Parallel computing with task scheduling

Koalas: pandas API on Apache Spark

Extract data from a wide range of Internet sources into a pandas DataFrame.

STUMPY is a powerful and scalable Python library for computing a Matrix Profile, which can be used for a variety of time series data mining tasks

A distributed task scheduler for Dask

Clean APIs for data cleaning. Python implementation of R package Janitor

PyData, The Complete Works of

Machine learning with scikit-learn tutorial at PyData Chicago 2016

Notebooks for the Seattle PyData 2017 talk on Scattertext

A consistent table management library in python

Python client for OmniSci GPU-accelerated SQL engine and analytics platform

vtreat is a data frame processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. Distributed under a BSD-3-Clause license.

Repo for my talk at the PyData Berlin 2017 conference

Companion repo to the GitBook, notes on Data Science topics

Slides and notebooks for my tutorial at PyData London 2018

Speaker slides from monthly meetups and conference

This is the code and presentation for my PyData2017 talk "Reverse Image Search Using Out-of-the-box Machine Learning Libraries

Social network analyses code examples for PyCon 2019 talk

@matthewbrems and I presented "Recreating, Understanding, and Visualizing FiveThirtyEight's Elections Forecast" at PyData DC 2018

PyData Korea 공식 홈페이지입니다. (준비중)

Pydata 2017 workshop: build a clickbait detector with python

Scrapy Project Sample for Baseball(NPB)

Material for working alongside my workshop session at PyData Berlin 2018

Introduction to Machine Learning with Time Series at PyData Festival Amsterdam 2020

Implementation of Neural Image Captioning model using Keras with Theano backend

An example of how the LIME algorithm can be used to provide real-world insight into the decision processes of a 'black-box' machine learning algorithm - in this case a Radom Forest regressor.

A friendly pandas wrapper with a more composable grammar support.

Deployment of PyData Parallel Tutorial on Kubernetes

Battle-hardened advice on efficient data loading for deep learning on videos.

All the documents for PyDataBratislava