How to Scrape a Website into Excel without programming

How to Scrape a Website into Excel without programming

This web scraping tutorial will teach you visually step by step how to scrape or extract or pull data from websites using import.io(Free Tool) without programming skills into Excel.

Personally, I use web scraping for analysing my competitors’ best-performing blog posts or content such as what blog posts or content received most comments or social media shares.

In this tutorial,We will scrape the following data from a blog:

    All blog posts URLs.
    Authors names for each post.
    Blog posts titles.
    The number of social media shares each post received.

Then we will use the extracted data to determine what are the popular blog posts and their authors,which posts received much engagement from users through social media shares and on page comments.

Let’s get started.

Step 1:Install import.io app

The first step is to install import.io app.A free web scraping tool and one of the best web scraping software.It is available for Windows,Mac and Linux platforms.Import.io offers advanced data extraction features without coding by allowing you to create custom APIs or crawl entire websites.

After installation, you will need to sign up for an account.It is completely free so don’t worry.I will not cover the installation process.Once everything is set correctly you will see something similar to the window below after your first login.

Step 2:Choose how to scrape data using import.io extractor

With import.io you can do data extraction by creating custom APIs or crawling the entire websites.It comes equipped with different tools for data extraction such as magic,extractor,crawler and connector.

In this tutorial,I will use a tool called “extractor” to create a custom API for our data extraction process.

To get started click the “new” red button on the right top of the page and then click “Start Extractor” button on the pop-up window.

After clicking  “Start Extractor” the Import.io app internal browser window will open as shown below.

Step 3:Data scraping process

Now after the import.io browser is open navigate to the blog URL you want to scrape data from. Then once you already navigated to the target blog URL turn on extraction.In this tutorial,I will use this blog URL bongo5.com  for data extraction.

You can see from the window below I already navigated to www.bongo5.com but extraction switch is still off.

Turn extraction switch “ON” as shown in the window below and move to the next step.

Step 4:Training the “columns” or specifying the data we want to scrape

In this step,I will specify exactly what kind of data I want to scrape from the blog.On import.io app specifying the data you want to scrape is referred to as “training the columns”.Columns represent the data set I want to scrape(post titles,authors’ names and posts URLs).

In order to understand this step, you need to know the difference between a blog page and a blog post.A page might have a single post or multiple posts depending on the blog configuration.

A blog might have several blog posts,even hundreds or thousands of posts.But I will take only one session to train the “extractor” about the data I want to extract.I will do so by using an import.io visual highlighter.Once the data extraction is turned on the-the highlighter will appear by default.

I will do the training session for a single post in a single blog page with multiple posts then the extractor will extract data automatically for the remaining posts on the “same” blog page.
Step 4a:Creating “post_title” column

I will start by renaming “my_column” into the name of the data I want to scrape.Our goal in this tutorial is to scrape the blog posts titles,posts URLs,authors names and get social statistics later so I will create columns for posts titles,posts URLs,authors names.Later on, I will teach you how to get social statistics for the post URLs.

After editing “my_column” into “post_title” then point the mouse cursor over to any of the Posts title on the same blog page and the visual highlighter will automatically appear.Using the highlighter I can select the data I want to extract.

You can see below I selected one of the blog post titles on the page.The rectangular box with orange border is the visual highlighter.

The app will ask you how is the data arranged on the page.Since I have more than one post in a single page then you have rows of repeating data.This blog is having 25 posts per page.So you will select “many rows”.Sometimes you might have a single post on a page for that case you need to select “Just one row”.

Source: http://nocodewebscraping.com/web-scraping-for-dummies-tutorial-with-import-io-without-coding/

Are You Screen Scraping or Data Mining?

Are You Screen Scraping or Data Mining?

Many of us seem to use these terms interchangeably but let’s make sure we are clear about the differences that make each of these approaches different from the other.

