Scraping data from a list of web pages using Google Docs

Quite often when you’re looking for data as part of a story, that data will not be on a single page, but on a series of pages. To manually copy the data from each one – or even scrape the data individually – would take time. Here I explain a way to use Google Docs to grab the data for you.

Some basic principles

Although Google Docs is a pretty clumsy tool to use to scrape webpages, the method used is much the same as if you were writing a scraper in a programming language like Python or Ruby. For that reason, I think this is a good quick way to introduce the basics of certain types of scrapers.

Here’s how it works:

Firstly, you need a list of links to the pages containing data.

Quite often that list might be on a webpage which links to them all, but if not you should look at whether the links have any common structure, for example “http://www.country.com/data/australia” or “http://www.country.com/data/country2″. If it does, then you can generate a list by filling in the part of the URL that changes each time (in this case, the country name or number), assuming you have a list to fill it from (i.e. a list of countries, codes or simple addition).

Second, you need the destination pages to have some consistent structure to them. In other words, they should look the same (although looking the same doesn’t mean they have the same structure – more on this below).

The scraper then cycles through each link in your list, grabs particular bits of data from each linked page (because it is always in the same place), and saves them all in one place.

Scraping with Google Docs using =importXML – a case study

If you’ve not used =importXML before it’s worth catching up on my previous 2 posts How to scrape webpages and ask questions with Google Docs and =importXML and Asking questions of a webpage – and finding out when those answers change.

This takes things a little bit further.

In this case I’m going to scrape some data for a story about local history – the data for which is helpfully published by the Durham Mining Museum. Their homepage has a list of local mining disasters, with the date and cause of the disaster, the name and county of the colliery, the number of deaths, and links to the names and to a page about each colliery.

However, there is not enough geographical information here to map the data. That, instead, is provided on each colliery’s individual page.

So we need to go through this list of webpages, grab the location information, and pull it all together into a single list.

Finding the structure in the HTML

To do this we need to isolate which part of the homepage contains the list. If you right-click on the page to ‘view source’ and search for ‘Haig’ (the first colliery listed) we can see it’s in a table that has a beginning tag like so: <table border=0 align=center style=”font-size:10pt”>

We can use =importXML to grab the contents of the table like so:

=Importxml(“http://www.dmm.org.uk/mindex.htm”, ”//table[starts-with(@style, ‘font-size:10pt’)]“)

But we only want the links, so how do we grab just those instead of the whole table contents?

The answer is to add more detail to our request. If we look at the HTML that contains the link, it looks like this:

<td valign=top><a href=”http://www.dmm.org.uk/colliery/h029.htm“>Haig&nbsp;Pit</a></td>

So it’s within a <td> tag – but all the data in this table is, not surprisingly, contained within <td> tags. The key is to identify which <td> tag we want – and in this case, it’s always the fourth one in each row.

So we can add “//td[4]” (‘look for the fourth <td> tag’) to our function like so:

=Importxml(“http://www.dmm.org.uk/mindex.htm”, ”//table[starts-with(@style, ‘font-size:10pt’)]//td[4]“)

Now we should have a list of the collieries – but we want the actual URL of the page that is linked to with that text. That is contained within the value of the href attribute – or, put in plain language: it comes after the bit that says href=”.

So we just need to add one more bit to our function: “//@href”:

=Importxml(“http://www.dmm.org.uk/mindex.htm”, ”//table[starts-with(@style, ‘font-size:10pt’)]//td[4]//@href”)

So, reading from the far right inwards, this is what it says: “Grab the value of href, within the fourth <td> tag on every row, of the table that has a style value of font-size:10pt”

Note: if there was only one link in every row, we wouldn’t need to include //td[4] to specify the link we needed.

Scraping data from each link in a list

Now we have a list – but we still need to scrape some information from each link in that list

Firstly, we need to identify the location of information that we need on the linked pages. Taking the first page, view source and search for ‘Sheet 89′, which are the first two words of the ‘Map Ref’ line.

The HTML code around that information looks like this:

<td valign=top>(Sheet 89) NX965176, 54° 32' 35" N, 3° 36' 0" W</td>

Looking a little further up, the table that contains this cell uses HTML like this:

<table border=0 width=”95%”>

So if we needed to scrape this information, we would write a function like this:

=importXML(“http://www.dmm.org.uk/colliery/h029.htm”, “//table[starts-with(@width, ‘95%’)]//tr[2]//td[2]“)

…And we’d have to write it for every URL.

But because we have a list of URLs, we can do this much quicker by using cell references instead of the full URL.

So. Let’s assume that your formula was in cell C2 (as it is in this example), and the results have formed a column of links going from C2 down to C11. Now we can write a formula that looks at each URL in turn and performs a scrape on it.

In D2 then, we type the following:

=importXML(C2, “//table[starts-with(@width, ‘95%’)]//tr[2]//td[2]“)

If you copy the cell all the way down the column, it will change the function so that it is performed on each neighbouring cell.

In fact, we could simplify things even further by putting the second part of the function in cell D1 – without the quotation marks – like so:

//table[starts-with(@width, ‘95%’)]//tr[2]//td[2]

And then in D2 change the formula to this:

=ImportXML(C2,$D$1)

(The dollar signs keep the D1 reference the same even when the formula is copied down, while C2 will change in each cell)

Now it works – we have the data from each of 8 different pages. Almost.

Troubleshooting with =IF

The problem is that the structure of those pages is not as consistent as we thought: the scraper is producing extra cells of data for some, which knocks out the data that should be appearing there from other cells.

So I’ve used an IF formula to clean that up as follows:

In cell E2 I type the following:

=if(D2=””, ImportXML(C2,$D$1), D2)

Which says ‘If D2 is empty, then run the importXML formula again and put the results here, but if it’s not empty then copy the values across‘

That formula is copied down the column.

But there’s still one empty column even now, so the same formula is used again in column F:

=if(E2=””, ImportXML(C2,$D$1), E2)

A hack, but an instructive one

As I said earlier, this isn’t the best way to write a scraper, but it is a useful way to start to understand how they work, and a quick method if you don’t have huge numbers of pages to scrape. With hundreds of pages, it’s more likely you will miss problems – so watch out for inconsistent structure and data that doesn’t line up.

Source: http://onlinejournalismblog.com/2011/10/14/scraping-data-from-a-list-of-webpages-using-google-docs/

Data Scraping - What Are Hand-Scraped Hardwood Floors and What Are the Benefits?

If you love the look of hardwood flooring with lots of character, then you may want to check out hand-scraped hardwood flooring. Hand-scraped wood provides a warm vintage look, providing the floor instant character. These types of scraped hardwoods are suitable for living rooms, dining rooms, hallways and bedrooms. But what exactly is hand-scraped hardwood flooring?

Well, it is literally what you think it is. Hand-scraped hardwood flooring is created by hand using specialized wood working tools to make each board unique and giving an overall "old worn" appearance.

At Innovation Builders we offer solid wood floors finished on site with an actual hand-scraping technique followed by stain and sealer. Solid wood floors are installed by an expert team of technicians who work each board with skilled craftsman-like attention to detail. Following the scraping procedure the floor is stained by hand with a customer selected stain color, and then protected with multiple coats of sealing and finishing polyurethane. This finishing process of staining, sealing and coating the wood floors contributes to providing the look and durability of an old reclaimed wood floor, but with today's tough, urethane finishes.

There are many, many benefits to hand-scraped wood flooring. Overall, these floors are extremely durable and hard wearing, providing years of trouble-free use. These wood floors remain looking newer for longer because the texture that the process provides hides the typical dents, dings and scratches that other floors can't hide so easily. That's great news for households with kids, dogs, and cats.

These types of wood flooring have another unique advantage as well. When you do scratch these floors during their lifetime, the scratches are easily repaired. As long as the scratch isn't too deep you can make them practically disappear without ever having to hire a professional. It's simple to hide the scratch by using a color-matched stain marker or repair kit that is readily available through local flooring distributors. These features make hand-scraped hardwood flooring a lot more durable and hassle-free to maintain than other types of wood flooring.

The expert processes utilized in the creation of these floors provides a custom look of worn wood with deep color and subtle highlights. When the light hits the wood at different times during the day, it provides an understated but powerful effect of depth and beauty. They instantly offer your rooms a rustic look full of character, allowing your home to become a warm and inviting environment. The rustic look of this wood provides a texture, style and rustic appeal that cannot be matched by any other type of flooring.

Hand-Scraped Hardwood Flooring is a floor that says welcome and adds a touch of elegance to any home. If you are looking to buy a new home and you haven't had the opportunity to see or feel hand scraped hardwoods, stop in any of the model homes at Innovation Builders in Keller, North Richland Hills or Grand Prairie, Texas and check it out!

Source: http://ezinearticles.com/?What-Are-Hand-Scraped-Hardwood-Floors-and-What-Are-the-Benefits?&id=6026646

Web scraping in under 60 seconds: the magic of import.io

Import.io is a very powerful and easy-to-use tool for data extraction that has the aim of getting data from any website in a structured way. It is meant for non-programmers that need data (and for programmers who don’t want to overcomplicate their lives).

