ipykernel VS NumPy

The way I work is to use an interactive IPython REPL. It has a lot of features to make interactive development comfortable. I solve a problem incrementally, attempting to get a little piece of code right many times, changing it a little each time to fix bugs. Then I go back and copypaste the lines into a file and tidy it up a little. Then I go back to IPython and solve the next little stage of the problem. Maybe this style of development will be more fitting for you?

IPython

If you're talking about history in the python shell, you're probably looking for IPython.

For this kind of thing I mostly use Jupyter and the IPython kernel, with data analysis packages like numpy and pandas and matplotlib for charts.

Otherwise you should be able to convert the .ipynb's to .py using the ipython libraries:

Nice product, I noticed that you are updating the next Jupyter cell; what was your solution to doing that reliably since `set_next_input` is so damn flakey?

I personally grew so frustrated with the state of GUI development in Jupyter that I tried to fix it in such a way that would allow proper message passing between cells and python code (because you can't wait on Comm events).

> https://github.com/ipython/ipykernel/pull/589

But sadly the priorities of big open source projects don't always match your own. So I had to extract that logic into my own kernel.

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]