If you ever created a class in Python, you probably accessed it using dot notation (i.e. instance_name.attribute_name).

That’s python’s way of calling getattr by means of an alias:

class A:
    var = 10
    pass

a = A()
# this is how Python accesses attributes
getattr(a, 'var')

10

a.__getattribute__('var') # above is an alias for this

10

The most “pythonic” way of getting and setting attributes is using dot notation:

A.var = 11
print(A.var)

11

which is short for the dunder getattribute method

A decorator is the gateway drug into the world of Python metaprogramming. In python, everything, everything, is an object (specifically a dictionary but let’s not go there). That means that we can pass in and return any object regardless of its types, especially regardless of its type.

If I define a function:

def fn(*args, **kwargs):
    pass

and now call type on fn

type(fn)

function

the type is function (No surprises there). But remember, we can return anything. So if I really wanted to, I could do the following:

A few weeks ago I had to figure out how to perform a mapping of pandas column values to other values. This was not necessarily a discrete mapping, as in the initial column value needed to match a range.

The dataframe I was working with resembled the following:

   value
0     88
1      3
2      5
3     65
4     72
5     54

And there were a set of conditions by which I needed to replace. Think of it like this, if the above were a group of marks for an exam, I would want to map it based on the value ranges.

This was purely out of sheer need, and this was the fastest way I could’ve gotten it done (I ended up learning a LOT about CLI and the awk command from this, so I’m really grateful for that)

The problem: I have a column in a utf-8 CSV file of type Integer, which should actually be type string and zero-padded up to (let’s say length N).

~/projects/awk_pad ❯ cat out.csv             
a,Y,1
b,N,10
c,Y,12223253

What I want, is the following (output from the cat tool):

This is taken from Diffusion Convolutional Neural Networks (referenced in the footer). According to the authors, a diffusion convolution scans a diffusion process across each node. Analog to biology, where the information is allowed to propagate conditional of its density and environment.

It was applied to node classification, edge classification and graph classification, but node-classification is the task I wanted to focus on. When first presented, it was a novel way to effectively apply convolutions (invariant to location and rotation), to arbitrarily-structured data (i.e. graphs). Based on the results presented, the DCNN model outperformed a probabilistic-relational model in citation (a conditional-random field, no I do not know much about that) in graph topic-classification.