1. numpy.empty

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

        返回一个给定shape和type的新数组，但不初始化entries。

        Parameters:         shape: int or tuple of int  e.g. (2,3) or 2.

                                     dtype: data-type, optional        Default is numpy.float64

                                     order: {'C','F'},optional,              Default:'C'        

                                                 row-major(C-style )

                                                 column-major(Fortran-style)

                                      like:array_like, optional

        Returns:                out:ndarray

  例： 生成空数组(可做参量初始化)

import numpy as np

a = np.empty(shape=[0, 2])
print(a.shape)
'''
>>> (0,2)
'''
print(a)
'''
>>> []
'''

例： 生成非空数组

a = np.empty(shape=[2, 2])
print(a.shape)
'''
>>> (2,2)
'''
print(a)
'''
>>> [[-1.93601037e+091  7.17958114e-312]
     [ 7.17958108e-312 -6.47269325e-165]]
'''

        和zeros不同，它不把值设置为0，因此生成可能稍微会快一点。但是，不做初始化可能会造成意外影响，存在风险。

2. numpy.append

numpy.append(arr, values, axis=None)

        数组末尾追加值

        Parameters:         arr: array_like

                                                Values are appended to a copy of this array.

                                     values: array_like

                                                These values are appended to a copy of arr. 

                                     axis: int, optional

                                                 If axis is not given, both arr and values are flattened before use.

        Returns:                append: ndarray

例: axis为默认

a = np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]])
print(a)
'''
>>> [1 2 3 4 5 6 7 8 9]
'''

例: axis =0

a = np.append([[1, 2, 3]], [[4, 5, 6], [7, 8, 9]], axis=0)
print(a)
'''
>>> [[1 2 3]
     [4 5 6]
     [7 8 9]]
'''

3. np.empty和np.append追加组合

例：行初始化以及行追加

import numpy as np

a = np.empty(shape=(0, 3))
for i in range(5):
    values = [i, i ** 2, i + i ** 2]
    row_values = np.expand_dims(np.array(values), axis=0)
    a = np.append(a, row_values, axis=0)
print(a)
'''
>>>[[ 0.  0.  0.]
    [ 1.  1.  2.]
    [ 2.  4.  6.]
    [ 3.  9. 12.]
    [ 4. 16. 20.]]
'''