How to Extract Data from Multiple PDFs into Excel

Extracting data from one PDF is usually manageable. Extracting data from 20, 50, or 100 PDFs into a single Excel spreadsheet is a different problem.

Even when the files contain similar information, their tables may use different column names, date formats, currencies, or layouts. Some PDFs contain selectable text. Others are scanned images. And a successful conversion does not guarantee that the extracted numbers are correct.

The goal is not simply to convert PDF files into Excel. It is to create a consistent, verifiable dataset that you can filter, compare, and analyze.

This guide explains how to extract data from multiple PDFs into Excel using Microsoft Power Query, Python, and AI-assisted workflows. It also covers scanned documents, field standardization, duplicate detection, and accuracy checks.

Quick Answer: How to Extract Data from Multiple PDFs into Excel

The best method depends on how consistent your PDF files are.

• Similar, text-based PDFs: Start with Excel Power Query. It can import PDF tables and support repeatable transformations.

• Large batches or custom extraction rules: Consider Python with libraries such as pdfplumber and pandas.

• Different document layouts: AI-assisted extraction may help identify fields, but the results require validation.

• Scanned PDFs: Run OCR first when the extraction method requires searchable text.

A reliable workflow follows six stages:

1. Inspect the source PDFs.

2. Define the fields you want to extract.

3. Choose an extraction method.

4. Preserve the source filename and page number.

5. Standardize and combine the extracted data.

6. Validate the final Excel workbook against the original PDFs.

If you only need to extract a table from one file, start with our guide to extracting tables from PDFs with AI.

Before You Start: Check Your PDF Files

The first mistake in batch extraction is assuming every PDF has the same structure.

Before choosing a tool, inspect a representative sample of your documents.

1. Are the PDFs Text-Based or Scanned?

Open a PDF and try selecting a sentence.

If you can select and copy the text correctly, the document likely contains a usable text layer.

If the page behaves like an image, it may require OCR.

Some files contain a mixture of both types.

For scanned documents, follow our guide to making scanned PDFs searchable with OCR.

Remember that OCR can introduce recognition errors, especially in prices, dates, product codes, and tables.

2. Do the Files Use the Same Layout?

Compare the tables and field labels across several files.

For example:

PDF	Product Field	Quantity Field	Price Field
Supplier A	Product Code	Qty	Unit Price
Supplier B	Item No.	Quantity	Price/Unit
Supplier C	SKU	Units	Unit Cost

These fields may describe the same information, but their names are not identical.

A batch extraction process must map them to a common structure.

3. What Data Do You Actually Need?

Define your output schema before extracting anything.

For a supplier quotation comparison, you might need:

• Source filename.

• Source page number.

• Supplier name.

• Product code.

• Quantity.

• Unit price.

• Currency.

• Extraction status.

Avoid extracting every available field if you only need a small subset. A focused schema makes verification easier.

4. What Does One Excel Row Represent?

This is an important decision.

One row might represent:

• One PDF document.

• One invoice.

• One invoice line item.

• One product quotation.

• One transaction.

For the example in this guide, one row represents one quoted product line item.

That means a PDF containing ten products should produce ten rows, not one.

Which Method Should You Choose?

Method	Best For	Main Strength	Main Limitation
Excel Power Query	Similar PDF tables	Repeatable Excel-based transformations	Layout changes can break the workflow
Python + pdfplumber	Batch automation	Custom extraction and processing rules	Requires programming and layout-specific adjustments
AI-assisted extraction	Variable document formats	Can help interpret different labels and structures	May omit, misread, or invent values
OCR + extraction	Scanned documents	Makes image-based text accessible	OCR does not automatically reconstruct tables

These are workflow recommendations, not controlled accuracy rankings.

The right method is the one that produces a verifiable result for your document collection.

Method 1: Extract Data from Multiple PDFs Using Excel Power Query

Power Query is a useful starting point when you have several PDFs containing similar tables.