Basically, screen scraping is a process where you use a computer program or software to extract information from a website.  This is different than crawling, searching or mining a site because you are not indexing everything on the page – a screen scraper simply extracts precise information selected by the user.  Screen scraping is a useful application when you want to do real-time, price and product comparisons, archive web pages, or acquire data sets that you want to evaluate or filter.

When you perform screen scraping, you are able to scrape data more directly and, you can automate the process if you are using the right solution. Different types of screen scraping services and solutions offer different ways of obtaining information. Some look directly at the html code of the webpage to grab the data while others use more advanced, visual abstraction techniques that can often avoid “breakage” errors when the web source experiences a programming or code change.

On the other hand, data mining is basically the process of automatically searching large amounts of information and data for patterns. This means that you already have the information and what you really need to do is analyze the contents to find the useful things you need. This is very different from screen scraping as screen scraping requires you to look for the data, collect it and then you can analyze it.

Data mining also involves a lot of complicated algorithms often based on various statistical methods. This process has nothing to do with how you obtain the data. All it cares about is analyzing what is available for evaluation.

Screen scraping is often mistaken for data mining when, in fact, these are two different things. Today, there are online services that offer screen scraping. Depending on what you need, you can have it custom tailored to meet your specific needs and perform precisely the tasks you want. But screen scraping does not guarantee any kind of analysis of the data.

Source: http://www.connotate.com/are-you-screen-scraping-or-data-mining/

Scraping data from LinkedIn

Scraping data from LinkedIn

How to scrape data from LinkedIn public profile for marketing purposes?

You can scrape data from a LinkedIn public profile using data scraper software. LinkedIn data extraction is most beneficial for marketers and most medium size companies rely on LinkedIn for their marketing purpose.

I would recommend you to use "LinkedIn Lead Extractor" software, which helps to quickly scrape public profiles from LinkedIn. With this tool your can scrape profile link, First Name, Last Name, Email, Phone Address, Twitter id, Yahoo messenger id, Skype Id, Google Talk ID, Job Role, Company Name, Address, Country, Connections. This company has built this tool specially for LinkedIn marketers who are not satisfied with their drop ship supplier's digital data.

LinkedIn advance search provides you the targeted customers profiles list with your requirements like country, country, city, company, job title, and much more.

In few weeks you can developed new ways to set-up differently the sales teams and create a much more technologic environment in the strategy department. An internal platform that generated targeted leads can be of a very big help. You can easily execute go to market to any area or city in so much little time compared with some years ago.

Source: http://www.ahmadsoftware.com/blogs/4/scraping-data-from-linkedin.html

Extract Data from Multiple Web Pages into Excel using import.io

In this tutorial, i will show you how to extract data from multiple web pages of a website or blog and save the extracted data into Excel spreadsheet for further processing.There are various methods and tools to do that but I found them complicated and I prefer to use import.io to accomplish the task.Import.io doesn’t require you to have programming skills.The platform is quite powerful,user-friendly with a lot of support online and above all FREE to use.

You can use the online version of their data extraction software or a desktop application.The online version will be covered in this tutorial.

Let us get started.

Step 1:Find a web page you want to extract data from.
You can extract data such as prices, images, authors’ names, addresses,dates etc

Step 2:Enter the URL for that web page into the text box here and click “Extract data”.

Then click  “Extract data” Import.io will transform the web page into data in seconds.Data such as authors,images,posts published dates and posts title will be pulled from the web page as shown in the image below.

Import.io extracted only 40 posts or articles from the first page of the blog!.
If you visit bongo5.com you will notice that the web page is having a total of 600+ pages at the time of writing this article and each page has 40 posts or articles on it as can be shown by the image below.
Next step will show you how to extract data from multiple pages of the web page into excel.

