How to Extract Data from PDF to Excel: A Comprehensive Guide

PDF files are popular for several purposes, such as managing professional documents, analyzing reports, and preparing for a research project. They are also highly effective for sharing and viewing content. However, extracting data from them can be challenging when you need it in a structured format like Excel for detailed analysis or visualization.

This guide will help you learn five smarter ways of how to extract data from PDF to Excel.

Five Ways to Extract Data from PDF to Excel

To save time and maintain accuracy in your work, here are five reliable methods to streamline the process of transferring data from PDF to Excel.

Manual Copy Paste: An Unproductive Method to Transform PDF to Excel

You can manually transform data from PDF to Excel using copy and paste method:

• Launch the PDF file using a PDF viewer like Adobe Acrobat Reader.
• Choose the fields you need to copy into an Excel file.
• Copy the data by pressing the CTRL + C buttons.
• Launch Microsoft Excel and press CTRL + V to paste the data into a cell where you need it.
• Manually adjust the formatting in Excel if needed and save the file.

This copying and pasting approach might work if you only need to do it once or twice for small datasets. However, when you deal with hundreds of PDFs, this process becomes time-consuming.

Airbyte: An Automated Approach to Move Data From PDF to Excel

Airbyte is an AI-powered, no-code data movement platform that helps you extract data from diverse data sources and load them into a target system. It offers a user-friendly interface and 550+ pre-built connectors to streamline this process.

To extract textual data from PDFs, Airbyte introduces an experimental Document File Type Format in source connectors like S3, Azure Blob Storage, and Google Drive. These connectors utilize the open-source Unstructured library to perform data extraction from PDFs.

Rather than retrieving tabular data as individual fields, the connectors help you pull the data and convert them into markdown using this library. As a result, you can preserve the structural elements, such as headings and lists, from the original document during data retrieval. You can also retrieve text from scanned PDF documents using OCR technology. Additionally, you can convert images to Excel for seamless data analysis and management.

Here’s how you can build a pipeline to move data from PDFs stored in Google Drive to Excel spreadsheets:

Step 1: Configure Your Source to Extract Data From PDFs

Prerequisite:

Create a Google Drive folder and upload the PDF files you want to move to Excel sheets.

Steps:

• Sign in to your Airbyte Cloud account or register if you do not have one already. You can also deploy Airbyte on your local system.
• Navigate to the Sources section and search Google Drive.
• During the configuration, fill in the Folder Url field with the Google Drive folder link where your PDF files are stored.

• Then, create a new stream by clicking the Add button near The list of streams to sync. Select Document File Type Format (Experimental), fill in the Name as pdf_files, and specify a path pattern *.pdf in the Globs field. 

• Select Authenticate via Google (OAuth) from the Authentication dropdown and click Sign in with Google.
• Finally, click the Set up source button to finish the source configuration.

Step 2: Configure Your Destination to Load Extracted Data into Excel

To store data in an Excel sheet, you can leverage the Google Sheets connector, which is a Marketplace destination connector maintained by the Airbyte community. Google Sheets is also a spreadsheet application that allows you to organize and manage your data, just like in Excel.

One key benefit of storing data in Google Sheets over Excel is its cloud integration, allowing real-time collaboration, automatic saving, and easy access from any device. Unlike Excel, Google Sheets enables you to reduce manual file-sharing needs and ensures that everyone is working on the latest version.

If you require Excel for specific use cases, you can download the loaded PDF file data from Google Sheets in Microsoft Excel format.

Here is the step-by-step guide on how to ingest data from PDF files into Google Sheets/Excel Spreadsheets.

• Once you set up the Google Drive source connector, you will be redirected to a Connection page that enables you to add the target destination. On this page, click the Create a connection button and search for your destination Google Sheets under the Set up a new destination option.

• You can start configuring your Google Sheets destination by signing into your Google account.
• Create a spreadsheet file in your connected Google Sheet account and provide its link in the Spreadsheet Link field.

• When you click the Set up destination button, you will navigate to the Select streams page. Here, you can choose sync mode and the required data streams. In this example, there is only one stream to select (pdf_files). Once done, click the Next button.

• Now, you can change the Replication Frequency if required in the Configure connection page and then click the Finish & Sync button.

• All your selected streams from Google Drive are successfully synced with the Google Sheets.

• In this spreadsheet file, you can see a pdf_files sheet (marked in red bordered eclipse), which is the PDF file loaded from Google Drive.

