Difference between Data Mining and KDD

Difference between Data Mining and KDD

Data, in its raw form, is just a collection of things, where little information might be derived. Together with the development of information discovery methods(Data Mining and KDD), the value of the info is significantly improved.

Data mining is one among the steps of Knowledge Discovery in Databases(KDD) as can be shown by the image below.KDD is a multi-step process that encourages the conversion of data to useful information. Data mining is the pattern extraction phase of KDD. Data mining can take on several types, the option influenced by the desired outcomes.

Knowledge Discovery in Databases Steps
Data Selection

KDD isn’t prepared without human interaction. The choice of subset and the data set requires knowledge of the domain from which the data is to be taken. Removing non-related information elements from the dataset reduces the search space during the data mining phase of KDD. The sample size and structure are established during this point, if the dataset can be assessed employing a testing of the info.
Pre-processing

Databases do contain incorrect or missing data. During the pre-processing phase, the information is cleaned. This warrants the removal of “outliers”, if appropriate; choosing approaches for handling missing data fields; accounting for time sequence information, and applicable normalization of data.
Transformation

Within the transformation phase attempts to reduce the variety of data elements can be assessed while preserving the quality of the info. During this stage, information is organized, changed in one type to some other (i.e. changing nominal to numeric) and new or “derived” attributes are defined.
Data mining

Now the info is subjected to one or several data-mining methods such as regression, group, or clustering. The information mining part of KDD usually requires repeated iterative application of particular data mining methods. Different data-mining techniques or models can be used depending on the expected outcome.
Evaluation

The final step is documentation and interpretation of the outcomes from the previous steps. Steps during this period might consist of returning to a previous step up the KDD approach to help refine the acquired knowledge, or converting the knowledge in to a form clear for the user.In this stage the extracted data patterns are visualized for further reviews.
Conclusion

Data mining is a very crucial step of the KDD process.

For further reading aboud KDD and data mining ,please check this link.

Source: http://nocodewebscraping.com/difference-data-mining-kdd/

Three Common Methods For Web Data Extraction

Three Common Methods For Web Data Extraction

Probably the most common technique used traditionally to extract data from web pages this is to cook up some regular expressions that match the pieces you want (e.g., URL's and link titles). Our screen-scraper software actually started out as an application written in Perl for this very reason. In addition to regular expressions, you might also use some code written in something like Java or Active Server Pages to parse out larger chunks of text. Using raw regular expressions to pull out the data can be a little intimidating to the uninitiated, and can get a bit messy when a script contains a lot of them. At the same time, if you're already familiar with regular expressions, and your scraping project is relatively small, they can be a great solution.

Other techniques for getting the data out can get very sophisticated as algorithms that make use of artificial intelligence and such are applied to the page. Some programs will actually analyze the semantic content of an HTML page, then intelligently pull out the pieces that are of interest. Still other approaches deal with developing "ontologies", or hierarchical vocabularies intended to represent the content domain.

There are a number of companies (including our own) that offer commercial applications specifically intended to do screen-scraping. The applications vary quite a bit, but for medium to large-sized projects they're often a good solution. Each one will have its own learning curve, so you should plan on taking time to learn the ins and outs of a new application. Especially if you plan on doing a fair amount of screen-scraping it's probably a good idea to at least shop around for a screen-scraping application, as it will likely save you time and money in the long run.

So what's the best approach to data extraction? It really depends on what your needs are, and what resources you have at your disposal. Here are some of the pros and cons of the various approaches, as well as suggestions on when you might use each one:

Raw regular expressions and code

Advantages:

- If you're already familiar with regular expressions and at least one programming language, this can be a quick solution.

- Regular expressions allow for a fair amount of "fuzziness" in the matching such that minor changes to the content won't break them.

- You likely don't need to learn any new languages or tools (again, assuming you're already familiar with regular expressions and a programming language).

- Regular expressions are supported in almost all modern programming languages. Heck, even VBScript has a regular expression engine. It's also nice because the various regular expression implementations don't vary too significantly in their syntax.

Disadvantages:

- They can be complex for those that don't have a lot of experience with them. Learning regular expressions isn't like going from Perl to Java. It's more like going from Perl to XSLT, where you have to wrap your mind around a completely different way of viewing the problem.

- They're often confusing to analyze. Take a look through some of the regular expressions people have created to match something as simple as an email address and you'll see what I mean.

- If the content you're trying to match changes (e.g., they change the web page by adding a new "font" tag) you'll likely need to update your regular expressions to account for the change.

- The data discovery portion of the process (traversing various web pages to get to the page containing the data you want) will still need to be handled, and can get fairly complex if you need to deal with cookies and such.

When to use this approach: You'll most likely use straight regular expressions in screen-scraping when you have a small job you want to get done quickly. Especially if you already know regular expressions, there's no sense in getting into other tools if all you need to do is pull some news headlines off of a site.

Ontologies and artificial intelligence

Advantages:

- You create it once and it can more or less extract the data from any page within the content domain you're targeting.

- The data model is generally built in. For example, if you're extracting data about cars from web sites the extraction engine already knows what the make, model, and price are, so it can easily map them to existing data structures (e.g., insert the data into the correct locations in your database).

- There is relatively little long-term maintenance required. As web sites change you likely will need to do very little to your extraction engine in order to account for the changes.

Disadvantages:

- It's relatively complex to create and work with such an engine. The level of expertise required to even understand an extraction engine that uses artificial intelligence and ontologies is much higher than what is required to deal with regular expressions.

- These types of engines are expensive to build. There are commercial offerings that will give you the basis for doing this type of data extraction, but you still need to configure them to work with the specific content domain you're targeting.

- You still have to deal with the data discovery portion of the process, which may not fit as well with this approach (meaning you may have to create an entirely separate engine to handle data discovery). Data discovery is the process of crawling web sites such that you arrive at the pages where you want to extract data.

When to use this approach: Typically you'll only get into ontologies and artificial intelligence when you're planning on extracting information from a very large number of sources. It also makes sense to do this when the data you're trying to extract is in a very unstructured format (e.g., newspaper classified ads). In cases where the data is very structured (meaning there are clear labels identifying the various data fields), it may make more sense to go with regular expressions or a screen-scraping application.

Screen-scraping software

Advantages:

- Abstracts most of the complicated stuff away. You can do some pretty sophisticated things in most screen-scraping applications without knowing anything about regular expressions, HTTP, or cookies.

- Dramatically reduces the amount of time required to set up a site to be scraped. Once you learn a particular screen-scraping application the amount of time it requires to scrape sites vs. other methods is significantly lowered.

- Support from a commercial company. If you run into trouble while using a commercial screen-scraping application, chances are there are support forums and help lines where you can get assistance.

Disadvantages:

- The learning curve. Each screen-scraping application has its own way of going about things. This may imply learning a new scripting language in addition to familiarizing yourself with how the core application works.

- A potential cost. Most ready-to-go screen-scraping applications are commercial, so you'll likely be paying in dollars as well as time for this solution.

- A proprietary approach. Any time you use a proprietary application to solve a computing problem (and proprietary is obviously a matter of degree) you're locking yourself into using that approach. This may or may not be a big deal, but you should at least consider how well the application you're using will integrate with other software applications you currently have. For example, once the screen-scraping application has extracted the data how easy is it for you to get to that data from your own code?

When to use this approach: Screen-scraping applications vary widely in their ease-of-use, price, and suitability to tackle a broad range of scenarios. Chances are, though, that if you don't mind paying a bit, you can save yourself a significant amount of time by using one. If you're doing a quick scrape of a single page you can use just about any language with regular expressions. If you want to extract data from hundreds of web sites that are all formatted differently you're probably better off investing in a complex system that uses ontologies and/or artificial intelligence. For just about everything else, though, you may want to consider investing in an application specifically designed for screen-scraping.

