Web Scraping Github Python

Web scraping is an automated, programmatic process through which data can be constantly 'scraped' off webpages. Also known as screen scraping or web harvesting, web scraping can provide instant data from any publicly accessible webpage. On some websites, web scraping may be illegal.

Python Scrapy Github
Web Scraping Github Python Interview
Web Scraping Python Github
Python Scraper Github

Web Scraping com Python e BeautifulSoup. GitHub Gist: instantly share code, notes, and snippets.
Web Scraping Using Python What is Data Extraction? Data extraction is a process that involves retrieval of data from different website sources. Firms extract data in order to analyze it, migrate the data to a data repository (data warehouse) or use it in their businesses.

Navigate to the folder called PythonWebScrape that you downloaded to your desktop and double-click on the folder Within the PythonWebScrape folder, double-click on the file with the word “BLANK” in the name (PythonWebScrapeBLANK.ipynb). A pop-up window will ask you to Select Kernal — you should select the Python.

# Scraping using the Scrapy framework

First you have to set up a new Scrapy project. Enter a directory where you’d like to store your code and run:

To scrape we need a spider. Spiders define how a certain site will be scraped. Here’s the code for a spider that follows the links to the top voted questions on StackOverflow and scrapes some data from each page (source):

Save your spider classes in the projectNamespiders directory. In this case - projectNamespidersstackoverflow_spider.py.

Now you can use your spider. For example, try running (in the project's directory):

# Basic example of using requests and lxml to scrape some data

# Maintaining web-scraping session with requests

It is a good idea to maintain a web-scraping session to persist the cookies and other parameters. Additionally, it can result into a performance improvement because requests.Session reuses the underlying TCP connection to a host:

# Scraping using Selenium WebDriver

Some websites don’t like to be scraped. In these cases you may need to simulate a real user working with a browser. Selenium launches and controls a web browser.

Selenium can do much more. It can modify browser’s cookies, fill in forms, simulate mouse clicks, take screenshots of web pages, and run custom JavaScript.

# Scraping using BeautifulSoup4

# Modify Scrapy user agent

Sometimes the default Scrapy user agent ('Scrapy/VERSION (+http://scrapy.org)') is blocked by the host. To change the default user agent open settings.py, uncomment and edit the following line to what ever you want.

For example

# Simple web content download with urllib.request

The standard library module urllib.request can be used to download web content:

A similar module is also available in Python 2.

# Scraping with curl

imports:

Downloading:

-s: silent download

-A: user agent flag

Parsing:

# Remarks

# Useful Python packages for web scraping (alphabetical order)

# Making requests and collecting data

A simple, but powerful package for making HTTP requests.

Caching for requests; caching data is very useful. In development, it means you can avoid hitting a site unnecessarily. While running a real collection, it means that if your scraper crashes for some reason (maybe you didn't handle some unusual content on the site...? maybe the site went down...?) you can repeat the collection very quickly from where you left off.

Useful for building web crawlers, where you need something more powerful than using requests and iterating through pages.

Python bindings for Selenium WebDriver, for browser automation. Using requests to make HTTP requests directly is often simpler for retrieving webpages. However, this remains a useful tool when it is not possible to replicate the desired behaviour of a site using requests alone, particularly when JavaScript is required to render elements on a page.

# HTML parsing

Query HTML and XML documents, using a number of different parsers (Python's built-in HTML Parser,html5lib, lxml or lxml.html)

Processes HTML and XML. Can be used to query and select content from HTML documents via CSS selectors and XPath.

Introduction

Python Scrapy Github

Introduction

Before reading it, please read the warnings in my blog Learning Python: Web Scraping.

Scrapy is an application framework for crawling web sites and extracting structured data which can be used for a wide range of useful applications, like data mining, information processing or historical archival.You can install Scrapy via pip.

Don’t use the python-scrapy package provided by Ubuntu, they are typically too old and slow to catch up with latest Scrapy.Instead, use pip install scrapy to install.

Basic Usage

After installation, try python3 -m scrapy --help and get help information:

A basic flow of Scrapy usage:

Create a new Scrapy project.
Write a spider to crawl a site and extract data.
Export the scraped data using the command line.
Change spider to recursively follow links.
Try to use other spider arguments.

Create a Project

Create a new Scrapy project:

Then it will create a directory like:

Create a class of our own spider which is the subclass of scrapy.Spider in the file soccer_spider.py under the soccer/spiders directory.

name: identifies the Spider. It must be unique within a project.
start_requests(): must return an iterable of Requests which the Spider will begin to crawl from.
parse(): a method that will be called to handle the response downloaded for each of the requests made.
Scrapy schedules the scrapy.Request objects returned by the start_requests method of the Spider.

Running Spider

Go to the soccer root directory and run the spider using runspider or crawl commands:

When getting the page content in response.body or in a local saved file, you could use other libraries such as Beautiful Soup to parse it.Here, I will continue use the methods provided by Scrapy to parse the content.

Extracting Data

Scrapy provides CSS selectors .css() and XPath .xpath() for the response object. Some examples:

With that, you can extract the data according to the elements, CSS styles or XPath. Add the codes in the parse() method.

Sometimes, you may want to extract the data from another link in the page.Then you can find the link and get the response by sending another request like:

Use the .urljoin() method to build a full absolute URL (since sometimes the links can be relative).

Scrapy also provides another method .follow() that supports relative URLs directly.

Example

I will still use the data in UEFA European Cup Matches 2017/2018 as an example.

The HTML content in the page looks like:

I developed a new class extends the scrapy.Spider class and then run it via Scrapy to extract the data.

I prefer using XPath because it is more flexible.Learn more about XPath in XML and XPath in W3Schools or other tutorials.

Further