TECH BLOG

The article explains how breaking down complex problems, recognizing patterns, and approaching tasks methodically can help anyone think more like a coder. Whether you’re new to programming or looking to sharpen your skills, adopting this mindset can improve both your coding and logical thinking abilities.

Intro

In order to keep my programming skills sharp, from time to time I solve problems on the LeetCode. It’s great because it’s constantly evolving, it has a friendly interface, you can choose problems on topics that interest you and there’s an excellent community, from which you can learn a lot.

Bookmarking websites and pages is a common practice for many people who use the internet regularly. It allows us to easily access our favorite websites and pages without having to remember their exact URLs or search for them every time. However, as the number of websites and pages we bookmark grows, it can become difficult to keep track of them all. This is where a bookmark manager comes in handy.

In the previous article we figured out what an API is and how it works - a set of rules and protocols that allows different software applications to communicate with each other. Now, let’s explore how to choose the best framework for building APIs in Python.

Each article covers a different topic. If you’re just starting to understand how to write your own API, it’s useful to read everything in order. If you already know it and are interested in a particular topic, go straight to it.

The topic of API is very interesting and large, so I’ve divided the material into several parts, each of which is designed for a different audience. If you’re just starting to understand the subject, read everything in order. And if you know something already, choose the part that interests you.

1. Simple Explanation of API (this article 👇)
2. Best Framework for API
3. Build API with FastAPI (soon)
4. Build API with ChatGPT (soon)
5. Top Tools to Test API (soon)
6. Make Money on API (soon)

So, let’s go…

Introduction

Setting up a user-friendly development environment is extremely important for Python programmers working on macOS. The right tools can greatly increase your efficiency, reduce debugging time, and improve your overall programming experience. Whether you’re just starting your Python journey or you’re already an experienced developer, the right set of apps can make a big difference in your productivity.

In this article, we’ll look at the essential tools for Python developers on macOS, from terminal apps and text editors to full-featured IDEs and interactive notepads. We’ll highlight each tool’s strengths and features to help you find the perfect solution for your development needs.

This guide walks you through creating tables in Markdown, a simple yet powerful way to organize data in your content. It provides clear instructions and examples, making it easy to add clean, well-formatted tables to your blog posts or documentation. Whether you’re new to Markdown or looking to enhance your skills, this tutorial covers all the basics you need.

Intro

Let’s start by figuring out why tables are needed in markdown documents and look at the most convenient ways to insert a table into a markdown document quickly and easily.

A test is code that executes code. When you start developing a new feature for your Python project, you could formalize its requirements as code. When you do so, you not only document the way your implementation’s code shall be used, but you can also run all the tests automatically to always make sure your code matches your requirements. One such tool, which assists you in doing this is pytest and it’s probably the most popular testing tool in the Python universe.

You need a tool that allows you to manage your collection of links - organize easy access, sort by category, remove duplicates and broken links, synchronize everything between devices. Read about the best solutions for 2022.

Pyjion is a tool that can significantly boost Python performance by integrating a Just-In-Time (JIT) compiler into the Python runtime. This article explains how Pyjion works and how developers can use it to speed up their Python code without needing to make any major changes to their projects. It’s a great option for anyone looking to optimize Python’s execution speed while maintaining simplicity.

What is Pyjion

The most common way to speed up your Python code is to use CPython, the official Python interpreter. It compiles code into an intermediary language called bytecode and evaluates this code whenever your code is executed. This allows lots of flexibility, but has a downside. Simple calculations where CPUs are very efficient, like arithmetic of integers and floating point numbers are significantly slower in CPython than other interpreters.

Time series analysis is a fundamental domain in data science and machine learning, with massive applications in various sectors such as e-commerce, finance, capacity planning, supply chain management, medicine, weather, energy, astronomy, and many others.

Time Series Analysis

Time series analysis as a statistical technique is used to examine and model time-dependent data. Some common features of time series analysis tools include:

• Time series decomposition: the ability to break down a time series into its component parts, such as trend, seasonality, and residuals
• Forecasting: the ability to predict future values of a time series based on past data
• Anomaly detection: the ability to identify unusual or unexpected behavior in a time series
• Multivariate analysis: the ability to analyze multiple time series simultaneously, taking into account the relationships between them
• Feature extraction/embedding: the ability to extract meaningful features from time series data or to represent time series data in a lower-dimensional space for further analysis.

These are just a few examples of the types of functionality that may be included in a time series analysis tool. Let’s see what Kats can provide us with.