How To Build A Web Crawler Python
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
How to Build a Web Crawler with Python? – Best Proxy Reviews
Do you want to learn how to build a web crawler from scratch? Join me as I show you how to build a web crawler using Python as the language of choice for the you ever wondered how the Internet would be without search engines? Well, what if I tell you that web crawlers are some of the secrets that makes search engine what they have become have proven to be incredibly important, not only in the area of general web search but other aspects in academic research, lead generation, and even Search Engine Optimizations (SEO) project that intends to extract data from many pages on a website or the full Internet without a prior list of links to extract the data from will most likely make use of web crawlers to achieve you are interested in developing a web crawler for a project, then you need to know that the basics of a web crawler are easy, and everyone can design and develop one. However, depending on the complexity and size of your project, the befitting crawler could be difficult to build and maintain. In this article, you will learn how to build web crawlers yourself. Before going into the tutorial proper, let take a look at what web crawler actually are Web Crawlers? The terms web crawlers and web scrapers are used interchangeably, and many think they mean the same thing. While they loosely mean the same thing, if you go deep, you will discover web scraping and web crawling are not the same things – and you can even see that from the way web crawlers and web scrapers are crawlers, also known as web spiders, spiderbots, or simply crawlers, are web bots that have been developed to systematically visit webpages on the World Wide Web for the purpose of web indexing and collecting other data from the pages they Do They Differ from Web Scrapers? From the above, you can tell that they are different from web scrapers. They are both bots for web data extraction. However, you can see web scrapers as more streamlined and specialized workers designed for extracting specific data from a specific and defined list of web pages, such as Yelp reviews, Instagram posts, Amazon price data, Shopify product data, and so on…Web scrapers are fed a list of URLs, and it visits those URLs and scrapes required is not the case for web crawlers as they are fed a list of URLs, and from this list, the web crawler is meant to find other URLs to be crawled by themselves, following some set of rules. The reason why marketers use the terms interchangeably is that in the process of web crawling, web scraping is involved – and some web scrapers incorporate aspects of web Does Web Crawlers Work? Depending on the complexity and use cases of a web crawler, it could work in the basic way web crawlers work or have some modifications in working mechanism. At its most basic level, you can see web crawlers as web browsers that browse pages on the Internet collecting working mechanism for web crawlers is simple. For a web crawler to work, you will have to provide it a list of URLs – these URLs are known as seed URLs. These seed URLs are added to a list of URLs to be visited. The crawler then goes through the list of URLs to be visited and visit them one after the each URL the crawler visits, it extracts all the hyperlinks on the page and adds them to the list of URLs to be visited. Aside from collecting hyperlinks in other to cover the width and breadth of the site or web, as in the case of web crawlers not specifically designed for a specific website, web crawlers also collect other, for instance, Google bots, the most popular web crawler on the Internet aside from link data, also index the content of a page to make it easier to search. On the other hand, a web archive takes a snapshot of the pages it visits – other crawlers extract data they are interested in. Aside from a list of URLs to be visited, the crawler also keeps a list of URLs that have already been visited to avoid adding a crawled URLs into the list of sites to be are a good number of consideration you will have to look into, including a crawling policy that set the rule for URLs to be visited, a re-visit policy that dictates when to look out for a change on a web page, a politeness policy that determines whether you should respect the rules or not, and lastly, a parallelization policy for coordinating distributed web crawling exercise, among the above, we expect you to have an idea of what web crawlers are. It is now time to move into learning how to develop one yourself. Web crawlers are computer programs written using any of the general-purpose programming languages out can code a web crawler using Java, C#, PHP, Python, and even JavaScript. This means that the number one prerequisite of developing a web crawler is being able to code in any of the general-purpose programming lated: How to scrape HTML from a website Using Javascript? In this article, we are going to be making use of Python because of its simplicity, ease of use, beginner-friendliness, and extensive library support. Even if you are not a Python programmer, you can