Customized Web Data Scraping Services

To understand your customers’ behaviour it is crucial to organize the scattered data into a single repository. There are experts today who can scrape websites to extract data and develop analytics. Data extraction is a major requisite for any small or large company that deals with a massive volume of information that is stored in a complex structure. Premium data mining services help in extracting and structuring data from structured as well as semi-structured documents found on the internet or in other data warehouses.

Companies dealing with a large amount of data on a regular basis may need to convert these set of data into useful information. In that case, web scraping services will come to help. The experts offering such services will ensure that none of the data is missed. Customized data extraction is carried out mostly on the customer databases in order to analyse their behaviour and demographic characteristics. Personalized services offer a whole lot of benefits, which are;

Ensure Data Quality

The experts use a custom data extractor in order to ensure that the data extracted are of high quality. More than forty percent of the websites change their structure every month. Thus, it can be difficult for you to monitor the websites. A customized data extraction service will allow you to concentrate on your business’ larger goals, instead of wasting your time in trying DIY web data extraction.

Availability of Custom Scraper Tool

A reputed web scraping service provider is expected to have custom scraper tool with which they can extract information from the data efficiently without missing on anyone. By using the tools they can even scrape the most complex data and can provide it in any format.

Avoid Possible Human Errors

While extracting so many data sometimes even the professional service providers can also miss out on data. However, with customized services there will be no possibility of human error. Besides, a lot of time and cost can be saved too.

Great Speed

The custom web scraping service provider with their efficient tools can work really fast to convert the large amount of data into analytics. Also, they are able to extract data from multiple resources. The extracted data will be further preserved into customized structured formats such as, Microsoft Database, Text, script, HTML, SQL script etc.

Update Website

Additionally, you will have the leverage to update the website with the latest price and filter search by skipping the data, which do not match the keyword.

Tailor-made services even allow the professionals to extract data from emails and some other communication channels efficiently. With these data you will be able to spot the essentials required to implement in your business to convert the visitors into your customers. Also, you can make your business marketing plans accordingly.

Custom website data scraping service assist companies to have access to various on-demand data that are scraped from web, depending on the individual needs. The experts offering end-to-end data extraction services can also help in preparing the analytics for your business.

 Source : http://www.web-parsing.com/blog/customized-web-data-scrapping-services

Data Extraction: Tips to Get Exemplary Results

Data extraction is a skill, the more you master it – more are the chances of having a lucid picture of the volatile market and getting better perceptive of constantly changing trends. Escalating volatility in the market and intensifying competition has been the most contributing factors that have led to the rise of data extraction and data mining.

Data extraction is primarily used by companies (large and small, alike) to collect data from a specific industry, or data related to targeted customers or about their competition in the market. In fact, it has become a primary tool for marketers to plan their moves for branding and promoting particular products or services. It helps a wide plethora of industrial sectors to find and learn about specific data, based on their requirements.

And now with the rise of internet, web scraping has emerged as an important aspect that contributes to your success – the success of your venture or organization. It processes the HTML of a Web page to obtain data and convert it into to another format (i.e. HTML to XML).

Various extraction tools form an integral part of data extraction and data scrapping. Following offers a brief outline of some of these tools:

Email Extraction – An email extractor tool is used to acquire the email ids from any dependable sources automatically

Screen Scrapping – Screen scraping is a practice of reading text information from a screen and collecting visual data, rather than analyzing data as done in web scraping.

Data Mining as name suggests is a process of gathering patterns from information. It basically transforms the information into formats like CSV, MS excels, HTML and so and so forth, depending to your requirements

Web Spider – A Web spider is a computer program which browses internet in a systematic, automated manner. It is used by many search engines in order to provide up-to-date data

It is often seen that while extracting data; many get lost into the labyrinth of confusion, data overabundance, along with a lot of weird and not-so-familiar terms. Proper handling of these may sound easy, however; when not executed with appropriate procedure and processes; it may bring in disastrous results.

This no way means that data mining is a rocket science which only a few gifted and skilled people can take up. All it requires is undivided attention, keen preparation, and training, so brace up yourself for an overview of some practical tips that can help in successful data extraction and give a boost to your business.

Identify your Business Goals!:

Get a clear perspective in mind as to what are your business goals.

Data extraction can be bifurcated into various branches; and one needs to choose it wisely, depending on the business goals. E.g. your primary requirement is to get email ids of potential clients to conduct an email campaign; and for that you certainly need an email extractor. Use of this tool assists in extracting the email ids from trustworthy sources automatically. It essentially collects business contacts from various web pages, text files, HTML files, or any other format without duplicating the email ids. So, if you are not sure what you want; even applying the best tools will be of no use!

A crystal clear mindset helps in better understanding of market scenario and thus helps in formulation of powerful and effective strategies to get desired outcomes. E.g., people dealing in real estate business, should have a vision for it and which area they want to target specifically. With a clear vision they can clearly spell out what you want and where it should be.

Set Realistic Expectations:

Upon identifying your business goals, make sure to check out that they are realistic and attainable! Unrealistic and unachievable targets are the real cause for the obstacles and frustrations in the future.

Since, there are various tools that are and can be employed to extract data; vague or unclear goals make it difficult to determine which tool can be applied.

This crystal clear mindset; will help you give that insight about the direction your business is headed to.

Moreover, you can determine which method can be used to get excellent results. You can get a lucid picture of the past and present of your competitors and therefore helps in setting targets based on the others’ experiences. It is usually a wise move to set expectations that you have not achieved before.

Appoint Skilled Data Miner:

Skilled data miner with excellent data mining skills will reduce the painstaking and tiresome process of planning, devising and preparation.

For fresh start-ups, you can go ahead with the standard procedure however; if you have ample professionals at your disposal, pick up the right one who is not only knowledgeable but also reliable and sincere towards the task.

Prevent Data Deposits:

Being dead-sure of what you really want will help you avoid unnecessary data deposition.

Data mining just like real mining is a skill to know where the real treasure lies and being able to get it in the most efficient and effective way.

Being able to spot on authenticated & reliable resources, well researched information is what gives a short cut to locate the right and exact data.

If you are aimlessly opening every website; the results are bound to be ambiguous and would ultimately be a waste of time and effort.


Source:  http://www.habiledata.com/blog/data-extraction-is-not-a-rocket-science-follow-these-4-tips-to-get-exemplary-results

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: working with APIs

APIs present researchers with a diverse set of data sources through a standardised access mechanism: send a pasted together HTTP request, receive JSON or XML in return. Today we tap into a range of APIs to get comfortable sending queries and processing responses.

These are the slides from the final class in Web Scraping through R: Web scraping for the humanities and social sciences

This week we explore how to use APIs in R, focusing on the Google Maps API. We then attempt to transfer this approach to query the Yandex Maps API. Finally, the practice section includes examples of working with the YouTube V2 API, a few ‘social’ APIs such as LinkedIn and Twitter, as well as APIs less off the beaten track (Cricket scores, anyone?).

I enjoyed teaching this course and hope to repeat and improve on it next year. When designing the course I tried to cram in everything I wish I had been taught early on in my PhD (resulting in information overload, I fear). Still, hopefully it has been useful to students getting started with digital data collection, showing on the one hand what is possible, and on the other giving some idea of key steps in achieving research objectives.

Download the .Rpres file to use in Rstudio here

A regular R script with code-snippets only can be accessed here

Slides from the first session here

Slides from the second session here

Slides from the third session here

Source: http://www.r-bloggers.com/web-scraping-working-with-apis/

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

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…

Source: https://scraperwiki.wordpress.com/2012/12/28/scraping-the-royal-society-membership-list/