Jupyter lab（Jupyter notebook）中单元格图片的存储方式及导出（还原）方法（关键点：ipynb文件，图片base64编码）

问题描述

在Jupyter lab（Jupyter notebook）记事本（notebook）中单元格中包含图片的情况有三种：

• 在markdown单元格中显示图片。
  • 以markdwon语法插入本地图片。
  • 复制图片、粘贴到单元格中，以图片附件形式保存在单元格中。
• 代码单元格的输出中的图片。

但是Jupyter lab并不提供单元格直接导出图片的功能，如何将记事本中的图片导出呢？

分析问题

复制、粘贴图片到markdown单元格中

将上图复制粘贴到markdown单元格中。

运行后效果如下：

以文本形式打开.ipynb文件，可以发现以附件形式出现的图片，并不以文件形式保存在磁盘上，而是以Base64编码形式直接保存在.ipynb文件中 。

"cells": [
  {
    
    
   "attachments": {
    
    
    "ab7cc708-c91c-4147-89d4-2dbcfa4a361a.png": {
    
    
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    
    },
   "source": [ 
    "![图片.png](attachment:ab7cc708-c91c-4147-89d4-2dbcfa4a361a.png)"
   ]
  }
 ],

将上述Base64编码还原为图片，如下所示。

代码单元格输出图片

在代码单元格中输入以下内容并运行。

以文本形式打开.ipynb文件。

 "cells": [
  {
    
    
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    
    },
   "outputs": [
    {
    
    
     "data": {
    
    
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1957058a2e8>]"
      ]
     },
     "execution_count": 1,
     "metadata": {
    
    },
     "output_type": "execute_result"
    },
    {
    
    
     "data": {
    
    
      "image/png": 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vm/Wa7r7DjcBxAFV1O737EBcBs8DrwFVL/por+L+XJGkIK/HSjSTpMBh6SWqcoZekxhl6SWqcoZekxhl6SWqcoZekxv0fi4CaHnY3t8AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
    
    
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot([1, 1])"
   ]
  }
 ],

将上述Base64编码还原为图片，如下所示。

markdown单元格本地文件

 "cells": [
  {
    
    
   "cell_type": "markdown",
   "metadata": {
    
    },
   "source": [
    "![Image](1.png) "
   ]
  }
 ],

结论

markdown单元格中的粘贴来的临时图片和代码单元格生成的图片都是以附件（Base64编码）形式直接保存在ipynb文件中。如果本地没有保存或者上传图片，可以通过Base64编码保存图片。

markdown单元格中的本地图片将不会保存在ipynb文件中，仍然以本地文件形式存储。