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

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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

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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

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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

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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

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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

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The difference can be demonstrated by this example    generate a matrix   gt  gt  gt  A   numpy matrix numpy ones  3 3     gt  gt  gt  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    gt  gt  gt  numpy array A  2  2  gt  gt  gt  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   gt  gt  gt  numpy asarray A  2  2  gt  gt  gt  A matrix    1    1    1              1    1    1              2    2    2        Hope this helps

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

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