The purpose of this Pydon't is to show you what underscores are used for in Python, and to show you how to write more idiomatic code with them.
In this Pydon't we will take a look at the reduce function,
which used to be a built-in function and is currently
in the functools module.
In this Pydon't we explore what Boolean short-circuiting
for the and and or operators is, and how to use this
functionality to write more expressive code.
In this Pydon't we conclude the slicing trilogy and
take a look at the inner workings of Python slicing,
from the built-in slice type to the dunder method
__getitem__ and its siblings.
In this Pydon't we cover advanced topics related to sequence slicing, like (negative) steps, more idiomatic sequence slicing, slice assignment, and slice deletion.
In the fifth article of this short series we will be handling some subtleties that we overlooked in our experiment to classify handwritten digits from the MNIST dataset.
This article covers the basics of sequence slicing in Python and teaches you some idiomatic slicing patterns to write more elegant code.
In this article we use (finite state) automatons to count 698,438,863,898,480,640 passwords in a couple milliseconds.
A short article with all you need to know about sequence indexing in Python – and a bit more.
If you need to access the items of an iterable but also keep
track of their indices, have you considered using enumerate?
Let's talk about another of Python's amazing tools to work
with for loops.
In part 4 of this series we add some unit testing,
improve our tokenizer and implement the primitives ⍴ and ⍤.
for loops are the bread and butter of imperative programming
and Python has some really nice tools to work with them.
If you want to traverse several structures in parallel,
have you considered using zip?
Structural pattern matching is coming in Python 3.10 and
the previous Pydon't explored some interesting use cases
for the new match statement.
This article explores situations for which match isn't the answer.
In the fourth article of this short series we will apply our neural network framework to recognise handwritten digits.
Structural pattern matching is coming in Python 3.10 and this article
explores how to use it to write Pythonic code,
showing the best use cases for the match statement.
The third article of this short series concerns itself with the implementation of the backpropagation algorithm, the usual choice of algorithm used to enable a neural network to learn.
In the second article of this short series we will create a class for a generic neural network and we will also see how to assess the quality of the output of a network, essentially preparing ourselves to implement the backpropagation algorithm.
Learn the ins and outs of comparison operator chaining, and especially the cases you should avoid.
This is the first article in a series to implement a neural network from scratch. We will set things up in terms of software to install, knowledge we need, and some code to serve as backbone for the remainder of the series.
Deep unpacking (or nested unpacking) provides a more powerful way for you to write assignments in your code. Deep unpacking can be used to improve the readability of your code and help protect you against unexpected bugs. Learning about deep unpacking will also be very important in order to make the most out of the structural matching feature that is to be introduced in Python 3.10.
