Jeff Reback: Performance Pandas (PyData NYC 2015)

preview_player
Показать описание
PyData NYC 2015

Discuss and illustrate a few tips and tricks to get the most out of pandas. Will focus on idioms, computational efficiency, and memory optimization.

Discuss and illustrate some useful tips and techniques in pandas. We will be addressing questions such as these:

Do's and Dont's in Pandas. How to optimize performance. How to reduce memory usage in memory and permanent storage. Groupby Idioms MultiIndex Slicing

00:10 Help us add time stamps or captions to this video! See the description for details.

  1. PyData
  2. python
  3. pandas
Рекомендации по теме
Jeff Reback: Performance 0:43:11

Jeff Reback: Performance Pandas (PyData London 2015)

Jeff Reback: Performance 1:26:13

Jeff Reback: Performance Pandas (PyData NYC 2015)

Jeff Reback - 0:52:58

Jeff Reback - New Features in pandas

Jeff Reback - 0:31:13

Jeff Reback - What is the Future of Pandas

Jeff Reback: pandas 0:37:14

Jeff Reback: pandas at a Crossroads, the Past, Present, and Future | PyData NYC 2022

Marc Garcia, Jeff 1:20:44

Marc Garcia, Jeff Reback, Tom Augspurger: Introduction to pandas | PyData New York City 2019

pandas: Integer NA 0:31:17

pandas: Integer NA as a first class citizen - Jeff Reback

Two Sigma Presents 0:58:00

Two Sigma Presents Pandas at a Crossroads the Past Present and Future with Jeff Reback

Andreas Mueller, Brian 0:44:52

Andreas Mueller, Brian Granger, Jeff Reback, Michael Droettboom (Keynote): New Project Features

Li Jin - 0:24:16

Li Jin - Improving Pandas and PySpark performance and interoperability with Apache Arrow

Extending Pandas using 0:33:11

Extending Pandas using Apache Arrow and Numba - Uwe L Korn

Stephen Simmons - 1:01:51

Stephen Simmons - Pandas from the inside

PyData Tel Aviv 0:30:04

PyData Tel Aviv Meetup: Diving into Pandas is faster than reinventing it - Dean Langsam

Demystifying pandas internals 0:41:52

Demystifying pandas internals - Marc Garcia

Tom Augspurger | 1:26:09

Tom Augspurger | Pandas: .head() to .tail()

Jeffrey Tratner: Pandas 0:41:08

Jeffrey Tratner: Pandas Under The Hood: Peeking behind the scenes of a high performance data analys

Harizo Rajaona - 0:34:59

Harizo Rajaona - A Tour of the Many DataFrame Frameworks

Marc Garcia - 0:34:24

Marc Garcia - Towards Pandas 1.0

Li Jin, Hyonjee 0:36:49

Li Jin, Hyonjee Joo: Spark Backend for Ibis: Seamless Transition Between Pandas... | PyData NYC 2019

Joris Van den 0:30:18

Joris Van den Bossche: On Blocks, Copies and Views: updating pandas' internals

Ian Ozsvald- Skinny 0:32:34

Ian Ozsvald- Skinny Pandas Riding On A Rocket| PyData Global 2020

Tom Augspurger: Pandas: 1:58:01

Tom Augspurger: Pandas: .head() to .tail()

Diego Torres Quintanilla: 0:38:30

Diego Torres Quintanilla: Cleaning, optimizing and windowing pandas with numba | PyData NYC 2019

Cloud + Forsyth- 0:44:15

Cloud + Forsyth- Ibis- Expressive analytics in Python at any scale | PyData NYC 2022

Комментарии
Автор

I put this talk aside for so long thinking this was all about performance and optimization. Infact, this was a intermediate+ talk that I have been always looking for.

Jeff gets past the usual this is how you create DataFrame, and this is a Index stuff into more advanced stuff that should interest any journeyman. Thanks a lot, Jeff!

jeffjose