Step 3:Extract Data from Multiple Web Pages into Excel

Using the import.io online tool you can extract data from 20 web pages maximum.Go to the bottom right corner of the import.io online tool page and click “Download CSV” to save the extracted data from those 20 pages into Excel.
Note:Using the import.io desktop application you can extract an unlimited number of pages and pin point only the data you want to extract.Check out this tutorial on how to use the desktop application.
Once you click “Download CSV” the following pop up window will appear.You can specify the number of pages you want to get data from up to a maximum of 20 pages then click “Go!”
You will need to Sign up for a free account to download that data as a CSV, or save it as an API.If you save it as an API you can go back to the API later to extract new data if the web page is updated without the need to repeat the steps we have done so far.Also, you can use the API for integration into other platforms.
Below image shows 20 rows out of 800 rows of data extracted from the 20 pages of the web page.

Conclusion

The online tool doesn’t offer much flexibility than the desktop application.For example, you can not extract more than 20 pages and you can not pin point the type of data you want to extract.For a more advanced tutorial on how to use the desktop application, you can check out this tutorial I created earlier.

Source URL : http://nocodewebscraping.com/extract-multiple-web-pages-data-into-excel/

How to Avoid the Most Common Traps in Web Scraping?

A lot of industries are successfully using web scraping for creating massive data banks of applicable and actionable data which can be used on every day basis for further business interests as well as offer superior services to the customers. However, web scraping does have its own roadblocks and problems.

Using automated scraping, you could face many common problems. The web scraping spiders or programs present a definite picture to their targeted websites. Then, they use this behavior for making out between the human users as well as web scraping spiders. According to those details, a website can employ a certain web scraping traps for stopping your efforts. Here are some of the most common traps:

How Can You Avoid These Traps?

Some measures, which you can use to make sure that you avoid general web scraping traps include:

• Begin with caching pages, which you already have crawled and make sure that you are not required to load them again.
• Find out if any particular website, which you try to scratch has any particular dislikes towards the web scraping tools.
• Handle scraping in moderate phases as well as take the content required.
• Take things slower and do not overflow the website through many parallel requests, which put strain on the resources.
• Try to minimize the weight on every sole website, which you visit to scrape.
• Use a superior web scraping tool that can save and test data, patterns and URLs.
• Use several IP addresses to scrape efforts or taking benefits of VPN services and proxy servers. It will assist to decrease the dangers of having trapped as well as blacklisted through a website.

Source URL :http://www.3idatascraping.com/category/web-data-scraping

Scraping the Royal Society membership list

To a data scientist any data is fair game, from my interest in the history of science I came across the membership records of the Royal Society from 1660 to 2007 which are available as a single PDF file. I’ve scraped the membership list before: the first time around I wrote a C# application which parsed a plain text file which I had made from the original PDF using an online converting service, looking back at the code it is fiendishly complicated and cluttered by boilerplate code required to build a GUI. ScraperWiki includes a pdftoxml function so I thought I’d see if this would make the process of parsing easier, and compare the ScraperWiki experience more widely with my earlier scraper.

The membership list is laid out quite simply, as shown in the image below, each member (or Fellow) record spans two lines with the member name in the left most column on the first line and information on their birth date and the day they died, the class of their Fellowship and their election date on the second line.

Later in the document we find that information on the Presidents of the Royal Society is found on the same line as the Fellow name and that Royal Patrons are formatted a little differently. There are also alias records where the second line points to the primary record for the name on the first line.

pdftoxml converts a PDF into an xml file, wherein each piece of text is located on the page using spatial coordinates, an individual line looks like this:

<text top="243" left="135" width="221" height="14" font="2">Abbot, Charles, 1st Baron Colchester </text>

This makes parsing columnar data straightforward you simply need to select elements with particular values of the “left” attribute. It turns out that the columns are not in exactly the same positions throughout the whole document, which appears to have been constructed by tacking together the membership list A-J with that of K-Z, but this can easily be resolved by accepting a small range of positions for each column.

