O957

Polars Cheat Sheet

Here's a cheat sheet for the Polars Python package, covering many of its key functions and features:

Installation

pip install polars 

# Install Polars with all optional dependencies:
pip install 'polars[all]'

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

Falsehoods programmers believe about time

This is a compiled list of falsehoods programmers tend to believe about working with time.

Don't re-invent a date time library yourself. If you think you understand everything about time, you're probably doing it wrong.

Falsehoods

There are always 24 hours in a day.
February is always 28 days long.
Any 24-hour period will always begin and end in the same day (or week, or month).

EDIT from 2019: Hi folks. I wrote this gist for myself and some friends, and it seems like it's gotten posted somewhere that's generated some (ahem, heated) discussion. ~~The whitespace was correct when it was posted, and since then GitHub changed how it formats <pre> tags. Look at the raw text if you care about this. I'm sure someone could tell me how to fix it, but~~ (thank you @anzdaddy for suggesting a formatting workaround) honestly this is a random throwaway gist from 2015, and someone more knowledgable about this comparison should just write a proper blog post about it. If you comment here I'll hopefully see it and stick a link to it up here. Cheers. @oconnor663

Here's the canonical TOML example from the TOML README, and a YAML version of the same.

title = "TOML Example"

	from jax import random, jit
	import jax.numpy as jnp
	from jax.scipy import stats

	from util import ravelize_function, make_log_density

	__all__ = ["log_density", "log_density_vec", "init_draw_zero"]


	def constrain_parameters(sigma_unc, alpha, beta):

	import polars as pl

	# Making data frames ------------------------------------------
	# with dictionary
	df = pl.DataFrame({
	'name': ['foo', 'bar', 'baz'],
	'bar': [0, 1, 2],
	'qux': [0.0, 1.0, 2.0]
	})

	#Author: Cameron Davidson-Pilon, replicated styles from FiveThirtyEight.com
	# See https://www.dataorigami.net/blogs/fivethirtyeight-mpl
	# Modified by Nathan McIntosh to my personal tastes
	# Text is all very large, ideal for use in presentations

	lines.linewidth: 4
	lines.solid_capstyle: butt

	legend.fancybox: true

	# set number of cores to use fitting the model
	# no benefit on runtime if cores > chains which is set to 4 by default
	options(mc.cores = 4)

	# Packages ----------------------------------------------------------------
	# install.packages(c("data.table", "remotes", "ggplot2"))
	# remotes::install_github("epiforecasts/EpiNow2")
	# remotes::install_github("epiforecasts/covidregionaldata")
	library(data.table)
	library(ggplot2)