shortuuid
jellyfish
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shortuuid | jellyfish | |
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5 | 3 | |
1,975 | 1,989 | |
- | - | |
0.8 | 6.9 | |
about 1 year ago | 22 days ago | |
Python | Jupyter Notebook | |
BSD 3-clause "New" or "Revised" License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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shortuuid
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Ask HN: Could you show your personal blog here?
Unlike many people here, I don't like to write hundreds of mediocre posts. Instead, I prefer very few posts, that unfortunately are still mediocre.
If you're tired of all the perfection that exists on the internet, where every piece is deeply insightful and changes your life, I'd encourage you to read my articles, which only promise to shorten it:
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Short, friendly base32 slugs from timestamps
I use shortuuid[0] for that stuff, which also omits the capital letter I, and has some other niceties (I wrote the library). It works really well, and I like how small the IDs are.
- Ask HN: What are your favorite personal sites?
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Building a Micro Business: What Services I Pay For
skorokithakis: developer of django-annoying and shortuuid
- GUIDs Are Not the Only Answer
jellyfish
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Python Libraries
For sounds something like https://github.com/jamesturk/jellyfish ?
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Comparing Strings (Street Names) With Machine Learning
When comparing strings (in our case street names), there are plenty of off-the-shelf features that can be used, such as those provided by the jellyfish. This package also provides a number of phonetic encodings. We can combine an encoding with a metric, such as Levenshtein Distance, to measure the phonetic similarity between two street names.
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How to match names which differ slightly?
You can use a library like jellyfish which implements a bunch of string comparison algorithms, you'd just have to experiment and see which one gives the best results for you. I think I've had the best luck with Jaro-Winkler, then looking at the % match result and picking a cutoff above which I have good confidence that the match is real. It's still not perfect, and I really don't see how your last example would work with just about any automated comparison.
What are some alternatives?
pangu.py - Paranoid text spacing in Python
fuzzywuzzy - Fuzzy String Matching in Python
chardet - Python character encoding detector
TextDistance - 📐 Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.
Levenshtein - The Levenshtein Python C extension module contains functions for fast computation of Levenshtein distance and string similarity
Pygments
uniout - Never see escaped bytes in output.
ceja - PySpark phonetic and string matching algorithms
Charset Normalizer - Truly universal encoding detector in pure Python
汉字拼音转换工具(Python 版) - 汉字转拼音(pypinyin)