Basic Materials: Werdz an Regulah Expreshuns

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[Cross-posted from ICanHazDataScience]

Okay, that last post was a bit long for Emily… she fell asleep on my desk long before I’d finished typing.  So today we’re back to short and practical.

Data is not just numbers.  Numbers are one of the basic types of data that appear again and again in data science.   Two of those types are words (as in written text, like this blogpost) and networks (as in objects connected with links – like a diagram of your twitter friends and your friends’ friends etc).  Today we’re looking at words.

In the last post, I was looking at a set of online job descriptions.  We’ll leave the basics of webpage scraping til later (but if you’re curious, ScraperWiki’s notes are good) and assume that what we have is a set of text files that we’ve used the “Processing all the teh Files in Directory” post with the commands

fin = open(infile_fullname, “rb”)

bigstring += ” ” + fin.read()

to add the text from each file into one big string, which we’ve (rather imaginatively) called bigstring.

For Dan’s jobsite data, the first 200 characters of bigstring look like this to a human:

Title

Senior Director of Development

Department/Mall

SPG/Premium Outlets – NJ

Location

Roseland, NJ

 

Description

 

PRIMARY PURPOSE:

 

• Responsible for overseeing domestic

And like this to a Python program:

‘Title\r\n    Senior Director of Development\r\nDepartment/Mall\r\n    SPG/Premi

um Outlets – NJ\r\nLocation\r\n    Roseland, NJ\r\n\r\nDescription\r\n\r\n    PR

IMARY PURPOSE: \r\n\r\n    \x95 Responsible for overseeing domestic ‘

That’s what Python sees: a long sequence of characters, some of which are letters (both uppercase and lowercase), some punctuation (spaces, commas, dashes, full stops etc), character sequences (“\r\n”) to show line endings and special codes (“\x95”) for other characters like “•”.

Your program needs to split that sequence of characters into words.  We could do this the hard way – look at each character in bigstring, adding it to a word if it’s alphabetical, and creating a new word if it’s not alphabetical etc., but Python (and many other languages) has a really great shortcut for text processing, known as regular expressions.

Regular expressions – seen in Python as the “Re” library – are a fast way of searching for text patterns in strings (including very very large strings).  I’m not going to pretend that regular expressions are easy, but I am going to insist that you’ll find them very useful, and it’s worth the pain of learning about them because that allows you to do much more powerful things to your text.  For today, I’m going to show you the regular expression that I used to convert bigstring into a list of all the words in it (within limits: for this application, I ignored things like hyphenated words and words with numbers and non-alphabetical characters in them).

The regular expression I used for the jobsites was:

import re

words = re.sub(‘[\W_]+’, ‘ ‘, bigstring.lower()).split()

This combined the re library function “sub” with some standard python string functions (“lower” and “split”).  First, I wanted the words returned to be all-lowercase (there’s nothing more annoying than getting separate frequencies for “Follow”, “FOLLOW” and “follow” in your results).  For this, I used the string expression “bigstring.lower()”.  You can do this to any string in Python, and it will lowercase all your text.   The results was

lowerstring = ‘title\r\n    senior director of development\r\ndepartment/mall\r\n    spg/premium outlets – nj\r\nlocation\r\n    roseland, nj\r\n\r\ndescription\r\n\r\n    primary purpose: \r\n\r\n    \x95 responsible for overseeing domestic’

(I’ve called this lowerstring so you can see what happens next). Next, I used re.sub(‘[\W_]+’, ‘ ‘, lowerstring) to convert any sets of characters in the text that AREN’T alphabetical into spaces.  The result of this is

cleantext = ‘title senior director of development department mall spg premium outlets nj location roseland nj description primary purpose responsible for overseeing domestic ‘

Which just leaves the final step of using the spaces to split the whole text into words.  This is what the “split()” function does – if you use characters as parameters,e.g. split(“,”), it will convert the text string into a list of all the text between each of those characters, but if you leave the parameter blank, e.g. split(), it creates a list of all the text between spaces. For the jobsite example, that list starts like this:

>>> words[:200]

[‘title’, ‘senior’, ‘director’, ‘of’, ‘development’, ‘department’, ‘mall’, ‘spg’, ‘premium’, ‘outlets’, ‘nj’, ‘location’, ‘roseland’, ‘nj’, ‘description’, ‘primary’, ‘purpose’, ‘responsible’, ‘for’, ‘overseeing’, ‘domestic’ …]

Next post, we’re going to look at the code needed to do useful things with this simple list of words, and at some of the issues (like wordstems) that simple lists of words can have.

Wut 2 Do Wif Data?

