Text-Processing in Python
Q1. What are the testing frameworks are commonly used for text processing in python?
- unittest: There are some built-in testing frameworks in Python
- pytest: This is the third-party testing frameworks that are known for its simplicity.
- nose2: this is nose testing frameworks with additional features.
- doctest: A module for testing docstrings by running examples embedded in documentation.
Q2. How do i run tests in Python?
You can run tests using the following commands:
- For unittest: ‘python -m unittest <test_module>’
- For Pytest: ‘pytest<test_module>
Q3. How can i mock objects in Python tests?
Python provides the ‘unittest.mock’ module, that allow to create mock objects for testing.
Q4. How do i write basic test in Python?
You can create a test class which inherits from ‘unittest.TestCase’ and write test methods within it. You can write function name and start with “test_” to use assertions just to check output that are expected.
Text Preprocessing in Python
Text Processing pertains to the analysis of text data using a programming language such as Python. Text Processing is an essential task in NLP as it helps to clean and transform raw data into a suitable format used for analysis or modeling.
In this article, we will learn by using various Python Libraries and Techniques that are involved in Text Processing.
Prerequisites: Introduction to NLP
Whenever we have textual data, we need to apply several processing and pre-processing steps to the data to transform words into numerical features that work with machine learning algorithms. The pre-processing steps for a problem depend mainly on the domain and the problem itself, hence, we don’t need to apply all steps to every problem.
In this article, we are going to see text preprocessing in Python. We will be using the NLTK (Natural Language Toolkit) library here.
# import the necessary libraries
import nltk
import string
import re