How to make good reproducible pandas examples - Stack Overflow Having spent a decent amount of time watching both the r and pandas tags on SO, the impression that I get is that pandas questions are less likely to contain reproducible data This is something that the R community has been pretty good about encouraging, and thanks to guides like this , newcomers are able to get some help on putting together
How to make a great R reproducible example - Stack Overflow You are most likely to get good help with your R problem if you provide a reproducible example A reproducible example allows someone else to recreate your problem by just copying and pasting R code You need to include four things to make your example reproducible: required packages, data, code, and a description of your R environment
How to create a Minimal, Reproducible Example - Help Center Reproducible: Test the code you're about to provide to make sure it reproduces the problem The rest of this help article provides guidance on these aspects of writing a minimal, reproducible example For more information on how to debug your program so that you can create a minimal example, Eric Lippert has written a fantastic blog post on the
Tips on creating a minimal reproducible example for a coding challenge . . . With this information you can reduce the code to just run with this particular input and post it as a minimal reproducible example Some things to be aware of specifically in the context of such online code challenge platforms: Be careful with global static variables This includes any other state that outlives a single execution of your
Why should I provide a Minimal Reproducible Example for a very simple . . . Those that take the trouble to provide minimal, reproducible examples are far more likely to be answered quickly, and accurately It's easy to provide a sample data set It doesn't have to be real data It just has to fairly reflect the nature of the problem at hand
How to Get Reproducible Results (Keras, Tensorflow): TF_Support, thank you for detailed and structured answer If to divide the 'reproducible code as a reference' you gave in the Jupiter notebook on 2 cells: 1) all the rows but 3 last 2) 3 last (to begin from 'histpry = model fit(x_train ) and run 2 cells consequently, it always shows the identical result
Reproducibility of python pseudo-random numbers across systems and . . . So far, I've been using numpy random RandomState for reproducible pseudo-randomness, though it too does not make the formal promise you're after If you want full reproducibility, you might want to include a copy of random's source in your program, or hack together a "P²RNG" (pseudo-pseudo-RNG) from hashlib
Any good strategies for dealing with not reproducible bugs? It is important to categorize such bugs (rarely reproducible) and act on them differently than bugs that are frequently reproducible based on specific user actions Clear issue description along with steps to reproduce and observed behavior : Unambiguous reporting helps in understanding of the issue by entire team eliminating incorrect conclusions
How to generate a random UUID which is reproducible (with a seed) in . . . if your goal is a reproducible UUID, here's one concise approach import uuid seeded_uuid = uuid UUID(bytes=b"z123456789101112") # 7a313233-3435-3637-3839-313031313132 How does this work internally ? Using binary strings allows almost anything to act as a seed