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Efficient string matching with regular expressions

Home Page: https://trrex.readthedocs.io/en/latest/

License: MIT License

Python 100.00%
search-in-text keyword-extraction trie regex regular-expression nlp pandas python text-mining python-library string-matching

trex's Issues

Add Documentation

Add missing documentation detailing how to integrate trrex with different libraries. The documentation should use the
pydata-sphinx-theme

extra closed beckets are getting generated

Hello Developer,

Below is the query sent and it is generating extra ")".

code:
import trrex as tx
tx.make(['IBS(.|)TECH','IBS(.|)SOL','IBS(.|)TEHK'], prefix=r"\b(", suffix=r")")

output='\b(IBS(.(?:|)SOL|*|)TE(?:HK|CH)))'

Please let me know if any further information is required on the same.

Thanks,
Rangam

List of regex patterns possible?

First, great package. Love using it! Makes my life much easier, and the speed is phenomenal!

I find that it works with a list of words, such as ['Love', 'Hello', ' Book',...]

Does it work with a list of regex patterns?

For example

regex_patterns = [
    r'(?!^\d+)(?=.*)(\b\d+$\b)'               #Remove any numbers that end string i.e "SHELL 545436"
   ,r'^\bSQ\b'                                #Remove the "SQ" if it starts the string ie. "SQ NORDSTROM"
   ,r'(?!\b(ST|^\w{1,2})\b$)\b\w{1,2}\b$'     #Remove any words that are one or two chars at end of string i.e. "Burger King CA" -- except if they = "ST"

]

tx_maker = tx.make(entity_final_clean_regex_patterns,  prefix=r"", suffix=r"") 


#clean column
df['clean'] = df.STRING_COLUMN.str.replace(tx_maker, "", regex=True)

Remove automatic escaping of characters of input words

What is happening?
Currently we escape every character of the input words even if they do not need escaping.

What should happen?
Remove this behavior because it prevents the end user from adding patterns and also it makes the processing slow.
Document how to escape regex characters.

Remove compile function

The compile function offers a vanilla wrapper for using with the re Python module, is better to remove this function and then add it later if needed.

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