What is the difference between ndarray and array in numpy?
Solution 1
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
,zeros
orempty
... 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
Solution 2
numpy.array
is a function that returns a numpy.ndarray
. There is no object type numpy.array.
Solution 3
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))
Solution 4
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
-
flxb almost 2 years
What is the difference between
ndarray
andarray
in Numpy? And where can I find the implementations in the numpy source code? -
flxb about 11 yearsI think array() is implemented in core/src/multiarray/methods.c in array_getarray().
-
Steve L almost 7 yearsThis 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 over 5 yearsStill have no clue why should avoid using ndarray? Coz it's low-level?
-
user2357112 over 5 years@flxb: No,
array_getarray
is the implementation ofnumpy.ndarray.__array__
.numpy.array
starts at_array_fromobject
, at least in the current implementation. -
NoName over 4 yearsSo why is it not recommended?
-
David Waterworth over 3 yearsUsually when you have a type (i.e.
ndarray
) which is complicated to construct factory methods such asarray
,zeros
etc are provided to ensure that it's constructed correctly. Also perhaps the numpy developers prefer to keep the interfaces ofarray
,zeros
unchanged from release to release but don't make the same guarantees ofndarray()
-
Maf over 3 yearsWhat a great answer. Thanks a lot!