知行编程网知行编程网  2022-07-12 01:00 知行编程网 隐藏边栏 |   抢沙发  33 
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最强的Python 办公自动化之 PDF 攻略来了(全)

办公自动化应该算是打工人上班摸鱼的极致追求了,况且对于 Python 爱好者来说,办公自动化简直是太简单了

比如,今天的办公自动化主题:Python 操作 PDF

关于 Python 与 PDF 的操作,前面也有提到几篇,文末也会 列出相关几篇文章,感兴趣的可以都学习一下

今天的具体内容将会从以下几个小节展开:

  1. 相关介绍
  2. 批量拆分
  3. 批量合并
  4. 提取文字内容
  5. 提起表格内容
  6. 提起图片内容
  7. 转换为PDF图片
  8. 添加水印
  9. 加密与解码

上述操作比较常用,也可以解决较多的办公内容,下面直接开始本节内容:

1. 相关介绍

Python 操作 PDF 会用到两个库,分别是:PyPDF2 和 pdfplumber

其中 PyPDF2 可以更好的读取、写入、分割、合并PDF文件,而 pdfplumber 可以更好的读取 PDF 文件中内容和提取 PDF 中的表格

对应的官网分别是:

PyPDF2:https://pythonhosted.org/PyPDF2/
pdfplumber:https://github.com/jsvine/pdfplumber

由于这两个库都不是 Python 的标准库,所以在使用之前都需要单独安装

win+r 后输入 cmd 打开 command 窗口,依次输入如下命令进行安装:

pip install PyPDF2
pip install pdfplumber

安装完成后显示 success 则表示安装成功

最强的Python 办公自动化之 PDF 攻略来了(全)

2. 批量拆分

将一个完整的 PDF 拆分成几个小的 PDF,因为主要涉及到 PDF 整体的操作,所以本小节需要用到 PyPDF2 这个库

拆分的大概思路如下:

  • 读取 PDF 的整体信息、总页数等
  • 遍历每一页内容,以每个 step 为间隔将 PDF 存成每一个小的文件块
  • 将小的文件块重新保存为新的 PDF 文件

需要注意的是,在拆分的过程中,可以手动设置间隔,例如:每5页保存成一个小的 PDF 文件

拆分的代码如下:

以“易方达中小盘混合型证券投资基金2020年中期报告”为例,整个 PDF 文件一共 46 页,每5页为间隔,最终生成了10个小的 PDF 文件

最强的Python 办公自动化之 PDF 攻略来了(全)

3. 批量合并

比起拆分来,合并的思路更加简单:

  • 确定要合并的 文件顺序
  • 循环追加到一个文件块中
  • 保存成一个新的文件

对应的代码比较简单:

concat_pdf(filename, read_dirpath, save_filepath)

4. 提取文字内容

涉及到具体的 PDF 内容 操作,本小节需要用到 pdfplumber 这个库

在进行文字提取的时候,主要用到 extract_text 这个函数

具体代码如下:

可以看到,直接通过下标即可定位到相应的页码,从而通过 extract_text 函数提取该也的所有文字

而如果想要提取所有页的文字,只需要改成:

例如,提取“易方达中小盘混合型证券投资基金2020年中期报告” 第一页的内容时,源文件是这样的:

最强的Python 办公自动化之 PDF 攻略来了(全)

运行代码后提取出来是这样的:

最强的Python 办公自动化之 PDF 攻略来了(全)

5. 提取表格内容

同样的,本节是对具体内容的操作,所以也需要用到 pdfplumber 这个库

和提取文字十分类似的是,提取表格内容只是将 extract_text 函数换成了 extract_table 函数

对应的代码如下:

上面代码可以获取到第 18 页的第一个表格内容,并且将其保存为 csv 文件存在本地

但是,如果说第 18 页有多个表格内容呢?

因为读取的表格会被存成二维数组,而多个二维数组就组成一个三维数组

遍历这个三位数组,就可以得到该页的每一个表格数据,对应的将 extract_table 函数 改成 extract_tables 即可

具体代码如下:

以“易方达中小盘混合型证券投资基金2020年中期报告” 第 xx 页的第一个表格为例:

源文件中的表格是这样的:

最强的Python 办公自动化之 PDF 攻略来了(全)

提取并存入 excel 之后的表格是这样的:

最强的Python 办公自动化之 PDF 攻略来了(全)

6. 提取图片内容

提取 PDF 中的图片和将 PDF 转存为图片是不一样的(下一小节),需要区分开。

提取图片:顾名思义,就是将内容中的图片都提取出来;

转存为图片:则是将每一页的 PDF 内容存成一页一页的图片,下一小节会详细说明

转存为图片中,需要用到一个模块叫 fitz,fitz 的最新版 1.18.13,非最新版的在部分函数名称上存在差异,代码中会标记出来

使用 fitz 需要先安装 PyMuPDF 模块,安装方式如下:

pip install PyMuPDF

提取图片的整体逻辑如下:

