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https://github.com/code-charity/patterns

🎇Vision: great List of Regex, Patterns, Google queries, public SPARQL, SQL, NoSQL - Regular Expressions(=Search Patterns=Data type definitions) are one of the oldest but most common and most efficient dicsiplines in programming. Thinking regularly, thinking universally, thinking mathematically . . . . . . . . . . . . . . . . . . . . . .
https://github.com/code-charity/patterns

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🎇Vision: great List of Regex, Patterns, Google queries, public SPARQL, SQL, NoSQL - Regular Expressions(=Search Patterns=Data type definitions) are one of the oldest but most common and most efficient dicsiplines in programming. Thinking regularly, thinking universally, thinking mathematically . . . . . . . . . . . . . . . . . . . . . .

Awesome Lists containing this project

README 

          
> ####  `thinking regularly, thinking universally, thinking mathematically`



# List of Patterns 🎇 'Pattern-Collector'   

The ['awesome List'](https://github.com/sindresorhus/awesome#contents) of Patterns         \*_(the only repo of it's kind)_  

Please edit this draft wildy 🎉: [Spreadsheet](https://docs.google.com/spreadsheets/d/1EjeZ2RtNpM_mANdO1VPXmZmbIb5vANUXodPBFtdg3zU/edit) / [Readme](https://github.com/code4charity/PATTERNs--The-RegEx-Collector-queries-ontologies-sql-sparql-nosql-structured-unstructured-data/edit/main/README.md) ) 

Please don't hesitate to add sublists for specific scientific fields such as DNA
 Exploration: Patterns make bite-sized tools🍒🍟 ( Searching such list (,once well populated,) you will already have mentally defined the specific, regular scope of your goal (the task of identifying specific data / matches). That can be more efficient / versatile than searching Stack Overflow Answers or node.js NPM's. Yet each regex could also be an NPM or module/package in any language.



----



## 1. **Reg**ular **Ex**pressions(=Search Patterns=Data format definitions.) 

Regex are most common & most efficient to type. (Despite they are one of the oldest dicsiplines in programming to make sense of data, convert it, clean it or spell-check it. https://en.wikipedia.org/wiki/Regular_expression)   

Regex are versatile, because they work in most languages and editors and many apps.

 

| Common Data Formats² | **match** | replacement | _comment/justify_ | extra³_ |

| --: | :-: | :--| --: | --: |

|ISBN ||

|Youtube Video ID |`[^\w-]([\w-]{11})[^\w-]`| $1 | 11char base64 is almost unique| `(?:https?://\|//)?(?:www\.\|m\.)?youtu/?be(?:\.com)?/(?:embed/\|v/\|watch\/?\?[&\w=]{,128}v=([\w-]{11})[^\w-]`| 

| **Hashes, Public Keys, Signatures** | **match** |  

| MD6 ||

| SHA256, Bitcoin, ... ||

| **Convert** | **match** | **replacement** |

|MarkDown links to HTML links | `\[([^\]]*)\]\(([^\)]*)\)`|`$1`|

|**this table**2Javascript |\\|\`([^\`]\*)\`\\\|\`([^\`]\*)\`\\||`replaceAll(/$1/g, "$2").replaceAll("\\|","\|")`| 

|Javascript 2 Python | _..._|_$1$2$3_|



*² date, postal code, formal greeting, formal __, ...* 
 _³extra: match typos too (common) and/or add precision ('no false positives' / perfectionism)_



[**we could add 1000s**]



---- 



## 1.1 Automatic pattern generation / AI



Currently (2021) little of this is automated. Solutions such as Microsoft Power Automate for Desktop (Windows 11) want to change some of it.



## 1.2 Pre-processing Patterns 

A raw text / data source material - or a list or category of patterns - can sometimes be analyzed for similarities and thus be combined in one preprocessing step. i.e. Preprocessing might Reduce Input data by 90% already in a fraction of the time / CPU



## 2. Contextual & Semantic patterns 

#### word-lists, topics, frequencies, thesaurus, antonyms,  semantic dictionaries, psychologic & sentiment dictionaries

wordnet, framenet, google ngrams, google trends, ....

#### Google Search: 

~synonyms a|b AROUND(3) c|d  -e|f|g|h|i|j|k|l|m|n|o|p|q|r|s|t|u|v|w|x|y|z    

https://ahrefs.com/blog/google-advanced-search-operators/

#### Human Grammar & Natural language processing (NLP):  

https://github.com/edobashira/speech-language-processing#readme



## 3. Structured Data. Querying Public Databases & the internet. SPARQL, SQL, NoSQL

#### Semantic web

#### WikiData

#### AWS public databases



## 4. Merging the above "1.-3."

### vs 5. Human work VS machine learning models



----



# All Patterns  

## https://docs.google.com/spreadsheets/d/1EjeZ2RtNpM_mANdO1VPXmZmbIb5vANUXodPBFtdg3zU/edit

- Others Lists  // potential Sources: ___ , ___ , ___ , ____ ,____ , ( not a list but 1 repo per regex: [https://github.com/regexhq](github/regexhq), takes clicks to see one: [regexhq/youtube-regex/index.js](https://github.com/regexhq/youtube-regex/blob/master/index.js))



- Compare:  https://www.mulesoft.com/exchange/?type=connector&view=list   (>10000 'enterprise converts')



| Name | **pattern match** | replacement | language | comment/justify | raw³ | extra context/precision |

| --: | :-: | :--|  --: |  --: |  --: |  --: |

| | | | regex |

| | | | google | 

| | | | css |

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