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Understanding Python Regex Features, with Examples

Common expressions (regex) are particular sequences of characters used to search out or match patterns in strings, as this introduction to regex explains. We’ve beforehand proven find out how to use common expressions with JavaScript and PHP. The main target of this text is Python regex, with the aim of serving to you higher perceive find out how to manipulate common expressions in Python.

You’ll discover ways to use Python regex features and strategies successfully in your applications as we cowl the nuances concerned in dealing with Python regex objects.

Common Expression Modules in Python: re and regex

Python has two modules — re and regex — that facilitate working with common expressions. The re module is inbuilt to Python, whereas the regex module was developed by Matthew Barnett and is offered on PyPI. The regex module by Barnett is developed utilizing the built-in re module, and each modules have related functionalities. They differ by way of implementation. The built-in re module is the extra well-liked of the 2, so we’ll be working with that module right here.

Python’s Constructed-in re Module

As a rule, Python builders use the re module when executing common expressions. The final assemble of normal expression syntax stays the identical (characters and symbols), however the module gives some features and methodology to successfully execute regex in a Python program.

Earlier than we are able to use the re module, now we have to import it into our file like every other Python module or library:

import re

This makes the module obtainable within the present file in order that Python’s regex features and strategies are simply accessible. With the re module, we are able to create Python regex objects, manipulate matched objects, and apply flags the place essential.

A Collection of re Features.

The re module has features reminiscent of re.search(), re.match(), and re.compile(), which we’ll talk about first.

re.search(sample, string, flags=0) vs re.match(sample, string, flags=0)

The re.search() and re.match() search by means of a string for a Python regex sample and return a match if discovered or None if no match object is discovered.

Each features at all times return the primary matched substring present in a given string and preserve a default worth 0 for flag. However whereas the search() perform scans by means of a complete string to discover a match, match() solely searches for a match firstly of a string.

Python’s re.search() documentation:

Scan by means of string on the lookout for the primary location the place the common expression sample produces a match, and return a corresponding match object. Return None if no place within the string matches the sample; be aware that that is completely different from discovering a zero-length match in some unspecified time in the future within the string.

Python’s re.match() documentation:

If zero or extra characters firstly of string match the common expression sample, return a corresponding match object. Return None if the string doesn’t match the sample; be aware that that is completely different from a zero-length match.

Let’s see some code examples to additional make clear:

search_result = [re.search](http://re.search)(r'd{2}', 'I stay at 22 Backyard Street, East Legon')




<re.Match object; span=(10, 12), match='22'>

match_result = re.match(r'd{2}', 'I stay at 22 Backyard Street, East Legon')





Traceback (most up-to-date name final):

File "/house/ini/Dev./sitepoint/regex.py", line 4, in <module>


AttributeError: 'NoneType' object has no attribute 'group'

From the above instance, None was returned as a result of there was no match firstly of the string. An AttributeError was raised when the group() methodology was known as, as a result of there’s no match object:

match_result = re.match(r'd{2}', "45 vehicles had been used for the president's convoy")




<re.Match object; span=(0, 2), match='45'>


With 45, the match object firstly of the string, the match() methodology works simply tremendous.

re.compile(sample, flags=0)

The compile() perform takes a given common expression sample and compiles it into an everyday expression object utilized in discovering a match in a string or textual content. It additionally accepts a flag as an elective second argument. This methodology is helpful as a result of the regex object could be assigned to a variable and used later in our Python code. At all times keep in mind to make use of a uncooked string r"..." when making a Python regex object.

Right here’s an instance of the way it works:

regex_object = re.compile(r'b[ae]t')

mo = regex_object.search('I wager, you wouldn't let a bat be your president')




re.fullmatch(sample, string, flags=0)

This perform takes two arguments: a string handed as an everyday expression sample, a string to go looking, and an elective flag argument. A match object is returned if all the string matches the given regex sample. If there’s no match, it returns None:

regex_object = re.compile(r'Tech is the longer term')

mo = regex_object.fullmatch('Tech is the longer term, be a part of now')





Traceback (most up-to-date name final):

File "/house/ini/Dev./sitepoint/regex.py", line 16, in <module>


AttributeError: 'NoneType' object has no attribute 'group'

The code raises an AttributeError, as a result of there’s no string matching.

re.findall(sample, string, flags=0)

The findall() perform returns a listing of all match objects present in a given string. It traverses the string left to proper, till all matches are returned. See the code snippet beneath:

regex_object = re.compile(r'[A-Z]w+')

mo = regex_object.findall('Pick all of the Phrases that Start with a Capital letter')



['Pick', 'Words', 'Begin', 'Capital']

Within the code snippet above, the regex consists of a personality class and a phrase character, which ensures that the matched substring begins with a capital letter.

re.sub(sample, repl, string, depend=0, flags=0)

Elements of a string could be substituted with one other substring with the assistance of the sub() perform. It takes at the least three arguments: the search sample, the alternative string, and the string to be labored on. The unique string is returned unchanged if no matches are discovered. With out passing a depend argument, by default the perform finds a number of occurrences of the common expression and replaces all of the matches.

Right here’s an instance:

regex_object = re.compile(r'disagreed')

mo = regex_object.sub('agreed',"The founder and the CEO disagreed on the corporate's new course, the traders disagreed too.")



The founder and the CEO agreed on the corporate's new course, the traders agreed too.

subn(sample, repl, string, depend=0, flags=0)

The subn() perform performs the identical operation as sub(), however it returns a tuple with the string and variety of alternative carried out. See the code snippet beneath:

regex_object = re.compile(r'disagreed')

mo = regex_object.subn('agreed',"The founder and the CEO disagreed on the corporate's new course, the traders disagreed too.")



("The founder and the CEO agreed on the corporate's new course, the traders agreed too.", 2)

Match Objects and Strategies

A match object is returned when a regex sample matches a given string within the regex object’s search() or match() methodology. Match objects have a number of strategies that show helpful whereas maneuvering regex in Python.

Match.group([group1, …])

This methodology returns a number of subgroups of a match object. A single argument will return a sign subgroup; a number of arguments will return a number of subgroups, based mostly on their indexes. By default, the group() methodology returns all the match substring. When the argument within the group() is greater than or lower than the subgroups, an IndexError exception is thrown.

Right here’s an instance:

regex_object = re.compile(r'(+d{3}) (d{2} d{3} d{4})')

mo = regex_object.search('Pick the nation code from the cellphone quantity: +233 54 502 9074')




The argument 1 handed into the group(1) methodology — as seen within the above instance — picks out the nation code for Ghana +233. Calling the strategy with out an argument or 0 as an argument returns all subgroups of the match object:

regex_object = re.compile(r'(+d{3}) (d{2} d{3} d{4})')

mo = regex_object.search('Pick the cellphone quantity: +233 54 502 9074')



+233 54 502 9074


teams() returns a tuple of subgroups that match the given string. Regex sample teams are at all times captured with parentheses — () — and these teams are returned when there’s a match, as components in a tuple:

regex_object = re.compile(r'(+d{3}) (d{2}) (d{3}) (d{4})')

mo = regex_object.search('Pick the cellphone quantity: +233 54 502 9074')



('+233', '54', '502', '9074')

Match.begin([group]) & Match.finish([group])

The begin() methodology returns the beginning index, whereas the finish() methodology returns the tip index of the match object:

regex_object = re.compile(r'sw+')

mo = regex_object.search('Match any phrase after an area')

print('Match begins at', mo.begin(), 'and ends', mo.finish())



Match begins at 5 and ends 9


The instance above has a regex sample for matching any phrase character after a whitespace. A match was discovered — ' any' — ranging from place 5 and ending at 9.

Sample.search(string[, pos[, endpos]])

The pos worth signifies the index place the place the seek for a match object ought to start. endpos signifies the place the seek for a match ought to cease. The worth for each pos and endpos could be handed as arguments within the search() or match() strategies after the string. That is the way it works:

regex_object = re.compile(r'[a-z]+[0-9]')

mo = regex_object.search('discover the alphanumeric character python3 within the string', 20 , 30)




The code above picks out any alphanumeric character within the search string.

The search begins at string index place of 20 and stops at 30.

re Regex Flags

Python permits using flags when utilizing re module strategies like search() and match(), which provides extra context to common expressions. The flags are elective arguments that specify how the Python regex engine finds a match object.


