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ci: move JupyterLab Playwright tests to dedicated CI job (#534)
* ci: move JupyterLab Playwright tests from build.yml to ci.yml Extract the inline JupyterLab Playwright test step from the monolithic build job into its own dedicated job in ci.yml, matching the pattern used by the Marimo, WASM Marimo, Server, and Storybook Playwright jobs. The new job downloads the pre-built wheel artifact and runs the test script directly. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * ci: set JupyterLab Playwright timeout to 5 minutes Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * ci: restore integration test notebooks accidentally removed in abd5e85 These were removed in the bulk cleanup of generated files but are needed by the JupyterLab Playwright tests. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * ci: add error diagnostics to test_polars_dfviewer notebook Print the actual traceback when PolarsDFViewer fails so we can see the root cause in CI logs. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: handle non-class items in find_most_specific_styling PolarsBuckarooWidget.analysis_klasses contains @stat-decorated functions (from PL_ANALYSIS_V2) alongside classes. The find_most_specific_styling helper called issubclass() on every item without checking if it was actually a class first, causing "issubclass() arg 1 must be a class" when PolarsDFViewer was used. Add isinstance(x, type) guard before the issubclass() check. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
1 parent eb00f96 commit 3492d2e

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‎.github/workflows/build.yml‎

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@@ -82,32 +82,6 @@ jobs:
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run: |
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bash scripts/test_playwright_storybook.sh
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- name: Run JupyterLab Playwright Tests
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continue-on-error: true
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if: ${{ matrix.os == 'depot-ubuntu-latest' }}
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run: |
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# Ensure uv environment is set up
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uv sync --all-extras --group dev
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# Install the built buckaroo wheel
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uv pip install --force-reinstall dist/*.whl
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# Install test dependencies (polars, jupyterlab)
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uv pip install polars jupyterlab
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# Detect venv location (uv sync creates .venv by default, but check if it exists)
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if [ -d ".venv" ]; then
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VENV_LOCATION=".venv"
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else
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# Fallback: try to detect from uv's Python environment
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VENV_LOCATION=$(uv run python -c "import sys; print(sys.prefix)" 2>/dev/null || echo ".venv")
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fi
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# Run the integration test script with venv location
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bash scripts/test_playwright_jupyter.sh --venv-location="$VENV_LOCATION"
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env:
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UV_PYTHON: ${{ matrix.python-version }}
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# ui-test:
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# needs: [build]

‎.github/workflows/ci.yml‎

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- name: Run Marimo Playwright Tests
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run: bash scripts/test_playwright_marimo.sh
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TestJupyterLab:
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name: JupyterLab Playwright Tests
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needs: [BuildWheel]
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runs-on: depot-ubuntu-latest
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timeout-minutes: 5
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continue-on-error: true
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steps:
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- uses: actions/checkout@v4
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- name: Install uv
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uses: astral-sh/setup-uv@v7
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with:
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enable-cache: true
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python-version: "3.13"
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prune-cache: false
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- name: Setup pnpm
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uses: pnpm/action-setup@v4
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with:
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version: 9.10.0
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- name: Setup Node.js with pnpm cache
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uses: actions/setup-node@v4
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with:
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cache: 'pnpm'
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cache-dependency-path: 'packages/pnpm-lock.yaml'
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- name: Install pnpm dependencies
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working-directory: packages
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run: pnpm install --frozen-lockfile
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- name: Download build artifacts
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uses: actions/download-artifact@v4
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with:
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name: buckaroo-build
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- name: Install the project
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run: uv sync --all-extras --dev
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- name: Install built wheel and test dependencies
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run: |
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uv pip install --force-reinstall dist/*.whl
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uv pip install polars jupyterlab
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- name: Cache Playwright browsers
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uses: actions/cache@v4
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with:
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path: ~/.cache/ms-playwright
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key: playwright-${{ hashFiles('packages/buckaroo-js-core/package.json') }}
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- name: Run JupyterLab Playwright Tests
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run: bash scripts/test_playwright_jupyter.sh --venv-location=".venv"
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TestWASMMarimo:
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name: WASM Marimo Playwright Tests
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needs: [BuildWheel]

