- Python Text Processing - Home
- Python Text Processing - Introduction
- Python Text Processing - Environment
- Python Text Processing - String Immutability
- Python Text Processing - Sorting Lines
- Python Text Processing - Counting Token in Paragraphs
- Python Text Processing - Binary ASCII Conversion
- Python Text Processing - Strings as Files
- Python Text Processing - Backward File Reading
- Python Text Processing - Filter Duplicate Words
- Python Text Processing - Extract Emails from Text
- Python Text Processing - Extract URL from Text
- Python Text Processing - Pretty Print
- Python Text Processing - State Machine
- Python Text Processing - Capitalize and Translate
- Python Text Processing - Tokenization
- Python Text Processing - Remove Stopwords
- Python Text Processing - Synonyms and Antonyms
- Python Text Processing - Translation
- Python Text Processing - Word Replacement
- Python Text Processing - Spelling Check
- Python Text Processing - WordNet Interface
- Python Text Processing - Corpora Access
- Python Text Processing - Tagging Words
- Python Text Processing - Chunks and Chinks
- Python Text Processing - Chunk Classification
- Python Text Processing - Classification
- Python Text Processing - Bigrams
- Python Text Processing - Process PDF
- Python Text Processing - Process Word Document
- Python Text Processing - Reading RSS feed
- Python Text Processing - Sentiment Analysis
- Python Text Processing - Search and Match
- Python Text Processing - Text Munging
- Python Text Processing - Text wrapping
- Python Text Processing - Frequency Distribution
- Python Text Processing - Summarization
- Python Text Processing - Stemming Algorithms
- Python Text Processing - Constrained Search
Python Text Processing Useful Resources
Python Text Processing - Munging
Munging in general means cleaning up anything messy by transforming them. In our case we will see how we can transform text to get some result which gives us some desirable changes to data. At a simple level it is only about transforming the text we are dealing with.
Example - Munging
In the below example we plan to shuffle and then rearrange all the letters of a sentence except the first and the last one to get the possible alternate words which may get generated as a mis-spelled word during writing by a human. This rearrangement helps us in
main.py
import random
import re
def replace(t):
inner_word = list(t.group(2))
random.shuffle(inner_word)
return t.group(1) + "".join(inner_word) + t.group(3)
text = "Hello, You should reach the finish line."
print(re.sub(r"(\w)(\w+)(\w)", replace, text))
print(re.sub(r"(\w)(\w+)(\w)", replace, text))
Output
When we run the above program we get the following output −
Hlleo, You slohud recah the fniish line. Hello, You soulhd reach the fniish line.
Here you can see how the words are jumbled except for the first and the last letters. By taking a statistical approach to wrong spelling we can decided what are the commonly misspelled words and supply the correct spelling for them.