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我正在尝试使用 python 中的 camelot 从 pdf 文件中提取表和表名。尽管我知道如何使用 camelot 提取表（这非常简单），但我仍在努力寻找有关如何提取表名的任何帮助。目的是提取此信息并显示表格及其名称的视觉效果，以便用户从列表中选择相关表格。

我尝试过提取表格，然后也从 pdf 中提取文本。我在这两个方面都很成功，但在将表名连接到表方面却没有。

def tables_from_pdfs(filespath):
    pdffiles = glob.glob(os.path.join(filespath, "*.pdf"))
    print(pdffiles)
    dictionary = {}
    keys = []
    for file in pdffiles:
        print(file)
        n = PyPDF2.PdfFileReader(open(file, 'rb')).getNumPages()
        print(n)
        tables_dict = {}
        for i in range(n):
            tables = camelot.read_pdf(file, pages = str(i))
            tables_dict[i] = tables
        head, tail = os.path.split(file)
        tail = tail.replace(".pdf", "")
        keys.append(tail)
        dictionary[tail] = tables_dict
    return dictionary, keys

预期结果是一个表格和 pdf 文件中所述的表格名称。例如：pdf 名称第 x 页上的表格：Table 1. Blah Blah blah '''Table'''

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2 回答 2

0

我能够找到一个相对的解决方案。至少对我有用。

import os, PyPDF2, time, re, shutil
import pytesseract
from pdf2image import convert_from_path
import camelot
import datefinder
from difflib import SequenceMatcher

def similar(a, b):
    return SequenceMatcher(None, a, b).ratio()

similarityAmt = 0.6 # find with 60% similarity
def find_table_name(dataframe, documentString):
    
    # Assuming that you extracted the text from a PDF, it should be multi-lined. We split by line
    stringsSeparated = text.split("\n")
    for i, string in enumerate(stringsSeparated):
        
        # Split by word
        words = string.split()
        for k, word in enumerate(words):
            
            # Get the keys from the dataframe as a list (it is initially extracted as a generator type)
            dfList = list(dataframe.keys())
            keys = str(dfList)
            
            # If the first key is a digit, we assume that the keys are from the row below the keys instead
            if keys[0].isdigit():
                keys = dataframe[dfList[0]]

            # Put all of the keys in a single string
            keysAll = ""
            for key in keys:
                keysAll += key

            # Since a row should be horizontal, we check the similarity between that of the text by line.
            similarRating = similar(words, keysAll)
            if similarRating > similarityAmt: # If similarity rating (which is a ratio from 0 to 1) is above the similarity amount, we approve of it
                for j in range(10): # Iterate upwards 10 lines above until we are capable of finding a line that is longer than 4 characters (this is an arbitrary number just to ignore blank lines).
                    try:
                        separatedString = stringsSeparated[i-j-1]
                        if len(separatedString) > 4:
                            return stringsSeparated[i-j-2]+separatedString # Return the top two lines to hopefully have an accurate name
                        else:
                            continue
                    except:
                        continue
    return "Unnamed"

# Retreive the text from the pdf
pages = convert_from_path(pdf_path, 500) # pdf_path would be the path of the PDF which you extracted the table from
pdf_text = ""
# Add all page strings into a single string, so the entire PDF is one single string
for pageNum, imgBlob in enumerate(pages):
    extractedText = pytesseract.image_to_string(imgBlob, lang='eng')
    pdf_text += extractedText + "\n"

# Get the name of the table using the table itself and pdf text
tableName = find_table_name(table.df, pdf_text) # A table you extracted with your code, which you want to find the name of
于 2021-07-28T10:03:30.147 回答
-3

表格与 Camelot API 中的 TableList 和 Table 函数一起列出： https ://camelot-py.readthedocs.io/en/master/api.html#camelot.core.TableList

从它说的网页开始：

低层类

Camelot 没有对表名的引用，只是单元格数据描述。它确实使用了 python 的 panda 数据库 API，尽管其中可能包含表名。

结合使用 Camelot 和 Pandas 来获取表名。

获取 pandas DataFrame 的名称

附加更新以回答

来自 https://camelot-py.readthedocs.io/en/master/

import camelot
tables = camelot.read_pdf('foo.pdf')
tables
<TableList n=1>
tables.export('foo.csv', f='csv', compress=True) # json, excel, html
tables[0]
<Table shape=(7, 7)>
tables[0].parsing_report
{
'accuracy': 99.02,
'whitespace': 12.24,
'order': 1,
'page': 1
}
tables[0].to_csv('foo.csv') # to_json, to_excel, to_html
df_table = tables[0].df # get a pandas DataFrame!

#add
df_table.name = 'name here'


#from https://stackoverflow.com/questions/31727333/get-the-name-of-a-pandas-dataframe
import pandas as pd
df = pd.DataFrame( data=np.ones([4,4]) )
df.name = 'Ones'

print df.name

注意：添加的“名称”属性不是 df 的一部分。在序列化 df 时，添加的 name 属性会丢失。

更多附加答案，“名称”属性实际上称为“索引”。

Getting values

>>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
...      index=['cobra', 'viper', 'sidewinder'],
...      columns=['max_speed', 'shield'])
>>> df
            max_speed  shield
cobra               1       2
viper               4       5
sidewinder          7       8

Single label. Note this returns the row as a Series.

>>> df.loc['viper']
max_speed    4
shield       5
Name: viper, dtype: int64
于 2019-10-03T12:48:40.820 回答