• You can perform further analysis of this data using Google Sheets functions, which are the same as Excel functions. However, if you prefer to use this data in Microsoft Excel, you can download it by clicking File > Download > Microsoft Excel (.xlsx).

With Airbyte, you can do more than just transfer data from source to destination. This includes creating and applying custom transformations right after the initial sync using dbt Cloud integration to make your data in a consistent format.

To keep your data up-to-date, Airbyte provides various sync modes, including Incremental | Append, Full Refresh | Overwrite, Full Refresh | Append, and Incremental | Append + Deduped. These modes help you automatically sync updated data from new or existing PDF files to your Google Sheets based on the specified replication frequency. This way, you can ensure your Excel sheets stay up-to-date with minimal effort.

PDF to Excel Converters: Free & Paid Online Solutions

PDF to Excel converters like SmallPDF or Docparser help you convert a PDF to an Excel file format for data analysis and exploration. However, free versions of these tools have certain limitations:

• PDF to Excel converter tools restrict the size of the files you can process, making them unsuitable for massive datasets.
• Some tools add watermarks to the output, which can be inconvenient for professional use.

To handle larger datasets for professional use, you must purchase the full version. Investing in a paid tool just for this specific purpose may not be practical. It adds yet another SaaS app to your tech stack, performing only one task while increasing your operational costs.

Here’s where a tool like Airbyte shows its true potential. Unlike single-purpose tools, Airbyte offers a comprehensive platform for various data integration tasks rather than just converting PDF to Excel. Its extensive catalog of pre-built connectors facilitates this efficient data movement across multiple sources.

Let’s check out a few more features this multi-faced platform offers to simplify your entire data workflow:

• Personalized Connector Development: If no suitable connector exists, you can create a custom one using its Low-code CDKs, Language-specific CDKs, or no-code Connector Builder with AI Assistant.
• ‍Streamline GenAI workflows: Airbyte allows you to load unstructured data into vector databases—Pinecone, Milvus, Quadrant, Weavite, and more. You can also perform RAG-based transformations like LangChain-powered chunkings and OpenAI-enabled embeddings while integrating data into Vector databases.‍
• Incremental Data Updates: Certain Airbyte connectors support the CDC approach, allowing you to track source schema changes and replicate them to the destination.
• ‍Schema Management: You can configure how Airbyte should manage any source schema change for each connection. For Cloud users, source schema checks happen every 15 minutes, and for Self-hosted users, once every 24 hours.
• ‍Adheres to Industry-specific Regulations: Airbyte complies with GDPR, HIPAA, SOC 2 Type II assessment, and ISO 27001 regulatory standards for a secure data integration process.
• ‍Open-source: Besides the Cloud-based edition, Airbyte also offers an open-source version. This version enables you to deploy an Airbyte instance locally. With the open-source edition, you can leverage pre-built connectors, low-code CDKs, and schema propagation features.

Power Query: A Microsoft ETL Engine to Load Data from PDF File to Excel

Power Query is a data transformation and preparation engine offered by Microsoft. It allows you to collect data from different sources and transform it into a consistent format for analysis and reporting.

You can access Power Query across several Microsoft products. It is a built-in capability within Microsoft Excel 2016 and Excel for Office 365. For older versions like Excel 2010 and 2013 versions, you can install Power Query as a free add-in. Power Query is also integrated within Power BI Desktop, Microsoft Dataverse, and Azure Data Lake Storage for seamless data integration and preparation. As a result, the destination for storing the processed data depends on the platform where you use Power Query.

Learn how to extract data from PDF to Excel using Microsoft Excel’s Power Query feature:

• Launch the Microsoft Excel 2016 or above.
• Go to the Data > Get & Transform Data > Get Data > From File > From PDF. Browse for a necessary PDF file, and click on the Import button.

• Once you are redirected to the Navigator page, select the items you need to load into an Excel file.

• If you want to process the PDF file data, click the Transform Data, which will navigate you to the Power Query Editor. Otherwise, click on the Load button.

• Once data is transformed, click on the Close & Load option. This will load the data from the chosen PDF file to Excel.

Python Data Pipeline: A Developer-Friendly Approach

Due to its simplicity and rich set of libraries, Python is one of the most powerful programming languages for building data pipelines. A Python-based data pipeline allows you to efficiently extract, transform, and load data from diverse sources to your desired destinations. Python libraries and frameworks like Pandas, Apache Airflow, and Luigi help you simplify many aspects of pipeline creation. However, integrating diverse data sources can be challenging using these frameworks alone.

