numpy.require() – The NumPy require Python Function

Introduction to NumPy require

When the right flags are returned, the numpy.require() function is useful for the array and meets the criteria for forwarding to compiled code (perhaps through ctypes).

Syntax:

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

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’: ‘WRITABLE’ – ensure a writable array.
    • ‘O’: ‘OWNDATA’ – ensure an array that owns its own data.
    • ‘E’: ‘ENSUREARRAY’ – ensure a base array, instead of a subclass.

Return: ndarray

Exception: ValueError – Raises ValueError

Code Examples of NumPy require

Example 01:

# welcome to softhunt.net
# Python program explaining
# numpy.require() function

# importing numpy
import numpy as np

# creating 3 x 3 array
data = np.arange(9).reshape(3, 3)
print(data)
print(data.flags)

Output:

[[0 1 2]
 [3 4 5]
 [6 7 8]]
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

Note: These programs will not run in online IDEs. Please test them on your systems to see how they operate.

Example 02:

# welcome to softhunt.net
import numpy as np

# Python program explaining
# numpy.require()
data = np.arange(9).reshape(3, 3)
b = np.require(data, dtype=np.float32,
			requirements=['A', 'W', 'O', 'C'])
print(data)
print(b.flags)

Output:

[[0 1 2]
 [3 4 5]
 [6 7 8]]
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

Note: These programs will not run in online IDEs. Please test them on your systems to see how they operate.

FAQs

Is SciPy required for NumPy?

NumPy is the backbone for SciPy. SciPy contains all of the numerical code. The NumPy functions are all contained in the SciPy module. It is, however, preferable to utilize NumPy for fast processing.

Which is better NumPy or SciPy?

In terms of convenience and a wide range of functions, modules, and packages, NumPy and SciPy are both major Python libraries. They are use in data science, machine learning, deep learning, and other fields that need mathematical computations.

In terms of speed, NumPy has more evacuation speed than SciPy. NumPy is written in C and so has a faster computational speed. However, SciPy is written in Python and so has a slower execution speed but vast functionality.

Conclusion

That’s all for this article, if you have any confusion contact us through our website or email us at [email protected] or by using LinkedIn. And make sure you check out our NumPy tutorials.

Suggested Articles:

  1. numpy.asscalar()

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