Microsoft provides a PDF connector for importing tables and a Folder connector for combining files.

However, combining multiple PDFs is not always a one-click operation. The selected PDF objects and transformation steps must work consistently across the files.

Step 1: Organize Your PDF Files

Create a folder such as:

PDF-Quotations

Place the PDFs you want to process inside it.

Use descriptive filenames:

• supplier-a-quote.pdf

• supplier-b-quote.pdf

• supplier-c-quote.pdf

Avoid mixing unrelated documents into the same extraction folder.

Step 2: Test the PDF Connector on One File

In a supported Excel version, open:

Data → Get Data → From File → From PDF

Choose a representative PDF.

Power Query will attempt to identify available tables and pages.

Use the Navigator to inspect the detected objects.

Select the table that contains the information you need, then choose Transform Data.

If the table is not detected correctly, review the document layout before building a batch process around it.

Step 3: Clean the Sample Table

In Power Query Editor:

1. Remove unnecessary header or footer rows.

2. Promote the correct row to column headers when appropriate.

3. Rename columns to your target schema.

4. Remove empty rows.

5. Set appropriate data types.

6. Check whether values remain aligned with the correct records.

For example:

Item No. → Product Code

Qty → Quantity

Price/Unit → Unit Price

Do not convert currency values without confirming the currency and number format.

Step 4: Import Files From a Folder

Return to Excel and use the Folder connector:

Data → Get Data → From File → From Folder

Select the folder containing your PDFs.

Power Query will display a file list.

Filter the list to include only the intended PDF files.

At this stage, you can use the Combine Files workflow or build a custom transformation using the PDF connector.

For PDF files, the transformation commonly involves applying Pdf.Tables to each file's binary content.

The exact steps depend on the structure of your documents.

Step 5: Apply the Extraction to Each PDF

The important requirement is consistency.

If every file contains a comparable table, you may be able to apply the same transformation function across the folder.

If table names or layouts differ, the function may need additional logic.

For example, selecting a table by a fixed identifier can fail when another PDF uses a different identifier.

A more robust workflow may need to inspect available tables and select the appropriate one based on its content or structure.

Step 6: Preserve the Source Filename

Keep the file's Name column, or rename it to:

Source File

This lets you trace extracted rows back to the original PDF.

If page-level traceability is required, preserve page information where the extraction method provides it.

Do not fabricate a page number when the connector does not expose one.

Step 7: Combine and Load Into Excel

After confirming that the transformation works across your sample files:

1. Append the extracted records.

2. Apply consistent column names.

3. Set the correct data types.

4. Review errors and null values.

5. Select Close & Load to load the result into Excel.

Step 8: Refresh After Adding New PDFs

A properly configured folder-based query can be refreshed when new files are added.

However, new files must still be compatible with the transformation rules.

If a supplier changes its PDF layout, the query may need updating.

When Power Query works best: Similar, digitally generated PDFs with consistent tables.

When it struggles: Scanned files, irregular layouts, merged cells, or changing table structures.

For current product behavior, consult Microsoft's PDF connector documentation.

Advanced Example: Combine PDF Tables With Power Query M

If your PDFs contain similar tables, you can use Power Query's M language to apply a PDF extraction function to every file in a folder.

This example assumes:

• Your PDFs contain machine-readable text.

• Each file has at least one table that Power Query can detect.

• The first detected table contains the data you want.

• Its first row contains column headers.

• The documents have broadly consistent structures.

These assumptions matter. The first detected table is not necessarily the correct table in every PDF.

Step 1: Create a Folder

For example, on Windows:

C:\PDF-Quotations

Place the PDFs inside this folder.

Step 2: Open Power Query

In a supported Excel version, create a blank query and open the Advanced Editor.