take a crash course fin python programming in other to understand what will be discussed as all of the codes will be written in project we will be building will be a very easy one and what can be called a proof of concept. The crawler we will be developing will accept a seed URL and visit all pages on the website, outing the links and title to the won’t be respecting files, no proxy usage, no multithreading, and any other complexities – we are making it easy for you to follow and quirements for the ProjectEarlier, I stated that Python has an extensive library of tools for web crawling. The most important of them all for web crawling is Scrapy, a web crawling framework that makes it easy for the development of web crawlers in fewer lines of code. However, we won’t be using Scrapy as it hides some details; let make use of the Requests and BeautifulSoup combination for the development. Python: While many Operating Systems come with Python preinstalled, the version installed is usually old, and as such, you will need to install a recent version of Python. You can visit the official download page to download an updated version of the Python programming language. Requests: Dubbed HTTP for Humans, Requests is the best third-party library for sending HTTP requests to web servers. It is very simple and easy to use. under the hood, this library makes use of the urllib package but abstract it and provide you better APIs for handling HTTP requests and responses. This is a third-party library, and as such, you will need to download it. You can use thepip install Requeststo download it. BeautifulSoup: while the Requests library is for sending HTTP requests, BeautifulSoup is for parsing HTML and XML documents. With BeautifulSoup, you do not have to deal with using regular expressions and the standard HTM parser that are not easy to use and prone to errors if you are not skilled in their usage. BeautifulSoup makes it easy for you to transverse HTML documents and parse out the required data. This tool is also a third-party library and not included in the standard python distribution. You can use download it using the pip command:pip install Vs. Beautifulsoup Vs. Selenium for Web ScrapingWhat is Data Parsing and Parsing Techniques involved? As stated earlier, the process of developing a web crawler can be complex, but the crawler we are developing in this tutorial is very easy. In fact, if you already know how to scrape data from web pages, there is a high chance that you already know how to develop a simple web crawler. The Page Title Extractor project will be contained in only one module. You can create a new Python file and name The module will have a class named TitleExtractor with 2 methods. The two classes arecrawlfor defining main crawling logic andstartfor giving the crawl method directive on the URL to the Necessary LibrariesLet start by importing the required libraries for the project. We require requests, beautifulsoup, and urlparse. Requests is for sending web requests, beautifulsoup for parsing title, and URLs from web pages downloaded by requests. The urlparse library is bundled inside the standard Python library and use for parsing rseimport urlparse
import requests
from bs4 import BeautifulSoupWeb Crawler Class Definition After importing the required library, let create a new class name TitleExtractor. This will be the crawler TitleCrawler:
“””
Crawler class accepts a URL as argument.
This seed url will be the url from which other urls will be discovered
def __init__(self, start_url):
self. urls_to_be_visited = []
(start_url)
sited = []
= “” + urlparse(start_url). netlocFrom the above, you can see the initialization function – it accepts a URL as argument. There are 3 variables – theurls_to_be_visitedis for keeping a list of URLs to visit, thevisitedvariable is for keeping a list of visited URLs to avoid crawling a URL more than once, and the domain variable is for thedomainof the site you are scraping from. You will need it so that only links from the domain are Method Coding def start(self):
for urlin self. urls_to_be_visited:
(url)
x = TitleCrawler(“)
()The start method above belongs to the TitleExtractor class. You can see a for loop that loops through the urls_to_be_visitedand pass URLs to the crawl method. The crawl method is also a method of the TitleExtractor class. The x variable is for creating an instance of the TitleExtractor class and then calling the start method to get the crawler to start crawling. From the above code snippets, nothing has actually been done. The main work is done in the crawl method. Below is the code for the crawl Method Coding def crawl(self, link):
page_content = (link)
soup = BeautifulSoup(page_content, “”)
title = (“title”)
print(“PAGE BEING CRAWLED: ” + + “|” + link)
(link)
urls = nd_all(“a”)
for urlin urls:
url = (“href”)
if urlis not None:
if artswith():
if urlnot in sited:
print(“Number of Crawled pages:” + str(len(sited)))
print(“Number of Links to be crawled:” + str(len(self. urls_to_be_visited)))
