本文将介绍获取不同范围的所有numpy切片的有效方法的详细情况,特别是关于numpy获取指定行列的相关信息。我们将通过案例分析、数据研究等多种方式,帮助您更全面地了解这个主题,同时也将涉及一些关于"i
本文将介绍获取不同范围的所有 numpy 切片的有效方法的详细情况,特别是关于numpy获取指定行列的相关信息。我们将通过案例分析、数据研究等多种方式,帮助您更全面地了解这个主题,同时也将涉及一些关于"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的知识。
本文目录一览:- 获取不同范围的所有 numpy 切片的有效方法(numpy获取指定行列)
- "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
获取不同范围的所有 numpy 切片的有效方法(numpy获取指定行列)
如何解决获取不同范围的所有 numpy 切片的有效方法
我想多次切片相同的 numpy 数组 (data_arra) 以查找每次不同范围内的值
data_ar shpe: (203,)
range_ar 形状:(1000,)
我用 for 循环实现了它,但它需要很长时间,因为我有很多 data_arrays:
#create results array
results_ar = np.zeros(shape=(1000),dtype=object)
i=0
for range in range_ar:
results_ar[i] = data_ar[( (data_ar>=(range-delta)) & (data_ar<(range+delta)) )].values
i+=1
例如:
data_ar = [1,3,4,6,10,12]
range_ar = [7,2]
delta= 3
预期输出:
(注意results_ar shpae=(3,) dtype=object,每个元素都是一个数组)
results_ar[[6,10];
[1,6];
[1,4]]
关于如何解决这个问题的一些想法?
解决方法
您可以使用 numba 来加快计算速度。
import numpy as np
import numba
from numba.typed import List
import timeit
data_ar = np.array([1,3,4,6,10,12])
range_ar = np.array([7,2])
delta = 3
def foo(data_ar,range_ar):
results_ar = list()
for i in range_ar:
results_ar.append(data_ar[( (data_ar>=(i-delta)) & (data_ar<(i+delta)) )])
print(timeit.timeit(lambda :foo(data_ar,range_ar)))
@numba.njit(parallel=True,fastmath=True)
def foo(data_ar,range_ar):
results_ar = List()
for i in range_ar:
results_ar.append(data_ar[( (data_ar>=(i-delta)) & (data_ar<(i+delta)) )])
print(timeit.timeit(lambda :foo(data_ar,range_ar)))
15.53519330600102
1.6557575029946747
几乎是 9.8 倍的加速。
,您可以像这样使用 np.searchsorted
:
data_ar = np.array([1,2])
delta = 3
bounds = range_ar[:,None] + delta * np.array([-1,1])
result = [data_ar[slice(*row)] for row in np.searchsorted(data_ar,bounds)]
"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
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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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