--- title: "pdfplumber in Python: Extract text and tables" canonical_url: "https://www.nutrient.io/blog/pdfplumber-python-guide/" md_url: "https://www.nutrient.io/blog/pdfplumber-python-guide.md" last_updated: "2026-10-13T17:58:08.396Z" description: "Extract text and tables from PDFs in Python with pdfplumber: the line and text table strategies, word coordinates, cropping, MIT licensing, and where it stops." --- **TL;DR** pdfplumber extracts text, tables, and coordinates from PDFs with an existing text layer. Start with its default line strategy for bordered tables, and try text-based boundaries for unruled tables. Inspect the visual debugger when results are wrong. It can render page images but doesn’t perform optical character recognition (OCR) or write PDFs. For varied documents and scans, Nutrient’s Data Extraction API is an option that combines parsing and OCR. A table can look clear on a PDF page and still extract as an empty list. The visible columns may be aligned words, with no drawn borders for the default table finder to follow. This guide shows how to choose a strategy, inspect the detected cells, and extract text or coordinates when a table isn’t the right representation. ## What is pdfplumber? pdfplumber is a pure-Python library for reading structured information from a PDF’s content streams: characters with coordinates and font attributes, lines, rectangles, curves, and images. It adds higher-level helpers on top of those primitives for text and table extraction. It’s one of several options in Python — the [Python PDF library comparison](https://www.nutrient.io/blog/best-python-pdf-libraries/) covers where each one fits. It’s built on [pdfminer.six](https://pdfminersix.readthedocs.io/en/latest/) and released under the MIT license. It can rasterize pages through `page.to_image()`, using `pypdfium2`, but doesn’t perform OCR or write PDFs. Without a text layer, text extraction returns an empty string; image and vector objects can still be inspected. ## Prerequisites - Python 3.12 for the examples tested here - A PDF with extractable text, such as a digitally generated document or a scan with an OCR text layer ## Installation Install the version used for these examples: ```bash python -m pip install pdfplumber==0.11.10 ``` The installer pulls in `pdfminer.six` for parsing and `pypdfium2` for the optional page-image rendering used by the visual debugging helpers. Standard wheels bundle the rendering dependencies on supported platforms; no OCR model is included. ## Extracting text from a PDF `pdfplumber.open()` returns a PDF object. Accessing `pages` builds the page list, while pdfplumber computes page layout data when needed and caches it. For large files, `page.close()` can release a processed page’s caches. The following example extracts the text of the first page: ```python import pdfplumber with pdfplumber.open("document.pdf") as pdf: first_page = pdf.pages[0] print(first_page.extract_text()) ``` The [Python text-extraction guide](https://www.nutrient.io/blog/extract-text-from-pdf-using-python.md) covers the same task across other libraries. To collect the whole document, iterate the pages and guard against the empty result. `extract_text()` returns an empty string on a page with no extractable characters, so `if text` skips it. Testing `text is not None` would accept the empty string: ```python import pdfplumber def extract_document_text(path: str) -> str: pages = [] with pdfplumber.open(path) as pdf: for page in pdf.pages: text = page.extract_text() if text: pages.append(text) return "\n\n".join(pages) print(extract_document_text("document.pdf")) ``` Empty text is a reason to inspect the PDF, not proof that it’s a scan. A page may be blank, contain image-only content, use outlined text, or have encoding problems. OCR can help when visible text isn’t extractable. ## Extracting tables Table extraction is a core pdfplumber feature, and it’s configuration-driven. The [PDF data extraction guide](https://www.nutrient.io/blog/pdf-data-extraction-developer-guide.md) covers the layered alternative: parsing, OCR, layout analysis, and schema-shaped JSON. It suits cases where the goal is the extracted data rather than the parsing stack. `extract_tables()` returns a list of tables, each containing rows of cell strings or `None` values. It returns `[]` when no tables are found. `extract_table()` returns the largest table, or `None` if none is found. ```python import pdfplumber with pdfplumber.open("statement.pdf") as pdf: page = pdf.pages[0] for table in page.extract_tables(): for row in table: print(row) ``` ### Bordered tables — The default line strategy The defaults are `vertical_strategy: "lines"` and `horizontal_strategy: "lines"`, meaning pdfplumber takes cell boundaries from the ruling lines and rectangle edges drawn on the page. This is a useful starting point for bordered tables, although broken lines or closely spaced edges can require tuning. ### Unruled tables — Switching to the text strategy For a table without drawn cell boundaries, try deriving boundaries from aligned words. The following settings are a starting point