Welcome To Datagrunt

Datagrunt is a Python library designed to simplify the way you work with CSV, Excel, Parquet, and PDF files. It provides a streamlined approach to reading, processing, and transforming your data into various formats, making data manipulation efficient and intuitive.

Why Datagrunt?

Born out of real-world frustration, Datagrunt eliminates the need for repetitive coding when handling CSV, Excel, Parquet, and PDF files. Whether you're a data analyst, data engineer, or data scientist, Datagrunt empowers you to focus on insights, not tedious data wrangling.

What Datagrunt Is Not

Datagrunt is not an extension of or a replacement for DuckDB, Polars, or PyArrow, nor is it a comprehensive data processing solution. Instead, it's designed to simplify the way you work with CSV, Excel, Parquet, and PDF files — solving the pain point of inferring delimiters when a CSV structure is unknown, reading Excel workbooks sheet by sheet, reading and converting columnar Parquet data, and turning PDFs into structured, queryable data. Datagrunt provides an easy way to convert CSV, Excel, and Parquet files to dataframes and export them to various formats, and to extract text, tables, and images from PDFs. One of Datagrunt's value propositions is its relative simplicity and ease of use.

Key Features

• Intelligent Delimiter Inference: Datagrunt automatically detects and applies the correct delimiter for your CSV files.
• Rust-Accelerated Core (v4.0+): CSV delimiter and dialect inference run in a bundled Rust extension (datagrunt._native) for multiple-times-faster scanning of large files. The native engine is the default and is required on supported platforms (it ships as a prebuilt wheel). A byte-for-byte-equivalent pure-Python implementation lives alongside it as the differential-parity oracle — validated against the Rust engine in CI and selectable via a hidden diagnostics toggle — so results are identical no matter which path runs.
• Path Object Support: Full support for both string paths and pathlib.Path objects for modern, cross-platform file handling.
• Multiple Processing Engines: Choose from three powerful engines - DuckDB, Polars, and PyArrow - to handle your data processing needs.
• Flexible Data Transformation: Easily convert your processed CSV, Excel, and Parquet data into various formats including CSV, Excel, JSON, JSONL, and Parquet.
• Excel Reading & Writing: Read .xlsx/.xls workbooks sheet by sheet into DataFrames, dicts, Arrow, or SQL, and export any sheet (or all of them) to CSV, JSON, JSONL, Parquet, or Excel.
• Parquet Reading & Writing: Read .parquet files into DataFrames, dicts, Arrow tables, or SQL, and export to CSV, JSON, JSONL, Parquet, or Excel. Backed by a single Polars engine with full read_options/write_options passthrough.
• Robust by Default: Fail-fast validation with clear errors (invalid engine names, missing paths, directories, encrypted PDFs), graceful handling of empty files, no UnicodeDecodeError when constructing a reader over a non-UTF-8 file, and sane comment semantics — only leading # lines are treated as comments, so #-prefixed data rows such as hex colors are preserved on all engines.
• PDF Parsing & OCR: Read PDFs into a typed document of headings, paragraphs, key-value pairs, tables, lists, and figures, as DataFrames, Arrow tables, dicts, Markdown, or SQL, and write JSON, JSONL, Markdown, Parquet, CSV, images, or a checksummed bundle. Hidden text is flagged, never dropped, and scanned pages are read with optional Tesseract OCR. Powered by the permissively-licensed PDFium engine.
• Schema-Driven PDF Rationalization (v5.1+): Extract Pydantic records from v5 PDF documents (rationalize()) with cross-family model consensus (min_families=2), verbatim quote and key grounding against visible elements, cross-page entity folding, and deterministic Polars/Arrow export (to_dataframe(flatten=...)).
• Pythonic API: Enjoy a clean and intuitive API that integrates seamlessly into your existing Python workflows.

Powertools Under The Hood

Installation

We recommend using uv as the default package manager.

