Author: Oliviero Andreussi, Department of Chemistry and Biochemistry, Boise State University
E-mail: olivieroandreuss@boisestate.edu
Content of the project:
Basic Jupyter Lectures on Python Programming for Undergraduate Chemistry Courses and Labs
- Introduction: covers basic programming strategies, assignments, conditionals, loops, formatting, and plotting
- Errors: covers concepts on measurement errors, some basic statistics (binomial and normal distributions), error propagation, reading files, Pandas dataframes, and linear regression with Sklearn
Assignments:
- Python Worksheet: four tasks on error analysis, basic math on a dataset, more advanced operations on the columns of a dataset, and linear regression fit.
Datafiles used in the notebooks:
- test.csv: absorbance vs. time data collected in a kinetic experiment
- protein.csv: atomic properties (element, environ descriptors, coordinates, mass, distance from center of mass) of atoms in a small protein