Improving your data science workflows with

Overview

Make Better Defaults

Author: Kjell Wooding [email protected]

This is the git repo for Makefiles: One great trick for making your conda environments more managable. A Pydata Global 2021 talk given on October 28, 2021 by Kjell Wooding.

Getting Started

To get started, type "make".

To follow along, watch the video once it's posted.

To learn more about Easydata, the framework that generated this repo, see the Getting Started Guide.

The Tips

  1. Use git and virtual environments. Always.
  2. Good workflow trumps good tooling
  3. Good workflow means not having to remember things
  4. Use one virtual environment per git repo. Give them both the same name.
  5. Maintain virtual environments as code.
  6. Use Lockfiles: Separate "what you want" from "what you need".
  7. Auto-document your workflow
  8. Don't be afraid to "Nuke it from orbit"

The Implementation

See https://github.com/hackalog/make_better_defaults

Directory Structure

See Project Organization for details on how this project is organized on disk.


This project was built using Easydata, a python framework aimed at making your data science workflow reproducible.

Owner
Kjell Wooding
Computer Engineer. Mathematician. Current Obsession: Reproducible Data Science
Kjell Wooding
Data and code accompanying the paper Politics and Virality in the Time of Twitter

Politics and Virality in the Time of Twitter Data and code accompanying the paper Politics and Virality in the Time of Twitter. In specific: the code

Cardiff NLP 3 Jul 02, 2022
pyhsmm MITpyhsmm - Bayesian inference in HSMMs and HMMs. MIT

Bayesian inference in HSMMs and HMMs This is a Python library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and expli

Matthew Johnson 527 Dec 04, 2022
Tools for working with MARC data in Catalogue Bridge.

catbridge_tools Tools for working with MARC data in Catalogue Bridge. Borrows heavily from PyMarc

1 Nov 11, 2021
A collection of robust and fast processing tools for parsing and analyzing web archive data.

ChatNoir Resiliparse A collection of robust and fast processing tools for parsing and analyzing web archive data. Resiliparse is part of the ChatNoir

ChatNoir 24 Nov 29, 2022
PandaPy has the speed of NumPy and the usability of Pandas 10x to 50x faster (by @firmai)

PandaPy "I came across PandaPy last week and have already used it in my current project. It is a fascinating Python library with a lot of potential to

Derek Snow 527 Jan 02, 2023
A lightweight interface for reading in output from the Weather Research and Forecasting (WRF) model into xarray Dataset

xwrf A lightweight interface for reading in output from the Weather Research and Forecasting (WRF) model into xarray Dataset. The primary objective of

National Center for Atmospheric Research 43 Nov 29, 2022
Churn prediction with PySpark

It is expected to develop a machine learning model that can predict customers who will leave the company.

3 Aug 13, 2021
Recommendations from Cramer: On the show Mad-Money (CNBC) Jim Cramer picks stocks which he recommends to buy. We will use this data to build a portfolio

Backtesting the "Cramer Effect" & Recommendations from Cramer Recommendations from Cramer: On the show Mad-Money (CNBC) Jim Cramer picks stocks which

Gábor Vecsei 12 Aug 30, 2022
A distributed block-based data storage and compute engine

Nebula is an extremely-fast end-to-end interactive big data analytics solution. Nebula is designed as a high-performance columnar data storage and tabular OLAP engine.

Columns AI 131 Dec 26, 2022
Random dataframe and database table generator

Random database/dataframe generator Authored and maintained by Dr. Tirthajyoti Sarkar, Fremont, USA Introduction Often, beginners in SQL or data scien

Tirthajyoti Sarkar 249 Jan 08, 2023
An extension to pandas dataframes describe function.

pandas_summary An extension to pandas dataframes describe function. The module contains DataFrameSummary object that extend describe() with: propertie

Mourad 450 Dec 30, 2022
MEAD: A Large-scale Audio-visual Dataset for Emotional Talking-face Generation [ECCV2020]

MEAD: A Large-scale Audio-visual Dataset for Emotional Talking-face Generation [ECCV2020] by Kaisiyuan Wang, Qianyi Wu, Linsen Song, Zhuoqian Yang, Wa

112 Dec 28, 2022
Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks.

pgmpy pgmpy is a python library for working with Probabilistic Graphical Models. Documentation and list of algorithms supported is at our official sit

pgmpy 2.2k Dec 25, 2022
PLStream: A Framework for Fast Polarity Labelling of Massive Data Streams

PLStream: A Framework for Fast Polarity Labelling of Massive Data Streams Motivation When dataset freshness is critical, the annotating of high speed

4 Aug 02, 2022
An Aspiring Drop-In Replacement for NumPy at Scale

Legate NumPy is a Legate library that aims to provide a distributed and accelerated drop-in replacement for the NumPy API on top of the Legion runtime. Using Legate NumPy you do things like run the f

Legate 502 Jan 03, 2023
Intake is a lightweight package for finding, investigating, loading and disseminating data.

Intake: A general interface for loading data Intake is a lightweight set of tools for loading and sharing data in data science projects. Intake helps

Intake 851 Jan 01, 2023
Powerful, efficient particle trajectory analysis in scientific Python.

freud Overview The freud Python library provides a simple, flexible, powerful set of tools for analyzing trajectories obtained from molecular dynamics

Glotzer Group 195 Dec 20, 2022
Fast, flexible and easy to use probabilistic modelling in Python.

Please consider citing the JMLR-MLOSS Manuscript if you've used pomegranate in your academic work! pomegranate is a package for building probabilistic

Jacob Schreiber 3k Jan 02, 2023
VevestaX is an open source Python package for ML Engineers and Data Scientists.

VevestaX Track failed and successful experiments as well as features. VevestaX is an open source Python package for ML Engineers and Data Scientists.

Vevesta 24 Dec 14, 2022
pipeline for migrating lichess data into postgresql

How Long Does It Take Ordinary People To "Get Good" At Chess? TL;DR: According to 5.5 years of data from 2.3 million players and 450 million games, mo

Joseph Wong 182 Nov 11, 2022