To install Datagrunt using uv:

uv pip install datagrunt

Datagrunt 5.0 requires Python 3.11 or newer.

PDF parsing is an optional extra — install it with uv pip install "datagrunt[pdf]". For schema-driven PDF extraction (rationalize), install uv pip install "datagrunt[pdf,rationalize]". See PDF parsing and Schema-driven extraction (rationalize) below.

Quick Start

Reading CSV Files with Multiple Engine Options

from datagrunt import CSVReader
from pathlib import Path

# Load your CSV file with different engines
# Accepts both string paths and Path objects
csv_file = 'electric_vehicle_population_data.csv'
csv_path = Path('electric_vehicle_population_data.csv')

# Choose your engine: 'polars' (default), 'duckdb', or 'pyarrow'
reader_polars = CSVReader(csv_file, engine='polars')    # String path - fast DataFrame ops
reader_duckdb = CSVReader(csv_path, engine='duckdb')    # Path object - best for SQL queries
reader_pyarrow = CSVReader(csv_file, engine='pyarrow')  # Arrow ecosystem integration

# Get a sample of the data (streams the first rows — the whole file is never
# materialized, so sampling large files stays memory-bounded on every engine)
reader_duckdb.get_sample()

DuckDB Integration for Performant SQL Queries

from datagrunt import CSVReader

# Set up DuckDB engine for SQL capabilities
dg = CSVReader('electric_vehicle_population_data.csv', engine='duckdb')

# Construct your SQL query using the auto-generated table name
query = f"""
WITH core AS (
    SELECT
        City AS city,
        "VIN (1-10)" AS vin
    FROM {dg.db_table}
)
SELECT
    city,
    COUNT(vin) AS vehicle_count
FROM core
GROUP BY 1
ORDER BY 2 DESC
"""

# Execute the query and get results as a Polars DataFrame
df = dg.query_data(query).pl()
print(df)

With the DuckDB engine, datagrunt imports the CSV once per reader or writer and reuses it: repeated reads (to_dataframe, to_arrow_table, get_sample), query_data calls, and multi-format exports (write_csv + write_excel + write_json + write_parquet) all skip re-importing the file, so follow-up operations run dramatically faster.

You never have to manage this — the connection is released automatically when the reader or writer goes out of scope (its connection is reference-counted). If you want to release it early (for example, to free memory deterministically in a long-running process), use the reader as a context manager or call close(); this is optional and the object stays usable afterward:

with CSVReader('vehicles.csv', engine='duckdb') as dg:
    df = dg.query_data(f"SELECT city, COUNT(*) FROM {dg.db_table} GROUP BY 1").pl()
# connection released here, even if the block raises

Consistent Column Names with normalize_columns

Pass normalize_columns=True at construction to work in normalized column names (lowercase, underscores, collision-safe) everywhere — including SQL:

from datagrunt import CSVReader

dg = CSVReader('electric_vehicle_population_data.csv', engine='duckdb', normalize_columns=True)

# The DuckDB table is imported with normalized names, so you write your
# query and read your results in the same vocabulary — no aliases needed.
query = f"SELECT city, vin_1_10 FROM {dg.db_table} LIMIT 5"
df = dg.query_data(query).pl()

# Every other output honors the same setting
dg.to_dataframe()   # columns: city, vin_1_10, ...
dg.get_sample()     # same normalized names

CSVWriter(..., normalize_columns=True) does the same for every exported file. The older per-call form (to_dataframe(normalize_columns=True)) still works but is deprecated and emits a DeprecationWarning.

