Python Extract Substring Using Regex

In a programming language, a Regular Expression written as (RE or regex) is a text string that is used to describe a search pattern. It’s perfect for extracting data from text files, logs, spreadsheets, and even papers. When utilizing a Python regular expression, remember that everything is fundamentally a character. We create patterns that match a specific sequence of characters, generally referred to as a string. Latin letters or Ascii are the letters you see on your keyboards; on the other hand, Unicode is primarily used to match the foreign text. All numerals, punctuation, and special characters, such as $#@! are included.

A Python regular expression, for example, may instruct a program to search a string for specified text and then print the result. A set of characters are known as a “string.” Whether we’re working on software or any other competitive programming, we’re constantly dealing with strings. While developing programs, we occasionally need to access sub-parts of a string. Substrings are the names for these sub-parts. A substring is a string’s subset. We can easily achieve this by using the string slicing technique or a regular expression (RE).

Expression includes text matching, branching, repetition, and pattern building. RE is a regular expression or RegEx that is imported via the re module in Python. A regular expression is supported by Python libraries. Identifiers, Modifiers, and White Space Characters are supported by RegEx in Python. For the best use of Regular Expressions, you must import the re module; otherwise, it may not work properly. We have structured this piece into three sections that are not exactly related to each other, and you may go right into any of them to get started, but if you are new to RegEx, we recommend reading it in order. We’ll use the findall, search, and match functions in the re module to solve our problems throughout this post. Let’s get started.

Example 1:

We will use a regular expression in Python to extract the substring in this example. We will utilize Python’s built-in package re for regular expressions. The search() function in the preceding code looks for the first instance of the pattern supplied as an argument in the passed text. It gives you a Match object as a result. The span of the substring, as well as the starting and ending indexes of the substring, are all characteristics of a Match object that define the output. It’s worth noting that some properties may be missing because dir() calls the _dir_() method, which provides a list of all the attributes. And this technique can be changed or overridden.

Here is the output when we run the above code.

Example 2:

We will apply the re.match() method in our next example. In Python, the re.match() function looks for and returns the first occurrence of a regular expression pattern. In Python, this Match function will look for a match at the beginning only. If a match is discovered in the first line, the match object is returned. The Match method of Python RegEx, on the other hand, returns null if a match is successfully found in another line. Consider the following Python code for the re.match() function. The expressions “w+” and “W” will match words that begin with the letter “g,” and anything that does not begin with the letter “g” will be ignored. In this Python re.match() example, we use the for loop to check for matches for each element in the list or text.

Here is the output of the above code when executed.

Example 3:

In our last example, we will use the findall method of Python. Findall() is a module that searches for “all” instances of a pattern in a given input. In contrast, the search() module returns the first occurrence that only matches the pattern. findall() will check all the lines in the file and return the non-overlapping pattern matches in a single step. Observe the code below and see that we have some e-mail addresses and some text and want to fetch the email addresses only, so we use the re.findall() function for this purpose. It will search the entire list for e-mail addresses.

The result of the above code is as follows.


Regular expressions (RegEx) are useful for extracting character patterns from text and processing them. Regular Expressions are quick and very easy to use, and they save you time by avoiding the use of redundant loops in your application to match and retrieve data. We have shown you how to utilize regular expressions in Python to tackle specific situations in this post. We have also included examples of utilizing RegEx to address various text processing challenges. We mostly focused on extracting words from strings in this post.

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Kalsoom Bibi

Hello, I am a freelance writer and usually write for Linux and other technology related content