numpy.ma.empty#

ma.empty(shape, dtype=float, order='C', *, device=None, like=None)[source]#

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.
devicestr, optional: The device on which to place the created array. Default: None. For Array-API interoperability only, so must be "cpu" if passed.

New in version 2.0.0.
likearray_like, optional: 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:

outMaskedArray: 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

Unlike other array creation functions (e.g. zeros, ones, full), empty does not initialize the values of the array, and may therefore be marginally faster. However, the values stored in the newly allocated array are arbitrary. For reproducible behavior, be sure to set each element of the array before reading.

Examples

>>> import numpy as np
>>> 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