REGULAR EXPRESSION IN PYTHON 2023

REGULAR EXPRESSION IN PYTHON 2023 In the early days of computing, processing text and matching text patterns was a huge challenge.

What is a Python regular expression REGULAR EXPRESSION IN PYTHON methood 1 :

At the time, no standards or pattern-matching characters were proposed. Matching text patterns in a bulk size file was an extremely difficult task at the time REGULAR EXPRESSION IN PYTHON 2023.

It wasn’t until the 1950s that the American mathematician Stephen Kleene invented regular expressions, which completely revolutionized text processing, pattern matching, and bulk data manipulation. A regular expression metacharacter called Kleene Star(*) is named after him REGULAR EXPRESSION IN PYTHON 2023.

A Python regular expression is a sequence of metacharacters that defines a search pattern. We use these patterns in the string search algorithm to “find” or “find and replace” on strings. They are strings in which “what to match” is defined or written REGULAR EXPRESSION IN PYTHON 2023.

The term “regular expressions” is often shortened to “regex” in some places.

In this we will learn the basics of regular expressions in Python. We will use the “re” module for this. So let’s import our module REGULAR EXPRESSION IN PYTHON 2023.

python training in delhi REGULAR EXPRESSION IN PYTHON methood 2 :

At the time, no standards or pattern-matching characters were proposed. Matching text patterns in a bulk size file was an extremely difficult task at the time REGULAR EXPRESSION IN PYTHON 2023.

It wasn’t until the 1950s that the American mathematician Stephen Kleene invented regular expressions, which completely revolutionized text processing, pattern matching, and bulk data manipulation. A regular expression metacharacter called Kleene Star(*) is named after him REGULAR EXPRESSION IN PYTHON 2023.

At the time, no standards or pattern-matching characters were proposed. Matching text patterns in a bulk size file was an extremely difficult task at the time REGULAR EXPRESSION IN PYTHON 2023.

It wasn’t until the 1950s that the American mathematician Stephen Kleene invented regular expressions, which completely revolutionized text processing, pattern matching, and bulk data manipulation. A regular expression metacharacter called Kleene Star(*) is named after him.

REGULAR EXPRESSION IN PYTHON 2023 Regular expressions are typically used in many applications that require a large amount of text processing. Many programming languages ​​include support for regular expressions in the language syntax (Perl, Ruby, etc.). While some languages ​​like C, C++ and Python support regular expressions through extension libraries REGULAR EXPRESSION IN PYTHON 2023.

What are the uses of regular expressions REGULAR EXPRESSION IN PYTHON ?

Since the job of regular expressions is to find and/or replace a given pattern, they can be used in many places where pattern matching is a top priority. Some of its main applications are as follows:

Text editors REGULAR EXPRESSION IN PYTHON 2023
Search engines and back-end search engine of websites and APIs
Code editors and IDEs
Data entry software
Validation of form data and user input
Data Analytics, Web ScrapingREGULAR EXPRESSION IN PYTHON 2023

PYTHON REGEX – METACHARACTERS

REGULAR EXPRESSION IN PYTHON 2023 Every character in Python Regex is either a metacharacter or a regular character. A metacharacter has a special meaning, while a regular character matches itself.

A raw string does not handle backslashes in any special way. To do this, add an “r” before the pattern. Without it, you may have to use “\\\\” for a single backslash. But with this one you only need r’\’.

Some of the basic metacharacters used in a regular expression are:

Python 3

Finding patterns in text REGULAR EXPRESSION IN PYTHON 2023

The most common use of a regular expression is to find patterns in text. The search() function takes a pattern and text to scan and returns a Match object when the pattern is found. If the pattern is not found, the search() function returns None REGULAR EXPRESSION IN PYTHON 2023.

Each Match object contains information about the nature of the match, including the original input string, the regular expression used, and the location in the original string where the pattern occurs.