This flag is used when performing a case-insentive match. The regex engine will ignore uppercase or lowercase variation of normal expression patterns:

regex_object = [re.search](http://re.search)('django', 'My tech stack contains of python, Django, MySQL, AWS, React', re.I)




The re.I ensures {that a} match object is discovered, no matter whether or not it’s in uppercase or lowercase.

re.S (re.DOTALL)

The '.' particular character matches any character besides a newline. Introducing this flag will even match a newline in a block of textual content or string. See the instance beneath:

regex_object= [re.search](http://re.search)('.+', 'What's your favorite espresso taste nI want the Mocha')



What is your favorite espresso taste

The '.' character solely finds a match from the start of the string and stops on the newline. Introducing the re.DOTALL flag will match a newline character. See the instance beneath:

regex_object= [re.search](http://re.search)('.+', 'What's your favorite espresso taste nI want the Mocha', re.S)



What is your favorite espresso taste

I want the Mocha


By default the '^' particular character solely matches the start of a string. With this flag launched, the perform searches for a match firstly of every line. The '$' character solely matches patterns on the finish of the string. However the re.M flag ensures it additionally finds matches on the finish of every line:

regex_object = [re.search](http://re.search)('^Jw+', 'Widespread programming languages in 2022: nPython nJavaScript nJava nRust nRuby', re.M)




re.X (re.VERBOSE)

Typically, Python regex patterns can get lengthy and messy. The re.X flag helps out when we have to add feedback inside our regex sample. We will use the ''' string format to create a multiline regex with feedback:

email_regex = [re.search](http://re.search)(r'''

[a-zA-Z0-9._%+-]+ # username composed of alphanumeric characters

@ # @ image

[a-zA-Z0-9.-]+ # area identify has phrase characters

(.[a-zA-Z]{2,4}) # dot-something

''', 'extract the e-mail deal with on this string [kwekujohnson1@gmail.com](mailto:kwekujohnson1@gmail.com) and ship an e-mail', re.X)




Sensible Examples of Regex in Python

Let’s now dive in to some extra sensible examples.

Python password energy check regex

One of the well-liked use instances for normal expressions is to check for password energy. When signing up for any new account, there’s a test to make sure we enter an acceptable mixture of letters, numbers, and characters to make sure a powerful password.

Right here’s a pattern regex sample for checking password energy:

password_regex = re.match(r"""

^(?=.*?[A-Z]) # this ensures person inputs at the least one uppercase letter

(?=.*?[a-z]) # this ensures person inputs at the least one lowercase letter

(?=.*?[0-9]) # this ensures person inputs at the least one digit

(?=.*?[#?!@$%^&*-]) # this ensures person inputs one particular character

.{8,}$ #this ensures that password is at the least 8 characters lengthy

""", '@Sit3po1nt', re.X)

print('Your password is' ,password_regex.group())


Your password is @Sit3po1nt

Be aware using '^' and '$' to make sure the enter string (password) is a regex match.

Python search and substitute in file regex

Right here’s our aim for this instance:

  • Create a file ‘pangram.txt’.
  • Add a easy some textual content to file, "The 5 boxing wizards climb rapidly."
  • Write a easy Python regex to go looking and substitute “climb” to “bounce” so now we have a pangram.

Right here’s some code for doing that:

import re


textual content="climb"


def search_and_replace(filePath, textual content, subs, flags=0):

with open(file_path, "r+") as file:

file_contents = [file.read](http://file.learn)()

text_pattern = re.compile(re.escape(textual content), flags)

file_contents = text_pattern.sub(subs, file_contents)




search_and_replace(file_path, textual content, subs)

Python net scraping regex

Typically you would possibly want to reap some knowledge on the Web or automate easy duties like net scraping. Common expressions are very helpful when extracting sure knowledge on-line. Under is an instance:

import urllib.request

phone_number_regex = r'(d{3}) d{3}-d{4}'

url = 'https://www.summet.com/dmsi/html/codesamples/addresses.html'

response = urllib.request.urlopen(url)

string_object = [response.read](http://response.learn)().decode("utf8")

regex_object = re.compile(phone_regex)

mo = regex_object.findall(string_object)

print(mo[: 5])


['(257) 563-7401', '(372) 587-2335', '(786) 713-8616', '(793) 151-6230', '(492) 709-6392']


Common expressions can fluctuate from easy to complicated. They’re an important a part of programming, because the examples above display. To raised perceive regex in Python, it’s good to start by getting accustomed to issues like character lessons, particular characters, anchors, and grouping constructs.

There’s loads additional we are able to go to deepen our understanding of regex in Python. The Python re module makes it simpler to stand up and operating rapidly.

Regex considerably reduces the quantity of code we’d like write to do issues like validate enter and implement search algorithms.

It’s additionally good to have the ability to reply questions on using common expressions, as they usually come up in technical interviews for software program engineers and builders.



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