Replace the query with the following example, adjusting the folder path:

let
    Source = Folder.Files("C:\PDF-Quotations"),

    PDFOnly = Table.SelectRows(
        Source,
        each Text.Lower([Extension]) = ".pdf"
    ),

    ExtractPDF = Table.AddColumn(
        PDFOnly,
        "Extracted",
        each
            try
                let
                    PDFTables = Pdf.Tables([Content]),
                    FirstTable = PDFTables{0}[Data],
                    Promoted = Table.PromoteHeaders(
                        FirstTable,
                        [PromoteAllScalars = true]
                    )
                in
                    Promoted
            otherwise null
    ),

    ValidFiles = Table.SelectRows(
        ExtractPDF,
        each [Extracted] <> null
    ),

    WithSource = Table.AddColumn(
        ValidFiles,
        "Result",
        each
            let
                FileName = [Name],
                DataTable = [Extracted]
            in
                Table.AddColumn(
                    DataTable,
                    "Source File",
                    each FileName,
                    type text
                )
    ),

    Combined =
        if Table.RowCount(WithSource) = 0
        then #table({}, {})
        else Table.Combine(WithSource[Result])

in
    Combined

Step 3: Review the Combined Table

Power Query will attempt to:

1. List the files in the specified folder.

2. Keep PDF files.

3. Extract detected tables from each PDF.

4. Select the first detected table.

5. Promote its first row to headers.

6. Add the source filename.

7. Combine the extracted tables.

If files contain different column names, Power Query may produce separate columns rather than automatically recognizing equivalent fields.

For example, Qty and Quantity may appear as two columns.

You must normalize those columns before relying on the dataset.

Step 4: Identify Failed Files

The example uses try ... otherwise null so that an extraction failure does not stop the entire query.

However, this also means unsuccessful files are excluded from the combined output.

For a reliable workflow, create a separate query or processing report that identifies files where extraction returned null.

Do not interpret a successfully loaded table as proof that every PDF was processed.

Step 5: Handle Different PDF Layouts

If files contain different table structures, the example may need a more specific selection rule.

Possible approaches include:

• Selecting tables by known identifiers.

• Inspecting table headers.

• Using separate transformations for different document templates.

• Cleaning each supplier's data before appending the results.

For inconsistent PDFs, a custom Power Query function or a Python extraction workflow may be more appropriate.

Important: This example is designed to demonstrate folder-based PDF extraction. It is not a universal PDF-to-Excel converter, and it does not guarantee correct values, complete row coverage, or preservation of source page numbers.

See the Microsoft PDF connector documentation for supported behavior and additional options.

Method 2: Extract Data from Multiple PDFs With Python

Python is useful when you need to process a collection of PDFs repeatedly, preserve source information, and apply your own data-cleaning rules.

In this example, we'll use:

• pdfplumber to detect and extract tables from text-based PDFs.

• pandas to organize the extracted records.

• openpyxl as the Excel-writing engine.

The script handles several common variations in column names and creates a processing log so that files with no recognized data are not silently ignored.

It is designed for relatively simple, machine-readable tables. Scanned documents and complex layouts require additional processing.

Step 1: Install the Python Libraries

Open a terminal and run:

python -m pip install pdfplumber pandas openpyxl

Step 2: Create Your Project Folder

Organize your files like this:

pdf-extraction/
├── extract_pdfs.py
├── input/
│   ├── supplier-a.pdf
│   ├── supplier-b.pdf
│   └── supplier-c.pdf
└── output/

Place the PDFs inside the input folder.

