Python - Processing Unstructured Data

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.
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.
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, '1991,': 1, 'is': 1, 'programming.': 1, 'dynamic': 1, 'an': 1, 'design': 1, 'in': 1, 'high-level': 1, 'management.': 1, 'features': 1, 'readability,': 1, 'van': 1, 'both': 1, 'for': 1, 'Rossum': 1, 'system': 1, 'provides': 1, 'memory': 1, 'has': 1, 'type': 1, 'enable': 1, 'Created': 1, 'philosophy': 1, 'constructs': 1, 'emphasizes': 1, 'general-purpose': 1, 'notably': 1, 'released': 1, 'significant': 1, 'Guido': 1, 'using': 1, 'interpreted': 1, 'by': 1, 'on': 1, 'language': 1, 'whitespace.': 1, 'clear': 1, 'It': 1, 'large': 1, 'small': 1, 'automatic': 1, 'scales.': 1, 'first': 1})

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