I understand why numpy arrays of "standard" types are almost always more efficient and than a list containing the same type of data. Therefore it is better to get into the habit of using numpy arrays for the simple stuff, yes?
However, I'd like to know what the pro's and con's are to using numpy arrays to 'store' custom class instances, compared to using lists for that.
Consider
import numpy as np
class Foo:
def __init__(self, name):
self.name = name
class Bar:
def __init__(self, name):
self.name = name
self.myFoos = np.zeros(0, dtype = Foo)
def add_foo(self, some_foo):
self.myFoos = np.append(self.myFoos, some_foo)
I'd be able to do just fine using
self.myFoos = []
What should I keep in mind when making this decision?
Does the complexity of the Foo class make a big difference? (in my use case, it contains maybe 20 or 30 standard types, one or two fixed-sized integer arrays and then about 10 simple methods.)
Does the amount of Foos typically in the myFoos make a difference? (in my use case, it will be zero to 10)
Does the amount of times myFoos get handled make a difference? (In my real use case, it will be called maybe 10 to 20 times between user actions.)
P.s. Although the code works fine, pyCharm doesn't like that last append statement, it warns me that
Expected type 'Union[ndarray, iterable]' got 'Foo' instead.
Thanks in advance!
