QuackWrangler

Open, clean, explore, and export data without leaving VS Code. Your files stay on your machine.

QuackWrangler does not upload rows or cell values. If you choose to use the optional AI transform planner, it sends only your instruction and the table schema—column names, types, and nullability—to OpenAI. It shows the proposed steps before changing anything.

QuackWrangler ships with DuckDB built in. Open CSV, Parquet, JSON, Excel, and ODS files directly—no Python environment, Jupyter kernel, or database setup required.

QuackWrangler is an independent open-source project inspired by the visual workflow of data-wrangling tools.

Install: code --install-extension quackwrangler.quackwrangler

Open world_cup.csv → inspect column profiles → browse nested structs → visualize correlations — all without leaving VS Code.

Fast workflows

• Quick filter: right-click a table cell to keep its value, exclude it, find matching text, or show nulls. The result is a normal undoable transform.
• Formula Builder: open Transform → Formula Builder to create conditional columns, date differences, regex extractions, or combined text without SQL.
• Join or append files: open Combine Files, choose another supported file, then configure a join or append rows by matching column names.
• Save an analysis: run QuackWrangler: Save Workspace from the Command Palette to create a portable .qw file. Use QuackWrangler: Open Workspace to restore its source and transform history.
• Recent files: the QuackWrangler activity-bar view keeps the ten most recently opened data files.
• Work with nested data: select a struct, list, map, or JSON cell to browse it as a tree, copy a JSON Pointer, extract a field, flatten an object, or explode an array into rows.
• Open cloud data: run QuackWrangler: Open Remote HTTPS/S3 Data to query a supported remote file through DuckDB httpfs.
• Visible remote loading: HTTPS/S3 opens show staged progress and surface actionable connection, authentication, and format errors in the editor.
• Generate a cleaning plan: configure an OpenAI key in VS Code SecretStorage, then run Generate Visual Transforms with AI. Only column names/types/nullability and your instruction are sent; generated steps require approval and pass the normal transform validator.
• Compare schemas: select multiple files with Compare File Schemas, or recursively inspect up to 100 files with Detect Folder Schema Drift.

Why QuackWrangler?

QuackWrangler sits between a basic file preview and a full notebook. It gives you a quick visual workflow for everyday data work, while keeping each change reproducible.

Performance benchmark

On a deterministic one-million-row Parquet workload, QuackWrangler's production Node DuckDB engine completed the analytical transform faster than both comparison engines. Polars loaded the file faster and had the lowest end-to-end time; QuackWrangler finished the complete workflow about 2× faster than Pandas.

These are medians of seven measured runs after two warmups on an Apple M3 Pro with 18 GB RAM, recorded August 1, 2026. Every engine ran equivalent eager operations and returned the same validated 20-row result. Lower is better.

Scaling and peak memory

Fresh-process measurements include each language runtime and its loaded libraries:

At 10 million rows, QuackWrangler was 1.6× faster than Polars and 3.9× faster than Pandas in this workload, while its measured peak process RSS was 56% lower than Polars and 61% lower than Pandas. All engines completed successfully; no unmeasured OOM claim is made. See the raw scalability results.

10M rows — total workflow time (lower is better)

QuackWrangler  148 ms  ███████
Polars         242 ms  ████████████
Pandas         572 ms  █████████████████████████████

An exploratory single 50M-row run also completed for every engine:

This 50M result is one fresh-process sample rather than a multi-run median. System swap usage increased during the experiment, so it is published as exploratory evidence and not used for an OOM or “under 1 GB” claim. See the raw 50M result.

File-format loading

For the same generated 100K-row dataset, DuckDB materialized Parquet in 13.98 ms, NDJSON in 50.62 ms, and CSV in 63.49 ms. Parquet was 4.5× faster to load than CSV and produced a much smaller file. See the raw format results.

This is a reproducible workload, not a universal engine ranking; results depend on hardware, versions, data shape, cache state, swap pressure, and query. Peak RSS is more defensible than an allocator delta but still includes different runtime overheads. Large runs remain opt-in because they can materially affect workstation responsiveness.

