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<h1 class="post-title"><a href="https://blog.dich.bid/windows-5-py/">Windows系列(5):Python开发配置</a></h1>
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2024-05-31
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:: tags:
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<a class="post-tag" href="https://blog.dich.bid/tags/windows/">#Windows</a></span>
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<div class="post-content">
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<p>前言 由于 Windows 中开发环境较 linux 复杂，这里总结 Windows 中使用 Jupyter 开发 Python 的环境配置。</p>
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<span id="continue-reading"></span><h2 id="an-zhuang">安装</h2>
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<p>Python是一种跨平台的编程语言,社区生态丰富，有许多现成的包可以调用。传统的安装方法如下：</p>
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<ul>
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<li>下载、安装Pythond解释器；</li>
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<li>验证安装；</li>
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<li>安装VScode以及Python的拓展；</li>
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</ul>
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<p>但Python开发项目时往往需要不同版本，不同的第三方包，如果用传统方法难以管理；因此现在的主流方法是：</p>
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<ul>
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<li>安装Anaconda或miniconda等Python集成包；</li>
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<li>使用conda创建并启动一个Python环境；</li>
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<li>安装jupyter编辑器编写python。</li>
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</ul>
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<p>在<a href="https://www.anaconda.com/">Anaconda官网</a>下载并安装，安装成功后，命令行中敲<code>conda info</code>，会显示conda的版本和python的版本等详细信息；再敲<code>conda list</code>，会列出当前环境下所有安装的包。</p>
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<p>安装好了Anaconda，就相当于同时有了Python、环境管理器、包管理器以及一大堆开箱即用的科学计算工具包。</p>
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<h2 id="shi-yong">使用</h2>
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<p>安装好了，默认是在base虚拟环境下，此时我们从base环境复制一份出来，在新环境里工作。</p>
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<ul>
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<li>复制base环境, 创建test环境</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda create --name test --clone base
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</span></code></pre>
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<ul>
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<li>激活test环境</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda activate test
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</span></code></pre>
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<ul>
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<li>取消Conda默认激活base虚拟环境</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda config --set auto_activate_base false
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</span></code></pre>
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<ul>
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<li>列出本机的所有环境，如下，可见当前有2个环境，当前激活的是test环境：</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>(test) ➜ ~ conda info -e
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</span><span>- conda environments:
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</span><span>#
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</span><span>base /Volumes/300g/opt/anaconda3
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</span><span>test * /Volumes/300g/opt/anaconda3/envs/test
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</span></code></pre>
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<ul>
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<li>Anaconda默认安装了jupyter，打开jupyter：</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>jupyter notebook
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</span></code></pre>
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<p>此时会自动弹出浏览器窗口打开Jupyter Notebook网页，默认为<code>http://localhost:8888</code></p>
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<blockquote>
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<p>Jupyter汉化/下载中文包：<code>pip install jupyterlab-language-pack-zh-CN</code></p>
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</blockquote>
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<h3 id="xu-ni-huan-jing-guan-li">虚拟环境管理</h3>
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<ul>
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<li>创建环境，后面的python=3.6是指定python的版本</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda create --name env_name python=3.6
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</span></code></pre>
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<ul>
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<li>创建包含某些包的环境（也可以加上版本信息）</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda create --name env_name python=3.7 numpy scrapy
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</span></code></pre>
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<ul>
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<li>激活某个环境</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda activate env_name
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</span></code></pre>
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<ul>
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<li>关闭某个环境</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda deactivate
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</span></code></pre>
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<ul>
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<li>复制某个环境</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda create --name new_env_name --clone old_env_name
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</span></code></pre>
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<ul>
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<li>删除某个环境</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda remove --name env_name --all
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</span></code></pre>
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<ul>
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<li>生成需要分享环境的yml文件（需要在虚拟环境中执行）</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda env export > environment.yml
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</span></code></pre>
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<ul>
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<li>别人在自己本地使用yml文件创建虚拟环境</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda env create -f environment.yml
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</span></code></pre>
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<h3 id="bao-guan-li">包管理</h3>
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<ul>
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<li>列出当前环境下所有安装的包</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda list
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</span></code></pre>
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<ul>
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<li>列举一个指定环境下的所有包</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda list -n env_name
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</span></code></pre>
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<ul>
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<li>查询库</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda search scrapys
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</span></code></pre>
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<ul>
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<li>安装库安装时可以指定版本例如：（scrapy=1.5.0）</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda install scrapy
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</span></code></pre>
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<ul>
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<li>为指定环境安装某个包</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda install --name target_env_name package_name
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</span></code></pre>
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<ul>
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<li>更新安装的库</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda update scrapy
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</span></code></pre>
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<ul>
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<li>更新指定环境某个包</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda update -n target_env_name package_name
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</span></code></pre>
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<ul>
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<li>更新所有包</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda update --all
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</span></code></pre>
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<ul>
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<li>删除已经安装的库</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda remove scrapy
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</span></code></pre>
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<ul>
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<li>删除指定环境某个包</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda remove -n target_env_name package_name
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</span></code></pre>
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<ul>
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<li>更多命令请查看官方文档或者查询帮助命令：</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda --help
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</span><span>
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</span><span>conda install --help
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</span></code></pre>
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<p>有了Conda包管理器，为什么Anaconda环境中，可能还需要用pip安装包呢？因为Anaconda本身只提供部分包，远没有pip提供的包多，有时conda无法安装我们需要的包，此时需要用pip将其装到conda环境里。</p>
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<p>安装特定版本的包，conda用=，pip用==。例如：</p>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda install xxx=1.0.0
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</span><span>pip install xxx==1.0.0
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</span></code></pre>
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<h2 id="jupytershi-yong">Jupyter使用</h2>
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<p>安装Anaconda并启动一个环境之后，如何让Jupyter Notebook在我们要的环境中启动呢？</p>
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<ul>
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<li>激活目标环境</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda activate myenv
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</span></code></pre>
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<ul>
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<li>安装 ipykernel（如尚未安装）
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为了让 Jupyter Notebook 能识别该环境中的 Python 解释器，你需要在该环境中安装 ipykernel：</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda install ipykernel
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</span><span>
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</span><span># 或者使用 pip
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</span><span>
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</span><span>pip install ipykernel
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</span></code></pre>
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<ul>
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<li>注册环境内核
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将该环境注册为 Jupyter 的一个内核（kernel），这样启动 Jupyter Notebook 后就能选择这个内核：</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>python -m ipykernel install --user --name myenv --display-name "Python (myenv)"
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</span><span>
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</span><span># 这里 --name 指定内核的名称，--display-name 是在 Jupyter Notebook 界面中显示的名称，你可以根据需要自定义。
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</span></code></pre>
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<ul>
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<li>启动 Jupyter Notebook：依然在激活后的环境中，启动 Jupyter Notebook：</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>jupyter notebook
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</span></code></pre>
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<ul>
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<li>启动后，你在新建 notebook 时可以选择刚刚注册的内核 “Python (myenv)” 来确保使用该环境的 Python 解释器。</li>
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</ul>
