fuzzywuzzy | Fuzzy String Matching in Python | Search Engine library
kandi X-RAY | fuzzywuzzy Summary
kandi X-RAY | fuzzywuzzy Summary
Fuzzy String Matching in Python
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Top functions reviewed by kandi - BETA
- UWRatio between two strings
- Get opcodes
- Get all matching blocks
- Return the similarity between two strings
- Uratio ratio
- Return the similarity between two sequences
- Return the ratio between two strings
- Compare two strings
- Removes duplicates from a list
- Extract elements from a query
- Extract a single item from a query
- Print the result from a timeit
- Extract the best matches from the query
- Extracts the best match from choices
- Generate a quick ratio
fuzzywuzzy Key Features
fuzzywuzzy Examples and Code Snippets
Lines of Code : 3License : Strong Copyleft (CC BY-SA 4.0)
Copydef example_task(words, beginning):
return [w for w in words if w.startswith(beginning)]
#Run this to install the required libraries
#pip install python-levenshtein fuzzywuzzy
from fuzzywuzzy import fuzz
l_data =[
['Robert','9185 Pumpkin Hill St.']
,['Rob','9185 Pumpkin Hill Street']
,['Mike','1296 Tunnel St.']
def match_groups(addresses, threshold):
subgroups = [i for i in range(1, len(addresses)+1)]
for i, val_i in enumerate(addresses):
for j, val_j in enumerate(addresses):
if j>i:
ratio = fuzz.ratimport pandas as pd
from fuzzywuzzy import fuzz
# Setup
df1.columns = [f"df1_{col}" for col in df1.columns]
# Add new columns
df1["fuzz_ratio_lname"] = (
df1["df1_lname"]
.apply(
lambda x: max(
[(value, fuzz.rfrom functools import cache
import pandas as pd
from fuzzywuzzy import fuzz
# First, define indices and values to check for matches
indices_and_values = [(i, value) for i, value in enumerate(df2["lname"] + df2["fname"])]
# Define helperbest_match = process.extractOne(text, choices_dict, score_cutoff=80)
if best_match:
value, score, key = best_match
print(f"best match is {key}:{value} with the similarity {score}")
else:
print("no match found")
>>> from rapidfuzz.distance import Levenshtein
>>> Levenshtein.distance('controlled', 'comparative')
8
>>> Levenshtein.similarity('controlled', 'comparative')
3
>>> Levenshtein.normalized_distance('contrfrom fuzzywuzzy import fuzz
df['score'] = df[['Name Left','Name Right']].apply(lambda x : fuzz.partial_ratio(*x),axis=1)
df
Out[134]:
Match ID Name Left Name Right score
0 1 LemonFarms Lemon Farms Inc import os
import csv
import shutil
import usaddress
import pandas as pd
from fuzzywuzzy import process
with open(r"TEST_Cass_Howard.csv") as csv_file, \
open(".\Scratch\Final_Test_Clean.csv", "w") as f, \
open(r"TEST_Uniquimport usaddress
from fuzzywuzzy import process
data1 = "3176 DETRIT ROAD"
choices = ["DETROIT RD"]
try:
data1 = usaddress.tag(data1)
except usaddress.RepeatedLabelError:
pass
parts = [
data1[0].get("StreetNamePreDirectionalCommunity Discussions
Trending Discussions on fuzzywuzzy
QUESTION
I am confused about a simple task
the user will give me a string and my program will check if this string equals the first letters of a list of words ( like this example)
...ANSWER
Answered 2022-Mar-28 at 14:59No need for some weird libraries, Python has a nice builtin str function called startswith that does just that.
QUESTION
I am trying to migrate from google cloud composer composer-1.16.4-airflow-1.10.15 to composer-2.0.1-airflow-2.1.4, However we are getting some difficulties with the libraries as each time I upload the libs, the scheduler fails to work.
here is my requirements.txt
...ANSWER
Answered 2022-Mar-27 at 07:04We have found out what was happening. The root cause was the performances of the workers. To be properly working, composer expects the scanning of the dags to take less than 15% of the CPU ressources. If it exceeds this limit, it fails to schedule or update the dags. We have just taken bigger workers and it has worked well
QUESTION
When I try to run pipreqs /path/to/project it comes back with
ANSWER
Answered 2022-Mar-21 at 23:52Are you on Windows? Your file contains a Unicode byte-order mark. Some services don't like that. If you remove the BOM, it should work.
QUESTION
I have the following dataset:
...ANSWER
Answered 2022-Mar-21 at 07:59One way might be to create a parallel DataFrame, then join. Here are a couple of variations on that approach. There may well be a better way.
Here's a slightly modified match_groups function, so that it takes a Series rather than a DataFrame:
QUESTION
I have referred to this post but cannot get it to run for my particular case. I have two dataframes:
...ANSWER
Answered 2021-Dec-26 at 17:50You could try this:
QUESTION
I'm currently doing some string product similarity matches between two different retailers and I'm using the fuzzywuzzy process.extractOne function to find the best match.
However, I want to be able to set a scoring threshold so that the product will only match if the score is above a certain threshold, because currently it is just matching every single product based on the closest string.
The following code gives me the best match: (currently getting errors)
title, index, score = process.extractOne(text, choices_dict)
I then tried the following code to try set a threshold:
title, index, score = process.extractOne(text, choices_dict, score_cutoff=80)
Which results in the following TypeError:
TypeError: cannot unpack non-iterable NoneType object
Finally, I also tried the following code:
title, index, scorer, score = process.extractOne(text, choices_dict, scorer=fuzz.token_sort_ratio, score_cutoff=80)
Which results in the following error:
ValueError: not enough values to unpack (expected 4, got 3)
ANSWER
Answered 2022-Feb-23 at 14:12process.extractOne will return None, when the best score is below score_cutoff. So you either have to check for None, or catch the exception:
QUESTION
I have a university activity that makes the following dataframe available:
...ANSWER
Answered 2022-Feb-21 at 12:43You can't use fuzz.ratio this way directly, the function is not vectorial.
You need to pass it to apply:
QUESTION
Can someone explain me how this function of the library fuzzywuzzy in Python works? I know how the Levenshtein distance works but I don't understand how the ratio is computed.
...ANSWER
Answered 2022-Feb-17 at 05:13As you probably already know the Levenshtein distance is the minimum amount of insertions / deletions / substitutions to convert one sequence into another sequence. It can be normalized as dist / max_dist, where max_dist is the maximum distance possible given the two sequence lengths. In the case of the Levenshtein distance this results in the normalization dist / max(len(s1), len(s2)). In addition a normalized similarity can be calculated by inverting this: 1 - normalized distance.
QUESTION
I want to check for fuzzy duplicates in a column of the dataframe using fuzzywuzzy. In this case, I have to iterate over the rows one by one using two nested for loops.
...ANSWER
Answered 2022-Feb-15 at 08:30For your use case I would recommend the usage of RapidFuzz (I am the author). In particular the function process.cdist should allow you to implement this very efficiently:
QUESTION
I'm not sure where I'm going wrong here and why my data is returning wrong. Writing this code to use fuzzywuzzy to clean bad input road names against a list of correct names, replacing the incorrect with the closest match.
It's returning all lines of data2 back. I'm looking for it to return the same, or replaced lines of data1 back to me.
My Minimal, Reproducible Example:
...ANSWER
Answered 2022-Jan-25 at 18:21Okay, I'm not certain I've fully understood your issue, but modifying your reprex, I have produced the following solution.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install fuzzywuzzy
You can use fuzzywuzzy like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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