numpy.empty¶

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

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

Parameters:

Parameters:	shape : int or tuple of int Shape of the empty array dtype : data-type, optional Desired output data-type. order : {‘C’, ‘F’}, optional 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.

shape : int or tuple of int

Shape of the empty array

dtype : data-type, optional

Desired output data-type.

order : {‘C’, ‘F’}, optional

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, zeros, ones

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]])         #random

>>> np.empty([2, 2], dtype=int)
array([[-1073741821, -1067949133],
       [  496041986,    19249760]])                     #random