python-course | Problem sets for Python Lunchtime Programming Sessions | REST library

I have defined a graph by using from pyvis.network import Network and defining a network as net = Network().

I added to the net edges and nodes using the functions net.add_nodes(nodes) and net.add_edge(x, y). There is any chance to deal with this graph not only using a visualization? So for e.g. to find all paths between two specific nodes? Because searching in the documentation there is only the possibility to plot the graph. I saw other options (e.g. class) to deal with graphs (example) but is very annoying to define the graph with a dict of a dict.

I have generate a spec-list below with conda list --explicit > spec-list.txt

When a divide-and-conquer recursive function doesn't yield runtimes low enough, which other improvements could be done?

Let's say, for example, this power function taken from here:

This is a very basic question relating to setting attributes that I don't find a definitive answer to. I have a python class as follows

I have a Networkx graph like the following image (image source)

I perform edge attacks and observe the change in values at the node of the resulting subgraph.

Example, If I attack edge (a,2): edge (a, 2) and (2, 1) will be removed. To explain a bit, when edge (a, 2) is attacked the node 2 will have a degree < 2. So the edge that's connected to node 2 is also removed.

The above attack results in a subgraph

Each time an edge is attacked, the value of the terminal node labelled e observed over time changes. Let's say I perform 5 (attack = 5) attacks, I have a time x attack matrix (time=25, attack=5) that stores the time-series data of node e.

I would like to ask for suggestions on how to visualize the effect of these attacks on the value of node e changing over time. EDIT:

What information do you want to be able to see or identify from your visualizations?

I want to see the attack on which edge has the maximum effect on the time course value observed at e. We could imagine this to be a transportation network and the values at node reflect the amount of a product that has reached the location/node. From the source node b, the goods are transported to target node e. The observation made is the change in node values after an edge is attacked and no observation of the edge value is available.

Please find the code that is used to attack edges

Is it possible to overload arbitrary operators in Python? Or is one restricted to the list of operators which have associated magic methods as listed here: https://www.python-course.eu/python3_magic_methods.php ?

I'm asking because I noticed that Numpy uses the @ operator to perform matrix multiplication e.g. C=A@B where A,B are Numpy arrays, and I was wondering how they did it.

Edit: The @ operator is not in the list I linked to.

Could someone point me to the Numpy source where this is done?

MAIN TROUBLE

As an example, say I would like to decorate split() method of str class (the example is representative of what I try to do, except I would like to decorate agg method of pandas DataFrame class).

At least, I get the same error.

I prefer not to use @ nor to create a child of str class to keep things simple (and maybe it is why it is not working...)

I encounter following trouble.

In this example below, the method m on class A is called just once.

I understand this is a feature, this is the Pythonic way to solve the issue where A's m method would be called twice (if it was implemented in the naive way) in this diamond-like inheritance scenario.

This is all described here:
https://www.python-course.eu/python3_multiple_inheritance.php

(1) But under the hood... how did they achieve this behavior i.e. that class A's m method is called ONLY once?!
Asked simplistically: which line is being "skipped" during execution - is it line #1 or line # 2 ?

Could someone shed more light on this?
I have never used multiple inheritance seriously because I mostly program in Java. So I am really curious about this scenario here and more specifically to the inner-workings behind it.

Note: I just want to get the general idea of how this works in Python, not really understand every tiny detail here.

(2) What if I want (in this same scenario and for some reason) A's m method to be called twice (or N times depending on how many base classes of D we have), while still going through using super(). Is this possible? Does super() support such mode of operation?

(3) Is this just some tree or DAG visiting algorithm where they keep track which class's m method has already been visited and just don't visit it (call it) twice? If so then simplistically speaking I guess '# 2' is the line which is skipped.

First post on Overstack.

I'm new to Python and coding in general. I'm now learning about functions and presently looking at some examples that describe global variables on Python-Course.eu: