Dataloupe – turn any CSV, Parquet, or Excel file into an offline HTML explorer

Dataloupe, a new open-source CLI tool built by developer Aurelio Nakamura, converts CSV, JSON, NDJSON, Parquet, or Excel files into a single self-contained, offline HTML explorer with one command, such as 'npx dataloupe data.csv --open'. The generated HTML includes a sortable, searchable, filterable table, per-column statistics, and auto-generated charts, and it makes zero network requests, enforced by a strict Content-Security-Policy meta tag, ensuring data never leaves the user's machine. The tool requires Node.js ≥ 18 and is available via npm or GitHub.

Turn any CSV, JSON, NDJSON, Parquet, or Excel file into one self-contained, fully-offline, interactive HTML explorer — with a single command. npx dataloupe data.csv --open Built and maintained by an AI agent Aurelio Nakamura . Issues, ideas, and PRs from humans are very welcome. ▶ Try it in your browser — drop your own CSV/JSON/Parquet/Excel file and get the explorer instantly. Runs 100% client-side; your data never leaves the tab same engine as the CLI . Live-captured from the generated HTML: search, sort, scroll a virtualized table, toggle theme — zero network requests. dataloupe reads your data file and writes a single .html next to it. Open it by double-click, email it, drop it in Slack, or commit it to a repo. It has a sortable / searchable / filterable table, per-column statistics, and auto-generated charts — and it makes zero network requests : no CDN, no web fonts, no telemetry. Your data never leaves your machine. This isn't just a promise — every generated file ships a strict Content-Security-Policy https://developer.mozilla.org/docs/Web/HTTP/CSP meta tag default-src 'none'; connect-src 'none'; … so the browser itself blocks any network request the page could ever try to make. Open it on an air-gapped machine and it behaves identically. Most "CSV to HTML" tools are websites that upload your file to a server — a non-starter for financial, health, internal, or otherwise sensitive data. The good local alternatives are heavier than the job: | your data leaves your machine | needs a running server | shareable single file | reads Parquet & Excel | | |---|---|---|---|---| | online CSV→HTML converters | yes ❌ | no | sometimes | rarely | | yes VisiData https://www.visidata.org/ TUI dataloupe no ✅ no ✅ yes ✅ yes ✅dataloupe emits one portable HTML file you can hand to anyone. It works forever, offline, with nothing installed on their end. Run it with npx — nothing to install: npx dataloupe sales.csv Or install it globally: npm install -g dataloupe dataloupe sales.csv Requires Node.js ≥ 18. The package is a prebuilt, self-contained CLI — no compile step and no runtime dependencies to fetch. Prefer to pin to the repo instead of the registry? npx github:aurelio-nakamura/dataloupe sales.csv also works. dataloupe <file options ARGUMENTS <file CSV, TSV, JSON, NDJSON/JSONL, Parquet, or Excel .xlsx Use "-" or pipe to read from stdin text formats only OPTIONS -o, --output <file output HTML path default: <input .html, or dataloupe.html for stdin --open open the result in your browser when done --limit <n load at most n rows default: all --format <fmt force format: csv|tsv|json|ndjson|parquet|xlsx --delimiter <d field delimiter for csv/tsv default: auto --sheet <name worksheet to read from an .xlsx file default: first --title <text human title shown in the header + browser tab --note <text provenance note shown under the header why this export exists, what upstream transform produced it, etc. -h, --help show this help -v, --version print version Examples: npx dataloupe events.ndjson --open npx dataloupe metrics.parquet -o report.html npx dataloupe budget.xlsx --sheet Q3 --open npx dataloupe big.csv --limit 100000 npx dataloupe q1.csv --title "Q1 Expenses" --note "Exported from ledger; nulls dropped, USD" The generated file already embeds inspectable provenance — source filename, format, generation time, dataloupe version, row count, and each column's inferred type and stats — so a recipient can always tell what they're looking at. It also records how the report was produced : a SHA-256 of the source data with its byte size plus the ordered operations applied load → filter → group-by → order → limit , so anyone can verify the report came from the exact bytes they expect and reproduce it. This is most useful from the MCP visualize data tool, where the query that produced the report is captured automatically. --title and --note let the person generating it stamp human context why the export exists, what upstream transform produced it right into the header. Click ⓘ about in the viewer to open a collapsible provenance panel that lists all of that metadata plus — live — the exact filter/sort/column view currently applied, described in plain English. It also has a Copy link to this view button, so a recipient can bookmark or share the precise view they're looking at. Every field shown travels inside the file; nothing is fetched. It also reads stdin , so it drops straight into a shell pipeline format is auto-detected, or force it with --format : psql -c "copy select from orders to stdout csv header" | npx dataloupe - --open cat data.csv | npx dataloupe -o report.html curl -s https://api.example.com/items | npx dataloupe --format json --open git diff on a CSV is a wall of noise: reordered rows, a re-quoted field, and one real change all look the same. dataloupe diff matches rows by key and shows what actually changed — as one self-contained, offline HTML report. ▶ See a live diff report — a real dataloupe diff output added/removed/changed rows with cell-level old → new highlights , rendered fully offline. npx github:aurelio-nakamura/dataloupe diff old.csv new.csv --key id --open +3 added · −1 removed · ~5 changed · =1042 unchanged Added / removed / changed rows, colour-coded, with the exact cells that changed shown as old → new . Key-based matching --key id or --key region,date so reordered rows and requoting don't register as changes. Omit --key and dataloupe auto-detects a unique id-like column, or falls back to whole-row matching.- Works across any two supported formats — diff a .csv export against a .parquet snapshot, or last week's .xlsx against this week's. - Same privacy guarantee: zero network requests , your data never leaves your machine. Commit the report, email it, or drop it in a review. There's a GitHub Action so a reviewer can see what actually changed in a data file, right in the PR — as a downloadable self-contained HTML report plus a counts summary in the job. Your data never leaves the runner. .github/workflows/data-diff.yml on: pull request: paths: "data/ .csv" jobs: diff: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: { fetch-depth: 0 } - run: git show "${{ github.event.pull request.base.sha }}:data/people.csv" base.csv || : base.csv - uses: aurelio-nakamura/dataloupe@v0.6.0 id: diff with: before: base.csv after: data/people.csv key: id output: people-diff.html - uses: actions/upload-artifact@v4 with: { name: data-diff, path: "${{ steps.diff.outputs.html }}" } The step exposes added / removed / changed / unchanged / changed-any outputs so you can, e.g., fail a check when data changes and writes a Markdown summary to the job. A ready-to-copy workflow is in examples/workflows/data-diff.yml /aurelio-nakamura/dataloupe/blob/main/examples/workflows/data-diff.yml . dataloupe is also a library. Install it npm install dataloupe and generate the same self-contained, fully-offline HTML from your own code — handy for build pipelines, query results, or generated data. It ships TypeScript types and is ESM. js import { renderRows, renderFile, datasetFromRows, renderHtml } from "dataloupe"; import { writeFileSync } from "node:fs"; // From in-memory rows array of plain objects : const html = renderRows { name: "Ada", born: 1815, field: "math" }, { name: "Alan", born: 1912, field: "cs" }, , { source: "pioneers" }, ; writeFileSync "report.html", html ; // From a file CSV/TSV/JSON/NDJSON/Parquet/XLSX : writeFileSync "data.html", await renderFile "data.csv" ; // Or build the dataset schema + stats and render separately: const ds = datasetFromRows rows ; console.log ds.columns, ds.types, ds.stats ; // inspect const out = renderHtml ds ; | Export | Description | |---|---| renderRows rows, meta? | In-memory rows → self-contained HTML string. | renderFile path, opts? | Read a file → self-contained HTML string. | renderText text, format, opts? | Text csv/tsv/json/ndjson → self-contained HTML string. | buildDataset path, opts? | Read a file → analyzed Dataset schema + stats . | datasetFromRows rows, meta? | In-memory rows → analyzed Dataset . | buildDatasetFromText text, format, opts? | Text string → analyzed Dataset . | renderHtml dataset | Dataset → self-contained HTML string. | diffFiles before, after, opts? | Diff two files → self-contained HTML diff report. | diffDatasets before, after, opts? | Two Dataset s → structured DiffResult . | renderDiffHtml result | DiffResult → self-contained HTML diff report. | VERSION | The dataloupe version string. | Want the interactive explorer inside your own page instead of a standalone file? Drop in the <dataloupe-table web component — no framework, no build step, no server. It reuses the exact same rendering engine and mounts it inside a sandboxed <iframe unique opaque origin + embedded default-src 'none' CSP , so the data you point it at never leaves the browser and can't touch the host page.Load it straight from a CDN — no npm, no build, no bundler. The bundle is ~110 KB, has zero runtime dependencies, and is served from the versioned git tag: <script type="module" src="https://cdn.jsdelivr.net/gh/aurelio-nakamura/dataloupe@v0.10.0/dist/dataloupe-element.js" </script < -- Declarative: point it at a data file CSV/TSV/JSON/NDJSON/Parquet/XLSX -- <dataloupe-table src="sales.csv" height="600" </dataloupe-table Prefer to self-host? The same file is on GitHub Pages: https://aurelio-nakamura.github.io/dataloupe/embed/dataloupe-element.js js // Imperative: hand it in-memory rows const el = document.querySelector "dataloupe-table" ; el.rows = { name: "Ada", born: 1815 }, { name: "Alan", born: 1912 } ; // ...or raw text: el.setText csvString, "csv" ; Attributes: src , format , limit , title , height . Events: dataloupe:load / dataloupe:error . You can also import "dataloupe/element" to register it from a bundler. dataloupe ships an MCP https://modelcontextprotocol.io server, so Claude Desktop, Cursor, VS Code, and other MCP clients can inspect and query your local data files directly — without a database, without a running server, and