Exporting Data to Multiple Formats

from datagrunt import CSVWriter
from pathlib import Path

# Create writer with your preferred engine (accepts both strings and Path objects)
input_file = Path('input.csv')
writer = CSVWriter(input_file, engine='duckdb')  # Default for exports

# Export to various formats
writer.write_csv('output.csv')          # Clean CSV export
writer.write_excel('output.xlsx')       # Excel workbook
writer.write_json('output.json')        # JSON format
writer.write_parquet('output.parquet')  # Parquet for analytics

# Use PyArrow engine for optimized Parquet exports
writer_arrow = CSVWriter('input.csv', engine='pyarrow')  # String path also works
writer_arrow.write_parquet('optimized.parquet')  # Native Arrow Parquet

Every write_* method — including write_parquet — honors lenient=True for ragged CSVs, and empty source files produce empty output instead of an error.

Reading and writing Excel

ExcelReader and ExcelWriter read .xlsx/.xls-family workbooks sheet by sheet. Reading is backed by a single canonical engine — Polars + calamine (via fastexcel, a core dependency) — so there is no engine argument. A non-Excel or missing path is rejected at construction (ValueError / FileNotFoundError).

from datagrunt import ExcelReader, ExcelWriter

xl = ExcelReader("workbook.xlsx")
xl.sheets                      # ['Sheet1', 'Sheet2', ...]
xl.to_dataframe()              # first sheet as a Polars DataFrame
xl.to_dataframe(sheet="Sheet2")  # by name
xl.to_dicts(sheet=1)           # by position
xl.to_arrow_table()            # also: get_sample(), all accept sheet=
xl.query_data(f"SELECT * FROM {xl.db_table}", sheet="Sheet2")

# Read options are keyword arguments forwarded verbatim to pl.read_excel
# (constructor default or per call). Pass any pl.read_excel parameter:
xl.to_dataframe(has_header=False)
xl.to_dataframe(read_options={"skip_rows": 2, "n_rows": 100})

w = ExcelWriter("workbook.xlsx")
w.write_csv("out.csv")                    # first sheet
w.write_parquet("out.parquet", sheet="Sheet2")
w.write_csv("out.csv", all_sheets=True)   # one file per sheet: out_Sheet1.csv, ...
w.write_excel("all.xlsx", all_sheets=True) # one multi-tab workbook

Every reader/writer method takes an optional sheet= (name or zero-based index), defaulting to the first sheet; an invalid sheet raises a ValueError listing the available sheets, and empty/blank workbooks return empty results. The keys source, sheet_id, and sheet_name are reserved (sheet selection is controlled via sheet=).

normalize_columns=True (constructor or per call) normalizes column names exactly as the CSV API does. Values beginning with =, +, -, @ are written verbatim — sanitize at the application layer if your source is untrusted and the output may be opened in a spreadsheet (CWE-1236).

How read options work (same for Excel and Parquet). In both subsystems you pass read options as keyword arguments, and Datagrunt forwards them verbatim to the underlying Polars function — pl.read_excel for Excel, pl.read_parquet for Parquet. The mechanism is identical; only the available option names differ, because the two Polars functions differ. pl.read_excel happens to expose a parameter that is itself a dict named read_options (the calamine engine's option bag, where skip_rows/n_rows live), which is why you see read_options={...} above. pl.read_parquet has no such dict — its options (columns, n_rows, …) are plain top-level parameters. So xl.to_dataframe(read_options={"skip_rows": 2}) and pq.to_dataframe(columns=["id"], n_rows=100) use the same Datagrunt keyword-passthrough; the shape of the arguments comes straight from Polars.

Reading and writing Parquet

ParquetReader and ParquetWriter work with .parquet files as a single-table columnar format — there are no sheets and no engine argument. Reading is backed by a single canonical Polars engine. A non-Parquet or missing path is rejected at construction (ValueError / FileNotFoundError).

from datagrunt import ParquetReader, ParquetWriter

reader = ParquetReader("data.parquet")
reader.get_sample()                          # first rows as a Polars DataFrame (n_rows= adjusts the sample size)
reader.to_dataframe()                        # full file as a Polars DataFrame
reader.to_arrow_table()                      # Apache Arrow Table
reader.to_dicts()                            # list of row dicts
reader.query_data(f"SELECT * FROM {reader.db_table} LIMIT 5")

