📘 Day 17: Regular Expressions for Text Pattern Matching

"""
Day 17: Using Regular Expressions for Text Pattern Matching (Refactored)

This script demonstrates how to use the 're' module for common
business text processing tasks like finding patterns, validating text,
and cleaning data. This version is refactored into testable functions.
"""

import re
from collections import Counter
from typing import List, Tuple


def find_most_common_words(text: str, top_n: int) -> List[Tuple[str, int]]:
    """
    Finds the most common words in a given text string.
    The text is converted to lowercase and punctuation is removed before counting.
    """
    # Use regex to find all words (sequences of alphabetic characters)
    words = re.findall(r"\b[a-z]+\b", text.lower())
    return Counter(words).most_common(top_n)


def extract_and_analyze_numbers(text: str) -> dict:
    """
    Extracts all integer numbers from a text and returns the sorted list
    and the distance between the maximum and minimum numbers.
    """
    # Regex to find all integers, including negative ones
    numbers = [int(n) for n in re.findall(r"-?\d+", text)]
    if not numbers:
        return {"sorted_numbers": [], "distance": 0}

    numbers.sort()
    distance = numbers[-1] - numbers[0]
    return {"sorted_numbers": numbers, "distance": distance}


def is_valid_python_variable(name: str) -> bool:
    """
    Checks if a string is a valid Python variable name using regex.
    Valid: starts with a letter or underscore, followed by letters, numbers, or underscores.
    """
    # ^[a-zA-Z_] matches the start of the string with a letter or underscore.
    # \w* matches zero or more "word" characters (letters, numbers, underscore).
    # $ matches the end of the string.
    return bool(re.fullmatch(r"[a-zA-Z_]\w*", name))


def clean_text_advanced(text: str) -> str:
    """
    Cleans a text string by removing all non-alphanumeric characters
    (except spaces) and converting to lowercase.
    """
    # [^a-z0-9\s] is a character set that matches anything that is NOT
    # a lowercase letter, a digit, or a whitespace character.
    return re.sub(r"[^a-z0-9\s]", "", text.lower())


def main():
    """Main function to demonstrate regex capabilities."""
    print("--- Finding Most Common Words ---")
    paragraph = (
        "I love teaching. If you do not love teaching what else can you love. I love Python if you do not love "
        "something which can give you all the capabilities to develop an application what else can you love."
    )
    top_words = find_most_common_words(paragraph, 5)
    print(f"Original Text: '{paragraph[:50]}...'")
    print(f"Top 5 most common words: {top_words}")
    print("-" * 20)

    print("--- Extracting and Analyzing Numbers ---")
    para_with_nums = (
        "The position of some particles on the x-axis are -12, -4, -3, -1, 0, 4, and 8."
    )
    analysis = extract_and_analyze_numbers(para_with_nums)
    print(f"Original Text: '{para_with_nums}'")
    print(f"Extracted and sorted numbers: {analysis['sorted_numbers']}")
    print(f"Distance between max and min: {analysis['distance']}")
    print("-" * 20)

    print("--- Validating Python Variable Names ---")
    print(
        f"'first_name' is a valid variable name: {is_valid_python_variable('first_name')}"
    )
    print(
        f"'first-name' is a valid variable name: {is_valid_python_variable('first-name')}"
    )
    print(
        f"'2nd_place' is a valid variable name: {is_valid_python_variable('2nd_place')}"
    )
    print("-" * 20)

    print("--- Advanced Text Cleaning ---")
    messy_sentence = """%I $am@% a %tea@cher%, &and& I lo%#ve %tea@ching%;."""
    cleaned = clean_text_advanced(messy_sentence)
    print(f"Original: '{messy_sentence}'")
    print(f"Cleaned: '{cleaned}'")
    print("-" * 20)


if __name__ == "__main__":
    main()

# Day 17: Regular Expressions - Solutions

import re
import string
from collections import Counter

## Exercise 1: Find all numbers in a string

para = """The position of some particles on the horizontal x-axis are -12, -4, -3 and -1 in the negative direction,
0 at origin, 4 and 8 in the positive direction. """

num_list = list(map(int, re.findall(r"[-+]?[.]?[\d]+", para)))
print("Numbers found in the string:", num_list)


## Exercise 2: Validate a variable name


def is_valid_variable(potential_variable):
    if re.search(r"^[a-zA-Z_]\w*$", potential_variable):
        return True
    else:
        return False


print("Is '_name' a valid variable?", is_valid_variable("_name"))  # Expected: True
print(
    "Is 'first_name' a valid variable?", is_valid_variable("first_name")
)  # Expected: True
print("Is '1name' a valid variable?", is_valid_variable("1name"))  # Expected: False
print("Is 'name-1' a valid variable?", is_valid_variable("name-1"))  # Expected: False


## Exercise 3: Clean up a messy sentence

sentence = """%I $am@% a %tea@cher%, &and& I lo%#ve %tea@ching%;. There $is nothing; &as& mo@re rewarding as
educa@ting &and& @emp%o@wering peo@ple. ;I found tea@ching m%o@re interesting tha@n any other %jo@bs. %Do@es thi%s
mo@tivate yo@u to be a tea@cher!? """


def clean_text(text):
    text = text.lower()
    text = re.sub(r"\[.*?]", "", text)
    text = re.sub(r"https?://\S+|www\.\S+", "", text)
    text = re.sub(r"<.*?>+", "", text)
    text = re.sub(r"[%s]" % re.escape(string.punctuation), "", text)
    text = re.sub(r"\n", "", text)
    text = re.sub(r"\w*\d\w*", "", text)
    return text


def most_common_words(text, n):
    split_it = text.split()
    return Counter(split_it).most_common(n)


cleaned_sentence = clean_text(sentence)
print("Cleaned sentence:", cleaned_sentence)
print("Top 3 most common words:", most_common_words(cleaned_sentence, 3))