I almost forgot!! Apart from everything, it is also a free tool (o_O)

The purpose of this post is to teach you how to scrape a website and make a dataset and/or API in under 60 seconds. Are you ready?

It’s very simple. You just have to go to http://magic.import.io; post the URL of the site you want to scrape, and push the “GET DATA” button. Yes! It is that simple! No plugins, downloads, previous knowledge or registration are necessary. You can do this from any browser; it even works on tablets and smartphones.

For example: if we want to have a table with the information on all items related to Chewbacca on MercadoLibre (a Latin American version of eBay), we just need to go to that site and make a search – then copy and paste the link (http://listado.mercadolibre.com.mx/chewbacca) on Import.io, and push the “GET DATA” button.

You’ll notice that now you have all the information on a table, and all you need to do is remove the columns you don’t need. To do this, just place the mouse pointer on top of the column you want to delete, and an “X” will appear.

Good news for those of us who are a bit more technically-oriented! There is a button that says “GET API” and this one is good to, well, generate an API that will update the data on each request. For this you need to create an account (which is also free of cost).

As you saw, we can scrape any website in under 60 seconds, even if it includes tons of results pages. This truly is magic, no? For more complex things that require logins, entering subwebs, automatized searches, et cetera, there is downloadable import.io software… But I’ll explain that in a different post.

Source: http://schoolofdata.org/2014/12/09/web-scraping-in-under-60-seconds-the-magic-of-import-io/

Web Scraping Services : Making Modern File Formats More Accessible

Data scraping is the process of automatically sorting through information contained on the internet inside html, PDF or other documents and collecting relevant information to into databases and spreadsheets for later retrieval. On most websites, the text is easily and accessibly written in the source code but an increasing number of businesses are using Adobe PDF format (Portable Document Format: A format which can be viewed by the free Adobe Acrobat software on almost any operating system. See below for a link.). The advantage of PDF format is that the document looks exactly the same no matter which computer you view it from making it ideal for business forms, specification sheets, etc.; the disadvantage is that the text is converted into an image from which you often cannot easily copy and paste. PDF Scraping is the process of data scraping information contained in PDF files. To PDF scrape a PDF document, you must employ a more diverse set of tools.

There are two main types of PDF files: those built from a text file and those built from an image (likely scanned in). Adobe's own software is capable of PDF scraping from text-based PDF files but special tools are needed for PDF scraping text from image-based PDF files. The primary tool for PDF scraping is the OCR program. OCR, or Optical Character Recognition, programs scan a document for small pictures that they can separate into letters. These pictures are then compared to actual letters and if matches are found, the letters are copied into a file. OCR programs can perform PDF scraping of image-based PDF files quite accurately but they are not perfect.

Once the OCR program or Adobe program has finished PDF scraping a document, you can search through the data to find the parts you are most interested in. This information can then be stored into your favorite database or spreadsheet program. Some PDF scraping programs can sort the data into databases and/or spreadsheets automatically making your job that much easier.

Quite often you will not find a PDF scraping program that will obtain exactly the data you want without customization. Surprisingly a search on Google only turned up one business, that will create a customized PDF scraping utility for your project. A handful of off the shelf utilities claim to be customizable, but seem to require a bit of programming knowledge and time commitment to use effectively. Obtaining the data yourself with one of these tools may be possible but will likely prove quite tedious and time consuming. It may be advisable to contract a company that specializes in PDF scraping to do it for you quickly and professionally.

Let's explore some real world examples of the uses of PDF scraping technology. A group at Cornell University wanted to improve a database of technical documents in PDF format by taking the old PDF file where the links and references were just images of text and changing the links and references into working clickable links thus making the database easy to navigate and cross-reference. They employed a PDF scraping utility to deconstruct the PDF files and figure out where the links were. They then could create a simple script to re-create the PDF files with working links replacing the old text image.

A computer hardware vendor wanted to display specifications data for his hardware on his website. He hired a company to perform PDF scraping of the hardware documentation on the manufacturers' website and save the PDF scraped data into a database he could use to update his webpage automatically.

PDF Scraping is just collecting information that is available on the public internet. PDF Scraping does not violate copyright laws.

PDF Scraping is a great new technology that can significantly reduce your workload if it involves retrieving information from PDF files. Applications exist that can help you with smaller, easier PDF Scraping projects but companies exist that will create custom applications for larger or more intricate PDF Scraping jobs.

Source: http://ezinearticles.com/?PDF-Scraping:-Making-Modern-File-Formats-More-Accessible&id=193321

On-line directory tree webscraping

As you surf around the internet — particularly in the old days — you may have seen web-pages like this:

The former image is generated by Apache SVN server, and the latter is the plain directory view generated for UserDir on Apache.

In both cases you have a very primitive page that allows you to surf up and down the directory tree of the resource (either the SVN repository or a directory file system) and select links to resources that correspond to particular files.

Now, a file system can be thought of as a simple key-value store for these resources burdened by an awkward set of conventions for listing the keys where you keep being obstructed by the ‘/‘ character.

My objective is to provide a module that makes it easy to iterate through these directory trees and produce a flat table with the following helpful entries:

Although there is clearly redundant data between the fields url, abspath, fname, name, ext, having them in there makes it much easier to build a useful front end.

The function code (which I won’t copy in here) is at https://scraperwiki.com/scrapers/apache_directory_tree_extractor/. This contains the functions ParseSVNRevPage(url) and ParseSVNRevPageTree(url), both of which return dicts of the form:

{'url', 'rev', 'dirname', 'svnrepo',

 'contents':[{'url', 'abspath', 'fname', 'name', 'ext'}]}

I haven’t written the code for parsing the Apache Directory view yet, but for now we have something we can use.

I scraped the UK Cave Data Registry with this scraper which simply applies the ParseSVNRevPageTree() function to each of the links and glues the output into a flat array before saving it:

lrdata = ParseSVNRevPageTree(href)

ldata = [ ]

for cres in lrdata["contents"]:

    cres["svnrepo"], cres["rev"] = lrdata["svnrepo"], lrdata["rev"]

    ldata.append(cres)

scraperwiki.sqlite.save(["svnrepo", "rev", "abspath"], ldata)

Now that we have a large table of links, we can make the cave image file viewer based on the query:

select abspath, url, svnrepo from swdata where ext=’.jpg’ order by abspath limit 500

By clicking on a reference to a jpg resource on the left, you can preview what it looks like on the right.

If you want to know why the page is muddy, a video of the conditions in which the data was gathered is here.

Image files are usually the most immediately interesting out of any unknown file system dump. And they can be made more interesting by associating meta-data with them (given that no convention for including interesting information in the EXIF sections of their file formats). This meta-data might be floating around in other files dumped into the same repository — eg in the form of links to them from html pages which relate to picture captions.

But that is a future scraping project for another time.

Source: https://scraperwiki.wordpress.com/2012/09/14/on-line-directory-tree-webscraping/

Data Scraping Services - Scraping Yelp Business Data With Python Scraping Script

Yelp is a great source of business contact information with details like address, postal code, contact information; website addresses etc. that other site like Google Maps just does not. Yelp also provides reviews about the particular business. The yelp business database can be useful for telemarketing, email marketing and lead generation.

Are you looking for yelp business details database? Are you looking for scraping data from yelp website/business directory? Are you looking for yelp screen scraping software? Are you looking for scraping the business contact information from the online Yelp? Then you are at the right place.

Here I am going to discuss how to scrape yelp data for lead generation and email marketing. I have made a simple and straight forward yelp data scraping script in python that can scrape data from yelp website. You can use this yelp scraper script absolutely free.

I have used urllib, BeautifulSoup packages. Urllib package to make http request and parsed the HTML using BeautifulSoup, used Threads to make the scraping faster.