# Read options are keyword arguments forwarded verbatim to pl.read_parquet
# (the same passthrough as Excel; only "source" is reserved). Pass any
# pl.read_parquet parameter — they are plain top-level kwargs, not a dict:
reader_subset = ParquetReader("data.parquet", columns=["id", "name"], n_rows=1000)

writer = ParquetWriter("data.parquet")
writer.write_csv("out.csv")
writer.write_json("out.json")
writer.write_json_newline_delimited("out.jsonl")
writer.write_parquet("out.parquet")
writer.write_excel("out.xlsx")

# write_options are forwarded to the Polars writer:
writer.write_parquet("recompressed.parquet", compression="zstd")

normalize_columns=True normalizes column names exactly as the CSV and Excel APIs do. Empty or blank source files return empty results from reader methods and produce 0-byte output files from writer methods. Values beginning with =, +, -, @ are written verbatim — sanitize at the application layer if your source is untrusted and the output may be opened in a spreadsheet (CWE-1236).

Read options work exactly as they do for Excel — keyword arguments forwarded verbatim to Polars (here pl.read_parquet). There is no Datagrunt-specific convention and no dict to wrap them in: pl.read_parquet simply exposes its options as top-level parameters (columns, n_rows, …), whereas pl.read_excel groups some of its calamine options inside a dict named read_options. Same Datagrunt passthrough, different Polars signatures.

PDF parsing

PDF support is an optional extra:

uv pip install "datagrunt[pdf]"

The extra installs pypdfium2 (PDFium) and Pillow. import datagrunt works without it; constructing a PDFReader or PDFWriter without it raises an ImportError that names the install command.

Datagrunt 5.0 reads every PDF with one engine, PDFium, into a typed document: headings, paragraphs, key-value pairs, tables, lists, and figures, in reading order. Each element carries its page, its bounding box (in points, top-left origin), and the headings it sits under (scope_path). Coming from 4.x? engine=, native=, drop_layout_tables=, and the PyMuPDF, pdfplumber, and pytesseract dependencies are gone, and the output shapes changed; the migration guide lists every change.

Reading a PDF

from datagrunt import PDFReader

reader = PDFReader("report.pdf")

df = reader.to_dataframe()          # Polars: one row per element, in document order
arrow = reader.to_arrow_table()     # the same rows as a PyArrow table
rows = reader.to_dicts()            # the same rows as dicts
reader.get_sample()                 # the first 20 rows (n_rows=...)
markdown = reader.to_markdown()     # the whole document as Markdown

# The typed document model
for table in reader.tables:
    print(table.scope_path, table.to_dataframe())
reader.headings, reader.paragraphs, reader.key_values, reader.lists, reader.figures
reader.sections, reader.pages, reader.page_images, reader.metadata

# SQL over the element rows (an in-memory DuckDB connection per reader)
result = reader.query_data(f"SELECT kind, count(*) AS n FROM {reader.db_table} GROUP BY kind")
result.pl()                         # also .pa() and .dicts()

Every row has the same columns: doc_id, element_id, page_number, reading_order, kind (heading, paragraph, key_value, table, list, or figure), scope_path, text, markdown, bbox, route, integrity_verified, and hidden_reasons.