As an aside, I thought I should also mention a recent project we've been involved with that has actually required a hybrid approach of two of the aforementioned methods. We're currently working on a project that deals with extracting newspaper classified ads. The data in classifieds is about as unstructured as you can get. For example, in a real estate ad the term "number of bedrooms" can be written about 25 different ways. The data extraction portion of the process is one that lends itself well to an ontologies-based approach, which is what we've done. However, we still had to handle the data discovery portion. We decided to use screen-scraper for that, and it's handling it just great. The basic process is that screen-scraper traverses the various pages of the site, pulling out raw chunks of data that constitute the classified ads. These ads then get passed to code we've written that uses ontologies in order to extract out the individual pieces we're after. Once the data has been extracted we then insert it into a database.

Source: http://ezinearticles.com/?Three-Common-Methods-For-Web-Data-Extraction&id=165416

Tips for scraping business directories

Tips for scraping business directories

Are you looking to scrape business directories to generate leads?

Here are a few tips for scraping business directories.

Web scraping is not rocket science. But there are good and bad and worst ways of doing it.

Generating sales qualified leads is always a headache. The old school ways are to buy a list from sites like Data.com. But they are quite expensive.

Scraping business directories can help generate sales qualified leads. The following tips can help you scrape data from business directories efficiently.

1) Choose a good framework to write the web scrapers. This can help save a lot of time and trouble. Python Scrapy is our favourite, but there are other non-pythonic frameworks too.

2) The business directories might be having anti-scraping mechanisms. You have to use IP rotating services to do the scrape. Using IP rotating services, crawl with multiple changing IP addresses which can cover your tracks.

3) Some sites really don’t want you to scrape and they will block the bot. In these cases, you may need to disguise your web scraper as a human being. Browser automation tools like selenium can help you do this.

4) Web sites will update their data quite often. The scraper bot should be able to update the data according to the changes. This is a hard task and you need professional services to do that.

One of the easiest ways to generate leads is to scrape from business directories and use enrich them. We made Leadintel for lead research and enrichment.

Source: http://blog.datahut.co/tips-for-scraping-business-directories/

4 Web Scraping Tools To Save You Time On Data Extraction

Either you are working on a product website, struggling to add live data feed to your app or merely need to pull out a huge amount of online data for analysis, an accurate web scraping tool can save you loads of time and keep you sane. Here are four powerful web scraping tools to save you from copy-pasting or spending time on writing your own scripts.

Uipath  specializes in developing various process automation software including web scraping and screen scraping software for desktop and web. Uipath web scraper is perfect for non-coders and easily surpasses most common data extraction challenges including page navigation, digging through flash and even scraping PDF files. All you need to do is open the web scraping wizard and simply highlight the data you need to extract. The tool will scrape all the data following this pattern at all pages you’ve chosen and sort it accordingly. You can add as many items for scraping as you like and have them sorted in respective columns. As a result, you receive a neat Excel or CSV document with all the data eliminated from duplicates.

Moreover, Uipath isn’t just about scraping. This software can be used not only for extracting data, but to manipulate the interface of another app, thus establishing data transfers among the two of them. Basically, this tool could be used to conduct any repetitive task a human could do, yet much faster and with higher accuracy.

Pros: You can automate form filling, clicking buttons, navigation etc. Uipath scraper is impressively accurate, fast and simple to use. It “reads” all types of data on screen (JS, HTML, Silverlight and more), plus you can train the software to emulate human actions of various complexity.

Cons: Premium software runs at a premium price. Uipath is an affordable professional solution, but may be a bit too pricey for personal use.

 Import.io  offers you a free desktop app to help you scrap all the data you need from an unlimited amount of web pages. The service treats each page as a potential data source to generate API from. If the page you’ve submitted has been previously processed, you can access its API and get some of the data. In other case, Import.io will guide you through the process of creating the scraping matrix by building connectors (for navigation) or extractors (to pull out the needed data). Afterwards, you submit a request for extraction and it’s typically processed within 24 hours. All the data is private and you can schedule auto refreshments at any chosen period of time.

