[Dowwie]

I've found the faker library [1] useful.

The fake data that I've bothered to model are weighted age ranges. Fortunately, as of Python3.6, you can access it from random.choices [2] in the stdlib

[1] https://github.com/joke2k/faker

[2] https://docs.python.org/3/library/random.html#random.choices

example: https://gist.github.com/Dowwie/8409d871ddae913e44c61bc4d47ce...

[logv]

part of what prompted the work on plait.py was that joke2k/faker was reasonably slow to generate 10K fake names for me: https://paste.ubuntu.com/26354987/

PS. that's a really cool python tip!

[Dowwie]

Well done, then! :) Is plait a drop-in replacement for faker?

[logv]

it's a drop in replacement for stympy/faker, but not joke2k faker

joke2k/faker is python and the data is stored in code (all or most of the random values are in .py files around the codebase), perhaps leading to its slowness.

stympy/faker is ruby and its random values are in yaml files, with some fields defined as ruby functions (those are not supported by plait.py).

can use 'plait -l' and 'plait -ll name' and 'plait -ll name.name' (more info in the README) to get a list of fake fields available.

[Dowwie]

if/when you tire with the latest performance improvement, consider porting to Rust and adapting it to python via cffi