Python

It is a new selection of tips and tricks about Python and programming from my Telegram-channel @pythonetc.

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Different asyncio tasks obviously have different stacks. You can view at all of them at any moment using asyncio.all_tasks() to get all currently running tasks and task.get_stack() to get a stack for each task.

Tips and tricks from my Telegram-channel @pythonetc, November 2019

It is a new selection of tips and tricks about Python and programming from my Telegram-channel @pythonetc.

← Previous publications.



PATH is an environment variable that stores paths where executables are looked for. When you ask your shell to run ls, the shell looks for the ls executable file across all paths that are presented in PATH.

tl;dr

github.com/QratorLabs/fastenum

pip install fast-enum

What are enums

(If you think you know that — scroll down to the “Enums in Standard Library” section).

Imagine that you need to describe a set of all possible states for the entities in your database model. You'll probably use a bunch of constants defined as module-level attributes:

# /path/to/package/static.py:
INITIAL = 0
PROCESSING = 1
PROCESSED = 2
DECLINED = 3
RETURNED = 4
...

...or as class-level attributes defined in their own class:

class MyModelStates:
  INITIAL = 0
  PROCESSING = 1
  PROCESSED = 2
  DECLINED = 3
  RETURNED = 4

That helps you refer to those states by their mnemonic names, while they persist in your storage as simple integers. By this, you get rid of magic numbers scattered through your code and make it more readable and self-descriptive.

But, both the module-level constant and the class with the static attributes suffer from the inherent nature of python objects: they are all mutable. You may accidentally assign a value to your constant at runtime, and that is a mess to debug and rollback your broken entities. So, you might want to make your set of constants immutable, which means both the number of constants declared and the values they are mapped to must not be modified at runtime.

It is a new selection of tips and tricks about Python and programming from my Telegram-channel @pythonetc.

← Previous publications


If you want to iterate over several iterables at once, you can use the zip function (it has nothing to do with ZIP file format):

It is a new selection of tips and tricks about Python and programming from my Telegram-channel @pythonetc.

← Previous publications


If an instance of a class doesn’t have an attribute with the given name, it tries to access the class attribute with the same name.

>>> class A:
...     x = 2
...
>>> A.x
2
>>> A().x
2

Nowadays, talking to conversational agents is becoming a daily routine, and it is crucial for dialogue systems to generate responses as human-like as possible. As one of the main aspects, primary attention should be given to providing emotionally aware responses to users. In this article, we are going to describe the recurrent neural network architecture for emotion detection in textual conversations, that participated in SemEval-2019 Task 3 “EmoContext”, that is, an annual workshop on semantic evaluation. The task objective is to classify emotion (i.e. happy, sad, angry, and others) in a 3-turn conversational data set.

It is a new selection of tips and tricks about Python and programming from my Telegram-channel @pythonetc.

← Previous publications

Everyone uniquely perceives texts, regardless of whether this person reads news on the Internet or world-known classic novels. This also applies to a variety of algorithms and machine learning techniques, which understand texts in a more mathematical way, namely, using high-dimensional vector space.

This article is devoted to visualizing high-dimensional Word2Vec word embeddings using t-SNE. The visualization can be useful to understand how Word2Vec works and how to interpret relations between vectors captured from your texts before using them in neural networks or other machine learning algorithms. As training data, we will use articles from Google News and classical literary works by Leo Tolstoy, the Russian writer who is regarded as one of the greatest authors of all time.

We go through the brief overview of t-SNE algorithm, then move to word embeddings calculation using Word2Vec, and finally, proceed to word vectors visualization with t-SNE in 2D and 3D space. We will write our scripts in Python using Jupyter Notebook.

Hi everyone!

Winter has arrived, and so I had to check the thermal insulation of my out of town residence dacha. And it just turned out a famous Chinese marketplace started to sell cheap thermal camera modules. So I decided to DIY it up and build a rather exotic and useful thing — a heat visor for the home. Why not? Especially since I had a Raspberry Pi lying around anyway… The result is down below.

It is new selection of tips and tricks about Python and programming from my Telegram-channel @pythonetc.

Previous publications:

• December 2018

It is new selection of tips and tricks about Python and programming from my Telegram-channel @pythonetc.