本文将分享Python列表比numpy数组表现更好？Python列表麻木的详细内容，并且还将对python列表和数组有啥区别进行详尽解释，此外，我们还将为大家带来关于"importnumpyasnp"

本文将分享Python 列表比 numpy 数组表现更好？ Python 列表麻木的详细内容，并且还将对python列表和数组有啥区别进行详尽解释，此外，我们还将为大家带来关于"import numpy as np" ImportError: No module named numpy、3.7Python 数据处理篇之 Numpy 系列 (七)---Numpy 的统计函数、Anaconda Numpy 错误“Importing the Numpy C Extension Failed”是否有另一种解决方案、cvxpy 和 numpy 之间的版本冲突：“针对 API 版本 0xe 编译的模块，但此版本的 numpy 是 0xd”的相关知识，希望对你有所帮助。

• Python 列表比 numpy 数组表现更好？ Python 列表麻木（python列表和数组有啥区别）
• "import numpy as np" ImportError: No module named numpy
• 3.7Python 数据处理篇之 Numpy 系列 (七)---Numpy 的统计函数
• Anaconda Numpy 错误“Importing the Numpy C Extension Failed”是否有另一种解决方案
• cvxpy 和 numpy 之间的版本冲突：“针对 API 版本 0xe 编译的模块，但此版本的 numpy 是 0xd”

Python 列表比 numpy 数组表现更好？ Python 列表麻木（python列表和数组有啥区别）

如何解决Python 列表比 numpy 数组表现更好？ Python 列表麻木

我最近报名参加了一门科学的 Python 课程，并且在课堂上展示了在某些情况下 numpy 数组如何比列表表现得更好。对于统计模拟，我尝试了两种方法，令人惊讶的是，numpy 数组需要更长的时间才能完成该过程。有人能帮我找出我的（可能的）错误吗？

我的第一个想法是代码的编写方式可能有问题，但我无法弄清楚它是如何出错的。该脚本会计算一个人平均需要多少次尝试才能完成一组已排序的贴纸：

Python 列表

我使用了一个函数，没有使用外部模块。

import random as rd
import statistics as st
def collectStickers(experiments,collectible):
    obtained = []
    attempts = 0
    while(len(obtained) < collectible):
        new_sticker = rd.randint(1,collectible)
        if new_sticker not in obtained:
            obtained.append(new_sticker)
        attempts += 1
    experiments.append(attempts)
experiments = []
collectible = 20
rep_experiment = 100000
for i in range(1,rep_experiment):
    collectStickers(experiments,collectible)
print(st.mean(experiments))

结果

对于像这样的简单实验来说，处理时间似乎还可以，但对于更复杂的目的，13.8 秒太多了。

72.06983069830699
[Finished in 13.8s]

麻木

我无法使用任何函数，因为当我遵循与上述相同的逻辑时出现以下错误：

• 运行时警告：空切片的平均值。

• RuntimeWarning：在 double_scalars 中遇到无效值

所以我只是选择了天真的方式：

import random as rd
import numpy as np
experiments = np.array([])
rep_experiment = 100000
for i in range(1,rep_experiment):
    obtained = np.array([])
    attempts = 0
    while(len(obtained) < 20):
        new_sticker = rd.randint(1,20)
        if new_sticker not in obtained:
            obtained = np.append(obtained,new_sticker)
        attempts += 1
    experiments = np.append(experiments,attempts)
print(np.mean(experiments))

结果

几乎慢了 4 倍！

是函数使用的区别吗？

72.03112031120311
[Finished in 54.2s]

解决方法

def vectorized():
    rep_experiment = 100000
    collectible = 20
    # array of falses
    obtained = np.zeros(rep_experiment,dtype=bool)
    attempts = np.zeros(rep_experiment,dtype=int)
    targets = np.zeros((rep_experiment,collectible),dtype=bool)
    x = np.arange(0,100000,step=1,dtype=int)
    while False in targets:
        r = np.random.randint(0,collectible,size=rep_experiment)
        # add the new stickers to the collected target
        targets[x,r] = True
        # if collected all set obtained to True
        obtained[np.sum(targets,axis=1)==collectible] = True
        # increments the not obtained values
        attempts[~obtained] += 1
    print(attempts.mean(),attempts.std())

experiments = np.append(experiments,attempts)

import random
import statistics
import timeit
import numpy as np
collectible = 20
rep_experiment = 10000
def original_collect_stickers():
    obtained = []
    attempts = 0
    while len(obtained) < collectible:
        new_sticker = random.randint(1,collectible)
        if new_sticker not in obtained:
            obtained.append(new_sticker)
        attempts += 1
    return attempts
def set_collect_stickers():
    obtained = set()
    attempts = 0
    n = 0
    while n < collectible:
        new_sticker = random.randint(1,collectible)
        if new_sticker not in obtained:
            obtained.add(new_sticker)
            n += 1
        attempts += 1
    return attempts
def repeat_with_list(fn):
    experiments = []
    for i in range(rep_experiment):
        experiments.append(fn())
    return statistics.mean(experiments)
def repeat_with_numpy(fn):
    experiments = np.zeros(rep_experiment)
    for i in range(rep_experiment):
        experiments[i] = fn()
    return np.mean(experiments)
def time_fn(name,fn,n=3):
    time_taken = timeit.timeit(fn,number=n) / n
    result = fn()  # once more to get the result too
    print(f"{name:15}: {time_taken:.6f},result {result}")
for wrapper in (repeat_with_list,repeat_with_numpy):
    for fn in (original_collect_stickers,set_collect_stickers):
        time_fn(f"{wrapper.__name__} {fn.__name__}",lambda: wrapper(fn))