Yelp Scraping Python Script

import urllib from bs4 import BeautifulSoup import re from threading import Thread #List of yelp urls to scrape url=['http://www.yelp.com/biz/liman-fisch-restaurant-hamburg','http://www.yelp.com/biz/casa-franco-caramba-hamburg','http://www.yelp.com/biz/o-ren-ishii-hamburg','http://www.yelp.com/biz/gastwerk-hotel-hamburg-hamburg-2','http://www.yelp.com/biz/superbude-hamburg-2','http://www.yelp.com/biz/hotel-hafen-hamburg-hamburg','http://www.yelp.com/biz/hamburg-marriott-hotel-hamburg','http://www.yelp.com/biz/yoho-hamburg'] i=0 #function that will do actual scraping job def scrape(ur): html = urllib.urlopen(ur).read() soup = BeautifulSoup(html) title = soup.find('h1',itemprop="name") saddress = soup.find('span',itemprop="streetAddress") postalcode = soup.find('span',itemprop="postalCode") print title.text print saddress.text print postalcode.text print "-------------------" threadlist = [] #making threads while i<len(url): t = Thread(target=scrape,args=(url[i],)) t.start() threadlist.append(t) i=i+1 for b in
threadlist: b.join()

import urllib

from bs4 import BeautifulSoup

import re

from threading import Thread

 #List of yelp urls to scrape

url=['http://www.yelp.com/biz/liman-fisch-restaurant-hamburg','http://www.yelp.com/biz/casa-franco-caramba-hamburg','http://www.yelp.com/biz/o-ren-ishii-hamburg','http://www.yelp.com/biz/gastwerk-hotel-hamburg-hamburg-2','http://www.yelp.com/biz/superbude-hamburg-2','http://www.yelp.com/biz/hotel-hafen-hamburg-hamburg','http://www.yelp.com/biz/hamburg-marriott-hotel-hamburg','http://www.yelp.com/biz/yoho-hamburg']

 i=0

#function that will do actual scraping job

def scrape(ur):

           html = urllib.urlopen(ur).read()

          soup = BeautifulSoup(html)

       title = soup.find('h1',itemprop="name")

          saddress = soup.find('span',itemprop="streetAddress")

          postalcode = soup.find('span',itemprop="postalCode")

          print title.text

          print saddress.text

          print postalcode.text

          print "-------------------"

 threadlist = []

#making threads

while i<len(url):

          t = Thread(target=scrape,args=(url[i],))

          t.start()

          threadlist.append(t)

          i=i+1

for b in threadlist:

          b.join()

Recently I had worked for one German company and did yelp scraping project for them and delivered data as per their requirement. If you looking for scraping data from business directories like yelp then send me your requirement and I will get back to you with sample.

Source: http://webdata-scraping.com/scraping-yelp-business-data-python-scraping-script/

Data Mining Services

Data Minng Services, through its data mining services can mine required data for you from any of the available sources. Over the years, we have successfully catered to wide variety of outsource data mining requirements, which specifies our competency in dealing with your data mining requirements.

Based on your requirements, we can mine data from your preferred data sources, or we will use our own reliable sources to mine the data required by you. We have been using automated as well manual data mining strategies to deliver superior data mining services.

Types of data mining services delivered by us

With an extensive variety of data mining services provided by us, you will definitely be able to find the most perfect service package to cater to your requirements. Below listed are just some of the data mining services offered by us:

•    Web data mining
•    Data extraction
•    Data capture
•    Data gathering
•    Collection of required data
•    Validation of data

Outsource data mining requirements to us, and we are sure that the data mining India unit of Hi-Tech BPO Services will be able to formulate the most appropriate and cost effective solutions to include your entire requirements.

Highlights of our data mining services:

•    Most affordable rates
•    Dedicated data mining India unit
•    Latest data mining technologies used to mine all required data
•    Data will be mined, gathered, processed and validated as per your requirements
•    Mined data can be directly included into your database

Competitive advantage of using our data mining services

To mine accurate and relevant data, some level of internet knowledge is essential. And it would also consume a lot of your valuable time. With our data mining services, we will take care of all your data mining tasks, while you look after your business and its core functions.

The affordably priced data mining services delivered by the data mining India unit will also help you to save considerable amount of your money, which you can put into more productive purposes.

Source: http://www.hitechbposervices.com/data-mining.php

Improving performance for web scraping code

2 down vote favorite

I have a website in which the code scrapes other websites for getting the accurate data. While the code works good but there a decent lag in performance because the code firsts downloads the html stream from various sites(some times 9 websites), extracts the relative part and then renders the html page.

What should I do to get an optimal performance. Should I change from shared hosting (godaddy) to my own server or it has nothing to do with my hosting and I need to make changes to my code?

1 Answer

API/CSV

Ask those websites if they provide an API, or, if you don't need an up-to-date information or the information you need doesn't change frequently, if they can sell/give you for free the data itself (for example in an CSV file). Some small websites may have fancier ways to access data, like a CSV file for the older information, and an RSS feed for the changed one.

Those websites would probably be happy to help you, since providing you with an API would reduce their own CPU and bandwidth usage by you.

Profile

Screen scrapping is really ugly when it comes to performance and scaling. You may be limited by:

    your machine performance, since parsing, sometimes an invalid HTML file, takes time,

    your network speed,

    their network speed usage, i.e. how fast can you access the pages of their website depending on the restrictions they set, like the DOS protection and the number of requests per second for screen scrappers and search engine crawlers,

    their machine performance: if they spend 500 ms. to generate every page, you can't do anything to reduce this delay.

If, despite your requests to them, those websites cannot provide any convenient way to access their data, but they give you a written consent to screen scrape their website, then profile your code to determine the bottleneck. It may be the internet speed. It may be your database queries. It may be anything.

For example, you may discover that you spend too much time finding with regular expressions the relevant information in the received HTML. In that case, you would want to stop doing it wrong and use a parser instead of regular expressions, then see how this improve the performance.

You may also find that the bottleneck is the time the remote server spends generating every page. In this case, there is nothing to do: you may have the fastest server, the fastest connection and the most optimized code, the performance will be the same.

Do things in parallel:

Remember to use parallel computing wisely and to always profile what you're doing, instead of doing premature optimization, in hope that you're smarter than the profiler.

Especially when it comes to using network, you may be very surprised. For example, you may believe that making more requests in parallel will be faster, but as Steve Gibson explains in episode 345 of Security Now, this is not always the case.

Legal aspects

Also note that screen scrapping is explicitly forbidden by the conditions of use (like on IMDB) on many websites. And if nothing is said on this subject in conditions of use, it doesn't mean that you can screen scrape those websites.

The fact that the information is available publicly on the internet doesn't give you the right to copy and reuse it this way neither.

Why? you may ask. For two reasons:

    Most websites are relying on advertisement and marketing. When people use one of those websites directly, they waste some CPU/network bandwidth of the website, but in response, they may click on an ad or buy something sold on the website. When you screen scrape, your bot waste their CPU/network bandwidth, but will never click on an ad or buy something.

    Displaying the information you screen scrapped on your website can have even worse effects. Example: in France, there are two major websites selling hardware. The first one is easy and fast to use, has a nice visual design, better SEO, and in general is very well done. The second one is a crap, but the prices are lower. If you screen scrape them and give the raw results (prices with links) to your users, they will obviously click on the lower price every time, which means that the website with pretty design will have less chances to sell the products.

    People made an effort in collecting, processing and displaying some data. Sometimes they paid to get it. Why would they enjoy seeing you pulling this data conveniently and for free?

Source: http://programmers.stackexchange.com/questions/141403/improving-performance-for-web-scraping-code/141406#141406

Web scraping using Python without using large frameworks like Scrapy

scrapy-big-logoIf you need publicly available data from scraping the Internet, before creating a webscraper, it is best to check if this data is already available from public data sources or APIs. Check the site’s FAQ section or Google for their API endpoints and public data.

Even if their API endpoints are available you have to create some parser for fetching and structuring the data according to your needs.

Scrapy is a well established framework for scraping, but it is also a very heavy framework. For smaller jobs, it may be overkill and for extremely large jobs it is very slow.

So if you would like to roll up your sleeves and build your own scraper, continue reading.

Here are some basic steps performed by most webspiders:

1) Start with a URL and use a HTTP GET or PUT request to access the URL
2) Fetch all the contents in it and parse the data
3) Store the data in any database or put it into any data warehouse
4) Enqueue all the URLs in a page
5) Use the URLs in queue and repeat from process 1
Here are the 3 major modules in every web crawler:
1) Request/Response handler.
2) Data parsing/data cleansing/data munging process.
3) Data serialization/data pipelines.

Lets look at each of these modules and see what they do and how to use them.

Request/Response handler

Request/response handlers are managers who make http requests to a url or a group of urls, and fetch the response objects as html contents and pass this data to the next module. If you use Python for performing request/response url-opening process libraries such as the following are most commonly used

1) urllib(20.5. urllib – Open arbitrary resources by URL – Python v2.7.8 documentation) -Basic python library yet high-level interface for fetching data across the World Wide Web.

2) urllib2(20.6. urllib2 – extensible library for opening URLs – Python v2.7.8 documentation) – extensible library of urllib, which would handle basic http requests, digest authentication, redirections, cookies and more.

3) requests(Requests: HTTP for Humans) – Much advanced request library

which is built on top of basic request handling libraries.

Data parsing/data cleansing/data munging process

This is the module where the fetched data is processed and cleaned. Unstructured data is transformed into structured during this processing. Usually  a set of Regular Expressions (regexes) which perform pattern matching and text processing tasks on the html data are used for this processing.

In addition to regexes, basic string manipulation and search methods are also used to perform this cleaning and transformation. You must have a thorough knowledge of regular expressions and so that you could design the regex patterns.

Data serialization/data pipelines

Once you get the cleaned data from the parsing and cleaning module, the data serialization module will be used to serialize the data according to the data models that you require. This is the final module that will output data in a standard format that can be stored in databases, JSON/CSV files or passed to any data warehouses for storage. These tasks are usually performed by libraries listed below

1) pickle (pickle – Python object serialization) –  This module implements a fundamental, but powerful algorithm for serializing and de-serializing a Python object structure

2) JSON (JSON encoder and decoder)

3) CSV (https://docs.python.org/2/library/csv.html)

4) Basic database interface libraries like pymongo (Tutorial – PyMongo),mysqldb ( on python.org), sqlite3(sqlite3 – DB-API interface for SQLite databases)

And many more such libraries based on the format and database/data storage.

