider1():
name = ‘Wikipedia’
start_urls = [‘(electricity)’]
def parse(self, response):
passThe above class consists of the following components:a name for identifying the spider or the crawler, “Wikipedia” in the above example. a start_urls variable containing a list of URLs to begin crawling from. We are specifying a URL of a Wikipedia page on clustering algorithms. a parse() method which will be used to process the webpage to extract the relevant and necessary can run the spider class using a simple command ‘scrapy runspider ‘. The output looks something like above output contains all the links and the information (text content) on the website in a wrapped format. A more focused web crawler to pull product information and links from an e-commerce website looks something like this:import requests
from bs4 import BeautifulSoup
def web(page, WebUrl):
if(page>0):
url = WebUrl
code = (url)
plain =
s = BeautifulSoup(plain, “”)
for link in ndAll(‘a’, {‘class’:’s-access-detail-page’}):
tet = (‘title’)
print(tet)
tet_2 = (‘href’)
print(tet_2)
web(1, ’)This snippet gives the output in the following above output shows that all the product names and their respective links have been enlisted in the output. This is a piece of more specific information pulled by the Read: How Web Scraping Helps Private Equity Firms Improve Due Diligence EfficiencyOther crawlers in the marketThere are multiple open-source crawlers in the market that can help you collect/mine data from the Internet. You can conduct your due research and use the best possible tool for collecting information from the web. A lot of these crawlers are written in different languages like Java, PHP, Node, etc. While some of these crawlers can work across multiple operating software, some are tailor-made for specific platforms like Linux. Some of them are the GNU Wget written in C, the PHP-crawler in PHP, JSpider in Java among many others. To chose the right crawler for your use, you must consider factors like the simplicity of the program, speed of the crawler, ability to crawl over various web sites (flexibility) and memory usage of these tools before you make your final choice. Web Crawling with DatahutWhile there are multiple open source data crawlers, they might not be able to crawl complicated web pages and sites on a large scale. You will need to tweak the underlying code so that the code works for your target page. Moreover, as mentioned earlier, it might not function for all the operating software present in your ecosystem. The speed and computational requirements might be another hassle. To overcome these difficulties, Datahut can crawl multiple pages irrespective of your platforms, devices or the code language and store the content in simple readable file formats like or even in database systems. Datahut has a simple and transparent process of mining data from the web. You can read more about our process and the multiple use-cases we have helped solve with data mining from the web. Get in touch with Datahut for your web scraping and crawling needs. #webcrawling #Python #scrapy #webscraping #crawler #webcrawler #webscrapingwithpython
Frequently Asked Questions about python web crawler source code
How do you code a web crawler in Python?
Building a Web Crawler using Pythona name for identifying the spider or the crawler, “Wikipedia” in the above example.a start_urls variable containing a list of URLs to begin crawling from. … a parse() method which will be used to process the webpage to extract the relevant and necessary content.Aug 12, 2020
What is Web crawling and scraping in Python?
Web crawling is a component of web scraping, the crawler logic finds URLs to be processed by the scraper code. A web crawler starts with a list of URLs to visit, called the seed. For each URL, the crawler finds links in the HTML, filters those links based on some criteria and adds the new links to a queue.Dec 11, 2020
Is Octoparse open source?
Vision RPA, which is both free and Open Source. Other great apps like Octoparse are Scrapy (Free, Open Source), ParseHub (Freemium), Portia (Free, Open Source) and import.io (Paid).