Step 3: Create the Extraction Script

Save the following code as extract_pdfs.py:

from pathlib import Path

import pandas as pd
import pdfplumber

BASE_DIR = Path(__file__).resolve().parent
INPUT_DIR = BASE_DIR / "input"
OUTPUT_FILE = BASE_DIR / "output" / "combined.xlsx"

ALIASES = {
    "product code": "Product Code",
    "item no.": "Product Code",
    "sku": "Product Code",
    "qty": "Quantity",
    "quantity": "Quantity",
    "units": "Quantity",
    "unit price": "Unit Price",
    "price/unit": "Unit Price",
    "unit cost": "Unit Price",
    "currency": "Currency",
}

REQUIRED = [
    "Product Code",
    "Quantity",
    "Unit Price",
    "Currency",
]

records = []
processing_log = []

for pdf_path in sorted(INPUT_DIR.glob("*.pdf")):
    extracted_count = 0
    issues = []

    try:
        with pdfplumber.open(pdf_path) as pdf:
            for page_number, page in enumerate(
                pdf.pages, start=1
            ):
                tables = page.extract_tables() or []

                for table_number, table in enumerate(
                    tables, start=1
                ):
                    if not table or len(table) < 2:
                        continue

                    headers = [
                        ALIASES.get(
                            " ".join(str(cell or "").split()).lower()
                        )
                        for cell in table[0]
                    ]

                    if not all(
                        field in headers for field in REQUIRED
                    ):
                        issues.append(
                            f"Page {page_number}, table "
                            f"{table_number}: unmatched headers"
                        )
                        continue

                    for row_number, row in enumerate(
                        table[1:], start=2
                    ):
                        if not row or not any(
                            str(cell or "").strip()
                            for cell in row
                        ):
                            continue

                        values = dict(zip(headers, row))

                        record = {
                            "Source File": pdf_path.name,
                            "Page Number": page_number,
                            "Table Number": table_number,
                            "Source Row": row_number,
                        }

                        for field in REQUIRED:
                            record[field] = str(
                                values.get(field) or ""
                            ).strip()

                        record["Review Status"] = (
                            "Review"
                            if any(
                                not record[field]
                                for field in REQUIRED
                            )
                            else "Check source"
                        )

                        records.append(record)
                        extracted_count += 1

        processing_log.append({
            "Source File": pdf_path.name,
            "Rows Extracted": extracted_count,
            "Status": (
                "Processed"
                if extracted_count
                else "Needs review"
            ),
            "Notes": "; ".join(issues),
        })

    except Exception as error:
        processing_log.append({
            "Source File": pdf_path.name,
            "Rows Extracted": 0,
            "Status": "Failed",
            "Notes": str(error),
        })

OUTPUT_FILE.parent.mkdir(
    parents=True, exist_ok=True
)

data_columns = [
    "Source File",
    "Page Number",
    "Table Number",
    "Source Row",
    *REQUIRED,
    "Review Status",
]

log_columns = [
    "Source File",
    "Rows Extracted",
    "Status",
    "Notes",
]

with pd.ExcelWriter(
    OUTPUT_FILE, engine="openpyxl"
) as writer:

    pd.DataFrame(
        records, columns=data_columns
    ).to_excel(
        writer,
        sheet_name="Extracted Data",
        index=False,
    )

    pd.DataFrame(
        processing_log, columns=log_columns
    ).to_excel(
        writer,
        sheet_name="Processing Log",
        index=False,
    )

print(f"Extracted {len(records)} rows.")
print(f"Processed {len(processing_log)} files.")
print(f"Saved workbook: {OUTPUT_FILE}")

Step 4: Run the Script

From the project folder, run:

python extract_pdfs.py

The script creates:

output/combined.xlsx

The workbook contains two sheets:

Extracted Data

Stores extracted line items, normalized column names, source filenames, page numbers, and review status.

Processing Log

Records which PDF files were processed, how many rows were extracted, and whether errors or unmatched headers were encountered.

Step 5: Review the Output

Do not treat the exported workbook as verified data.

Open it and check:

1. Every expected PDF appears in the processing log.

2. Files marked Needs review or Failed are investigated.

3. The extracted row counts match the original tables.

4. Product codes and quantities match the source PDFs.

5. Prices and currencies have not been misinterpreted.

6. Important records can be traced to their original page.

The script intentionally keeps extracted field values as text before export. This avoids making unsupported assumptions about number separators and currencies, although Excel or spreadsheet libraries may still apply type interpretation.

