Scraping R-bloggers with Python – Part 2

In my previous post I showed how to write a small simple python script to download the pages of R-bloggers.com. If you followed that post and ran the script, you should have a folder on your hard drive with 2409 .html files labeled post1.html , post2.html and so forth. The next step is to write a small script that extract the information we want from each page, and store that information in a .csv file that is easily read by R. In this post I will show how to extract the post title, author name and date of a given post and store it in a .csv file with a unique id.

To do this open a document in your favorite python editor (I like to use aquamacs) and name it: extraction.py. As in the previous post we start by importing the modules that we will use for the extraction:

from BeautifulSoup import BeautifulSoup

import os
import re

As in the previous post we will be using the BeautifulSoup module to extract the relevant information from the pages. The os module is used to get a list of file from the directory where we have saved the .html files, and finally the re module allows us to use regular expressions to format the titles that include a comma value or a newline value (\n). We need to remove these as they would mess up the formatting of the .csv file.

After having read in the modules, we need to get a list of files that we can iterate over. First we need to specify the path were the files are saved, and then we use the os module to get all the filenames in the specified directory:

path = "/Users/thomasjensen/Documents/RBloggersScrape/download"

listing = os.listdir(path)

It might be that there are other files in the given directory, hence we apply a filter, in shape of a list comprehension, to weed out any file names that do not match our naming scheme:

listing = [name for name in listing if re.search(r"post\d+\.html",name) != None]

Notice that a regular expression was used to determine whether a given name in the list matched our naming scheme. For more on regular expressions have a look at this site.

The final steps in preparing our extraction is to change the working directory to where we have our .html files, and create an empty dictionary:

os.chdir(path)
data = {}

Dictionaries are one of the great features of Python. Essentially a dictionary is a mapping of a key to a specific value, however the fact that dictionaries can be nested within each other, allows us to create data structures similar to R’s data frames.

Now we are ready to begin extracting information from our downloaded pages. Much as in the previous post, we will loop over all the file names, read each file into Python and create a BeautifulSoup object from the file:

for page in listing:
    site = open(page,"rb")
    soup = BeautifulSoup(site)

In order to store the values we extract from a given page, we update the dictionary with a unique key for the page. Since our naming scheme made sure that each file had a unique name, we simply remove the .html part from the page name, and use that as our key:

key = re.sub(".html","",page)

data.update({key:{}})

This will create a mapping between our key and an empty dictionary, nested within the data dictionary. Once this is done we can start extract information and store it in our newly created nested dictionary. The content we want is located in the main column, which has the id tag “leftcontent” in the html code. To get at this we use find() function on soup object created above:

content = soup.find("div", id = "leftcontent")

The first “h1” tag in our content object contains the title, so again we will use the find() function on the content object, to find the first “h1” tag:

title = content.findNext("h1").text

To get the text within the “h1” tag the .text had been added to our search with in the content object.

To find the author name, we are lucky that there is a class of “div” tags called “meta” which contain a link with the author name in it. To get the author name we simply find the meta div class and search for a link. Then we pull out the text of the link tag:

author = content.find("div",{"class":"meta"}).findNext("a").text

Getting the date is a simple matter as it is nested within div tag with the class “date”:

date = content.find("div",{"class":"date"}).text

Once we have the three variables we put them in dictionaries that are nested within the nested dictionary we created with the key:

data[key]["title"] = title
data[key]["author"] = author
data[key]["date"] = date

Once we have run the loop and gone through all posts, we need to write them in the right format to a .csv file. To begin with we open a .csv file names output:

output = open("/Users/thomasjensen/Documents/RBloggersScrape/output.csv","wb")

then we create a header that contain the variable names and write it to the output.csv file as the first row:

variables = unicode(",".join(["id","date","author","title"]))
header = variables + "\n"
output.write(header.encode("utf8"))

Next we pull out all the unique keys from our dictionary that represent individual posts:

keys = data.keys()

Now it is a simple matter of looping through all the keys, pull out the information associated with each key, and write that information to the output.csv file:

for key in keys:
    print key
    id = key
    date = re.sub(",","",data[key]["date"])
    author = data[key]["author"]
    title = re.sub(",","",data[key]["title"])
    title = re.sub("\\n","",title)
    linelist = [id,date,author,title]
    linestring = unicode(",".join(linelist))
    linestring = linestring + "\n"
    output.write(linestring.encode("utf-8"))

