副本和视图

在操作数组时，它们的数据有时会被复制到一个新的数组中，有时则不会。对于初学者来说，这常常是一个困惑的来源。有三种情况:

完全没有副本

简单的赋值不复制对象或它们的数据。

>>> a = np.array([[ 0,  1,  2,  3],
...               [ 4,  5,  6,  7],
...               [ 8,  9, 10, 11]])
>>> b = a            # no new object is created
>>> b is a           # a and b are two names for the same ndarray object
True

Python将可变对象作为引用传递，因此函数调用不进行复制。

>>> def f(x):
...     print(id(x))
...
>>> id(a)    # id is a unique identifier of an object
148293216  # may vary
>>> f(a)
148293216  # may vary

视图或浅拷贝

不同的数组对象可以共享相同的数据。该方法创建一个新的数组对象，该对象将查看相同的data.view

>>> c = a.view()
>>> c is a
False
>>> c.base is a                        # c is a view of the data owned by a
True
>>> c.flags.owndata
False
>>>
>>> c = c.reshape((2, 6))                      # a's shape doesn't change
>>> a.shape
(3, 4)
>>> c[0, 4] = 1234                      # a's data changes
>>> a
array([[   0,    1,    2,    3],
       [1234,    5,    6,    7],
       [   8,    9,   10,   11]])

对数组进行切片将返回数组的视图

>>> s = a[ : , 1:3]     # spaces added for clarity; could also be written "s = a[:, 1:3]"
>>> s[:] = 10    # s[:] is a view of s. Note the difference between s = 10 and s[:] = 10
>>> a
array([[   0,   10,   10,    3],
       [1234,   10,   10,    7],
       [   8,   10,   10,   11]])

深拷贝

该方法生成数组及其数据的完整副本data.copy

>>> d = a.copy()                          # a new array object with new data is created
>>> d is a
False
>>> d.base is a                           # d doesn't share anything with a
False
>>> d[0,0] = 9999
>>> a
array([[   0,   10,   10,    3],
       [1234,   10,   10,    7],
       [   8,   10,   10,   11]])

如果不再需要原始数组，有时应该在切片后调用。例如，假设是一个巨大的中间结果，而最终结果只包含一小部分，在使用切片构造时应该做一个深度复制。

>>> a = np.arange(int(1e8))
>>> b = a[:100].copy()
>>> del a  # the memory of ``a`` can be released.

函数和方法概述

下面是按类别排序的一些有用的NumPy函数和方法名称的列表。参见完整列表。
数组创建
arange, array, copy, empty, empty_like, eye, fromfile, fromfunction, identity, linspace, logspace, mgrid, ogrid, ones, ones_like, r_, zeros, zeros_like

转换

ndarray.astype, atleast_1d, atleast_2d, atleast_3d, mat

处理

array_split, column_stack, concatenate, diagonal, dsplit, dstack, hsplit, hstack, ndarray.item, newaxis, ravel, repeat, reshape, resize, squeeze, swapaxes, take, transpose, vsplit, vstack

问题

all, any, nonzero, where

组合

argmax, argmin, argsort, max, min, ptp, searchsorted, sort

操作

choose, compress, cumprod, cumsum, inner, ndarray.fill, imag, prod, put, putmask, real, sum

基本统计

cov, mean, std, var

基础线性代数

cross, dot, outer, linalg.svd, vdot