print(“::::::::::::::::::::::::::::::::::::::”)The URL to be crawled is passed into the crawl method by the start function, and it does that by iterating through the urls_to_be_visited list variable. The first line in the code above sends a request to the URL and returns the content of the Beautifulsoup, the title of the page and URLs present on the page are scraped. The web crawler is meant for crawling only URLs for a target website, and such, URLs for external sources are not considered – you can see that from the second if statement. For a URL to be added to the list of URLs to be visited, it must be a valid URL and has not been visited Codefrom rseimport urlparse
from bs4 import BeautifulSoup
class TitleCrawler:
= “” + urlparse(start_url)
def crawl(self, link):
print(“::::::::::::::::::::::::::::::::::::::”)
def start(self):
()You can change the seed URL to any other URL. In the code above, we use. If you run the code above, you will get something like the result BEING CRAWLED: Sneaker Bots • Cop Guru|
Number of Crawled pages:4
Number of Links to be crawled:1535::::::::::::::::::::::::::::::::::::::
PAGE BEING CRAWLED: All in One Bots • Cop Guru|
Number of Crawled pages:5
Number of Links to be crawled:1666::::::::::::::::::::::::::::::::::::::
PAGE BEING CRAWLED: Adidas Bots • Cop Guru|
Number of Crawled pages:6
Number of Links to be crawled:1763A Catch: There is a lot of improvement in the Project Looking at the above code, you will most likely run it without any problem, but when an exception is experienced, that’s when the code will stop running. No exception handling was considered in the code for from exception handling, you will discover that no anti-bot system evading technique was incorporated, while in reality, many popular websites have them in place to discourage bot access. There is also the issue of speed, which you can solve by making the bot multithreaded and making the code more efficient. Aside from these, there are other rooms for nclusion Looking at the code of the web crawler we developed, you will agree with me that web crawlers are like web scrapers but have a wider scope. Another thing you need to know is that depending on the number of URLs discovered, the running time of the crawler can be long, but with multithreading, this can be shortened. Also, you need to also have it at the back of your mind that complex web crawlers for real projects will require a more planned Crawling Vs. Web ScrapingPython Web Scraping Libraries and FrameworkBuilding a Web Crawler Using Selenium and ProxiesHow to Use Selenium to Web Scrape with Python
Making Web Crawlers Using Scrapy for Python – DataCamp
If you would like an overview of web scraping in Python, take DataCamp’s Web Scraping with Python course.
In this tutorial, you will learn how to use Scrapy which is a Python framework using which you can handle large amounts of data! You will learn Scrapy by building a web scraper for which is an e-commerce website. Let’s get scrapping!
Scrapy Overview
Scrapy Vs. BeautifulSoup
Scrapy Installation
Scrapy Shell
Creating a project and Creating a custom spider
A basic HTML and CSS knowledge will help you understand this tutorial with greater ease and speed. Read this article for a fresher on HTML and CSS.
Source
Web scraping has become an effective way of extracting information from the web for decision making and analysis. It has become an essential part of the data science toolkit. Data scientists should know how to gather data from web pages and store that data in different formats for further analysis.
Any web page you see on the internet can be crawled for information and anything visible on a web page can be extracted [2]. Every web page has its own structure and web elements that because of which you need to write your web crawlers/spiders according to the web page being extracted.
Scrapy provides a powerful framework for extracting the data, processing it and then save it.
Scrapy uses spiders, which are self-contained crawlers that are given a set of instructions [1]. In Scrapy it is easier to build and scale large crawling projects by allowing developers to reuse their code.
In this section, you will have an overview of one of the most popularly used web scraping tool called BeautifulSoup and its comparison to Scrapy.
Scrapy is a Python framework for web scraping that provides a complete package for developers without worrying about maintaining code.
Beautiful Soup is also widely used for web scraping. It is a Python package for parsing HTML and XML documents and extract data from them. It is available for Python 2. 6+ and Python 3.
Here are some differences between them in a nutshell:
Scrapy
BeautifulSoup
Functionality
—
Scrapy is the complete package for downloading web pages, processing them and save it in files and databases
BeautifulSoup is basically an HTML and XML parser and requires additional libraries such as requests, urlib2 to open URLs and store the result [6]
Learning Curve
Scrapy is a powerhouse for web scraping and offers a lot of ways to scrape a web page. It requires more time to learn and understand how Scrapy works but once learned, eases the process of making web crawlers and running them from just one line of command. Becoming an expert in Scrapy might take some practice and time to learn all functionalities.
