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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>
<div class="post-meta-inline">
<span class="post-date">
2024-05-31
</span>
</div>
<span class="post-tags-inline">
:: tags:&nbsp;
<a class="post-tag" href="https://blog.dich.bid/tags/windows/">#Windows</a></span>
<div class="post-content">
<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>
<span id="continue-reading"></span><h2 id="an-zhuang">安装</h2>
<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>
<ul>
<li>下载、安装Pythond解释器；</li>
<li>验证安装；</li>
<li>安装VScode以及Python的拓展；</li>
</ul>
<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>
<ul>
<li>安装Anaconda或miniconda等Python集成包；</li>
<li>使用conda创建并启动一个Python环境；</li>
<li>安装jupyter编辑器编写python。</li>
</ul>
<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>
<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>
<blockquote>
<p>linux中安装Miniconda</p>
</blockquote>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span># Miniconda安装脚本
</span><span>wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
</span><span># 执行以下命令启动安装程序：
</span><span>bash Miniconda3-latest-Linux-x86_64.sh
</span><span># 验证安装
</span><span>conda --version
</span></code></pre>
<h2 id="shi-yong">使用</h2>
<ul>
<li>创建环境，后面的python=3.6是指定python的版本</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda create --name env_name python=3.6
</span></code></pre>
<ul>
<li>创建包含某些包的环境（也可以加上版本信息）</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda create --name env_name python=3.7 numpy scrapy
</span></code></pre>
<ul>
<li>激活某个环境</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda activate env_name
</span></code></pre>
<ul>
<li>关闭某个环境</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda deactivate env_name
</span></code></pre>
<ul>
<li>复制某个环境</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda create --name new_env_name --clone old_env_name
</span></code></pre>
<ul>
<li>删除某个环境</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda remove --name env_name --all
</span></code></pre>
<ul>
<li>生成需要分享环境的yml文件（需要在虚拟环境中执行）</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda env export &gt; environment.yml
</span></code></pre>
<ul>
<li>在本地使用yml文件创建虚拟环境</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda env create -f environment.yml
</span></code></pre>
<ul>
<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>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>(test) ➜ ~ conda info -e
</span><span>- conda environments:
</span><span>#
</span><span>base /Volumes/300g/opt/anaconda3
</span><span>test * /Volumes/300g/opt/anaconda3/envs/test
</span></code></pre>
<h3 id="bao-guan-li">包管理</h3>
<ul>
<li>列出当前环境下所有安装的包</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda list
</span></code></pre>
<ul>
<li>列举一个指定环境下的所有包</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda list -n env_name
</span></code></pre>
<ul>
<li>查询库</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda search scrapys
</span></code></pre>
<ul>
<li>安装库安装时可以指定版本例如：（scrapy=1.5.0）</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda install scrapy
</span></code></pre>
<ul>
<li>为指定环境安装某个包</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda install --name target_env_name package_name
</span></code></pre>
<ul>
<li>更新安装的库</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda update scrapy
</span></code></pre>
<ul>
<li>更新指定环境某个包</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda update -n target_env_name package_name
</span></code></pre>
<ul>
<li>更新所有包</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda update --all
</span></code></pre>
<ul>
<li>删除已经安装的库</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda remove scrapy
</span></code></pre>
<ul>
<li>删除指定环境某个包</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda remove -n target_env_name package_name
</span></code></pre>
<ul>
<li>更多命令请查看官方文档或者查询帮助命令：</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda --help
</span><span>
