The data that is already present in a row and column format or which can be easily converted to rows and columns so that later it can fit nicely into a database is known as structured data. Examples are CSV, TXT, XLS files etc. These files have a delimiter and either fixed or variable width where the missing values are represented as blanks in between the delimiters. But sometimes we get data where the lines are not fixed width, or they are just HTML, image or pdf files. Such data is known as unstructured data. While the HTML file can be handled by processing the HTML tags, a feed from twitter or a plain text document from a news feed can without having a delimiter does not have tags to handle. In such scenario we use different in-built functions from various python libraries to process the file.

Reading Data

In the below example we take a text file and read the file segregating each of the lines in it. Next we can divide the output into further lines and words. The original file is a text file containing some paragraphs describing the python language.

filename = 'path\input.txt'

with open(filename) as fn:

# Read each line
ln = fn.readline()

# Keep count of lines
lncnt = 1
while ln:
print("Line {}: {}".format(lncnt, ln.strip()))
ln = fn.readline()
lncnt += 1

When we execute the above code, it produces the following result.
Line 1: Python is an interpreted high-level programming language for general-purpose programming. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace. It provides constructs that enable clear programming on both small and large scales.
Line 2: Python features a dynamic type system and automatic memory management. It supports multiple programming paradigms, including object-oriented, imperative, functional and procedural, and has a large and comprehensive standard library.
Line 3: Python interpreters are available for many operating systems. CPython, the reference implementation of Python, is open source software and has a community-based development model, as do nearly all of its variant implementations. CPython is managed by the non-profit Python Software Foundation.

Counting Word Frequency

We can count the frequency of the words in the file using the counter function as follows.
from collections import Counter

with open(r’pathinput2.txt’) as f:
p = Counter(f.read().split())
print(p)
When we execute the above code, it produces the following result.
Counter({‘and’: 3, ‘Python’: 3, ‘that’: 2, ‘a’: 2, ‘programming’: 2, ‘code’: 1, ‘199

Python – Word Tokenization

Word tokenization is the process of splitting a large sample of text into words. This is a requirement in natural language processing tasks where each word needs to be captured and subjected to further analysis like classifying and counting them for a particular sentiment etc. The Natural Language Tool kit(NLTK) is a library used to achieve this. Install NLTK before proceeding with the python program for word tokenization.
conda install -c anaconda nltk
Next we use the word_tokenize method to split the paragraph into individual words.
import nltk

word_data = “It originated from the idea that there are readers who prefer learning new skills from the comforts of their drawing rooms”
nltk_tokens = nltk.word_tokenize(word_data)
print (nltk_tokens)
When we execute the above code, it produces the following result.
[‘It’, ‘originated’, ‘from’, ‘the’, ‘idea’, ‘that’, ‘there’, ‘are’, ‘readers’,
‘who’, ‘prefer’, ‘learning’, ‘new’, ‘skills’, ‘from’, ‘the’,
‘comforts’, ‘of’, ‘their’, ‘drawing’, ‘rooms’]

Tokenizing Sentences

We can also tokenize the sentences in a paragraph like we tokenized the words. We use the method sent_tokenize to achieve this. Below is an example.
import nltk
sentence_data = “Sun rises in the east. Sun sets in the west.”
nltk_tokens = nltk.sent_tokenize(sentence_data)
print (nltk_tokens)
When we execute the above code, it produces the following result.
[‘Sun rises in the east.’, ‘Sun sets in the west.’]

Python – Stemming and Lemmatization

In the areas of Natural Language Processing we come across situation where two or more words have a common root. For example, the three words – agreed, agreeing and agreeable have the same root word agree. A search involving any of these words should treat them as the same word which is the root word. So it becomes essential to link all the words into their root word. The NLTK library has methods to do this linking and give the output showing the root word.
The below program uses the Porter Stemming Algorithm for stemming.
import nltk
from nltk.stem.porter import PorterStemmer
porter_stemmer = PorterStemmer()

word_data = “It originated from the idea that there are readers who prefer learning new skills from the comforts of their drawing rooms”
# First Word tokenization
nltk_tokens = nltk.word_tokenize(word_data)
#Next find the roots of the word
for w in nltk_tokens:
print “Actual: %s Stem: %s” % (w,porter_stemmer.stem(w))
When we execute the above code, it produces the following result.
Actual: It Stem: It
Actual: originated Stem: origin
Actual: from Stem: from
Actual: the Stem: the
Actual: idea Stem: idea
Actual: that Stem: that
Actual: there Stem: there
Actual: are Stem: are
Actual: readers Stem: reader
Actual: who Stem: who
Actual: prefer Stem: prefer
Actual: learning Stem: learn
Actual: new Stem: new
Actual: skills Stem: skill
Actual: from Stem: from
Actual: the Stem: the
Actual: comforts Stem: comfort
Actual: of Stem: of
Actual: their Stem: their
Actual: drawing Stem: draw
Actual: rooms Stem: room
Lemmatization is similar ti stemming but it brings context to the words.So it goes a steps further by linking words with similar meaning to one word. For example if a paragraph has words like cars, trains and automobile, then it will link all of them to automobile. In the below program we use the WordNet lexical database for lemmatization.
import nltk
from nltk.stem import WordNetLemmatizer
wordnet_lemmatizer = WordNetLemmatizer()

word_data = “It originated from the idea that there are readers who prefer learning new skills from the comforts of their drawing rooms”
nltk_tokens = nltk.word_tokenize(word_data)
for w in nltk_tokens:
print “Actual: %s Lemma: %s” % (w,wordnet_lemmatizer.lemmatize(w))
When we execute the above code, it produces the following result.
Actual: It Lemma: It
Actual: originated Lemma: originated
Actual: from Lemma: from
Actual: the Lemma: the
Actual: idea Lemma: idea
Actual: that Lemma: that
Actual: there Lemma: there
Actual: are Lemma: are
Actual: readers Lemma: reader
Actual: who Lemma: who
Actual: prefer Lemma: prefer
Actual: learning Lemma: learning
Actual: new Lemma: new
Actual: skills Lemma: skill
Actual: from Lemma: from
Actual: the Lemma: the
Actual: comforts Lemma: comfort
Actual: of Lemma: of
Actual: their Lemma: their
Actual: drawing Lemma: drawing
Actual: rooms Lemma: room

So, this brings us to the end of blog. This Tecklearn ‘NLP in Python’ blog helps you with commonly asked questions if you are looking out for a job in Python Programming. If you wish to learn Python and build a career in Python Programming domain, then check out our interactive, Python with Data Science Training, that comes with 24*7 support to guide you throughout your learning period.

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