fuzzyMatch | Fuzzy String matching/scoring algorithm thingy | Search Engine library

by jbt JavaScript Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | fuzzyMatch Summary

fuzzyMatch is a JavaScript library typically used in Database, Search Engine applications. fuzzyMatch has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can install using 'npm i fuzzzzz' or download it from GitHub, npm.

Fuzzy String matching/scoring algorithm thingy

Support

Quality

Security

License

Reuse

Support

fuzzyMatch has a low active ecosystem.

It has 11 star(s) with 5 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 13 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of fuzzyMatch is current.

Quality

fuzzyMatch has 0 bugs and 0 code smells.

Security

fuzzyMatch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

fuzzyMatch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

fuzzyMatch is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

fuzzyMatch releases are not available. You will need to build from source code and install.

Deployable package is available in npm.

Top functions reviewed by kandi - BETA

kandi has reviewed fuzzyMatch and discovered the below as its top functions. This is intended to give you an instant insight into fuzzyMatch implemented functionality, and help decide if they suit your requirements.

Returns the score of a given item .
Fuzzily test results .
Calculates the similarity of a character and returns the score
Convert a character to a string .
Escapes characters in a string .
Fuzzily finds the similarity .

Get all kandi verified functions for this library.

fuzzyMatch Key Features

No Key Features are available at this moment for fuzzyMatch.

fuzzyMatch Examples and Code Snippets

No Code Snippets are available at this moment for fuzzyMatch.

Community Discussions

Trending Discussions on fuzzyMatch

Return multiple possible matches when fuzzy joining two dataframes or vectors in R if they share a word in common

Fuzzymatcher returns NaN for best_match_score

Why use both conda and pip?

OperationalError: no such module: fts4 ? Also No Extensions Available in SQLite in Python

Breaking a wide dataframe to two uneven column dataframe and change it to long formate

FuzzyWuzzy partial_ratio string match

Compare two date columns in pandas DataFrame to validate third column

How to also get the name of the city when geocoding?

scala increment nested for comprehension

Fuzzy match columns and merge/join dataframes

QUESTION

Return multiple possible matches when fuzzy joining two dataframes or vectors in R if they share a word in common

Asked 2022-Mar-15 at 18:03

Is there a way of joining two dataframes via where a row in the first dataframe is joined with every row in the second dataframe if they share a word in common?

For example:

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ANSWER

Answered 2022-Mar-15 at 18:03

With fuzzy_join:

Source https://stackoverflow.com/questions/71486862

QUESTION

Fuzzymatcher returns NaN for best_match_score

Asked 2021-Mar-21 at 20:29

I'm observing odd behaviour while performing fuzzy_left_join from fuzzymatcher library. Trying to join two df, left one with 5217 records and right one with 8734, the all records with best_match_score is 71 records, which seems really odd . To achieve better results I even remove all the numbers and left only alphabetical charachters for joining columns. In the merged table the id column from the right table is NaN, which is also strange result.

left table - column for join "amazon_s3_name". First item - limonig

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ANSWER

Answered 2021-Mar-21 at 20:29

You could give polyfuzz a try. Use the examples' setup, for example using TF-IDF or Bert, then run:

Source https://stackoverflow.com/questions/66619277

QUESTION

Why use both conda and pip?

Asked 2021-Feb-21 at 03:24

In this article, the author suggests the following

To install fuzzy matcher, I found it easier to conda install the dependencies (pandas, metaphone, fuzzywuzzy) then use pip to install fuzzymatcher. Given the computational burden of these algorithms you will want to use the compiled c components as much as possible and conda made that easiest for me.

Can someone explain why he is suggesting to use Conda to install dependencies and then use pip to install the actual package i.e fuzzymatcher? Why can't we just use Conda for both? Also, how do we know if we are using the compiled C packages as he suggested?

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ANSWER

Answered 2021-Feb-21 at 00:34

For the compiled C packages, you could import a package, see where it's located, and check the package itself to see what it imports. At some point, you would read into an import of a compiled module (.so extension on *nix). There's possibly an easier way, but that may depend on at what point in the import sequence of the package the compiled module is loaded.

Fuzzymatcher may not be available through Conda, or only an outdated version, or only a version that matches an outdated set of dependencies. Then you may end up with an out-of-date set of packages. Pip may have a more recent version of fuzzymatcher, and likely cares less (for better or worse) on the versions of various other packages in your environment. I'm not familiar with fuzzymatcher, so I can't give you an exact reason: you'd have to ask the author.

Note that the point of that paragraph, on installing the necessary packages with Conda, is that some packages require (C) libraries (not necessary compiled packages, though these will depend on these libraries) that may not be installed by default on your system. Conda will install these for you; Pip will not.

Source https://stackoverflow.com/questions/66297879

QUESTION

OperationalError: no such module: fts4 ? Also No Extensions Available in SQLite in Python

Asked 2020-Nov-02 at 07:14

I am trying to use fuzzymatcher, but when I run the code I get the following error:

...

ANSWER

Answered 2020-Nov-02 at 07:14

These are the Steps I Followed & Extensions got enabled,

Source https://stackoverflow.com/questions/64586694

QUESTION

Breaking a wide dataframe to two uneven column dataframe and change it to long formate

Asked 2020-Oct-20 at 21:34

I have a wide table with more than 22 columns. This table is the result of fuzzymatch and that's why it's in wide format. The column names are shown below (in order) (I will try to create a sample data frame for better demonstration):

...

