The ndarray objects can be saved to and loaded from the disk files. The IO functions available are −

• load() and save() functions handle /numPy binary files (with npy extension)
• loadtxt() and savetxt() functions handle normal text files
  load() and save() functions handle /numPy binary files (with npy extension)
  loadtxt() and savetxt() functions handle normal text files
  NumPy introduces a simple file format for ndarray objects. This .npy file stores data, shape, dtype and other information required to reconstruct the ndarray in a disk file such that the array is correctly retrieved even if the file is on another machine with different architecture.

  numpy.save()

  The numpy.save() file stores the input array in a disk file with npy extension.

  import numpy as np
  a = np.array([1,2,3,4,5])
  np.save('outfile',a)

  To reconstruct array from outfile.npy, use load() function.

  import numpy as np
  b = np.load('outfile.npy')
  print( b)

  It will produce the following output −

  array([1, 2, 3, 4, 5])

  The save() and load() functions accept an additional Boolean parameter allow_pickles. A pickle in Python is used to serialize and de-serialize objects before saving to or reading from a disk file.

  savetxt()

  The storage and retrieval of array data in simple text file format is done with savetxt() and loadtxt() functions.

  Example

  import numpy as np
  a = np.array([1,2,3,4,5])
  np.savetxt('out.txt',a)
  b = np.loadtxt('out.txt')
  print( b)

  It will produce the following output −

  [ 1. 2. 3. 4. 5.]

  The savetxt() and loadtxt() functions accept additional optional parameters such as header, footer, and delimiter.