ipynb1.7 KB

Jupyter Notebook (IPYNB)

A valid Jupyter notebook (nbformat 4.5) with markdown cells, code cells, and real outputs (stdout stream and an execute result) — for testing notebook parsers, nbconvert, and JSON tooling.

JSON parsing Conversion testing Editor testing

Preview — first 50 linesipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monthly Revenue Analysis\n",
    "\n",
    "A short example notebook: load a small revenue table, compute the total, and ",
    "find the best month. Free to use for testing notebook parsers and converters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],\n",
    "    'revenue': [42, 48, 55, 51, 63, 70],\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total revenue: 329\n"
     ]
    }
   ],
   "source": [
    "print('Total revenue:', df['revenue'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {

… 86 lines total — download for the full file.

Specifications

Format: nbformat 4.5
Cells: 5
Cell Types: markdown + code
Outputs: stream + execute_result

What is a .ipynb file?

An IPYNB file is a Jupyter notebook: a JSON document holding an ordered list of cells and, unusually for a source file, the output each code cell produced. Markdown cells hold prose; code cells carry `source` as an array of lines, an `execution_count`, and an `outputs` array of stdout streams, result values, base64 images, and tracebacks, while `metadata.kernelspec` names the interpreter. The extension survives from the 2011 IPython Notebook, which became Project Jupyter in 2014.

How to use this file

Use an example notebook to test nbformat parsers, nbconvert and jupytext, and diff tooling — checking that multi-line `source` arrays round-trip, that every output type including tracebacks and embedded images survives, and that a null `execution_count` reads as never-run rather than zero.

How to use this file for testing

“Jupyter Notebook (IPYNB)” is a deterministic Novus Examples fixture for JSON parsing, Conversion testing, Editor testing. Flat, deeply nested, JSON Lines, and intentionally invalid JSON for testing parsers and error handling.

Documented properties for this file: nbformat 4.5. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.

Data fixtures document their exact quirks — delimiters, encodings, null handling, schema, and row counts — in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.

Feed the file to your parser and assert it handles the documented quirks — quoted delimiters, embedded newlines, ragged rows, or invalid syntax; the valid↔invalid distinction is labelled in the title.

Code examples

jupyter nbconvert --to html monthly-revenue-analysis.ipynb
jupyter nbconvert --to script monthly-revenue-analysis.ipynb

Generated by generation/data_dev_artifacts.py. Free for any use, no attribution required — license.

Specifications

What is a .ipynb file?

How to use this file

How to use this file for testing

Code examples

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