pax_global_header00006660000000000000000000000064152500060370014510gustar00rootroot0000000000000052 comment=e34715d1e9e2bd6f5306662e8ad495eaf0c1325a narwhals-2.26.0/000077500000000000000000000000001525000603700134165ustar00rootroot00000000000000narwhals-2.26.0/.github/000077500000000000000000000000001525000603700147565ustar00rootroot00000000000000narwhals-2.26.0/.github/CODE_OF_CONDUCT.md000066400000000000000000000121511525000603700175550ustar00rootroot00000000000000# Contributor Covenant Code of Conduct ## Our Pledge We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socioeconomic status, nationality, personal appearance, race, religion, or sexual identity and orientation. We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community. ## Our Standards Examples of behavior that contributes to a positive environment for our community include: * Demonstrating empathy and kindness toward other people * Being respectful of differing opinions, viewpoints, and experiences * Giving and gracefully accepting constructive feedback * Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience * Focusing on what is best not just for us as individuals, but for the overall community Examples of unacceptable behavior include: * The use of sexualized language or imagery, and sexual attention or advances of any kind * Trolling, insulting or derogatory comments, and personal or political attacks * Public or private harassment * Publishing others' private information, such as a physical or email address, without their explicit permission * Other conduct which could reasonably be considered inappropriate in a professional setting ## Enforcement Responsibilities Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful. Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate. ## Scope This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at hello_narwhals@proton.me. All complaints will be reviewed and investigated promptly and fairly. All community leaders are obligated to respect the privacy and security of the reporter of any incident. ## Enforcement Guidelines Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct: ### 1. Correction **Community Impact**: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community. **Consequence**: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested. ### 2. Warning **Community Impact**: A violation through a single incident or series of actions. **Consequence**: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban. ### 3. Temporary Ban **Community Impact**: A serious violation of community standards, including sustained inappropriate behavior. **Consequence**: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban. ### 4. Permanent Ban **Community Impact**: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals. **Consequence**: A permanent ban from any sort of public interaction within the community. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 2.0, available at https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. Community Impact Guidelines were inspired by [Mozilla's code of conduct enforcement ladder](https://github.com/mozilla/diversity). [homepage]: https://www.contributor-covenant.org For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https://www.contributor-covenant.org/translations. narwhals-2.26.0/.github/ISSUE_TEMPLATE/000077500000000000000000000000001525000603700171415ustar00rootroot00000000000000narwhals-2.26.0/.github/ISSUE_TEMPLATE/bug_report.yml000066400000000000000000000032031525000603700220320ustar00rootroot00000000000000name: Bug report description: Report to help us reproduce the bug title: "[Bug]: " body: - type: markdown attributes: value: > ## Thanks for taking the time to fill out this report - type: markdown attributes: value: > ### Before submitting a bug, please make sure it has not been reported in the [list of issues](https://github.com/narwhals-dev/narwhals/issues). - type: textarea attributes: label: Describe the bug description: > A clear description of the bug. validations: required: true - type: textarea attributes: label: Steps or code to reproduce the bug description: | Please add a minimal code example to reproduce the bug. validations: required: true - type: textarea attributes: label: Expected results description: > Please paste or describe the expected results. placeholder: > Example: No error is thrown. validations: required: true - type: textarea attributes: label: Actual results description: | Please paste or describe the results you observe instead of the expected results. validations: required: true - type: textarea attributes: label: Please run narwhals.show_versions() and enter the output below. render: shell description: | You could do: ```python import narwhals narwhals.show_versions() ``` validations: required: true - type: textarea attributes: label: Relevant log output description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. render: shell narwhals-2.26.0/.github/ISSUE_TEMPLATE/config.yml000066400000000000000000000004641525000603700211350ustar00rootroot00000000000000contact_links: - name: Discord server url: https://discord.gg/V3PqtB4VA4 about: | Developers and users can be found on the Discord server. Please note that GitHub Discussions should be used in most cases instead. - name: Blank issue url: https://github.com/narwhals-dev/narwhals/issues/new narwhals-2.26.0/.github/ISSUE_TEMPLATE/doc_issue.yml000066400000000000000000000016131525000603700216420ustar00rootroot00000000000000name: Documentation improvement description: Report to improve the docs. You could also directly open a PR with your suggestions. title: "[Doc]: " body: - type: markdown attributes: value: > ## Thanks for taking the time to fill out this form - type: dropdown id: TYPE attributes: label: What type of report is this? options: - 'Correction' - 'Improvement' validations: required: true - type: textarea attributes: label: Please describe the issue. description: > Tell us if something is unclear or incorrect, and where. validations: required: true - type: textarea attributes: label: If you have a suggestion on how it should be, add it below. description: > How can we improve it - type: markdown attributes: value: > ### If you'd be interested in opening a pull request to fix this, please let us know! narwhals-2.26.0/.github/ISSUE_TEMPLATE/feature_request.yml000066400000000000000000000031041525000603700230650ustar00rootroot00000000000000name: Feature request description: Form to request a new feature title: "[Enh]: " body: - type: markdown attributes: value: > ## Thanks for helping us improve Narwhals! - type: markdown attributes: value: > ### Before submitting a request, please check if it has already been discussed in the [list of issues](https://github.com/narwhals-dev/narwhals/issues?q=is%3Aissue+is%3Aclosed%2Bopen+). - type: textarea attributes: label: > We would like to learn about your use case. For example, if this feature is needed to adopt Narwhals in an open source project, could you please enter the link to it below? description: > Link to the project where this will be used if possible. - type: textarea attributes: label: Please describe the purpose of the new feature or describe the problem to solve. description: > A clear description of the objective. validations: required: true - type: textarea attributes: label: Suggest a solution if possible. description: > Please suggest a solution if you can. validations: required: false - type: textarea attributes: label: If you have tried alternatives, please describe them below. description: > What you have tried if applicable. - type: textarea attributes: label: Additional information that may help us understand your needs. description: > Context or useful information - type: markdown attributes: value: > ### If you'd be interested in opening a pull request to fix this, please let us know! narwhals-2.26.0/.github/pull_request_template.md000066400000000000000000000026601525000603700217230ustar00rootroot00000000000000 # Description ## What type of PR is this? (check all applicable) - [ ] ๐Ÿ’พ Refactor - [ ] โœจ Feature - [ ] ๐Ÿ› Bug Fix - [ ] ๐Ÿ”ง Optimization - [ ] ๐Ÿ“ Documentation - [ ] โœ… Test - [ ] ๐Ÿณ Other ## Related issues - Related issue #\ - Closes #\ ## AI assistance - [ ] No AI tools were used for this PR. - [ ] AI tools were used. ## Checklist - [ ] Code follows style guide (ruff) - [ ] Tests added - [ ] Documented the changes - [ ] If this is your first PR to narwhals, attach a screenshot of `pytest` passing locally (not CI): ```bash PYTEST_ADDOPTS="--numprocesses=logical" \ make run-ci DEPS="--extra pandas --extra dask --group core-tests --group sklearn --group plugins" \ CMD="pytest tests --cov=src --cov=tests --runslow --constructors=pandas,pandas[nullable],pandas[pyarrow],pyarrow,polars[eager],polars[lazy],dask,duckdb,sqlframe" ``` narwhals-2.26.0/.github/release-drafter.yml000066400000000000000000000015231525000603700205470ustar00rootroot00000000000000exclude-labels: - ignore - release - dependencies name-template: 'Narwhals unreleased' version-template: unreleased change-template: '- $TITLE (#$NUMBER)' categories: - title: ๐Ÿ† Highlights labels: - highlight - title: ๐Ÿ’ฅ Breaking changes labels: - breaking - title: โš ๏ธ Deprecations labels: - deprecation - title: ๐Ÿš€ Performance improvements labels: - performance - title: โœจ Enhancements labels: - enhancement - title: ๐Ÿž Bug fixes labels: - fix - title: ๐Ÿ“– Documentation labels: - documentation - title: ๐Ÿ“ฆ Build system labels: - build - title: ๐Ÿ› ๏ธ Other improvements labels: - internal template: | ## Changes $CHANGES Thank you to all our contributors for making this release possible! $CONTRIBUTORS narwhals-2.26.0/.github/workflows/000077500000000000000000000000001525000603700170135ustar00rootroot00000000000000narwhals-2.26.0/.github/workflows/check_docs_build.yml000066400000000000000000000015711525000603700230060ustar00rootroot00000000000000name: Check Docs Build on: pull_request: workflow_call: permissions: contents: read env: PY_COLORS: 1 UV_LOCKED: 1 UV_NO_DEV: 1 UV_SYSTEM_PYTHON: 1 jobs: zensical-strict-build: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV - name: Check docs build run: make docs-build narwhals-2.26.0/.github/workflows/check_tpch_queries.yml000066400000000000000000000021511525000603700233650ustar00rootroot00000000000000name: Tests for TPCH Queries on: pull_request: paths: - "**.py" - .github/workflows/check_tpch_queries.yml - pyproject.toml - uv.lock env: PY_COLORS: 1 UV_LOCKED: 1 UV_NO_DEV: 1 permissions: contents: read jobs: validate-queries: strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: ${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: generate-data run: make run-ci DEPS="--extra dask --extra pandas --group core-tests" CMD="--module tpch.generate_data --debug" - name: tpch-tests run: make run-ci DEPS="--extra dask --extra pandas --group core-tests" CMD="pytest tpch/tests -s" narwhals-2.26.0/.github/workflows/codeql.yml000066400000000000000000000020341525000603700210040ustar00rootroot00000000000000name: CodeQL on: pull_request: schedule: - cron: '24 7 * * 3' permissions: contents: read jobs: analyze: name: Analyze runs-on: ubuntu-latest permissions: actions: read contents: read security-events: write strategy: fail-fast: false matrix: language: ["python"] # CodeQL supports [ $supported-codeql-languages ] # Learn more about CodeQL language support at https://aka.ms/codeql-docs/language-support steps: - name: Checkout repository uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Initialize CodeQL uses: github/codeql-action/init@cdf488f595d80d6e07e03d4674febd5ab45fa938 # v4.37.9 with: languages: ${{ matrix.language }} - name: Perform CodeQL Analysis uses: github/codeql-action/analyze@cdf488f595d80d6e07e03d4674febd5ab45fa938 # v4.37.9 with: category: "/language:${{ matrix.language }}" narwhals-2.26.0/.github/workflows/deploy-docs.yml000066400000000000000000000025661525000603700217710ustar00rootroot00000000000000name: Publish Docs on: push: branches: - main env: cache_id: "" UV_LOCKED: 1 UV_NO_DEV: 1 UV_SYSTEM_PYTHON: 1 permissions: contents: read jobs: deploy: permissions: contents: read pages: write id-token: write environment: name: github-pages url: ${{ steps.deployment.outputs.page_url }} runs-on: ubuntu-latest steps: - uses: actions/configure-pages@45bfe0192ca1faeb007ade9deae92b16b8254a0d # v6.0.0 - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Configure Git Credentials run: | git config user.name github-actions[bot] git config user.email 41898282+github-actions[bot]@users.noreply.github.com - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: "3.14" - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV - name: Build docs run: make docs-build - uses: actions/upload-pages-artifact@fc324d3547104276b827a68afc52ff2a11cc49c9 # v5.0.0 with: path: site - uses: actions/deploy-pages@cd2ce8fcbc39b97be8ca5fce6e763baed58fa128 # v5.0.0 id: deployment narwhals-2.26.0/.github/workflows/downstream_tests.yml000066400000000000000000000647021525000603700231540ustar00rootroot00000000000000name: Test Downstream Libraries - Fast on: pull_request: paths: - src/** - .github/workflows/downstream_tests.yml - pyproject.toml - uv.lock workflow_call: inputs: skip-fairlearn: type: boolean default: false env: PY_COLORS: 1 permissions: contents: read jobs: altair: if: github.head_ref != 'bump-version' env: UV_SYSTEM_PYTHON: 1 strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: altair-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: clone-altair run: | git clone https://github.com/vega/altair.git cd altair git log - name: install-basics run: uv pip install --upgrade tox virtualenv setuptools - name: install-altair-dev run: | cd altair uv sync --all-extras uv pip install -e . - name: install-narwhals-dev run: | uv pip uninstall narwhals uv pip install -e . - name: show-deps run: uv pip freeze - name: Run pytest run: | cd altair uv run --no-sync --all-extras pytest tests - name: Run mypy run: | cd altair uv run --no-sync --all-extras mypy altair tests marimo: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] dependencies: ["core,optional"] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: marimo-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: clone-marimo run: | git clone https://github.com/marimo-team/marimo.git --depth=1 cd marimo git log - name: install-narwhals-dev run: | cd marimo uv add ../ --editable - name: show-deps run: | cd marimo uv tree --group test-optional - name: Create assets directory, copy over index.html continue-on-error: true run: | mkdir -p marimo/marimo/_static/assets cp marimo/frontend/index.html marimo/marimo/_static/index.html cp marimo/frontend/public/favicon.ico marimo/marimo/_static/favicon.ico - name: Run tests with full dependencies if: ${{ matrix.dependencies == 'core,optional' }} run: | cd marimo uv run --python ${{ matrix.python-version }} python -c "import narwhals; print(narwhals.__file__)" scripts/test-narwhals.sh --python=${{ matrix.python-version }} timeout-minutes: 15 scikit-lego: if: github.head_ref != 'bump-version' env: UV_SYSTEM_PYTHON: 1 strategy: matrix: python-version: ["3.11"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: sklego-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: clone-scikit-lego run: git clone https://github.com/koaning/scikit-lego.git --depth 1 - name: install-basics run: uv pip install --upgrade tox virtualenv setuptools - name: install-scikit-lego-dev run: | cd scikit-lego # Temporary pin to get CI green https://github.com/koaning/scikit-lego/issues/818. uv pip install "pandas<3" uv pip install -e ".[test]" - name: install-narwhals-dev run: | uv pip uninstall narwhals uv pip install -e . - name: show-deps run: uv pip freeze - name: Run pytest run: | cd scikit-lego pytest --numprocesses=logical --disable-warnings --cov=sklego -m "not cvxpy and not formulaic and not umap" shiny: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: shiny-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: clone-shiny run: | git clone https://github.com/posit-dev/py-shiny.git cd py-shiny git log - name: Narwhals integration setup # Deliberately unpinned: the `clone-shiny` step above already checks out py-shiny at # `main` and the steps below run its test suite, so hash-pinning these composite # actions adds no security benefit and only risks skew against the cloned revision. uses: posit-dev/py-shiny/.github/py-shiny/narwhals-setup@main # zizmor: ignore[unpinned-uses] with: shiny-dir: py-shiny - name: install-narwhals-dev env: UV_SYSTEM_PYTHON: "1" run: | cd py-shiny uv pip uninstall narwhals uv pip install -e ./.. - name: show-deps env: UV_SYSTEM_PYTHON: "1" run: | cd py-shiny uv pip freeze - name: Narwhals integration test uses: posit-dev/py-shiny/.github/py-shiny/narwhals-test@main # zizmor: ignore[unpinned-uses] with: shiny-dir: py-shiny tea-tasting: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: clone-tea-tasting run: | git clone https://github.com/e10v/tea-tasting.git --depth=1 cd tea-tasting git log - name: install-uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: true cache-suffix: tea-tasting-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" python-version: ${{ matrix.python-version }} - name: install-tea-tasting-test run: | cd tea-tasting uv sync --group test - name: install-narwhals-dev run: | cd tea-tasting uv pip uninstall narwhals uv pip install -e ./.. - name: show-deps run: | cd tea-tasting uv pip freeze - name: run-pytest run: | cd tea-tasting # empty pytest.ini to avoid pytest using narwhals configs touch pytest.ini uv run --no-sync pytest tests tubular: if: github.head_ref != 'bump-version' env: UV_SYSTEM_PYTHON: 1 strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: tubular-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: clone-tubular run: | git clone https://github.com/lvgig/tubular --depth=1 cd tubular git log - name: install-basics run: uv pip install --upgrade tox virtualenv setuptools pytest-env - name: install-tubular-dev run: | cd tubular uv pip install -e ".