How to manage dynamic settings in Django

By Jorge Michelena • September 28, 2020 • 0 Comments

I recently finished working on a small project in Django when I realized that it could be improved greatly by adding a single feature. There were a few functionalities depending directly on constant values defined in the settings file but given their nature, it seemed like a good idea to be able

Recommendation Systems: How Suggestion Algorithms Work

By Anthony Figueroa • March 19, 2020 • 0 Comments

Oftentimes, we are surprised by the accuracy of recommendations on what to buy on Amazon, watch on Netflix, or listen on Spotify. We feel that somehow these companies know how our brain works and monetizing this magical guessing game. They have a deep foundation on behavioral sciences, and our job

A Quick Classification Experiment Using Machine Learning

By Bruno Michetti • December 27, 2019 • 1 Comment

My team recently faced a brand new challenge: developing a way to classify job positions written in natural language by lots of different people. It sounds simple, but there are a few factors that made this problem hard to solve. Job positions can be ambiguous depending on language usage and

Django Best Practices and Tips for Beginners

By Bruno Michetti • September 23, 2019 • 2 Comments

The Django framework saves time and effort and helps to create apps and REST APIs with maintainable code. This post gives some practical examples of what to do and what not to do when you code in Python using Django. In this post, I’ll explain why good code structure and

Improve data quality by using the pandas library and Python

By Anthony Figueroa • July 18, 2019 • 0 Comments

Data quality is a broad concept with multiple dimensions. I detail that information in another introductory article. This tutorial explores a real-life example. We identify what we want to improve, create the code to achieve our goals, and wrap up with some comments about things that can happen in real-life situations. To follow along, you need a basic understanding of Python.

Python Data Analysis Library (pandas) is an open-source, BSD-licensed library that provides high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

You can install pandas by entering this code in a command line: python3 -m pip install — upgrade pandas.