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UID:pretalx-pyconde-pydata-2026-GPJGH3@pretalx.com
DTSTART;TZID=CET:20260416T101500
DTEND;TZID=CET:20260416T114500
DESCRIPTION:If you have worked with real-world data before\, you know that 
 processing it can be challenging. Data often comes scattered across tables
 \, in inconsistent encodings\, with duplicated rows and is generally dirty
 . In this tutorial\, you will learn how to process large amounts of data r
 eliably and quickly using `polars` and `dataframely`.\n\nWhat we love abou
 t `polars` is that it's easy to use\, fast and elegant — it allows us to
  build and compose complex transformations with ease. On this basis\, we b
 uilt `dataframely`: a library for defining and validating contents of pola
 rs data frames. With `dataframely`\, we can build pipelines without ever g
 etting confused about what's in our data frames. We document and validate 
 our expectations and assumptions clearly\, which makes our pipeline code s
 impler and easier to understand. "Is this join correct?"\, and "where did 
 this column come from?" are questions you will not have to worry about any
 more.\n\nIn this tutorial\, you will become familiar with `polars` basics 
 by writing a simple pipeline: you will read data\, transform it to make it
  ready for use\, and you will learn how to do that fast. With `dataframely
 ` schemas\, you will upgrade your code from "it works" to "it's beautiful!
 "\, and along the way\, `dataframely` will help you eliminate entire class
 es of bugs you will never have to think about again. After the tutorial\, 
 you will be all set to use these tools in your own work.
DTSTAMP:20260412T141905Z
LOCATION:Ferrum [2nd Floor]
SUMMARY:Building reliable data pipelines with polars and dataframely - Oliv
 er Borchert\, Andreas Albert
URL:https://pretalx.com/pyconde-pydata-2026/talk/GPJGH3/
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