Notice that we first create four variables that contain the id, date, author and title information. With regards to the title we use two regular expressions to remove any commas and “\n” from the title, as these would create new columns or new line breaks in the output.csv file. Finally we put the variables together in a list, and turn the list into a string with the list items separated by a comma. Then a linebreak is added to the end of the string, and the string is written to the output.csv file. As a last step we close the file connection:

output.close()

And that is it. If you followed the steps you should now have a csv file in your directory with 2409 rows, and four variables – ready to be read into R. Stay tuned for the next post which will show how we can use this data to see how R-bloggers has developed since 2005. The full extraction script is shown below:

from BeautifulSoup import BeautifulSoup

import os
import re

 path = "/Users/thomasjensen/Documents/RBloggersScrape/download"
 listing = os.listdir(path)

listing = [name for name in listing if re.search(r"post\d+\.html",name) != None]
 os.chdir(path)
 data = {}
 for page in listing:
site = open(page,"rb")
soup = BeautifulSoup(site)
key = re.sub(".html","",page)
print key
data.update({key:{}})
 content = soup.find("div", id = "leftcontent")
title = content.findNext("h1").text
author = content.find("div",{"class":"meta"}).findNext("a").text
date = content.find("div",{"class":"date"}).text
data[key]["title"] = title
data[key]["author"] = author
data[key]["date"] = date

 output = open("/Users/thomasjensen/Documents/RBloggersScrape/output.csv","wb")

 keys = data.keys()
 variables = unicode(",".join(["id","date","author","title"]))
 header = variables + "\n"
 output.write(header.encode("utf8"))
 for key in keys:
print key
id = key
date = re.sub(",","",data[key]["date"])
author = data[key]["author"]
title = re.sub(",","",data[key]["title"])
title = re.sub("\\n","",title)
linelist = [id,date,author,title]
linestring = unicode(",".join(linelist))
linestring = linestring + "\n"
output.write(linestring.encode("utf-8"))
 output.close()

Source:http://www.r-bloggers.com/scraping-r-bloggers-with-python-part-2/

Outsourcing Data Mining is a Wise Business Decision

Most businesses nowadays have a large volume of raw data that is never processed, because of the lack of time or resources. If your business is facing a similar situation, then you are missing out on valuable information. Without the right information, your company will be unable to make accurate business decisions.

The right information can play a key role in promoting the growth of your business. When unprocessed data is entered, filtered, classified and converted into a workable format, it can be used to maximize your profits, ameliorate your risks and run a seamless workflow.

Over the years, data mining has proved to be extremely useful in various industries, be it, healthcare, direct marketing, e-commerce, finance, customer relationship management or telecommunications. With the right information, companies have been able to make fast and effective business decisions.

Why outsource data mining?

Data mining requires the expertise of professional business and financial analysts who understand how to acquire important information from vast amounts of data. If data mining is done in-house, it can become expensive and time consuming. It can also shift your focus away from core business activities. Outsourcing data mining on the other hand is more fast, cost-effective and can give you access to professional services.

4 commonly outsourced data mining functions

Most companies outsource one or more of the following data mining functions to India:

1. Data congregation: Data is extracted from various web pages and websites, by using methods like web and screen scraping. The collected data is then entered into a database.

2. Contact data collection: Different websites are searched and information concerning contacts is collected.

3. E-commerce data: Data about varied online stores are collected, taking into account information about prices, discounts and products.

4. Data about competitors: Data about business competitors are collected to help a company gauge itself against its competition. With such valuable data, you can effectively re-design your marketing strategy and pricing matrix.

8 advantages of outsourcing data mining to India

With data mining out of your hands, your business can make huge savings in terms of time, money and infrastructure. The following are some of the benefits that you can leverage by outsourcing data mining to India:

    Get qualified and highly skilled data mining experts to work for you at an extremely affordable cost

    Be assured of the quality of information, as Indian data entry companies only extract information from reliable websites and databases

    Save on the cost of investing on the latest data mining software and technology, as your Indian service provider will be making these investments

    Get your data processed within a short turnaround time of 3,6 or 12 hours as Indian data mining companies can provide efficient data mining within a few hours

    When compared to in-house data mining, outsourcing data mining can be a lot cheaper and also bring you better results

    Stay assured about the complete privacy, security and confidentiality of your valuable data as Indian data mining companies use the latest technology to ensure 100% safety

    Get access to data with a wide market coverage as your Indian data mining provider will be serving many business with varied data mining needs

    Improve your overall productivity and generate more profits by making informed decisions about your business

Have you outsourced data mining before? If yes, which data mining service did you outsource? Did you find outsourcing more advantageous that in-house data mining. Let us know.

