If you've been coding in Python for a while, you already know re for regex and maybe NLTK for NLP. But you also know the pain: text full of messy encodings, weird punctuation, invisible characters, and HTML soup. Parsing it with vanilla tools feels like untangling headphones from 2010.

I've been there. I've built scrapers, log parsers, and natural language pipelines. Over the years I've collected a small arsenal of lesser-known but insanely useful libraries that make text cleaning, parsing and analysis dead simple. Here are my favourites.

1. ftfy — Fix Text for You (Invisible Unicode Problems Gone)

Ever seen "Français" instead of "Français"? That's Mojibake. ftfy fixes that automatically.

import ftfy

text = "Français - piñata"
print(ftfy.fix_text(text))
# Français - piñata

It also normalizes quotes, dashes, and whitespace. No regex headaches.

2. Unidecode — Strip Accents Cleanly

When you need ASCII-only slugs or filenames, unidecode transliterates anything:

from unidecode import unidecode

print(unidecode("Супер пример - Français"))
# Super primer - Francais

Perfect for generating URLs or filenames from messy text.

3. dateparser — Parse Human Dates Like a Boss

Stop writing brittle regexes for "yesterday" or "last Friday 8pm":

import dateparser

print(dateparser.parse("next Thursday at 5pm"))
# 2025-09-25 17:00:00

It understands 200+ languages and time zones out of the box.

4. Textacy — NLP Building Blocks Without Boilerplate

Sits on top of spaCy but gives you higher-level helpers like keyword extraction, readability, or term frequency.

import spacy, textacy

nlp = spacy.load("en_core_web_sm")
doc = nlp("Python makes text processing simple.")
keywords = textacy.extract.keyterms.textrank(doc, topn=3)
print(list(keywords))

Fewer lines, more power.

5. PySBD — Sentence Boundary Detection Without False Splits

You think splitting on . works? It doesn't. PySBD (by the open-source community) handles abbreviations and multilingual text:

import pysbd

seg = pysbd.Segmenter(language="en", clean=True)
print(seg.segment("Dr. Smith went to Washington. It rained."))
# ['Dr. Smith went to Washington.', 'It rained.']

Great for preparing text before summarisation or tokenization.

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6. justext — Strip Boilerplate from HTML

Need only the article body without ads or nav bars? justext does it better than BeautifulSoup alone.

import requests, justext

html = requests.get("https://example.com").text
paragraphs = justext.justext(html, justext.get_stoplist("English"))
clean_text = " ".join(p.text for p in paragraphs if not p.is_boilerplate)
print(clean_text)

Boom: instant readable content.

7. Clean-Text — One-Liner Cleaning Pipelines

Lowercase, remove URLs, punctuation, emojis, or stopwords in one go:

from cleantext import clean

txt = "Visit https://example.com 😃!!"
print(clean(txt, no_urls=True, no_emoji=True, lower=True))
# visit !!

Chain options instead of chaining regexes.

8. Polyglot — Fast Multilingual Named Entity Recognition

Need language detection, transliteration, or entity extraction in 130+ languages?

from polyglot.text import Text

txt = Text("Elon Musk vive en Texas.")
print(txt.entities)  # [['Elon Musk', 'I-PER']]
print(txt.language)  # ('es', 1.0)

Great for global datasets where English-only tools choke.

9. FlashText — Keyword Extraction at Lightning Speed

Regular expressions scale badly. flashtext can replace or extract thousands of keywords in O(n) time.

from flashtext import KeywordProcessor

kp = KeywordProcessor()
kp.add_keywords_from_dict({'python': ['python', 'py']})
print(kp.extract_keywords("I love Py and python libraries"))
# ['python', 'python']

Ideal for massive logs or real-time filtering.

10. LangDetect — Dead Simple Language Detection

Detect the language of any string with one call:

from langdetect import detect

print(detect("Ceci est un test"))
# fr

Use it as a gatekeeper before choosing the right pipeline.

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