BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//programme.europython.eu//europython-2023//talk//AEAPDB
BEGIN:VTIMEZONE
TZID:CET
BEGIN:STANDARD
DTSTART:20001029T040000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-europython-2023-AEAPDB-0@programme.europython.eu
DTSTART;TZID=CET:20230718T134500
DTEND;TZID=CET:20230718T151500
DESCRIPTION:We will explore possibilities for making our data analyses and 
 transformations in Pandas robust and production ready. We will see how adv
 anced group-by\, resample or rolling aggregations work on large time serie
 s weather data. (As a bonus\, you will learn about Prague climate.) We wil
 l use type annotations and schema validations with the Pandera library to 
 make our code more readable and robust. We will also show the potential of
  property-based testing using the Hypothesis package\, with strategies gen
 erated from Pandera schemas. We will show how to avoid issues with time zo
 nes when working with time series data. By the end of the tutorial\, you w
 ill have a deeper understanding of advanced Pandas aggregations and be abl
 e to write robust\, production ready Pandas code.
DTSTAMP:20260310T194054Z
LOCATION:Club H
SUMMARY:Robust Data Transformation with Pandas: Typing\, Validation\, Testi
 ng - Jakub Urban\, Jan Pipek
URL:https://programme.europython.eu/europython-2023/talk/AEAPDB/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-europython-2023-AEAPDB-1@programme.europython.eu
DTSTART;TZID=CET:20230718T153000
DTEND;TZID=CET:20230718T170000
DESCRIPTION:We will explore possibilities for making our data analyses and 
 transformations in Pandas robust and production ready. We will see how adv
 anced group-by\, resample or rolling aggregations work on large time serie
 s weather data. (As a bonus\, you will learn about Prague climate.) We wil
 l use type annotations and schema validations with the Pandera library to 
 make our code more readable and robust. We will also show the potential of
  property-based testing using the Hypothesis package\, with strategies gen
 erated from Pandera schemas. We will show how to avoid issues with time zo
 nes when working with time series data. By the end of the tutorial\, you w
 ill have a deeper understanding of advanced Pandas aggregations and be abl
 e to write robust\, production ready Pandas code.
DTSTAMP:20260310T194054Z
LOCATION:Club H
SUMMARY:Robust Data Transformation with Pandas: Typing\, Validation\, Testi
 ng - Jakub Urban\, Jan Pipek
URL:https://programme.europython.eu/europython-2023/talk/AEAPDB/
END:VEVENT
END:VCALENDAR