repeat_with_list original_collect_stickers: 0.747183,result 71.7912
repeat_with_list set_collect_stickers: 0.688952,result 72.1002
repeat_with_numpy original_collect_stickers: 0.752644,result 72.0978
repeat_with_numpy set_collect_stickers: 0.685355,result 71.7515

repeat_with_list original_collect_stickers: 0.761186,result 72.0185
repeat_with_list set_collect_stickers: 0.690244,result 71.878
repeat_with_list set_collect_stickers_fastrand: 0.116410,result 71.9323
repeat_with_numpy original_collect_stickers: 0.759154,result 71.8604
repeat_with_numpy set_collect_stickers: 0.696563,result 71.5482
repeat_with_numpy set_collect_stickers_fastrand: 0.114212,result 71.6369

"import numpy as np" ImportError: No module named numpy

问题：没有安装 numpy

解决方法：

下载文件，安装

numpy-1.8.2-win32-superpack-python2.7

安装运行 import numpy，出现

Traceback (most recent call last):
  File "<pyshell#2>", line 1, in <module>
    import numpy
  File "C:\Python27\lib\site-packages\numpy\__init__.py", line 153, in <module>
    from . import add_newdocs
  File "C:\Python27\lib\site-packages\numpy\add_newdocs.py", line 13, in <module>
    from numpy.lib import add_newdoc
  File "C:\Python27\lib\site-packages\numpy\lib\__init__.py", line 8, in <module>
    from .type_check import *
  File "C:\Python27\lib\site-packages\numpy\lib\type_check.py", line 11, in <module>
    import numpy.core.numeric as _nx
  File "C:\Python27\lib\site-packages\numpy\core\__init__.py", line 6, in <module>
    from . import multiarray
ImportError: DLL load failed: %1 不是有效的 Win32 应用程序。

原因是：python 装的是 64 位的，numpy 装的是 32 位的

重新安装 numpy 为：numpy-1.8.0-win64-py2.7

3.7Python 数据处理篇之 Numpy 系列 (七)---Numpy 的统计函数

目录

[TOC]

前言

具体我们来学 Numpy 的统计函数

（一）函数一览表

调用方式：np.*

（二）统计函数 1

（1）说明

（2）输出

.sum(a)

.mean(a)

.average(a)

.std(a)

.var(a)

（三）统计函数 2

（1）说明

（2）输出

.max(a) .min(a)

.ptp(a)

.median(a)

.argmin(a)

.argmax(a)

.unravel_index(index,shape)

作者：Mark

日期：2019/02/11 周一

Anaconda Numpy 错误“Importing the Numpy C Extension Failed”是否有另一种解决方案

  \Traceback (most recent call last):
File "C:\Conda\envs\gcp\lib\site-packages\numpy\core\__init__.py",line 22,in <module>
from . import multiarray
File "C:\Conda\envs\gcp\lib\site-packages\numpy\core\multiarray.py",line 12,in <module>
from . import overrides
File "C:\Conda\envs\gcp\lib\site-packages\numpy\core\overrides.py",line 7,in <module>
from numpy.core._multiarray_umath import (
ImportError: DLL load Failed while importing _multiarray_umath: The specified module Could not be found.

During handling of the above exception,another exception occurred:

Traceback (most recent call last):
File "c:\API\citi-bike.py",line 4,in <module>
import numpy as np
File "C:\Conda\envs\gcp\lib\site-packages\numpy\__init__.py",line 150,in <module>
from . import core
File "C:\Conda\envs\gcp\lib\site-packages\numpy\core\__init__.py",line 48,in <module>
raise ImportError(msg)
ImportError:

IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!

Importing the numpy C-extensions Failed. This error can happen for
many reasons,often due to issues with your setup or how NumPy was
installed.

We have compiled some common reasons and troubleshooting tips at:

https://numpy.org/devdocs/user/troubleshooting-importerror.html

Please note and check the following:

* The Python version is: python3.9 from "C:\Conda\envs\gcp\python.exe"
* The NumPy version is: "1.21.1"

and make sure that they are the versions you expect.
Please carefully study the documentation linked above for further help.

Original error was: DLL load Failed while importing _multiarray_umath: The specified module Could not be found.

RuntimeError: module compiled against API version 0xe but this version of numpy is 0xd
Traceback (most recent call last):
  File "test.py",line 1,in <module>
    import cvxpy
  File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\__init__.py",line 18,in <module>
    from cvxpy.atoms import *
  File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\atoms\__init__.py",line 20,in <module>
    from cvxpy.atoms.geo_mean import geo_mean
  File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\atoms\geo_mean.py",in <module>
    from cvxpy.utilities.power_tools import (fracify,decompose,approx_error,lower_bound,File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\utilities\power_tools.py",in <module>
    from cvxpy.atoms.affine.reshape import reshape
  File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\atoms\affine\reshape.py",in <module>
    from cvxpy.atoms.affine.hstack import hstack
  File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\atoms\affine\hstack.py",in <module>
    from cvxpy.atoms.affine.affine_atom import AffAtom
  File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\atoms\affine\affine_atom.py",line 22,in <module>
    from cvxpy.cvxcore.python import canonInterface
  File "c:\Projekte\algo5\venv\lib\site-packages\cvxpy\cvxcore\python\__init__.py",line 3,in <module>
    import _cvxcore
ImportError: numpy.core.multiarray Failed to import

cvxpy 
numpy~=1.19.2 # tensorflow 2.4.1 requires this version

import cvxpy

if __name__ == ''__main__'':
    pass