[dev]" - name: install-narwhals-dev run: | uv pip uninstall narwhals uv pip install -e . - name: show-deps run: uv pip freeze - name: Run pytest run: | cd tubular pytest tests --config-file=pyproject.toml plotly: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: plotly-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: clone-plotly run: | git clone https://github.com/plotly/plotly.py --depth=1 cd plotly.py git log - name: install-deps run: | cd plotly.py uv venv -p ${{ matrix.python-version }} uv sync --extra dev_optional - name: install-narwhals-dev run: | cd plotly.py . .venv/bin/activate uv pip uninstall narwhals uv pip install -e ./.. - name: show-deps run: | cd plotly.py . .venv/bin/activate uv pip freeze - name: Run pytest on plotly express run: | cd plotly.py . .venv/bin/activate pytest tests/test_optional/test_px hierarchicalforecast: if: github.head_ref != 'bump-version' env: UV_SYSTEM_PYTHON: 1 strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: hierarchicalforecast-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: clone-hierarchicalforecast run: | git clone https://github.com/Nixtla/hierarchicalforecast --depth=1 --recurse-submodules --shallow-submodules cd hierarchicalforecast git log - name: install-basics run: uv pip install --upgrade tox virtualenv setuptools - name: install-deps run: | cd hierarchicalforecast uv pip install datasetsforecast "statsforecast>=1.0.0" scipy "polars[numpy]<=1.32.0" pytest pytest-benchmark pytest-cov uv pip install . - name: install-narwhals-dev run: | uv pip uninstall narwhals uv pip install -e . - name: show-deps run: uv pip freeze - name: Run tests run: | cd hierarchicalforecast pytest tests --no-cov --import-mode=importlib formulaic: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: formulaic-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: clone-formulaic run: | git clone https://github.com/matthewwardrop/formulaic.git --depth=1 cd formulaic git log - name: create-hatch-env run: | cd formulaic uv pip install hatch hatch-vcs "click!=8.3.0" --system - name: install-formulaic-dev run: | cd formulaic # Temporary pin until it's addressed upstream. hatch run uv pip install "pandas<3" hatch run uv pip install -e ".[arrow,calculus]" - name: install-narwhals-dev run: | cd formulaic hatch run uv pip uninstall narwhals hatch run uv pip install -e ./.. - name: show-deps run: hatch run uv pip freeze - name: run test run: | cd formulaic hatch run tests --config-file=pyproject.toml pointblank: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: pointblank-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: clone-pointblank run: | git clone https://github.com/posit-dev/pointblank.git --depth=1 cd pointblank git log - name: install-basics run: uv pip install --upgrade tox virtualenv setuptools --system - name: install-deps run: | cd pointblank uv venv -p ${{ matrix.python-version }} . .venv/bin/activate uv pip install . --group dev uv pip install pytest pytest-cov pytest-snapshot pandas polars "ibis-framework[duckdb,mysql,postgres,sqlite]>=9.5.0" chatlas shiny # Temporary pin to get CI green: sqlglot 30.18.0 refactored `DROP TABLE` # to use a `tables` argument instead of `this` (tobymao/sqlglot#8229), # which breaks ibis's in-memory table creation (it still builds # `sge.Drop(this=...)`), causing "syntax error at end of input" from # duckdb/sqlite in pointblank's test fixtures. uv pip install "sqlglot<30.18.0" - name: install-narwhals-dev run: | cd pointblank . .venv/bin/activate uv pip uninstall narwhals uv pip install -e . - name: show-deps run: | cd pointblank . .venv/bin/activate uv pip freeze - name: Run tests run: | cd pointblank . .venv/bin/activate # make sure that we use the .venv when running tests, so that # the local narwhals install is picked up sed -i 's/uv run pytest/uv run --no-sync pytest/g' Makefile make test validoopsie: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.10", "3.13"] # these are enough to cover all the tests os: ["ubuntu-latest"] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: validoopsie-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: clone-validoopsie run: | git clone https://github.com/akmalsoliev/Validoopsie.git --depth=1 validoopsie cd validoopsie git log - name: install-validoopsie-dev run: | cd validoopsie uv sync --dev --upgrade - name: install-narwhals-dev run: | cd validoopsie uv remove narwhals uv add ./.. - name: show-deps run: | cd validoopsie which python uv pip freeze - name: Run tests run: | cd validoopsie # empty pytest.ini to avoid pytest using narwhals configs touch pytest.ini touch tests/__init__.py touch tests/utils/__init__.py # Keep `--no-sync` in case Narwhals is re-synced with PyPI version uv run --no-sync pytest tests timeout-minutes: 15 darts: if: github.head_ref != 'bump-version' env: UV_SYSTEM_PYTHON: 1 strategy: matrix: python-version: ["3.12"] os: ["ubuntu-latest"] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: darts-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: clone-darts run: | git clone https://github.com/unit8co/darts.git --depth=1 cd darts git log - name: install-deps run: | cd darts uv build --wheel uv pip install dist/darts-*.whl \ --group dev \ llvmlite==0.46.0 \ numba==0.63.1 - name: install-narwhals-dev run: | uv pip uninstall narwhals # TODO(FBruzzesi): Install polars & pyarrow from darts requirements when they introduce it uv pip install -e ".[polars, pyarrow]" - name: show-deps run: uv pip freeze - name: Run tests run: | cd darts pytest darts/tests/test_timeseries.py pytest darts/tests/test_timeseries_multivariate.py pytest darts/tests/test_timeseries_static_covariates.py gt-extras: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: gt-extras-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: clone-gt-extras run: | git clone https://github.com/posit-dev/gt-extras.git --depth 1 cd gt-extras git log - name: install-gt-extras-dev run: | cd gt-extras uv sync --all-extras --dev - name: install-narwhals-dev run: | cd gt-extras uv pip uninstall narwhals uv pip install -e ./.. - name: show-deps run: | cd gt-extras uv pip freeze - name: run-pytest run: | cd gt-extras uv run --no-sync pytest fairlearn: if: github.head_ref != 'bump-version' && !inputs.skip-fairlearn env: UV_SYSTEM_PYTHON: 1 strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: fairlearn-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: clone-fairlearn run: | git clone https://github.com/fairlearn/fairlearn.git --depth 1 cd fairlearn git log - name: install-deps run: | cd fairlearn # TODO(FBruzzesi): Align with fairlearn team to get a minimal requirement to test narwhals features uv pip install -e . lightgbm matplotlib polars pyarrow pytest typing-extensions - name: install-narwhals-dev run: | cd fairlearn uv pip uninstall narwhals uv pip install -e ./.. uv pip freeze - name: run-pytest run: | cd fairlearn pytest test/unit -m "narwhals" lightgbm: if: github.head_ref != 'bump-version' env: UV_SYSTEM_PYTHON: 1 strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: lightgbm-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: clone-lightgbm run: | git clone https://github.com/lightgbm-org/LightGBM.git --recursive --depth=1 cd LightGBM git log - name: install-lightgbm run: | cd LightGBM uv pip install pip build sh ./build-python.sh install - name: install-deps run: | # scikit-learn's dev floor can briefly outpace the nightly wheel index # right after a