Pros: The service is easy-to-use with no tech skills needed. It can  pages with data (those that needed login/pass), plus it’s free. Minimalistic effective design and simple navigation comes along.

Cons: Improt.io has hard times navigating through combinations of javascript/POST and cannot navigate from one page to another (e.g. click next, second page etc).  Sometimes, it takes over 24 hours to receive the report.  Besides, it’s a browser-only app, non-compatible with other applications.

Kimono is a popular web scraper among app developers who prefer to power up their products with live data and no additional code. It saves you tons of time when you need to fill up your app with mashing data. Install Kimono Browser bookmarklet; highlight page elements you need to and provide some positive/negative examples to train the tool. After labeling all the data you can download it in CSV/JSON/a web endpoint format. The APIs created for your pages are stored in the cloud and you can run them on schedule. So far, Kimono is free to use with pro and enterprise solutions to be launched soon.

Pros: The tool works pretty fast and works great with scraping newsfeeds and prices. The data is rather accurate.

Cons: No page navigation available and you need to spend quite a lot of time to train Kimono before it starts to pull out the multi items data accurate enough. In general, I’d say Kimono is more of an app mash-ups creator than a full-scale web scraper.

 Screen Scraper  is pretty neat and tackles a lot of difficult tasks including navigation and precise data extractions, however it requires a bit of programming/tokenization skills if you’d like to run it super smooth. Launch the software, add a proxy, start recording the list of your actions and creating extracting patterns (some coding required). Works great with HTML and Javascript, however you should test it with Citrix and other platforms. Basically, screen scraper helps you writing simple web scraping scripts and lets you download the extracted data in txt/csv/excel format.

Pros: When set correctly, there’s no data extraction tasks Screen scraper fails to handle.
Cons: The tool is pricey and you’ll have to go through documentation and have basic coding skills to use it.

Source URL :  http://tech.co/4-web-scraping-tools-save-time-data-extraction-2015-03

ECJ clarifies Database Directive scope in screen scraping case

EC on the legal protection of databases (Database Directive) in a case concerning the extraction of data from a third party’s website by means of automated systems or software for commercial purposes (so called 'screen scraping').

Flight data extracted

The case, Ryanair Ltd vs. PR Aviation BV, C-30/14, is of interest to a range of companies such as price comparison websites. It stemmed from  Dutch company PR Aviation operation of a website where consumers can search through flight data of low-cost airlines  (including Ryanair), compare prices and, on payment of a commission, book a flight. The relevant flight data is extracted from third-parties’ websites by means of ‘screen scraping’ practices.

Ryanair claimed that PR Aviation’s activity:

• amounted to infringement of copyright (relating to the structure and architecture of the database) and of the so-called sui generis database right (i.e. the right granted to the ‘maker’ of the database where certain investments have been made to obtain, verify, or present the contents of a database) under the Netherlands law implementing the Database Directive;

• constituted breach of contract. In this respect, Ryanair claimed that a contract existed with PR Aviation for the use of its website. Access to the latter requires acceptance, by clicking a box, of the airline’s general terms and conditions which, amongst others, prohibit unauthorized ‘screen scraping’ practices for commercial purposes.

Ryanair asked Dutch courts to prohibit the infringement and order damages. In recent years the company has been engaged in several legal cases against web scrapers across Europe.

The Local Court, Utrecht, and the Court of Appeals of Amsterdam dismissed Ryanair’s claims on different grounds. The Court of Appeals, in particular, cited PR Aviation’s screen scraping of Ryanair’s website as amounting to a “normal use” of said website within the meaning of the lawful user exceptions under Sections 6 and 8 of the Database Directive, which cannot be derogated by contract (Section 15).

Ryanair appealed

Ryanair appealed the decision before the Netherlands Supreme Court (Hoge Raad der Nederlanden), which decided to refer the following question to the ECJ for a preliminary ruling: “Does the application of [Directive 96/9] also extend to online databases which are not protected by copyright on the basis of Chapter II of said directive or by a sui generis right on the basis of Chapter III, in the sense that the freedom to use such databases through the (whether or not analogous) application of Article[s] 6(1) and 8, in conjunction with Article 15 [of Directive 96/9] may not be limited contractually?.”

