Preprocessing is a critical step in the analysis pipeline of spectroscopic data. However, students are rarely introduced to preprocessing when learning spectral techniques in laboratory courses which in turn may affect and delay their progress in the field. Despite its undoubtable importance, students will be mainly performing spectroscopic analysis in the context of a research project where preprocessing is encountered as part of a routine or "recipe" to follow. In this work, a Python-based application has been developed that allows facile application of common spectral preprocessing techniques with instantaneous results to support student learning. The developed application, i.e. Porchlight, and supplied Jupyter notebooks can substitute costly commercial software and make spectroscopic analysis widely available to students, trainees, and users in general.