for an unruled table. Word alignment and minimum-word thresholds still affect detection: ```python table_settings = { "vertical_strategy": "text", "horizontal_strategy": "text", } with pdfplumber.open("report.pdf") as pdf: table = pdf.pages[0].extract_table(table_settings) ``` For a table with vertical column rules but no horizontal row rules, try mixing `"lines"` vertically with `"text"` horizontally: ```python table_settings = { "vertical_strategy": "lines", "horizontal_strategy": "text", "snap_y_tolerance": 5, "intersection_x_tolerance": 15, } ``` The tolerance families do different jobs. `snap_*` aligns nearby parallel edges, `join_*` joins collinear segments, and `intersection_*` controls how close orthogonal edges must be to form a cell corner. The settings above are illustrative, not defaults for every document. An empty result can come from a strategy mismatch, missing text, broken borders, or unsuitable tolerances. `page.debug_tablefinder()` returns a `TableFinder` object for inspecting edges, intersections, cells, and tables. For a visual overlay, use the method on a page image instead: ```python import pdfplumber with pdfplumber.open("statement.pdf") as pdf: page = pdf.pages[0] image = page.to_image(resolution=150) image.debug_tablefinder(table_settings={}) image.save("table-debug.png") ``` This uses the default strategies. Pass the settings you’re testing in place of `{}` to see their effect. The [pdfplumber table extraction reference](https://github.com/jsvine/pdfplumber#extracting-tables) also documents `"lines_strict"` and `"explicit"` strategies. ### Try a bordered-table example Download the [sample statement](https://www.nutrient.io/downloads/pdfplumber/statement.pdf) and save it as `statement.pdf` beside your script. It contains a bordered table with three made-up transactions. Run the table-extraction and visual-debugging examples from that directory using the default settings. With Python 3.12 and pdfplumber 0.11.10, the extraction returned one table: a header row and all three transaction rows. The amounts were strings: `1000.00`, `-42.18`, and `250.00`. Download the [extracted rows as CSV](https://www.nutrient.io/downloads/pdfplumber/extracted-rows.csv) to compare the values with your result.![pdfplumber debug overlay showing the bordered sample table, detected cell boundaries, and intersections](@/assets/images/blog/2026/pdfplumber-python-guide/table-debug.png) This overlay came from the visual-debugging example above. The sample demonstrates the default line strategy on a simple table; test your own layouts separately. ## Working with words and coordinates Beyond text and tables, pdfplumber exposes the positional data the page is assembled from. `extract_words()` returns each word with its bounding box. `extra_attrs` restricts words to characters that share the same font attributes, which helps isolate headings, totals, or a specific typeface: ```python with pdfplumber.open("document.pdf") as pdf: words = pdf.pages[0].extract_words(extra_attrs=["fontname", "size"]) for word in words[:5]: print(word["text"], word["x0"], word["top"], word["fontname"], word["size"]) ``` Coordinates make region-based extraction possible. `crop()` takes an `(x0, top, x1, bottom)` bounding box and returns a page restricted to it, so you can read a known layout positionally: ```python with pdfplumber.open("invoice.pdf") as pdf: page = pdf.pages[0] header = page.crop((0, 0, page.width, 120)) print(header.extract_text()) ``` For fields at known positions, crop to the region containing the value and extract text from that crop. Fixed coordinates may need adjustment when the layout changes. ## pdfplumber vs. PyMuPDF Both libraries expose text, tables, and layout data. Compare the specific operations your application needs rather than assuming only one supports a feature. | Capability | pdfplumber | PyMuPDF | | ------------------------- | ---------------------------------------------------------- | ---------------------------------------------------------------- | | License | MIT | AGPL v3 or commercial | | Parsing implementation | Python, using `pdfminer.six` | MuPDF engine with Python bindings | | Table extraction | `extract_tables()` with line, text, or explicit strategies | `Page.find_tables()` with configurable strategies and tolerances | | Character and layout data | Character dictionaries and drawn objects | `get_text("rawdict")` and drawing APIs | | Visual inspection | `to_image().debug_tablefinder()` overlay | Page rendering and drawing APIs | | Page rasterization | `to_image()`, using `pypdfium2` | `Page.get_pixmap()` | | Writing PDFs | Not supported | Supported | | OCR | Not built in | Tesseract integration; requires Tesseract installed separately | Choose pdfplumber when its table settings, object dictionaries, and visual debugging fit your extraction task. Evaluate PyMuPDF if you also need PDF editing or its rendering and extraction APIs. Benchmark both on your own files if throughput is a deciding factor. See the [PyMuPDF text extraction guide](https://www.nutrient.io/blog/extract-text-from-pdf-pymupdf/) for examples. For