cleantextify 0.1.6

A lightweight Python package for cleaning text data.

cleantextify 🧹✨

A lightweight, customizable Python package for cleaning text data.
Removes URLs, HTML tags, emojis, numbers, punctuation, and normalizes whitespace — perfect for text preprocessing in NLP, data cleaning, and web scraping projects.

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📦 Installation

Install from PyPI:

pip install cleantextify

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✨ Features

• 🚀 Remove URLs
• 🧼 Remove HTML tags
• 😂 Remove emojis
• 🔢 Remove numbers
• 📏 Normalize whitespace
• ! Remove punctuations

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📖 Usage

Basic Example:

from cleantextify import clean_text

text = "Hello world! 🌟 Visit https://example.com <br> 123"
cleaned = clean_text(text)

print(cleaned)
# Output: Hello world Visit

Output:

Hello world Visit

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🔧 Customizable Options

Parameter	Type	Default	Description
remove_urls	bool	True	Remove URLs from text.
remove_html	bool	True	Remove HTML tags and entities.
remove_emojis	bool	True	Remove emojis.
remove_numbers	bool	True	Remove numbers.
remove_punct	bool	True	Remove punctuation marks.
normalize_spaces	bool	True	Replace multiple spaces with a single space.

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📚 Full Usage Examples:

Remove only URLs and Emojis:

from cleantextify import clean_text

text = "Check this out! 😃 https://example.com"
cleaned = clean_text(text, remove_urls=True, remove_emojis=True, remove_numbers=False, remove_punct=False, normalize_spaces=True)

print(cleaned)

Output:

Check this out

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Remove All but Keep Numbers:

from cleantextify import clean_text

text = "Score: 99! 🎉 Visit: https://test.com"
cleaned = clean_text(text, remove_numbers=False)

print(cleaned)

Output:

Score 99 Visit

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Only Normalize Spaces:

from cleantextify import clean_text

text = "This    is    a    test."
cleaned = clean_text(text, remove_urls=False, remove_html=False, remove_emojis=False, remove_numbers=False, remove_punct=False, normalize_spaces=True)

print(cleaned)

Output:

This is a test.

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📜 License

MIT License

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📞 Author

Rajdeep Pandhere
[e-mail]

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📦 PyPI Link

https://pypi.org/project/cleantextify/