Basic spider rules

The rules to follow while building a spider are to be nice to the sites you are scraping and follow the rules in the site’s spider policies outlined in the site’s robots.txt.

Limit the  number of requests in a second and build enough delays in the spiders so that  you don’t adversely affect the site.

It just makes sense to be nice.

We will cover more techniques in future articles

Source: http://learn.scrapehero.com/webscraping-using-python-without-using-large-frameworks-like-scrapy/

How Web Data Extraction Services Impact Startups

Starting a business has its fair share of ebbs and flows – it can be extremely challenging to get a new business off the blocks, and extremely rewarding when everything goes according to plan and yields desired results. For startups, it is important to get the nuances of running a business right from day one. To succeed in an immensely competitive space, startups need to perform above and beyond expectation right from the start, and one of the factors that can be of great help during the growing years of a startup is web data extraction.

Web data extraction through crawling and scraping, a highly efficient information gathering process, can be used in many creative ways to bring about major change in the performance graph of a startup. With effective web data extraction services acquired by outsourcing to a reputed company, the business intelligence gathered and the numerous possibilities associated with it, web crawling and extraction services can indeed become the difference maker for a startup, propelling it to the heights of success.

What drives the success of web data extraction?

When it comes to figuring out the perfect, balanced web data collection methodology for startups, there are a lot of crucial factors that come into play. Some of these are associated with the technical aspects of data collection, the approach used, the time invested, and the tools involved. Others have more to do with the processing and analysis of collected information and its judicious use in formulating strategies to take things forward.

Web Crawling Services & Web Scraping Services

With the advent of highly professional web data extraction services providers, massive amounts of structured, relevant data can be gathered and stored in real time, and in time, productively used to further the business interests of a startup. As a new business owner, it is important to have a high-level knowledge of the modern and highly functional web scraping tools available for use. This will help to utilize the prowess of competent data extraction services. This in turn can assist both in the immediate and long-term revenue generation context.

Web Data Extraction for Startups

From the very beginning, the dynamics of startups is different from that of older, well-established businesses. The time taken by the new business entity in proving its capabilities and market position needs to be used completely and effectively. Every day of growth and learning needs to add up to make a substantial difference. In this period, every plan and strategy, every execution effort, and every move needs to be properly thought out.

In such a trying situation where there is little margin for error, it pays to have accurate, reliable, relevant and actionable business intelligence. This can put you in firm control of things by allowing you to make informed business decisions and formulate targeted, relevant and growth oriented business strategies. With powerful web crawling, the volume of data gathered is varied, accurate and relevant. This data can then be studied minutely, analyzed in detail and arranged into meaningful clusters. With this weapon in your arsenal, you can take your startup a long way with smart decisions and clever implementations.

Web data extraction is a task best handled by professionals who have had rich experience in the field. Often, in-house web scraping teams are difficult to assemble and not economically viable to maintain, especially for startups. For a better solution, you can outsource your web scraping needs to a reliable web data extraction service for data collection. This way, you can get all the relevant intelligence you need without overstraining your workforce or having to employ additional personnel to handle web scraping. The company you outsource your work to can easily scrape data from multiple sources as per your requirements, and furnish you with actionable business intelligence that can help you take a lead in a competitive market.

Different Ways for Startups to use Web Data Extraction

Web scraping can be employed for many different purposes to yield different kinds of relevant data that generate actionable insights. For a startup, the important decision is how to use this powerful technique to provide valuable information that can make a difference for the future prospects of the company. Here are some interesting possibilities when it comes to impactful web data extraction for startups –

Fishing for Social Rankings and Backlinks

One of the most important business processes for a startup is competition analysis. This is one area where web data extraction can come across as an invaluable enabler. In the past, many startups have effectively used web scraping to fish for backlinks and social rankings related to competing companies.

Backlinks are important to reach a greater mass of better-targeted audiences, which can go on to increase customer base with minimal efforts. Social ranking is also an immensely important factor, as social actions on the internet are building blocks of opinion and reputation generation in this day and age. Keeping this in mind, you can use web data extraction to scrape for social rankings and backlinks related to content generated by your competing companies. After careful analysis, it is possible to arrive at concrete conclusions regarding what your competitors are doing well, and what sells the best.

This information is gold for marketers and sales personnel, and can be used to discern exactly what needs to be done to increase social buzz, generate favorable opinion, and win over customers from your competitors. You can also use this technique to develop high authority backlinks that help with SEO, targeted reach and organic traffic for your business website. For competition analysis, web scraping is a formidable tool.

Sourcing Contact Information

Another important aspect of business that startups can never ignore is good networking. Whether it is with customers, prospective customers, industry peers, partners, or competitors, excellent networking and open, transparent communication is essential for the success of your startup. For effective communication and networking, you need a large, solid list of contact information pertaining to your exact requirements.

Scraping data from multiple web sources gives you the perfect method of achieving this. With automated, fast web scraping, you can in a short time collect a wealth of important contact information that can be leveraged in many different ways. Whether it is the formation of lasting business relationships or making potential customers aware of what you have on offer, this information has the power to propel your startup to new levels of recognition.

For Ecommerce

If you sell your products and services online and want to stay on top of the competition when it comes to variety, pricing analysis, and special deals and offers, web scraping is the way to go. For many e-commerce startups, the problem of high CTR and low conversion is a stumbling block to higher bottom lines. To remedy problems like these and to ensure better sales, it is always a good idea to have a clear insight about your competition.

Future of Retail Industry

With web data extraction, you can be always aware of what competing companies are doing in terms of pricing strategies, product diversity and special customer offers. By considering that information while evaluating and cementing your own strategies, you can always ensure that you provide better value and range of products and services than your competitors, and therefore stay ahead of the competition.

For Marketing, Brand Promotion and Advertisement

For startups, the first wave of promotion and marketing is the one that holds the key to your long-term business success. It is during this phase that the first and most important public perception of your company is formed, and the rudiments of public opinion start taking shape. For this reason, it is crucial to be on point with your marketing and promotion during the early, formative years of your business.

To achieve this, you need a clear, in-depth understanding of your target audience. You need to categorize your target audience on the basis of many factors like age, gender, demographics, income groups and tastes and preferences. Such detailed understanding can only be possible when you have a large wealth of social data pertaining to your target audience. There is no better way of achieving this than by web data extraction.

Love your brand

With the help of data extraction services, you can gather large chunks of relevant data regarding your target audience which can help you accurately evaluate the potential of each prospective customers as a possible addition to your business family. To ensure that you have a steady, early wave of customers to take your business off the blocks at a rapid pace, you need to devise marketing campaigns, promotional strategies and advertisements in accordance with the customer knowledge you drive through your web scraping efforts. This is a foolproof strategy to have marketing and promotional plans in place that achieve goals, bring in new business and provide your company with enough initial momentum to carry it through the later years of success.

To conclude, web data extraction can be a veritable tool in the hands of a startup. With the proper use and leveraging of this technique, your startup can gather the required business intelligence to shine in a competitive market and become a favorite with the customer base. Working with the right web data extraction company can be one of the most important business decisions you make as a startup owner.

Source: https://www.promptcloud.com/blog/web-data-extraction-services-for-startups/

What is Blog Scraping Service?

Blog scraping is the one of the best service to increase the traffic of the site by commenting about blogs or writing review about blogs in SEO field. Most of the Blogs will allow their reader to review or write their own comments or suggestion or ideas or thoughts in the blogs.

Nowadays in the internet world we can find the number of blogs and sites related to various topics or various products. Main concept of this service is increase traffic of website by commenting others blogs. This is very simple and easiest method. But the main difficultly we face here is getting approval from moderator of the site which may take more time or maybe we won’t get the approval.

Hence Web Scraping seo is planning to provide this blog scraping service without approval as many moderators do not have the time to read and approved each and every comment written by various visitors. We will find the High PR pages on the various blogs related to your website content and write the own comment about those blogs and provide the link of your website or anchor text. We don’t have the option or the way to track the blogs whether it is approved by moderator or not. We will give the link with comments what we have typed on the blogs as a report. It will increase the back link and increase the traffic.

What are the features of Blog scraping Service?

•    Will provide the comments or reviews to blogs which having related niche to your product.
•    Will write comments only high density or high ranking blogs.
•    Fast and More accurate promotion compared to other service
•    Understand the Blogs by reading carefully and comment accordingly
•    This service is optimized and SEO friendly.

What are the benefits of Blog scraping Service?

•    Effect of time spending for this service is very less.
•    This service is best method to increase your site traffic with minimal effect and cost.
•    Increase your web site rank in all search engines.
•    Reach your site to more number of audiences.
•    Increase your product sale.
•    Fast and more results.

