DataScience VS NumPy

For all topics that explained briefly, I provided the links with more thorough documentation. In addition, I would highly recommend reading the Julia Data Science online book and learn the great set of machine learning examples in Julia Academy Data Science GitHub repository.

So, I just decided to try to learn Julia, and started by following the Julia for DataScience lectures on JuliaAcademy. In the first lecture, I get instructed to clone the DataScience repository on GitHub. According to instructions, I activated the environment with activate and check the status (status). I then ran instantiate to update any necessary packages, and get the following error message:

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]