REGULAR EXPRESSION IN PYTHON 2023 Here is an example of our pattern and the text we want to perform a regular expression function on.

Now we use the search() function of the regular expression and store it in a variable.

We take two variables to get the index number of the match pattern in our text data.

We get the output using the print() function.

The start() and end() methods will put indices into the string showing where the text matching the pattern occurs.

So our output will be like this REGULAR EXPRESSION IN PYTHON 2023.

Compilation of expressions

Our re module contains module-level functions for working with regular expressions as text strings, it is more convenient to compile expressions that are frequently used by the program. The compile() function converts an expression string to a Regex object.

Take a look at one expression compilation example, we use the given understanding to compile two text patterns using the compile() function REGULAR EXPRESSION IN PYTHON 2023.

 

Now we apply a for loop over the regex variable and first properly print what is being searched for in the text data.

Now we use a conditional if statement to get the response when our compilation pattern is found in the text data.

Then we list other conditional statements to get a response when the compiled pattern is not found in the text data.

Finally, the total output will be:

Module-level functions maintain a cache of compiled expressions, but its size is limited, and using compiled expressions directly eliminates the overhead associated with cache lookups. By compiling all expressions when the module is loaded, compilation is moved to application startup time rather than occurring when the program can respond to user action, an advantage of using a precompiled regular expression object.

More matches

All sample examples use search() to find individual instances of text strings. The find all() function returns all substrings of the input that match the pattern without overlapping.

For example, we take pattern and text data

Now we will use the find all() function to find all “ab” from our text data.

The output will be REGULAR EXPRESSION IN PYTHON 2023:

The finder() function returns an iterator that creates Match instances instead of the strings returned by find all().

To see the use of finder(), let’s use pattern and text data again.

Now we run a for loop through the finditer() function containing our pattern and string data

We get the output of both “ab” along with their starting and ending index numbers in the string.

conclusion:

These were the basics of Python regular expressions. Try experimenting and make a small project with it.

For example, take a whole volume of data that might contain people’s names, their phone number, or their email address, and then try to find out everyone’s phone number/email address from that. Honestly, it’s really cool to have such a tool in hand REGULAR EXPRESSION IN PYTHON 2023.

Regular expressions are a powerful language for matching text patterns. This page provides a basic introduction to regular expressions that is sufficient for our Python exercises and shows how regular expressions work in Python. The Python “re” module provides support for regular expressions.
In Python, a regular expression search is usually written as:

The re.search() method takes a regular expression pattern and a string and searches for that pattern in the string. If the search is successful, the search() function returns a match object or else None. Therefore, the search is usually immediately followed by an if statement that tests whether the search was successful, as shown in the following example, which searches for the pattern “word:” followed by a 3-letter word (details below) REGULAR EXPRESSION IN PYTHON 2023:

import d

print (‘not found’)
The code match = re.search(pat, str) stores the search result in a variable named “match”. Then the if statement tests for a match – if true, the search was successful and match.group() is the matching text (eg ‘word:cat’). Otherwise, if the match is false (None to be more specific), then the search was not successful and there is no matching text.

The ‘r’ at the beginning of the pattern string indicates a Python “raw” string that passes backslashes unchanged, which is very useful for regular expressions (Java badly needs this feature!). I recommend that you always write pattern strings with ‘r’ as a habit.

Basic patterns

The power of regular expressions is that they can specify patterns, not just fixed characters. Here are the most basic patterns that correspond to individual characters REGULAR EXPRESSION IN PYTHON 2023:

a, X, 9, < — common characters match exactly. Metacharacters that do not match themselves because they have a special meaning are: . ^ $ * + ? { [ ] \ | ( ) (details below)
. (period) — matches any single character except newline ‘\n’
\w — (lowercase w) matches the character “word”: letter or number or underscore [a-zA-Z0-9_]. Note that although “word” is a mnemonic, it only matches a single word character, not an entire word. \W (uppercase W) matches any non-word character.
\b — boundary between word and non-word
\s — (lowercase s) matches one blank character — space, newline, return, tab, shape [ \n\r\t\f]. \S (uppercase S) matches any non-space character.
\t, \n, \r — tab, newline, return
\d — decimal digit [0-9] (some older regex tools don’t support \d, but all do \w and \s)
^ = start, $ = end — matches the start or end of a string
\ — suppress a character’s “specialty”. So for example use \. to match a dot or \\ to match a slash. If you’re not sure if a character has a special meaning, such as ‘@’, you can try putting a forward slash, \@, before it. If this is not a valid escape sequence, such as \c, your Python program will stop with an error.
Basic examples

Joke: what do you call a pig with three eyes? piiig!

The basic rules for finding a pattern in a string with a regular expression are:

The search proceeds in a chain from beginning to end, stopping at the first match found
The entire pattern must match, but not the entire string
If match = re.search(pat, str) is successful, the match is not None and specifically match.group() is the matching text REGULAR EXPRESSION IN PYTHON 2023

 

Repetition

Things get more interesting when you use + and * to specify repeats in a pattern

+ — 1 or more occurrences of the pattern on the left, eg ‘i+’ = one or more i’s
* — 0 or more occurrences of the pattern on the left
? — matches 0 or 1 occurrences of the pattern on the left
Leftmost and largest
First, the search finds the leftmost match for the pattern, and second, it tries to consume as much of the string as possible — ie + and * go as far as possible (+ and * are said to be “greedy”).

 

## ^ = matches the beginning of the string, so this will fail:
match = re.search(r’^b\w+’, ‘foobar’) # not found, match == None
## but without ^ succeeds:
match = re.search(r’b\w+’, ‘foobar’) # found, match.group() == “bar”
Example of emails
Suppose you want to find an email address in the string ‘xyz [email protected] purple monkey’. We’ll use this as a running example to demonstrate other features of regular expressions. Here’s a try using the r’\w+@\w+’ pattern:

str = ‘purple [email protected] monkey dishwasher’
match = re.search(r’\w+@\w+’, str)
if they match:
print(match.group()) ## ‘b@google’
The search in this case will not retrieve the entire email address because \w does not match ‘-‘ or ‘.’ in the address. We’ll fix this using the regular expression functions below.

Square brackets
Square brackets can be used to indicate a set of characters, so [abc] matches ‘a’ or ‘b’ or ‘c’. The codes \w, \s, etc. also work in square brackets, with the only exception that a dot (.) means only a literal dot. If you have a problem with emails, square brackets are an easy way to add a ‘.’ and ‘-‘ to the set of characters that can appear around @ with the pattern r'[\w.-]+@[\w.-]+’ to get the full email address:

match = re.search(r'[\w.-]+@[\w.-]+’, str)
if they match:
print(match.group()) ## ‘[email protected]’
(More functions of square brackets) You can also use a dash to indicate a range, so [a-z] matches all lowercase letters. To use a dash without specifying a range, place the dash last, eg [abc-]. An up-hat (^) at the beginning of a set of square brackets inverts it, so [^ab] means any character except ‘a’ or ‘b’.
Group extraction
Function “group” o REGULAR EXPRESSION IN PYTHON 2023

Regular expressions are a powerful language for matching text patterns. This page provides a basic introduction to regular expressions, sufficient for our Python exercises, and shows how regular expressions work in Python. The Python “re” module provides support for regular expressions.
In Python, a regular expression search is usually written as:

match = re.search(pat, str)
The re.search() method takes a regular expression pattern and a string and searches for that pattern in the string. If the search is successful, the search() function returns a match object or else None. Therefore, the search is usually immediately followed by an if statement that tests whether the search was successful, as shown in the following example, which searches for the pattern “word:” followed by a 3-letter word (details below):

import d

str = ‘word example:cat!!’
match = re.search(r’word:\w\w\w’, str)
# The if statement after search() tests whether it was successful
if they match:
print(‘found’, match.group()) ## ‘found word:cat’
other:
print (‘not found’)
The code match = re.search(pat, str) stores the search result in a variable named “match”. Then the if statement tests for a match – if true, the search was successful and match.group() is the matching text (eg ‘word:cat’). Otherwise, if the match is false (None to be more specific), the search was not successful and there is no matching text.