To address these challenges, you can leverage PyAirbyte, an open-source Python library offered by Airbyte. PyAirbyte helps you develop robust data pipelines by using Airbyte’s large catalog of connectors in your Python workflows.

The example below will help you understand how to extract data from PDF to Excel using PyAirbyte. In this process, data is initially extracted from a sample dataset (source-faker) and loaded into a Python DataFrame. This DataFrame is then saved as a PDF file, which can be subsequently ingested into an Excel file for advanced data analysis and visualization.

• Open Google Colab and add virtual environment support.

!apt-get install -qq python3.10-venv

• Install PyAirbyte.

%pip install --quiet airbyte

• Create and install the source “source-faker.” 

import airbyte as ab
source: ab.Source = ab.get_source("source-faker")

• Configure the source and verify the config and creds by running the check() function.

source.set_config(
    config={
        "count": 50_000,  # You can modify this to get a larger or smaller dataset
        "seed": 123,
    },
)
source.check()

• Select all of the source's streams and read data into the internal cache.

source.select_all_streams()
read_result: ab.ReadResult = source.read()

• Install fpdf and pdfplumber packages to move the data from the Python PandaFrame. into a PDF file.

pip install fpdf
pip install pdfplumber

• Save the DataFrame to a PDF file after performing certain data transformations.

from fpdf import FPDF
import pandas as pd
pdf = FPDF()
pdf.add_page()
pdf.set_font("Arial", size=12)
products_df = read_result["products"].to_pandas()
display(products_df)

# Add DataFrame content to the PDF
for index, row in products_df.iterrows():
    pdf.cell(200, 10, txt=str(row.to_dict()), ln=True)

# Save the PDF file
pdf_file_path = "source_data.pdf"
pdf.output(pdf_file_path)

• Finally, load the pdf file into the Excel format.

import pdfplumber
data = []
with pdfplumber.open(pdf_file_path) as pdf:
    for page in pdf.pages:
        text = page.extract_text()
        lines = text.split("\n")
        for line in lines:
            data.append(line.split(","))  

# Create a DataFrame from the extracted PDF data
pdf_df = pd.DataFrame(data)

# Save the DataFrame as an Excel file
excel_file_path = "source_data.xlsx"
pdf_df.to_excel(excel_file_path, index=False)

print(f"PDF saved at {pdf_file_path}")
print(f"Excel saved at {excel_file_path}")

Here is the directory of the Google Colab directory, which includes sample_data, source_data.pdf, and source_data.xlsx:

Which Method Should You Choose?

All the above-mentioned data extraction methods have their strengths, and the best choice depends on your data volumes and specific use cases.  

Manual Copy-Pasting

This method is ideal for small datasets or single tasks where accuracy is not critical. It is a quick solution for extracting data from a few pages but is inefficient for larger volumes.

PDF to Excel Converters

These tools are good for medium-sized datasets and can quickly handle structured PDF data. However, they could encounter difficulties with unstructured or complex PDFs. You cannot transform it into a usable format for further use cases. PDF to Excel converters are also limited to different data integration tasks, which makes it a single-purpose solution.

Power Query

Power Query is a great choice if your business is already using Microsoft products like Excel, Power BI, or Dataverse for data preparation needs. It can handle only medium volumes of data but offers various transformation features. If you are working within the Microsoft ecosystem and need a tool that helps you integrate well with other Office apps, Power Query is ideal.

Python Data Pipelines

If you are comfortable with coding and need flexibility in data processing, Python-based pipelines are excellent. These pipelines are suitable for handling large, complex datasets and allow fine-tuned control over the data extraction process. However, they require technical knowledge and may take longer to set up, making them less suitable for non-technical users.

Airbyte

When dealing with large datasets and complex end-to-end integrations, Airbyte will be the best choice compared to all the other methods. It offers a large catalog of pre-built connectors to support various data movements—not just PDF to Excel conversions.

Compared to all the other methods, Airbyte is one of the best data extraction tools if your organization needs automation, scalability, and flexibility to handle diverse data integration tasks.

Conclusion

In this comprehensive guide, you have learned how to extract data from PDF to Excel. The data extraction methods—copy-pasting, PDF to Excel converters, and Power Query are effective for small to medium-scale datasets. In contrast, Python-based data pipelines can help you handle large datasets but require extensive coding. By providing scalable solutions for your data integration needs, Airbyte will solve all these methods’ constraints.

‍To use Airbyte for your use case, feel free to connect with experts.