从PDF中提取文本的矢量化Python函数

Question

我目前正在做一个程序，从数以万计的法庭意见PDF中提取文本。我对Python比较陌生，正在努力使这段代码尽可能高效。我从这个网站和其他地方的许多帖子中收集到，我应该尝试对代码进行矢量化，但我尝试了三种方法，但都没有结果

我的reprex使用这些包和此示例数据

import os
import pandas as pd
import pdftotext
import wget

df = pd.DataFrame({'OpinionText': [""], 'URLs': ["https://cases.justia.com/federal/appellate-courts/ca6/20-6226/20-6226-2021-09-17.pdf?ts=1631908842"]})
df = pd.concat([df]*50, ignore_index=True)

我首先定义了这个函数，它下载PDF，提取文本，删除PDF，然后返回文本

def Link2Text(Link):
    OpinionPDF = wget.download(Link, "Temporary_Opinion.pdf")
    with open(OpinionPDF, "rb") as f:
        pdf = pdftotext.PDF(f)
    OpinionText = "\n\n".join(pdf)
    if os.path.exists("Temporary_Opinion.pdf"):
        os.remove("Temporary_Opinion.pdf")
    return(OpinionText)

我调用该函数的第一种方法是：

df['OpinionText'] = df['URLs'].apply(Link2Text)

根据我读到的有关矢量化的内容，我尝试使用以下方法调用函数：

df['OpinionText'] = Link2Text(df['URLs'])

#and, alternatively:

df['OpinionText'] = Link2Text(df['URLs'].values)

两者都返回了相同的错误，即：

Traceback (most recent call last):
  File "/Users/brendanbernicker/Downloads/Reprex for SO Vectorization Q.py", line 22, in <module>
    df['OpinionText'] = Link2Text(df['URLs'])
  File "/Users/brendanbernicker/Downloads/Reprex for SO Vectorization Q.py", line 10, in Link2Text
    OpinionPDF = wget.download(Link, "Temporary_Opinion.pdf")
  File "/Applications/anaconda3/lib/python3.8/site-packages/wget.py", line 505, in download
    prefix = detect_filename(url, out)
  File "/Applications/anaconda3/lib/python3.8/site-packages/wget.py", line 483, in detect_filename
    if url:
  File "/Applications/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py", line 1442, in __nonzero__
    raise ValueError(
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
[Finished in 0.683s]

我推测这意味着Python不知道如何处理输入，因为它是一个向量，所以我尝试用下面的调用替换该调用，并得到了这个回溯

df['OpinionText'] = Link2Text(df['URLs'].item)

Traceback (most recent call last):
  File "/Users/brendanbernicker/Downloads/Reprex for SO Vectorization Q.py", line 22, in <module>
    df['OpinionText'] = Link2Text(df['URLs'].item)
  File "/Users/brendanbernicker/Downloads/Reprex for SO Vectorization Q.py", line 10, in Link2Text
    OpinionPDF = wget.download(Link, "Temporary_Opinion.pdf")
  File "/Applications/anaconda3/lib/python3.8/site-packages/wget.py", line 505, in download
    prefix = detect_filename(url, out)
  File "/Applications/anaconda3/lib/python3.8/site-packages/wget.py", line 484, in detect_filename
    names["url"] = filename_from_url(url) or ''
  File "/Applications/anaconda3/lib/python3.8/site-packages/wget.py", line 230, in filename_from_url
    fname = os.path.basename(urlparse.urlparse(url).path)
  File "/Applications/anaconda3/lib/python3.8/urllib/parse.py", line 372, in urlparse
    url, scheme, _coerce_result = _coerce_args(url, scheme)
  File "/Applications/anaconda3/lib/python3.8/urllib/parse.py", line 124, in _coerce_args
    return _decode_args(args) + (_encode_result,)
  File "/Applications/anaconda3/lib/python3.8/urllib/parse.py", line 108, in _decode_args
    return tuple(x.decode(encoding, errors) if x else '' for x in args)
  File "/Applications/anaconda3/lib/python3.8/urllib/parse.py", line 108, in <genexpr>
    return tuple(x.decode(encoding, errors) if x else '' for x in args)
AttributeError: 'function' object has no attribute 'decode'

我尝试将.decode('utf-8')添加到我的函数调用中，并在函数中添加到输入中，但得到了相同的回溯。在这一点上，我不知道还有什么可以加速我的代码

我还尝试了numpy.vectorize使用.apply工作的版本，但它大大降低了执行速度。我认为这两个词不应该同时使用

为了完整起见，基于这里的一些优秀答案，我还尝试：

from numba import njit

@njit
def Link2Text(Link, Opinion):
    res = np.empty(Link.shape)
    for i in range(length(Link)):
        OpinionPDF = wget.download(Link[i], "Temporary_Opinion.pdf")
        with open(OpinionPDF, "rb") as f:
            pdf = pdftotext.PDF(f)
        OpinionText = "\n\n".join(pdf)
        if os.path.exists("Temporary_Opinion.pdf"):
            os.remove("Temporary_Opinion.pdf")
        Opinion[i] = OpinionText

Link2Text(df['URLs'].values, df['OpinionText'].values)

我推测这不起作用，因为numba不适用于我在函数中调用的包，它更多地用于数学运算。如果这是不正确的，我应该尝试使用麻木为这个，请让我知道