See the benchmark methodology and run instructions and raw machine-readable result. Re-run it locally with:

npm run benchmark
npm run benchmark:all # primary, scalability, and format suites

Highlights

• DuckDB-native execution with automatic disk spilling
• No Python, Jupyter, or database setup required — works immediately
• Local-first privacy: file contents, rows, and cell values are processed on your machine and are never sent to external servers
• Virtualized grid with synchronized headers and consistent column sizing
• Professional column profiles with completeness, cardinality, ranges, percentiles, and distributions
• Inline histogram, bar, scatter, line, box-plot, and correlation-heatmap charts backed by full DuckDB queries
• Data-quality summary for nulls, duplicates, and potential IQR outliers
• Expandable nested-data tree for structs, lists, maps, and JSON values, with JSON Pointer copying and extract/flatten/explode transforms
• Parquet, CSV, TSV, JSON, JSONL/NDJSON, XLSX, and ODS viewing
• Filters, sorting, deduplication, column rename/drop/add/cast, null filling, and grouping/aggregation
• Full-dataset search across every column (Cmd/Ctrl+F)
• Pivot and unpivot reshaping
• Drag-and-drop transformation ordering
• Custom read-only DuckDB SQL with results shown in the grid
• Multi-row selection and spreadsheet-friendly TSV, CSV, pipe, or JSON copying
• Full transformed-data export to Parquet, CSV, or JSON
• Undo and redo for the transformation pipeline
• Visual IF/date/regex/text formula builder
• Multi-file joins and union-by-name
• Shareable .qw workspaces and recent-file shortcuts
• HTTPS/S3 sources, schema comparison/drift reports, and opt-in schema-only AI transform plans
• Contextual dbt project detection with copyable model SQL and CTE snippets

Why DuckDB is the default engine

The extension currently uses DuckDB because it can run analytical queries larger than the configured memory limit by spilling intermediate data to disk. An eager Polars DataFrame normally materializes its working data in RAM, which can use more memory on large files. Polars also has a streaming engine and can be very efficient, so this is not a claim that DuckDB always uses less memory; it describes QuackWrangler's current workload and the measured results above.

For the next version after 0.1.2, we plan to explore a hybrid engine: DuckDB for file access, SQL, and out-of-core work, with Polars available for operations where its expression engine or streaming execution is a better fit. That work will only ship after both engines follow the same transform history, output, packaging, and test contracts.

Install

From the VS Code Marketplace

Open Extensions in VS Code, search for QuackWrangler, and select Install. You can also open the Marketplace listing. The command-line equivalent is:

code --install-extension quackwrangler.quackwrangler

Marketplace releases use separate native packages for Windows, Linux, Alpine Linux, Intel/Apple Silicon macOS, and x64/ARM64 systems. VS Code selects the matching package automatically from the single QuackWrangler listing.

From a local VSIX

git clone https://github.com/mohsinsurani/quackwrangler.git
cd quackwrangler
npm ci
npm --prefix webview-ui ci
npm run package
code --install-extension quackwrangler-<version>.vsix

Alternatively, run Extensions: Install from VSIX... from the VS Code Command Palette and select the generated file.

Use QuackWrangler

1. Open QuackWrangler from the Activity Bar.
2. Choose Open data file for one file, or Open data folder to scan a directory.
3. The folder view preserves subdirectories, hides unsupported files and empty folders, and opens a data file when selected.
4. You can also right-click a supported file in Explorer and choose Open in QuackWrangler.
5. Clicking a Parquet file opens the QuackWrangler visual editor by default. Other supported formats remain available through Open in QuackWrangler without replacing their normal VS Code editor association.
6. Inspect column profiles and values in the synchronized data grid.
7. Select an operation in the left panel, enter its parameters, and apply it.
8. Use Custom DuckDB query to run one read-only SELECT, WITH, or VALUES statement.
9. Export the complete transformed dataset, or select rows and copy them as TSV, CSV, pipe-separated text, or JSON.

Custom queries run against current_data:

SELECT country, COUNT(*) AS matches
FROM current_data
WHERE year >= 2000
GROUP BY country
ORDER BY matches DESC;

Mutation statements are rejected because the query box is an exploration surface, not an unrestricted database console.

Filters and transforms

Filters support equality and inequality, >, >=, <, <=, contains, does not contain, starts with, ends with, IN, NOT IN, null checks, and range checks. Values are safely converted for numeric, boolean, date/time, and text columns.

Other operations include:

QuackWrangler only offers numeric aggregations such as average for compatible scalar columns. Nested JSON values remain inspectable without being sent to invalid DuckDB aggregate functions. Click a nested cell to open its tree. Node actions can copy an RFC 6901-style JSON Pointer, extract the selected value as a column, flatten a selected object, or explode a selected array into rows. These actions become ordinary pipeline steps, so they support undo, redo, reordering, and .qw workspace persistence.

Supported file formats

HTTPS and S3 URLs are supported for formats handled by the corresponding DuckDB reader. Remote access uses DuckDB httpfs; private S3 authentication follows DuckDB/AWS credential-chain configuration, so QuackWrangler does not place cloud credentials in workspace files.

ORC files are detected and produce an actionable compatibility message, but they are not advertised as readable: the embedded DuckDB 1.5 runtime does not currently expose a supported ORC reader. Convert ORC to Parquet or CSV before opening it. QuackWrangler will enable native ORC loading only when a supported, testable DuckDB reader is available.

Spreadsheet support can require DuckDB to download an extension the first time it is used. Legacy .xls files are not currently supported.

AI privacy and configuration

AI transform generation is disabled until you explicitly store an OpenAI API key using QuackWrangler: Configure OpenAI API Key. The key is kept in VS Code SecretStorage, never settings or .qw files. Requests contain your instruction and schema metadata only—no cell values or sampled rows. The proposed plan is shown for approval, cannot contain raw SQL, and is limited to the same validated operations available in the visual UI. Configure quackwrangler.ai.model to choose the model; the default is gpt-4o-mini.

When an opened local file is inside a workspace containing an ancestor dbt_project.yml, QuackWrangler reveals Copy as dbt SQL in the editor toolbar and Command Palette. It can copy a complete dbt model query or reusable CTEs built from the active validated transform history. QuackWrangler asks for the upstream model used by ref() and does not parse or modify the dbt project. Generated expressions retain DuckDB SQL semantics; review adapter compatibility when the dbt target is not DuckDB.

Settings

Open VS Code Settings and search for QuackWrangler, or configure values directly:

{
  "quackwrangler.duckdb.memoryLimit": "1GB",
  "quackwrangler.duckdb.tempDirectory": "",
  "quackwrangler.duckdb.maxTempDirectorySize": "15GB",
  "quackwrangler.display.pageSize": 100,
  "quackwrangler.display.maxRows": 10000
}

When duckdb.tempDirectory is empty, spill files use a unique directory under VS Code's extension storage and are removed when QuackWrangler shuts down. A configured directory is created if needed and is never automatically deleted.

Develop locally

Requirements: Node.js 20 or newer, npm 10 or newer, and VS Code 1.85 or newer. Python is not required. Install uv only when running the optional Polars/Pandas benchmarks.

git clone https://github.com/mohsinsurani/quackwrangler.git
cd quackwrangler
npm ci
npm --prefix webview-ui ci
code .

In VS Code, open Run and Debug, select Run QuackWrangler Extension, and press F5. A new Extension Development Host window opens. Open a folder with data in that window and use Open in QuackWrangler from the file context menu.

Useful commands:

npm run build          # type-check and build the extension and webview
npm test               # unit and integration tests
npm run test:coverage  # coverage report
npm run lint           # static checks
npm run watch          # rebuild the extension while developing
npm run benchmark      # compare DuckDB, Polars, and Pandas locally
npm run benchmark:all  # primary, scalability, and file-format suites
npm run package        # production build and VSIX package

Before a release, follow the complete QA checklist, including the Extension Development Host smoke tests that exercise VS Code APIs.

Python, Polars, and Pandas are not extension runtime dependencies. Benchmark scripts use isolated uv environments.

Maintainers

Marketplace releases are maintainer-controlled. See Release and Marketplace publishing for the private-credential, QA, packaging, and publishing workflow.

Contributing

See CONTRIBUTING.md. Bug reports and pull requests are welcome at github.com/mohsinsurani/quackwrangler.

• Ask questions and share ideas in GitHub Discussions.
• Browse good first issues if you are new to the project.
• Read the Code of Conduct before participating.
• Report security issues privately by following the Security Policy; do not open a public issue for a suspected vulnerability.
• See CHANGELOG.md for versioned release notes.

License

Copyright (c) 2026 QuackWrangler Contributors. Released under the MIT License, which permits private and commercial use, modification, distribution, and sublicensing subject to preserving the license notice.