fuzzy-search

I wish to create a fuzzy search algorithm. However, upon hours of research I am really struggling. I want to create an algorithm that performs a fuzzy search on a list of names of schools. This is what I have looked at so far: Most of my research keep pointing to " string metrics " on Google and Stackoverflow such as: Levenshtein distance Damerau-Levenshtein distance Needleman–Wunsch algorithm However this just gives a score of how similar 2 strings are. The only way I can think of implementing it as a search algorithm is to perform a linear search and executing the string metric algorithm for

I have 2 dataframes currently, 1 for donors and 1 for fundraisers. Ideally what I'm trying to find is if any fundraisers also gave donations and if so copy some of that information into my fundraiser data set (donor name, email and their first donation). Problems with my data are 1) I need to match by name and email, but a user might have slightly different names (ex Kat and Kathy). 2) Duplicate names for donors and fundraisers. 2a) With donors I can get unique name/email combinations since I just care about the first donation date 2b) With fundraisers though I need to keep both rows and not

I'm trying to fuzzy match two csv files, each containing one column of names, that are similar but not the same. My code so far is as follows: import pandas as pd from pandas import DataFrame from fuzzywuzzy import process import csv save_file = open('fuzzy_match_results.csv', 'w') writer = csv.writer(save_file, lineterminator = '\n') def parse_csv(path): with open(path,'r') as f: reader = csv.reader(f, delimiter=',') for row in reader: yield row if __name__ == "__main__": ## Create lookup dictionary by parsing the products csv data = {} for row in parse_csv('names_1.csv'): data[row[0]] = row

I'm looking to implement fuzzy search for a small PHP/MySQL application. Specifically, I have a database with about 2400 records (records added at a rate of about 600 per year, so it's a small database). The three fields of interest are street address, last name and date. I want to be able to search by one of those fields, and essentially have tolerance for spelling/character errors. i.e., an address of "123 Main Street" should also match "123 Main St", "123 Main St.", "123 Mian St", "123 Man St", "132 Main St", etc. and likewise for name and date. The main issues I have with answers to other

Can you suggest some light weight fuzzy text search library? What I want to do is to allow users to find correct data for search terms with typos. I could use full-text search engines like Lucene, but I think it's an overkill. Edit: To make question more clear here is a main scenario for that library: I have a large list of strings. I want to be able to search in this list (something like MSVS' intellisense) but it should be possible to filter this list by string which is not present in it but close enough to some string which is in the list. Example: Red Green Blue When I type 'Gren' or 'Geen

I'm trying to use the similarity function in Postgres to do some fuzzy text matching, however whenever I try to use it I get the error: function similarity(character varying, unknown) does not exist If I add explicit casts to text I get the error: function similarity(text, text) does not exist My query is: SELECT (similarity("table"."field"::text, %s::text)) AS "similarity", "table".* FROM "table" WHERE similarity > .5 ORDER BY "similarity" DESC LIMIT 10 Do I need to do something to initalize pg_trgm? You have to install pg_trgm. In debian, source this sql: /usr/share/postgresql/8.4/contrib/pg