stable release, so install it separately from plain # PyPI first; the rest of the file then installs against it as already # satisfied, while numpy/pandas/pyarrow/etc still resolve as nightlies. sklearn_spec=$(grep -E '^scikit-learn' LightGBM/.ci/pip-envs/requirements-latest.txt) uv pip install "$sklearn_spec" uv pip install -r LightGBM/.ci/pip-envs/requirements-latest.txt - name: install-narwhals-dev run: | uv pip uninstall narwhals uv pip install -e . - name: show-deps run: uv pip freeze - name: Run pytest run: | cd LightGBM pytest --config-file python-package/pyproject.toml tests/python_package_test scikit-learn: if: github.head_ref != 'bump-version' env: UV_SYSTEM_PYTHON: 1 strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: ${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Clone scikit-learn run: | git clone https://github.com/scikit-learn/scikit-learn.git --depth=1 cd scikit-learn git log - name: Build scikit-learn run: | cd scikit-learn uv pip install -U ninja meson-python numpy cython joblib threadpoolctl scipy uv pip install --editable . --verbose --no-build-isolation --config-settings editable-verbose=true uv pip install -U pandas polars pyarrow pytest pytest-xdist - name: Install Narwhals branch run: | uv pip uninstall narwhals uv pip install -e . - name: Run tests #ย Based on https://github.com/scikit-learn/scikit-learn/pull/31127#issuecomment-4425039398 run: | cd scikit-learn tests=( sklearn/utils/tests/test_indexing.py sklearn/utils/tests/test_validation.py sklearn/compose/tests/test_column_transformer.py sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py ) pytest "${tests[@]}" narwhals-2.26.0/.github/workflows/extremes.yml000066400000000000000000000124131525000603700213730ustar00rootroot00000000000000name: Min, old, and nightly versions on: pull_request: paths: - "**.py" - .github/workflows/extremes.yml - pyproject.toml - uv.lock env: PY_COLORS: 1 PYTEST_ADDOPTS: "--numprocesses=logical" UV_LOCKED: 1 UV_NO_DEV: 1 permissions: contents: read jobs: minimum_versions: strategy: matrix: python-version: ["3.10"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: min-versions-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Run pytest run: make run-ci DEPS="--group extreme-minimum-versions --group plugins" CMD="pytest tests --cov=src --cov=tests --cov-fail-under=50 --runslow --constructors=pandas,pyarrow,polars[eager],polars[lazy],duckdb" pretty_old_versions: strategy: matrix: python-version: ["3.10"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: pretty-old-versions-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Run pytest run: make run-ci DEPS="--group extreme-pretty-old-versions --group plugins" CMD="pytest tests --cov=src --cov=tests --cov-fail-under=50 --runslow --constructors=pandas,pyarrow,polars[eager],polars[lazy],duckdb" not_so_old_versions: strategy: matrix: python-version: ["3.10"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: not-so-old-versions-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Run pytest run: make run-ci DEPS="--group extreme-not-so-old-versions --group plugins" CMD="pytest tests --cov=src --cov=tests --cov-fail-under=50 --runslow --constructors=pandas,pyarrow,polars[eager],polars[lazy],dask,duckdb" nightlies: strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} env: UV_LOCKED: 0 steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: nightlies-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" #ย Manually install pre-releases as it's not possible to specify pre-releases per index # See: https://github.com/astral-sh/uv/issues/15780 - name: Create venv run: uv venv --python ${{ matrix.python-version }} - name: install-reqs run: uv pip install -e . --group tests --group plugins - name: install polars pre-release run: | uv pip uninstall polars uv pip install -U --pre polars - name: install-pandas-nightly run: | uv pip uninstall pandas uv pip install pandas --pre --index https://pypi.anaconda.org/scientific-python-nightly-wheels/simple -U - name: install pyarrow nightly run: | uv pip uninstall pyarrow uv pip install pyarrow --pre --index https://pypi.fury.io/arrow-nightlies/ -U - name: install numpy nightly run: | uv pip uninstall numpy uv pip install numpy --pre --index https://pypi.anaconda.org/scientific-python-nightly-wheels/simple -U - name: install dask run: | uv pip uninstall dask dask-expr uv pip install "git+https://github.com/dask/dask[dataframe]" - name: install duckdb nightly run: | uv pip uninstall duckdb uv pip install -U --pre duckdb - name: show-deps run: uv pip freeze - name: Assert nightlies dependencies run: | DEPS=$(uv pip freeze) echo "$DEPS" | grep -E 'pandas.*(dev|rc|\+)' echo "$DEPS" | grep 'pyarrow.*dev' echo "$DEPS" | grep 'numpy.*dev' echo "$DEPS" | grep 'dask.*@' - name: Run pytest run: | uv run --no-sync pytest tests --cov=src --cov=tests --cov-fail-under=50 --runslow \ --constructors="pandas,pandas[nullable],pandas[pyarrow],pyarrow,polars[eager],polars[lazy],dask,duckdb" narwhals-2.26.0/.github/workflows/pre-commit-hooks.yml000066400000000000000000000014601525000603700227340ustar00rootroot00000000000000name: Pre-commit hooks on: pull_request: workflow_call: env: PY_COLORS: 1 permissions: contents: read jobs: prek: if: github.head_ref != 'bump-version' runs-on: ubuntu-latest steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: "3.12" - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: enable-cache: "true" cache-suffix: pre-commit-hooks cache-dependency-glob: "pyproject.toml" - name: Run prek run: uvx prek run --all-files --show-diff-on-failure --color=always narwhals-2.26.0/.github/workflows/prek-autoupdate.yml000066400000000000000000000036751525000603700226630ustar00rootroot00000000000000name: prek autoupdate on: schedule: # Every Tuesday at 06:00 UTC, just after renovate's github-actions window. - cron: "0 6 * * 2" workflow_dispatch: permissions: contents: write pull-requests: write jobs: autoupdate: runs-on: ubuntu-latest steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: "3.12" - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 - name: Run prek autoupdate # Same 7-day supply-chain cooldown as renovate's `minimumReleaseAge`. prek never # downgrades: if the newest eligible rev is older than the pinned one, it keeps the pin. run: uvx prek autoupdate --cooldown-days 7 - name: Open PR if changed env: GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} run: | set -euo pipefail if git diff --quiet -- .pre-commit-config.yaml; then echo "No hook updates." exit 0 fi branch="chore/prek-autoupdate-$(date -u +%Y-%m-%d)" git config user.name "github-actions[bot]" git config user.email "41898282+github-actions[bot]@users.noreply.github.com" git checkout -b "$branch" git add .pre-commit-config.yaml git commit -m "chore: prek autoupdate" # `persist-credentials: false` leaves `origin` unauthenticated; the gh credential # helper supplies the token on demand instead of writing it into .git/config. gh auth setup-git git push --set-upstream origin "$branch" gh pr create \ --title "chore: prek autoupdate" \ --body "Automated hook version bump via \`prek autoupdate\`." \ --base main \ --head "$branch" narwhals-2.26.0/.github/workflows/publish_to_pypi.yml000066400000000000000000000126311525000603700227520ustar00rootroot00000000000000# This is taken from # https://packaging.python.org/en/latest/guides/publishing-package-distribution-releases-using-github-actions-ci-cd-workflows/#the-whole-ci-cd-workflow # but with the following differences # - removed the TestPyPI part # - instead of `on: push`, we have `tags` in there too # # To gate the release on a new workflow: # 1. In the gating workflow, add `workflow_call:` to its `on:` triggers. # 2. Add `if: github.head_ref != 'bump-version'` to every job in that workflow. #ย  This skips it on the release PR opened from `bump-version` # (otherwise jobs would run twice: once on the PR, once via the workflow_call below). # 3. Below, add a job that `uses: ./.github/workflows/.yml` and append its name to `build.needs:`. name: Publish Python ๐Ÿ distribution ๐Ÿ“ฆ to PyPI on: push: tags: - "v[0-9]+.[0-9]+.