{"id":4224,"date":"2023-11-04T23:14:09","date_gmt":"2023-11-04T23:14:09","guid":{"rendered":"http:\/\/localhost:10003\/how-to-create-a-web-crawler-with-scrapy-and-python\/"},"modified":"2023-11-05T05:47:56","modified_gmt":"2023-11-05T05:47:56","slug":"how-to-create-a-web-crawler-with-scrapy-and-python","status":"publish","type":"post","link":"http:\/\/localhost:10003\/how-to-create-a-web-crawler-with-scrapy-and-python\/","title":{"rendered":"How to Create a Web Crawler with Scrapy and Python"},"content":{"rendered":"<h2>Introduction<\/h2>\n<p>Web scraping is the process of extracting data from websites. It often involves automating the web browsing experience and parsing the HTML of a website to extract useful information. Scrapy is a powerful and flexible web scraping framework built with Python. In this tutorial, we will learn how to create a web crawler with Scrapy and Python.<\/p>\n<h2>Prerequisites<\/h2>\n<p>To follow along with this tutorial, you will need the following:<\/p>\n<ul>\n<li>Python 3.5 or above installed on your system.<\/li>\n<li>The <code>pip<\/code> package manager, which typically comes with Python.<\/li>\n<li>Basic understanding of HTML and CSS.<\/li>\n<li>Familiarity with Python programming.<\/li>\n<\/ul>\n<h2>Install Scrapy<\/h2>\n<p>Let&#8217;s start by installing Scrapy. Open a terminal or command prompt and run the following command:<\/p>\n<pre><code>pip install scrapy\n<\/code><\/pre>\n<p>Scrapy and its dependencies will be installed in your Python environment.<\/p>\n<h2>Set up a New Scrapy Project<\/h2>\n<p>To create a new Scrapy project, open a terminal or command prompt and navigate to the directory where you want to create the project. Run the following command:<\/p>\n<pre><code>scrapy startproject tutorial\n<\/code><\/pre>\n<p>This will create a new directory named <code>tutorial<\/code> with the basic structure of a Scrapy project.<\/p>\n<h2>Define a Spider<\/h2>\n<p>A spider is the main component of a Scrapy project. It defines how to follow links and what information to extract from the web pages. In Scrapy, spiders are Python classes that define how requests are made and how responses are processed.<\/p>\n<p>Open the <code>tutorial\/spiders<\/code> directory and create a new Python file named <code>example_spider.py<\/code>. In this file, define a class named <code>ExampleSpider<\/code> that extends <code>scrapy.Spider<\/code>. Here&#8217;s an example:<\/p>\n<pre><code class=\"language-python\">import scrapy\n\nclass ExampleSpider(scrapy.Spider):\n    name = 'example'\n    start_urls = ['https:\/\/example.com']\n\n    def parse(self, response):\n        # Extract information from the response\n        # using XPath or CSS selectors\n        pass\n<\/code><\/pre>\n<p>In the <code>ExampleSpider<\/code> class, we provide a name for our spider using the <code>name<\/code> attribute. This name will be used to identify the spider when running Scrapy commands.<\/p>\n<p>The <code>start_urls<\/code> attribute is a list of URLs that the spider will start crawling from. In this example, we only have a single URL, but you can add more if needed.<\/p>\n<p>The <code>parse<\/code> method is called for each response received by the spider. Inside this method, we can extract information from the response using XPath or CSS selectors. For now, let&#8217;s leave it empty as <code>pass<\/code>.<\/p>\n<h2>Make Requests<\/h2>\n<p>To start making requests to the URLs defined in <code>start_urls<\/code>, we need to run the spider. Open a terminal or command prompt and navigate to the project&#8217;s directory (<code>tutorial<\/code> in our case). Run the following command:<\/p>\n<pre><code>scrapy crawl example\n<\/code><\/pre>\n<p>This command runs the spider named <code>example<\/code>. Scrapy will make requests to the URLs defined in <code>start_urls<\/code> and call the <code>parse<\/code> method for each response received.<\/p>\n<h2>Extract Data<\/h2>\n<p>In the <code>parse<\/code> method, we can use XPath or CSS selectors to extract data from the web pages. Scrapy provides a convenient way to select elements from the HTML using these selectors.<\/p>\n<p>For example, let&#8217;s say we want to extract the titles of all the articles on a web page. We can use the following CSS selector to accomplish this:<\/p>\n<pre><code class=\"language-python\">def parse(self, response):\n    for article in response.css('article'):\n        title = article.css('h2 a::text').get()\n        yield {\n            'title': title\n        }\n<\/code><\/pre>\n<p>In this example, we are selecting all the <code>&lt;article&gt;<\/code> elements and then selecting the text of the <code>&lt;a&gt;<\/code> element inside the <code>&lt;h2&gt;<\/code> element, which represents the title of each article. We use the <code>yield<\/code> keyword to return a dictionary with the extracted data. The <code>yield<\/code> keyword is used because Scrapy supports asynchronous processing of requests and responses.