Madyson  Reilly

Madyson Reilly

1601055000

Regular Expressions: What and Why?

Regular expressions is a powerful search and replace technique that you probably have used even without knowing. Be it your text editor’s “Find and Replace” feature, validation of your http request body using a third party npm module or your terminal’s ability to return list of files based on some pattern, all of them use Regular Expressions in one way or the other. It is not a concept that programmers must definitely learn but by knowing it you are able to reduce the complexity of your code in some cases.

_In this tutorial we will be learning the key concepts as well as some use cases of Regular Expressions in _javascript.

How do you write a Regular Expression?

There are two ways of writing Regular expressions in Javascript. One is by creating a **literal **and the other is using **RegExp **constructor.

//Literal
const myRegex=/cat/ig

//RegExp
const myRegex=new RegExp('cat','ig')

While both types of expressions will return the same output when tested on a particular string, the benefit of using the RegExp constructor is that it is evaluated at runtime hence allowing use of javascript variables for dynamic regular expressions. Moreover as seen in this benchmark test the RegExp constructor performs better than the literal regular expression in pattern matching.

The syntax in either type of expression consists of two parts:

  • pattern : The pattern that has to be matched in a string.
  • flags : these are modifiers which are rules that describe how pattern matching will be performed.

#regular-expressions #javascript #programming #js #regex #express

What is GEEK

Buddha Community

Regular Expressions: What and Why?

Mad Libs: Using regular expressions

From Tiny Python Projects by Ken Youens-Clark

Everyone loves Mad Libs! And everyone loves Python. This article shows you how to have fun with both and learn some programming skills along the way.


Take 40% off Tiny Python Projects by entering fccclark into the discount code box at checkout at manning.com.


When I was a wee lad, we used to play at Mad Libs for hours and hours. This was before computers, mind you, before televisions or radio or even paper! No, scratch that, we had paper. Anyway, the point is we only had Mad Libs to play, and we loved it! And now you must play!

We’ll write a program called mad.py  which reads a file given as a positional argument and finds all the placeholders noted in angle brackets like <verb>  or <adjective> . For each placeholder, we’ll prompt the user for the part of speech being requested like “Give me a verb” and “Give me an adjective.” (Notice that you’ll need to use the correct article.) Each value from the user replaces the placeholder in the text, and if the user says “drive” for “verb,” then <verb>  in the text replaces with drive . When all the placeholders have been replaced with inputs from the user, print out the new text.

#python #regular-expressions #python-programming #python3 #mad libs: using regular expressions #using regular expressions

Madyson  Reilly

Madyson Reilly

1601055000

Regular Expressions: What and Why?

Regular expressions is a powerful search and replace technique that you probably have used even without knowing. Be it your text editor’s “Find and Replace” feature, validation of your http request body using a third party npm module or your terminal’s ability to return list of files based on some pattern, all of them use Regular Expressions in one way or the other. It is not a concept that programmers must definitely learn but by knowing it you are able to reduce the complexity of your code in some cases.

_In this tutorial we will be learning the key concepts as well as some use cases of Regular Expressions in _javascript.

How do you write a Regular Expression?

There are two ways of writing Regular expressions in Javascript. One is by creating a **literal **and the other is using **RegExp **constructor.

//Literal
const myRegex=/cat/ig

//RegExp
const myRegex=new RegExp('cat','ig')

While both types of expressions will return the same output when tested on a particular string, the benefit of using the RegExp constructor is that it is evaluated at runtime hence allowing use of javascript variables for dynamic regular expressions. Moreover as seen in this benchmark test the RegExp constructor performs better than the literal regular expression in pattern matching.

The syntax in either type of expression consists of two parts:

  • pattern : The pattern that has to be matched in a string.
  • flags : these are modifiers which are rules that describe how pattern matching will be performed.

#regular-expressions #javascript #programming #js #regex #express

Regular Expressions in Python [With Examples]: How to Implement?

While processing raw data from any source, extracting the right information is important so that meaningful insights can be obtained from the data. Sometimes it becomes difficult to take out the specific pattern from the data especially in the case of textual data.

The textual data consist of paragraphs of information collected via survey forms, scrapping websites, and other sources. The Channing of different string accessors with pandas functions or other custom functions can get the work done, but what if a more specific pattern needs to be obtained? Regular expressions do this job with ease.

What is a Regular Expression (RegEx)?

Examples to Understand The Workaround

How to Implement it in Python?

Conclusion

#data science #python #regular expression #regular expression in python

Osiki  Douglas

Osiki Douglas

1620184320

Regular Expressions in Python [With Examples]: How to Implement? | upGrad blog

While processing raw data from any source, extracting the right information is important so that meaningful insights can be obtained from the data. Sometimes it becomes difficult to take out the specific pattern from the data especially in the case of textual data.