For production use, add explicit numeric parsing and validation rules based on the source formats.

Important Limitations

This script assumes that the first row of each detected table contains recognizable column headers.

It will not automatically handle:

• Scanned PDFs without a usable text layer.

• Multi-row table headers.

• Tables with missing required columns.

• Complex merged cells.

• Different table orientations.

• Documents that place supplier information outside the table.

• Tables whose structure changes significantly between pages.

It also does not independently verify that extracted values are correct.

For scanned files, see our guide to OCR and searchable PDFs.

For advanced extraction settings, consult the pdfplumber project documentation.

The main benefit of this approach is control and traceability, not guaranteed extraction accuracy.

Method 3: Use AI to Extract Data From Multiple PDFs

AI-assisted extraction can help when documents contain similar information but use different labels or layouts.

For example, one quotation may label a field Unit Cost, while another uses Price Per Unit.

An AI assistant may be able to recognize that these fields serve a similar purpose.

However, this flexibility comes with an important risk: the system may produce plausible but incorrect values.

Step 1: Define a Strict Output Schema

Before uploading documents, specify the exact fields you need.

For example:

Field	Expected Type
Source File	Text
Supplier	Text
Product Code	Text
Quantity	Number
Unit Price	Number
Currency	ISO currency code or original currency label
Page Number	Number, when available
Review Status	Text

Step 2: Use a Source-Grounded Extraction Prompt

Use a prompt such as:

Extract product quotation line items from the attached PDF files.

Return one row per product line item.

Use these columns: Source File, Supplier, Product Code, Quantity, Unit Price, Currency, Page Number, and Review Status.

Do not invent missing values. Use null when a value cannot be found reliably.

Preserve product codes exactly as written.

Do not convert currencies or infer exchange rates.

Keep each row associated with its original source file.

Flag ambiguous numbers, merged cells, or unclear table relationships for manual review.

Before finalizing, report which files and pages were processed and which could not be read.

This prompt reduces ambiguity, but it does not guarantee accuracy.

Step 3: Process Documents in Manageable Groups

Tool file limits and context handling vary.

For large collections, processing smaller groups may make verification easier.

Maintain a manifest of the files you intended to process.

Compare that manifest with the files the AI reports having analyzed.

Step 4: Export Structured Data

Request CSV or spreadsheet-compatible output.

If your tool supports creating Excel files directly, inspect the resulting workbook.

If it only returns text, save the structured output and import it into Excel.

Step 5: Verify the Extracted Values

Check the output against the original PDFs.

Prioritize:

• Product codes.

• Quantities.

• Prices.

• Currencies.

• Dates.

• Supplier names.

• Missing rows.

• Duplicate records.

Do not assume a row is correct simply because the AI provides a source reference.

The referenced page must actually support the extracted value.

When AI Extraction Is Useful

AI can be helpful for interpreting variable field names and identifying candidate values in inconsistent documents.

It is less suitable as an unverified source of truth for financial calculations, legal records, or other high-stakes data.

For an explanation of document interpretation problems, see Why ChatGPT Misreads PDFs and How to Fix It.

How to Extract Data From Scanned PDFs

Scanned PDFs require special attention because their content may exist only as images.

A typical workflow is:

1. Identify which PDFs are scanned.

2. Preserve the original files.

3. Run OCR using an appropriate tool.

4. Verify recognized text on representative pages.

5. Extract the relevant fields or tables.

6. Compare the structured output with the scanned originals.

OCR makes text accessible, but it does not automatically preserve table relationships.

For example, a scanned invoice may contain:

Item	Quantity	Price
AX-100	500	2.50
BX-210	300	3.20

OCR might recognize all six values but place them in the wrong order.

If the output is used for procurement or financial analysis, that error could lead to an incorrect calculation.

Read our OCR guide for scanned PDFs before building a scanned-document extraction workflow.

