Docker Packaging Guide for Python https://t.co/rCvUFm0KJu
— PyCoder’s Weekly (@pycoders) August 4, 2019
Docker Packaging Guide for Python https://t.co/rCvUFm0KJu
— PyCoder’s Weekly (@pycoders) August 4, 2019
Xarray is an open source library providing high-level, easy-to-use data structures and analysis tools for working with multidimensional labeled datasets and arrays in #Python.https://t.co/VoJK9lgb5j#NumFOCUS Sponsored Project since 2018#xarray @xarray_dev pic.twitter.com/P3T9xXrWxt
— PyData (@PyData) August 3, 2019
PEP 479 (accepted within a week of proposal) breaks generator composition in Python if you want composed functions to dynamically determine when to stop generation. :(
— Jeremy Howard (@jeremyphoward) August 1, 2019
Here's a little function to help work around the problem.https://t.co/Y2w3So3od2 pic.twitter.com/O2uAsNvrIJ
🐍 TIL: Creating pretty visual diffs in Python is much easier than I thought!
— Ines Montani 〰️ (@_inesmontani) August 1, 2019
I needed a script to refine tokenization alignment between word pieces (transformer models) & linguistically-motivated tokens (e.g. spaCy) & wrote a simple diff printer.
Gist: https://t.co/ZpiLADpqsX pic.twitter.com/zwxkVIvdyE
Floating point can store integers perfectly between:
— Smerity (@Smerity) July 28, 2019
± 2048 (2^11) for halfs (fp16)
± 16,777,216 (2^24) for floats (fp32)
± 9,007,199,254,740,992 (2^53) for doubles (fp64)
Just in case you ever needed to know ^_^https://t.co/baaxJIKzNL
Did you know about `np.meshgrid` and `np.apply_along_axis`? If not, that's OK - not many people do!
— Jeremy Howard (@jeremyphoward) July 27, 2019
They're useful little functions to know about, and used together make it easy to apply functions over coordinate grids pic.twitter.com/yINPRFKLep
Domain Driven Design for Python https://t.co/QfxZ0oqdUV
— PyCoder’s Weekly (@pycoders) July 13, 2019
pandas-flavor: a #Python package for integrating custom functions into the pandas DataFrame interface. #programming #DataSciencehttps://t.co/cyb5pDonQh
— Randy Olson (@randal_olson) July 11, 2019
Looks like a great way to standardize + improve readability of data cleaning code.
Thanks for the tip, @ericmjl! pic.twitter.com/28Mta5paI2
Take a live look at how I'm building my tutorial "A Real-Life Massive Machine Learning Pipeline with scikit-learn". Hope to release it soon. Big thanks to @mybinderteam #machinelearning #MasterDataAnalysisWithPython https://t.co/GSERYJyUGf
— Ted Petrou (@TedPetrou) July 10, 2019
The scikit-learn ColumnTransformer is a huge game changer and can be used within a Pipeline to create one single object to do all transformations as well as machine learning. Here is a visual representation and the code that creates it. #machinelearning #python #datascience pic.twitter.com/SNtjvvNmYx
— Ted Petrou (@TedPetrou) July 10, 2019
SimuPy: A Python Framework for Modeling and Simulating Dynamical Systems by Benjamin Margolis - https://t.co/tDNlzBkase. This talk introduces SimuPy, a framework for simulating interconnected dynamical system models & how to use SimuPy to simulate canonical dynamical systems.
— Python Software (@ThePSF) July 9, 2019
I started seeing type hints in Python code I was working on about a year ago now.
— Vicki Boykis (@vboykis) July 9, 2019
My first thought: Nice!!
My second thought: Wait, what are type hints?
So I recently did a deep dive on what type hints bring to Python and whether we need to use them. https://t.co/cR1k1o0uVb