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This might be related to linking problem, which appears as 'not found' many times in Alpine Linux when something is wrong with symlinks. When you build your numpy dependencies in the Debian based distro, it is linked to glibc in specific path. Usually, similar paths are also in Alpine. I'm not sure how this works with venv.

I would suggest that, as you managed (I think) to install numpy directly in Alpine based container, to use Alpine distro as builder as well. Otherwise you might need to change some linked paths manually.

If you look for example folder /lib64 in Alpine, the ld-linux-x86-64.so.2 should be in there, so it is not totally missing. (after installing libc6-compat package).

But in general, Alpine is not the best choice for Python as the programming language is based on C, and musl is not perfect. Alpine could be also much slower for Python

I tried it out and actually it is homogeneous! Check this out:

Usually you would open the anaconda navigator, then go to Enviroments on the left side, then at the bottom you would click "Add" to add a new enviroment. after that you click on the newly created enviroment and "open terminal". in that terminal you use: conda install -c anaconda numpy. Now you should be able to open your jupyter notebook either directly from the anaconda navigator or start it via the terminal and everything should work fine.

If it still doesn't work try to restart anaconda, it shouldn't be requried, but sometimes it worked for me.

As I stress in my earlier answers, tensordot is an extension of np.dot, allowing us to specify which dimensions are used in the sum-of-products. The dot default is last of A, 2nd to the last of B.

This illustrates how dot handles dimensions greater than 2:

VSCode lets you choose which interpreter you are using,This is most probabaly your probelm because VSCode is using a differnt interpreter,with different pacakages, than your system.

The fisrt step is:

First you need to know what version of python your system is using, to know which version of python your system uses type python3 --version in the terminal

The second step is:

Then you need to make VSCode use the same version of python as your system ,to do this type ctrl+shift+p to open the command palette in VSCode then in the command palette type or choose “Python: Select Interpreter then select the same version that your system is using.

The last step is:

Since the system install of python doesnt have numpy installed you need to install it by typing pip3 install numpy

The shuffle function in your above snippet is functioning as intended. Array a only has 3 elements (the rows in your 2d array).

Because of this, when you call shuffle on a, it will only shuffle the rows.

If you wanted to shuffle every number together in your nd-array, you could flatten a to a 1x9 and shuffle that way.

a would then have 9 elements, each one gets shuffled among itself.

From here you can resize your 1x9 back into a 3x3 and achieve your desired result.

One possible issues is channel mixing. Different channels often use different build stacks, which can result in referencing different symbols in linked libraries. In this case, try ensuring everything comes from conda-forge, e.g.

Solved by following a suggestion on Numpy mailing list (permalink).

Python >= 3.8 will no longer use PATH for resolving dependencies of extension modules. Use os.add_dll_directory(mkl_bin_path) https://docs.python.org/3/library/os.html#os.add_dll_directory in all your scripts before importing numpy or add the call to a _distributor_init.py file in the numpy package directory.

Boolean scalar indexing is not well-documented, but you can trace how it is handled in the source code. See for example this comment and associated code in the numpy source: