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SUMMARY:Annotating the dynamic: Type Annotation for DataFrames - Frank Sau
 erburger
DTSTART;TZID=Europe/Warsaw:20250819T083000
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DTSTAMP:20260915T035410Z
UID:pretalx-euroscipy-2025-DASHT7@pretalx.com
DESCRIPTION:While type annotation has significantly improved the readabili
 ty and structure of general application code\, its applicability to DataFr
 ames—a fundamental component in data science—has yet to be fully reali
 zed. The dynamic and runtime-defined nature of DataFrames contrasts the de
 velopment-time nature of type annotation. As a DataFrame schema is often o
 nly known at runtime\, e.g.\, after reading an input file\, utilizing type
  annotations to enhance schema validation and code readability presents a 
 challenge.\n\nThe tutorial is intended for hands-on Python enthusiasts who
  work with DataFrames. The tutorial introduces type annotation and present
 s its advantages regarding readability\, maintainability\, tooling\, and s
 tatic code analysis. The tutorial explores libraries and tools to leverage
  the advantages of type annotation at development-time and libraries to en
 force runtime validation. The tutorial dives into the benefits of type ann
 otations of DataFrames and highlights the limitations of type annotations 
 specific to the dynamic nature of DataFrames. Therefore\, the tutorial wil
 l present best practices for leveraging type annotations with DataFrames.
LOCATION:Room 1.20 (Ground Floor\, Shannon)
URL:https://pretalx.com/euroscipy-2025/talk/DASHT7/
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