The ECJ’s ruling

The ECJ (without the need of the opinion of the advocate general) ruled that the Database Directive is not applicable to databases which are not protected either by copyright or by the sui generis database right. Therefore, exceptions to restricted acts set forth by Sections 6 and 8 of the Directive do not prevent the database owner from establishing contractual limitations on its use by third parties. In other words, restrictions to the freedom to contract set forth by the Database Directive do not apply in cases of unprotected databases. Whether Ryanair’s website may be entitled to copyright or sui generis database right protection needs to be determined by the competent national court.

The ECJ’s decision is not particularly striking from a legal standpoint. Yet, it could have a significant impact on the business model of price comparison websites, aggregators, and similar businesses. Owners of databases that could not rely on intellectual property protection may contractually prevent extraction and use (“scraping”) of content from their online databases. Thus, unprotected databases could receive greater protection than the one granted by IP law.

Antitrust implications

However, the lawfulness of contractual restrictions prohibiting access and reuse of data through screen scraping practices should be assessed under an antitrust perspective. In this respect, in 2013 the Court of Milan ruled that Ryanair’s refusal to grant access to its database to the online travel agency Viaggiare S.r.l. amounted to an abuse of dominant position in the downstream market of information and intermediation on flights (decision of June 4, 2013 Viaggiare S.r.l. vs Ryanair Ltd). Indeed, a balance should be struck between the need to compensate the efforts and investments made by the creator of the database with the interest of third parties to be granted with access to information (especially in those cases where the latter are not entitled to copyright protection).

Additionally, web scraping triggers other issues which have not been considered by the ECJ’s ruling. These include, but are not limited to trademark law (i.e., whether the use of a company’s names/logos by the web scraper without consent may amount to trademark infringement), data protection (e.g., in case the scraping involves personal data), or unfair competition.


Source URL :http://yellowpagesdatascraping.blogspot.in/2015/07/ecj-clarifies-database-directive-scope.html

An Easy Way For Data Extraction

There are so many data scraping tools are available in internet. With these tools you can you download large amount of data without any stress. From the past decade, the internet revolution has made the entire world as an information center. You can obtain any type of information from the internet. However, if you want any particular information on one task, you need search more websites. If you are interested in download all the information from the websites, you need to copy the information and pate in your documents. It seems a little bit hectic work for everyone. With these scraping tools, you can save your time, money and it reduces manual work.

The Web data extraction tool will extract the data from the HTML pages of the different websites and compares the data. Every day, there are so many websites are hosting in internet. It is not possible to see all the websites in a single day. With these data mining tool, you are able to view all the web pages in internet. If you are using a wide range of applications, these scraping tools are very much useful to you.

The data extraction software tool is used to compare the structured data in internet. There are so many search engines in internet will help you to find a website on a particular issue. The data in different sites is appears in different styles. This scraping expert will help you to compare the date in different site and structures the data for records.

And the web crawler software tool is used to index the web pages in the internet; it will move the data from internet to your hard disk. With this work, you can browse the internet much faster when connected. And the important use of this tool is if you are trying to download the data from internet in off peak hours. It will take a lot of time to download. However, with this tool you can download any data from internet at fast rate.There is another tool for business person is called email extractor. With this toll, you can easily target the customers email addresses. You can send advertisement for your product to the targeted customers at any time. This the best tool to find the database of the customers.

 Source  URL : http://ezinearticles.com/?An-Easy-Way-For-Data-Extraction&id=3517104

Beginner’s guide to Web Scraping in Python (using Beautiful Soup)

Introduction

The need and importance of extracting data from the web is becoming increasingly loud and clear. Every few weeks, I find myself in a situation where we need to extract data from the web. For example, last week we were thinking of creating an index of hotness and sentiment about various data science courses available on the internet. This would not only require finding out new courses, but also scrape the web for their reviews and then summarizing them in a few metrics! This is one of the problems / products, whose efficacy depends more on web scrapping and information extraction (data collection) than the techniques used to summarize the data.