BeautifulSoup is relatively easy to understand for newbies in programming and can get smaller tasks done in no time
Speed and Load
Scrapy can get big jobs done very easily. It can crawl a group of URLs in no more than a minute depending on the size of the group and does it very smoothly as it uses Twister which works asynchronously (non-blocking) for concurrency.
BeautifulSoup is used for simple scraping jobs with efficiency. It is slower than Scrapy if you do not use multiprocessing.
Extending functionality
Scrapy provides Item pipelines that allow you to write functions in your spider that can process your data such as validating data, removing data and saving data to a database. It provides spider Contracts to test your spiders and allows you to create generic and deep crawlers as well. It allows you to manage a lot of variables such as retries, redirection and so on.
If the project does not require much logic, BeautifulSoup is good for the job, but if you require much customization such as proxys, managing cookies, and data pipelines, Scrapy is the best option.
Information: Synchronous means that you have to wait for a job to finish to start a new job while Asynchronous means you can move to another job before the previous job has finished
Here is an interesting DataCamp BeautifulSoup tutorial to learn.
With Python 3. 0 (and onwards) installed, if you are using anaconda, you can use conda to install scrapy. Write the following command in anaconda prompt:
conda install -c conda-forge scrapy
To install anaconda, look at these DataCamp tutorials for Mac and Windows.
Alternatively, you can use Python Package Installer pip. This works for Linux, Mac, and Windows:
pip install scrapy
Scrapy also provides a web-crawling shell called as Scrapy Shell, that developers can use to test their assumptions on a site’s behavior. Let us take a web page for tablets at AliExpress e-commerce website. You can use the Scrapy shell to see what components the web page returns and how you can use them to your requirements.
Open your command line and write the following command:
scrapy shell
If you are using anaconda, you can write the above command at the anaconda prompt as well. Your output on the command line or anaconda prompt will be something like this:
You have to run a crawler on the web page using the fetch command in the Scrapy shell. A crawler or spider goes through a webpage downloading its text and metadata.
fetch()
Note: Always enclose URL in quotes, both single and double quotes work
The output will be as follows:
The crawler returns a response which can be viewed by using the view(response) command on shell:
view(response)
And the web page will be opened in the default browser.
You can view the raw HTML script by using the following command in Scrapy shell:
print()
You will see the script that’s generating the webpage. It is the same content that when you left right-click any blank area on a webpage and click view source or view page source. Since, you need only relevant information from the entire script, using browser developer tools you will inspect the required element. Let us take the following elements:
Tablet name
Tablet price
Number of orders
Name of store
Right-click on the element you want and click inspect like below:
Developer tools of the browser will help you a lot with web scraping. You can see that it is an tag with a class product and the text contains the name of the product:
You can extract this using the element attributes or the css selector like classes. Write the following in the Scrapy shell to extract the product name:
(“. product::text”). extract_first()
The output will be:
extract_first() extract the first element that satisfies the css selector. If you want to extract all the product names use extract():
(“. extract()
Following code will extract price range of the products:
(“”). extract()
Similarly, you can try with a number of orders and the name of the store.
XPath is a query language for selecting nodes in an XML document [7]. You can navigate through an XML document using XPath. Behind the scenes, Scrapy uses Xpath to navigate to HTML document items. The CSS selectors you used above are also converted to XPath, but in many cases, CSS is very easy to use. But you should know how the XPath in Scrapy works.
Go to your Scrapy Shell and write fetch() the same way as before. Try out the following code snippets [3]:
(‘/html’). extract()
This will show you all the code under the tag. / means direct child of the node. If you want to get the
(‘/html//div’). extract()
For XPath, you must learn to understand the use of / and // to know how to navigate through child and descendent nodes. Here is a helpful tutorial for XPath Nodes and some examples to try out.
If you want to get all
(“//div”). extract()
You can further filter your nodes that you start from and reach your desired nodes by using attributes and their values. Below is the syntax to use classes and their values.