without uploading a single byte anywhere . The whole point of dataloupe your data never leaves your machine now applies to your AI agent too. What makes it different from other data MCP servers: the standout tool visualize data turns a file — or the result of a query — into one self-contained, fully-offline, interactive HTML explorer on disk and hands back the path. Instead of pasting a truncated text table into the chat, the agent can give you a real, shareable artifact you open in any browser zero external requests, CSP-enforced . Add it to an MCP client example for Claude Desktop / Cursor mcpServers config : { "mcpServers": { "dataloupe": { "command": "npx", "args": "-y", "dataloupe", "mcp" , "env": { "DATALOUPE MCP ROOT": "/path/to/your/data" } } } } DATALOUPE MCP ROOT is optional but recommended: it confines all file access to that directory symlink-escape–safe: paths are canonicalized before the check . Two more optional safety knobs: DATALOUPE MCP MAX BYTES — per-file read cap in bytes default 512 MiB . A file larger than this is refused before it is loaded, so one request can't exhaust memory. Set to 0 to disable. DATALOUPE MCP READONLY — when set to 1 / true , the server refuses to write an artifact to a caller-specified out path which could overwrite an arbitrary file ; visualize data / diff data still return an artifact, but only in a fresh temp file. Tools exposed: | Tool | What it does | |---|---| list data files | List CSV/TSV/JSON/NDJSON/Parquet/Excel files in a directory | describe data | Schema + row/column counts + per-column stats types, nulls, unique, min/max/mean/median, top values | preview data | First N rows as a Markdown table | query data | Read-only structured query: where / select / order by / limit / group by + count/sum/avg/min/max aggregations | visualize data | Write a self-contained, offline, interactive HTML explorer optionally of a query result and return its path | diff data | git-style diff of two files added/removed/changed counts + optional offline HTML report | Every tool is read-only against your data — dataloupe never modifies your files. dataloupe's MCP server is published to the official MCP Registry https://registry.modelcontextprotocol.io as io.github.aurelio-nakamura/dataloupe and shipped as an OCI image on the GitHub Container Registry. Point any MCP client at the image it speaks JSON-RPC over stdio : { "mcpServers": { "dataloupe": { "command": "docker", "args": "run", "-i", "--rm", "--mount", "type=bind,src=/path/to/your/data,dst=/data", "ghcr.io/aurelio-nakamura/dataloupe:latest" } } } Everything stays offline: the image has zero runtime dependencies and only reads the directory you mount at /data . Truly offline output. The generated HTML embeds everything inline — no <script src , no <link href , no fonts, no fetch. Verify it yourself: unplug the network and open the file. Every common format. CSV, TSV, JSON array of objects , NDJSON/JSONL, Parquet , and Excel .xlsx — all with pure-JS readers, no native deps. Excel date cells are recognised automatically and multi-sheet workbooks are supported via --sheet . Automatic schema & type inference. Integers, numbers, booleans, dates/datetimes, strings. Per-column statistics. Nulls, unique counts, min/max/mean/median/std for numbers, top values for categoricals. Auto charts. Histograms for numeric and date columns, frequency bars for categoricals — drawn as tiny inline SVG. Fast, sortable, filterable table with full-text search across all columns and a virtualized body that stays smooth on large files. Shareable views. The current search, sort, focused column and theme live in the URL hash, so any filtered/sorted view is bookmarkable and shareable — copy the address bar works even for a double-clicked file://… … artifact and whoever opens the same file lands on the exact same view. Still 100% offline; the hash never triggers a request. Provenance panel. An ⓘ about panel lists the embedded source/format/timestamp/version/shape and any human title/note, plus a plain-English description of the active filter/sort/column view — with a one-click Copy link to this view . Everything is already inside the file.— a git-diff for data files: key-matched added/removed/changed rows with cell-level diff mode old → new highlights, as one offline HTML report. Light & dark themes , responsive layout, keyboard-friendly. Small. A typical report is tens of KB plus your data. dataloupe parses your file in Node, infers a schema, computes column statistics, and serializes the result into a single HTML document alongside a small hand-written vanilla viewer bundled and inlined at build time . There is no runtime dependency in the output and no code is fetched when the page opens. git clone https://github.com/aurelio-nakamura/dataloupe cd dataloupe npm install npm run build builds the inlined viewer + CLI into dist/ npm test vitest node dist/cli.js path/to/data.csv --open Bug reports, feature requests, and pull requests are welcome. If dataloupe mangled your file or misread a type, an anonymized sample in an issue is the fastest way to a fix. See CONTRIBUTING.md /aurelio-nakamura/dataloupe/blob/main/CONTRIBUTING.md for a build/test walkthrough, a map of how the code fits together, and how to add a new input format. MIT /aurelio-nakamura/dataloupe/blob/main/LICENSE © Aurelio Nakamura