Ways to extract information from web

There are several ways to extract information from the web. Use of APIs being probably the best way to extract data from a website. Almost all large websites like Twitter, Facebook, Google, Twitter, StackOverflow provide APIs to access their data in a more structured manner. If you can get what you need through an API, it is almost always preferred approach over web scrapping. This is because if you are getting access to structured data from the provider, why would you want to create an engine to extract the same information.

Sadly, not all websites provide an API. Some do it because they do not want the readers to extract huge information in structured way, while others don’t provide APIs due to lack of technical knowledge. What do you do in these cases? Well, we need to scrape the website to fetch the information.

There might be a few other ways like RSS feeds, but they are limited in their use and hence I am not including them in the discussion here.

What is Web Scraping?

Web scraping is a computer software technique of extracting information from websites. This technique mostly focuses on the transformation of unstructured data (HTML format) on the web into structured data (database or spreadsheet).

You can perform web scrapping in various ways, including use of Google Docs to almost every programming language. I would resort to Python because of its ease and rich eocsystem. It has a library known as ‘Beautiful Soup’ which assists this task. In this article, I’ll show you the easiest way to learn web scraping using python programming.

For those of you, who need a non-programming way to extract information out of web pages, you can also look at import.io . It provides a GUI driven interface to perform all basic web scraping operations. The hackers can continue to read this article!

Libraries required for web scraping

As we know, python is a open source programming language. You may find many libraries to perform one function. Hence, it is necessary to find the best to use library. I prefer Beautiful Soup (python library), since it is easy and intuitive to work on. Precisely, I’ll use two Python modules for scraping data:

Urllib2: It is a Python module which can be used for fetching URLs. It defines functions and classes to help with URL actions (basic and digest authentication, redirections, cookies, etc). For more detail refer to the documentation page.

Beautiful Soup: It is an incredible tool for pulling out information from a webpage. You can use it to extract tables, lists, paragraph and you can also put filters to extract information from web pages. In this article, we will use latest version Beautiful Soup 4. You can look at the installation instruction in its documentation page.

Beautiful Soup does not fetch the web page for us. That’s why, I use urllib2 in combination with the BeautifulSoup library.

Python has several other options for HTML scraping in addition to Beatiful Soup. Here are some others:

    -mechanize
    -scrapemark
    -scrapy

Basics – Get familiar with HTML (Tags)

While performing web scarping, we deal with html tags. Thus, we must have good understanding of them.                     
 you already know basics of HTML, you can skip this section. Below is the basic syntax of HTML:
  This syntax has various tags as elaborated below:

    <!DOCTYPE html> : HTML documents must start with a type declaration
      HTML document is contained between <html> and </html>
      The visible part of the HTML document is between <body> and </body>
       HTML headings are defined with the <h1> to <h6> tags
       HTML paragraphs are defined with the <

Scrapping a web Page using Beautiful Soup

Here, I am scraping data from a Wikipedia page. Our final goal is to extract list of state, union territory capitals in India. And some basic detail like establishment, former capital and others form this wikipedia page. Let’s learn with doing this project step wise step:

Import necessary libraries:

#import the library used to query a website
import urllib2
#specify the url
wiki = "https://en.wikipedia.org/wiki/List_of_state_and_union_territory_capitals_in_India"
#Query the website and return the html to the variable 'page'
page = urllib2.urlopen(wiki)
#import the Beautiful soup functions to parse the data returned from the website
from bs4 import Beautiful Soup
#Parse the html in the 'page' variable, and store it in Beautiful Soup format
soup = Beautiful Soup(page)

Use function “prettify” to look at nested structure of HTML page

Above, you can see that structure of the HTML tags. This will help you to know about different available tags and how can you play with these to extract information.

