blocks|key|3962437|text|尝试使用numpy==1.20.0，这在这里有效，即使其他情况不同(高山3.12上的Python3.8)。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3962438|entityMap^0|4|D|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|K|8|@]|D|@]|E|$]]]|G|$]]

try with <code>numpy==1.20.0</code> this worked here, even though other circumstances are different (python3.8 on alpine 3.12).

blocks|key|3962465|text|对于几乎相同的图像:python:3.7-slim+buster|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3962466|我今天才开始有这个问题，以前是不存在的。|3962467|我通过从requirement.txt文件中删除numpy并在Dockerfile文件中执行以下操作来解决这个问题：|3962468|RUN+pip3+install+--upgrade++--no-binary+numpy==1.18.1+numpy==1.18.1+\
&&+pip3+install+-r+requirements.txt+|code-block|syntax|javascript|3962469|我使用了一些旧版本的keras和它的库，升级到numpy+1.20.0对这些库不起作用。但我认为解决方案在于我给你的第一个命令，告诉pip不要编译numpy并下载一个预编译的版本。|3962470|命令中的诀窍是，您可能会发现有人告诉您使用pip的--no-binary选项来解决问题，但他们没有指定如何解决问题，而且这可能很棘手(就像我遇到的那样)；您必须在命令中编写两次包才能使其工作，否则pip会抛出错误。|3962471|我认为第一个命令中的--upgrade选项是不必要的。|3962472|entityMap^0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|T|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|U|8|@]|9|@]|A|$]]|$1|F|3|G|5|H|7|V|8|@]|9|@]|A|$I|J]]|$1|K|3|L|5|6|7|W|8|@]|9|@]|A|$]]|$1|M|3|N|5|6|7|X|8|@]|9|@]|A|$]]|$1|O|3|P|5|6|7|Y|8|@]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|R|$]]

For almost the same image : python:3.7-slim-buster
I started to have this problem just today, it was non exitent before.
I solved it by removing numpy from requirement.txt file and doing instead the following in my Dockerfile:
<pre><code>RUN pip3 install --upgrade --no-binary numpy==1.18.1 numpy==1.18.1 \
&amp;&amp; pip3 install -r requirements.txt 
</code></pre>
I use some old versions of keras and its librairies and updrading to numpy 1.20.0 didn't work for those librairies. But I think the solution consist in the first command that I gave you wich tell pip to try to not compile numpy and download a pre-compiled version.
The trick in the command is that you might find people telling you to use --no-binary option of pip to solve the problem, but they don't specify how and it might be tricky (as it happened to me); you have to write the package two times in the command in order for it to work or else pip will throw you an error.
I think the --upgrade option in the first command isn't necessary.

blocks|key|5380026|text|我使用的是Python+3.8.5。这听起来太简单了，但我也遇到了同样的问题，我所做的就是重新安装numpy。消失了。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|5380027|pip+install+--upgrade+numpy|code-block|syntax|javascript|5380028|或|5380029|pip+uninstall+numpy
pip+install+numpy|5380030|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

I'm in Python 3.8.5. It sounds too simple to be real, but I had this same issue and all I did was reinstall numpy. Gone.
<pre><code>pip install --upgrade numpy
</code></pre>
or
<pre><code>pip uninstall numpy
pip install numpy
</code></pre>

blocks|key|1547983|text|我在使用tensorflow对象api时遇到了这个问题。Tensorflow目前与numpy==1.20不兼容(尽管这个问题要到以后才会显现出来)。在我的例子中，这个问题是由pycocotools引起的。我通过安装一个较旧的版本进行了修复。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1547984|pip+install+pycocotools==2.0.0|code-block|syntax|javascript|1547985|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I had this issue when using the tensorflow object api. Tensorflow is currently NOT compatible with numpy==1.20 (although this issue is not apparent until later).
In my case, the issue was caused by pycocotools. I fixed by installing an older version.
<pre><code>pip install pycocotools==2.0.0
</code></pre>

