Why you may need them and how to implement a custom one

Decorators can seem a little mysterious when dealing with them for the first time, but without any doubt, it’s a great tool to enhance a function’s behavior.

In fact, there are two types of decorators in Python — class decorators and function decorators — but I will focus on function…


Basic tools that help find bottlenecks in your application

When experimenting with a project, it can be helpful (or simply fun!) to try and see which parts of the code are, for example, the most memory-consuming. If those places in the code pose a problem, it’s worth figuring out how they can be improved. Sometimes, all that’s needed is…


Making Sense of Big Data

A concise overview of approaches available in Python

If you’re about to start a big data project you will be either retrieving a lot of information or crunching big numbers on your machine, or both. However, if the code is sequential or synchronous your application may start struggling.

Let’s see which concepts and Python libraries can improve your…


New features that can simplify your data processing code — what are they and what do they improve?

Python 3.9 has accumulated a lengthy list of improvements with some pretty significant changes such as a new type of parser. The new PEG parser takes a little more memory but is a little faster and should be able to handle certain cases better compared to the LL(1) parser. Although…


Using the examples of Naive Bayes, MaxEnt (Logistic Regression), HMM, and CRF

There are a few types of classical supervised machine learning algorithms: Naive Bayes, MaxEnt (also called Logistic Regression), Decision Trees, Hidden Markov Models (HMMs), Support Vector Machines, Conditional Random Fields (CRFs), Neural Networks, and some others. The top n list may differ depending on the field they are used in.


Essential tips for working with large amounts of data in Python

While you will only occasionally get to the point where you need to run a profiler to analyze your code and find bottlenecks, it’s definitely a good idea to get into the habit of writing efficient code and spotting the places where you can improve right away.

An important thing…


Methods and tricks that I found useful for exploring and processing data on the go, back when I just started learning Python a few years ago.

Have you just started self-teaching Python? Great decision! Python is a pretty popular language in a few domains, and particularly in Data Science according to the 2018 Kaggle Machine Learning and Data Science survey. …

Anna Astori

Software Engineer. AWS Certified Solutions Architect. Python developer. Ex-Amazon (Alexa AI). I’m also a big figure skating fan and a foodie.

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