  • 使用 fitz 打开文档,获取文档详细数据
  • 遍历每一个元素,通过正则找到图片的索引位置
  • 使用 Pixmap 将索引对应的元素生成图片
  • 通过 size 函数过滤较小的图片

实现的具体代码如下:

以本节示例的“易方达中小盘混合型证券投资基金2020年中期报告” 中的图片为例,代码运行后提取的图片如下:

最强的Python 办公自动化之 PDF 攻略来了(全)

这个结果和文档中的共 1 张图片的 结果符合

7. 转换为图片

转换为照片比较简单,就是将一页页的 PDF 转换为一张张的图片。大致过程如下:

安装 pdf2image

首先需要安装对应的库,最新的 pdf2image 库版本应该是 1.14.0

它的 github地址 为:https://github.com/Belval/pdf2image ,感兴趣的可以自行了解

安装方式如下:

pip install pdf2image
安装组件

对于不同的平台,需要安装相应的组件,这里以 windows 平台和 mac 平台为例:

Windows 平台

对于 windows 用户需要安装 poppler for Windows,安装链接是:http://blog.alivate.com.au/poppler-windows/

另外,还需要添加环境变量, 将 bin 文件夹的路径添加到环境变量 PATH 中

注意这里配置之后需要重启一下电脑才会生效,不然会报错

Mac

对于 mac 用户,需要安装 poppler for Mac,具体可以参考这个链接:http://macappstore.org/poppler/

详细代码如下:

以本节示例的“易方达中小盘混合型证券投资基金2020年中期报告” 中的图片为例,该文档共 46 页,保存后的 PDF 照片如下:

最强的Python 办公自动化之 PDF 攻略来了(全)

一共 46 张图片

8. 添加水印

添加水印后的效果如下:

最强的Python 办公自动化之 PDF 攻略来了(全)

在制作水印的时候,可以自定义水印内容、透明度、斜度、字间宽度等等,可操作性比较好。

前面专门写过一篇文章,讲的特别详细:Python快速给PDF文件添加自定义水印 

9. 文档加密与解密

你可能在打开部分 PDF 文件的时候,会弹出下面这个界面:

最强的Python 办公自动化之 PDF 攻略来了(全)

这种就是 PDF 文件被加密了,在打开的时候需要相应的密码才行

本节所提到的也只是基于 PDF 文档的加密解密,而不是所谓的 PDF 密码破解。

在对 PDF 文件加密需要使用 encrypt 函数,对应的加密代码也比较简单:

代码执行成功后再次打开 PDF 文件则需要输入密码才行

根据这个思路,破解 PDF 也可以通过暴力求解实现,例如:通过本地密码本一个个去尝试,或者根据数字+字母的密码形式循环尝试,最终成功打开的密码就是破解密码

上述破解方法耗时耗力,不建议尝试

另外,针对已经加密的 PDF 文件,也可以使用 decrypt 函数进行解密操作

解密代码如下:

解密完成后的 PDF 文档打开后不再需要输入密码,如需加密可再次执行加密代码。


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data-darkmode-bgcolor-15900529136199="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15900529136199="rgb(255, 255, 255)" data-darkmode-color-15900529136199="rgb(230, 230, 230)" data-darkmode-original-color-15900529136199="rgb(0, 0, 0)" data-darkmode-bgimage-15900529136199="1" style="padding: 18px 15px 20px 10px;max-width: 100%;background-size: 100% 100%;background-image: url('https://www.zkxjob.com/wp-content/uploads/2022/07/wxsync-2022-07-a2a8a5e1e58f30392066a170034ee027.png');color: rgb(86, 146, 214);letter-spacing: 1.5px;background-repeat: no-repeat;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section data-darkmode-bgcolor-15860613985508="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15860613985508="rgb(255, 255, 255)" data-darkmode-color-15860613985508="rgb(230, 230, 230)" data-darkmode-original-color-15860613985508="rgb(0, 0, 0)" data-darkmode-bgimage-15860613985508="1" data-darkmode-bgcolor-15870356070738="rgb(36, 36, 36)" 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data-darkmode-original-color-15870356070738="rgb(0, 0, 0)" data-darkmode-bgimage-15870356070738="1" data-darkmode-bgcolor-15870356071023="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356071023="rgb(255, 255, 255)" data-darkmode-color-15870356071023="rgb(230, 230, 230)" data-darkmode-original-color-15870356071023="rgb(0, 0, 0)" data-darkmode-bgimage-15870356071023="1" data-darkmode-bgcolor-15882384789136="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882384789136="rgb(255, 255, 255)" data-darkmode-color-15882384789136="rgb(230, 230, 230)" data-darkmode-original-color-15882384789136="rgb(0, 0, 0)" data-darkmode-bgimage-15882384789136="1" data-darkmode-bgcolor-15882396318564="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882396318564="rgb(255, 255, 255)" data-darkmode-color-15882396318564="rgb(230, 230, 230)" data-darkmode-original-color-15882396318564="rgb(0, 0, 0)" data-darkmode-bgimage-15882396318564="1" data-darkmode-bgcolor-15900529136199="rgb(36, 36, 36)" 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