What are the advantages of using this service in Web Scraping SEO?

•    Web Scraping SEO is one the top SEO service provider in the SEO Market.
•    Expert people working on Blog commenting service will always do analysis to find the high traffic blogs.
•    Web Scraping SEO will get the approval from Blogs administrator easily.
•    Provides High Quality Service with reasonable price.
•    Provides on time delivery.
•    More flexible to clients.
•    Always met the Client expectation and Provide quality service.

Frequently Asked Questions

Q: Will you provide the approval for each comment you typed on the blogs from blogsite moderator?

A: No, we are only responsible for creating comments for your website but we won’t wait for moderation approval, because Moderator is responsible for Approval, He may take time for approval that is according to Moderator’s scope. We will give only the blog links and the comments to you as a report.

Q: Do you have any system or software to track the approval of blog?

A: We don’t have any system or software to track the approval, we do comments in those top blog sites according to the matching keyword. That is only our job approval is from moderator side.

Q: Why you can’t get the approval for comments from moderator?

A: I can clearly answer this one, Because nowadays everyone is busy particularly the blogsite Moderators for that reason our comments got approved late. But we are not going to wait for that because we have a lot of works to do, But I assure you, that with the final reports that contains how many sites we have uploaded with your comments in MS Excel format will reach you.

Q: How do you select the blogs for commenting?

A: We are going to select top ranking blog sites related to your keywords, According to the benefits of your product we will give proper and attractive comments carefully.

Source: http://www.Web Scrapingseo.com/blog-scraping-service.aspx

Web Scraping: Startups, Services & Market

I got recently interested in startups using web scraping in a way or another and since I find the topic very interesting I wanted to share with you some thoughts. [Note that I’m not an expert. To correct me / share your knowledge please use the comment section]

Web scraping is everything but a new technique. However with more and more data shared on internet (from user generated content like social networks & review websites to public/government data and the growing number of online services) the amount of data collected and the use cases possible are increasing at an incredible pace.

We’ve entered the age of “Big Data” and web scraping is one of the sources to feed big data engines with fresh new data, let it be for predictive analytics, competition monitoring or simply to steal data.

From what I could see the startups and services which are using “web scraping” at their core can be divided into three categories:

•    the shovel sellers (a.k.a we sell you the technology to do web scraping)

•    the shovel users (a.k.a we use web scraping to extract gold and sell it to our users)

•    the shovel police (a.k.a the security services which are here to protect website owners from these bots)

The shovel sellers

From a technology point of view efficient web scraping is quite complicated. It exists a number of open source projects (like Beautiful Soup) which enable anyone to get up and running a web scraper by himself. However it’s a whole different story when it has to be the core of your business and that you need not only to maintain your scrapers but also to scale them and to extract smartly the data you need.

This is the reason why more and more services are selling “web scraping” as a service. Their job is to take care about the technical aspects so you can get the data you need without any technical knowledge. Here some examples of such services:

    Grepsr
    Krakio
    import.io
    promptcloud
    80legs
    Proxymesh (funny service: it provides a proxy rotator for web scraping. A shovel seller for shovel seller in a way)
    scrapingHub
    mozanda

The shovel users

It’s the layer above. Web scraping is the technical layer. What is interesting is to make sense of the data you collect. The number of business applications for web scraping is only increasing and some startups are really using it in a truly innovative way to provide a lot of value to their customers.

Basically these startups take care of collecting data then extract the value out of it to sell it to their customers. Here some examples:

Sales intelligence. The scrapers screen marketplaces, competitors, data from public markets, online directories (and more) to find leads. Datanyze, for example, track websites which add or drop javascript tags from your competitors so you can contact them as qualified leads.

Marketing. Web scraping can be used to monitor how your competitors are performing. From reviews they get on marketplaces to press coverage and financial published data you can learn a lot. Concerning marketing there is even a growth hacking class on udemy that teaches you how to leverage scraping for marketing purposes.

Price Intelligence. A very common use case is price monitoring. Whether it’s in the travel, e-commerce or real-estate industry monitoring your competitors’ prices and adjusting yours accordingly is often key. These services not only monitor prices but with their predictive algorithms they can give you advice on where the puck will be. Ex: WisePricer, Pricing Assistant.

Economic intelligence, Finance intelligence etc. with more and more economical, financial and political data available online a new breed of services, which collect and make sense of it, are rising. Ex: connotate.

The shovel police

Web scraping lies in a gray area. Depending on the country or the terms of service of each website, automatically collecting data via robots can be illegal. Whatever the laws say it becomes crucial for some services to try to block these crawlers to protect themselves. The IT security industry has understood it and some startups are starting to tackle this problem. Here are 3 services which claim to provide solutions to stop bots from crawling your website:

•    Distil
•    ScrapeSentry
•    Fireblade

From a market point of view

A couple of points on the market to conclude:

•    It’s hard to assess how big the “web scraping economy” is since it is at the intersection of several big industries (billion dollars): IT security, sales, marketing & finance intelligence. This technique is of course a small component of these industries but is likely to grow in the years to come.

•    A whole underground economy also exists since a lot of web scraping is done through “botnets” (networks of infected computers)

•    It’s a safe bet to say that more and more SaaS (like Datanyze pr Pricing Assistant) will find innovative applications for web scraping. And more and more startups will tackle web scraping from the security point of view.

•    Since these startups are often entering big markets through a niche product / approach (web scraping is a not the solution to everything, there are more a feature) they are likely to be acquired by bigger players (in the security, marketing or sales tools industries). The technological barrier are there.

Source: http://clementvouillon.com/article/web-scraping-startups-services-market/

Lawyers & Attorneys Website Data Scraping Services

There are so many instances where one end’s up needing information from lawyers or bar associations. However, if you approach them directly or look for other ways to get information it might either be difficult or you might not get the information you are looking for. Thus, the best way to go about the scraping lawyer data.

Scraping lawyer data allow you to get information from various attorney websites, bar association websites, or other related websites. Using web scraping tools for getting such information makes it much easier to get all the relevant and important information without actually having to worry about the same.

If you wish to scrape data from lawyer, you are entitled to information such as lawyer name, firm names, address, contact details, history about the lawyers, educational qualifications, the bar association they are part of and much more.

Scraping lawyer data ensure that you also have images of the lawyer you are concentrating on. The result of scrape data form lawyer can be obtained in any format the user wants such as csv, excel, MySql etc. Scraping lawyer data also ensures that none of the information provided are repetitive or redundant.

If you are in need of information regarding any lawyer such as their contact details, address etc. it could end up being a huge and difficult task to get it manually or physically. Thus, taking off the help of scraping tools would ensure that you get all the needed information without actually having to bother about anything at all. The presence of lots of attorney websites and the fact that more and more lawyers are moving to the internet makes getting information easy with the help of some great tools. Scraping data is a very useful and handy method in which one can get all the required and relevant information and that too in a very easy to read format, which makes the method even worthier.

There are quite a few tools or services that you can take help of to get lawyers data scraped. Most of these services also provide with a sample demo and that free of cost. From the sample one can decide if they wish to continue with the services or try some other services. Thus, if you want any information from attorney websites or information about any lawyers, data scraping is a great way to get the same.

Source: https://3idatascraping.wordpress.com/2014/03/18/lawyers-attorneys-website-data-scraping-services/

A Guide to Web Scraping Tools

Web Scrapers are tools designed to extract / gather data in a website via crawling engine usually made in Java, Python, Ruby and other programming languages.Web Scrapers are also called as Web Data Extractor, Data Harvester , Crawler and so on which most of them are web-based or can be installed in local desktops.

Its main purpose is to enable webmasters, bloggers, journalist and virtual assistants to harvest data from a certain website whether text, numbers, contact details and images in a structured way which cannot be done easily thru manual copy and paste method. Typically, it transforms the unstructured data on the web, from HTML format into a structured data stored in a local database or spreadsheet or automates web human browsing.

Web Scraper Usage

Web Scrapers are also being used by SEO and Online Marketing Analyst to pull out some data privately from the competitor’s website such as high targeted keywords, valuable links, emails & traffic sources that were also perform by SEOClerk, Google and many other web crawling sites.

Includes:

•    Price comparison
•    Weather data monitoring
•    Website change detection
•    Research
•    Web mash up
•    Info graphics
•    Web data integration
•    Web Indexing & rank checking
•    Analyze websites quality links

List of Popular Web Scrapers

There are hundreds of Web Scrapers today available for both commercial and personal use. If you’ve never done any web scraping before, there are basic

Web scraping tools like YahooPipes, Google Web Scrapers and Outwit Firefox extensions that it’s good to start with but if you need something more flexible and has extra functionality then,  check out the following:

HarvestMan [ Free Open Source]

HarvestMan is a web crawler application written in the Python programming language. HarvestMan can be used to download files from websites, according to a number of user-specified rules. The latest version of HarvestMan supports as much as 60 plus customization options. HarvestMan is a console (command-line) application. HarvestMan is the only open source, multithreaded web-crawler program written in the Python language. HarvestMan is released under the GNU General Public License.Like Scrapy, HarvestMan is truly flexible however, your first installation would not be easy.

