Regarding the linalg package - the scipy functions will call lapack and blas, which are available in highly optimised versions on many platforms and offer very good performance, particularly for operations on reasonably large dense matrices. On the other hand, they are not easy libraries to compile, requiring a fortran compiler and many platform specific tweaks to get full performance. Therefore, numpy provides simple implementations of many common linear algebra functions which are often good enough for many purposes.

选自“数量经济学”讲座

SciPy是一个包，它包含各种构建在NumPy之上的工具，使用NumPy的数组数据类型和相关功能

事实上，当我们导入SciPy时，我们也得到了NumPy，这可以从SciPy初始化文件中看到

# Import numpy symbols to scipy name space
import numpy as _num
linalg = None
from numpy import *
from numpy.random import rand, randn
from numpy.fft import fft, ifft
from numpy.lib.scimath import *

__all__  = []
__all__ += _num.__all__
__all__ += ['randn', 'rand', 'fft', 'ifft']

del _num
# Remove the linalg imported from numpy so that the scipy.linalg package can be
# imported.
del linalg
__all__.remove('linalg')

但是，显式地使用NumPy功能是更常见和更好的实践

import numpy as np

a = np.identity(3)

SciPy中有用的是其子包中的功能吗

scipy。优化，scipy，整合，scipy。统计数据等。

SciPy参考指南:

…所有Numpy函数都有 被纳入scipy 命名空间，所有这些 函数不需要 另外导入Numpy。

这样做的目的是让用户不必知道scipy和numpy名称空间之间的区别，不过显然您已经发现了一个例外。

上次检查时，scipy __init__方法执行了一个

from numpy import *

以便在导入scipy模块时将整个numpy名称空间包含到scipy中。

您描述的log10行为很有趣，因为两个版本都来自numpy。一个是ufunc，另一个是numpy。自由的功能。为什么scipy更喜欢库函数而不是ufunc，我不知道在我的头顶上。

编辑:事实上，我可以回答log10的问题。在scipy __init__方法中，我看到了以下内容:

# Import numpy symbols to scipy name space
import numpy as _num
from numpy import oldnumeric
from numpy import *
from numpy.random import rand, randn
from numpy.fft import fft, ifft
from numpy.lib.scimath import *

在scipy中获得的log10函数来自numpy.lib.scimath。看看这段代码，它说:

"""
Wrapper functions to more user-friendly calling of certain math functions
whose output data-type is different than the input data-type in certain
domains of the input.

For example, for functions like log() with branch cuts, the versions in this
module provide the mathematically valid answers in the complex plane:

>>> import math
>>> from numpy.lib import scimath
>>> scimath.log(-math.exp(1)) == (1+1j*math.pi)
True

Similarly, sqrt(), other base logarithms, power() and trig functions are
correctly handled.  See their respective docstrings for specific examples.
"""

该模块似乎覆盖了sqrt, log, log2, logn, log10, power, arccos, arcsin和arctanh的基本numpy ufuncs。这就解释了你所看到的行为。这样做的潜在设计原因可能隐藏在某个邮件列表帖子中。

Regarding the linalg package - the scipy functions will call lapack and blas, which are available in highly optimised versions on many platforms and offer very good performance, particularly for operations on reasonably large dense matrices. On the other hand, they are not easy libraries to compile, requiring a fortran compiler and many platform specific tweaks to get full performance. Therefore, numpy provides simple implementations of many common linear algebra functions which are often good enough for many purposes.

从SciPy FAQ来看，NumPy的一些函数是由于历史原因而出现的，而这是应该的 只有在SciPy:

What is the difference between NumPy and SciPy? In an ideal world, NumPy would contain nothing but the array data type and the most basic operations: indexing, sorting, reshaping, basic elementwise functions, et cetera. All numerical code would reside in SciPy. However, one of NumPy’s important goals is compatibility, so NumPy tries to retain all features supported by either of its predecessors. Thus NumPy contains some linear algebra functions, even though these more properly belong in SciPy. In any case, SciPy contains more fully-featured versions of the linear algebra modules, as well as many other numerical algorithms. If you are doing scientific computing with python, you should probably install both NumPy and SciPy. Most new features belong in SciPy rather than NumPy.

这就解释了为什么scipy.linalg.solve比numpy.linalg.solve提供了一些额外的特性。

我没有看到塞瑟姆·莫顿对相关问题的回答