gonum VS Testify

But if you want to see what can be done for numeric stuff, check out gonum. Personally, I still wouldn't use Go, and I rather suspect it's still pretty easy to reach for something like what you're trying to do and not find it because Go just can't write that type sensibly, but you can at least see what is available, written by people who disagree with me about Go not being a great language for this.

Numpy functionality is largely covered by https://www.gonum.org/ but for pandas I'm not sure if there is an equivalent as widely accepted. However, you might try https://github.com/rocketlaunchr/dataframe-go which I have not tried but it looks like it covers some of what you're looking for

Math libraries. It's just gonum right now. Missing things that often require people to link C or Python libs. E.g. https://github.com/gonum/gonum/issues/354

Maybe this way you could avoid having Mul, Mul_Inplace, Mul_Into variants. Gonum mostly follows the same pattern.

How does this compare to gonum graph? https://github.com/gonum/gonum/tree/master/graph

Go is quite a bit cleaner than Python and its concurrency/parallelism primitives can be well suited to scientific workloads.

You may want to have a look at Gonum (https://www.gonum.org), and the Go HEP package developed by CERN (https://go-hep.org).

I was also surprised to see DSP and pretty sophisticated packages, although I never used them: https://awesome-go.com/science-and-data-analysis

And of course Go has Jupyter integration, it's almost like running a script thanks to its fast compilation time.

You should check out this https://github.com/gonum/gonum

No, using CPU extensions and GPUs is a different thing than doing multitasking. There is Gonum but it is still slower than Numpy: https://github.com/gonum/gonum/issues/511

github.com/stretchr/testify

I then verify that there are no issues with writing the body with require.NoError() from the testify toolkit. This will ensure the test fails if something happens to go wrong at this point.

For testing purposes, Go provides a go test command that automatically discovers tests within your application and supports features such as caching and code coverage. However, if you require more advanced testing capabilities such as suites or mocking, you will need to install a toolkit like testify. Overall, while Go provides a highly effective testing experience, it's worth noting that writing tests in Python using pytest is arguably one of the most enjoyable testing experiences I have encountered across all programming languages.

Things I can't live without in a new Go project in no particular order:

- https://github.com/golangci/golangci-lint - meta-linter

- https://goreleaser.com - automate release workflows

- https://magefile.org - build tool that can version your tools

- https://github.com/ory/dockertest/v3 - run containers for e2e testing

- https://github.com/ecordell/optgen - generate functional options

- https://golang.org/x/tools/cmd/stringer - generate String()

- https://mvdan.cc/gofumpt - stricter gofmt

- https://github.com/stretchr/testify - test assertion library

- https://github.com/rs/zerolog - logging

- https://github.com/spf13/cobra - CLI framework

FWIW, I just lifted all the tools we use for https://github.com/authzed/spicedb

We've also written some custom linters that might be useful for other folks: https://github.com/authzed/spicedb/tree/main/tools/analyzers

IDE: use whatever make you productive. I personally use vscode. VCS: git, as golang communities use github heavily as base for many libraries. AFAIK Linter: use staticcheck for linting as it looks like mostly used linting tool in go, supported by many also. In Vscode it will be recommended once you install go plugin. Libraries/Framework: actually the standard libraries already included many things you need, decent enough for your day-to-day development cycles(e.g. `net/http`). But here are things for extra: - Struct fields validator: validator - Http server lib: chi router , httprouter , fasthttp (for non standard http implementations, but fast) - Web Framework: echo , gin , fiber , beego , etc - Http client lib: most already covered by stdlib(net/http), so you rarely need extra lib for this, but if you really need some are: resty - CLI: cobra - Config: godotenv , viper - DB Drivers: sqlx , postgre , sqlite , mysql - nosql: redis , mongodb , elasticsearch - ORM: gorm , entgo , sqlc(codegen) - JS Transpiler: gopherjs - GUI: fyne - grpc: grpc - logging: zerolog - test: testify , gomock , dockertest - and many others you can find here

FWIW, I've been using testify and mockery for years.

To answer OP directly, I am largely quite happy with mockery (and testify) to write expressive tests.

Como herramientas te recomiendo: FastJson https://github.com/valyala/fastjson : Si necesitas leer jsons Testify https://github.com/stretchr/testify : Para mockear y testear

Indispensable for testing your code: https://github.com/stretchr/testify