Python Regular Expression

Discover PYTHON regular expressions: find basic and COMPLEX PATTERNS, repetitions, or to do (non-)greedy matching, work with the REGEX library and much more!

Regular expressions are used to identify whether a pattern exists in a given sequence of characters (string) or not. They help in manipulating textual data, which is often a pre-requisite for data science projects that involve text mining. You must have come across some application of regular expressions: they are used at the server side to validate the format of email addresses or password during registration, used for parsing text data files to find, replace or delete certain string, etc.

You see, regular expressions are extremely powerful and in this tutorial, you will learn to use them in Python. You will cover the following topics:

• egular Expressions in Python
• Basic Characters: Ordinary Characters
• Wild Card Characters: Special Characters
• Repetitions
• Groups and Grouping using Regular Expressions
• Greedy vs Non-Greedy Matching
• re Python Library

#python #regex

What is GEEK

Buddha Community

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

…

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Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

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.

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

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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.

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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

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

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