(“//div[@class=’quote’]/span[@class=’text’]”). extract()
(“//div[@class=’quote’]/span[@class=’text’]/text()”). extract()
Use text() to extract all text inside nodes
Consider the following HTML code:
You want to get the text inside the tag, which is child node of
(‘//div[@class=”site-notice-container container”]/a[@class=”notice-close”]/text()’). extract()
Creating a Scrapy project and Custom Spider
Web scraping can be used to make an aggregator that you can use to compare data. For example, you want to buy a tablet, and you want to compare products and prices together you can crawl your desired pages and store in an excel file. Here you will be scraping for tablets information.
Now, you will create a custom spider for the same page. First, you need to create a Scrapy project in which your code and results will be stored. Write the following command in the command line or anaconda prompt.
scrapy startproject aliexpress
This will create a hidden folder in your default python or anaconda installation. aliexpress will be the name of the folder. You can give any name. You can view the folder contents directly through explorer. Following is the structure of the folder:
file/folder
Purpose
deploy configuration file
aliexpress/
Project’s Python module, you’ll import your code from here
__
Initialization file
project items file
project pipelines file
project settings file
spiders/
a directory where you’ll later put your spiders
Once you have created the project you will change to the newly created directory and write the following command:
[scrapy genspider aliexpress_tablets]()
This creates a template file named in the spiders directory as discussed above. The code in that file is as below:
import scrapy
class AliexpressTabletsSpider():
name = ‘aliexpress_tablets’
allowed_domains = [”]
start_urls = [”]
def parse(self, response):
pass
In the above code you can see name, allowed_domains, sstart_urls and a parse function.
name: Name is the name of the spider. Proper names will help you keep track of all the spider’s you make. Names must be unique as it will be used to run the spider when scrapy crawl name_of_spider is used.
allowed_domains (optional): An optional python list, contains domains that are allowed to get crawled. Request for URLs not in this list will not be crawled. This should include only the domain of the website (Example:) and not the entire URL specified in start_urls otherwise you will get warnings.
start_urls: This requests for the URLs mentioned. A list of URLs where the spider will begin to crawl from, when no particular URLs are specified [4]. So, the first pages downloaded will be those listed here. The subsequent Request will be generated successively from data contained in the start URLs [4].
parse(self, response): This function will be called whenever a URL is crawled successfully. It is also called the callback function. The response (used in Scrapy shell) returned as a result of crawling is passed in this function, and you write the extraction code inside it!
Information: You can use BeautifulSoup inside parse() function of the Scrapy spider to parse the html document.
Note: You can extract data through css selectors using () as discussed in scrapy shell section but also using XPath (XML) that allows you to access child elements. You will see the example of () in the code edited in pass() function.
You will make changes to the file. I have added another URL in start_urls. You can add the extraction logic to the pass() function as below:
# -*- coding: utf-8 -*-
start_urls = [”,
”]
print(“procesing:”)
#Extract data using css selectors
(‘. product::text’). extract()
(”). extract()
#Extract data using xpath
(“//em[@title=’Total Orders’]/text()”). extract()
(“//a[@class=’store $p4pLog’]/text()”). extract()
row_data=zip(product_name, price_range, orders, company_name)
#Making extracted data row wise
for item in row_data:
#create a dictionary to store the scraped info
scraped_info = {
#key:value
‘page’,
‘product_name’: item[0], #item[0] means product in the list and so on, index tells what value to assign
‘price_range’: item[1],
‘orders’: item[2],
‘company_name’: item[3], }
#yield or give the scraped info to scrapy
yield scraped_info
Information: zip() takes n number of iterables and returns a list of tuples. ith element of the tuple is created using the ith element from each of the iterables. [8]
The yield keyword is used whenever you are defining a generator function. A generator function is just like a normal function except it uses yield keyword instead of return. The yield keyword is used whenever the caller function needs a value and the function containing yield will retain its local state and continue executing where it left off after yielding value to the caller function. Here yield gives the generated dictionary to Scrapy which will process and save it!
Now you can run the spider:
scrapy crawl aliexpress_tablets
You will see a long output at the command line like below:
Exporting data
You will need data to be presented as a CSV or JSON so that you can further use the data for analysis. This section of the tutorial will take you through how you can save CSV and JSON file for this data.
To save a CSV file, open from the project directory and add the following lines:
FEED_FORMAT=”csv”
FEED_URI=””
After saving the, rerun the scrapy crawl aliexpress_tablets in your project directory.