scanned pages, pdfplumber needs an OCR text layer supplied by another tool. PyMuPDF provides [integrated OCR methods](https://pymupdf.readthedocs.io/en/latest/recipes-ocr.html), including `Page.get_textpage_ocr()`, through a separately installed Tesseract engine. ## Where pdfplumber fits, and where it stops pdfplumber suits projects that work from digitally generated PDFs with known layouts. In those projects, teams can tune the extraction logic per document type, and per-page speed isn’t the binding constraint.![PDF extraction stages: pdfplumber parses and assembles content; application code handles OCR routing, interpretation, configuration, and throughput.](@/assets/images/blog/2026/pdfplumber-python-guide/where-pdfplumber-stops.png) The limits show up as document variety increases: - **OCR** — Image-only text needs recognition before pdfplumber can extract it. Mixed documents may need routing by page. - **Layout changes** — A table configuration that works on one template may need adjustment on another. Keep representative files for regression checks. - **Throughput** — Measure text and table extraction separately, with runtime and peak memory limits that match your workload. - **Field meaning** — Coordinates and cell strings don’t identify business fields by themselves. Your application still maps values and validates them. These are the tasks between parsing a table and delivering validated records to another system. The [intelligent document processing guide](https://www.nutrient.io/blog/what-is-intelligent-document-processing.md) explains how a broader document workflow handles them. ## Reaching the same outcome with Nutrient For projects that need extraction across varied layouts and scanned documents, consider [Nutrient’s Data Extraction API](https://www.nutrient.io/api/data-extraction-api/). It combines parsing with OCR and can reduce the amount of extraction infrastructure and layout tuning your team maintains. The following request uses `/extraction/parse` in `understand` mode and asks for spatial document elements. It doesn’t return Markdown at the same time. Install `requests` with `python -m pip install requests` and replace the placeholder with your Nutrient API key before running it: ```python import requests with open("statement.pdf", "rb") as document: response = requests.post( "https://api.nutrient.io/extraction/parse", headers={"Authorization": "Bearer your_api_key_goes_here"}, files={"file": document}, data={ "instructions": '{"mode":"understand","output":{"format":"spatial"}}' }, timeout=120, ) response.raise_for_status() print(response.json()) ``` Use the [document element parsing guide](https://www.nutrient.io/guides/dws-data-extraction/parsing/extract-document-elements.md) to select an output format. The [table extraction API](https://www.nutrient.io/api/table-extraction-api/) page covers the tabular use case. Your application still needs to validate results, map them to its data model, and handle request failures. For named fields, use schema extraction through `/extraction/extract`. Its citations can include page regions and match labels where available. Not every element returned by the parsing request above carries those labels. See [confidence scores and source grounding](https://www.nutrient.io/blog/document-extraction-confidence-scores.md) and the [extraction case studies](https://www.nutrient.io/blog/pdf-extraction-document-case-studies.md) for how to use evidence during validation. If documents must stay in your own environment, evaluate the [Python SDK](https://www.nutrient.io/sdk/python/) against your required operations. Check the SDK’s supported operations and output formats separately from the hosted API. [Extract PDF tables with Nutrient](https://dashboard.nutrient.io/sign_up/?product=data-extraction) ## Conclusion pdfplumber is a useful choice for text and table extraction when you can test and tune against known layouts. Start with the examples above, inspect failed table detections, and measure performance on the files you’ll process. Teams that handle varied layouts and scans may want less extraction infrastructure to maintain. Run Nutrient’s Data Extraction API against the same documents and validation criteria. **Call to Action** Try Nutrient Data Extraction on your PDFs [Learn More](https://www.nutrient.io/api/data-extraction-api/) ## FAQ #### Is pdfplumber free for commercial use? Yes. Its [MIT license](https://github.com/jsvine/pdfplumber/blob/stable/LICENSE.txt) permits commercial use, subject to retaining the required copyright and permission notice. Check the license text when distributing software that includes it. #### Which is better, pdfplumber or PyMuPDF? Use pdfplumber for its table extraction settings and visual debugger. PyMuPDF also extracts tables and character data, and it adds PDF editing and Tesseract-based OCR integration. Compare extraction quality, runtime, and licensing on your own files. #### Can pdfplumber read scanned PDFs? It can extract an existing OCR text layer from a scanned PDF, but it can’t recognize text in images itself. For image-only pages, add OCR