The textual data consist of paragraphs of information collected via survey forms, scrapping websites, and other sources. The Channing of different string accessors with pandas functions or other custom functions can get the work done, but what if a more specific pattern needs to be obtained? Regular expressions do this job with ease.

What is a Regular Expression (RegEx)?

A regular expression is a representation of a set of characters for strings. It presents a generalized formula for a particular pattern in the strings which helps in segregating the right information from the pool of data. The expression usually consists of symbols or characters that help in forming the rule but, at first glance, it may seem weird and difficult to grasp. These symbols have associated meanings that are described here.

Meta-characters in RegEx

  1. ‘.’: is a wildcard, matches a single character (any character, but just once)
  2. ^: denotes start of the string
  3. $: denotes the end of the string
  4. [ ]: matches one of the sets of characters within [ ]
  5. [a-z]: matches one of the range of characters a,b,…,z
  6. [^abc] : matches a character that is not a,b or c.
  7. a|b: matches either a or b, where a and b are strings
  8. () : provides scoping for operators
  9. \ : enables escape for special characters (\t, \n, \b, .)
  10. \b: matches word boundary
  11. \d : any digit, equivalent to [0-9]
  12. \D: any non digit, equivalent to [^0-9]
  13. \s : any whitespace, equivalent to [ \t\n\r\f\v]
  14. \S : any non-whitespace, equivalent to [^\t\n\r\f\v]
  15. \w : any alphanumeric, equivalent to [a-zA-Z0-9_]
  16. \W : any non-alphanumeric, equivalent to [^a-zA-Z0-9_]
  17. ‘*’: matches zero or more occurrences
  18. ‘+’: matches one or more occurrences
  19. ‘?’: matches zero or one occurrence
  20. {n}: exactly n repetitions, n>=0
  21. {n,}: at least n repetitions
  22. {,n}: at most n repetitions
  23. {m,n}: at least m repetitions and at most n repetitions

#data science #python #regular expression #regular expression in python

A Gentle Introduction to Regular Expressions with R

We live in a data-centric age. Data has been described as the new oil. But just like oil, data isn’t always useful in its raw form. One form of data that is particularly hard to use in its raw form is unstructured data.

A lot of data is unstructured data. Unstructured data doesn’t fit nicely into a format for analysis, like an Excel spreadsheet or a data frame. Text data is a common type of unstructured data and this makes it difficult to work with. Enter regular expressions, or regex for short. They may look a little intimidating at first, but once you get started, using them will be a picnic!

More comfortable with python? Try my tutorial for using regex with python instead:

A Gentle Introduction to Regular Expressions with Python

Regular expressions are the data scientist’s most formidable weapon against unstructured text

towardsdatascience.com

The stringr Library

We’ll use the stringr library. The stringr library is built off a C library, so all of its functions are very fast.

To install and load the stringr library in R, use the following commands:

## Install stringer
install.packages("stringr")

## Load stringr
library(stringr)

See how easy that is? To make things even easier, most function names in the stringr package start with str. Let’s take a look at a couple of the functions we have available to us in this module:

  1. str_extract_all(string, pattern): This function returns a list with a vector containing all instances of pattern in string
  2. str_replace_all(string, pattern, replacement): This function returns string with instances of pattern in string replaced with replacement

You may have already used these functions. They have pretty straightforward applications without adding regex. Think back to the times before social distancing and imagine a nice picnic in the park, like the image above. Here’s an example string with what everyone is bringing to the picnic. We can use it to demonstrate the basic usage of the regex functions:

basicString <- "Drew has 3 watermelons, Alex has 4 hamburgers, Karina has 12 tamales, and Anna has 6 soft pretzels"

If I want to pull every instance of one person’s name from this string, I would simply pass the name and basic_string to str_extract_all():

basicExtractAll <- str_extract_all(basicString, "Drew")
print(basicExtractAll)

The result will be a list with all occurrences of the pattern. Using this example, basicExtractAll will have the following list with 1 vector as output:

[[1]]
[1] "Drew"

Now let’s imagine that Alex left his 4 hamburgers unattended at the picnic and they were stolen by Shawn. str_replace_all can replace any instances of Alex with Shawn:

basicReplaceAll <- str_replace_all(basicString, "Alex", "Shawn")
print(basicReplaceAll)

The resulting string will show that Shawn now has 4 hamburgers. What a lucky guy 🍔.

"Drew has 3 watermelons, Shawn has 4 hamburgers, Karina has 12 tamales, and Anna has 6 soft pretzels"

The examples so far are pretty basic. There is a time and place for them, but what if we want to know how many total food items there are at the picnic? Who are all the people with items? What if we need this data in a data frame for further analysis? This is where you will start to see the benefits of regex.

#regex #regular-expressions #r #text-processing #unstructured-data #express