Featured Post

Step-by-Step Guide to Reading Different Files in Python

Image
 In the world of data science, automation, and general programming, working with files is unavoidable. Whether you’re dealing with CSV reports, JSON APIs, Excel sheets, or text logs, Python provides rich and easy-to-use libraries for reading different file formats. In this guide, we’ll explore how to read different files in Python , with code examples and best practices. 1. Reading Text Files ( .txt ) Text files are the simplest form of files. Python’s built-in open() function handles them effortlessly. Example: # Open and read a text file with open ( "sample.txt" , "r" ) as file: content = file.read() print (content) Explanation: "r" mode means read . with open() automatically closes the file when done. Best Practice: Always use with to handle files to avoid memory leaks. 2. Reading CSV Files ( .csv ) CSV files are widely used for storing tabular data. Python has a built-in csv module and a powerful pandas library. Using cs...

Python Regex: The 5 Exclusive Examples

 Regular expressions (regex) are powerful tools for pattern matching and text manipulation in Python. Here are five Python regex examples with explanations:


Regular expression examples


01 Matching a Simple Pattern


import re


text = "Hello, World!"

pattern = r"Hello"

result = re.search(pattern, text)

if result:

    print("Pattern found:", result.group())

Output:


Output:

Pattern found: Hello

This example searches for the pattern "Hello" in the text and prints it when found.


02 Matching Multiple Patterns


import re


text = "The quick brown fox jumps over the lazy dog."

patterns = [r"fox", r"dog"]

for pattern in patterns:

    if re.search(pattern, text):

        print(f"Pattern '{pattern}' found.")

Output:


Pattern 'fox' found.

Pattern 'dog' found.

It searches for both "fox" and "dog" patterns in the text and prints when they are found.


03 Matching Any Digit

 

import re


text = "The price of the product is $99.99."

pattern = r"\d+"

result = re.search(pattern, text)

if result:

    print("Price:", result.group())

Output:


Price: 99

This example extracts digits (numbers) from the text.


04 Matching Email Addresses


import re


text = "Contact us at support@example.com or info@example.org."

pattern = r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,7}\b"

emails = re.findall(pattern, text)

for email in emails:

    print("Email:", email)

Output:

Email: support@example.com

Email: info@example.org

It extracts email addresses from the text using a common email pattern.


05. Replacing Text

 

import re

text = "Please visit our website at http://www.example.com."

pattern = r"http://www\.[A-Za-z]+\.[A-Za-z]+"

replacement = "https://www.example.com"

updated_text = re.sub(pattern, replacement, text)

print("Updated Text:", updated_text)

Output:

Updated Text: Please visit our website at https://www.example.com.

This example replaces a URL with a different URL in the text.


These are just a few examples of what you can do with regular expressions in Python. Regex is a versatile tool for text processing, and you can create complex patterns to match specific text structures or extract information from text data.

Comments

Popular posts from this blog

SQL Query: 3 Methods for Calculating Cumulative SUM

5 SQL Queries That Popularly Used in Data Analysis

A Beginner's Guide to Pandas Project for Immediate Practice