advent-of-code-data VS NumPy

The one additional trick I'd point out is using https://github.com/wimglenn/advent-of-code-data to help automate the process of acquiring your input:

By the way, I use https://github.com/wimglenn/advent-of-code-data to get the data. I removed it from the code to avoid confusion.

advent-of-code-data updated to match this format in v1.2.3 (#91)

I think that's a one-time thing though, so if you get the session cookie now you can use it for the rest of December. There's a python module to grab inputs and submit answers here, would love to see something similar in Haskell!

In addition to doing previous years’, I’d check out https://github.com/wimglenn/advent-of-code-data, which you can use to write some automation about loading data and submitting your responses. If you don’t know how to write unit tests, this might be a good chance to learn the basics. In most problems, he gives you a sample input and the expected result.

If you absolutely must have something download the input for you, then please use an existing, well-tested system like aocd. More people using it and contributing to it makes it less likely that it will have any significant defects.

While researching this I came across https://github.com/wimglenn/advent-of-code-data . If you are looking for something a little more fleshed out than a command line. It lets you access previous years' data as well. Pretty cool.

Since you're using Python, maybe just use https://github.com/wimglenn/advent-of-code-data/tree/master/aocd that seems to have already done these things.

advent-of-code-data might be of use for you, it provides some helpers to fetch inputs and submit solutions to structure your solution around.

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]