blocks|key|1852700|text|实际上，正如本answer+below所指出的那样，(构建和)安装numpy>=1.20.0应该是可行的。然而，我认为一些背景知识可能会很有趣--并提供替代的解决方案。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1852701|在numpy+1.20.0中有一个change+in+the+C+API。在某些情况下，pip似乎会为构建阶段下载最新版本的numpy，但随后该程序将与已安装的numpy版本一起运行。如果在<1.20中使用了build版本，但安装的版本是=>1.20，这将导致错误。|1852702|(反之亦然，因为向后兼容。但如果使用的是已安装的numpy<1.20版本，他们就没有预料到即将发生的变化。)|1852703|这导致了几种可能的解决问题的方法：|1852704|在带有numpy>=1.20.0|1852705|use+--no-binary|unordered-list-item|1852706|
|1852707|install和--no-build-isolation|1852708|
的中，将|1852709|升级(内部版本)到pyproject.toml最低支持的numpy版本|1852710|1852711|有关潜在解决方案的更详细讨论，请参阅https://github.com/scikit-learn-contrib/hdbscan/issues/457#issuecomment-773671043。|1852712|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/66066058/11589255|1|https://numpy.org/doc/stable/release/1.20.0-notes.html#size-of-np-ndarray-and-np-void-changed|2|https://github.com/scikit-learn-contrib/hdbscan/issues/457#issuecomment-773671043^0|X|D|7|C|0|0|1|C|18|3|1Q|5|28|5|2N|5|3B|6|H|J|1|0|O|A|0|0|3|D|0|4|B|0|0|8|K|0|1|9|E|1|0|I|29|2|0^^$0|@$1|2|3|4|5|6|7|1D|8|@$9|1E|A|1F|B|C]]|D|@$9|1G|A|1H|1|1I]]|E|$]]|$1|F|3|G|5|6|7|1J|8|@$9|1K|A|1L|B|C]|$9|1M|A|1N|B|C]|$9|1O|A|1P|B|C]|$9|1Q|A|1R|B|C]|$9|1S|A|1T|B|C]|$9|1U|A|1V|B|C]]|D|@$9|1W|A|1X|1|1Y]]|E|$]]|$1|H|3|I|5|6|7|1Z|8|@$9|20|A|21|B|C]]|D|@]|E|$]]|$1|J|3|K|5|6|7|22|8|@]|D|@]|E|$]]|$1|L|3|M|5|6|7|23|8|@$9|24|A|25|B|C]]|D|@]|E|$]]|$1|N|3|O|5|P|7|26|8|@$9|27|A|28|B|C]]|D|@]|E|$]]|$1|Q|3|R|5|6|7|29|8|@]|D|@]|E|$]]|$1|S|3|T|5|P|7|2A|8|@$9|2B|A|2C|B|C]]|D|@]|E|$]]|$1|U|3|V|5|6|7|2D|8|@]|D|@]|E|$]]|$1|W|3|X|5|P|7|2E|8|@$9|2F|A|2G|B|C]]|D|@]|E|$]]|$1|Y|3|-4|5|6|7|2H|8|@]|D|@]|E|$]]|$1|Z|3|10|5|6|7|2I|8|@]|D|@$9|2J|A|2K|1|2L]]|E|$]]|$1|11|3|-4|5|6|7|2M|8|@]|D|@]|E|$]]]|12|$13|$5|14|15|16|E|$17|18]]|19|$5|14|15|16|E|$17|1A]]|1B|$5|14|15|16|E|$17|1C]]]]

Indeed, (building and) installing with <code>numpy&gt;=1.20.0</code> should work, as pointed out e.g. by this <a href="https://stackoverflow.com/a/66066058/11589255">answer below</a>. However, I thought some background might be interesting -- and provide also alternative solutions.
There was a <a href="https://numpy.org/doc/stable/release/1.20.0-notes.html#size-of-np-ndarray-and-np-void-changed" rel="noreferrer">change in the C API</a> in <code>numpy 1.20.0</code>. In some cases, <code>pip</code> seems to download the latest version of <code>numpy</code> for the build stage, but then the program is run with the installed version of <code>numpy</code>. If the build version used in <code>&lt;1.20</code>, but the installed version is <code>=&gt;1.20</code>, this will lead to an error.
(The other way around it should not matter, because of backwards compatibility. But if one uses an installed version <code>numpy&lt;1.20</code>, they did not anticipate the upcoming change.)
This leads to several possible ways to solve the problem:
<ul>
<li>upgrade (the build version) to <code>numpy&gt;=1.20.0</code></li>
<li>use minmum supported numpy version in <code>pyproject.toml</code> (<code>oldest-supported-numpy</code>)</li>
<li>install with <code>--no-binary</code></li>
<li>install with <code>--no-build-isolation</code></li>
</ul>
For a more detailed discussion of potential solutions, see
<a href="https://github.com/scikit-learn-contrib/hdbscan/issues/457#issuecomment-773671043" rel="noreferrer">https://github.com/scikit-learn-contrib/hdbscan/issues/457#issuecomment-773671043</a>.

blocks|key|3962598|text|对于任何使用Poetry的人来说，有必要将experimental.new-installer设置为true，以便正确构建具有numpy<1.20依赖关系的应用程序，即：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3962599|poetry+config+experimental.new-installer+true|code-block|syntax|javascript|3962600|默认情况下，它是true，但如果(就像我的情况一样)它被更改了，它就会让您大吃一惊。|3962601|我的应用程序使用Tensorflow，因此我没有升级到>1.20的选项。Poetry也不支持--no-binary依赖项。|3962602|entityMap|0|LINK|mutability|MUTABLE|url|https://python-poetry.org/^0|L|Q|1E|4|1R|A|6|6|0|0|0|8|4|0|R|5|1A|B|0^^$0|@$1|2|3|4|5|6|7|W|8|@$9|X|A|Y|B|C]|$9|Z|A|10|B|C]|$9|11|A|12|B|C]]|D|@$9|13|A|14|1|15]]|E|$]]|$1|F|3|G|5|H|7|16|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|17|8|@$9|18|A|19|B|C]]|D|@]|E|$]]|$1|M|3|N|5|6|7|1A|8|@$9|1B|A|1C|B|C]|$9|1D|A|1E|B|C]]|D|@]|E|$]]|$1|O|3|-4|5|6|7|1F|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]]]

For anyone using <a href="https://python-poetry.org/" rel="nofollow noreferrer">Poetry</a> it is necessary to have <code>experimental.new-installer</code> set to <code>true</code> for an application with a <code>numpy&lt;1.20</code> dependency to be built correctly i.e:
<pre><code>poetry config experimental.new-installer true
</code></pre>
It is <code>true</code> by default but if (as was the case for me) it has been changed it can catch you out.
My application uses Tensorflow and I did not therefore have the option of upgrading to <code>&gt;1.20</code>. Poetry also does not support <code>--no-binary</code> dependencies.

blocks|key|1852738|text|通过pip安装任何包后，请确保重新启动内核，并且应该可以正常工作。通常情况下，包会自动升级，您所需要的就是快速重启。至少，这在我的情况下是有效的，当我试图安装和使用石榴石时，我得到了同样的错误。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1852739|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

After you pip install any package, makes sure you restart the Kernel and should work. usually packages get upgraded automatically and all you need is a quick restart. At least, this what worked in my situation and I was getting the same error when I tried to install and use pomegranate.

blocks|key|1852744|text|对我起作用的是：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1852745|pip+uninstall+numpy
conda+install+-y+-c+conda-forge+numpy|code-block|syntax|javascript|1852746|这听起来可能很奇怪……我甚至不需要用conda卸载它，这对我来说似乎很奇怪。我使用的是python+3.9|1852747|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

what worked for me was:
<pre><code>pip uninstall numpy
conda install -y -c conda-forge numpy
</code></pre>
as bizzare as it might sound...I didn't even have to uninstall it with conda which seemed odd to me. I am using python 3.9

blocks|key|3962657|text|将python版本升级到numpy+==1.21.1对我很有效！|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3962658|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Upgrading python version to numpy ==1.21.1 worked for me!

