poplarstand

Hey folks,

I've recently been tasked with doing some code cleanup for a hefty .Net codebase. It quickly became apparent that this wasn't going to be possible without some sanity checks to make sure that my refactors didn't introduce any regressions.

I've been writing mini functional tests for each Swagger API endpoint using a custom Python script and some .json files. Ideally though, this feels like something that should be formalized and automated.*

Got any suggestions for a tool or approach?

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*For context, the way this is currently done by other devs is opening up the Swagger web interface, manually entering some data, and checking the response.

One of the projects I work on has a Git Rebase workflow. Developers looking to get their changes into the main branch:

1. Do a rebase to get their branch in-sync with main, 2. Squash their branch's commits, 3. Make a push/force push to their branch, and 4. Create a pull request.

Which is lovely, but the process has a gnarly learning curve for our junior devs.

Is anyone familiar with documentation or tooling that does a good job of streamlining this sort of workflow?

Hey folks,

I started work as a consultant roughly half a year ago, and in doing so, have found myself pulled into more and more web development projects.

Although it isn't really my area of expertise, I've been happy to provide support for the poor overworked tech leads involved, and would like to build out a better understanding of the State of the Art.

Are there any mid-level books or resources that you'd recommend? The 18f engineering guide* is my current standard for helpful information.

https://engineering.18f.gov/

Hey all,

Some time ago I published a Kaggle dataset as part of a passion project. It was generated by a fairly involved handwritten Python pipeline that I hacked together while learning Pandas. The dataset became far more popular than I expected, and now I'm anxious about possible bugs.

I'd like to solicit your advice on building out a decent test suite. How would you go about validating data? Are there any books on best practices that you'd recommend?

Like many C.S. graduates, my education was theory heavy and practice light. I have spent much of the past few years rapidly building out missing areas of expertise to fulfill an increasingly wide pool of presumed knowledge. But one of the areas I have found difficult to get a handle on is security.

Would anyone here be willing to offer recommended reading? I regularly work with SSH, SSL/TLS, HTTPS, LDAP, AD, and OAuth with only cursory knowledge of any of them. I would be grateful for any guides that provide context for why these technologies exist and what best practices look like.

Thank you for any help you can provide.

I've accidentally assumed the role of local Python guru at my current company. Coworkers ask me about best practices, popular libraries, and deployment tooling. I find it very flattering, but I'm really just a tinkerer with a language preference.

Would anyone be able and willing to recommend me some sources to help in answering their questions? In particular, I'd be interested in hearing about:

* Publicly hosted examples of great Python code

* Concise, well-written books that discuss Python best practices and "gotchas"

* Tip sheets and style guides

Thank you in advance for any assistance you can offer.