Python

Why Learn OOP? Lately, I’ve been revisiting some fundamentals to reinforce Python’s role as a powerful tool for object-oriented programming (OOP). My mentor encouraged me to use Python like a developer—not just as an analyst—and I completely agree. Understanding data structures is essential, but incorporating OOP principles makes you a much stronger problem solver. Surprisingly, many analysts either never learn OOP theory or only encounter it after they’ve already become proficient in Python. ...

Introduction As a Python developer, managing dependencies and ensuring a clean development environment has always been a top priority for me. Virtual environments have become my go-to tool for handling project-specific dependencies, avoiding conflicts, and maintaining a tidy global Python environment. In this blog post, I’ll share the benefits I’ve experienced using virtual environments and walk you through a quick tutorial on using virtualenvwrapper, an extension that has significantly enhanced my workflow. ...

As a Data Scientist at the ShapeUp Project, I’ve been working on data analysis projects at Shape Up The Vote Data Project that activate barbershops and salons as voter engagement hubs in 13 states with 5,500 participating shops by 2024. We use relational organizing principles and remote communication through text and phone banking. My role involves collecting, cleaning, and enriching data on potential participants to identify the best shops to contact. ...

When writing functions in Python, you may sometimes find yourself assigning a function parameter to a new variable. While this can improve readability and functionality, it’s not always necessary. In this article, we’ll explore when and why mapping parameters to new variables makes sense—and when it doesn’t. 1. Preserving the Original Parameter (Avoiding Side Effects) Some data types in Python, like lists and dictionaries, are mutable, meaning that modifications inside a function can affect the original object. If you need to modify the data but want to avoid altering the original input, creating a new variable is a good practice. ...

Choosing between long and wide data formats can make or break your workflow—especially when it comes to analysis, visualization, or preparing reports. This guide will help you figure out the best structure for your dataset based on your goals and tools. 🧠 Start With the Right Questions Before reshaping your data, ask yourself: 1. What are you trying to do with the data? Analyze it (e.g., run time-series or trend models)? Visualize it (e.g., bar charts, line graphs)? Use it in tools like R, Python, Excel, or Tableau? 🔧 Pro tip: Tools like ggplot2 in R or pandas in Python often prefer long format. ...