Fixes some problems with Unicode text after the fact
Project description
ftfy: fixes text for you
>>> print(fix_encoding("(ง'⌣')ง"))
(ง'⌣')ง
The full documentation of ftfy is available at ftfy.readthedocs.org. The documentation covers a lot more than this README, so here are some links into it:
- Fixing problems and getting explanations
- Configuring ftfy
- Encodings ftfy can handle
- “Fixer” functions
- Is ftfy an encoding detector?
- Heuristics for detecting mojibake
- Support for “bad” encodings
- Command-line usage
- Citing ftfy
Testimonials
- “My life is livable again!” — @planarrowspace
- “A handy piece of magic” — @simonw
- “Saved me a large amount of frustrating dev work” — @iancal
- “ftfy did the right thing right away, with no faffing about. Excellent work, solving a very tricky real-world (whole-world!) problem.” — Brennan Young
- “I have no idea when I’m gonna need this, but I’m definitely bookmarking it.” — /u/ocrow
- “9.2/10” — pylint
What it does
Here are some examples (found in the real world) of what ftfy can do:
ftfy can fix mojibake (encoding mix-ups), by detecting patterns of characters that were clearly meant to be UTF-8 but were decoded as something else:
>>> import ftfy
>>> ftfy.fix_text('✔ No problems')
'✔ No problems'
Does this sound impossible? It's really not. UTF-8 is a well-designed encoding that makes it obvious when it's being misused, and a string of mojibake usually contains all the information we need to recover the original string.
ftfy can fix multiple layers of mojibake simultaneously:
>>> ftfy.fix_text('The Mona Lisa doesn’t have eyebrows.')
"The Mona Lisa doesn't have eyebrows."
It can fix mojibake that has had "curly quotes" applied on top of it, which cannot be consistently decoded until the quotes are uncurled:
>>> ftfy.fix_text("l’humanité")
"l'humanité"
ftfy can fix mojibake that would have included the character U+A0 (non-breaking space), but the U+A0 was turned into an ASCII space and then combined with another following space:
>>> ftfy.fix_text('Ã\xa0 perturber la réflexion')
'à perturber la réflexion'
>>> ftfy.fix_text('à perturber la réflexion')
'à perturber la réflexion'
ftfy can also decode HTML entities that appear outside of HTML, even in cases where the entity has been incorrectly capitalized:
>>> # by the HTML 5 standard, only 'PÉREZ' is acceptable
>>> ftfy.fix_text('PÉREZ')
'PÉREZ'
These fixes are not applied in all cases, because ftfy has a strongly-held goal of avoiding false positives -- it should never change correctly-decoded text to something else.
The following text could be encoded in Windows-1252 and decoded in UTF-8, and it would decode as 'MARQUɅ'. However, the original text is already sensible, so it is unchanged.
>>> ftfy.fix_text('IL Y MARQUÉ…')
'IL Y MARQUÉ…'
Installing
ftfy is a Python 3 package that can be installed using pip:
pip install ftfy
(Or use pip3 install ftfy on systems where Python 2 and 3 are both globally
installed and pip refers to Python 2.)
You can also clone this Git repository and install it with
python setup.py install.
Who maintains ftfy?
I'm Robyn Speer. You can find me on GitHub. I created ftfy as part of my work at the text understanding company Luminoso, and now maintain it independently.
Citing ftfy
ftfy has been used as a crucial data processing step in major NLP research.
It's important to give credit appropriately to everyone whose work you build on in research. This includes software, not just high-status contributions such as mathematical models. All I ask when you use ftfy for research is that you cite it.
ftfy has a citable record on Zenodo. A citation of ftfy may look like this:
Robyn Speer. (2019). ftfy (Version 5.5). Zenodo.
http://doi.org/10.5281/zenodo.2591652
In BibTeX format, the citation is::
@misc{speer-2019-ftfy,
author = {Robyn Speer},
title = {ftfy},
note = {Version 5.5},
year = 2019,
howpublished = {Zenodo},
doi = {10.5281/zenodo.2591652},
url = {https://doi.org/10.5281/zenodo.2591652}
}
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