Scraperwiki [Commercial]

Using a minimal programming you will be able to extract anything. Off course, you can also request a private scraper if there’s an exclusive in there you want to protect. In other words, it’s a marketplace for data scraping.

Scraperwiki is a site that encourages programmers, journalists and anyone else to take online information and turn it into legitimate datasets. It’s a great resource for learning how to do your own “real” scrapes using Ruby, Python or PHP. But it’s also a good way to cheat the system a little bit. You can search the existing scrapes to see if your target website has already been done. But there’s another cool feature where you can request new scrapers be built.  All in all, a fantastic tool for learning more about scraping and getting the desired results while sharpening your own skills.

Best use: Request help with a scrape, or find a similar scrape to adapt for your purposes.

FiveFilters.org [Commercial]   

Is an online web scraper available for commercial use. Provides easy content extraction using Full-Text RSS tool which can identify and extract web content (news articles, blog posts, Wikipedia entries, and more) and return it in an easy to parse format. Advantages; speedy article extraction, Multi-page support, has a Autodetection and  you can deploy  on the cloud server without database required.

Kimono

Produced by Kimono labs this tool lets you convert data to into apis for automated export.   Benjamin Spiegel did a great Youmoz post on how to build a custom ranking tool with Kimono, well worth checking out!

Mozenda [Commercial]

This is a unique tool for web data extraction or web scarping.Designed for easiest and fastest way of getting data from the web for everyone. It has a point & click interface and with the power of the cloud you can scrape, store, and manage your data all with Mozenda’s incredible back-end hardware. More advance, you can automate your data extraction leaving without a trace using Mozenda’s  anonymous proxy feature that could rotate tons of IP’s .

Need that data on a schedule? Every day? Each hour? Mozenda takes the hassle out of automating and publishing extracted data. Tell Mozenda what data you want once, and then get it however frequently you need it. Plus it allows advanced programming using REST API the user can connect directly Mozenda account.

Mozenda’s Data Mining Software is packed full of useful applications especially for sales people. You can do things such as “lead generation, forecasting, acquiring information for establishing budgets, competitor pricing analysis. This software is a great companion for marketing plan & sales plan creating.

Using Refine Capture tetx tool, Mozenda is smart enough to filter the text you want stays clean or get  the specific text or split them into pieces.

80Legs [Commercial]

The first time I heard about 80Legs my mind really got confused of what really this software does. 80Legs like Mozenda is a web-based data extraction  tool with customizable features:

•    Select which websites to crawl by entering URLs or uploading a seed list
•    Specify what data to extract by using a pre-built extractor or creating your own
•    Run a directed or general web crawler
•    Select how many web pages you want to crawl
•    Choose specific file types to analyze

80 legs offers customized web crawling that lets you get very specific about your crawling parameters, which tell 80legs what web pages you want to crawl and what data to collect from those web pages and also the general web crawling which can collect data like web page content, outgoing links and other data. Large web crawls take advantage of 80legs’ ability to run massively parallel crawls.

Also crawls data feeds and offers web extraction design services. (No installation needed)

ScrapeBox [Commercial]

ScrapeBox are most popular web scraping tools to SEO experts, online marketers and even spammers with its very user-friendly interface you can easily harvest data from a website;

•    Grab Emails
•    Check page rank
•    Checked high value backlinks
•    Export URLS
•    Checked Index
•    Verify working proxies
•    Powerful RSS Submission

Using thousands of rotating proxies you will be able to sneak on the competitor’s site keywords, do research on .gov sites, harvesting data, and commenting without getting blocked.

The latest updates allow the users to spin comments and anchor text to avoid getting detected by search engines.

You can also check out my guide to using Scrapebox for finding guest posting opportunities:

Scrape.it [Commercial]

Using a simple point & click Chrome Extension tool, you can extract data from websites that render in javascript. You can automate filling out forms, extract data from popups, navigate and crawl links across multiple pages, extract images from even the most complex websites with very little learning curve. Schedule jobs to run at regular intervals.

When a website changes layout or your web scraper stops working, scrape.it  will fix it automatically so that you can continue to receive data uninterrupted and without the need for you to recreate or edit it yourself.

They work with enterprises using our own tool that we built to deliver fully managed solutions for competitive pricing analysis, business intelligence, market research, lead generation, process automation and compliance & risk management requirements.

Features:

    Very easy web date extraction with Windows like Explorer interface

    Allowing you to extract text, images and files from modern Web 2.0 and HTML5 websites which uses Javascript & AJAX.

    The user could select what features they’re going to pay with

    lifetime upgrade and support at no extra charge on premium license

Scrapy [Free Open Source]

Off course the list would not be cool without Scrapy, it is a fast high-level screen scraping and web crawling 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.

Features:

•         Design with simplicity- Just writes the rules to extract the data from web pages and let Scrapy crawl the entire web site. It can crawl 500 retailers’ sites daily.

•         Ability to attach new code for extensibility without having to touch the framework core

•         Portable, open-source, 100% Python- Scrapy is completely written in Python and runs on Linux, Windows, Mac and BSD

•         Scrapy comes with lots of functionality built in.

•         Scrapy is extensively documented and has an comprehensive test suite with very good code coverage

•         Good community and commercial support

 Cons: The installation process is hard to perfect especially for beginners

Needlebase [Commercial]

Many organizations, from private companies to government agencies, store their info in a searchable database that requires you navigate a list page listing results, and a detail page with more information about each result.  Grabbing all this information could result in thousands of clicks, but as long as it fits the same formula, Needlebase can do it for you.  Point and click on example data from one page once to show Needlebase how your site is structured, and it will use that pattern to extract the information you’re looking for into a dataset.  You can query the data through Needle’s site, or you can output it as a CSV or other file format of your choice.  Needlebase can also rerun your scraper every day to continuously update your dataset.

OutwitHub [Free]

This Firefox extension is one of the more robust free products that exists Write your own formula to help it find information you’re looking for, or just tell it to download all the PDFs listed on a given page.  It will suggest certain pieces of information it can extract easily, but it’s flexible enough for you to be very specific in directing it.  The documentation for Outwit is especially well written, they even have a number of tutorials for what you might be looking to do.  So if you can’t easily figure out how to accomplish what you want, investing a little time to push it further can go a long way.

Best use: more text

irobotsoft [Free}

This is a free program that is essentially a GUI for web scraping. There’s a pretty steep learning curve to figure out how to work it, and the documentation appears to reference an old version of the software. It’s the latest in a long tradition of tools that lets a user click through the logic of web scraping. Generally, these are a good way to wrap your head around the moving parts of a scrape, but the products have drawbacks of their own that makes them little easier than doing the same thing with scripts.

Cons: The documentation seems outdated

Best use: Slightly complex scrapes involving multiple layers.

iMacros [Free]

The  same ethos on how microsoft macros works, iMacros automates repetitive task.Whether you choose the website, Firefox extension, or Internet Explorer add-on flavor of this tool, it can automate navigating through the structure of a website to get to the piece of info you care about. Record your actions once, navigating to a specific page, and entering a search term or username where appropriate.  Especially useful for navigating to a specific stock you care about, or campaign contribution data that’s mired deep in an agency website and lacks a unique Web address.  Extract that key piece (pieces) of info into a usable form.  Can also help convert Web tables into usable data, but OutwitHub is really more suited to that purpose.  Helpful video and text tutorials enable you to get up to speed quickly.

Best use: Eliminate repetition in navigating to a particular datapoint in a website that you’re checking up on often by recording a repeatable action that pulls the datapoint out of the clutter it’s naturally surrounded by.

InfoExtractor [Commercial]

This is a neat little web service that generates all sorts of information given a list of urls. Currently, it only works for YouTube video pages, YouTube user profile pages, Wikipedia entries, Huffingtonpost posts, Blogcatalog blog posts and The Heritage Foundation blog (The Foundry). Given a url, the tool will return structured information including title, tags, view count, comments and so on.

Google Web Scraper [Free]

A browser-based web scraper works like Firefox’s Outwit Hub, it’s designed for plain text extraction from any online pages and export to spreadsheets via Google docs. Google Web Scraper can be downloaded as an extension and you can install it in your Chrome browser without seconds. To use it: highlight a part of the webpage you’d like to scrape, right-click and choose “Scrape similar…”. Anything that’s similar to what you highlighted will be rendered in a table ready for export, compatible with Google Docs™. The latest version still had some bugs on spreadsheets.

Cons: It doesn’t work for images and sometimes it can’t perform well on huge volume of text but it’s easy and fast to use.


Tutorials:

Scraping Website Images Manually using Google Inspect Elements

The main purpose of Google Inspect Elements is for debugging like the Firefox Firebug however, if you’re flexible you can use this tool also for harvesting images in a website. Your main goal is to get the specific images like web backgrounds, buttons, banners, header images and product images which is very useful for web designers.

