standlib 0.0.7

A custom standard library module for Python

Standlib 🐍

This project is a set of pre-built modules, functions and classes that were made to make your life easier when programming in Python, it works like a Swiss army knife.

Version:

• This library is still only in beta version, it is worth mentioning that this means that it is in constant development.

Instalation:

• To install it is very simple, just install it via pip `pip install pystdlib` and then you can use it without any problems.

Purpose: The Double class provides a custom implementation for double-precision floating-point numbers in Python, offering greater control over precision and formatting than the built-in float type.

Attributes:

precision (int): Specifies the number of decimal places to maintain. Methods:

• init(value, precision=None): Creates a new Double object.

• value (int, float, or str): The initial value.

• precision (int, optional): The desired precision. Defaults to the class-wide default precision.

• repr(): Returns a string representation suitable for debugging.

• str(): Returns a formatted string representation.

• add, sub, mul, truediv, mod, pow, floordiv: Overloaded arithmetic operators for performing various mathematical operations on Double objects.

• set_default_precision(precision): Sets the default precision for all Double objects.

• get_default_precision(): Gets the current default precision.

Features:

Customizable precision: Control the number of decimal places to retain. Arithmetic operations: Supports addition, subtraction, multiplication, division, modulo, exponentiation, and floor division. String formatting: Provides flexible formatting options. Default precision: Sets a global default precision for all Double objects. Internal Implementation:

Stores values as strings to maintain exact precision. Implements custom arithmetic operations for accurate calculations. Handles edge cases like division by zero and large numbers. Benefits:

Improved accuracy: Avoids floating-point errors common in standard float types. Flexibility: Customize precision to suit specific use cases. Control: Offers fine-grained control over number representation. Use Cases:

Financial calculations: Where precise calculations are essential. Scientific computing: For applications requiring high-precision numbers. Custom data types: As a building block for more complex data structures.