A monkey dancing with a lion and a bear

William J. Turkel and Adam Crymble

This lesson builds on ‘Keywords in Context (Using N-grams)’, where n-grams were extracted from a text. Here, you will learn how to output all of the n-grams of a given keyword in a document downloaded from the Internet, and display them clearly in your browser window.


edited by

  • Miriam Posner

reviewed by

  • Jim Clifford
  • Frederik Elwert


| 2012-07-17


| 2012-07-17


| Medium

DOI id icon https://doi.org/10.46430/phen0016

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Available in: EN (original) | ES
This lesson is part of a series of 15 lessons - You are on lesson 14 | previous lesson | next lesson


Lesson Goals

This lesson builds on Keywords in Context (Using N-grams), where n-grams were extracted from a text. Here, you will learn how to output all of the n-grams of a given keyword in a document downloaded from the Internet, and display them clearly in your browser window.

Files Needed For This Lesson

  • obo.py

If you do not have these files from the previous lesson, you can download a zip file from the previous lesson

Making an N-Gram Dictionary

Our n-grams have an odd number of words in them for a reason. At this point, our n-grams don”t actually have a keyword; they’re just a list of words. However, if we have an odd numbered n-gram the middle word will always have an equal number of words to the left and to the right. We can then use that middle word as our keyword. For instance, [“it”, “was”, “the”, “best”, “of”, “times”, “it”] is a 7-gram of the keyword “best”.

Since we have a long text, we want to be able to output all n-grams for our keyword. To do this we will put each n-gram into a dictionary, using the middle word as the key. To figure out the keyword for each n-gram we can use the index positions of the list. If we are working with 5-grams, for example, the left context will consist of terms indexed by 0, 1, the keyword will be indexed by 2, and the right context terms indexed by 3,

  1. Since Python indexes start at 0, a 5-gram’s keyword will always be at index position 2.

That’s fine for 5-grams, but to make the code a bit more robust, we want to make sure it will work for any length n-gram, assuming its length is an odd number. To do this we’ll take the length of the n-gram, divide it by 2 and drop the remainder. We can achieve this using Python’s floor division operator, represented by two slashes, which divides and then returns an answer to the nearest whole number, always rounding down – hence the term “floor”.

print(7 // 2)
print(5 // 2)
print(3 // 2)

Let’s build a function that can identify the index position of the keyword when given an n-gram with an odd number of words. Save the following to obo.py.

# Given a list of n-grams identify the index of the keyword.

def nGramsToKWICDict(ngrams):
    keyindex = len(ngrams[0]) // 2

    return keyindex

To determine the index of the keyword, we have used the len property to tell us how many items are in the first n-gram, then used floor division to isolate the middle index position. You can see if this worked by creating a new program, get-keyword.py and running it. If all goes well, since we are dealing with a 5-gram, you should get 2 as the index position of the keyword as we determined above.


import obo

test = 'this test sentence has eight words in it'
ngrams = obo.getNGrams(test.split(), 5)


Now that we know the location of the keywords, let’s add everything to a dictionary that can be used to output all KWIC n-grams of a particular keyword. Study this code and then replace your nGramsToKWICDict with the following in your obo.py module.

# Given a list of n-grams, return a dictionary of KWICs,
# indexed by keyword.

def nGramsToKWICDict(ngrams):
    keyindex = len(ngrams[0]) // 2

    kwicdict = {}

    for k in ngrams:
        if k[keyindex] not in kwicdict:
            kwicdict[k[keyindex]] = [k]
    return kwicdict

A for loop and if statement checks each n-gram to see if its keyword is already stored in the dictionary. If it isn’t, it’s added as a new entry. If it is, it’s appended to the previous entry. We now have a dictionary named kwicdict that contains all the n-grams, sortable by keyword and we can turn to the task of outputting the information in a more useful format as we did in Output Data as HTML File.

Try rerunning the get-keyword.py program and you should now see what’s in your KWIC dictionary.

Outputting to HTML

Pretty Printing a KWIC

“Pretty printing” is the process of formatting output so that it can be easily read by human beings. In the case of our keywords in context, we want to have the keywords lined up in a column, with the terms in the left-hand context right justified, and the terms in the right-hand context left justified. In other words, we want our KWIC display to look something like this:

               amongst them a black there was one
                first saw the black i turned to
             had observed the black in the mob
                 say who that black was no seeing
                      i saw a black at first but
                 swear to any black yes there is
                   swear to a black than to a

This technique is not the best way to format text from a web designer’s perspective. If you have some experience with HTML we encourage you to use another method that will create a standards compliant HTML file, but for new learners, we just can’t resist the ease of the technique we’re about to describe. After all, the point is to integrate programming principles quickly into your research.

To get this effect, we are going to need to do a number of list and string manipulations. Let’s start by figuring out what our dictionary output will look like as it currently stands. Then we can work on refining it into what we want.

# html-to-pretty-print.py
import obo

# create dictionary of n-grams
n = 7
url = 'http://www.oldbaileyonline.org/browse.jsp?id=t17800628-33&div=t17800628-33'

text = obo.webPageToText(url)
fullwordlist = obo.stripNonAlphaNum(text)
ngrams = obo.getNGrams(fullwordlist, n)
worddict = obo.nGramsToKWICDict(ngrams)


As you can see when you run the above program, the output is not very readable yet. What we need to do is split the n-gram into three parts: before the keyword, the keyword, and after the keyword. We can then use the techniques learned in the previous chapters to wrap everything in HTML so that it is easy to read.