Now, this is a very easy task. First, you will definitely need to download and install the Google Chrome browser in your computer. After the installation do the following:

1. Open the desired webpage in Google Chrome

2. Highlight any part of the website and right click > choose Google Inspect Elements

3. In the Google Inspect Elements, go to Resources tab

4. Under Resources tab, expand all folders. You will eventually see script folders and IMAGES folders

5. In the Images folders, just use arrow keys to find the images you need to have (see the screenshot above)

6. Next, right click the images and choose Open the Image in New Tab

7. Finally, right click the image > choose Save Image As… . (save to your local folder)

You’re done!

How to Extract Links from a Web Page with OutWit Hub

In this tutorial we are going to learn how to extract links from a webpage with OutWit Hub.

Sometimes it can be useful to extract all links from a given web page. OutWit Hub is the easiest way to achieve this goal.

1. Launch OutWit Hub

If you haven’t installed OutWit Hub yet, please refer to the Getting Started with OutWit Hub tutorial.

Begin by launching OutWit Hub from Firefox. Open Firefox then click on the OutWit Button in the toolbar.

If the icon is not visible go to the menu bar and select Tools -> OutWit -> OutWit Hub

OutWit Hub will open displaying the Web page currently loaded on Firefox.


2. Go to the Desired Web Page

In the address bar, type the URL of the Website.

Go to the Page view where you can see the Web page as it would appear in a traditional browser.

Now, select “Links” from the view list.

In the “Links” widget, OutWit Hub displays all the links from the current page.

If you want to export results to Excel, just select all links using ctrl/cmd + A, then copy using ctrl/cmd + C and paste it in Excel (ctrl/cmd + V).

Source: http://www.garethjames.net/a-guide-to-web-scrapping-tools/

I Don’t Need No Stinking API: Web Scraping For Fun and Profit

If you’ve ever needed to pull data from a third party website, chances are you started by checking to see if they had an official API. But did you know that there’s a source of structured data that virtually every website on the internet supports automatically, by default?

scraper toolThat’s right, we’re talking about pulling our data straight out of HTML — otherwise known as web scraping. Here’s why web scraping is awesome:

Any content that can be viewed on a webpage can be scraped. Period.

If a website provides a way for a visitor’s browser to download content and render that content in a structured way, then almost by definition, that content can be accessed programmatically. In this article, I’ll show you how.

Over the past few years, I’ve scraped dozens of websites — from music blogs and fashion retailers to the USPTO and undocumented JSON endpoints I found by inspecting network traffic in my browser.

There are some tricks that site owners will use to thwart this type of access — which we’ll dive into later — but they almost all have simple work-arounds.

Why You Should Scrape

But first we’ll start with some great reasons why you should consider web scraping first, before you start looking for APIs or RSS feeds or other, more traditional forms of structured data.

Websites are More Important Than APIs

The biggest one is that site owners generally care way more about maintaining their public-facing visitor website than they do about their structured data feeds.

We’ve seen it very publicly with Twitter clamping down on their developer ecosystem, and I’ve seen it multiple times in my projects where APIs change or feeds move without warning.

Sometimes it’s deliberate, but most of the time these sorts of problems happen because no one at the organization really cares or maintains the structured data. If it goes offline or gets horribly mangled, no one really notices.

Whereas if the website goes down or is having issues, that’s a more of an in-your-face, drop-everything-until-this-is-fixed kind of problem, and gets dealt with quickly.

No Rate-Limiting

Another thing to think about is that the concept of rate-limiting is virtually non-existent for public websites.

Aside from the occasional captchas on sign up pages, most businesses generally don’t build a lot of defenses against automated access. I’ve scraped a single site for over 4 hours at a time and not seen any issues.

Unless you’re making concurrent requests, you probably won’t be viewed as a DDOS attack, you’ll just show up as a super-avid visitor in the logs, in case anyone’s looking.

Anonymous Access

There are also fewer ways for the website’s administrators to track your behavior, which can be useful if you want gather data more privately.

With APIs, you often have to register to get a key and then send along that key with every request. But with simple HTTP requests, you’re basically anonymous besides your IP address and cookies, which can be easily spoofed.

The Data’s Already in Your Face

Web scraping is also universally available, as I mentioned earlier. You don’t have to wait for a site to open up an API or even contact anyone at the organization. Just spend some time browsing the site until you find the data you need and figure out some basic access patterns — which we’ll talk about next.

Let’s Get to Scraping

So you’ve decided you want to dive in and start grabbing data like a true hacker. Awesome.

Just like reading API docs, it takes a bit of work up front to figure out how the data is structured and how you can access it. Unlike APIs however, there’s really no documentation so you have to be a little clever about it.

I’ll share some of the tips I’ve learned along the way.

Fetching the Data

So the first thing you’re going to need to do is fetch the data. You’ll need to start by finding your “endpoints” — the URL or URLs that return the data you need.

If you know you need your information organized in a certain way — or only need a specific subset of it — you can browse through the site using their navigation. Pay attention to the URLs and how they change as you click between sections and drill down into sub-sections.

The other option for getting started is to go straight to the site’s search functionality. Try typing in a few different terms and again, pay attention to the URL and how it changes depending on what you search for. You’ll probably see a GET parameter like q= that always changes based on you search term.

Try removing other unnecessary GET parameters from the URL, until you’re left with only the ones you need to load your data. Make sure that there’s always a beginning ? to start the query string and a & between each key/value pair.

Dealing with Pagination

At this point, you should be starting to see the data you want access to, but there’s usually some sort of pagination issue keeping you from seeing all of it at once. Most regular APIs do this as well, to keep single requests from slamming the database.

Usually, clicking to page 2 adds some sort of offset= parameter to the URL, which is usually either the page number or else the number of items displayed on the page. Try changing this to some really high number and see what response you get when you “fall off the end” of the data.

With this information, you can now iterate over every page of results, incrementing the offset parameter as necessary, until you hit that “end of data” condition.

The other thing you can try doing is changing the “Display X Per Page” which most pagination UIs now have. Again, look for a new GET parameter to be appended to the URL which indicates how many items are on the page.

Try setting this to some arbitrarily large number to see if the server will return all the information you need in a single request. Sometimes there’ll be some limits enforced server-side that you can’t get around by tampering with this, but it’s still worth a shot since it can cut down on the number of pages you must paginate through to get all the data you need.

AJAX Isn’t That Bad!

Sometimes people see web pages with URL fragments # and AJAX content loading and think a site can’t be scraped. On the contrary! If a site is using AJAX to load the data, that probably makes it even easier to pull the information you need.

The AJAX response is probably coming back in some nicely-structured way (probably JSON!) in order to be rendered on the page with Javscript.

All you have to do is pull up the network tab in Web Inspector or Firebug and look through the XHR requests for the ones that seem to be pulling in your data.

Once you find it, you can leave the crufty HTML behind and focus instead on this endpoint, which is essentially an undocumented API.

(Un)structured Data?

Now that you’ve figured out how to get the data you need from the server, the somewhat tricky part is getting the data you need out of the page’s markup.

Use CSS Hooks

In my experience, this is usually straightforward since most web designers litter the markup with tons of classes and ids to provide hooks for their CSS.

You can piggyback on these to jump to the parts of the markup that contain the data you need.

Just right click on a section of information you need and pull up the Web Inspector or Firebug to look at it. Zoom up and down through the DOM tree until you find the outermost <div> around the item you want.

This <div> should be the outer wrapper around a single item you want access to. It probably has some class attribute which you can use to easily pull out all of the other wrapper elements on the page. You can then iterate over these just as you would iterate over the items returned by an API response.

A note here though: the DOM tree that is presented by the inspector isn’t always the same as the DOM tree represented by the HTML sent back by the website. It’s possible that the DOM you see in the inspector has been modified by Javascript — or sometime even the browser, if it’s in quirks mode.

Once you find the right node in the DOM tree, you should always view the source of the page (“right click” > “View Source”) to make sure the elements you need are actually showing up in the raw HTML.

This issue has caused me a number of head-scratchers.

Get a Good HTML Parsing Library

It is probably a horrible idea to try parsing the HTML of the page as a long string (although there are times I’ve needed to fall back on that). Spend some time doing research for a good HTML parsing library in your language of choice.

Most of the code I write is in Python, and I love BeautifulSoup for its error handling and super-simple API. I also love its motto:

    You didn’t write that awful page. You’re just trying to get some data out of it. Beautiful Soup is here to help. :)

You’re going to have a bad time if you try to use an XML parser since most websites out there don’t actually validate as properly formed XML (sorry XHTML!) and will give you a ton of errors.

A good library will read in the HTML that you pull in using some HTTP library (hat tip to the Requests library if you’re writing Python) and turn it into an object that you can traverse and iterate over to your heart’s content, similar to a JSON object.

Some Traps To Know About

I should mention that some websites explicitly prohibit the use of automated scraping, so it’s a good idea to read your target site’s Terms of Use to see if you’re going to make anyone upset by scraping.

For two-thirds of the website I’ve scraped, the above steps are all you need. Just fire off a request to your “endpoint” and parse the returned data.