blocks|key|1852781|text|使用python虚拟环境，并使用以下命令安装gensim：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1852782|pip+install+gensim==3.8.3|code-block|syntax|javascript|1852783|entityMap^0|M|6|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

Use python virtual environments and install <code>gensim</code> using :
<pre class="lang-sh prettyprint-override"><code>pip install gensim==3.8.3
</code></pre>

blocks|key|1852791|text|升级numpy版本：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1852792|pip+install+-U+numpy|code-block|syntax|javascript|1852793|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Upgrade numpy version:
<pre><code>pip install -U numpy
</code></pre>

blocks|key|1852819|text|无需升级numpy的解决方案|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1852820|虽然升级numpy版本通常可以解决这个问题，但并不总是可行的。一个很好的例子是当您使用tensorflow==2.6.0时，它与最新的numpy版本不兼容(它需要~=1.19.2)。|1852821|正如在FZeiser's+answer中已经提到的，在版本1.20.0中，numpy的C应用编程接口有一个变化。有一些包在构建时依赖于这个C，例如pycocotools。鉴于pip的依赖关系解析器不保证安装包的任何顺序，可能会发生以下情况：|1852822|1852823|pip认为它需要安装numpy，并选择了最新的版本，在写这个答案的时候是1.21.2。|ordered-list-item|1852824|然后构建一个依赖于numpy和它的C应用程序接口的包，例如pycocotools。此程序包现在与numpy+1.21.2+C应用编程接口兼容。|1852825|稍后需要安装一个对旧版本numpy有要求的程序包，例如尝试安装numpy==1.19.5的tensorflow==2.6.0。因此，numpy==1.21.2被卸载，旧版本是运行使用pycocotools的代码的installed.|1852826|When，其当前安装依赖于更新的numpy+C应用编程接口，但numpy版本被降级，这将导致错误。|1852827|1852828|解决方案|1852829|您应该使用过时的numpy+C+API用法重新构建包，例如用于pycocotools|1852830|pip+uninstall+pycocotools
pip+install+pycocotools+--no-binary+pycocotools|code-block|syntax|javascript|1852831|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/66743692/2572994^0|4|5|0|4|5|17|H|1V|5|29|8|0|T|6|11|5|21|B|2F|3|3|G|0|0|0|0|3|A|5|10|6|0|9|5|T|B|1C|C|0|C|5|V|D|19|H|1U|D|2J|B|0|G|5|V|5|0|0|0|8|5|V|B|0|0^^$0|@$1|2|3|4|5|6|7|1B|8|@$9|1C|A|1D|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|1E|8|@$9|1F|A|1G|B|C]|$9|1H|A|1I|B|C]|$9|1J|A|1K|B|C]|$9|1L|A|1M|B|C]]|D|@]|E|$]]|$1|H|3|I|5|6|7|1N|8|@$9|1O|A|1P|B|C]|$9|1Q|A|1R|B|C]|$9|1S|A|1T|B|C]|$9|1U|A|1V|B|C]]|D|@$9|1W|A|1X|1|1Y]]|E|$]]|$1|J|3|-4|5|6|7|1Z|8|@]|D|@]|E|$]]|$1|K|3|L|5|M|7|20|8|@$9|21|A|22|B|C]|$9|23|A|24|B|C]|$9|25|A|26|B|C]]|D|@]|E|$]]|$1|N|3|O|5|M|7|27|8|@$9|28|A|29|B|C]|$9|2A|A|2B|B|C]|$9|2C|A|2D|B|C]]|D|@]|E|$]]|$1|P|3|Q|5|M|7|2E|8|@$9|2F|A|2G|B|C]|$9|2H|A|2I|B|C]|$9|2J|A|2K|B|C]|$9|2L|A|2M|B|C]|$9|2N|A|2O|B|C]]|D|@]|E|$]]|$1|R|3|S|5|M|7|2P|8|@$9|2Q|A|2R|B|C]|$9|2S|A|2T|B|C]]|D|@]|E|$]]|$1|T|3|-4|5|6|7|2U|8|@]|D|@]|E|$]]|$1|U|3|V|5|6|7|2V|8|@]|D|@]|E|$]]|$1|W|3|X|5|6|7|2W|8|@$9|2X|A|2Y|B|C]|$9|2Z|A|30|B|C]]|D|@]|E|$]]|$1|Y|3|Z|5|10|7|31|8|@]|D|@]|E|$11|12]]|$1|13|3|-4|5|6|7|32|8|@]|D|@]|E|$]]]|14|$15|$5|16|17|18|E|$19|1A]]]]

