Skip to main content

What are Python PEP Documents (example: PEP 3147)

 

What Are Python PEP Documents?

PEP stands for Python Enhancement Proposal.

A PEP is a design document that describes:

  • New features or changes in Python
  • Design decisions
  • Best practices
  • Information about the Python community or processes

PEPs serve as the primary channel for proposing and discussing new features or improvements to Python.


PEP Structure

A PEP typically includes:

  • PEP Number (e.g., PEP 3147)
  • Title
  • Author(s)
  • Status (Accepted, Rejected, Final, etc.)
  • Abstract (Summary of the proposal)
  • Motivation
  • Specification
  • Rationale
  • Reference Implementation

Example PEP: PEP 3147

PEP 3147: PYC Repository Directories

 What does it do? It introduced a new system for storing .pyc files (compiled bytecode files) in a dedicated folder called __pycache__.

Before PEP 3147 (Python 3.1 and earlier):

  • Bytecode files were stored directly in the same directory as the source files like:
module.py
module.pyc

After PEP 3147 (Python 3.2+):

  • Bytecode files are stored inside a __pycache__ directory:
module.py
__pycache__/
    module.cpython-310.pyc

This makes directories cleaner and easier to manage across multiple Python versions.


Why Are PEPs Important?

  • They provide transparency into the decision-making process.
  • Anyone can propose new features.
  • They ensure that the community agrees on changes before they are implemented.

Popular PEPs You Should Know:

PEP Number Description Status
PEP 8 Python Style Guide Final
PEP 20 Zen of Python Informational
PEP 257 Docstring Conventions Final
PEP 484 Type Hints Final
PEP 572 Assignment Expressions (:=) Final

How to Find PEPs?

All PEPs are publicly available on the official Python website:  https://peps.python.org/



Comments

Popular posts from this blog

Simple Linear Regression - and Related Regression Loss Functions

Today's Topics: a. Regression Algorithms  b. Outliers - Explained in Simple Terms c. Common Regression Metrics Explained d. Overfitting and Underfitting e. How are Linear and Non Linear Regression Algorithms used in Neural Networks [Future study topics] Regression Algorithms Regression algorithms are a category of machine learning methods used to predict a continuous numerical value. Linear regression is a simple, powerful, and interpretable algorithm for this type of problem. Quick Example: These are the scores of students vs. the hours they spent studying. Looking at this dataset of student scores and their corresponding study hours, can we determine what score someone might achieve after studying for a random number of hours? Example: From the graph, we can estimate that 4 hours of daily study would result in a score near 80. It is a simple example, but for more complex tasks the underlying concept will be similar. If you understand this graph, you will understand this blog. Sim...

What problems can AI Neural Networks solve

How does AI Neural Networks solve Problems? What problems can AI Neural Networks solve? Based on effectiveness and common usage, here's the ranking from best to least suitable for neural networks (Classification Problems, Regression Problems and Optimization Problems.) But first some Math, background and related topics as how the Neural Network Learn by training (Supervised Learning and Unsupervised Learning.)  Background Note - Mathematical Precision vs. Practical AI Solutions. Math can solve all these problems with very accurate results. While Math can theoretically solve classification, regression, and optimization problems with perfect accuracy, such calculations often require impractical amounts of time—hours, days, or even years for complex real-world scenarios. In practice, we rarely need absolute precision; instead, we need actionable results quickly enough to make timely decisions. Neural networks excel at this trade-off, providing "good enough" solutions in seco...

Activation Functions in Neural Networks

  A Guide to Activation Functions in Neural Networks 🧠 Question: Without activation function can a neural network with many layers be non-linear? Answer: Provided at the end of this document. Activation functions are a crucial component of neural networks. Their primary purpose is to introduce non-linearity , which allows the network to learn the complex, winding patterns found in real-world data. Without them, a neural network, no matter how deep, would just be a simple linear model. In the diagram below the f is the activation function that receives input and send output to next layers. Commonly used activation functions. 1. Sigmoid Function 2. Tanh (Hyperbolic Tangent) 3. ReLU (Rectified Linear Unit - Like an Electronic Diode) 4. Leaky ReLU & PReLU 5. ELU (Exponential Linear Unit) 6. Softmax 7. GELU, Swish, and SiLU 1. Sigmoid Function                       The classic "S-curve," Sigmoid squashes any input value t...