This is my undergraduate summative lab report for my year 1 undergraduate physics degree at Durham University.
I independently developed a solid framework for sorting and analysing my data (measuring electric force, distance, as well as some additional columns of data that I ended up omitting from the report due to their irrelevance).
Instead of extracting from an Excel spreadsheet, I extracted from a dedicated CSV file for simplicity.
I then represented the data in the form of an innovative graph - instead of using traditional error bars, I sought to express them more vividly and demonstrate the range of errors/uncertainties in a more unique way. Note how the error bars now extrapolated across the plot as opposed to existing point-per-point.
This makes data analysis much more straightforward, as instead of relying on 10 or so bars to conduct further analyses, you can use the developed extrapolation.
NumPy, Pandas, Matplotlib (importing pyplot)
I am one of the few, if not the only, year 1 student this year, to use Python. Most students used Excel or LaTeX - and whilst Python is one of my strongest skills, I realised that the best way of winning this award would be to use Python - there's so much more freedom and potential, unlike pre-set graphs in more commonly used software.
{This section will be updated accordingly in case I win.}