clean-text

library0.7.1pypypiunverified

Clean-Text (pypi: clean-text) provides functions to preprocess and normalize text, making it suitable for various NLP tasks. It offers features like lowercasing, removing emojis, URLs, digits, punctuation, and normalizing whitespace. It is currently at version 0.7.1 and has an active but infrequent release cadence.

ai-ml data

pip install clean-text

package ↗

INSTALL

IMPORT

SIG · CLEAN-TEXT

clean-text

ai-mlpythonv0.7.1

Install

1.9s avg

Import

—

Disk

25MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.7.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.920 runs

installs and imports cleanly · install 0.0s · import 0.000s · 29.6MB

glibc

py 3.10–3.920 runs

installs and imports cleanly · install 1.9s · import 0.000s · 30MB

25MB installed

● package 25MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

clean

✓ from clean_text import clean

Demonstrates the basic usage of the `clean` function. By default, it aggressively preprocesses text by lowercasing, removing emojis, URLs, digits, and punctuation. The example also shows how to customize cleaning behavior by disabling some default transformations.

from clean_text import clean text = " Hello World! 👋 Check out my site: https://example.com This is a test. 123 😊 " cleaned_text = clean(text) print(f"Original: '{text}'") print(f"Cleaned: '{cleaned_text}'") # To customize cleaning, for example, keep emojis and punctuation: text_custom = " Hello World! 👋 This is a test. :) " cleaned_custom = clean(text_custom, no_emoji=False, no_punct=False) print(f"Custom Cleaned: '{cleaned_custom}'")

Debug

Known issues

gotchaThe default behavior of the `clean` function is highly aggressive, performing lowercasing, removing emojis, URLs, digits, and punctuation. This might lead to unexpected data loss if not explicitly configured.

fix

Carefully review the `clean` function's parameters (e.g., `lower=False`, `no_emoji=False`, `no_digits=False`, `no_punct=False`) and set them according to your specific text cleaning needs.

affects: >=0.1.0

gotchaFunctions like `clean_text.remove_emoji` or direct usage of `clean_text.normalize` (for diacritics) require optional dependencies `emoji` and `unidecode` respectively. Calling these directly without installing their corresponding extras will result in an `ImportError`.

fix

Install the necessary optional dependencies using `pip install clean-text[emoji]` for emoji features, `pip install clean-text[normalize]` for diacritic normalization, or `pip install clean-text[all]` for both.

affects: >=0.1.0

gotchaThe `clean` function's `lang` parameter defaults to 'en', which primarily affects stop word removal (if enabled). Using this with non-English text can lead to incorrect behavior if stop words are to be removed.

fix

Specify `lang='your_language_code'` (e.g., `lang='pt'` for Portuguese) when calling `clean` if your text is not English and you plan to use language-dependent features like `remove_stop_words=True`.

affects: >=0.6.0

Upgrade

Version history

0.7.1latest on PyPI · released Jan 28, 2026

Audit

Dependencies

emojioptionalRequired for emoji-related cleaning features (e.g., remove_emoji). Install with `pip install clean-text[emoji]`.

unidecodeoptionalRequired for diacritic removal and advanced text normalization. Install with `pip install clean-text[normalize]` or `pip install clean-text[all]`.

Agent activity

22 hits · last 30 days

node

OpenAI (training)

Resources

packagehttps://pypi.org/project/clean-text/ ↗