Was asked recently about how many lines of python code was in my most recent project. A simple command line check, cd into your project directory and run:

find -type f -name "*.py" | xargs wc -l

(If on windows, you can download the GOW tools to be able to use these same tools by default available on unix/mac.) This will include whitespace and non-functional lines (like docstrings), but that I think is ok. Doing this for my current main project at Gainwell, I have about 30k lines of python code. Myself (and now about 4 other people) have been working on that code base for nearly a year.

For my first production project at (then) HMS, the total lines of python code are 20k, and I developed the bulk of that in around 7 months of work. Assuming 20 work days in a month, that results in around 20000/140 ~ 143 lines of code per workday. I did other projects during that time span, but this was definitely my main focus (and I was the sole developer/data scientist). I think that is high (more code is not necessarily better, overall code might have decreased as future development of this project happened over time), but is ballpark reasonable expectations for working data scientists (I would have guessed closer to around 100 per day). In the grand scheme of things, this is like 2 functions or unit tests per work day (when considering white space and docstrings).

Doing this for all of my python code on my personal machine is around 60k (I do around, as I am removing counts for projects that are just cloned). And for all the python code on my work machine is around 140k (for 3 years of work). (I am only giving fuzzy numbers, I have some projects joint work I am half counting, and some cloned code I am not counting at all.)

Doing this same exercise for R code, I only get around 40k lines of code on my personal machine. For instance, my ptools package has under 3k lines of "*.R" code total. I am guessing this is due to not only R code being more precise than python, but to take code into production takes more work. Maybe worth another blog post, but the gist of the difference between an academic project is that you need the code to run one time, whereas for a production project the code needs to keep running on a regular schedule indefinitely.

I have written much more SPSS code over my career than R code, but I have most of it archived on Dropbox, so cannot easily get a count of the lines. I have a total of 1846 sps files (note that this does not use xargs).

find -type f -name "*.sps" | wc -l

It is possible that the average sps file on my machine is 200 lines per file (it definitely is over 100 lines). So my recent python migration I don’t think has eclipsed my cumulative SPSS work going back over a decade (but maybe in two more years will).