numpy.empty¶

numpy.empty(shape, dtype=float, order='C', *, like=None)¶

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

Parameters

shapeint or tuple of int: Shape of the empty array, e.g., (2, 3) or 2.
dtypedata-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.
likearray_like: Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.

New in version 1.20.0.

Returns

outndarray: 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

Array creation routines

numpy.empty_like