Work with HTML tags

    soup.<tag>: Return content between opening and closing tag including tag.
    In[30]:soup.title
    Out[30]:<title>List of state and union territory capitals in India - Wikipedia, the free encyclopedia</title>
    soup.<tag>.string: Return string within given tag
    In [38]:soup.title.string
    Out[38]:u'List of state and union territory capitals in India - Wikipedia, the free encyclopedia'

Find all the links within page’s <a> tags::  We know that, we can tag a link using tag “<a>”. So, we should go with option soup.a and it should return the links available in the web page. Let’s do it.

    In [40]:soup.a
    Out[40]:<a id="top"></a>

Above, you can see that, we have only one output. Now to extract all the links within <a>, we will use

Above, it is showing all links including titles, links and other information.  Now to show only links, we need to iterate over each a tag and then return the link using attribute “href” with get.

Find the right table: As we are seeking a table to extract information about state capitals, we should identify the right table first. Let’s write the command to extract information within all table tags.

all_tables=soup.find_all('table')

Now to identify the right table, we will use attribute “class” of table and use it to filter the right table. In chrome, you can check the class name by right click on the required table of web page –> Inspect element –> Copy the class name OR go through the output of above command find the class name of right table.

right_table=soup.find('table', class_='wikitable sortable plainrowheaders')

\right_table

Extract the information to DataFrame: Here, we need to iterate through each row (tr) and then assign each element of tr (td) to a variable and append it to a list. Let’s first look at the HTML structure of the table (I am not going to extract information for table heading <th>)
Above, you can notice that second element of <tr> is within tag <th> not <td> so we need to take care for this. Now to access value of each element, we will use “find(text=True)” option with each element.  Let’s look at the code

#Generate lists

A=[]
B=[]
C=[]
D=[]
E=[]
F=[]
G=[]
for row in right_table.findAll("tr"):

    cells = row.findAll('td')
    states=row.findAll('th') #To store second column data
    if len(cells)==6: #Only extract table body not heading
        A.append(cells[0].find(text=True))
        B.append(states[0].find(text=True))
        C.append(cells[1].find(text=True))
        D.append(cells[2].find(text=True))
        E.append(cells[3].find(text=True))
        F.append(cells[4].find(text=True))
        G.append(cells[5].find(text=True))

#import pandas to convert list to data frame

import pandas as pd
df=pd.DataFrame(A,columns=['Number'])
df['State/UT']=B
df['Admin_Capital']=C
df['Legislative_Capital']=D
df['Judiciary_Capital']=E
df['Year_Capital']=F
df['Former_Capital']=G
df

Similarly, you can perform various other types of web scraping using “Beautiful Soup“. This will reduce your manual efforts to collect data from web pages. You can also look at the other attributes like .parent, .contents, .descendants and .next_sibling, .prev_sibling and various attributes to navigate using tag name. These will help you to scrap the web pages effectively.-

But, why can’t I just use Regular Expressions?

Now, if you know regular expressions, you might be thinking that you can write code using regular expression which can do the same thing for you. I definitely had this question. In my experience with Beautiful Soup and Regular expressions to do same thing I found out:

Code written in Beautiful Soup is usually more robust than the one written using regular expressions. Codes written with regular expressions need to be altered with any changes in pages. Even Beautiful Soup needs that in some cases, it is just that Beautiful Soup is relatively better.

Regular expressions are much faster than Beautiful Soup, usually by a factor of 100 in giving the same outcome.

So, it boils down to speed vs. robustness of the code and there is no universal winner here. If the information you are looking for can be extracted with simple regex statements, you should go ahead and use them. For almost any complex work, I usually recommend BeautifulSoup more than regex.

End Note

In this article, we looked at web scraping methods using “Beautiful Soup” and “urllib2” in Python. We also looked at the basics of HTML and perform the web scraping step by step while solving a challenge. I’d recommend you to practice this and use it for collecting data from web pages.


 Source : http://www.analyticsvidhya.com/blog/2015/10/beginner-guide-web-scraping-beautiful-soup-python/