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<blockquote>
|
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<p>当然，你也可以使用其他编辑器/IDE如 Sublime Text 或者 JetBrains 系列的 PyCharm 。</p>
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</blockquote>
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<blockquote>
|
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<p>linux中使用Miniconda</p>
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</blockquote>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span># Miniconda安装脚本
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</span><span>wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
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</span><span># 执行以下命令启动安装程序：
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</span><span>bash Miniconda3-latest-Linux-x86_64.sh
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</span><span># 验证安装
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</span><span>conda --version
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</span></code></pre>
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<h2 id="shi-yong-uvti-dai-conda">使用UV替代Conda</h2>
|
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<blockquote>
|
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<p>UV（由 Astral 团队开发）是一个用 Rust 编写的高性能包管理器，提供了类似 Conda 的虚拟环境管理和依赖解析功能，并且在大多数场景下比 pip 和 Conda 快 10–100 倍。它通过命令行工具如 uv venv（创建/管理虚拟环境）和 uv pip（安装/锁定/同步依赖）来覆盖传统的 conda create、conda install、conda env export 等操作，但本身并不管理底层的 C/C++ 库，因此对于诸如 GDAL、SciPy 等需要系统级二进制依赖的包，仍建议在 Conda/系统包管理器中预装相关库，然后用 UV 来管理 Python 包。</p>
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</blockquote>
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<p><strong>安装与激活</strong></p>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>wget -qO- https://astral.sh/uv/install.sh | sh
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</span></code></pre>
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<ul>
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<li>在当前目录下创建 .venv，使用系统默认 Python（若不存在则自动下载）</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>uv venv
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</span></code></pre>
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<ul>
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<li>指定环境名称或路径</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>uv venv myenv
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</span></code></pre>
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<ul>
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<li>指定 Python 版本（需系统已有或可下载）</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>uv venv --python 3.11
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</span></code></pre>
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<ul>
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<li>激活</li>
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</ul>
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<pre style="background-color:#151515;color:#e8e8d3;"><code><span>source .venv/bin/activate
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</span></code></pre>
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<p><strong>安装包</strong></p>
|
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<pre data-lang="bash" style="background-color:#151515;color:#e8e8d3;" class="language-bash "><code class="language-bash" data-lang="bash"><span style="color:#888888;"># 安装单个包
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</span><span style="color:#ffb964;">uv</span><span> pip install requests
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</span><span>
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</span><span style="color:#888888;"># 批量安装并自动锁定依赖
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</span><span style="color:#ffb964;">uv</span><span> pip install fastapi uvicorn sqlalchemy
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</span></code></pre>
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<p><strong>生成与同步锁文件</strong></p>
|
||
<pre data-lang="bash" style="background-color:#151515;color:#e8e8d3;" class="language-bash "><code class="language-bash" data-lang="bash"><span style="color:#888888;"># 从 requirements.in 生成统一依赖文件
|
||
</span><span style="color:#ffb964;">uv</span><span> pip compile docs/requirements.in \
|
||
</span><span style="color:#ffb964;"> --universal </span><span>\
|
||
</span><span style="color:#ffb964;"> --output-file</span><span> docs/requirements.txt
|
||
</span><span>
|
||
</span><span style="color:#888888;"># 根据锁文件同步环境
|
||
</span><span style="color:#ffb964;">uv</span><span> pip sync docs/requirements.txt
|
||
</span></code></pre>
|
||
<p>此流程替代 <code>conda env export</code> + <code>conda env update</code>，并保证跨平台一致性 ([GitHub][3])。</p>
|
||
<p><strong>查看与卸载</strong></p>
|
||
<pre data-lang="bash" style="background-color:#151515;color:#e8e8d3;" class="language-bash "><code class="language-bash" data-lang="bash"><span style="color:#ffb964;">uv</span><span> pip list </span><span style="color:#888888;"># 列出已安装包（类似 conda list）
|
||
</span><span style="color:#ffb964;">uv</span><span> pip uninstall numpy
|
||
</span></code></pre>
|
||
<p><strong>替代常见 Conda 工作流</strong></p>
|
||
<table><thead><tr><th>Conda 操作</th><th>UV 对应</th></tr></thead><tbody>
|
||
<tr><td><code>conda create -n env python=3.x</code></td><td><code>uv venv --python 3.x</code></td></tr>
|
||
<tr><td><code>conda activate env</code></td><td><code>source .venv/bin/activate</code> 或 <code>activate</code></td></tr>
|
||
<tr><td><code>conda install pkg1 pkg2</code></td><td><code>uv pip install pkg1 pkg2</code></td></tr>
|
||
<tr><td><code>conda env export > env.yml</code></td><td><code>uv pip compile requirements.in</code></td></tr>
|
||
<tr><td><code>conda env update -f env.yml</code></td><td><code>uv pip sync requirements.txt</code></td></tr>
|
||
<tr><td><code>conda list</code></td><td><code>uv pip list</code></td></tr>
|
||
</tbody></table>
|
||
<p><strong>最佳实践</strong>：</p>
|
||
<ol>
|
||
<li><strong>系统依赖</strong>：用 Conda/Mamba 安装较难编译的 C 库（<code>conda install gdal</code>）。</li>
|
||
<li><strong>Python 包</strong>：用 UV 管理所有纯 Python 依赖（<code>uv pip install pandas scikit-learn</code>）。</li>
|
||
<li><strong>统一锁定</strong>：把 <code>uv pip compile</code> 生成的 <code>requirements.txt</code> 放入版本控制，确保团队环境一致。</li>
|
||
</ol>
|
||
<hr />
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||
<p><strong>Done.</strong></p>
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