[0-9]+*" env: UV_LOCKED: 1 UV_NO_DEV: 1 permissions: {} jobs: pytest: name: PyTest uses: $/.github/workflows/pytest.yml permissions: contents: read pytest-pyspark: name: PyTest PySpark uses: $/.github/workflows/pytest-pyspark.yml permissions: contents: read pytest-ibis: name: PyTest Ibis uses: $/.github/workflows/pytest-ibis.yml permissions: contents: read pytest-modin: name: PyTest Modin uses: $/.github/workflows/pytest-modin.yml permissions: contents: read pytest-fireducks: name: PyTest Fireducks uses: $/.github/workflows/pytest-fireducks.yml permissions: contents: read typing: name: Type checking uses: $/.github/workflows/typing.yml permissions: contents: read downstream-tests: name: Downstream tests uses: $/.github/workflows/downstream_tests.yml with: skip-fairlearn: true permissions: contents: read check-docs-build: name: Check docs build uses: $/.github/workflows/check_docs_build.yml permissions: contents: read pre-commit-hooks: name: Pre-commit hooks uses: $/.github/workflows/pre-commit-hooks.yml permissions: contents: read test-sdist: name: Test sdist uses: $/.github/workflows/test_sdist.yml permissions: contents: read build: name: Build distribution ๐Ÿ“ฆ needs: - pytest - pytest-pyspark - pytest-ibis - pytest-modin - typing - downstream-tests - check-docs-build - pre-commit-hooks - test-sdist runs-on: ubuntu-latest permissions: contents: read steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: "3.13" enable-cache: false - name: Build a binary wheel and a source tarball run: uv build - name: Check wheel content run: uv run python utils/check_dist_content.py - name: Smoke tests (wheel and source distribution) run: | uv run --isolated --no-project --with dist/*.whl tests/version_test.py uv run --isolated --no-project --with dist/*.tar.gz tests/version_test.py - name: Store the distribution packages uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 with: name: python-package-distributions path: dist/ publish-to-pypi: name: >- Publish Python ๐Ÿ distribution ๐Ÿ“ฆ to PyPI if: startsWith(github.ref, 'refs/tags/') # only publish to PyPI on tag pushes needs: - build runs-on: ubuntu-latest environment: name: pypi url: https://pypi.org/p/narwhals permissions: id-token: write # IMPORTANT: mandatory for trusted publishing steps: - name: Download all the dists uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1 with: name: python-package-distributions path: dist/ - name: Publish distribution ๐Ÿ“ฆ to PyPI uses: pypa/gh-action-pypi-publish@dc37677b2e1c63e2034f94d8a5b11f265b73ba33 # v1.14.2 github-release: name: >- Sign the Python ๐Ÿ distribution ๐Ÿ“ฆ with Sigstore and upload them to GitHub Release needs: - publish-to-pypi runs-on: ubuntu-latest permissions: contents: write # IMPORTANT: mandatory for making GitHub Releases id-token: write # IMPORTANT: mandatory for sigstore steps: - name: Download all the dists uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1 with: name: python-package-distributions path: dist/ - name: Sign the dists with Sigstore uses: sigstore/gh-action-sigstore-python@790bc6befb9d733738f18d8f895854b453640ec9 # v3.5.0 with: inputs: >- ./dist/*.tar.gz ./dist/*.whl - name: Create GitHub Release env: GITHUB_TOKEN: ${{ github.token }} run: >- gh release create "$GITHUB_REF_NAME" --repo "$GITHUB_REPOSITORY" --notes "" - name: Upload artifact signatures to GitHub Release env: GITHUB_TOKEN: ${{ github.token }} # Upload to GitHub Release using the `gh` CLI. # `dist/` contains the built packages, and the # sigstore-produced signatures and certificates. run: >- gh release upload "$GITHUB_REF_NAME" dist/** --repo "$GITHUB_REPOSITORY" narwhals-2.26.0/.github/workflows/pytest-fireducks.yml000066400000000000000000000017701525000603700230500ustar00rootroot00000000000000name: PyTest FireDucks on: pull_request: paths: - src/** - tests/** - .github/workflows/pytest-fireducks.yml - pyproject.toml - uv.lock workflow_call: env: PY_COLORS: 1 UV_LOCKED: 0 UV_NO_DEV: 1 permissions: contents: read jobs: pytest-fireducks: if: github.head_ref != 'bump-version' runs-on: ubuntu-latest steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: "3.13" enable-cache: "true" cache-suffix: fireducks-3.13 cache-dependency-glob: "pyproject.toml" - name: Run pytest run: make run-ci DEPS="--group core-tests --extra pandas" RUN_ONLY="--with fireducks --with 'pandas>2.2.0'" CMD="python -m fireducks.pandas --hook-import-module -m pytest tests/fireducks_test.py" narwhals-2.26.0/.github/workflows/pytest-ibis.yml000066400000000000000000000022631525000603700220150ustar00rootroot00000000000000name: PyTest Ibis constructor on: pull_request: paths: - src/narwhals/_expression_parsing.py - src/narwhals/_ibis/** - src/narwhals/_sql/** - tests/** - .github/workflows/pytest-ibis.yml - pyproject.toml - uv.lock workflow_call: env: PY_COLORS: 1 PYTEST_ADDOPTS: "--numprocesses=logical" UV_LOCKED: 1 UV_NO_DEV: 1 permissions: contents: read jobs: pytest-ibis-constructor: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.13"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: ibis-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Run pytest run: make run-ci DEPS="--group core-tests --extra ibis --extra pandas" CMD="pytest tests --constructors=ibis" narwhals-2.26.0/.github/workflows/pytest-modin.yml000066400000000000000000000022351525000603700221740ustar00rootroot00000000000000name: PyTest Modin constructor on: pull_request: paths: - src/narwhals/_expression_parsing.py - src/narwhals/_pandas_like/** - tests/** - .github/workflows/pytest-modin.yml - pyproject.toml - uv.lock workflow_call: env: PY_COLORS: 1 PYTEST_ADDOPTS: "--numprocesses=logical" UV_LOCKED: 1 UV_NO_DEV: 1 permissions: contents: read jobs: pytest-modin-constructor: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.13"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: modin-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Run pytest run: make run-ci DEPS="--group core-tests --extra modin" CMD="pytest tests --constructors=modin[pyarrow]" narwhals-2.26.0/.github/workflows/pytest-pyspark.yml000066400000000000000000000103011525000603700225500ustar00rootroot00000000000000name: PyTest PySpark constructor on: pull_request: paths: - .github/workflows/pytest-pyspark.yml - src/narwhals/_expression_parsing.py - src/narwhals/_spark_like/** - src/narwhals/_sql/** - tests/*scan*.py - tests/frame/*sink*.py - pyproject.toml - uv.lock workflow_call: schedule: - cron: 0 12 * * 0 # Sunday at mid-day env: PY_COLORS: 1 PYTEST_ADDOPTS: "--numprocesses=logical" UV_LOCKED: 1 UV_NO_DEV: 1 permissions: contents: read jobs: pytest-pyspark-constructor: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.11"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: pyspark-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Run pytest run: make run-ci DEPS="--group core-tests --extra pyspark --extra pandas" CMD="pytest tests --cov=src/narwhals/_spark_like --cov-fail-under=95 --runslow --constructors=pyspark" pytest-pyspark-min-version-constructor: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.11"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: pyspark-min-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Run pytest run: make run-ci DEPS="--group min-pyspark-version --extra pandas" CMD="pytest tests --cov=src/narwhals/_spark_like --cov-fail-under=95 --runslow --constructors=pyspark" pytest-pyspark-connect-constructor: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.11"] os: [ubuntu-latest] env: SPARK_VERSION: 4.1.0 SPARK_PORT: 15002 SPARK_CONNECT: true runs-on: ${{ matrix.os }} container: # env context isn't available here (https://docs.github.com/en/actions/learn-github-actions/contexts#context-availability), so this must match SPARK_VERSION image: apache/spark:4.2.0@sha256:fc64959c04bd87b0ac686be9aaa9008b69cdb1afc695400528279c8b01f43d89 options: --user root env: HOME: /root steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: pyspark-connect-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Create venv run: uv venv --python ${{ matrix.python-version }} - name: install-reqs # temporary pin to get CI green run: uv pip install -e ".