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Engineering LibreTexts

9.5: Advanced Text Parsing

In the above example using the file romeo.txt, we made the file as simple as possible by removing all punctuation by hand. The actual text has lots of punctuation, as shown below.

But, soft! what light through yonder window breaks?
It is the east, and Juliet is the sun.
Arise, fair sun, and kill the envious moon,
Who is already sick and pale with grief,

Since the Python split function looks for spaces and treats words as tokens separated by spaces, we would treat the words "soft!" and "soft" as different words and create a separate dictionary entry for each word.

Also since the file has capitalization, we would treat "who" and "Who" as different words with different counts.

We can solve both these problems by using the string methods lowerpunctuation, and translate. The translate is the most subtle of the methods. Here is the documentation for translate:

line.translate(str.maketrans(fromstr, tostr, deletestr))

Replace the characters in fromstr with the character in the same position in tostr and delete all characters that are in deletestr. The fromstr and tostr can be empty strings and the deletestr parameter can be omitted.

We will not specify the table but we will use the deletechars parameter to delete all of the punctuation. We will even let Python tell us the list of characters that it considers "punctuation":

>>> import string
>>> string.punctuation

The parameters used by translate were different in Python 2.0.

We make the following modifications to our program:

import string

fname = input('Enter the file name: ')
    fhand = open(fname)
    print('File cannot be opened:', fname)

counts = dict()
for line in fhand:
    line = line.rstrip()
    line = line.translate(line.maketrans('', '', string.punctuation))
    line = line.lower()
    words = line.split()
    for word in words:
        if word not in counts:
            counts[word] = 1
            counts[word] += 1


# Code:

Part of learning the "Art of Python" or "Thinking Pythonically" is realizing that Python often has built-in capabilities for many common data analysis problems. Over time, you will see enough example code and read enough of the documentation to know where to look to see if someone has already written something that makes your job much easier.

The following is an abbreviated version of the output:

Enter the file name: romeo-full.txt
{'swearst': 1, 'all': 6, 'afeard': 1, 'leave': 2, 'these': 2,
'kinsmen': 2, 'what': 11, 'thinkst': 1, 'love': 24, 'cloak': 1,
a': 24, 'orchard': 2, 'light': 5, 'lovers': 2, 'romeo': 40,
'maiden': 1, 'whiteupturned': 1, 'juliet': 32, 'gentleman': 1,
'it': 22, 'leans': 1, 'canst': 1, 'having': 1, ...}

Looking through this output is still unwieldy and we can use Python to give us exactly what we are looking for, but to do so, we need to learn about Python tuples. We will pick up this example once we learn about tuples.