PySwissLib 1.0.1

A comprehensive utility library for Python

PySwissLib - A Comprehensive Python Utility Library

PySwissLib is a versatile Python library designed to streamline common programming and data-related tasks. It offers a structured collection of modules for system operations, string manipulation, mathematics, data science, visualization, file handling, and more. Authored by Gautham Nair, this library aims to be a single, convenient toolkit for developers and data scientists.

---

Installation

You can easily install pyswisslib using pip.

pip install pyswisslib

This command will automatically install all required dependencies, including numpy, pandas, matplotlib, seaborn, torch, scikit-learn, and requests.

---

Core Features

1. General Utilities (pyswisslib.basic)

• System and Platform Info: Get details about the operating system and Python version.
• Screen Clearing: A cross-platform function to clear the console.
• Package Installation: Install new Python packages directly from your script.

2. String & Math (pyswisslib.string, pyswisslib.math)

• String Manipulation: Perform common operations like string comparison, word counting, and palindrome checks.
• Mathematical Functions: Calculate factorials, check for prime numbers, and generate Fibonacci sequences.

3. Data Science (pyswisslib.datascience)

• DataFrames: Create and manipulate Pandas DataFrames from various sources.
• Statistical Analysis: Get descriptive statistics and calculate correlation matrices.
• Array Operations: Utilize NumPy for efficient array creation and statistical summaries.

4. Visualization (pyswisslib.visualization)

• Plotting: Generate a variety of plots, including line, bar, scatter, and histograms, using Matplotlib and Seaborn.
• Heatmaps: Visualize data correlations with heatmaps.

5. File & Network (pyswisslib.filehandler, pyswisslib.network)

• File Handling: Read, write, and manage files and directories easily.
• Network Utilities: Ping hosts and fetch JSON data from URLs with built-in caching.

6. AI & Machine Learning (pyswisslib.aiml)

• PyTorch Integration: Access PyTorch utilities for model training and evaluation.
• GPU Detection: Automatically detect and utilize available GPUs for accelerated computing.

---

Usage Example

The library is designed for simple imports and static method calls.

import pandas as pd
from pyswisslib import string, math, datascience, visualization, aiml

# String and Math
my_string = "Hello, PySwissLib!"
print(f"Word count: {string.word_count(my_string)}")
print(f"Is 13 prime? {math.is_prime(13)}")

# Data Science
try:
    data = {'numbers': [1, 2, 3, 4, 5], 'squares': [1, 4, 9, 16, 25]}
    df = datascience.create_dataframe(data)
    print("\nDataFrame statistics:\n", datascience.basic_stats(df))

    # Visualization
    visualization.line_plot(df['numbers'], df['squares'], title="Numbers vs. Squares")

except Exception as e:
    print(f"An error occurred: {e}")

# AI/ML - Get the best available device
try:
    device = aiml.get_device()
    print(f"\nUsing device: {device}")
except Exception as e:
    print(f"An error occurred: {e}")

---

Author

• Gautham Nair