[pandas]" --group core-tests --group sklearn "pyarrow<22.0" - name: install pyspark run: uv pip install -e . "pyspark[connect]==${SPARK_VERSION}" "setuptools<78" - name: show-deps run: uv pip freeze - name: Start Spark Connect server run: | /opt/spark/sbin/start-connect-server.sh --master 'local[*]' --conf spark.connect.grpc.binding.port=${SPARK_PORT} sleep 5 echo "Spark Connect server started" - name: Run pytest run: uv run --no-sync pytest tests --cov=src/narwhals/_spark_like --cov-fail-under=95 --runslow --constructors "pyspark[connect]" - name: Stop Spark Connect server if: always() run: /opt/spark/sbin/stop-connect-server.sh narwhals-2.26.0/.github/workflows/pytest.yml000066400000000000000000000150431525000603700210710ustar00rootroot00000000000000name: PyTest on: pull_request: paths: - "**.py" - .github/workflows/pytest.yml - pyproject.toml - uv.lock workflow_call: env: PY_COLORS: 1 PYTEST_ADDOPTS: "--numprocesses=logical" UV_LOCKED: 1 UV_NO_DEV: 1 permissions: contents: read jobs: pytest-310: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.10"] os: [windows-latest, ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: pytest-310-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Run pytest shell: bash run: make run-ci DEPS="--group tests --group plugins --extra pandas --extra polars --extra pyarrow" CMD="pytest tests --cov=src --cov=tests --cov-fail-under=75 --constructors=pandas,pyarrow,polars[eager],polars[lazy]" pytest-windows: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.10", "3.13"] os: [windows-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: pytest-windows-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" # we are not testing pyspark, modin, or dask on Windows here because nobody got time for that - name: Run pytest shell: bash run: make run-ci DEPS="--group core-tests --group sklearn --group plugins --extra pandas" CMD="pytest tests --cov=src --cov=tests --runslow --cov-fail-under=95 --durations=30 --constructors=pandas,pandas[nullable],pandas[pyarrow],pyarrow,polars[eager],polars[lazy],duckdb,sqlframe" pytest-full-coverage: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.11", "3.13"] os: [ubuntu-latest] include: - python-version: "3.11" polars_streaming: true runs-on: ${{ matrix.os }} env: NARWHALS_POLARS_NEW_STREAMING: ${{ matrix.polars_streaming == true }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: pytest-full-coverage-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Run pytest run: make test-full-coverage - name: Run doctests # reprs differ between versions, so we only run doctests on the latest Python if: matrix.python-version == '3.13' run: make doctest # Test against smaller dependency set, used e.g. on Gentoo. pytest-narrower-dependencies: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.13"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} env: UV_LOCKED: 0 steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: pytest-narrower-deps-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Run pytest (pandas and pandas[nullable]) run: make run-ci DEPS="-U --group tests --extra pandas" RUN_ONLY="--isolated" CMD="pytest tests --runslow --constructors=pandas,pandas[nullable]" - name: Run pytest (pandas[pyarrow] and pyarrow) run: make run-ci DEPS="-U --group tests --extra pandas --extra pyarrow" RUN_ONLY="--isolated" CMD="pytest tests --runslow --constructors=pandas[pyarrow],pyarrow" - name: Run pytest (polars) run: make run-ci DEPS="-U --group tests --extra polars" RUN_ONLY="--isolated" CMD="pytest tests --runslow --constructors=polars[eager],polars[lazy]" python-314: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.14"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: python-314-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Run pytest run: make run-ci DEPS="--group core-tests --group plugins --extra pandas" CMD="pytest tests --cov=src --cov=tests --cov-fail-under=50 --runslow --durations=30 --constructors=pandas,pandas[nullable],pandas[pyarrow],pyarrow,polars[eager],polars[lazy],duckdb,sqlframe" python-314t: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.14t"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} env: PYTHON_GIL: 0 UV_LOCKED: 0 steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: python-314t-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Run pytest run: make run-ci DEPS="--pre --group tests --group plugins --extra pandas --extra pyarrow" CMD="pytest tests --cov=src --cov=tests --cov-fail-under=50 --runslow --durations=30 --constructors=pandas,pandas[nullable],pandas[pyarrow],pyarrow" narwhals-2.26.0/.github/workflows/random_ci_pytest.yml000066400000000000000000000025741525000603700231110ustar00rootroot00000000000000name: Random Versions on: pull_request: paths: - "**.py" - .github/workflows/random_ci_pytest.yml - pyproject.toml - uv.lock env: PY_COLORS: 1 PYTEST_ADDOPTS: "--numprocesses=logical" UV_LOCKED: 1 UV_NO_DEV: 1 permissions: contents: read jobs: tox: strategy: matrix: python-version: ["3.10"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: ${{ matrix.python-version }} enable-cache: "true" cache-suffix: pytest-random-ci-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Generate random versions run: uv run --no-project python utils/generate_random_versions.py - name: Show dependencies run: uv pip compile random-requirements.txt --group tests --group plugins - name: Run pytest with random versions run: | uv run --group tests --group plugins --with-requirements random-requirements.txt \ pytest tests --cov=src --cov=tests --cov-fail-under=75 \ --constructors="pandas,pyarrow,polars[eager],polars[lazy]" narwhals-2.26.0/.github/workflows/release-drafter.yml000066400000000000000000000016761525000603700226150ustar00rootroot00000000000000name: Release Drafter on: push: # branches to consider in the event; optional, defaults to all branches: - main permissions: contents: read jobs: update_release_draft: permissions: # write permission is required to create a github release contents: write runs-on: ubuntu-latest steps: # (Optional) GitHub Enterprise requires GHE_HOST variable set #- name: Set GHE_HOST # run: | # echo "GHE_HOST=${GITHUB_SERVER_URL##https:\/\/}" >> $GITHUB_ENV # Drafts your next Release notes as Pull Requests are merged into "master" - uses: release-drafter/release-drafter@34d80673e067bdc0c24568d3af899c216adcfaa9 # v7.7.0 # (Optional) specify config name to use, relative to .github/. Default: release-drafter.yml with: config-name: release-drafter.yml # disable-autolabeler: true env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} narwhals-2.26.0/.github/workflows/scorecard.yml000066400000000000000000000042131525000603700215030ustar00rootroot00000000000000name: Scorecard supply-chain security on: # For Branch-Protection check. Only the default branch is supported. See # https://github.com/ossf/scorecard/blob/main/docs/checks.md#branch-protection branch_protection_rule: # To guarantee Maintained check is occasionally updated. See # https://github.com/ossf/scorecard/blob/main/docs/checks.md#maintained schedule: - cron: '15 18 * * 5' push: branches: [ "main" ] # Declare default permissions as read only. permissions: {} jobs: analysis: name: Scorecard analysis runs-on: ubuntu-latest permissions: # Needed to upload the results to code-scanning dashboard. security-events: write # Needed to publish results and get a badge (see publish_results below). id-token: write steps: - name: "Checkout code" uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: "Run analysis" uses: ossf/scorecard-action@2d1146689b8cda280b9bc96326124645441f03bc # v2.4.4 with: results_file: results.sarif results_format: sarif # Public repositories: # - Publish results to OpenSSF REST API for easy access by consumers # - Allows the repository to include the Scorecard badge. # - See https://github.com/ossf/scorecard-action#publishing-results. publish_results: true # Upload the results as artifacts (optional). Commenting out will disable uploads of run results in SARIF # format to the repository Actions tab. - name: "Upload artifact" uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 with: name: SARIF file path: results.sarif retention-days: 5 # Upload the results to GitHub's code scanning dashboard (optional). # Commenting out will disable upload of results to your repo's Code Scanning dashboard - name: "Upload to code-scanning" uses: github/codeql-action/upload-sarif@cdf488f595d80d6e07e03d4674febd5ab45fa938 # v4.37.9 with: sarif_file: results.sarif narwhals-2.26.0/.github/workflows/test_sdist.yml000066400000000000000000000050301525000603700217210ustar00rootroot00000000000000name: Test sdist is installable and self-contained # Builds the sdist, unpacks it OUTSIDE the repo (so there is no workspace, # mirroring a downstream packager unpacking the PyPI tarball), installs it with # the `tests` dependency group via plain `pip`, and runs the test suite from the # unpacked tree. This guards against regressions like https://github.com/narwhals-dev/narwhals/pull/3664, #ย where the sdist shipped without `tests/data/` and the suite could not run from the tarball. # # NOTE: `pip` (not `uv pip`) is used on purpose. on: pull_request: workflow_call: env: PY_COLORS: 1 PYTEST_ADDOPTS: "--numprocesses=logical" permissions: contents: read jobs: test-sdist: if: github.head_ref != 'bump-version' runs-on: ubuntu-latest steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: python-version: "3.13" enable-cache: "true" cache-suffix: test-sdist cache-dependency-glob: "pyproject.toml" - name: Build the sdist run: uv build --sdist - name: Unpack the sdist outside the repo run: | mkdir -p "${RUNNER_TEMP}/sdist" tar xzf dist/*.tar.gz -C "${RUNNER_TEMP}/sdist" # Fail loudly if the workspace leaked into the tarball: its presence # would mask exactly the kind of bug this job is meant to catch. if [ -d "${RUNNER_TEMP}/sdist"/narwhals-*/packages ]; then echo "::error::Unexpected 'packages/' (workspace) directory inside the sdist" exit 1 fi - name: Create an isolated venv with a recent pip run: | uv venv "${RUNNER_TEMP}/venv" --python 3.13 uv pip install --python "${RUNNER_TEMP}/venv" "pip>=25.1" - name: Install the unpacked sdist with the tests group (via pip) run: | src=$(echo "${RUNNER_TEMP}/sdist"/narwhals-*) "${RUNNER_TEMP}/venv/bin/python" -m pip install \ "${src}[pandas,pyarrow,polars]" \ --group "${src}/pyproject.toml:tests" - name: Run the test suite from the unpacked sdist run: | # Validate that the sdist is complete and usable src=$(echo "${RUNNER_TEMP}/sdist"/narwhals-*) cd "${src}" "${RUNNER_TEMP}/venv/bin/python" -m pytest tests \ --constructors="pandas,pyarrow,polars[eager],polars[lazy]" narwhals-2.26.0/.github/workflows/typing.yml000066400000000000000000000026621525000603700210560ustar00rootroot00000000000000name: Type checking on: pull_request: paths: - "**.py" - .github/workflows/typing.yml - pyproject.toml - uv.lock workflow_call: env: PY_COLORS: 1 UV_LOCKED: 1 UV_NO_DEV: 1 permissions: contents: read jobs: typing: if: github.head_ref != 'bump-version' strategy: matrix: python-version: ["3.12"] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: persist-credentials: false - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1 with: activate-environment: true enable-cache: "true" cache-suffix: typing-${{ matrix.python-version }} cache-dependency-glob: "pyproject.toml" - name: Create venv run: uv venv .venv --clear - name: install-reqs # TODO: add more dependencies/backends incrementally run: uv pip install -e ".[pyspark]" --group typing - name: show-deps run: uv pip freeze - name: Run mypy and pyright run: make typing - name: Check type-completeness run: | uv run pyrefly coverage check src/narwhals --public-only narwhals-2.26.0/.gitignore000066400000000000000000000006641525000603700154140ustar00rootroot00000000000000# Byte-compiled / optimized / DLL files __pycache__/ *.pyc # Distribution / packaging dist/ # Unit test / coverage reports .nox/ .coverage .coverage.* .cache coverage.xml .hypothesis/ .pytest_cache/ .mypy_cache # Documentation site/ todo.md docs/this.md docs/api-completeness/*.md !docs/api-completeness/index.md # Environments .venv # TPC-H data tpch/data/* # VSCode .vscode/ # IntelliJ IDEA / PyCharm .idea/ # MacOS .DS_Store narwhals-2.26.0/.pre-commit-config.yaml000066400000000000000000000107671525000603700177120ustar00rootroot00000000000000repos: - repo: https://github.com/astral-sh/ruff-pre-commit # Ruff version. rev: 'v0.16.5' hooks: # Run the formatter. - id: ruff-format # Run the linter. - id: ruff-check args: [--fix] - id: ruff name: check-docstrings alias: check-docstrings entry: python utils/check_docstrings.py - repo: https://github.com/astral-sh/uv-pre-commit rev: 0.12.8 hooks: - id: uv-lock - repo: https://github.com/codespell-project/codespell rev: 'v2.4.3' hooks: - id: codespell files: \.(py|rst|md)$ args: [--ignore-words-list=ser] exclude: ^docs/api-completeness.md$ - repo: https://github.com/zizmorcore/zizmor-pre-commit rev: 'v1.30.0' hooks: - id: zizmor args: [--no-progress, --fix] - repo: https://github.com/adhtruong/mirrors-typos # Use mirror to avoid autoupdate issue: https://github.com/crate-ci/typos/issues/390 rev: 'v1.50.0' hooks: - id: typos - repo: https://github.com/pycqa/flake8 rev: '7.3.0' hooks: - id: flake8 # TODO(unassigned): replace with ruff once https://github.com/astral-sh/ruff/issues/458 is addressed. name: darglint alias: darglint entry: flake8 --select DAR --ignore DAR101,DAR201,DAR401,DAR402 exclude: | (?x)^( tests/.*| # TODO: gradually enable src/narwhals/dependencies\.py| # Private, so less urgent to document too well src/narwhals/_.*| # Internal use only, less urgent ^utils/.*| # Soft-deprecated, so less crucial to document so carefully src/narwhals/stable/v1/.* )$ # Keep in sync with `flake8-typing-imports` so the same venv is reused. additional_dependencies: [darglint==1.8.1, flake8-typing-imports==1.17.0] - id: flake8 # TODO(unassigned): replace with ruff once https://github.com/astral-sh/ruff/issues/2302 is addressed. name: flake8-typing-imports alias: flake8-typing-imports entry: flake8 --select TYP --min-python-version=3.10.0 # Keep in sync with `darglint` so the same venv is reused. additional_dependencies: [darglint==1.8.1, flake8-typing-imports==1.17.0] - repo: local hooks: # `language: python` runs in an isolated venv. #ย The scripts that need `narwhals` add `src/` to `sys.path` themselves. - id: check-api-reference name: check-api-reference pass_filenames: false entry: uv run --extra polars utils/check_api_reference.py language: system - id: sort-api-reference name: sort-api-reference pass_filenames: false entry: uv run utils/sort_api_reference.py language: system - id: check-slotted-classes name: check-slotted-classes pass_filenames: false entry: uv run utils/check_slotted_classes.py language: system - id: imports-are-banned name: import are banned (use `get_pandas` instead of `import pandas`) entry: uv run utils/import_check.py language: system files: ^src/narwhals/ exclude: ^src/narwhals/dependencies\.py - id: self-self name: don't annotate `self` argument using `Self` entry: "self: Self" language: pygrep files: ^src/narwhals/ - id: dtypes-import name: don't import from narwhals.dtypes (use `Version.dtypes` instead) entry: | (?x) import\ narwhals(\.stable\.v\d)?\.dtypes| from\ narwhals(\.stable\.v\d)?\ import\ dtypes| ^from\ narwhals(\.stable\.v\d)?\.dtypes\ import \ (DType,\ )? ((Datetime|Duration|Enum)(,\ )?)+ ((,\ )?DType)? language: pygrep files: ^src/narwhals/ exclude: | (?x) ^( src/narwhals/_utils\.py| src/narwhals/stable/v./_?dtypes.py| src/narwhals/.*__init__.py| src/narwhals/.*typing\.py ) - id: pull-request-target name: don't use `pull_request_target` entry: pull_request_target language: pygrep files: ^\.github/workflows/ - repo: https://github.com/pre-commit/pre-commit-hooks rev: v6.0.0 hooks: - id: name-tests-test exclude: | (?x) ^(tests/utils\.py) |^(packages/test_plugin/) - id: no-commit-to-branch - id: end-of-file-fixer exclude: .svg$ - id: trailing-whitespace exclude: | (?x)^( tests/data/.*\.csv| .*\.svg )$ narwhals-2.26.0/AGENTS.md000066400000000000000000000203411525000603700147210ustar00rootroot00000000000000# AGENTS.md Narwhals is an extremely lightweight, zero-dependency compatibility layer between Python dataframe libraries. It lets library authors write dataframe-agnostic code once using a subset of the Polars API, and have it work across pandas, Polars, PyArrow, cuDF, Modin, Dask, DuckDB, PySpark, Ibis, and SQLFrame, without depending on any of them. The primary audience is **library maintainers**, not end users. Because of that, stability and backwards compatibility are taken extremely seriously. ## Read the docs first Almost everything an agent needs is already documented. Read the relevant page instead of inferring from the source, and update the page when you change the behaviour it describes. | Topic | Read | | --- | --- | | Internal architecture: expressions, nodes, expression metadata, broadcasting, `over` push-down, group-by | [docs/how_it_works.md](docs/how_it_works.md) | | Contributor workflow: env setup, test invocations, backend-specific rules, docstring style, PR conventions | [CONTRIBUTING.md](CONTRIBUTING.md) | | Stable API guarantees and the `main` vs `stable.v1` / `stable.v2` diff | [docs/backcompat.md](docs/backcompat.md) | | Adding a backend: compliant protocols, plugin entry points, the IO namespace contract | [docs/extending.md](docs/extending.md) | | Row-order semantics: what `DataFrame` guarantees, what `LazyFrame` doesn't, `over(order_by=...)