Source: http://blog.flatworldsolutions.com/outsourcing-data-mining-is-a-wise-business-decision/

Get started with screenscraping using Google Chrome’s Scraper extension

How do you get information from a website to a Excel spreadsheet? The answer is screenscraping. There are a number of softwares and plattforms (such as OutWit Hub, Google Docs and Scraper Wiki) that helps you do this, but none of them are – in my opinion – as easy to use as the Google Chrome extension Scraper, which has become one of my absolutely favourite data tools.

What is a screenscraper?

I like to think of a screenscraper as a small robot that reads websites and extracts pieces of information. When you are able to unleash a scraper on hundreads, thousands or even more pages it can be an incredibly powerful tool.

In its most simple form, the one that we will look at in this blog post, it gathers information from one webpage only.

Google Chrome’s Scraper

Scraper is an Google Chrome extension that can be installed for free at Chrome Web Store.

Image

Now if you installed the extension correctly you should be able to see the option “Scrape similar” if you right-click any element on a webpage.

The Task: Scraping the contact details of all Swedish MPs

Image

This is the site we’ll be working with, a list of all Swedish MPs, including their contact details. Start by right-clicking the name of any person and chose Scrape similar. This should open the following window.

Understanding XPaths

At w3schools you’ll find a broader introduction to XPaths.

Before we move on to the actual scrape, let me briefly introduce XPaths. XPath is a language for finding information in an XML structure, for example an HTML file. It is a way to select tags (or rather “nodes”) of interest. In this case we use XPaths to define what parts of the webpage that we want to collect.

A typical XPath might look something like this:

    //div[@id="content"]/table[1]/tr

Which in plain English translates to:

    // - Search the whole document...

    div[@id="content"] - ...for the div tag with the id "content".

    table[1] -  Select the first table.

    tr - And in that table, grab all rows.

Over to Scraper then. I’m given the following suggested XPath:

    //section[1]/div/div/div/dl/dt/a

The results look pretty good, but it seems we only get names starting with an A. And we would also like to collect to phone numbers and party names. So let’s go back to the webpage and look at the HTML structure.

Right-click one of the MPs and chose Inspect element. We can see that each alphabetical list is contained in a section tag with the class “grid_6 alpha omega searchresult container clist”.

 And if we open the section tag we find the list of MPs in div tags.

We will do this scrape in two steps. Step one is to select the tags containing all information about the MPs with one XPath. Step two is to pick the specific pieces of data that we are interested in (name, e-mail, phone number, party) and place them in columns.

Writing our XPaths

In step one we want to try to get as deep into the HTML structure as possible without losing any of the elements we are interested in. Hover the tags in the Elements window to see what tags correspond to what elements on the page.

In our case this is the last tag that contains all the data we are looking for:

    //section[@class="grid_6 alpha omega searchresult container clist"]/div/div/div/dl

Click Scrape to test run the XPath. It should give you a list that looks something like this.

Scroll down the list to make sure it has 349 rows. That is the number of MPs in the Swedish parliament. The second step is to split this data into columns. Go back to the webpage and inspect the HTML code.

I have highlighted the parts that we want to extract. Grab them with the following XPaths:

    name: dt/a
    party: dd[1]
    region: dd[2]/span[1]
    seat: dd[2]/span[2]
    phone: dd[3]
    e-mail: dd[4]/span/a

Insert these paths in the Columns field and click Scrape to run the scraper.

Click Export to Google Docs to get the data into a spreadsheet.

Source: http://dataist.wordpress.com/2012/10/12/get-started-with-screenscraping-using-google-chromes-scraper-extension/

Why Businesses Need Data Scraping Service?

With the ever-increasing popularity of internet technology there is an abundance of knowledge processing information that can be used as gold if used in a structured format. We all know the importance of information. It has indeed become a valuable commodity and most sought after product for businesses. With widespread competition in businesses there is always a need to strive for better performances.