‎buckaroo/dataflow/widget_extension_utils.py‎

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@@ -13,7 +13,7 @@ def find_most_specific_styling(klasses, df_display_name='main'):
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https://stackoverflow.com/questions/23660447/how-can-i-sort-a-list-of-python-classes-by-inheritance-depth
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"""
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styling_klasses = filter(lambda x: issubclass(x, DefaultMainStyling), klasses)
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styling_klasses = filter(lambda x: isinstance(x, type) and issubclass(x, DefaultMainStyling), klasses)
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klasses_by_depth = sorted(styling_klasses, key=lambda x: len(x.__mro__))
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return klasses_by_depth[-1]
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‎tests/integration_notebooks/test_blank_rows_200.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cell-0",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import buckaroo\n",
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"\n",
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"# Create a predictable 200-row DataFrame\n",
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"# Each row has: index=i, int_col=i+10, str_col=f'foo_{i}'\n",
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"N = 200\n",
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"df = pd.DataFrame({\n",
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" 'int_col': [i + 10 for i in range(N)],\n",
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" 'str_col': [f'foo_{i}' for i in range(N)],\n",
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" 'float_col': [i * 1.5 for i in range(N)],\n",
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"})\n",
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"print(f'Created DataFrame with {len(df)} rows')\n",
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"df"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.11.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎tests/integration_notebooks/test_buckaroo_infinite_widget.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import buckaroo\n",
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"import pandas as pd\n",
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"from buckaroo import BuckarooInfiniteWidget\n",
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"\n",
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"# Create test data\n",
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"df = pd.DataFrame({\n",
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" 'name': ['Alice', 'Bob', 'Charlie'],\n",
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" 'age': [25, 30, 35],\n",
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" 'score': [85.5, 92.0, 78.3]\n",
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"})\n",
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"print(f\"✅ Created DataFrame with shape: {df.shape}\")\n",
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"\n",
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"# Display the widget\n",
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"widget = BuckarooInfiniteWidget(df)\n",
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"print(\"✅ BuckarooInfiniteWidget created successfully\")\n",
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"widget\n"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎tests/integration_notebooks/test_buckaroo_widget.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import buckaroo\n",
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"import pandas as pd\n",
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"from buckaroo import BuckarooWidget\n",
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"\n",
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"# Create test data\n",
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"df = pd.DataFrame({\n",
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" 'name': ['Alice', 'Bob', 'Charlie'],\n",
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" 'age': [25, 30, 35],\n",
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" 'score': [85.5, 92.0, 78.3]\n",
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"})\n",
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"print(f\"✅ Created DataFrame with shape: {df.shape}\")\n",
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"\n",
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"# Display the widget\n",
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"widget = BuckarooWidget(df)\n",
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"print(\"✅ BuckarooWidget created successfully\")\n",
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"widget\n"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎tests/integration_notebooks/test_dfviewer.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import buckaroo\n",
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"import pandas as pd\n",
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"from buckaroo import DFViewer\n",
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"\n",
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"# Create test data\n",
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"df = pd.DataFrame({\n",
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" 'name': ['Alice', 'Bob', 'Charlie'],\n",
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" 'age': [25, 30, 35],\n",
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" 'score': [85.5, 92.0, 78.3]\n",
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"})\n",
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"print(f\"✅ Created DataFrame with shape: {df.shape}\")\n",
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"\n",
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"# Display the widget\n",
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"widget = DFViewer(df)\n",
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"print(\"✅ DFViewer created successfully\")\n",
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"widget\n"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎tests/integration_notebooks/test_dfviewer_infinite.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import buckaroo\n",
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"import pandas as pd\n",
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"from buckaroo.buckaroo_widget import DFViewerInfinite\n",
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"\n",
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"# Create test data\n",
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"df = pd.DataFrame({\n",
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" 'name': ['Alice', 'Bob', 'Charlie'],\n",
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" 'age': [25, 30, 35],\n",
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" 'score': [85.5, 92.0, 78.3]\n",
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"})\n",
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"print(f\"✅ Created DataFrame with shape: {df.shape}\")\n",
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"\n",
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"# Display the widget\n",
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"widget = DFViewerInfinite(df)\n",
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"print(\"✅ DFViewerInfinite created successfully\")\n",
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"widget\n"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎tests/integration_notebooks/test_infinite_scroll_transcript.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import buckaroo\n",
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"import polars as pl\n",
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"from buckaroo.polars_buckaroo import PolarsBuckarooInfiniteWidget\n",
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"\n",
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"# Create a large predictable DataFrame for testing infinite scroll\n",
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"# Row i has: int_col = i + 10, str_col = f\"foo_{i + 10}\"\n",
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"# 2000 rows ensures we need multiple data fetches to satisfy scroll requests\n",
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"N_ROWS = 2000\n",
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"df = pl.DataFrame({\n",
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" 'row_num': list(range(N_ROWS)),\n",
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" 'int_col': [i + 10 for i in range(N_ROWS)],\n",
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" 'str_col': [f'foo_{i + 10}' for i in range(N_ROWS)]\n",
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"})\n",
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"print(f\"✅ Created DataFrame with shape: {df.shape}\")\n",
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"print(f\" First row: row_num=0, int_col=10, str_col='foo_10'\")\n",
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"print(f\" Last row: row_num={N_ROWS-1}, int_col={N_ROWS-1+10}, str_col='foo_{N_ROWS-1+10}'\")\n",
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"\n",
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"# Display the widget with transcript recording enabled\n",
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"widget = PolarsBuckarooInfiniteWidget(df, record_transcript=True)\n",
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"print(\"✅ PolarsBuckarooInfiniteWidget created successfully with transcript recording enabled\")\n",
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"widget\n"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎tests/integration_notebooks/test_lazy_infinite_polars_widget.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import buckaroo\n",
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"import polars as pl\n",
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"from buckaroo.lazy_infinite_polars_widget import LazyInfinitePolarsBuckarooWidget\n",
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"\n",
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"# Create test data\n",
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"df = pl.DataFrame({\n",
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" 'name': ['Alice', 'Bob', 'Charlie'],\n",
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" 'age': [25, 30, 35],\n",
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" 'score': [85.5, 92.0, 78.3]\n",
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"})\n",
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"print(f\"✅ Created DataFrame with shape: {df.shape}\")\n",
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"\n",
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"# Display the widget (using lazy frame)\n",
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"ldf = df.lazy()\n",
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"widget = LazyInfinitePolarsBuckarooWidget(ldf)\n",
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"print(\"✅ LazyInfinitePolarsBuckarooWidget created successfully\")\n",
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"widget\n"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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