本文将分享将标准偏差函数转换为numpy形式的详细内容,并且还将对标准偏差值函数进行详尽解释,此外,我们还将为大家带来关于"importnumpyasnp"ImportError:Nomodulena
本文将分享将标准偏差函数转换为 numpy 形式的详细内容,并且还将对标准偏差值函数进行详尽解释,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、Difference between import numpy and import numpy as np的相关知识,希望对你有所帮助。
本文目录一览:- 将标准偏差函数转换为 numpy 形式(标准偏差值函数)
- "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
- Difference between import numpy and import numpy as np
将标准偏差函数转换为 numpy 形式(标准偏差值函数)
如何解决将标准偏差函数转换为 numpy 形式
我想获得由 number 定义的每个第 n 个数字的标准偏差。所以它需要列表中每第 5 个数字的标准偏差。那么我如何才能将下面代码中的函数 std
转为 np.std
的 numpy 格式。
import numpy as np
number = 5
list_= np.array([457.334015,424.440002,394.795990,408.903992,398.821014,402.152008,435.790985,423.204987,411.574005,404.424988,399.519989,377.181000,375.467010,386.944000,383.614990,375.071991,359.511993,328.865997,320.510010,330.079010,336.187012,352.940002,365.026001,361.562012,362.299011,378.549011,390.414001,400.869995,394.773010,382.556000])
for i in range(len(list_)-number):
y_mean = sum(list_[i:i+number])/number
#Standard Dev function = square root((first list value - y_mean)+(second list value - y_mean) + (third list value - y_mean)/n-1)
std = (sum([(k - y_mean)**2 for k in list_[i:i+number]])/(number-1))**0.5
解决方法
看来您要测量的是特定窗口的滚动标准偏差。您可以结合使用列表推导式 np.std
:
>>> [list_[i:i+number].std() for i in range(0,len(list_)-number)]
[22.67653382706694,10.394077295510364,14.600764816599321,13.828019444486458,...
11.249337331453159,15.436611278024934,13.65945977634702]
,
在我的脑海中,我不确定您是否可以使用相同的 np.std
函数以这种特定方式提供服务,但您至少可以创建自己的可以提供相同效果的函数。尝试使变量与函数同名可能会很棘手,因为它会导致程序出现一些混乱。如果经过调整,您的代码可能会起作用,但似乎没有必要,因为 numpy 已经在其库中内置了一个 std 函数。但是,如果您想创建一个自定义函数来查找每个第 N 个数字的标准偏差,您可以使用以下代码:
import numpy as np
data_set = np.array(
[457.334015,424.440002,394.795990,408.903992,398.821014,402.152008,435.790985,423.204987,411.574005,404.424988,399.519989,377.181000,375.467010,386.944000,383.614990,375.071991,359.511993,328.865997,320.510010,330.079010,336.187012,352.940002,365.026001,361.562012,362.299011,378.549011,390.414001,400.869995,394.773010,382.556000])
def nthStd(x,number):
std_dev_set = []
for i in x[::+number-1]:
std_dev_set.append(i)
return std_dev_set
您可以调用该函数来插入您想要使用的数据集以及您想要使用的第 N 个数字。然后,您可以将正态 np.std
应用于函数并返回标准偏差。
例如,调用该函数将如下所示并返回以下内容:
nthStd(data_set,5)
OUTPUT: [457.334015,375.46701,394.77301]
它返回的只是原始数据集中所有数字的列表,这些数字相隔 N 次。如果你想找到这些的标准偏差,你可以像这样使用 np.std
函数:
np.std(nthStd(data_set,5))
或者,您也可以将 np.std
步骤放入函数本身以节省空间。这样,函数就会返回标准偏差,并且可以一起调用。
def nthStd(x,number):
std_dev_set = []
for i in x[::+number-1]:
std_dev_set.append(i)
return np.std(std_dev_set)
nthStd(data_set,5)
OUTPUT: 35.05640152471951
编辑:根据一些评论,您似乎希望它遍历所有有效的 N 数字对,因此值为 2 的 [1,2,3,4,5,6] 列表将产生有效[1,2] [2,3] [3,4] [4,5] [5,6] 列表,然后计算每个列表的标准偏差。可以通过添加另一个 for 循环来将此更改添加到原始公式中,如下所示:
def nthStd(x,number):
std_dev_set = []
std_answer = []
for k in range(0,len(x)+1-number):
std_dev_set.clear()
for i in x[k:number+k]:
std_dev_set.append(i)
std_answer.append(np.std(std_dev_set))
return std_answer
"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.*
.sum(a) | 对数组 a 求和 |
---|---|
.mean(a) | 求数学期望 |
.average(a) | 求平均值 |
.std(a) | 求标准差 |
.var(a) | 求方差 |
.ptp(a) | 求极差 |
.median(a) | 求中值,即中位数 |
.min(a) | 求最大值 |
.max(a) | 求最小值 |
.argmin(a) | 求最小值的下标,都处里为一维的下标 |
.argmax(a) | 求最大值的下标,都处里为一维的下标 |
.unravel_index(index, shape) | g 根据 shape, 由一维的下标生成多维的下标 |
(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
如何解决Anaconda Numpy 错误“Importing the Numpy C Extension Failed”是否有另一种解决方案?
希望有人能在这里提供帮助。我一直在绕圈子一段时间。我只是想设置一个 python 脚本,它将一些 json 数据从 REST API 加载到云数据库中。我在 Anaconda 上设置了一个虚拟环境(因为 GCP 库推荐这样做),安装了依赖项,现在我只是尝试导入库并向端点发送请求。 我使用 Conda(和 conda-forge)来设置环境并安装依赖项,所以希望一切都干净。我正在使用带有 Python 扩展的 VS 编辑器作为编辑器。 每当我尝试运行脚本时,我都会收到以下消息。我已经尝试了其他人在 Google/StackOverflow 上找到的所有解决方案,但没有一个有效。我通常使用 IDLE 或 Jupyter 进行脚本编写,没有任何问题,但我对 Anaconda、VS 或环境变量(似乎是相关的)没有太多经验。 在此先感谢您的帮助!
\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.
解决方法
暂无找到可以解决该程序问题的有效方法,小编努力寻找整理中!
如果你已经找到好的解决方法,欢迎将解决方案带上本链接一起发送给小编。
小编邮箱:dio#foxmail.com (将#修改为@)
Difference between import numpy and import numpy as np
Difference between import numpy and import numpy as np
up vote 18 down vote favorite 5 |
I understand that when possible one should use This helps keep away any conflict due to namespaces. But I have noticed that while the command below works the following does not Can someone please explain this? python numpy
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4 Answers
active oldest votes
up vote 13 down vote |
numpy is the top package name, and doing When you do In your above code: Here is the difference between
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up vote 7 down vote |
The When you import a module via the numpy package is bound to the local variable Thus, is equivalent to, When trying to understand this mechanism, it''s worth remembering that When importing a submodule, you must refer to the full parent module name, since the importing mechanics happen at a higher level than the local variable scope. i.e. I also take issue with your assertion that "where possible one should [import numpy as np]". This is done for historical reasons, mostly because people get tired very quickly of prefixing every operation with Finally, to round out my exposé, here are 2 interesting uses of the 1. long subimports 2. compatible APIs
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up vote 1 down vote |
when you call the statement
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up vote 1 down vote |
This is a language feature. This feature allows:
Notice however that Said that, when you run You receive an
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