Taking this into consideration web data scraping service has become an inevitable component of businesses as it is highly useful in getting relevant information which is accurate. In the initial periods data scraping process included copying and pasting data information which was not relevant because it required intensive labor and was very costly. But now with the help of new data scraping tools like Mozenda, it is possible to extract data from websites easily. You can also take the help of data scrapers and data mining experts that scrape the data and automatically keep record of it.

How Professional Data Scraping Companies and Data Mining Experts Device a Solution?

Data Scraping Plan and Solutions

ImageCredit:http://www.loginworks.com/images/newscapingpage/data-as-service-plan.png

Why Data Scraping is Highly Essential for Businesses?

Data scraping is highly essential for every industry especially Hospitality, eCommerce, Research and Development, Healthcare, Financial and data scraping can be useful in marketing industry, real estate industry by scraping properties, agents, sites etc., travel and tourism industry etc. The reason for that is it is one of those industries where there is cut-throat competition and with the help of data scraping tools it is possible to extract useful information pertaining to preferences of customers, their preferred location, strategies of your competitors etc.

It is very important in today’s dynamic business world to understand the requirements of your customers and their preferences. This is because customers are the king of the market they determine the demand. Web data scraping process will help you in getting this vital information. It will help you in making crucial decisions which are highly critical for the success of business. With the help of data scraping tools you can automate the data scraping process which can result in increased productivity and accuracy.

Reasons Why Businesses Opt. For Website Data Scraping Solutions:

Website Scraping
Demand For New Data:

There is an overflowing demand for new data for businesses across the globe. This is due to increase in competition. The more information you have about your products, competitors, market etc. the better are your chances of expanding and persisting in competitive business environment. The manner in which data extraction process is followed is also very important; as mere data collection is useless. Today there is a need for a process through which you can utilize the information for the betterment of the business. This is where data scraping process and data scraping tools come into picture.

ImageCredit:3idatascraping.com
Capitalize On Hot Updates:

Today simple data collection is not enough to sustain in the business world. There is a need for getting up to date information. There are times when you will have the information pertaining to the trends in the market for your business but they would not be updated. During such times you will lose out on critical information. Hence; today in businesses it is a must to have recent information at your disposal.

The more recent update you have pertaining to the services of your business the better it is for your growth and sustenance. We are already seeing lot of innovation happening in the field of businesses hence; it is very important to be on your toes and collect relevant information with the help of data scrapers. With the help of data scrapping tools you can stay abreast with the latest developments in your business albeit; by spending extra money but it is necessary tradeoff in order to grow in your business or be left behind like a laggard.

Analyzing Future Demands:

Foreknowledge about the various major and minor issues of your industry will help you in assessing the future demand of your product / service. With the help of data scraping process; data scrapers can gather information pertaining to possibilities in business or venture you are involved in. You can also remain alert for changes, adjustments, and analysis of all aspects of your products and services.

Appraising Business:

It is very important to regularly analyze and evaluate your businesses. For that you need to evaluate whether the business goals have been met or not. It is important for businesses to know about your own performance. For example; for your businesses if the world market decides to lower the prices in order to grow their customer base you need to be prepared whether you can remain in the industry despite lowering the price. This can be done only with the help of data scraping process and data scraping tools.

Source:http://www.habiledata.com/blog/why-businesses-need-data-scraping-service

Web Scraping: Business Intelligence

Web scraping is simply getting of information that is both hidden and unhidden from the internet. Web scraping is one of the latest technologies used in harvesting data from WebPages. It has been used to extract useful information for practical and beneficial applications and its interpretation has been tested in decision making. Web scraping is a new term that overshadows the traditional data harvesting technique that was used before. It has been regarded as knowledge discovery in databases for research and even marketing monitoring.

This article explores the various business intelligence ways in which web scraping can be used to be of importance.

Web scraping services has been used by many companies that have a strong customer focus. These companies range from sectors like retail, financial services, and marketing and communication organizations. It quite important to realize that web scraping has great signifies and impact in the varied commercial applications for the better understanding and prediction of the critical data. The data may range from stocks to consumer behaviors. The consumer behaviors are better shown in trends like customer profiles, purchasing and industry analysis among others.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/web-scraping-business-intelligence/