HTTP Web-Sniffer App. SEO : Scraper facilement Google avec Excel. Préambule : ceci est le premier article d’une série consacrée à Excel pour le SEO.
Alors Stay Tuned ! Nous avons tous eu besoin un jour ou l’autre de scraper les résultats de Google, que ce soit pour un audit, un suivi de positionnement ou tout un tas d’autres choses. Il existe aujourd’hui sur le marché de très nombreuses solutions pour récupérer les résultats des pages de recherche. Je pense en premier lieu à l’excellent RDDZ Scraper, que j’utilise très souvent, mais il y en a d’autres. Le souci (mon souci) c’est que l’analyse s’effectue systématiquement sur Excel. Je vous propose en bas de cet article un document Excel que je mets à disposition gratuitement et qui va vous permettre de scraper directement dans Excel. Edit 24/09/14 – 10:15 : la version 1.5 contenait un bug, pensez à télécharger la dernière version (1.6). J’ai besoin de quoi pour scraper directement dans Excel ?
Il vous faut bien sûr une version d’Excel pas trop ancienne si possible. Ce que vous allez pouvoir faire. Using Kimono Labs to Scrape the Web for Free. Historically, I have written and presented about big data—using data to create insights, and how to automate your data ingestion process by connecting to APIs and leveraging advanced database technologies.
Recently I spoke at SMX West about leveraging the rich data in webmaster tools. After the panel, I was approached by the in-house SEO of a small company, who asked me how he could extract and leverage all the rich data out there without having a development team or large budget. I pointed him to the CSV exports and some of the more hidden tools to extract Google data, such as the GA Query Builder and the YouTube Analytics Query Builder. However, what do you do if there is no API? What do you do if you want to look at unstructured data, or use a data source that does not provide an export? Before we get into the actual "scraping" I want to briefly discuss how these tools work. Kimono Labs allows you to extract this data either on demand or as a scheduled job. A. B. 1. 2. 3. 4. 5. Build a Website Crawler based upon Scrapy. 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 and it is widely used in Industries.
In this article we are going to build a crawler which will crawl data from Hacker News and store it in the database which can be reproduced as per our requirement. Installation We are going to need the Scrapy library along with BeautifulSoup for screen scraping purpose and SQLAlchemy for storing the data. Install Scrapy by simple pip command if you are using ubuntu or any other variant of unix. If you are in Windows Machine, you will need to install various dependencies of scrapy manually.
Windows users will need pywin32, pyOpenSSL, Twisted, lxml and zope.interface. How to crawl a quarter billion webpages in 40 hours. More precisely, I crawled 250,113,669 pages for just under 580 dollars in 39 hours and 25 minutes, using 20 Amazon EC2 machine instances. I carried out this project because (among several other reasons) I wanted to understand what resources are required to crawl a small but non-trivial fraction of the web. In this post I describe some details of what I did. Of course, there’s nothing especially new: I wrote a vanilla (distributed) crawler, mostly to teach myself something about crawling and distributed computing.
Still, I learned some lessons that may be of interest to a few others, and so in this post I describe what I did. The post also mixes in some personal working notes, for my own future reference. What does it mean to crawl a non-trivial fraction of the web? Code: Originally I intended to make the crawler code available under an open source license at GitHub. There’s a more general issue here, which is this: who gets to crawl the web? Web Crawling and Metadata with Python. How this was made This document was created using Docutils/reStructuredText and S5.
Introduction to web crawling (using Scrapy) metadata extraction (using Schemato). What do we do? How do we do it? "A computer program that browses the World Wide Web in a methodical, automated manner or in an orderly fashion. " Open source examples: Apache Nutch: built by Doug Cutting, creator of Lucene/HadoopHeritrix: built by the Internet Archive 40 billion pages on the Web today (Google) Growing: size was "just" 15 billion in October 2010.