But sometimes, you’ll find that the response you get when scraping isn’t what you saw when you visited the site yourself.

When In Doubt, Spoof Headers

Some websites require that your User Agent string is set to something they allow, or you need to set certain cookies or other headers in order to get a proper response.

Depending on the HTTP library you’re using to make requests, this is usually pretty straightforward. I just browse the site in my web browser and then grab all of the headers that my browser is automatically sending. Then I put those in a dictionary and send them along with my request.

Note that this might mean grabbing some login or other session cookie, which might identify you and make your scraping less anonymous. It’s up to you how serious of a risk that is.

Content Behind A Login

Sometimes you might need to create an account and login to access the information you need. If you have a good HTTP library that handles logins and automatically sending session cookies (did I mention how awesome Requests is?), then you just need your scraper login before it gets to work.

Note that this obviously makes you totally non-anonymous to the third party website so all of your scraping behavior is probably pretty easy to trace back to you if anyone on their side cared to look.

Rate Limiting

I’ve never actually run into this issue myself, although I did have to plan for it one time. I was using a web service that had a strict rate limit that I knew I’d exceed fairly quickly.

Since the third party service conducted rate-limiting based on IP address (stated in their docs), my solution was to put the code that hit their service into some client-side Javascript, and then send the results back to my server from each of the clients.

This way, the requests would appear to come from thousands of different places, since each client would presumably have their own unique IP address, and none of them would individually be going over the rate limit.

Depending on your application, this could work for you.

Poorly Formed Markup

Sadly, this is the one condition that there really is no cure for. If the markup doesn’t come close to validating, then the site is not only keeping you out, but also serving a degraded browsing experience to all of their visitors.

It’s worth digging into your HTML parsing library to see if there’s any setting for error tolerance. Sometimes this can help.

If not, you can always try falling back on treating the entire HTML document as a long string and do all of your parsing as string splitting or — God forbid — a giant regex.

Source: https://blog.hartleybrody.com/web-scraping/

What is HTML Scraping and how it works

There are many reasons why there may be a requirement to pull data or information from other sites, and usually the process begins after checking whether the site has an official API. There are very few people who are aware about the presence of structured data that is supported by every website automatically. We are basically talking about pulling data right from the HTML, also referred to as HTML scraping. This is an awesome way of gleaning data and information from third party websites.

Any webpage content that can be viewed can be scraped without any trouble. If there is any way provided by the website to the browser of the visitor to download content and use the same in a highly structured manner, in that case, accessing of the content programmatically is possible. HTML scraping works in an amazing manner.

Before indulging in HTML scraping, one can inspect the browser for network traffic. Site owners have a couple of tricks up their sleeve to thwart this access, but majority of them can be worked around.

Before moving on to how HTML scraping works, we must understand the reasons behind the same. Why is scraping needed? Once you get a satisfactory answer to this question, you can start looking for RSS or API feeds or various other traditional structured data forms. It is significant to understand that when compared with APIs, websites are more significant.

The most important advantage of the same is the maintenance of their websites where a lot of visitors visit rather than safeguarding structured data feeds. With Tweeter, the same has been publicly seen when it clamps down on the developer ecosystem. Many times, API feeds change or move without any prior warning. Many times, it can also be a deliberate attempt, but mostly, such issues or problems erupt as there is no authority or an organization that maintains or takes care of the structured data. It is rarely noticed, if the same gets severely mangled or goes offline. In case the website has certain issues or the website no longer works, the problem is more in the form of a ball in your court requiring dealing with the same without losing any time. api-comic-image

Rate limiting is another factor that needs a lot of thinking and in case of public websites, it virtually doesn’t exist. Besides some occasional sign up pages or captchas, many business websites fail to create and built defenses against any unwarranted automated access. Many times, a single website can be scraped for four hours straight without anyone noticing. There are chances that you would not be viewed under DDOS attack unless concurrent requests are being made by you. You will be seen just as an avid visitor or an enthusiast in the logs, that too, in case anyone is looking.

Another factor in HTML scraping is that one can easily access any website anonymously. Behavior tracking can be done with a few ways by the administrator of the website and this turns out to be beneficial if you want to privately gather the data. Many times, registration is imperative with APIs in order to get key and with any request being sent, this key also needs to be sent. But, in case of simple and straightforward HTTP requests, the visitor can stay anonymous besides cookies and IP address, which can again be spoofed.

The availability of HTML scraping is universal and there is no need to wait for the opening of the site for an API or for contacting anyone in the organization. One simply needs to spend some time and browse websites at a leisurely pace until the data you want is available and then find out the basic patterns to access the same.

Now you need to don a hat of a professional scraper and simply dive in. Initially, it may take some time to work up figuring out the way the data have been structured and the way it can be accessed just as we read APIs. If there is no documentation unlike APIs, you need to be a little more smart about it and use clever tricks.

Some of the most used tricks are

Data Fetching
The first thing that is required is data fetching. Find endpoints to begin with, that is the URLs that can help in returning the data that is required. If you are pretty sure about the data and the way it should be structured so as to match your requirements, you will require a particular subset for the same and later you can indulge in site browsing using the navigation tools.

GET Parameter

The URLs must be paid attention to and see the way it changes as you indulge in clicking between the sections and the way they divide into various subsections. Before starting, the other option that can be used is to straight away go to the search functionality of the site. Certain terms can be typed and the URL needs to be focused again for watching the changes on the basis of what is being searched. A GET parameter will be probably seen like q which changes on the basis of the search term used by you. Other GET parameters that are not being used can be removed from the URL until only the ones that are needed are left for data loading. Before a query string, there must always be a “?” beginning.

Now the time has come when you would have started to come across the data that you would like to see and want to access, but sometimes, there may be certain pagination issues that require to be dealt with. Due to these issues, you may not be able to see the data in its entirety. Single requests are kept away by many APIs as well from database slamming. Many times, clicking the next page can add some offset parameter that helps in data visibility on the page. All these steps will help you succeed in HTML scraping.

Source: https://www.promptcloud.com/blog/what-is-html-scraping-and-how-it-works/

rvest: easy web scraping with R

rvest is new package that makes it easy to scrape (or harvest) data from html web pages, inspired by libraries like beautiful soup. It is designed to work with magrittr so that you can express complex operations as elegant pipelines composed of simple, easily understood pieces. Install it with:

install.packages("rvest")

rvest in action

To see rvest in action, imagine we’d like to scrape some information about The Lego Movie from IMDB. We start by downloading and parsing the file with html():

library(rvest)

lego_movie <- html("http://www.imdb.com/title/tt1490017/")

To extract the rating, we start with selectorgadget to figure out which css selector matches the data we want: strong span. (If you haven’t heard of selectorgadget, make sure to read vignette("selectorgadget") – it’s the easiest way to determine which selector extracts the data that you’re interested in.) We use html_node() to find the first node that matches that selector, extract its contents with html_text(), and convert it to numeric with as.numeric():

lego_movie %>%

  html_node("strong span") %>%

  html_text() %>%

  as.numeric()

#> [1] 7.9

We use a similar process to extract the cast, using html_nodes() to find all nodes that match the selector:

lego_movie %>%

  html_nodes("#titleCast .itemprop span") %>%

  html_text()

#>  [1] "Will Arnett"     "Elizabeth Banks" "Craig Berry"   

#>  [4] "Alison Brie"     "David Burrows"   "Anthony Daniels"

#>  [7] "Charlie Day"     "Amanda Farinos"  "Keith Ferguson"

#> [10] "Will Ferrell"    "Will Forte"      "Dave Franco"   

#> [13] "Morgan Freeman"  "Todd Hansen"     "Jonah Hill"

The titles and authors of recent message board postings are stored in a the third table on the page. We can use html_node() and [[ to find it, then coerce it to a data frame with html_table():

lego_movie %>%

  html_nodes("table") %>%

  .[[3]] %>%

  html_table()

#>                                              X 1            NA

#> 1 this movie is very very deep and philosophical   mrdoctor524

#> 2 This got an 8.0 and Wizard of Oz got an 8.1...  marr-justinm

#> 3                         Discouraging Building?       Laestig

#> 4                              LEGO - the plural      neil-476

#> 5                                 Academy Awards   browncoatjw

#> 6                    what was the funniest part? actionjacksin

Other important functions

•    If you prefer, you can use xpath selectors instead of css: html_nodes(doc, xpath = "//table//td")).

•    Extract the tag names with html_tag(), text with html_text(), a single attribute with html_attr() or all attributes with html_attrs().

•    Detect and repair text encoding problems with guess_encoding() and repair_encoding().

•    Navigate around a website as if you’re in a browser with html_session(), jump_to(), follow_link(), back(), and forward(). Extract, modify and submit forms with html_form(), set_values() and submit_form(). (This is still a work in progress, so I’d love your feedback.)

To see these functions in action, check out package demos with demo(package = "rvest").

Source: http://blog.rstudio.org/2014/11/24/rvest-easy-web-scraping-with-r/