Alexander Hultnér
Founder, Hultnér Technologies (https://hultner.se). Want me to speak at your company? Corporate training? Or just a fresh pair of eyes on your project? Contact me for contracts.
Live stream: https://youtu.be/kO5Es7KKUIY This talk provides a hands-on deep-dive into the wheel file format and python packaging. First, we will slash the tire, see what's inside, and then build new wheels from scratch. You will learn about the inner workings of a crucial part of the Python packaging ecosystem and understand what your tools do behind the covers.
Live stream: https://youtu.be/kO5Es7KKUIY This talk provides a hands-on deep-dive into the wheel file format and python packaging. First, we will slash the tire, see what's inside, and then build new wheels from scratch. You will learn about the inner workings of a crucial part of the Python packaging ecosystem and understand what your tools do behind the covers.
A talk about the fantastic pydantic library and how it makes your dataclasses better!
A talk about the fantastic pydantic library and how it makes your dataclasses better!
A talk about the fantastic pydantic library and how it makes your dataclasses better!
Keynote presentation for BelPy 2021
Keynote presentation for PyCon Sweden 2020
Automatically generate test-cases based on your API-schemas with Schemathesis.
Automatically generate test-cases based on your API-schemas with Schemathesis.
Automatically generate test-cases based on your API-schemas with Schemathesis.
A quick talk about the fantastic pydantic library and how it makes your dataclasses better!
Did you ever miss that corner case bug? Maybe it was a negative integer, strange timezone conversion behaviour, off by one error or something entirely else. These subtle bugs are often hard to catch and are easily missed in test cases. You like me have probably ran into plenty of code utilising only happy path testing, only to later discover subtle bugs which are easily fixed once pointed out. This is where property based testing comes into the picture. In this talk I will focus on a wonderful Python library called Hypothesis but the concepts apply to other languages as well. Hypethesis is based on the same concept as the famous QuickCheck library for Haskell, which in turn have been ported a large number of languages. Hypothesis uses a wide range of input to find edge cases that you could otherwise easily miss, once it finds these cases it narrows down the input to the minimal breaking example to provide failures which are easier to understand.
Did you ever miss that corner case bug? Maybe it was a negative integer, strange timezone conversion behaviour, off by one error or something entirely else. These subtle bugs are often hard to catch and are easily missed in test cases. You like me have probably ran into plenty of code utilising only happy path testing, only to later discover subtle bugs which are easily fixed once pointed out. This is where property based testing comes into the picture. In this talk I will focus on a wonderful Python library called Hypothesis but the concepts apply to other languages as well. Hypethesis is based on the same concept as the famous QuickCheck library for Haskell, which in turn have been ported a large number of languages. Hypothesis uses a wide range of input to find edge cases that you could otherwise easily miss, once it finds these cases it narrows down the input to the minimal breaking example to provide failures which are easier to understand.
Did you ever miss that corner case bug? Maybe it was a negative integer, strange timezone conversion behaviour, off by one error or something entirely else. These subtle bugs are often hard to catch and are easily missed in test cases. You like me have probably ran into plenty of code utilising only happy path testing, only to later discover subtle bugs which are easily fixed once pointed out. This is where property based testing comes into the picture. In this talk I will focus on a wonderful Python library called Hypothesis but the concepts apply to other languages as well. Hypethesis is based on the same concept as the famous QuickCheck library for Haskell, which in turn have been ported a large number of languages. Hypothesis uses a wide range of input to find edge cases that you could otherwise easily miss, once it finds these cases it narrows down the input to the minimal breaking example to provide failures which are easier to understand.
Python 3.7 is here and the @dataclass-decorator is a major new feature simplifying class-creation. In this talk, we will learn to use the power of data classes to make our codebases cleaner and leaner in a pythonic way. We will also learn how to use the back-port in Python 3.6 codebases before upgrading.
GothPy Meetup, 2018-05-17 Talk about Dataclasses from Python 3.7 showcase of using the backport in Python 3.6
A part of Hack The Castle 2018
A part of Hack The Castle 2018
A part of Hack The Castle 2018