Although writing a simple code from scratch can be an illuminative experience, we believe students can gain some intuition for the mathematical machinery of DFT by interacting with a transparent, readable program with visualizations. Jupyter notebooks are one increasingly common approach used by educators that seamlessly integrate text, diagrams, and code into 'computational essays' and facilitate engagement with data without extensive programming knowledge. This work provides three Jupyter notebooks written in Python, which can be run online through the Google Colaboratory 48 (Google Colab) interface, that explain DFT through a simple particle in a box (PIB) model system. They are organized by (1) 3D PIB energy levels and wavefunctions, (2) polycyclic aromatic hydrocarbons (PAHs) frontier HF orbitals, and (3) energy terms in DFT applied to the 3D PIB system. This approach was tested in a graduate-level physical chemistry course at Oregon State University in the Fall 2022 term after covering Hückel theory, HF theory, and basis sets. The material can equivalently be presented in a classroom or remote-learning setting. A pre-and post-activity survey was provided through Google Forms and results are summarized herein.