Using the same slice method as above, we will create our three parts. Open a Python shell and try the following examples. Pay close attention to what appears before and after the colon in each case. Knowing how to manipulate the slice method is a powerful skill for a new programming historian.

# ParseError: Could not check this chunk!
# calculate the length of the n-gram
kwic = 'amongst them a black there was one'.split()
n = len(kwic)
-> 7

# calculate the index position of the keyword
keyindex = n // 2
-> 3

# display the items before the keyword
-> ['amongst', 'them', 'a']

# display the keyword only
-> black

# display the items after the keyword
-> ['there', 'was', 'one']

Now that we know how to find each of the three segments, we need to format each to one of three columns in our display.

The right-hand context is simply going to consist of a string of terms separated by blank spaces. We’ll use the join method to turn the list entries into a string.

# ParseError: Could not check this chunk!
print(' '.join(kwic[(keyindex+1):]))
-> there was one

We want the keywords to have a bit of whitespace padding around them. We can achieve this by using a string method called center, which will align the text to the middle of the screen. We can add padding by making the overall string be longer than the keyword itself. The expression below adds three blank spaces (6/2) to either side of the keyword. We’ve added hash marks at the beginning and end of the expression so you can see the leading and trailing blanks.

print('#' + str(kwic[keyindex]).center(len(kwic[keyindex])+6) + '#')
-> #   black   #

Finally, we want the left-hand context to be right justified. Depending on how large n is, we are going to need the overall length of this column to increase. We do this by defining a variable called width and then making the column length a multiple of this variable (we used a width of 10 characters, but you can make it larger or smaller as desired). The rjust method handles right justification. Once again, we’ve added hash marks so you can see the leading blanks.

width = 10
print('#' + ' '.join(kwic[:keyindex]).rjust(width*keyindex) + '#')
-> #                 amongst them a#

We can now combine these into a function that takes a KWIC and returns a pretty-printed string. Add this to the obo.py module. Study the code to make sure you understand it before moving on.

# Given a KWIC, return a string that is formatted for
# pretty printing.

def prettyPrintKWIC(kwic):
    n = len(kwic)
    keyindex = n // 2
    width = 10

    outstring = ' '.join(kwic[:keyindex]).rjust(width*keyindex)
    outstring += str(kwic[keyindex]).center(len(kwic[keyindex])+6)
    outstring += ' '.join(kwic[(keyindex+1):])

    return outstring

Putting it All Together

We can now create a program that, given a URL and a keyword, wraps a KWIC display in HTML and outputs it in Firefox. This program begins and ends in a similar fashion as the program that computed word frequencies. Type or copy the code into your text editor, save it as html-to-kwic.py, and execute it. You will need to choose either obo.wrapStringInHTMLMac() or obo.wrapStringInHTMLWindows() as appropriate for your system, as done before.

# html-to-kwic.py

import obo

# create dictionary of n-grams
n = 7
url = 'http://www.oldbaileyonline.org/browse.jsp?id=t17800628-33&div=t17800628-33'

text = obo.webPageToText(url)
fullwordlist = ('# ' * (n//2)).split()
fullwordlist += obo.stripNonAlphaNum(text)
fullwordlist += ('# ' * (n//2)).split()
ngrams = obo.getNGrams(fullwordlist, n)
worddict = obo.nGramsToKWICDict(ngrams)

# output KWIC and wrap with html
target = 'black'
outstr = '<pre>'
if target in worddict:
    for k in worddict[target]:
        outstr += obo.prettyPrintKWIC(k)
        outstr += '<br />'
    outstr += 'Keyword not found in source'

outstr += '</pre>'
obo.wrapStringInHTML('html-to-kwic', url, outstr)

The first part is the same as above. In the second half of the program, we’ve wrapped everything in the HTML pre tag (pre-formatted), which tells the browser not to monkey with any of the spacing we’ve added.

Also, notice that we use the has_key dictionary method to make sure that the keyword actually occurs in our text. If it doesn’t, we can print a message for the user before sending the output to Firefox. Try changing the target variable to a few other keywords. Try one you know isn’t there to make sure your program doesn’t output something when it shouldn’t.

We have now created a program that looks for a keyword in a dictionary created from an HTML page on the web, and then outputs the n-grams of that keyword to a new HTML file for display on the web. All of the lessons up to this point have included parts of Python vocabulary and methods needed to create this final program. By referring to those lessons, you can now experiment with Python to create programs that accomplish specific tasks that will help in your research process.

Code Syncing

This marks the end of this series of original lessons on python. The finished code for the series can be downloaded as a zip file. If you are following along with the Mac / Linux version you may have to open the obo.py file and change “file:///Users/username/Desktop/programming-historian/” to the path to the directory on your own computer.

There is an additional lesson on using Python to download multiple records using Query Strings, marked as the next lesson.

About the authors

William J. Turkel is Professor of History at the University of Western Ontario.

Adam Crymble, University College London.

Suggested Citation

William J. Turkel and Adam Crymble, "Output Keywords in Context in an HTML File with Python," Programming Historian 1 (2012), https://doi.org/10.46430/phen0016.

Great Open Access tutorials cost money to produce. Join the growing number of people supporting Programming Historian so we can continue to share knowledge free of charge.