first or use [Nutrient’s Data Extraction API](https://www.nutrient.io/api/data-extraction-api/), which supports scanned documents. #### Why does extract_tables() return an empty list on a visible table? The table finder may lack usable boundaries or text, or its tolerances may not fit the layout. Try text strategies for unruled tables and inspect an overlay with `page.to_image().debug_tablefinder()`. When no tables are found, `extract_tables()` returns `[]`; `extract_table()` returns `None`. #### When is a document API a better fit than a pdfplumber pipeline? An API is a better fit when handling scans, layout variation, and extraction infrastructure takes more effort than your team wants to maintain. Test Nutrient’s parsing or schema extraction on representative documents, and check the output against your accuracy and validation requirements. #### Is pdfplumber fast enough for large documents? That depends on document complexity, the extraction operations, and your processing window. Benchmark representative files and record both runtime and peak memory. Calling `page.close()` after processing a page can release its cached layout data. ## Related reading - [Python PDF library comparison](https://www.nutrient.io/blog/best-python-pdf-libraries/) - [Extract text from a PDF using Python](https://www.nutrient.io/blog/extract-text-from-pdf-using-python.md) - [PDF data extraction: A developer guide](https://www.nutrient.io/blog/pdf-data-extraction-developer-guide.md) - [What is intelligent document processing?](https://www.nutrient.io/blog/what-is-intelligent-document-processing.md) - [Confidence scores and source grounding](https://www.nutrient.io/blog/document-extraction-confidence-scores.md) - [PDF extraction case studies](https://www.nutrient.io/blog/pdf-extraction-document-case-studies.md) --- ## Related pages - [The business case for accessibility: Five ways it drives enterprise value](/blog/5-ways-accessibility-drives-enterprise-value.md) - [Accessibility Untangled Why It Matters Guide](/blog/accessibility-untangled-why-it-matters-guide.md) - [Advanced Techniques For React Native Ui Components](/blog/advanced-techniques-for-react-native-ui-components.md) - [`vector_store` holds your indexed documents (see the multimodal RAG post](/blog/agentic-rag.md) - [Ai Assistant Agent Governance](/blog/ai-assistant-agent-governance.md) - [How to build an AI agent for contract redlining against a compliance playbook](/blog/ai-contract-redlining-compliance-playbook.md) - [Ai Document Automation Extraction To Action](/blog/ai-document-automation-extraction-to-action.md) - [Ai Document Workflows Ocr Compliance Heavy Teams](/blog/ai-document-workflows-ocr-compliance-heavy-teams.md) - [Ai Legal Assistant Document Authoring](/blog/ai-legal-assistant-document-authoring.md) - [Ai Schema Generator Document Extraction](/blog/ai-schema-generator-document-extraction.md) - [Amazon Textract Alternatives](/blog/amazon-textract-alternatives.md) - [Start (clears any prior buffer), navigate the document, then stop into a file.](/blog/android-faster-pdf-rendering.md) - [Android Pdf Out Of Memory Handling](/blog/android-pdf-out-of-memory-handling.md) - [Angular File Viewer Pdf Image Office Files](/blog/angular-file-viewer-pdf-image-office-files.md) - [Approval Workflow Software](/blog/approval-workflow-software.md) - [Approvals Matrix](/blog/approvals-matrix.md) - [Apryse To Nutrient Migration](/blog/apryse-to-nutrient-migration.md) - [Auto Tagging And Document Accessibility In Dotnet Sdk](/blog/auto-tagging-and-document-accessibility-in-dotnet-sdk.md) - [Simple PII redaction.](/blog/automated-pii-removal.md) - [Azure Document Intelligence Alternatives](/blog/azure-document-intelligence-alternatives.md) - [Best Ai Document Workflow Platforms](/blog/best-ai-document-workflow-platforms.md) - [Best Document Ai Platforms](/blog/best-document-ai-platforms.md) - [Best Document Classification Platforms](/blog/best-document-classification-platforms.md) - [Best document parser for RAG: LlamaParse vs. Unstructured vs. Reducto vs. Nutrient](/blog/best-document-parser-llamaparse-unstructured-reducto.md) - [Best Document Parsing Apis](/blog/best-document-parsing-apis.md) - [Best Document Viewers](/blog/best-document-viewers.md) - [Best Llm Document Understanding Platforms](/blog/best-llm-document-understanding-platforms.md) - [Best Multilingual Ocr Software](/blog/best-multilingual-ocr-software.md) - [Best Pdf Parsers For Rag](/blog/best-pdf-parsers-for-rag.md) - [Best Salesforce Document Generation Apps](/blog/best-salesforce-document-generation-apps.md) - [Best Secure Document Collaboration Platforms](/blog/best-secure-document-collaboration-platforms.md) - [Bpm Guide](/blog/bpm-guide.md) - [Bpm Tools](/blog/bpm-tools.md) - [Build Vs Buy Document Extraction](/blog/build-vs-buy-document-extraction.md) - [Business Automation](/blog/business-automation.md) - [Capex Vs Opex](/blog/capex-vs-opex.md) - [The CEO’s AI playbook: Why decision architecture beats model selection](/blog/ceo-ai-playbook-decision-architecture.md) - [1. 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