What is the difference between ndarray and array in NumPy? Where is their implementation in the NumPy source code?
Add a comment
|
6 Answers
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 numpy.ndarray:
Arrays should be constructed using
array,zerosorempty... 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:
https://github.com/numpy/numpy/blob/master/numpy/core/numeric.py
6 Comments
flxb
I think array() is implemented in core/src/multiarray/methods.c in array_getarray().
Steve L
This can bite you if you forget that
np.array is not a class, as I often do. x = np.array([1,2.1,3]) if isinstance(x,np.array): # will give you a TypeError
GabrielChu
Still have no clue why should avoid using ndarray? Coz it's low-level?
NoName
So why is it not recommended?
David Waterworth
Usually when you have a type (i.e.
ndarray) which is complicated to construct factory methods such as array,zeros etc are provided to ensure that it's constructed correctly. Also perhaps the numpy developers prefer to keep the interfaces of array,zeros unchanged from release to release but don't make the same guarantees of ndarray() |
numpy.array is a function that returns a numpy.ndarray object.
There is no object of type numpy.array.
answered Apr 8, 2013 at 12:46
Ramón J Romero y Vigil
18k8 gold badges84 silver badges139 bronze badges
Comments
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
print(type(a))
You will get
<class 'list'>
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
print(type(a))
You will get
<class 'numpy.ndarray'>
which tells you the type of the numpy array is numpy.ndarray
You can also check the type by
isinstance(a, (np.ndarray))
and you will get
True
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))
Comments
numpy.ndarray() is a class, while numpy.array() is a method / function to create ndarray.
In numpy docs if you want to create an array from ndarray class you can do it with 2 ways as quoted:
1- using array(), zeros() or empty() methods:
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.
2- from ndarray class directly:
There are two modes of creating an array using __new__:
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 example:
>>> np.ndarray((2,), buffer=np.array([1,2,3]), ... offset=np.int_().itemsize, ... dtype=int) # offset = 1*itemsize, i.e. skip first element array([2, 3])
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
Conclusion: use numpy.array() if you want to make a numpy.ndarray() object"
Comments
In [58]: type(np.array) Out[58]: builtin_function_or_method
In [59]: type(np.ndarray) Out[59]: type
Comments
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.