The ‘r’ at the beginning of the pattern string indicates a Python “raw” string that passes backslashes unchanged, which is very useful for regular expressions (Java really needs this feature!). I recommend always writing pattern strings with ‘r’ as a habit.

Basic patterns
The power of regular expressions is that they can specify patterns, not just fixed characters. Here are the most basic patterns that correspond to individual characters:

. (period) — matches any single character except newline ‘\n’
\w — (lowercase w) matches “word”: letter or number or underscore [a-zA-Z0-9_]. Note that although “word” is a mnemonic, it only corresponds to a single word character, not an entire word. \W (uppercase W) matches any non-word character.
\b — boundary between word and non-word
\s — (lowercase s) matches one blank character — space, newline, return, tab, shape [ \n\r\t\f]. \S (uppercase S) matches any character without a space.
\t, \n, \r — tab, newline, return
\d — decimal digit [0-9] (some older regular expression tools don’t support \d, but all \w and \s do)
^ = start, $ = end — matches the start or end of a string
\ — suppress a character’s “specialty”. So for example use \. to match a dot or \\ to match a slash. If you are not sure whether a character has a special meaning, such as ‘@’, you can try putting a forward slash \@ before it. If this is not a valid escape sequence such as \c, your Python program will stop with an error.
Basic examples
Joke: what do you call a pig with three eyes? piiig!

The basic rules for finding a pattern in a regular expression string are:

The search proceeds in a chain from beginning to end, stopping at the first match found
The entire pattern must match, but not the entire string
If match = re.search(pat, str) is successful, the match is not None and specifically match.group() is the matching text

Things get more interesting when you use + and * to specify repeats in a pattern

+ — 1 or more occurrences of the pattern on the left, eg ‘i+’ = one or more i’s
* — 0 or more occurrences of the pattern on the left
? — matches 0 or 1 occurrences of the pattern on the left
Far left and largest
First, the search finds the leftmost match for the pattern, and second, it tries to consume as much of the string as possible — ie + and * go as far as possible (+ and * are said to be “greedy”).

match = re.search(r’^b\w+’, ‘foobar’) # not found, match == None
## but without ^ succeeds:
match = re.search(r’b\w+’, ‘foobar’) # found, match.group() == “bar”
Example of emails
Suppose you want to find an email address in the string ‘xyz [email protected] purple monkey’. We’ll use this as a running example to demonstrate other features of regular expressions. Here’s an attempt to use the pattern

print(match.group()) ## ‘b@google’
The search in this case will not retrieve the entire email address because \w does not match ‘-‘ or ‘.’ in the address. We’ll fix this using the regular expression functions below REGULAR EXPRESSION IN PYTHON 2023.

Square brackets
Square brackets can be used to indicate a set of characters, so [abc] matches ‘a’ or ‘b’ or ‘c’. The codes \w, \s, etc. also work in square brackets, with the only exception that a dot (.) means only a literal dot. If you’re having trouble with emails, square brackets are an easy way to add a ‘.’ and ‘-‘ to the set of characters that can appear around @ with the pattern r'[\w.-]+@[\w.-]+’ to get the full email address:if they match:
print(match.group()) ## ‘[email protected]’
(Another function of square brackets) You can also use a dash to indicate a range, so [a-z] matches all lowercase letters. To use a dash without specifying a range, place the dash last, eg [abc-]. An up-hat (^) at the beginning of a set of square brackets inverts it, so [^ab] means any character except ‘a’ or ‘b’.
Group extraction
Function “gr REGULAR EXPRESSION IN PYTHON 2023

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