• PDFReader writes nothing to disk, not even temporary files. Figures load lazily (figure.read_bytes()); write them with PDFWriter.
• Each page has a page_image. A digital or hybrid page's is drawn at the reader's dpi only when you read it (page.page_image.read_bytes(), a PNG); a scanned page's is the image OCR read. An error page has none, nor does a page too large to draw (a warning on the page says so). A page image is drawn from the source PDF, so if the file at its path no longer holds the document's bytes, reading it raises datagrunt.pdf.PDFSourceChangedError.
• A heading's level (1 to 6) is ranked over the whole document by its font size and boldness (font_size_pt, is_bold), largest first, so a level means the same prominence on every page. Hidden headings never shift it.
• The PDF is read on first use, then cached for the reader's life. Errors in the arguments are raised at construction: a missing path raises FileNotFoundError; a file that isn't a PDF raises ValueError.
• An encrypted PDF needs password=; without it, reading raises PDFEncryptedError (also a ValueError). A structurally unreadable PDF raises PDFCorruptError (also a RuntimeError). Both derive from DatagruntPDFError.
• PDFReader also reads a bundle directory (see write_bundle below), its manifest.json, or a document's JSON as a dict.
• query_data runs your SQL as written: building it from untrusted input is your application's concern. DuckDB never installs an extension on its own; a query that needs one raises until you INSTALL it.

Writing outputs

from datagrunt import PDFWriter

writer = PDFWriter("report.pdf")

writer.write_json("out/report.json")          # the whole document, lossless
writer.write_jsonl("out/report.jsonl")        # one element per line
writer.write_markdown("out/report.md")
writer.write_parquet("out/report.parquet")    # nested columns kept
writer.write_csv("out/report.csv")            # bbox as four columns, lists as JSON arrays
writer.extract_images("out/images")           # each distinct figure once, as <sha256>.png
writer.render_pages_as_images("out/pages")    # every page as page_0001.png, page_0002.png, ...
writer.write_bundle("out/report_bundle")      # all of the above in one checked directory

• Every output path is required; there are no default filenames. Each write_* method returns the absolute path it wrote, and extract_images / render_pages_as_images return the paths they wrote; all of them create missing directories. write_bundle returns the PDFDocument it bundled, and its parent directory must exist.
• Every file is written atomically (a temporary file, then a rename), so a failed write never leaves a partial file. An existing file is replaced.
• extract_images(output_dir, dedupe=False) writes every figure, named by its element id (p0001_e003.png).
• render_pages_as_images(output_dir, dpi=None) writes 8-bit grayscale PNGs at the writer's dpi (or the one you pass), through a memory-capped renderer. A page that can't be rendered is logged and skipped; the others are written.
• write_bundle(output_dir) writes json/document.json, markdown/document.md, images/<sha256>.png, page_images/page_NNNN.png (plus the OCR bands of scanned pages), and a manifest.json that records every file's size and SHA-256, plus json/elements.jsonl and one Markdown file per page (markdown/pages/page_NNNN.md). output_dir must not exist or must be empty. If it already holds a bundle of the same PDF, written with the same options and extractor version, that bundle is returned as it is; overwrite=True replaces any other valid bundle there. A directory without a valid bundle manifest is never overwritten. PDFReader("out/report_bundle") loads it back, verifying every file: an invalid manifest.json raises ValueError at construction, and a file that doesn't match its manifest raises datagrunt.pdf.BundleCorruptError when the bundle is first loaded. A source PDF that changes while it's read or bundled raises datagrunt.pdf.PDFSourceChangedError.
• write_csv writes every value as extracted: a cell that starts with =, +, -, or @ is not escaped, so guarding a spreadsheet against formula injection is your application's concern.
• A PDFWriter also accepts a PDFDocument (for example reader.document). It has no PDF to render, so render_pages_as_images and write_bundle raise ValueError.

Hidden text

Text a human reader can't see is flagged, never dropped. Each element's hidden_reasons lists why: off_page, invisible, tiny_font, transparent, matches_background, zero_scale, duplicate, superseded, or unchecked_background. An empty list means the element is visible. Markdown (to_markdown(), write_markdown() and a bundle's Markdown) leaves hidden elements out by default; pass include_hidden=True to keep each behind a <!-- hidden: reasons --> comment. JSON and the DataFrame always keep every element. Filter on the column before you pass text on, for example to an LLM:

import polars as pl

visible = reader.to_dataframe().filter(pl.col("hidden_reasons").list.len() == 0)

Scanned pages and OCR

Each page is routed digital, hybrid, or vision (page.route). A vision page (a scan, or a page with too little text or mostly unreadable characters) is rendered at vision_dpi and read by the Tesseract program; there is no Python wrapper. The page's own digital text is kept, flagged superseded.

