Oops, that's what I get for mathing before coffee-- mixed up the formula for \sum (1+r)^n vs. \sum r^n
for product_id in unique_products:
product_items = [x for x in dataset_python if x[0]==product_id ]
This is O(unique_products * observations), and it looks like O(unique_products) = O(observations). Thus we have a quadratic scan when a linear one would suffice. You'll get the best performance using whichever solution lets you code this to linear the fastest. E.g. pure python, make a dict from product_id to observations and iterate over that, or for pandas, use groupby. >For example, in a software or tech company they would be:
> Software Engineers
> Data Scientists
> Visual Designers
> Structural & 3-D Prototype Designers
> Narrative Developers
This certainly doesn't agree with what almost anybody else uses to define high-performance employees. >Are you motivated more by mastery of skill/craft, or motivated more by earning money?
> Mastery of skill or craft
> Money
> Other
I don't think we can draw any conclusion from the fact that 76% of the people who answered this public google form chose 'mastery'.