ANSWER

Answered 2020-Oct-20 at 21:01

Try this. You can use bind_rows() and setNames() to define common names so that the values can be joined properly:

Source https://stackoverflow.com/questions/64453196

QUESTION

FuzzyWuzzy partial_ratio string match

Asked 2020-Aug-17 at 12:28

I have a sub-string that needs to checked against main-string , I had used FuzzyMatch Partial Ratio algorithm, but somehow, the score seems to be inappropriate

sub string :

Aspire 1 14

Main String: Acer Aspire 1 14 Inch Celeron 4GB 64GB Cloudbook - Red This sleek HD Acer Aspire 1 delivers an inviting tactile finish, featuring 4GB of RAM and an Intel Celeron Processor complete daily tasks and surf the internet seamlessly. Whilst 64GB of storage gives you enough space to easily store and share your important media and documents. #||#The classy look of the Aspire 1 is matched only by the convenience of its thin, easily portable design. #||#The Precision Touch-pad is more responsive than traditional touch-pads helping you work more effectively. #||#Model number: A114-32. #||#General features:#||#Size H1.79, W34.3, D24.5cm. #||#Weight 1.65kg. #||#Up to 10 hours battery life. #||#CPU, Memory and Operating System:#||#Intel Celeron N4000 processor. #||#Dual core processor. #||#1.1GHz processor speed with a burst speed of 2.6GHz. #||#4GB RAM DDR4. #||#64GB eMMC storage. #||#Microsoft Windows 10 S. #||#Display features:#||#14 inch screen. #||#High definition display. #||#Resolution 1366 x 768 pixels. #||#DVD optical drives:#||#Disc drive not included. #||#Graphics:#||#Intel UHD Graphics 600 graphics card. #||#Shared graphics card. #||#Interfaces and connectivity:#||#SD media card reader. #||#Secure Digital (SD), . #||#2 USB 2.0 ports. #||#1 USB 3.0 port. #||#1 Ethernet port. #||#1 HDMI port. #||#Bluetooth. #||#Wi-Fi enabled. #||#Multimedia features:#||#HD webcam. #||#Built-in mic. #||#Built-in audio sound system. #||#30 days Norton Security. #||#General information:#||#Manufacturer's 1 year guarantee. #||#EAN: 4710180446104. Size H1.79, W34.3, D24.5cm.#||#Weight 1.65kg.#||#Up to 10 hours battery life.#||#Intel Celeron N4000 processor.#||#Dual core processor.#||#1.1GHz processor speed with a burst speed of 2.6GHz.#||#4GB RAM DDR4.#||#64GB eMMC storage.#||#Microsoft Windows 10 S.#||#14 inch screen.#||#High definition display.#||#Resolution 1366 x 768 pixels.#||#Disc drive not included.#||#Intel UHD Graphics 600 graphics card.#||#Shared graphics card.#||#SD media card reader.#||#Secure Digital (SD), .#||#2 USB 2.0 ports.#||#1 USB 3.0 port.#||#1 Ethernet port.#||#1 HDMI port.#||#Bluetooth.#||#Wi-Fi enabled.#||#HD webcam.#||#Built-in mic.#||#Built-in audio sound system.#||#30 days Norton Security.#||#Manufacturer's 1 year guarantee.#||#EAN: 4710180446104.

Expected score is 100 but got only 55

Any suggestions are welcomed! Thanks in advance!

Heading ...

ANSWER

Answered 2020-Aug-17 at 12:28

Figured out that if either of strings length (# of characters) crosses threshold value, partial_ratio sets Sequence Matcher to be false and the scores are not 100% even if there is a partial string match

Source https://stackoverflow.com/questions/63286322

QUESTION

Compare two date columns in pandas DataFrame to validate third column

Asked 2020-Apr-20 at 21:36

Background info
I'm working on a DataFrame where I have successfully joined two different datasets of football players using fuzzymatcher. These datasets did not have keys for an exact match and instead had to be done by their names. An example match of the name column from two databases to merge as one is the following

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ANSWER

Answered 2020-Apr-20 at 21:28

IICU: Please Try np.where. Works as follows;

Source https://stackoverflow.com/questions/61332263

QUESTION

How to also get the name of the city when geocoding?

Asked 2020-Mar-06 at 14:11

I am using the Mapbox Geocoding API to find the latitude and longitude of a place provided by user input. This works great. I would also like to display the name of the city which is at this location.

This is an example request, which searches for "70176", a postcode in Germany:

...

ANSWER

Answered 2020-Mar-06 at 14:11

You actually get all this information from the request you made:

The coordinates of the center of the bounding box, boxing the city are returned in the "center" item of the features JSON object. The city name you get from the "place_name" item of the features object. You would have to parse the string and split it by comma, then select the second item of the returned array to get the city name.

Source https://stackoverflow.com/questions/59956252

QUESTION

scala increment nested for comprehension

Asked 2020-Mar-05 at 06:33

I am working on detecting PI/SI information within given dataset(spark). I have set of rules (in csv format) as below

...

ANSWER

Answered 2020-Mar-05 at 06:33

for turns into a map call which always checks every elements. You need to use collectFirst, which stops at the first match.

Source https://stackoverflow.com/questions/60538816

QUESTION

Fuzzy match columns and merge/join dataframes

Asked 2020-Feb-29 at 16:53

I am trying to merge 2 dataframes with multiple columns each based on matching values at one of the columns on each of them. This code from @Erfan does a great job fuzzymatching the target columns, but is there a way to carry the rest of columns too. https://stackoverflow.com/a/56315491/12802642

Dataframe

...

ANSWER

Answered 2020-Feb-29 at 16:53

For those who need this. Here's a solution I came up with.
merge = pd.merge(df, df2, left_on=['matches'],right_on=['Key'],how='outer').fillna(0)
From there you can drop unnecessary or duplicate columns and get a clean result like so:
clean = merge.drop(['matches', 'Key_y'], axis=1)

Source https://stackoverflow.com/questions/60379947

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install fuzzyMatch

You can install using 'npm i fuzzzzz' or download it from GitHub, npm.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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