RapidFuzz | Rapid fuzzy matching in Python | Learning library
kandi X-RAY | RapidFuzz Summary
kandi X-RAY | RapidFuzz Summary
RapidFuzz is a fast string matching library for Python and C++, which is using the string similarity calculations from FuzzyWuzzy. However there are a couple of aspects that set RapidFuzz apart from FuzzyWuzzy:.
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Top functions reviewed by kandi - BETA
- Get a single item from a query
- Return the best score and optimal score
- Calculate the cdist of the given queries
- Convert a dtype to an integer
- Benchmark function
- Return the platform name
- Import a module
- Return a list of values from a given query
- Extract an iterable from a given query
- Compute the similarity between two strings
- Calculate the partial ratio
- Computes the similarity between two tokens
- Computes the partial similarity between two strings
- Calculate partial ratio between two strings
- Compute the similarity between two tokens
- Convert a list of ops to a list of opcodes blocks
- Return a copy of this filter
- Compute similarity between two strings
- Convert a list of ops to edit operations
- Create a new Editops object
- Benchmark for testing
- Benchmarking function
- Create an editops object from an opcodes
- Construct opcodes from editops
- Run setup
- Compute the similarity between two blocks
- Returns a list of required requirements for the build wheel
RapidFuzz Key Features
RapidFuzz Examples and Code Snippets
Community Discussions
Trending Discussions on RapidFuzz
QUESTION
I am interested to loop through column to convert into processed series.
Below is an example of two row, four columns data frame:
ANSWER
Answered 2022-Apr-07 at 07:41If I get you right, try out this fast solution using numpy.where:
QUESTION
I'm trying to fuzzy-search for a short text in a larger text.
Common python libs, such as fuzzywuzzy and rapidfuzz, support the "partial_ratio" function, but those only return a score, not the location of the match.
Is there some library or function which I can use to also obtain where the fuzzy match was, (something like the span method of regex match)?
...ANSWER
Answered 2021-Nov-28 at 15:14I looked at fuzzywuzzy and noted that finding the index of a match is an open issue. The same is true for RapidFuzz.
This prompted me "(something like the span method of regex match)" to do some research around this method. During my research I found the Python package regex. The package's Readme talks about fuzzy matching. I haven't used this package, but it seem that it might be useful to solving your use case.
QUESTION
I need to build a NER system (Named Entity Recognition). For simplicity, I am doing it by using approximate string matching as input can contain typos and other minor modifications. I have come across some great libraries like: fuzzywuzzy or even faster RapidFuzz. But unfortunately I didn't find a way to return the position where the match occurs. As, for my purpose I not only need to find the match, but also I need to know where the match happened. As for NER, I need to replace those matches with some predefined string.
For example, If any one of the line is found in input string I want to replace them with the string COMPANY_NAME:
ANSWER
Answered 2021-Nov-24 at 07:57It seems modules fuzzywuzzy and RapidFuzz don't have function for this. You could try to use process.extract() or process.extractOne() but it would need to split text in smaller parts (ie. words) and check every part separatelly. For longer words like International Business Machine it would need to split in part with 3 words - so it would need even more work.
I think you need rather module fuzzysearch
QUESTION
I need to merge two large datasets based on string columns which don't perfectly match. I have wide datasets which can help me determine the best match more accurately than string distance alone, but I first need to return several 'top matches' for each string.
Reproducible example:
...ANSWER
Answered 2021-Oct-22 at 18:33This doesn't answer your question but I'd be curious to know if it speeds things up. Just returning dictionaries instead of DataFrames should be much more efficient:
QUESTION
I am hitting a wall with this. Rapidfuzz delivers different results for string score similarity if I run it within a pandas dataframe and if I run it by itself? Why the results for Adress Similarity 2 and for the last line are different?
...ANSWER
Answered 2021-Jul-29 at 06:54The error comes from the fact that you call the entire column when applying fuzz. If you do the following thing, which is to apply fuzz to the individual row, you get the same result:
QUESTION
i have a code and the prints look pretty weird. i want to fix it
*The Prints
...ANSWER
Answered 2021-Mar-30 at 09:05You create a new dataframe in each loop. You can store the result in a global dict and create dataframe from that dict after the loop.
QUESTION
I have a large (>500k rows) pandas df like so
orig_df = pd.DataFrame(columns=list('id', 'free_text1', 'something_inert', 'free_text2'))
free_textX is a string field containing user input imported from a csv. The goal is to have a function func that does various checks on each row of free_textX and then a performs Levenshtein fuzzy text recognition based on the contents of another df reference. Something like
ANSWER
Answered 2020-Oct-31 at 20:39I may be missing a point, but you can use apply function to get what I think you want:
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