numpy.empty
- 2019 年 10 月 11 日
- 筆記
版權聲明：本文為博主原創文章，遵循 CC 4.0 BY-SA 版權協議，轉載請附上原文出處鏈接和本聲明。
本文鏈接：https://blog.csdn.net/weixin_36670529/article/details/101756795
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