How to Combine Different PDF Layouts Into One Excel Table

The extraction step is only half the task.

To create a useful dataset, the extracted records must follow a consistent schema.

Step 1: Normalize Field Names

Map equivalent labels to standard column names.

Examples:

Original Label	Standard Column
Item No.	Product Code
SKU	Product Code
Qty	Quantity
Units	Quantity
Unit Cost	Unit Price
Price Per Unit	Unit Price

Document these mappings so they can be reviewed and reused.

Step 2: Normalize Data Types

A quantity should be stored as a number when appropriate.

A date should use a consistent date type.

A unit price should be separated from its currency.

Be careful with number formats.

For example:

1,250.50

and

1.250,50

may represent the same amount under different regional conventions.

Do not apply a global number conversion rule without checking the source format.

Step 3: Preserve Original Values When Necessary

For important workflows, consider keeping both:

• Original Price Text

• Normalized Unit Price

This allows reviewers to compare the transformed value with the source.

Step 4: Keep Source Metadata

At minimum, retain the source filename.

Where available, retain page number and table identifier.

For sensitive or regulated workflows, additional provenance information may be required.

Step 5: Handle Missing Values Explicitly

Do not replace missing prices with zero unless zero is actually the intended value.

Use a blank or null value and flag the record for review.

A missing price and a price of zero have different meanings.

Step 6: Detect Duplicates Carefully

Two rows are not necessarily duplicates just because they have the same product code.

They may represent different suppliers, dates, quantities, or price tiers.

Define duplicate rules based on your actual business logic.

For example, a possible composite key might include:

Source File + Product Code + Quantity + Unit Price

Even then, repeated identical rows within one source may be legitimate and should not automatically be deleted.

Practical Example: Combine Five Supplier Quotations

Consider a hypothetical workflow involving five supplier quotation PDFs.

The files contain product codes, quantities, prices, and currencies, but their layouts differ.

The goal is to create a single Excel table for comparing quotations.

This example uses illustrative data and does not represent an independent extraction benchmark.

Input Files

supplier-a.pdf
supplier-b.pdf
supplier-c.pdf
supplier-d.pdf
supplier-e.pdf

Target Output

Source File	Supplier	Product Code	Quantity	Unit Price	Currency
supplier-a.pdf	Supplier A	AX-100	500	2.50	USD
supplier-b.pdf	Supplier B	BX-210	300	3.20	USD
supplier-c.pdf	Supplier C	CX-305	800	1.85	EUR

Workflow

First, inspect all five PDFs and identify whether they contain selectable text.

Second, define the common output columns.

Third, choose Power Query if the layouts are consistent, Python if custom parsing is required, or AI-assisted extraction if the field labels and structures vary.

Fourth, extract each quotation while preserving its source filename.

Fifth, standardize field names and data types.

Sixth, validate the extracted product codes, quantities, unit prices, and currencies against the original PDFs.

Finally, save the combined Excel workbook.

The result should be a structured dataset whose records can be traced back to the original documents.

How to Validate Data Extracted From Multiple PDFs

A spreadsheet can look clean while containing serious extraction errors.

Validation should be part of the workflow, not an optional final step.

1. Check File Coverage

Compare the number of input PDFs with the number of files successfully processed.

Maintain a processing log.

For example:

Source File	Processing Status	Review Required
supplier-a.pdf	Processed	No
supplier-b.pdf	Processed	Yes
supplier-c.pdf	Failed	Yes

Do not silently omit failed files.

2. Check Row Counts

Where possible, compare the number of source line items with the number of extracted rows.

A PDF containing ten quotation lines should not unexpectedly produce eight rows without an explanation.

3. Check Critical Numbers

Compare important values directly with the source:

• Quantities.

• Unit prices.

• Totals.

• Percentages.

• Dates.

• Product identifiers.

4. Check for Missing or Unexpected Values

Filter the Excel table for:

• Blank product codes.

