Normalizing Textual Data with Python

In this article, we will learn How to Normalizing Textual Data with Python. Let’s discuss some concepts :

• Textual data ask systematically collected material consisting of written, printed, or electronically published words, typically either purposefully written or transcribed from speech.
• Text normalization is that the method of transforming text into one canonical form that it’d not have had before. Normalizing text before storing or processing it allows for separation of concerns since the input is sure to be consistent before operations are performed thereon. Text normalization requires being conscious of what sort of text is to be normalized and the way it’s to be processed afterwards; there’s no all-purpose normalization procedure.

Steps Required

Here, we will discuss some basic steps need for Text normalization.

• Input text String,
• Convert all letters of the string to one case(either lower or upper case),
• If numbers are essential to convert to words else remove all numbers,
• Remove punctuations, other formalities of grammar,
• Remove white spaces,
• Remove stop words,
• And any other computations.

We are doing Text normalization with above-mentioned steps, every step can be done in some ways. So we will discuss each and everything in this whole process.

Text String

Output:

”       Python 3.0, released in 2008, was a major revision of the language that is not completely backward compatible and much Python 2 code does not run unmodified on Python 3. With Python 2’s end-of-life, only Python 3.6.x[30] and later are supported, with older versions still supporting e.g. Windows 7 (and old installers not restricted to 64-bit Windows).”

Case Conversion (Lower Case)

In Python, lower() is a built-in method used for string handling. The lower() methods returns the lowercased string from the given string. It converts all uppercase characters to lowercase. If no uppercase characters exist, it returns the original string.

Output:

”       python 3.0, released in 2008, was a major revision of the language that is not completely backward compatible and much python 2 code does not run unmodified on python 3. with python 2’s end-of-life, only python 3.6.x[30] and later are supported, with older versions still supporting e.g. windows 7 (and old installers not restricted to 64-bit windows).”

Removing Numbers

Remove numbers if they’re not relevant to your analyses. Usually, regular expressions are used to remove numbers.

Output:

”       python ., released in , was a major revision of the language that is not completely backward compatible and much python  code does not run unmodified on python . with python ‘s end-of-life, only python ..x[] and later are supported, with older versions still supporting e.g. windows  (and old installers not restricted to -bit windows).”

Removing punctuation

The part of replacing with punctuation can also be performed using regex. In this, we replace all punctuation by empty string using certain regex.

Output:

‘       python  released in  was a major revision of the language that is not completely backward compatible and much python  code does not run unmodified on python  with python s endoflife only python x and later are supported with older versions still supporting eg windows  and old installers not restricted to bit windows’

Removing White space

The strip() function is an inbuilt function in Python programming language that returns a copy of the string with both leading and trailing characters removed (based on the string argument passed).

Output:

‘python  released in  was a major revision of the language that is not completely backward compatible and much python  code does not run unmodified on python  with python s endoflife only python x and later are supported with older versions still supporting eg windows  and old installers not restricted to bit windows’

Removing Stop Words

Stop words” are the foremost common words during a language like “the”, “a”, “on”, “is”, “all”. These words don’t carry important meaning and are usually faraway from texts. It is possible to get rid of stop words using tongue Toolkit (NLTK), a set of libraries and programs for symbolic and statistical tongue processing.

Output: 

In this, we can normalize the textual data using Python. Below is the complete python program:

Output: