从 N 个 numpy 数组生成相等大小的批次（使用numpy产生200个数的数组）

25-04-28 2

本文将分享从N个numpy数组生成相等大小的批次的详细内容，并且还将对使用numpy产生200个数的数组进行详尽解释，此外，我们还将为大家带来关于"importnumpyasnp"ImportErro

本文将分享从 N 个 numpy 数组生成相等大小的批次的详细内容，并且还将对使用numpy产生200个数的数组进行详尽解释，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：

从 N 个 numpy 数组生成相等大小的批次（使用numpy产生200个数的数组）
"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

从 N 个 numpy 数组生成相等大小的批次（使用numpy产生200个数的数组）

如何解决从 N 个 numpy 数组生成相等大小的批次

我有 N 个形状为 data[n,m,3] 的 NumPy 数组。我想将它们拟合/挤压/分割/切片/重塑为 N'' 个形状为 new_data_#[1000,3] 的数组，其中 # 是新数组的索引。问题是 n 可以更小，或大于 1000。当它以某种方式更小时，我应该用下一个数组填充 new_array 剩余的 1000 个容量，当它大于 1000 时，我应该创建一个 new_data_# 并添加休息到那个。我不知道如何管理这个。这是一个伪代码，但它不能以这种方式完成，例如， while 可能不是必需的。输出可以写入磁盘或以新的数据格式返回。

def array2blocks(array_files)
 for each N in array_files:
    N = data = np.random.rand(n,3)
    new_data = np.zeros((1000,3),dtype=np.float32)
    j=0
    index = 0
    while j <= new_data.shape[0]:
        for i in range(data.shape[0]):
            print("--->",data[i,:,:])
            print (i)
            if i <= new_data.shape[0]:
                # here first we should check the left capacity of new_data and then insert data into it
                # new_data[i,:] = data[i,:] #this overrides prevIoUs items so not correct
                print(new_data)
            else:
                print(''n>1000'')
                new_data_name = ''new_data'' + ''_'' + str(index)
                # here fill rest of the data in the new_data
                ...
                index += 1
            #when capacity is full write it to the disk
    print(new_data)

UPDATE 与 Aaron 的旧答案：我用 batch_size = 5 替换了 1000 以简化操作。

def numpyarrays2blocks(array_files):
    N1 = np.random.rand(7,4,3)
    N2 = np.random.rand(7,3)
    N3 = np.random.rand(4,3)
    # array_files = []
    array_files.append(N1)
    array_files.append(N2)
    array_files.append(N3)
    for N in array_files:
        n = N.shape[0]
        m = N.shape[1]
        batch_size = 5
        # N = data = np.random.rand(n,3)
        data = N
        # print(data)
        new_arrays = []
        i = 0  # the current row index to insert
        while i < n:
            new_data = np.zeros((batch_size,dtype=np.float32)
            j = min(i + batch_size,n)  # the last row (exclusive) to copy to new_data
            # j - i is the number of rows to copy
            new_data[:j - i,:] = data[i:j,:]
            print(''NEW DATA: '',new_data)
            i = j  # update the index
            new_arrays.append(new_data)
    print(new_arrays)

解决方法

data 用于存储临时结果，data_start 是向data 插入行的索引。
如果是 data 则分配 None
yield data 如果已满。

merge_and_split 是一个生成器，因此内存需求应该很低。

import random
from typing import Iterator

import numpy as np


def merge_and_split(arrays,batch_size) -> Iterator:
    arrays = tuple(arrays)

    dtype = arrays[0].dtype

    data_shape = (batch_size,) + arrays[0].shape[1:]

    assert all(a.shape[1:] == data_shape[1:] for a in arrays),"Shape mismatch"

    data = None
    data_start = 0

    for src in arrays:
        src_index = 0
        src_avail = src.shape[0]

        while src_avail >= 1:
            if data is None:
                # allocate if None
                data = np.zeros(data_shape,dtype=dtype)
                data_start = 0

            num_moved = min(batch_size - data_start,src_avail)
            data[data_start:data_start + num_moved,...] = src[src_index:src_index + num_moved,...]

            data_start += num_moved
            src_index += num_moved
            src_avail -= num_moved

            if data_start >= batch_size:
                yield data
                data = None

    if data is not None:
        yield data


def input_arrays():
    number = 10

    r = random.Random(13)

    return [np.random.randint(0,10,size=(r.randint(1,5),4,3)) for _ in range(number)]


def main():
    # Testing input and output
    arrays = input_arrays()

    # for i,item in enumerate(arrays):
    #     print(''input'',i,item.shape)
    #     print(item)

    result = list(merge_and_split(arrays,5))

    # for i,item in enumerate(result):
    #     print(''result'',item.shape)
    #     print(item)

    src_concat = np.vstack(arrays)
    row_number = sum(s.shape[0] for s in arrays)
    print(''concatenated'',src_concat.shape,row_number)

    out_concat = np.vstack(result)
    print(out_concat.shape)
    print((out_concat[0:row_number,...] == src_concat).all())  # They are indeed the same


if __name__ == ''__main__'':
    main()

你可以concatenate你所有的原始数组split它们：

ars = ... # list of N arrays
ars = np.concatenate(ars,axis=0)
ars = np.split(ars,np.arange(1000,ars.shape[0],1000))

最后一行可以写成 ars = np.split(ars,1000)，但前提是您确定元素总数是 1000 的倍数，否则 np.split 会导致呕吐。指定明确的分割点，就像 np.arange 一样，可以让您拥有更短的最后一段。

"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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