• Missing prices.

• Unexpected currencies.

• Negative quantities.

• Invalid dates.

• Duplicate-looking records.

Not every unusual value is an error, but each should be investigated.

5. Check the Source References

Every important record should be traceable to its source PDF.

If page numbers are available, retain them.

If the extraction method cannot provide reliable page references, do not invent them.

6. Reconcile Totals When Appropriate

If a PDF includes a reliable subtotal or total, compare it with the extracted line items.

Be careful: discounts, taxes, shipping charges, and rounding can affect reconciliation.

A mismatch is a signal to investigate, not automatic proof of an extraction error.

Common Problems When Extracting Data From Multiple PDFs

Problem 1: Power Query Finds Different Tables in Each PDF

Cause: The files use different layouts or detected table identifiers.

Fix: Inspect the selected PDF objects and adjust the transformation logic. Do not rely on a fixed table identifier unless it is consistent across the documents.

Problem 2: Some PDFs Produce No Data

Cause: The files may be scanned, protected, damaged, or structured differently.

Fix: Check the PDF type, permissions, and extraction method. Run OCR where appropriate and permitted.

Problem 3: Prices Are Assigned to the Wrong Products

Cause: Table extraction has lost the relationship between rows and columns.

Fix: Compare extracted records with the original tables. Use layout-specific extraction settings or manual review for problematic files.

Problem 4: Numbers Change After Importing Into Excel

Cause: Locale-dependent separators, automatic type conversion, or formatting rules.

Fix: Preserve original values and explicitly define numeric conversion rules.

Problem 5: Duplicate Rows Appear

Cause: Repeated headers, overlapping table detection, repeated pages, or legitimate duplicate records.

Fix: Identify the source of duplication before removing rows.

Problem 6: AI Produces Values That Are Not in the PDF

Cause: The model may infer missing information or misinterpret the document.

Fix: Require null values for missing fields, preserve source references, and verify important records directly.

Frequently Asked Questions

Can Excel extract data from multiple PDFs automatically?

Excel Power Query can support repeatable extraction from multiple PDFs, especially when the files have consistent structures. Different layouts may require additional transformations.

Can I combine 100 PDF files into one Excel spreadsheet?

Potentially, yes. The practical limits depend on file complexity, the extraction method, system resources, and the quality of the source documents. Large batches should include processing logs and error handling.

What is the best way to extract tables from multiple PDFs?

For similar text-based tables, Power Query may be a good starting point. For custom automation, Python offers more control. For inconsistent layouts, AI-assisted extraction may help, but it requires verification.

Can Python extract data from scanned PDFs?

Python can be part of a scanned-document workflow, but pdfplumber itself is not a general OCR engine. Scanned pages usually require OCR or image-based recognition before text-oriented extraction.

Can AI convert multiple PDFs into Excel?

Some AI tools can help extract structured information and generate spreadsheet-compatible output. However, file support, limits, and export capabilities vary. The resulting data should be checked against the original PDFs.

How do I avoid losing rows when combining PDFs?

Track source files, compare expected and extracted row counts, preserve source metadata, and investigate failed or incomplete extractions.

Should I remove duplicate rows automatically?

Not without defining what constitutes a duplicate. Identical-looking quotation lines may be valid records from different suppliers or pricing periods.

Conclusion: Build a Verifiable PDF-to-Excel Workflow

Extracting data from multiple PDFs into Excel is not simply a conversion task.

A dependable workflow needs to identify document types, select an appropriate extraction method, standardize fields, preserve source references, and validate important values.

For similar text-based PDFs, Power Query is often a practical starting point.

For custom automation, Python provides more control over extraction and transformation.

For inconsistent layouts, AI-assisted methods can help interpret fields, but they should not replace source verification.

Whatever method you choose, preserve the original PDFs and keep a record of which files and rows were processed.

The goal is not just one Excel workbook. It is a dataset you can trace, check, and confidently use.