Install Tesseract with its English (eng) and orientation (osd) data, e.g. brew install tesseract on macOS or apt-get install tesseract-ocr on Debian/Ubuntu. On Windows, use the UB-Mannheim installer or a package manager (winget install UB-Mannheim.TesseractOCR, choco install tesseract, or scoop install tesseract). Datagrunt runs the tesseract it finds in an absolute directory on your PATH, never one in the working directory, so add the Tesseract directory to PATH.

Every page reports how extraction went (page.status):

• ok: extracted on its route.
• ocr_fallback: the page couldn't be read digitally, so it was rendered and read by OCR.
• ocr_unavailable: a vision page OCR couldn't read (no Tesseract, say); its digital text is kept.
• error: nothing could be read; page.error says why. The rest of the document still extracts.

A page read by OCR at a mean word confidence under 70 carries a warning that it may be turned the wrong way or unreadable, so its text shouldn't be trusted as is; the text is kept. Tesseract's orientation detection can turn a page with little text the wrong way; such a page reads at low confidence and is flagged.

Extraction is always complete: a bad page is recorded, never dropped. Pass strict=True to raise PDFCorruptError on a bad page instead.

Options

PDFReader and PDFWriter take the same keyword-only options:

reader = PDFReader(
    "report.pdf",
    password=None,                  # an encrypted PDF's password
    workers=1,                      # processes to extract pages with
    ocr="auto",                     # "auto", "always" (every page to OCR), or "never"
    dpi=200,                        # page images (render_pages_as_images, bundles)
    vision_dpi=300,                 # pages read by OCR; at least dpi
    max_render_pixels=16_000_000,   # caps every render: a huge page gets a lower DPI
    min_image_dimension=40,         # smaller embedded images aren't figures
    strict=False,                   # raise on a bad page instead of recording it
)

Options are checked at construction: an out-of-range value raises ValueError and a wrong type raises TypeError. min_image_dimension drops embedded images narrower or shorter than that many pixels (rule lines, separators, icon slivers); it must be at least 1.

Parallel processing

Every PDF is parsed in worker processes, never in your own: pages are extracted in workers processes (one when workers=1) and come back in page order, and figure reads and page renders run in workers too. Each worker is a fresh, isolated python process with a memory cap (2 GiB), and it never imports your script, so no if __name__ == "__main__": guard is needed. A page that passes the cap, or its time bound, becomes an error page with the reason; the other pages are read by a new worker. Inside a distributed runtime (Apache Spark, Apache Beam, Apache Flink, or Celery, detected through variables such as SPARK_ENV_LOADED and BEAM_WORKER_ID) one worker extracts the pages. A frozen application (PyInstaller) cannot start workers yet, so reading a PDF there raises DatagruntPDFError saying so.

Schema-driven extraction (rationalize)

Install the [rationalize] extra (pydantic and httpx) alongside [pdf]:

uv pip install "datagrunt[pdf,rationalize]"

rationalize() extracts structured Pydantic records from one or more PDF documents (PDFDocument, PDF path, bundle path, or a sequence of them). Hidden elements (hidden_reasons non-empty) are excluded before any model request is built, bidi/tag/control characters are escaped in the wire envelope, and every model response is validated, compared across model families, grounded against the cited visible elements, and folded across pages by entity key:

from decimal import Decimal
from typing import Annotated
from pydantic import BaseModel
from datagrunt import Key, PDFReader, rationalize


class LineItem(BaseModel):
    part_no: Annotated[str, Key]
    qty: int
    unit_price: Decimal


class Invoice(BaseModel):
    invoice_id: Annotated[str, Key("Invoice #")]
    vendor: str
    items: list[LineItem] = []