</span><span>conda install --help
</span></code></pre>
<h2 id="jupytershi-yong">Jupyter使用</h2>
<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>
<ul>
<li>安装jupyter</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda install jupyter notebook
</span></code></pre>
<ul>
<li>配置虚拟机中允许宿主机访问</li>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span># 生成配置
</span><span>jupyter notebook --generate-config
</span><span># 编辑配置
</span><span>nano ~/.jupyter/jupyter_notebook_config.py
</span><span># 写入这三行
</span><span>c.NotebookApp.ip = &#39;0.0.0.0&#39; # 允许任何 IP 访问
</span><span>c.NotebookApp.port = 8888 # 指定端口
</span><span>c.NotebookApp.open_browser = False # 不自动开浏览器
</span><span># 重启jupyter
</span><span>jupyter notebook
</span></code></pre>
<ul>
<li>安装 ipykernel</li>
</ul>
<p>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：</p>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda install ipykernel
</span></code></pre>
<ul>
<li>注册环境内核</li>
</ul>
<p>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：</p>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>python -m ipykernel install --user --name myenv --display-name &quot;Python (myenv)&quot;
</span></code></pre>
<p>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。</p>
<ul>
<li>启动 Jupyter Notebook：依然在激活后的环境中，启动 Jupyter Notebook；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>
</ul>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>jupyter notebook
</span></code></pre>
<ul>
<li>汉化jupyter(可选)</li>
</ul>
<p>Jupyter Notebook 本身没有官方语言包，但可以用第三方扩展 <code>jupyter_contrib_nbextensions</code>和<code>notebook-translation</code>来实现部分汉化</p>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>pip install jupyter_contrib_nbextensions
</span><span>jupyter contrib nbextension install --user
</span><span>pip install jupyter-notebook-translation
</span></code></pre>
<blockquote>
<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>
</blockquote>
<h2 id="shi-yong-uv-ti-dai-conda">使用 UV 替代 Conda</h2>
<blockquote>
<p>UV（由 Astral 团队开发）是一个用 Rust 编写的高性能 Python 包管理器，提供类似 Conda 的虚拟环境管理和依赖解析功能，在大多数场景下比 pip 和 Conda 快 10–100 倍。它通过命令行工具如 <code>uv venv</code>（创建/管理虚拟环境）和 <code>uv pip</code>（安装/锁定/同步依赖）覆盖传统的 Conda 流程，但本身不管理底层的 C/C++ 库，因此对于 GDAL、SciPy 等需要系统级二进制依赖的包，仍建议先通过系统包管理器或 Conda 安装，然后用 UV 管理 Python 包。</p>
</blockquote>
<hr />
<ul>
<li>安装 UV</li>
</ul>
<pre data-lang="bash" style="background-color:#151515;color:#e8e8d3;" class="language-bash "><code class="language-bash" data-lang="bash"><span style="color:#ffb964;">wget -qO-</span><span> https://astral.sh/uv/install.sh | </span><span style="color:#ffb964;">sh
</span></code></pre>
<ul>
<li>创建与管理环境</li>
</ul>
<pre data-lang="bash" style="background-color:#151515;color:#e8e8d3;" class="language-bash "><code class="language-bash" data-lang="bash"><span style="color:#888888;"># 创建虚拟环境，指定 Python 版本
</span><span style="color:#ffb964;">uv</span><span> venv</span><span style="color:#ffb964;"> --python</span><span> 3.12
</span><span>
</span><span style="color:#888888;"># 激活环境
</span><span>source .venv/bin/activate
</span><span>
</span><span style="color:#888888;"># 退出环境
</span><span style="color:#ffb964;">deactivate
</span><span>
</span><span style="color:#888888;"># 删除环境
</span><span style="color:#ffb964;">rm -rf</span><span> .venv
</span></code></pre>
<ul>
<li>直接运行</li>
</ul>
<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> run python
</span><span style="color:#ffb964;">uv</span><span> run jupyter lab
</span></code></pre>
<ul>
<li>注册 Jupyter 内核</li>
</ul>
<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> run python</span><span style="color:#ffb964;"> -m</span><span> ipykernel install</span><span style="color:#ffb964;"> --user --name</span><span> bank</span><span style="color:#ffb964;"> --display-name </span><span style="color:#556633;">&quot;</span><span style="color:#99ad6a;">Python (bank)</span><span style="color:#556633;">&quot;
</span></code></pre>
<hr />
<ul>
<li>安装依赖</li>
</ul>
<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> add tensorflow
</span><span style="color:#ffb964;">uv</span><span> pip install requests fastapi uvicorn sqlalchemy
</span></code></pre>
<blockquote>
<p>安装完成后，UV 会自动更新 <code>uv.lock</code> 文件锁定依赖版本，保证环境可复现。</p>
</blockquote>
<ul>
<li>使用 TOML 配置管理依赖</li>
</ul>
<p>创建一个 <code>pyproject.toml</code>：</p>
<pre data-lang="toml" style="background-color:#151515;color:#e8e8d3;" class="language-toml "><code class="language-toml" data-lang="toml"><span>[</span><span style="color:#ffb964;">tool</span><span>.</span><span style="color:#ffb964;">uv</span><span>.</span><span style="color:#ffb964;">dependencies</span><span>]
</span><span style="color:#ffb964;">fastapi </span><span>= </span><span style="color:#556633;">&quot;</span><span style="color:#99ad6a;">*</span><span style="color:#556633;">&quot;
</span><span style="color:#ffb964;">uvicorn </span><span>= </span><span style="color:#556633;">&quot;</span><span style="color:#99ad6a;">*</span><span style="color:#556633;">&quot;
</span><span style="color:#ffb964;">sqlalchemy </span><span>= </span><span style="color:#556633;">&quot;</span><span style="color:#99ad6a;">*</span><span style="color:#556633;">&quot;
</span></code></pre>
<p>然后同步环境：</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 sync
</span></code></pre>
<p>这会根据 <code>pyproject.toml</code> + <code>uv.lock</code> 安装和锁定所有依赖。</p>
<ul>
<li>查看与卸载包</li>
</ul>
<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
</span><span style="color:#ffb964;">uv</span><span> pip uninstall numpy
</span></code></pre>
<hr />
<h3 id="ti-dai-chang-jian-conda-gong-zuo-liu">替代常见 Conda 工作流</h3>
<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>uv venv 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 &gt; env.yml</code></td><td>自动生成 <code>uv.lock</code> 或 <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</code>（根据 <code>uv.lock</code> 或 <code>pyproject.toml</code> 同步）</td></tr>
<tr><td><code>conda list</code></td><td><code>uv pip list</code></td></tr>
</tbody></table>
<h2 id="ipynbzhuan-markdown">ipynb转markdown</h2>
<p>首先安装 nbformat 和 nbconvert包：</p>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>conda install nbformat nbconvert -y
</span><span>touch ipynb2md.py &amp;&amp; nano ipynb2md.py
</span></code></pre>
<p>写入以下脚本：</p>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>import nbformat
</span><span>from nbconvert import MarkdownExporter
</span><span>from pathlib import Path
</span><span>
</span><span>def ipynb_to_md(ipynb_path: Path, output_dir: Path):
</span><span> &quot;&quot;&quot;单个 ipynb 转 md&quot;&quot;&quot;
</span><span> with open(ipynb_path, &quot;r&quot;, encoding=&quot;utf-8&quot;) as f:
</span><span> nb = nbformat.read(f, as_version=4)
</span><span>
</span><span> exporter = MarkdownExporter()
</span><span> body, resources = exporter.from_notebook_node(nb)
</span><span>
</span><span> output_file = output_dir / (ipynb_path.stem + &quot;.md&quot;)
</span><span> with open(output_file, &quot;w&quot;, encoding=&quot;utf-8&quot;) as f:
</span><span> f.write(body)
</span><span>
</span><span> print(f&quot;✔ 转换完成: {ipynb_path} -&gt; {output_file}&quot;)
</span><span>
</span><span>def batch_convert(input_dir: str, output_dir: str = &quot;markdown_output&quot;):
</span><span> input_dir = Path(input_dir)
</span><span> output_dir = Path(output_dir)
</span><span> output_dir.mkdir(parents=True, exist_ok=True)
</span><span>
</span><span> for ipynb_file in input_dir.glob(&quot;*.ipynb&quot;):
</span><span> ipynb_to_md(ipynb_file, output_dir)
</span><span>
</span><span>if __name__ == &quot;__main__&quot;:
</span><span> # 修改这里的目录路径即可
</span><span> batch_convert(input_dir=&quot;.&quot;)
</span></code></pre>
<p>运行脚本：</p>
<pre style="background-color:#151515;color:#e8e8d3;"><code><span>python ipynb2md.py
</span></code></pre>
<p>脚本会自动扫描当前目录下的所有 .ipynb 文件，并把 .md 文件输出到 markdown_output/ 文件夹。</p>
<hr />
<p><strong>Done.</strong></p>
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