fuzzywuzzy

I try compare each row with all rows in pandas DF through fuzzywuzzy.fuzzy.partial_ratio() >= 85 and write results in list for each row. in: df = pd.DataFrame( {'id':[1, 2, 3, 4, 5, 6], 'name':['dog', 'cat', 'mad cat', 'good dog', 'bad dog', 'chicken']}) use pandas function with fuzzywuzzy library get result: out: id name match_id_list 1 dog [4, 5] 2 cat [3, ] 3 mad cat [2, ] 4 good dog [1, 5] 5 bad dog [1, 4] 6 chicken [] But I don't understand how get this. The first step would be to find the indices that match the condition for a given name . Since partial_ratio only takes strings, we apply

I am trying to look for potential matches in a PANDAS column full of organization names. I am currently using iterrows() but it is extremely slow on a dataframe with ~70,000 rows. After having looked through StackOverflow I have tried implementing a lambda row (apply) method but that seems to barely speed things up, if at all. The first four rows of the dataframe look like this: index org_name 0 cliftonlarsonallen llp minneapolis MN 1 loeb and troper llp newyork NY 2 dauby o'connor and zaleski llc carmel IN 3 wegner cpas llp madison WI The following code block works but took around five days

I am trying to detect matches between an open text field (read: messy!) with a vector of names. I created a silly fruit example that highlights my main challenges. df1 <- data.frame(id = c(1, 2, 3, 4, 5, 6), entry = c("Apple", "I love apples", "appls", "Bannanas", "banana", "An apple a day keeps...")) df1$entry <- as.character(df1$entry) df2 <- data.frame(fruit=c("apple", "banana", "pineapple"), code=c(11, 12, 13)) df2$fruit <- as.character(df2$fruit) df1 %>% mutate(match = str_detect(str_to_lower(entry), str_to_lower(df2$fruit))) My approach grabs the low hanging fruit, if you will (exact

Is there any way to speed up the fuzzy string match using fuzzywuzzy in pandas. I have a dataframe as extra_names which has names that I want to run fuzzy matches for with another dataframe as names_df . >> extra_names.head() not_matching 0 Vij Sales 1 Crom Electronics 2 REL Digital 3 Bajaj Elec 4 Reliance Digi >> len(extra_names) 6500 >> names_df.head() names types 0 Vijay Sales 1 1 Croma Electronics 1 2 Reliance Digital 2 3 Bajaj Electronics 2 4 Pai Electricals 2 >> len(names_df) 250 As of now, I'm running the logic using the following code, but its taking forever to complete. choices =

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 have 2 lists of over a million names with slightly different naming conventions. The goal here it to match those records that are similar, with the logic of 95% confidence. I am made aware there are libraries which I can leverage on, such as the FuzzyWuzzy module in Python. However in terms of processing it seems it will take up too much resources having every string in 1 list to be compared to the other, which in this case seems to require 1 million multiplied by another million number of iterations. Are there any other more efficient methods for this problem? UPDATE: So I created a