python-data-utils

Practical Python utilities for data processing, validation, and small automation tasks, with clear structure and documentation.

Python Data Utilities

A collection of practical Python utilities for common data processing, validation, and automation tasks.

This repository focuses on clean, readable code and simple examples that demonstrate real-world usage.

Project Structure

data_cleaner.py
Functions for cleaning and normalizing text-based data.

file_utils.py
Helpers for basic file handling and validation.

examples/
Small scripts showing how the utilities can be used.

Usage

Clone the repository:

git clone https://github.com/Andrareed/python-data-utils.git cd python-data-utils

Run an example script:

python examples/example_clean.py

Requirements

Python 3.x
No external dependencies.

License

MIT License """ Simple data cleaning utilities. """

import re

def normalize_text(text: str) -> str: """ Convert text to lowercase and normalize whitespace. """ return re.sub(r"\s+", " ", text.strip().lower()) """ Basic file utility functions. """

import os

def file_exists(path: str) -> bool: """ Check whether a file exists. """ return os.path.exists(path)

def read_lines(path: str) -> list[str]: """ Read all lines from a text file. """ with open(path, "r", encoding="utf-8") as file: return file.read().splitlines() from data_cleaner import normalize_text

text = " Sample TEXT for Cleaning " print("Original:", text) print("Cleaned:", normalize_text(text)) from file_utils import file_exists, read_lines

path = "README.md"

print("File exists:", file_exists(path)) if file_exists(path): print("Number of lines:", len(read_lines(path))) # No external dependencies