<\/p>\n<h2>Follow Links<\/h2>\n<p>To crawl multiple pages, we can make requests to the links found on the current page. We can achieve this by calling the <code>follow<\/code> method and passing it the URL of the link.<\/p>\n<p>For example, let&#8217;s say we have a web page with pagination links at the bottom. We want to follow these links to scrape all the pages. We can modify our spider as follows:<\/p>\n<pre><code class=\"language-python\">def parse(self, response):\n    for article in response.css('article'):\n        title = article.css('h2 a::text').get()\n        yield {\n            'title': title\n        }\n\n    next_page_url = response.css('a.next-page-link::attr(href)').get()\n    if next_page_url:\n        yield response.follow(next_page_url, self.parse)\n<\/code><\/pre>\n<p>In this example, we select the URL of the next page using a CSS selector. If the URL exists, we call <code>response.follow<\/code> and pass it the URL and the <code>parse<\/code> method. This creates a new request to the next page and calls the <code>parse<\/code> method for that response.<\/p>\n<h2>Storing the Scraped Data<\/h2>\n<p>By default, Scrapy outputs the scraped data to the terminal. However, we can configure Scrapy to store the data in different formats like JSON, CSV, or a database.<\/p>\n<p>To configure the output format, open the <code>settings.py<\/code> file inside the <code>tutorial\/tutorial<\/code> directory. Uncomment the following lines and modify them as desired:<\/p>\n<pre><code class=\"language-python\">ITEM_PIPELINES = {\n   'tutorial.pipelines.ExamplePipeline': 300,\n}\n\nFEED_FORMAT = 'json'\nFEED_URI = 'output.json'\n<\/code><\/pre>\n<p>In this example, we set the output format to JSON and specify the output file name as <code>output.json<\/code>. We also specify <code>ExamplePipeline<\/code> as the pipeline to process the items. Pipelines in Scrapy are used to process and store the scraped data.<\/p>\n<p>To create a pipeline, open the <code>pipelines.py<\/code> file inside the <code>tutorial\/tutorial<\/code> directory. Define a class named <code>ExamplePipeline<\/code> and implement the <code>process_item<\/code> method. Here&#8217;s an example:<\/p>\n<pre><code class=\"language-python\">import json\n\nclass ExamplePipeline:\n    def open_spider(self, spider):\n        self.file = open('output.json', 'w')\n        self.file.write('[n')  # Start a JSON list\n\n    def close_spider(self, spider):\n        self.file.write('n]')  # End the JSON list\n        self.file.close()\n\n    def process_item(self, item, spider):\n        line = json.dumps(dict(item))\n        self.file.write(line + ',n')  # Write a JSON object to a new line\n        return item\n<\/code><\/pre>\n<p>In this example, we open the output file in the <code>open_spider<\/code> method and write a start indicator for the JSON list. In the <code>close_spider<\/code> method, we write an end indicator and close the file. Finally, in the <code>process_item<\/code> method, we convert the item to a JSON string and write it to the file.<\/p>\n<h2>Conclusion<\/h2>\n<p>In this tutorial, we learned how to create a web crawler with Scrapy and Python. We covered the basic structure of a Scrapy project, how to define a spider, how to make requests and extract data, how to follow links, and how to store the scraped data. Scrapy provides a robust and easy-to-use framework for web scraping automation, making it an ideal choice for scraping data from websites.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Introduction Web scraping is the process of extracting data from websites. It often involves automating the web browsing experience and parsing the HTML of a website to extract useful information. Scrapy is a powerful and flexible web scraping framework built with Python. In this tutorial, we will learn how to <a href=\"http:\/\/localhost:10003\/how-to-create-a-web-crawler-with-scrapy-and-python\/\" class=\"btn btn-link continue-link\">Continue Reading<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[1],"tags":[1769,1770,85,1537,75,1771,1768,739,1767],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.5 (Yoast SEO v21.5) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How to Create a Web Crawler with Scrapy and Python - Pantherax Blogs<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/localhost:10003\/how-to-create-a-web-crawler-with-scrapy-and-python\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Create a Web Crawler with Scrapy and Python\" \/>\n<meta property=\"og:description\" content=\"Introduction Web scraping is the process of extracting data from websites. 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