fuzzywuzzy

Python fuzzy matching of names with only first initials

由 左心房为你撑大大i 提交于 2019-12-11 03:54:34
问题 I have a case where I need to match a name from a given string to a database of names. Below I have given a very simple example of the issue that I am running into, and I am unclear as to why one case works over the other? If I'm not mistaken, the Python default algorithm for extractOne() is the Levenshtein distance algorithm. Is it because the Clemens' names provide the first two initials, opposed to only one in the Gonzalez's case? from fuzzywuzzy import fuzz from fuzzywuzzy import process

Python fuzzywuzzy error string or buffer expect

由 大城市里の小女人 提交于 2019-12-10 17:47:31
问题 I'm using fuzzywuzzy to find near matches in a csv of company names. I'm comparing manually matched strings with the unmatched strings in the hope of finding some useful proximity matches, however, I'm getting a string or buffer error within fuzzywuzzy. My code is: from fuzzywuzzy import process from pandas import read_csv if __name__ == '__main__': df = read_csv("usm_clean.csv", encoding = "ISO-8859-1") df_false = df[df['match_manual'].isnull()] df_true = df[df['match_manual'].notnull()] sss

Using fuzzywuzzy to create a column of matched results in the data frame

由 梦想的初衷 提交于 2019-12-09 20:05:56
问题 I'm running into a challenge with using the FuzzyWuzzy library to store all my results in a data frame column (I'm guessing it might require a loop?) I've been scratching my head over this all day, now I want to see if any of you can help me with the solution! Would be super helpful! As an example of what I'm trying to do, here's 2 data frame tables… Master Table +----+-----------------+ | ID | ITEM | +----+-----------------+ | | | | 1 | Pepperoni Pizza | | | | | 2 | Cheese Pizza | | | | | 3

fuzzy match between 2 columns (Python)

由 无人久伴 提交于 2019-12-07 17:40:28
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.ratio(df_combo['url_entrance'],df_combo['company_name']) but it returns only 1 number which is the

Searching one Python dataframe / dictionary for fuzzy matches in another dataframe

由 我是研究僧i 提交于 2019-12-06 11:14:23
问题 I have the following pandas dataframe with 50,000 unique rows and 20 columns (included is a snippet of the relevant columns): df1 : PRODUCT_ID PRODUCT_DESCRIPTION 0 165985858958 "Fish Burger with Lettuce" 1 185965653252 "Chicken Salad with Dressing" 2 165958565556 "Pork and Honey Rissoles" 3 655262522233 "Cheese, Ham and Tomato Sandwich" 4 857485966653 "Coleslaw with Yoghurt Dressing" 5 524156285551 "Lemon and Raspberry Cheesecake" I also have the following dataframe (which I also have saved

Fuzzy logic on big datasets using Python

由 烈酒焚心 提交于 2019-12-06 07:14:08
问题 My team has been stuck with running a fuzzy logic algorithm on a two large datasets. The first (subset) is about 180K rows contains names, addresses, and emails for the people that we need to match in the second (superset). The superset contains 2.5M records. Both have the same structure and the data has been cleaned already, i.e. addresses parsed, names normalized, etc. ContactID int, FullName varchar(150), Address varchar(100), Email varchar(100) The goal is to match values in a row of

Searching one Python dataframe / dictionary for fuzzy matches in another dataframe

由 ぐ巨炮叔叔 提交于 2019-12-04 17:01:46
I have the following pandas dataframe with 50,000 unique rows and 20 columns (included is a snippet of the relevant columns): df1 : PRODUCT_ID PRODUCT_DESCRIPTION 0 165985858958 "Fish Burger with Lettuce" 1 185965653252 "Chicken Salad with Dressing" 2 165958565556 "Pork and Honey Rissoles" 3 655262522233 "Cheese, Ham and Tomato Sandwich" 4 857485966653 "Coleslaw with Yoghurt Dressing" 5 524156285551 "Lemon and Raspberry Cheesecake" I also have the following dataframe (which I also have saved in dictionary form) which has 2 columns and 20,000 unique rows: df2 (also saved as dict_2) PROD_ID PROD

Using fuzzywuzzy to create a column of matched results in the data frame

由 感情迁移 提交于 2019-12-04 15:41:15
I'm running into a challenge with using the FuzzyWuzzy library to store all my results in a data frame column (I'm guessing it might require a loop?) I've been scratching my head over this all day, now I want to see if any of you can help me with the solution! Would be super helpful! As an example of what I'm trying to do, here's 2 data frame tables… Master Table +----+-----------------+ | ID | ITEM | +----+-----------------+ | | | | 1 | Pepperoni Pizza | | | | | 2 | Cheese Pizza | | | | | 3 | Chicken Salad | | | | | 4 | Plain Salad | +----+-----------------+ Lookup Table +--------------+---+

What does “the following packages will be superseded by a higher priority channel” mean?

由 与世无争的帅哥 提交于 2019-12-04 15:09:30
问题 Disclaimer: I am an ignorant Linux + Anaconda noob. Now, with that out of the way: I am trying to install fuzzywuzzy onto my Anaconda distribution in 64 bit Linux. When I do this, it tries to change my conda , and conda-env to conda-forge channels. As follows: I search anaconda for fuzzy wuzzy by writing: anaconda search -t fuzzywuzzy This showed that the most up to date version available for anaconda on 64 bit Linux is 0.13 provided on the channel conda-forge . To install, within the command

Pandas compare each row with all rows in data frame and save results in list for each row

由 梦想与她 提交于 2019-12-04 13:35:01
问题 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. 回答1: The first step would be to
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