doc = PDFReader("invoice.pdf").document
result = rationalize(
    doc,
    schema=Invoice,
    prompt="Extract each invoice and its line items.",
    models=("openai:gpt-5.4", "anthropic:claude-sonnet-4-6"),
    min_families=2,          # require agreement across >= 2 distinct model families
    cache_dir=".dg-cache",   # optional atomic on-disk response cache
)

df = result.to_dataframe(flatten="items")   # deterministic Polars schema + left-outer explode
review = result.needs_review                # disputed, ungrounded, or cross-page-conflict items

• Cross-family consensus (min_families=2): by default, a record reaches "consensus" only when a strict majority of requested models from at least two distinct model families agree on every field (must_agree=("vendor",) requires unanimity on named paths).
• Quote and key grounding: every supporting model must cite non-header unit elements (source_element_ids) and supply grounding_quotes that appear in those cited elements (exact substring on digital/hybrid pages; token containment ≥ 0.85 on vision/OCR pages) and contain the record's root Key values. Grounding checks root keys and verbatim quotes against cited elements; non-key field values are verified by cross-family consensus.
• Explicit provider endpoints: model strings require an explicit prefix (openai:, anthropic:, gemini:, ollama:, or openai-compatible:<model>@<url>). Google Cloud (Vertex AI / Agent Platform) endpoints are configured through GeminiProvider.google_cloud(...), GeminiProvider.google_cloud_express(...), AnthropicProvider.google_cloud(...), and OpenAICompatibleProvider.google_cloud(...) with GoogleCloudAuth.

Engine Comparison

The engines above apply to CSV processing. Whichever you pick, results are consistent: leading # comment lines, leading blank lines, logical record counts (quoted embedded newlines count as one record), and column-name normalization — including collision handling like Col A,col_a → col_a, col_a_1 — behave identically across all three. Mid-file lines starting with # are kept as data on all three engines. PDF parsing uses the PDFium engine (permissively licensed) — see PDF parsing.

Primary Classes

Readers

• CSVReader: Read and process CSV files with intelligent delimiter detection
• ExcelReader: Read Excel workbooks sheet by sheet into Polars DataFrames, dicts, PyArrow tables, or SQL query results
• ParquetReader: Read Parquet files into Polars DataFrames, dicts, PyArrow tables, or SQL query results
• PDFReader: Read PDF files into a typed document (headings, paragraphs, key-value pairs, tables, lists, figures) as Polars DataFrames, PyArrow tables, dicts, Markdown, or SQL query results

Writers

• CSVWriter: Export CSV data to multiple formats (CSV, Excel, JSON, Parquet)
• ExcelWriter: Export a sheet (or all sheets) of an Excel workbook to CSV, Excel, JSON, JSONL, or Parquet
• ParquetWriter: Export Parquet files to multiple formats (CSV, Excel, JSON, JSONL, Parquet)
• PDFWriter: Write a PDF's document to JSON, JSONL, Markdown, Parquet, or CSV, extract its figures, render whole pages to PNG files, or write a checksummed bundle

Rationalization

• rationalize: Extract, ground, reach cross-family consensus on, and fold Pydantic schema records over PDFDocuments
• Key: Annotated schema marker identifying entity key fields (and optional document label aliases)
• RationalizedResult: Consensus records (records), review queue (needs_review), quarantine log (quarantined), telemetry (telemetry), and deterministic Polars/Arrow/dict exports (to_dataframe, to_arrow_table, to_dicts)

Full Documentation

For complete documentation, detailed examples, and advanced usage patterns, see: 📖 Complete Documentation

License

This project is licensed under the MIT License

Acknowledgements

A HUGE thank you to the open source community and the creators of DuckDB, Polars, and PyArrow for their fantastic libraries that power Datagrunt.

Source Repository

https://github.com/pmgraham/datagrunt