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Data science is not about data. Data science is about insight – the knowledge and suggestions that you can glean by inspecting and using data. And that insight usually starts with a set of questions.  Here are some examples, hopefully making you think a bit more about your own questions (which in Emily’s case is the correlation between cuteness, cuddles and the amount of Meow Mix in her dish).

You don’t always know what the good questions are, but you usually know (or pick) the framework that you’re asking them in.  This is how I usually approach this:

  • Look at context – ask question (or get question from user)
  • Get data
  • Phrase question in way that data can answer
  • Write down issues with data
  • Clean data
  • Investigate question
  • Check conclusions and possible issues with conclusions
  • Describe possible further investigations / data gathering
  • Which might mean improving on the data that you obtained this time

Here are two examples, to help you think about your own questions. One example analyses text, the other numbers; both are simple but raise many difficult questions.

Example 1: Starting with a question

Look at context- ask question

I’ve somehow been spending a lot of time lately thinking about poo… erm… sanitation, open defecation and farm slurry.  Some of this stemmed from a question I asked about a UN ‘fact’ that was quoted without a data provenance – that more people have access to a mobile phone than to a toilet. My question was simple: “is this true?”.

Get data

Now at this point, I had no data.  So I looked at the resources I had available (me and an internet full of open data) and the value of the result (me satisfying my curiosity), and scoped out the size of the project: I’d look for open data (i.e. not ping any of my contacts for data, set up surveys or anything that involved other peoples’ goodwill – that’s a valuable resource), and use that to determine whether the question could be answered.  I’m spoiling the surprise, but this is something that happens a lot with development data: you start out with a clear question, find that the data isn’t there to answer it, then adapt either the data (by gathering more) or the question (by reducing its scope, or changing it to a set of also-valuable questions that the data can help you with).

So data. I searched all the usual suspects (see opencrisis.com for a list), but couldn’t find any dataset of surveys that included both access to toilets and mobile phones.  There’s probably been one or more of these done, they could probably be dug up with a lot of phone calls, but they weren’t easily visible online.  The datasets that I did find were one on sanitation from WSSinfo and another on mobile phone densities from ITU. And these have issues:

  • The datasets were hard to find.
  • I looked at the last 5 years (anything older than that in development isn’t that useful), but there was no data after 2010 in these datasets.
  • The datasets were unrelated
  • The dataset formats were hard to machine-read (they included merged cells, explanations etc).
  • It was difficult to track provenance – e.g. what decisions did the people creating these datasets make? What assumptions?
  •  There were data issues: numbers were rounded up, data was at country level, countrynames didn’t match between the two datasets, there were multiple charactersets in the files (e.g. Å, A, Ԇ).

Phrase question in way the data can answer

So onto the question.  Taking the question “more people have access to mobile phones than toilets” as a start point, we can rephrase this as: number of people with mobiles > number of people with toilets

or (mobile% – toilet%)*population > 0

or (mobile% – (100-opendef%)) > 0

Where mobile% is the percentage of people with mobile phones, toilet% is the percentage of people with access to a toilet (not, note, owning a toilet – or I’d be looking through the sanitaryware import and latrine digging figures for each country), opendef% is the number of people open defecating (pooing outside).  And we can answer this question using with the datasets.

Write down issues and clean data

And even once the numbers for open defecation (a polite phrase for “has no toilet and has to poo outside”) and telephones were compared, that comparison only created a bunch more questions.  Most of these questions exist because of the idea of statistical independence – if you gather two datasets independently of each other, it’s only possible to compare them under some really tight statistical conditions.  Some of these questions were:

  • Is there actually a correlation between the two datasets?  Phone densities are quoted as the number of phones per hundred people, and are often over 100 (I think I have 4 phones at home, but I’ve lost count now).  Most of the countries with phones > toilets are in the developing world: don’t some people in the developing world have more than one phone? In some cities (e.g. Benin City) I’ve visited, phone signal availability is so variable that people have up to 5 simcards each, on different carriers. Were the results uniform – the datasets were listed by country – what if the cities have lots of phones and toilets, and the rural areas don’t? What does that do to the numbers?
  • And how do you count up people without toilets? Are these percentages estimates or survey results?  If they’re surveys, how big were the surveys, and were they demographically and geographically representative (e.g. were city and country people surveyed proportionately, and how was this done – on paper or by phone?).  We’re talking about people here – how likely were they to be truthful about toilets – having to poo outside could be deeply embarassing, and perhaps hard to admit.
  • Where does my composting toilet fit in this?  If I have an ‘unusual’ outdoor toilet, does that count as a toilet or open defecation?
  • What do we do with a zero value in the datasets? What do we do with values over 100 per 100 people (I truncated these to 100, so extra phones had less of an effect, but I felt uneasy doing that).
  • Did we just list the people who, with the right tools, can campaign for more toilets?
  • Etc…

