fuzzywuzzy

问题 I have a dataframe df_sample with 10 parsed addresses and am comparing it to another dataframe with hundreds of thousands of parsed address records df . Both df_sample and df share the exact same structure: zip_code city state street_number street_name unit_number country 12345 FAKEVILLE FLORIDA 123 FAKE ST NaN US What I want to do is match a single row in df_sample against every row in df , starting with state and take only the rows where the fuzzy.ratio(df['state'], df_sample['state']) > 0

问题 I have a list of words: lst = ['dog', 'cat', 'mate', 'mouse', 'zebra', 'lion'] I also have a pandas dataframe: df = pd.DataFrame({'input': ['dog', 'kat', 'leon', 'moues'], 'suggested_class': ['a', 'a', 'a', 'a']}) input suggested_class dog a kat a leon a moues a I would like to populate the suggested_class column with the value from lst that has the highest levenshtein distance to a word in the input column. I am using the fuzzywuzzy package to calculate that. The expected output would be:

问题 I am trying to perform a fuzzywuzzy command comparing two columns in a dataframe. I want to know if a character string from one column ('Relationship') exists in another ('CUST_NAME'), even partially. Then repeat the process for a second column ('Dealer_Name'), on the same column as prior ('CUST_NAME'). I am currently trying to run the following code: Here is my dataframe: RapDF1 = RapDF[['APP_KEY','Relationship','Dealer_Name','CUST_NAME']] Here is the fuzzy matching: from fuzzywuzzy import

问题 I have a pandas dataframe called "df_combo" which contains columns "worker_id" , "url_entrance" , "company_name" . I am trying to produce an output column that would tell me if the URLs in "url_entrance" column contains any word in "company_name" column. Even a close match like fuzzywuzzy would work. For example, if the URL is "www.grandhotelseattle.com" and the "company_name" is "Hotel Prestige Seattle", then the fuzz ratio might be somewhere 70-80. I have tried the following script: >>>fuzz

问题 I have a dataframe in pandas where I am using fuzzywuzzy package in python to match first column in the dataframe with second column. I have defined a function to create an output with first column, second column and partial ratio score. But it is not working. Could you please help import csv import sys import os import numpy as np import pandas as pd from fuzzywuzzy import fuzz from fuzzywuzzy import process def match(driver): driver["score"]=driver.apply(lambda row: fuzz.partial_ratio(row

问题 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

问题 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

问题 I'm having some probems apllying a text search algorithm with parallelized dask insfrastructure. I'm tryng to find the best match for 40,000 stirngs in a series object against a 4000 string list. I could have done it using pandas.apply but it's to time expensive, so i decided try parallelization with map_partitions in dask. I'm using this text search library with python-Levenshtein https://marcobonzanini.com/2015/02/25/fuzzy-string-matching-in-python As you can see, it works ok on this

问题 I am new to python and I'm running a fuzzywuzzy string matching logic on a list with 2 million records. The code is working and it is giving output as well. The problem is that it is extremely slow . In 3 hours it processes only 80 rows. I want to speed things up by making it process multiple rows at once . If it it helps - I am running it on my machine with 16Gb RAM and 1.9 GHz dual core CPU. Below is the code I'm running. d = [] n = len(Africa_Company) #original list with 2m string records

问题 I have some data containing spelling errors. I'm correcting them and scoring how close the spelling is using the following code: import pandas as pd import difflib Li_A = ["potato", "tomato", "squash", "apple", "pear"] Q = {'one' : pd.Series(["potat0", "toma3o", "s5uash", "ap8le", "pea7"], index=['a', 'b', 'c', 'd', 'e']), 'two' : pd.Series(["po1ato", "2omato", "squ0sh", "2pple", "p3ar"], index=['a', 'b', 'c', 'd', 'e'])} df_Q = pd.DataFrame(Q) # Define the function that Corrects & Scores the