Backgound
I want to generate random numbers with distributions (np.random.normal, np.random.poisson, etc.), passing seveal keyword parameters (loc, scale, size, etc. each one is a list) into it.
loc = [1, 2, 3, 6, 10]
scale = [4, 6, 7, 8, 5]
size = [10, 9, 7, 8, 5]
# When I know which kwargs is in use, this lambda function works
list(map(lambda x, y: np.random.normal(loc=x, size=y), loc, size))
# however, the number of kwargs may change and the kwargs themselves may change. It won't work with codes below. How to generlize the function above?
list(map(lambda **params: np.random.normal(**params),**{'loc': loc, 'scale': scale, 'size': size}))
list(map(lambda x, y: np.random.poisson(loc=x, size=y), loc, size))
Outputs:
TypeError Traceback (most recent call last)
<ipython-input-48-1f3449886ea1> in <module>
----> 1 list(map(lambda x, y: np.random.poisson(loc=x, size=y), loc, size))
<ipython-input-48-1f3449886ea1> in <lambda>(x, y)
----> 1 list(map(lambda x, y: np.random.poisson(loc=x, size=y), loc, size))
mtrand.pyx in numpy.random.mtrand.RandomState.poisson()
TypeError: poisson() got an unexpected keyword argument 'loc'
Question
Is there a way to use built-in/numpy to iterate over kwargs's elements?