What is the difference between Numpy's array() and asarray() functions?
Question:
Answers:
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.
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.
The difference can be demonstrated by this example:
-
generate a matrix
>>> A = numpy.matrix(numpy.ones((3,3)))
>>> A
matrix([[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.]])
-
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.]])
-
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!
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.
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).
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()
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.
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.
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.
The difference can be demonstrated by this example:
-
generate a matrix
>>> A = numpy.matrix(numpy.ones((3,3))) >>> A matrix([[ 1., 1., 1.], [ 1., 1., 1.], [ 1., 1., 1.]])
-
use
numpy.array
to modifyA
. 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.]])
-
use
numpy.asarray
to modifyA
. It worked because you are modifyingA
itself>>> numpy.asarray(A)[2]=2 >>> A matrix([[ 1., 1., 1.], [ 1., 1., 1.], [ 2., 2., 2.]])
Hope this helps!
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.
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)
andnp.array(m)
will copy both, because that’s the default behavior.np.array(a, copy=False)
andnp.array(m, copy=False)
will copym
but nota
, becausem
is not anndarray
.np.array(a, copy=False, subok=True)
andnp.array(m, copy=False, subok=True)
will copy neither, becausem
is amatrix
, which is a subclass ofndarray
.np.array(a, dtype=int, copy=False, subok=True)
will copy both, because thedtype
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 compatiblendarray
(copy=False
).asanyarray
: The input will be returned uncopied iff it’s a compatiblendarray
or subclass likematrix
(copy=False
,subok=True
).ascontiguousarray
: The input will be returned uncopied iff it’s a compatiblendarray
in contiguous C order (copy=False
,order='C')
.asfortranarray
: The input will be returned uncopied iff it’s a compatiblendarray
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 anobject
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).
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()
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.