BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//cfp.scipy.org//2023//speaker//FVXBU9
BEGIN:VTIMEZONE
TZID:America/Chicago
BEGIN:DAYLIGHT
DTSTART:20220711T000000
TZNAME:CDT
TZOFFSETFROM:-0500
TZOFFSETTO:-0500
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20221106T020000
RDATE:20231105T020000
TZNAME:CST
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20230312T030000
RDATE:20240310T030000
TZNAME:CDT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
SUMMARY:Idiomatic Pandas - Matt Harrison
DTSTART;TZID=America/Chicago:20230711T080000
DTEND;TZID=America/Chicago:20230711T120000
DTSTAMP:20261017T033049Z
UID:pretalx-2023-8TAA7K@cfp.scipy.org
DESCRIPTION:Pandas can be tricky\, and there is a lot of bad advice floati
 ng around. This tutorial will cut through some of the biggest issues I've 
 seen with Pandas code after working with the library for a while and writi
 ng three books on it.\n\nWe will discuss:\n\n* Proper types\n* Chaining\n*
  Aggregation\n* Debugging
LOCATION:Classroom 106
URL:https://cfp.scipy.org/2023/talk/8TAA7K/
END:VEVENT
BEGIN:VEVENT
SUMMARY:[BoF Room 103] PyArrow in pandas and Dask - Matt Harrison\, James 
 Bourbeau\, Patrick Hoefler
DTSTART;TZID=America/Chicago:20230713T131500
DTEND;TZID=America/Chicago:20230713T141000
DTSTAMP:20261017T033049Z
UID:pretalx-2023-SZP3LA@cfp.scipy.org
DESCRIPTION:DataFrame libraries in general\, pandas and Dask specifically
 \, are moving towards a better integration with PyArrow. This has many ben
 efits\, like improved performance and a reduced memory footprint. We want 
 to connect with users to discuss how PyArrow can improve DataFrame librari
 es and what they expect out of PyArrow support. This can include things li
 ke improved performance\, more consistent behavior or better interoperabil
 ity with other libraries.
LOCATION:Classroom 103
URL:https://cfp.scipy.org/2023/talk/SZP3LA/
END:VEVENT
END:VCALENDAR