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numpy.require() in Python

  • Last Updated : 11 Oct, 2020

numpy.require() function is useful for the array when correct flags is returned satisfies requirements for passing to compiled code (perhaps through ctypes).

Syntax: numpy.require(a, dtype=None, requirements=None)

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Parameters:



a :  array_like

dtype : data-type

requirements : str or list of str 

The requirements list can be any of the following.

  • ‘F’ : ‘F_CONTIGUOUS’  – ensure a Fortran-contiguous array.
  • ‘C’  : ‘C_CONTIGUOUS’ – ensure a C-contiguous array.
  • ‘A’ : ‘ALIGNED’  – ensure a data-type aligned array.
  • ‘W’ : ‘WRITEABLE’  – ensure a writable array.
  • ‘O’ : ‘OWNDATA’ – ensure an array that owns its own data.
  • ‘E’ : ‘ENSUREARRAY’ – ensure a base array, instead of a subclass.

Returns :  ndarray

Exception : ValueError – Raises ValueError

Code #1:

Python3




# Python program explaining
# numpy.require() function
  
# importing numpy
import numpy as np
  
# creating 4 x 4 array
data = np.arange(16).reshape(4, 4)
  
data.flags

Output:

C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

Code #2:

Python3




import numpy as np
  
# Python program explaining
# numpy.require()
b = np.require(data, dtype=np.float32,
               requirements=['A', 'W', 'O', 'C'])
b.flags

Output:

C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False



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