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

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.

Since other questions are being redirected to this one which ask about asanyarray or other array creation routines, it's probably worth having a brief summary of what each of them does.

The differences are mainly about when to return the input unchanged, as opposed to making a new array as a copy.

array offers a wide variety of options (most of the other functions are thin wrappers around it), including flags to determine when to copy. A full explanation would take just as long as the docs (see Array Creation, but briefly, here are some examples:

Assume a is an ndarray, and m is a matrix, and they both have a dtype of float32:

• np.array(a) and np.array(m) will copy both, because that's the default behavior.
• np.array(a, copy=False) and np.array(m, copy=False) will copy m but not a, because m is not an ndarray.
• np.array(a, copy=False, subok=True) and np.array(m, copy=False, subok=True) will copy neither, because m is a matrix, which is a subclass of ndarray.
• np.array(a, dtype=int, copy=False, subok=True) will copy both, because the dtype is not compatible.

Most of the other functions are thin wrappers around array that control when copying happens:

• asarray: The input will be returned uncopied iff it's a compatible ndarray (copy=False).
• asanyarray: The input will be returned uncopied iff it's a compatible ndarray or subclass like matrix (copy=False, subok=True).
• ascontiguousarray: The input will be returned uncopied iff it's a compatible ndarray in contiguous C order (copy=False, order='C').
• asfortranarray: The input will be returned uncopied iff it's a compatible ndarray in contiguous Fortran order (copy=False, order='F').
• require: The input will be returned uncopied iff it's compatible with the specified requirements string.
• copy: The input is always copied.
• fromiter: The input is treated as an iterable (so, e.g., you can construct an array from an iterator's elements, instead of an object array with the iterator); always copied.

There are also convenience functions, like asarray_chkfinite (same copying rules as asarray, but raises ValueError if there are any nan or inf values), and constructors for subclasses like matrix or for special cases like record arrays, and of course the actual ndarray constructor (which lets you create an array directly out of strides over a buffer).

The difference can be demonstrated by this example:

1. generate a matrix

   >>> A = numpy.matrix(numpy.ones((3,3)))>>> Amatrix([[ 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>>> Amatrix([[ 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>>> Amatrix([[ 1.,  1.,  1.],        [ 1.,  1.,  1.],        [ 2.,  2.,  2.]])

Hope this helps!