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numpy.empty_like

numpy.empty

numpy.empty(shape, dtype=float, order='C')

Return a new array of given shape and type, without initializing entries.

Parameters:
shape : int or tuple of int

Shape of the empty array, e.g., (2, 3) or 2.

dtype : data-type, optional

Desired output data-type for the array, e.g, numpy.int8. Default is numpy.float64.

order : {‘C’, ‘F’}, optional, default: ‘C’

Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory.

Returns:
out : ndarray

Array of uninitialized (arbitrary) data of the given shape, dtype, and order. Object arrays will be initialized to None.

See also

empty_like
Return an empty array with shape and type of input.
ones
Return a new array setting values to one.
zeros
Return a new array setting values to zero.
full
Return a new array of given shape filled with value.

Notes

empty, unlike zeros, does not set the array values to zero, and may therefore be marginally faster. On the other hand, it requires the user to manually set all the values in the array, and should be used with caution.

Examples

>>> np.empty([2, 2])
array([[ -9.74499359e+001,   6.69583040e-309],
       [  2.13182611e-314,   3.06959433e-309]])         #uninitialized
>>> np.empty([2, 2], dtype=int)
array([[-1073741821, -1067949133],
       [  496041986,    19249760]])                     #uninitialized