<h2>Solution without upgrading <code>numpy</code></h2>
While upgrading the <code>numpy</code> version would often solve the issue, it's not always viable. Good example is the case when you're using <code>tensorflow==2.6.0</code> which isn't compatible with the newest <code>numpy</code> version (it requires <code>~=1.19.2</code>).
As already mentioned in <a href="https://stackoverflow.com/a/66743692/2572994">FZeiser's answer</a>, there was a change in <code>numpy</code>s C API in version <code>1.20.0</code>. There are packages that rely on this C API when they are being built, e.g. <code>pycocotools</code>. Given that <code>pip</code>s dependency resolver doesn't guarantee any order for installing the packages, the following might happen:
<ol>
<li><code>pip</code> figures out that it needs to install <code>numpy</code> and it chooses the latest version, <code>1.21.2</code> as of the time writing this answer.</li>
<li>It then builds a package that depends on <code>numpy</code> and its C API, e.g. <code>pycocotools</code>. This package is now compatible with <code>numpy 1.21.2</code> C API.</li>
<li>At a later point <code>pip</code> needs to install a package that has a requirement for an older version of <code>numpy</code>, e.g. <code>tensorflow==2.6.0</code> which would try to install <code>numpy==1.19.5</code>. As a result, <code>numpy==1.21.2</code> is uninstalled and the older version is installed.</li>
<li>When running code that uses <code>pycocotools</code>, its current installation relies on the updated <code>numpy</code> C API, yet the <code>numpy</code> version was downgraded which would result in the error.</li>
</ol>
<h3>Solution</h3>
You should rebuild the package with the outdated <code>numpy</code> C API usage, e.g. for <code>pycocotools</code>:
<pre><code>pip uninstall pycocotools
pip install pycocotools --no-binary pycocotools
</code></pre>

blocks|key|5380306|text|安装较旧版本的gensim，它可以工作！|type|unstyled|depth|inlineStyleRanges|entityRanges|data|5380307|pip+install+gensim==3.5.0|code-block|syntax|javascript|5380308|或|5380309|conda+install+gensim==3.5.0|5380310|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

Install older version of gensim, It works!
<pre><code>pip install gensim==3.5.0
</code></pre>
or
<pre><code>conda install gensim==3.5.0
</code></pre>

blocks|key|1852867|text|numpy版本1.22为我解决了这个问题。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1852868|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

numpy version 1.22 solved it for me.