Investigate question, check conclusions, describe possible future investigations

So, having found run the question against the data, here are the numbers for 2010:

country population opendefecation not opendefecation phones phones minus loos people affected
India 1.22E+09 51.09471 48.90529 61.4226 12.51732 153288799  
Indonesia 2.4E+08 26.25828 73.74172 88.08497 14.34325 34405290  
Brazil 1.95E+08 3.694356 96.30564 100 3.694356 7202000  
Morocco 31951000 15.86805 84.13195 100 15.86805 5070000  
South Africa 50133000 7.745397 92.2546 100 7.745397 3882999  
Viet Nam 87848000 4.177671 95.82233 100 4.177671 3669999  
Benin 8850000 56.39548 43.60452 79.94351 36.33899 3216000  
Cambodia 14138000 60.53897 39.46103 57.65042 18.1894 2571616  
Peru 29077000 7.232521 92.76748 100 7.232521 2102999  
Colombia 46295000 6.486662 93.51334 96.07475 2.561412 1185805  
Mauritania 3460000 53.64162 46.35838 80.23792 33.87954 1172232  
Guatemala 14389000 6.046285 93.95371 100 6.046285 870000  
Namibia 2283000 51.86159 48.13841 85.50451 37.36609 853067  
Ecuador 14465000 4.638783 95.36122 100 4.638783 670999  
Honduras 7601000 8.748849 91.25115 100 8.748849 664999  
Niger 15512000 78.85508 21.14492 24.53329 3.388367 525603  
El Salvador 6193000 5.926046 94.07395 100 5.926046 367000  
Botswana 2007000 15.39611 84.60389 100 15.39611 309000  
Mongolia 2756000 11.71988 88.28012 91.09104 2.810925 77469  
Suriname 525000 6.095238 93.90476 100 6.095238 32000  

Reading the whole table, the bottom line is that 200 million or so people have phones but not toilets, if you use the ITU and Wssinfo data, and ignore statistical independence (that’s an enormous ignore). That’s out of 7 billion people worldwide.  So yes, it’s potentially an issue, but it’s more interesting to think about where, and what that means.  For instance, there are 200 million people with phones who, if they get the right SMS apps or information, can lobby for governments and NGOs to build toilets in their areas, or for the plans, materials, money or labour to do this for themselves. If anyone wants to start a “givemealoo” site with an SMS connection and publicity through SMS and local radio, they now know where to start…

Example 2: Starting with a dataset

Sometimes you start with a dataset, and the question “what can you glean from this?”.  For instance, my partner had a set of job descriptions that he liked, and wanted to find more like them.  The long answer would be to do some supervised learning with these and other descriptions, and build a jobsite scraper that classified each description into “interesting” or “not interesting”.   The short answer was to look for patterns,  features and possibly clusters in the dataset.

The data was from a mix of different websites, all with a different structure (and different headings for ‘experience’ etc.), so I treated each page as unstructured text (e.g. I ignored labels and punctuation and treated each page as a huge collection of words).   I started by building a histogram of the words used: a list of the top 30 words I found across all the documents, with how many times each one appeared.  This list contained a lot of stopwords – common words that don’t add anything useful to the histogram, like “and”, “the”, “of”, “to” and “in”, that I then removed from the list, to give a list of terms that might be useful to Dan.

Removing stopwords is a common thing in text processing – normally I’d use a standard list of stopwords (e.g. Porter) for this, but I didn’t want to miss any industry-specific terms that might be on those lists, so I built my own stopword list.  For development data, you’ll probably do this a lot too, e.g. “crisis” isn’t a really useful term to find when you’re working on crisis information.  So I built a histogram (minus stopwords): the top 10 words in it were:  estate (26), real (26), development (12), design (11), manage (11), planning (11), sales (11), investment (10), senior (8), portfolio (8).

I showed this to Dan and he said “great – but what about pairs of words”. .. something that might have been triggered by the top 2 words on that list (“real” and “estate”).  So I modified the code to produce a histogram of adjacent words, and got: real estate (26), new york (5), trade marketing (4), job description (4), estate portfolio (4), senior strategist (4), city area (3), estate investment (3), funding approvals (3), area job (3).

I could have continued this – looking for chains of words, e.g. “real estate” linked to “estate portfolio” etc., and linked it to a jobsite scraper to automatically alert Dan to jobs that were similar to his “interesting” ones (you’ve probably worked out by now that he’s a real estate architectural designer), but the lists enough were enough for him: he got search terms that he hadn’t thought of, and is happily sifting through sites with them.  Which is another lesson to learn: sometimes a seemingly simple thing will have enough of an effect to make a user happy, without needing complex analysis.  Unless you’re playing with a dataset out of curiosity, that’s often a good place to stop.