different element data types within numpy array?
From the docs:

dtype : data-type, optional
The desired data-type for the array. If not given, then the type will
  be determined as the minimum type required to hold the objects in the
  sequence. This argument can only be used to ‘upcast’ the array. For
  downcasting, use the .astype(t) method.

So if you set dtype as float64, everything needs to be a float. You can mix types, but then you can't set it as a mismatching type. It will use a type that will fit all data, like a string for example in the case of array(['1', 'Foo', '3.123']).
Just like list in python where [1,"hello", {"python": 10}] it can have all different types within, can numpy array have this as well?
when numpyarray.dtype => dtype('float64') is it implying all elements are of type float?