Attempting to automatically parse all 395 pages of the document reveals some transcription errors: one Fellow was apparently elected on 16th March 197 – a bit of Googling reveals that the real date is 16th March 1978. Another fellow is classed as a “Felllow”, and whilst most of the dates of birth and death are separated by a dash some are separated by an en dash which as far as the code is concerned is something completely different and so on. In my earlier iteration I missed some of these quirks or fixed them by editing the converted text file. These variations suggest that the source document was typed manually rather than being output from a pre-existing database. Since I couldn’t edit the source document I was obliged to code around these quirks.

ScraperWiki helpfully makes putting data into a SQLite database the simplest option for a scraper. My handling of dates in this version of the scraper is a little unsatisfactory: presidential terms are described in terms of a start and end year but are rendered 1st January of those years in the database. Furthermore, in historical documents dates may not be known accurately so someone may have a birth date described as “circa 1782? or “c 1782?, even more vaguely they may be described as having “flourished 1663-1778? or “fl. 1663-1778?. Python’s default datetime module does not capture this subtlety and if it did the database used to store dates would need to support it too to be useful – I’ve addressed this by storing the original life span data as text so that it can be analysed should the need arise. Storing dates as proper dates in the database, rather than text strings means we can query the database using date based queries.

ScraperWiki provides an API to my dataset so that I can query it using SQL, and since it is public anyone else can do this too. So, for example, it’s easy to write queries that tell you the the database contains 8019 Fellows, 56 Presidents, 387 born before 1700, 3657 with no birth date, 2360 with no death date, 204 “flourished”, 450 have birth dates “circa” some year.

I can count the number of classes of fellows:

select distinct class,count(*) from `RoyalSocietyFellows` group by class

Make a table of all of the Presidents of the Royal Society

select * from `RoyalSocietyFellows` where StartPresident not null order by StartPresident desc

…and so on. These illustrations just use the ScraperWiki htmltable export option to display the data as a table but equally I could use similar queries to pull data into a visualisation.

Comparing this to my earlier experience, the benefits of using ScraperWiki are:

•    Nice traceable code to provide a provenance for the dataset;

•    Access to the pdftoxml library;

•    Strong encouragement to “do the right thing” and put the data into a database;

•    Publication of the data;

•    A simple API giving access to the data for reuse by all.

My next target for ScraperWiki may well be the membership lists for the French Academie des Sciences, a task which proved too complex for a simple plain text scraper…

Sources URL :                             http://yellowpagesdatascraping.blogspot.in/2015/06/scraping-royal-society-membership-list.html

Web Data Extraction Services and Data Collection Form Website Pages

For any business market research and surveys plays crucial role in strategic decision making. Web scrapping and data extraction techniques help you find relevant information and data for your business or personal use. Most of the time professionals manually copy-paste data from web pages or download a whole website resulting in waste of time and efforts.

Instead, consider using web scraping techniques that crawls through thousands of website pages to extract specific information and simultaneously save this information into a database, CSV file, XML file or any other custom format for future reference.

Examples of web data extraction process include:
• Spider a government portal, extracting names of citizens for a survey
• Crawl competitor websites for product pricing and feature data
• Use web scraping to download images from a stock photography site for website design

Automated Data Collection
Web scraping also allows you to monitor website data changes over stipulated period and collect these data on a scheduled basis automatically. Automated data collection helps you discover market trends, determine user behavior and predict how data will change in near future.

Examples of automated data collection include:
• Monitor price information for select stocks on hourly basis
• Collect mortgage rates from various financial firms on daily basis
• Check whether reports on constant basis as and when required

Using web data extraction services you can mine any data related to your business objective, download them into a spreadsheet so that they can be analyzed and compared with ease.

In this way you get accurate and quicker results saving hundreds of man-hours and money!

With web data extraction services you can easily fetch product pricing information, sales leads, mailing database, competitors data, profile data and many more on a consistent basis.

Source URL :    http://ezinearticles.com/?Web-Data-Extraction-Services-and-Data-Collection-Form-Website-Pages&id=4860417