sloria

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

itsmattsoria

SHORTCUTS

Key/Command	Description
Tab	Auto-complete files and folder names
Ctrl + A	Go to the beginning of the line you are currently typing on
Ctrl + E	Go to the end of the line you are currently typing on
Ctrl + U	Clear the line before the cursor
Ctrl + K	Clear the line after the cursor
Ctrl + W	Delete the word before the cursor
Ctrl + T	Swap the last two characters before the cursor

kevin-smets

Default

Powerlevel10k

bsweger

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)

azadkuh

Vim cheat sheet

Starting Vim

vim [file1] [file2] ...

---

balzer82

yoavram

import matplotlib.pyplot as plt
import numpy as np

import sklearn.datasets
import sklearn.cross_validation as cv
from sklearn import linear_model

dataset = sklearn.datasets.fetch_california_housing()
X = dataset['data']
y = dataset['target']

HarshadRanganathan

[alias]

  ##
  # One letter alias for our most frequent commands.
  #
  # Guidelines: these aliases do not use options, because we want
  # these aliases to be easy to compose and use in many ways.
  ##

  a = add

karpathy

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.