preface

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Project background

1. Online PDF conversion tools are dazzling, difficult to choose, some even charge;
2. The effect of opening PDF directly in other formats is generally poor;
3. Some cute little people use copy and paste to operate, which wastes a lot of time.

So, is there any way to solve these problems in seconds? Yes, Python can. Don't talk too much, just practice.

Project operation

1, PDF to Text

First, install pdfplumber, the library for PDF operation. Pdfplumer can read PDF file content and extract tables in PDF well. This library does not belong to Python standard library and needs to be installed separately.

pip3 install pdfplumber

 

After installation, we import pdfplumber.

import pdfplumber

 

Open the PDF paper and extract the content of page 2 of the paper.

with pdfplumber.open("Industrial Development B Research on risk management of trust project.pdf") as p:
    page = p.pages[2]
    textdata = page.extract_text()

 

Print the textdata and extract the content on page 2 as follows:

 

 

At this point, you may be wondering, a master's thesis at least more than 50 pages. Yes, I downloaded 75 pages of this paper. I have to add a for loop to extract all the pages.

with pdfplumber.open("Industrial Development B Research on risk management of trust project.pdf") as p:
    for i in range(75):
        page = p.pages[i]
        textdata = page.extract_text()
        #print(textdata)

 

The content is extracted and then saved as text. "a" specifies that the write mode is append write. The complete code is as follows:

#PDF turn Text
import pdfplumber
with pdfplumber.open("Industrial Development B Research on risk management of trust project.pdf") as p:
    for i in range(75):
        page = p.pages[i]
        textdata = page.extract_text()
        #print(textdata)
        data = open("text.text", "a")
        data.write(textdata)

 

 

2, PDF to Word

You need to install Python docx, the library for Word operation.

pip3 install python-docx

 

Import the Document method.

from docx import Document

 

Open the PDF paper and extract the content of page 2 of the paper.

with pdfplumber.open("Industrial Development B Research on risk management of trust project.pdf") as p:
        page = p.pages[2]
        textdata = page.extract_text()

 

Create a new Word document and store the extracted content in it.

document = Document()  #Create a new blank word file
content = document.add_paragraph(textdata)  #Add body paragraphs to the document, changing the textdata Lead in
document.save("word.docx")  #Save document docx，Named word

 

The complete code is as follows:

#PDF turn Word1import pdfplumber
from docx import Document
with pdfplumber.open("Industrial Development B Research on risk management of trust project.pdf") as p:
        page = p.pages[2]
        textdata = page.extract_text()
        #print(textdata)
        document = Document()  #Create a new blank word file
        content = document.add_paragraph(textdata)  #Add body paragraphs to the document, changing the textdata Lead in
        document.save("word.docx")  #Save document docx，Named word

 

If you want to extract all, add a loop.

 

3, PDF to Excel

PDF to excel is not full-text to excel, but format conversion of some tables in the paper, which is convenient for data filtering, calculation and other operations in Excel.

Page 69-75 of the PDF paper of this case is the appendix part, which pastes the financial statement data and intercepts part of the content, as follows:

First, install and import openpyxl, a library for manipulating Excel tables.

pip3 install openpyxlfrom openpyxl import Workbook

 

Open the PDF paper, extract the table content on page 69-75 of the paper, and notice that the range is left open and right closed here.

with pdfplumber.open("Industrial Development B Research on risk management of trust project.pdf") as p:
    for i in range(68,75):  #Traverse 69 pages-75 page
        page = p.pages[i]
        table = page.extract_table()
        #print(table)

 

Create a new blank Excel file, and write the table data content extracted from PDF by row. The complete code is as follows:

 #PDF turn Excel 
 import pdfplumber
 from openpyxl import Workbook
 with pdfplumber.open("Industrial Development B Research on risk management of trust project.pdf") as p:
     workbook = Workbook()  #New blank Excel workbook 
     sheet = workbook.active  #activation sheet
     for i in range(68,75):  #Traverse 69 pages-75 page
         page = p.pages[i]
         table = page.extract_table() #Extract table data
        #print(table)
        for row in table:  #Traverse all rows
            #print(row)
            sheet.append(row) #Append write data by row
        workbook.save("Excel.xlsx") #Save file named Excel
        i += 1
        print("The first%d page PDF Extraction complete"%i) #Prompt extraction progress

 

 

The final effect is as follows. It seems to be OK.

Conclusion

At this point, Python's simple operation on PDF paper is basically over. But there are still some problems worth exploring, such as what if there are 100 PDFs and the pages are not the same? Even if all the files are extracted, how to optimize the file format? How to merge multiple Excel tables extracted into one sheet? First of all, I'm sure to tell you that Python can solve all problems, and I will write related Python operations in the future.

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