What is the difference between
array in NumPy? Where is their implementation in the NumPy source code?
numpy.array is just a convenience function to create an
ndarray; it is not a class itself.
You can also create an array using
numpy.ndarray, but it is not the recommended way. From the docstring of
Arrays should be constructed using
empty… The parameters given here refer to a
low-level method (
ndarray(...)) for instantiating an array.
Most of the meat of the implementation is in C code, here in multiarray, but you can start looking at the ndarray interfaces here:
numpy.array is a function that returns a
There is no object of type
Just a few lines of example code to show the difference between numpy.array and numpy.ndarray
Warm up step: Construct a list
a = [1,2,3]
Check the type
You will get
Construct an array (from a list) using np.array
a = np.array(a)
Or, you can skip the warm up step, directly have
a = np.array([1,2,3])
Check the type
You will get
which tells you the type of the numpy array is numpy.ndarray
You can also check the type by
and you will get
Either of the following two lines will give you an error message
np.ndarray(a) # should be np.array(a) isinstance(a, (np.array)) # should be isinstance(a, (np.ndarray))
numpy.ndarray() is a class, while
numpy.array() is a method / function to create
In numpy docs if you want to create an array from
ndarray class you can do it with 2 ways as quoted:
Arrays should be constructed using array, zeros or empty (refer to the See Also section below). The parameters given here refer to a low-level method (
ndarray(…)) for instantiating an array.
ndarray class directly:
There are two modes of creating an array using
If buffer is None, then only shape, dtype, and order are used.
If buffer is an object exposing the buffer interface, then all keywords are interpreted.
The example below gives a random array because we didn’t assign buffer value:
np.ndarray(shape=(2,2), dtype=float, order='F', buffer=None) array([[ -1.13698227e+002, 4.25087011e-303], [ 2.88528414e-306, 3.27025015e-309]]) #random
another example is to assign array object to the buffer
2,), buffer=np.array([1,2,3]), offset=np.int_().itemsize, dtype=int) # offset = 1*itemsize, i.e. skip first element array([2, 3])np.ndarray((
from above example we notice that we can’t assign a list to “buffer” and we had to use numpy.array() to return ndarray object for the buffer
numpy.array() if you want to make a
I think with
np.array() you can only create C like though you mention the order, when you check using
np.isfortran() it says false. but with
np.ndarrray() when you specify the order it creates based on the order provided.