fuzzywuzzy | Java fuzzy string matching implementation of the well | Search Engine library
kandi X-RAY | fuzzywuzzy Summary
kandi X-RAY | fuzzywuzzy Summary
Fuzzy string matching for java based on the FuzzyWuzzy Python algorithm. The algorithm uses Levenshtein distance to calculate similarity between strings.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Processes the input string
- Compiles the pattern
- Process the input string
- Returns the maximum element in the array
fuzzywuzzy Key Features
fuzzywuzzy Examples and Code Snippets
License : Strong Copyleft (CC BY-SA 4.0)
Copy# pip install fuzzywuzzy
# conda install -c conda-forge fuzzywuzzy
from fuzzywuzzy.process import extractWithoutOrder as extract
from operator import itemgetter
ratio = df["Text"].apply(lambda s: list(map(itemgetter(1), extract(s, df["Tefrom fuzzywuzzy import fuzz
from fuzzywuzzy import process
def fuzzy_merge(df_1, df_2, key1, key2, threshold=90, limit=1):
s = df_2[key2].tolist()
m = df_1[key1].apply(lambda x: process.extract(x, s, limit=limit))
df_1from fuzzywuzzy import fuzz
import pyspark.sql.functions as F
@F.udf
def fuzzyudf(original_title, title):
return fuzz.partial_ratio(original_title, title)
df2 = df.withColumn('partial_ratio', fuzzyudf('column1', 'column2'))
df2.show(from pyspark.sql.functions import udf
from fuzzywuzzy import fuzz
@udf("int")
def fuzz_udf(a,b):
return fuzz.ratio(a,b)
communes_corrompues_ratio.withColumn("fuzzywuzzy_ratio", fuzz_udf(col("resultat"),col("corrompue")).show()
<s=df1.outcome_notes
df1['New']=s.str.findall('|'.join(s.iloc[:4])).str[0]
df1
Out[449]:
id outcome_notes New
0 1 complete complete
1 2 pending pending
2 3 import pandas as pd
from fuzzywuzzy import fuzz
name = pd.read_excel('Book1.xlsx', sheet_name='name')
unique = []
for i in name.columns:
for j in name.columns:
if i != j and fuzz.ratio(i, j) > 90 and from fuzzywuzzy import process
def get_perc(score):
# I put your dictionary up here so that it's always defined.
pct_dict = {
14: 0.016,
14.7: 0.021,
15.3: 0.026,
16: 0.034,
16.7: 0.04,
Community 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 Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the fuzzywuzzy component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
