BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//programme.europython.eu//europython-2023//talk//AEAPDB
BEGIN:VTIMEZONE
TZID:Europe/Prague
BEGIN:DAYLIGHT
DTSTART:20220718T000000
TZNAME:CEST
TZOFFSETFROM:+0200
TZOFFSETTO:+0200
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20221030T030000
RDATE:20231029T030000
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20230326T030000
RDATE:20240331T030000
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
SUMMARY:Robust Data Transformation with Pandas: Typing\, Validation\, Test
 ing - Jakub Urban\, Jan Pipek
DTSTART;TZID=Europe/Prague:20230718T134500
DTEND;TZID=Europe/Prague:20230718T151500
DTSTAMP:20260904T205156Z
UID:pretalx-europython-2023-AEAPDB-0@programme.europython.eu
DESCRIPTION:We will explore possibilities for making our data analyses and
  transformations in Pandas robust and production ready. We will see how ad
 vanced group-by\, resample or rolling aggregations work on large time seri
 es weather data. (As a bonus\, you will learn about Prague climate.) We wi
 ll use type annotations and schema validations with the Pandera library to
  make our code more readable and robust. We will also show the potential o
 f property-based testing using the Hypothesis package\, with strategies ge
 nerated from Pandera schemas. We will show how to avoid issues with time z
 ones when working with time series data. By the end of the tutorial\, you 
 will have a deeper understanding of advanced Pandas aggregations and be ab
 le to write robust\, production ready Pandas code.
LOCATION:Club H
URL:https://programme.europython.eu/europython-2023/talk/AEAPDB/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Robust Data Transformation with Pandas: Typing\, Validation\, Test
 ing - Jakub Urban\, Jan Pipek
DTSTART;TZID=Europe/Prague:20230718T153000
DTEND;TZID=Europe/Prague:20230718T170000
DTSTAMP:20260904T205156Z
UID:pretalx-europython-2023-AEAPDB-1@programme.europython.eu
DESCRIPTION:We will explore possibilities for making our data analyses and
  transformations in Pandas robust and production ready. We will see how ad
 vanced group-by\, resample or rolling aggregations work on large time seri
 es weather data. (As a bonus\, you will learn about Prague climate.) We wi
 ll use type annotations and schema validations with the Pandera library to
  make our code more readable and robust. We will also show the potential o
 f property-based testing using the Hypothesis package\, with strategies ge
 nerated from Pandera schemas. We will show how to avoid issues with time z
 ones when working with time series data. By the end of the tutorial\, you 
 will have a deeper understanding of advanced Pandas aggregations and be ab
 le to write robust\, production ready Pandas code.
LOCATION:Club H
URL:https://programme.europython.eu/europython-2023/talk/AEAPDB/
END:VEVENT
END:VCALENDAR