Python Code Cleanup: Tackle These 10 Common Issues

Struggling with slow, messy Python code? This workshop tackles 10 common pitfalls that trip up beginners and developers new to Python. Learn clean, efficient coding practices through hands-on examples. Ditch C-style loops, tame unruly regexes, and pick the perfect data structures for lightning-fast performance. Leave with the techniques to consistently produce high-quality, performant Python code.

Workshop format: Presentation with hands-on exercises.

Target audience: Researchers and students looking to improve their Python coding skills.

Knowledge prerequisites: Some experience with basic Python.

Hardware/software prerequisites: Participants will need to run Jupyter notebooks. This can be accomplished by (1) having a local installation of Anaconda Python, (2) using Google Colab or (3) using Juypter OnDemand on Adroit. For (1) see https://researchcomputing.princeton.edu/learn/workshops-live-training/requirements-picscie-person-and-virtual-workshops#jupyter. For (2) no action is required since Colab is available to all members of the university. For (3) request an account on Adroit: https://bit.ly/3wicSaH (VPN required if off-campus). Additional detail for Adroit can be found in this guide (https://bit.ly/3QER9Sv).

Learning objectives: Attendees will come away as better Python programmers.

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Co-hosted with: GradFUTURES

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