Brilliant tutorial! Really enjoyed working along in Jupyter notebook too. I’ve been needing to do a lot of fuzzy searching recently so this was a great demo of how the process works.
It would seem that the partial_ratio deinition changed - fuzz.partial_ratio('Hello World', 'Hello Hello World World World') now returns 100, and not 64 like in the video.
Awesome tutorial, how would you do this if you have a few hundred/thousands of strings in json and want to compare duplicates with slightly different names?
@NeuralNine , crisp & rich tutorial at the same time. Amazing .Thank you for this. In last extract example , how do I make sure I have extractions done only when all the 3 words are found. in your example that would mean all the three words/tokens 'science' 'data' & 'python' must be present. So in other words I do not want to match if anyone of the word is missing . [Hence 'Some Other DataScience ... ] should not come up in result.
Very nice Bro, this library is very impressive. I have a program that looks for a match of a specific text in another text and with this library it will help me to be more accurate.
I've build combinations of two strings in a list with itertools a = combinations(lines, 2). How can I compare these pairs of 2 strings with fuzzywuzzy?
Brilliant tutorial! Really enjoyed working along in Jupyter notebook too. I’ve been needing to do a lot of fuzzy searching recently so this was a great demo of how the process works.
I've fallen in love with your channel name and logo! Man they are just perfect 👌👌
Imo just need to change the intro music iwd say
I was actually looking for something like this, thanks
This is very useful stuff, keep up the good work. Appreciate you
It would seem that the partial_ratio deinition changed - fuzz.partial_ratio('Hello World', 'Hello Hello World World World') now returns 100, and not 64 like in the video.
This will very useful. Thanks!
These videos are awesome, great job!
Radical tutorial, bro!
As other has said, a great tutorial. Thank you!
Thanks! This is really helpful for my work!
so basically the last matching method is the best one hahaha thanks for the tutorial
Awesome tutorial, how would you do this if you have a few hundred/thousands of strings in json and want to compare duplicates with slightly different names?
Interesting library to work. Maybe I could use it. Thank you 👍
Super helpful. Thank you for this video!
my mans uploading so quick, damn
I'm waiting for ur vids bro keep going
Very helpful
This is so cool! 🤯
@NeuralNine , crisp & rich tutorial at the same time. Amazing .Thank you for this. In last extract example , how do I make sure I have extractions done only when all the 3 words are found. in your example that would mean all the three words/tokens 'science' 'data' & 'python' must be present. So in other words I do not want to match if anyone of the word is missing . [Hence 'Some Other DataScience ... ] should not come up in result.
Very nice Bro, this library is very impressive. I have a program that looks for a match of a specific text in another text and with this library it will help me to be more accurate.
Thanks for sharing, good job!
Hi this is so useful and precise! I would like to know if we could add a "score_cutoff" to the "process.extract"?
Thank you, very useful information.
Awesome video. Thank you
Good. How to implement this two match two tables?
Thanks for sharing
is it performant on large strings?
I've build combinations of two strings in a list with itertools a = combinations(lines, 2). How can I compare these pairs of 2 strings with fuzzywuzzy?
What keyboard are you using ?
why does fuzz.partial_ratio(paypal, trustpad-pancakeswap) gives result as 67??please help..
cause it matches only some part of the string
Try get_close_matches in python difflib.😊