Tech Talk with Toni

A bunch of skyscrapers Today's post is inspired by a combination of my recent trip to Miami and the Google Certification that I am currently pursuing. As I marveled at the towering buildings in the city, I couldn’t help but think about the foundations that hold them up.  This reflection reminded me of my own work in data analytics, where seemingly simple structures like lists and tuples form the bedrock of larger, more intricate solutions. One might think that revisiting such fundamental concepts as a data professional isn’t worthwhile—it's "something you already know."  However, as I worked through exercises on lists and tuples, I was struck by how many nuances I had overlooked during my initial learning. For example, revisiting the differences in mutability between lists and tuples revealed new ways to optimize my code. With these insights in mind, let's dive into some of the key similarities and differences between lists and tuples in the table below: A...

  We can easily see that a piece is missing here.  Last week, my friend and I began brushing up on our Data Analytics skills outside of our work hours, starting off with a Data Cleaning Tutorial on Kaggle. The first section of this tutorial focused on Handling Missing Values, and although it was somewhat familiar to me, I still learned a lot. In a previous post I focused on what to do when working with an incomplete dataset. Today I will be focusing on how to find the number of missing values in a dataset. Pick your path: Go on an adventure to find the missing values manually. Let Python do its thing Now, while option one can work for small datasets like the example below, option two is useful when working with thousands of rows and columns. It has been a year since I completed my Computer Science degree.  Initially, I assumed the process was as straightforward as finding the number of nulls using the .isnull() function and summing them with .sum().  However, to arr...