The CSV file will look like:
Note: Everytime you run the spider it will append the file.
FEED_FORMAT [5]: This sets the format you want to store the data. Supported formats are: + JSON
+ CSV
+ JSON Lines
+ XML
FEED_URI [5]: This gives the location of the file. You can store a file on your local file storage or an FTP as well.
Scrapy’s Feed Export can also add a timestamp and the name of spider to your file name, or you can use these to identify a directory in which you want to store. %(time)s: gets replaced by a timestamp when the feed is being created [5]%(name)s: gets replaced by the spider name [5]
For Example:
Store in FTP using one directory per spider [5]:
ftpuser:[email protected]/scraping/feeds/%(name)s/%(time)
The Feed changes you make in will apply to all spiders in the project. You can also set custom settings for a particular spider that will override the settings in the file.
custom_settings={ ‘FEED_URI’: “aliexpress_%(time)”,
‘FEED_FORMAT’: ‘json’}
#yield or give the scraped info to Scrapy
returns the URL of the page from which response is generated. After running the crawler using scrapy crawl aliexpress_tablets you can view the json file:
Following Links
You must have noticed, that there are two links in the start_urls. The second link is the page 2 of the same tablets search results. It will become impractical to add all links. A crawler should be able to crawl by itself through all the pages, and only the starting point should be mentioned in the start_urls.
If a page has subsequent pages, you will see a navigator for it at the end of the page that will allow moving back and forth the pages. In the case you have been implementing in this tutorial, you will see it like this:
Here is the code that you will see:
As you can see that under there is a tag with class class that is the current page you are on, and under that are all tags with links to the next page. Everytime you will have to get the tags after this tag. Here comes a little bit of CSS! In this, you have to get sibling node and not a child node, so you have to make a css selector that tells the crawler to find tags that are after tag with class.
Remember! Each web page has its own structure. You will have to study the structure a little bit on how you can get the desired element. Always try out (SELECTOR) on Scrapy Shell before writing them in code.
Modify your as below:
‘FEED_FORMAT’: ‘csv’}
NEXT_PAGE_SELECTOR = ‘ + a::attr(href)’
next_page = (NEXT_PAGE_SELECTOR). extract_first()
if next_page:
yield quest(
response. urljoin(next_page), )
In the above code:
you first extracted the link of the next page using next_page = (NEXT_PAGE_SELECTOR). extract_first() and then if the variable next_page gets a link and is not empty, it will enter the if body.
response. urljoin(next_page): The parse() method will use this method to build a new url and provide a new request, which will be sent later to the callback. [9]
After receiving the new URL, it will scrape that link executing the for body and again look for the next page. This will continue until it doesn’t get a next page link.
Here you might want to sit back and enjoy your spider scraping all the pages. The above spider will extract from all subsequent pages. That will be a lot of scraping! But your spider will do it! Below you can see the size of the file has reached 1. 1MB.
Scrapy does it for you!
In this tutorial, you have learned about Scrapy, how it compares to BeautifulSoup, Scrapy Shell and how to write your own spiders in Scrapy. Scrapy handles all the heavy load of coding for you, from creating project files and folders till handling duplicate URLs it helps you get heavy-power web scraping in minutes and provides you support for all common data formats that you can further input in other programs. This tutorial will surely help you understand Scrapy and its framework and what you can do with it. To become a master in Scrapy, you will need to go through all the fantastic functionalities it has to provide, but this tutorial has made you capable of scraping groups of web pages in an efficient way.
For further reading, you can refer to Offical Scrapy Docs.
Also, don’t forget to check out DataCamp’s Web Scraping with Python course.
References
[1] [2] [3] [4] [5] [6] [7] [8] [9]
Frequently Asked Questions about how to build a web crawler python
How do I create 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
How do you design a web crawler?
Design a web crawlerStep 1: Outline use cases and constraints. Gather requirements and scope the problem. … Step 2: Create a high level design. Outline a high level design with all important components.Step 3: Design core components. Dive into details for each core component. … Step 4: Scale the design.
What is crawler in Python?
Web crawling is a powerful technique to collect data from the web by finding all the URLs for one or multiple domains. Python has several popular web crawling libraries and frameworks.Dec 11, 2020
