按其中某些元素的频率过滤 numpy 数组

25-05-01 2

最近很多小伙伴都在问按其中某些元素的频率过滤numpy数组这两个问题，那么本篇文章就来给大家详细解答一下，同时本文还将给你拓展"importnumpyasnp"ImportError:Nomodule

最近很多小伙伴都在问按其中某些元素的频率过滤 numpy 数组这两个问题，那么本篇文章就来给大家详细解答一下，同时本文还将给你拓展"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等相关知识，下面开始了哦！

本文目录一览：

按其中某些元素的频率过滤 numpy 数组
"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

按其中某些元素的频率过滤 numpy 数组

如何解决按其中某些元素的频率过滤 numpy 数组

我有一个 numpy 数组和一个类似于下面的字典：

arr1 = np.array([[''a1'',''x''],[''a2'',[''a3'',''y''],[''a4'',[''a5'',''z'']])
d = {''x'':2,''z'':1,''y'':1,''w'':2}

对于 (k,v) 中的每个键值对 d，k 在其第二列中的 v 中应该恰好出现 arr1 次。很明显，这不会发生在这里。

所以我想要做的是，从 arr1 开始，我想创建另一个数组，其中第二列中的每个元素都恰好按照 d 出现的次数。换句话说，我想要的结果是：

np.array([[''a1'',''z'']])

我可以使用列表推导得到我想要的结果：

ans = [[x1,x2] for x1,x2 in arr1 if np.count_nonzero(arr1==x2)==d[x2]]

但我想知道是否可以仅使用 numpy 来做到这一点。

解决方法

这就是你想要的：

import numpy as np
arr1 = np.array([[''a1'',''x''],[''a2'',[''a3'',''y''],[''a4'',[''a5'',''z'']])
d = {''x'': 2,''z'': 1,''y'': 1,''w'': 2}
# get the actual counts of values in arr1
counts = dict(zip(*np.unique(arr1[:,1],return_counts=True)))
# determine what values to keep,as their count matches the desired count
keep = [x for x in d if x in counts and d[x] == counts[x]]
# filter down the array
result = arr1[list(map(lambda x: x[1] in keep,arr1))]

很可能在 numpy 中有一种更优化的方法来做到这一点，但我不知道你申请的集合有多大，或者你需要多久这样做一次，以说寻找它是否值得

编辑：请注意，您需要扩大规模以决定什么是好的解决方案。您的原始解决方案非常适合玩具示例，它的表现优于这两个答案。但是，如果您扩展到可能更现实的工作负载，@NewbieAF 提供的 numpy 解决方案可以轻松击败其他解决方案：

from random import randint
from timeit import timeit
import numpy as np
def original(arr1,d):
    return [[x1,x2] for x1,x2 in arr1 if np.count_nonzero(arr1 == x2) == d[x2]]
def f1(arr1,d):
    # get the actual counts of values in arr1
    counts = dict(zip(*np.unique(arr1[:,return_counts=True)))
    # determine what values to keep,as their count matches the desired count
    keep = [x for x in d if x in counts and d[x] == counts[x]]
    # filter down the array
    return arr1[list(map(lambda x: x[1] in keep,arr1))]
def f2(arr1,d):
    # create arrays from d
    keys,vals = np.array(list(d.keys())),np.array(list(d.values()))
    # count the unique elements in arr1[:,1]
    unqs,cts = np.unique(arr1[:,return_counts=True)
    # only keep track of elements that appear in arr1
    mask = np.isin(keys,unqs)
    keys,vals = keys[mask],vals[mask]
    # sort the unique values and corresponding counts according to keys
    idx1 = np.argsort(np.argsort(keys))
    idx2 = np.argsort(unqs)
    unqs,cts = unqs[idx2][idx1],cts[idx2][idx1]
    # filter values by whether the counts match
    correct = unqs[vals==cts]
    return arr1[np.isin(arr1[:,correct)]
def main():
    arr1 = np.array([[''a1'',''z'']])
    d = {''x'': 2,''w'': 2}
    print(timeit(lambda: original(arr1,d),number=10000))
    print(timeit(lambda: f1(arr1,number=10000))
    print(timeit(lambda: f2(arr1,number=10000))
    counts = [randint(1,3) for _ in range(10000)]
    arr1 = np.array([[''x'',f''{n}''] for n in range(10000) for _ in range(counts[n])])
    d = {f''{n}'': randint(1,3) for n in range(10000)}
    print(timeit(lambda: original(arr1,number=10))
    print(timeit(lambda: f1(arr1,number=10))
    print(timeit(lambda: f2(arr1,number=10))
main()

结果：

0.14045359999999998
0.2402685
0.5027185999999999
46.7569239
5.893172499999999
0.08729539999999503

numpy 解决方案在玩具示例上很慢，但在大输入上要快几个数量级。您的解决方案看起来不错，但在扩展时输给了避免额外调用的非 numpy 解决方案。

考虑问题的大小。如果问题很小，您应该选择自己的解决方案，以提高可读性。如果问题是中等规模的，您可能会选择我的来提高性能。如果问题很大（无论是规模还是使用频率），您应该选择全 numpy 解决方案，牺牲可读性以提高速度。

在玩弄 np.argsort() 之后，我找到了一个纯粹的 numpy 解决方案。只需根据 arr1 的数组版本中相同元素的位置对 d.values() 的第二行进行排序。

arr1 = np.array([[''a1'',''z'']])
d = {''x'':2,''z'':1,''y'':1,''w'':2}
# create arrays from d
keys,np.array(list(d.values()))
# count the unique elements in arr1[:,1]
unqs,return_counts=True)
# only keep track of elements that appear in arr1
mask = np.isin(keys,unqs)
keys,vals[mask]
# sort the unique values and corresponding counts according to keys
idx1 = np.argsort(np.argsort(keys))
idx2 = np.argsort(unqs)
unqs,cts[idx2][idx1]
# filter values by whether the counts match
correct = unqs[vals==cts]
# keep subarray where the counts match
ans = arr1[np.isin(arr1[:,correct)]
print(ans)
# [[''a1'' ''x'']
#  [''a2'' ''x'']
#  [''a5'' ''z'']]

"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

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

up vote 18 down vote favorite

I understand that when possible one should use

import numpy as np

This helps keep away any conflict due to namespaces. But I have noticed that while the command below works

import numpy.f2py as myf2py

the following does not

import numpy as np
np.f2py #throws no module named f2py

Can someone please explain this?

python numpy

shareimprove this question

edited Mar 24 ''14 at 23:20

mu 無

24.7k104471

asked Mar 24 ''14 at 23:19

user1318806

3001311

@roippi have you tried exit your python and enter it and just do import numpy then numpy.f2py ? It throws an error in my case too – aha Mar 24 ''14 at 23:24

Importing a module doesn''t import sub-modules. You need to explicitly import the numpy.f2py module regardless of whether or not/how numpy itself has been imported. – alecb Mar 24 ''14 at 23:39

add a comment

4 Answers

active oldest votes

up vote 13 down vote

numpy is the top package name, and doing import numpy doesn''t import submodule numpy.f2py.

When you do import numpy it creats a link that points to numpy, but numpy is not further linked to f2py. The link is established when you do import numpy.f2py

In your above code:

import numpy as np # np is an alias pointing to numpy, but at this point numpy is not linked to numpy.f2py
import numpy.f2py as myf2py # this command makes numpy link to numpy.f2py. myf2py is another alias pointing to numpy.f2py as well

Here is the difference between import numpy.f2py and import numpy.f2py as myf2py:

import numpy.f2py
- put numpy into local symbol table(pointing to numpy), and numpy is linked to numpy.f2py
- both numpy and numpy.f2py are accessible
import numpy.f2py as myf2py
- put my2py into local symbol table(pointing to numpy.f2py)
- Its parent numpy is not added into local symbol table. Therefore you can not access numpy directly

shareimprove this answer

edited Mar 25 ''14 at 0:31

answered Mar 24 ''14 at 23:33

aha

1,2291718

add a comment

up vote 7 down vote

The import as syntax was introduced in PEP 221 and is well documented there.

When you import a module via

import numpy

the numpy package is bound to the local variable numpy. The import as syntax simply allows you to bind the import to the local variable name of your choice (usually to avoid name collisions, shorten verbose module names, or standardize access to modules with compatible APIs).

Thus,

import numpy as np

is equivalent to,

import numpy
np = numpy
del numpy

When trying to understand this mechanism, it''s worth remembering that import numpy actually means import numpy as numpy.

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.

import numpy as np
import numpy.f2py   # OK
import np.f2py      # ImportError

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 numpy. It has never prevented a name collision for me (laziness of programmers actually suggests there''s a higher probability of causing a collision with np)

Finally, to round out my exposé, here are 2 interesting uses of the import as mechanism that you should be aware of:

1. long subimports

import scipy.ndimage.interpolation as warp
warp.affine_transform(I, ...)

2. compatible APIs

try:
    import pyfftw.interfaces.numpy_fft as fft
except:
    import numpy.fft as fft
# call fft.ifft(If) with fftw or the numpy fallback under a common name

shareimprove this answer

answered Mar 25 ''14 at 0:59

hbristow

68345

add a comment

up vote 1 down vote

numpy.f2py is actually a submodule of numpy, and therefore has to be imported separately from numpy. As aha said before:

When you do import numpy it creats a link that points to numpy, but numpy is not further linked to f2py. The link is established when you do import numpy.f2py

when you call the statement import numpy as np, you are shortening the phrase "numpy" to "np" to make your code easier to read. It also helps to avoid namespace issues. (tkinter and ttk are a good example of what can happen when you do have that issue. The UIs look extremely different.)

shareimprove this answer

answered Mar 24 ''14 at 23:47

bspymaster

760923

add a comment

up vote 1 down vote

This is a language feature. f2py is a subpackage of the module numpy and must be loaded separately.

This feature allows:

you to load from numpy only the packages you need, speeding up execution.
the developers of f2py to have namespace separation from the developers of another subpackage.

Notice however that import numpy.f2py or its variant import numpy.f2py as myf2py are still loading the parent module numpy.

Said that, when you run

import numpy as np
np.f2py

You receive an AttributeError because f2py is not an attribute of numpy, because the __init__() of the package numpy did not declare in its scope anything about the subpackage f2py.

shareimprove this answer

answered Mar 24 ''14 at 23:57

gg349

7,67321739

when you do import numpy.f2py as myf2py, how do you access its parent numpy? it seems import numpy.f2py allows you to access its parent numpy, but import numpy.f2py as myf2py doesn''t – aha Mar 25 ''14 at 0:00

You don''t access it because you decided you didn''t want to use anything from numpy, and you only care of using the subpackage. It is similar to using from foo import bar: the name foo will not be accessible. See the comment after the first example of the docs, LINK – gg349 Mar 25 ''14 at 0:05

add a comment

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