blocks|key|3962866|text|我在raspberry+pi+3中也遇到了同样的问题，实际上错误出在pandas上。虽然tensorflow需要numpy~=1.19.2，但pandas不符合它。所以，我已经升级(因为降级不是)我的numpy到最新版本，一切正常！。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3962867|root@raspberrypi:/home/pi#+python3
Python+3.7.3+(default,+Jan+22+2021,+20:04:44)+
[GCC+8.3.0]+on+linux
Type+"help",+"copyright",+"credits"+or+"license"+for+more+information.
>>>+import+numpy+as+np+
>>>+np.__version__
'1.21.5'
>>>+import+pandas+as+pd
>>>+pd.__version__
'1.3.5'
>>>+import+tensorflow+as+tf
>>>+tf.__version__
'2.4.0'
>>>+tf.keras.__version__
'2.4.0'
>>>+tf.keras.layers
<module+'tensorflow.keras.layers'+from+'/usr/local/lib/python3.7/dist-packages/tensorflow/keras/layers/__init__.py'>|code-block|syntax|javascript|3962868|这里也有同样的问题-+https://github.com/bitsy-ai/tensorflow-arm-bin/issues/5|3962869|Tensorflow来源：https://github.com/bitsy-ai/tensorflow-arm-bin|3962870|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/bitsy-ai/tensorflow-arm-bin/issues/5|1|https://github.com/bitsy-ai/tensorflow-arm-bin^0|Y|6|1K|D|1Z|6|2S|5|0|0|B|1J|0|0|D|1A|1|0^^$0|@$1|2|3|4|5|6|7|Y|8|@$9|Z|A|10|B|C]|$9|11|A|12|B|C]|$9|13|A|14|B|C]|$9|15|A|16|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|17|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|18|8|@]|D|@$9|19|A|1A|1|1B]]|E|$]]|$1|M|3|N|5|6|7|1C|8|@]|D|@$9|1D|A|1E|1|1F]]|E|$]]|$1|O|3|-4|5|6|7|1G|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]|W|$5|R|S|T|E|$U|X]]]]

I was facing same issue in raspberry pi 3. Actually the error is with <code>pandas</code>. Although tensorflow need <code>numpy~=1.19.2</code> , but <code>pandas</code> is not compliable with it. So, I have upgraded (because downgrading is not) my <code>numpy</code> to latest version and all works fine!!!!.
<pre><code>root@raspberrypi:/home/pi# python3
Python 3.7.3 (default, Jan 22 2021, 20:04:44) 
[GCC 8.3.0] on linux
Type &quot;help&quot;, &quot;copyright&quot;, &quot;credits&quot; or &quot;license&quot; for more information.
&gt;&gt;&gt; import numpy as np 
&gt;&gt;&gt; np.__version__
'1.21.5'
&gt;&gt;&gt; import pandas as pd
&gt;&gt;&gt; pd.__version__
'1.3.5'
&gt;&gt;&gt; import tensorflow as tf
&gt;&gt;&gt; tf.__version__
'2.4.0'
&gt;&gt;&gt; tf.keras.__version__
'2.4.0'
&gt;&gt;&gt; tf.keras.layers
&lt;module 'tensorflow.keras.layers' from '/usr/local/lib/python3.7/dist-packages/tensorflow/keras/layers/__init__.py'&gt;
</code></pre>
Same issue here also - <a href="https://github.com/bitsy-ai/tensorflow-arm-bin/issues/5" rel="nofollow noreferrer">https://github.com/bitsy-ai/tensorflow-arm-bin/issues/5</a>
Tensorflow source: <a href="https://github.com/bitsy-ai/tensorflow-arm-bin" rel="nofollow noreferrer">https://github.com/bitsy-ai/tensorflow-arm-bin</a>

Importing from pyxdameraulevenshtein gives the following error, I have
<pre><code>pyxdameraulevenshtein==1.5.3, 
pandas==1.1.4 and 
scikit-learn==0.20.2. 
Numpy is 1.16.1. 
Works well in Python3.6, Issue in Python3.7.
</code></pre>
Has anyone been facing similar issues with Python3.7 (3.7.9), docker image - python:3.7-buster
<pre><code>__init__.pxd:242: in init pyxdameraulevenshtein
 ???
E ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject
</code></pre>

ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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从pyxdameraulevenshtein导入时会出现以下错误，我有pyxdameraulevenshtein==1.5.3, pandas==1.1.4 and scikit-learn==0.20.2. Numpy is 1.16.1. Works well in Python3.6, Issue in Pyth...

问ValueError: numpy.ndarray大小已更改，可能指示二进制不兼容。预期来自C标头的88，来自PyObject的80
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问ValueError: numpy.ndarray大小已更改，可能指示二进制不兼容。预期来自C标头的88，来自PyObject的80EN