What is the difference between Numpy's array() and asarray() functions?

Question

What is the difference between Numpy's array() and asarray() functions? When should you use one rather than the other? They seem to generate identical output for all the inputs I can think of.

Answer 1

The definition of asarray is:

def asarray(a, dtype=None, order=None):
    return array(a, dtype, copy=False, order=order)

So it is like array, except it has fewer options, and copy=False. array has copy=True by default.

The main difference is that array (by default) will make a copy of the object, while asarray will not unless necessary.

Answer 2

The difference can be demonstrated by this example:

1. generate a matrix

   >>> A = numpy.matrix(numpy.ones((3,3)))
   >>> A
   matrix([[ 1.,  1.,  1.],
           [ 1.,  1.,  1.],
           [ 1.,  1.,  1.]])
   

2. use numpy.array to modify A. Doesn't work because you are modifying a copy

   >>> numpy.array(A)[2]=2
   >>> A
   matrix([[ 1.,  1.,  1.],
           [ 1.,  1.,  1.],
           [ 1.,  1.,  1.]])
   

3. use numpy.asarray to modify A. It worked because you are modifying A itself

   >>> numpy.asarray(A)[2]=2
   >>> A
   matrix([[ 1.,  1.,  1.],
           [ 1.,  1.,  1.],
           [ 2.,  2.,  2.]])
   

Hope this helps!

Answer 3

The differences are mentioned quite clearly in the documentation of array and asarray. The differences lie in the argument list and hence the action of the function depending on those parameters.

The function definitions are :

numpy.array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0)

and

numpy.asarray(a, dtype=None, order=None)

The following arguments are those that may be passed to array and not asarray as mentioned in the documentation :

copy : bool, optional If true (default), then the object is copied. Otherwise, a copy will only be made if __array__ returns a copy, if obj is a nested sequence, or if a copy is needed to satisfy any of the other requirements (dtype, order, etc.).

subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default).

ndmin : int, optional Specifies the minimum number of dimensions that the resulting array should have. Ones will be pre-pended to the shape as needed to meet this requirement.

Answer 4

asarray(x) is like array(x, copy=False)

Use asarray(x) when you want to ensure that x will be an array before any other operations are done. If x is already an array then no copy would be done. It would not cause a redundant performance hit.

Here is an example of a function that ensure x is converted into an array first.

def mysum(x):
    return np.asarray(x).sum()

Answer 5

Here's a simple example that can demonstrate the difference.

The main difference is that array will make a copy of the original data and using different object we can modify the data in the original array.

import numpy as np
a = np.arange(0.0, 10.2, 0.12)
int_cvr = np.asarray(a, dtype = np.int64)

The contents in array (a), remain untouched, and still, we can perform any operation on the data using another object without modifying the content in original array.

Answer 6

Let's Understand the difference between np.array() and np.asarray() with the example:

np.array(): Convert input data (list, tuple, array, or other sequence type) to an ndarray and copies the input data by default.

np.asarray(): Convert input data to an ndarray but do not copy if the input is already an ndarray.

#Create an array...

arr = np.ones(5);  # array([1., 1., 1., 1., 1.])

#Now I want to modify `arr` with `array` method. Let's see...

arr = np.array(arr)[3] = 200;  # array([1., 1., 1., 1., 1.])

No change in the array because we are modify a copy of the arr.

Now, modify arr with asarray() method.

arr = np.asarray(arr)[3] = 200;  # array([1., 200, 1., 1., 1.])

The change occur in this array because we are work with the original array now.