` | [docs/concepts/order_dependence.md](docs/concepts/order_dependence.md) | | Null vs NaN: which methods exist for which, and what pandas muddies | [docs/concepts/null_handling.md](docs/concepts/null_handling.md) | | Why the pandas `group_by` `UserWarning` exists and how to avoid triggering it | [docs/concepts/improve_group_by_operation.md](docs/concepts/improve_group_by_operation.md) | | Boolean semantics, the pandas index, non-string column names | [docs/concepts/](docs/concepts/) | | Which methods each backend implements | [docs/api-completeness/](docs/api-completeness/) (generated, do not hand-edit) | | Public API surface | [docs/api-reference/](docs/api-reference/) (member lists are validated by CI) | | `narwhals.sql`: generating SQL from Narwhals expressions | [docs/generating_sql.md](docs/generating_sql.md) | | Security reporting and release-permission policy | [docs/security.md](docs/security.md) | The one-sentence summary of [docs/how_it_works.md](docs/how_it_works.md), worth internalising before touching anything in `_pandas_like/`, `_arrow/`, or `_compliant/`: > An expression is a function from a DataFrame to a sequence of Series. ## Code structure and design Narwhals follows a layered design: 1. **Public API** ([src/narwhals/dataframe.py](src/narwhals/dataframe.py), [series.py](src/narwhals/series.py), [expr.py](src/narwhals/expr.py), ...): the user-facing Polars-like API. Thin wrappers that build `ExprNode`s and dispatch to compliant backends. 2. **Compliant wrappers**: one folder per backend (`_pandas_like/`, `_arrow/`, `_polars/`, `_duckdb/`, `_spark_like/`, `_dask/`, `_ibis/`), each backend implements Narwhals-compliant DataFrames, Series, Exprs, and Namespaces that translate the Polars-like API to native calls. Shared protocols and base classes live in `src/narwhals/_compliant/`. 3. **Native libraries** (pandas, Polars, PyArrow, ...): the actual computation engines, never directly depended on. Outside `src/`, the project has: - [tests/](tests/): test suite, see [CONTRIBUTING.md โ†’ Running tests](CONTRIBUTING.md#7-running-tests) - [tpch/](tpch/): TPC-H benchmark queries, see [tpch/README.md](tpch/README.md) - `packages/`: uv workspace members, built and installed independently of `src/narwhals`. Currently just a `test-plugin` that is a minimal fake backend used to test `narwhals.plugins`. ## Hard rules These are non-negotiable and the most common source of review comments. The long form, with rationale, is in [CONTRIBUTING.md](CONTRIBUTING.md). * **Zero dependencies.** Narwhals must never add a runtime dependency. It only uses what the user passes in. * **Never import anything for `isinstance` checks.** Use the functions in [src/narwhals/dependencies.py](src/narwhals/dependencies.py) (e.g. `is_pandas_dataframe`). * **Never iterate over rows.** Assume infinite rows. Column iteration is acceptable. * **Never modify user input data.** Especially with pandas: no inplace operations on user-provided objects. * **100% branch coverage** is enforced by the full-coverage CI job. When a branch is genuinely unreachable (e.g. gated on an unsupported backend version), mark it `# pragma: no cover` with a one-line reason. * **Breaking changes never land in `narwhals.stable.v1` or `narwhals.stable.v2`.** New public APIs land in the main `narwhals` namespace and graduate into the next stable version. See [docs/backcompat.md](docs/backcompat.md), and add an entry to its `main` vs `stable.*` diff when the namespaces diverge. Backend-specific rules (no pandas `apply`/`map`/`assign`/`drop`/`reset_index`/`rename`, no Polars `map_elements`, no ordering assumptions or materialisation on lazy backends, DuckDB Python API over SQL) are listed in full under [CONTRIBUTING.md โ†’ Backend-specific considerations](CONTRIBUTING.md#backend-specific-considerations). ## Verify changes before committing Run these from the repo root. If you have not activated `.venv`, prefix the non-`make` commands with `uv run`. 1. `prek run --all-files` โ€” lint, format, docstring/import checks 2. `make typing` โ€” static type checking (mypy, pyright, and pyrefly) (Optional: `make typing-coverage` for type-completeness) 3. `make test-full-coverage` โ€” full test suite with 100% coverage. Very slow, see [Faster testing](#faster-testing) for alternatives. 4. `make doctest` โ€” tests docstring examples 5. `make docs-build` โ€” run only if you touched `docs/` or a docstring. The build *executes* `exec="yes"` code blocks, so a stale snippet is a build failure. Docs are built with `zensical` (configured in [zensical.toml](zensical.toml)), not mkdocs โ€” the nav lives there, so a new page must be added to it. ### Faster testing Full coverage runs are slow. While iterating: ```bash uv run pytest tests/path/to/test_file.py # one file uv run pytest tests --constructors=pandas,polars[eager],pyarrow uv run pytest tests --all-cpu-constructors # needs --extra modin --extra pyspark ``` * Default constructors are `pandas,pandas[pyarrow],polars[eager],pyarrow,duckdb,sqlframe,ibis` (overridable via the `NARWHALS_DEFAULT_CONSTRUCTORS` env var). * Hypothesis tests are skipped unless you pass `--runslow`. * Dask and Modin are not in `local-dev`; add `--extra dask --extra modin` to test them locally. Do not treat a green fast run as sufficient: the coverage gate and the lazy/SQL constructors (`duckdb`, `sqlframe`, `dask`) catch a distinct class of bug, so run step 3 before committing. ### Test failure patterns * `request.applymarker(pytest.mark.xfail)` โ€” planned but not yet supported features. * `pytest.mark.skipif` โ€” conditional skips (e.g. version constraints). * `pytest.raises` โ€” expected exceptions. Always document the reason in a comment. Details and examples: [CONTRIBUTING.md โ†’ Test Failure Patterns](CONTRIBUTING.md#test-failure-patterns). ## Inline Comments Default to none. Only add one when the *why* is non-obvious (a hidden constraint, a workaround, a subtle invariant). Never to restate what the code already says, and never to narrate a diff. Comments that merely repeat the following line are a common tell of unreviewed AI-assisted output and are removed on sight in review. ## Pull requests and issues **A human opens the pull request or issue, not you.** Draft the diff, the description, and the commit messages if asked, but the submission is made by the person you are working with, under their account, in their words, after they have read the whole diff. The same holds for review replies and issue discussion. Unattended agent-filed issues and PRs can be closed without discussion. For guidelines about PRs (e.g. title rules) see [CONTRIBUTING.md โ†’ Pull requests](CONTRIBUTING.md#6-pull-requests). **AI-assisted contributions must be disclosed** in the dedicated PR-template field, and the author is accountable for every line. Read [CONTRIBUTING.md โ†’ AI-assisted contributions](CONTRIBUTING.md#ai-assisted-contributions) before opening a PR. narwhals-2.26.0/CONTRIBUTING.md000066400000000000000000000571711525000603700156620ustar00rootroot00000000000000# Contributing Thank you for your interest in contributing to Narwhals! Any kind of improvement is welcome! ## **TLDR** If you've got experience with open source contributions, the following instructions might suffice: - Install [uv](https://docs.astral.sh/uv/getting-started/installation/) (required). - Clone repo: `git clone git@github.com:narwhals-dev/narwhals.git narwhals-dev` - `cd narwhals-dev/` - `git remote rename origin upstream` - `git remote add origin ` - `uv sync --group local-dev` (creates `.venv` and installs project + dev deps) - Install prek as a git hook: `uv run prek install` - To run tests: `uv run pytest` - To run ruff formatting and linting: `make lint` - To run all pre-commit checks (which include ruff): `uv run prek run --all-files` - To run static typing checks: `make typing` For more detailed and beginner-friendly instructions, see below! ## 0. Prerequisites Narwhals uses [uv](https://docs.astral.sh/uv/) as its package and environment manager. You must have `uv` installed to follow the rest of this guide. The Makefile targets, dependency groups, and lockfile (`uv.lock`) all assume `uv` is present. To install `uv`, follow the official instructions at [uv installation](https://docs.astral.sh/uv/getting-started/installation/). If you already have `uv`, keep it up-to-date with: ```terminal uv self update ``` ### Dependency management with uv A few useful `uv` workflows when contributing: - `uv add --group local-dev ` โ€” add a dependency to a group (e.g. `local-dev`) - `uv add --optional dask ` โ€” add a dependency to an extra (e.g. `dask`) - `uv remove ` โ€” remove a dependency (with `--group`/`--optional` as appropriate) - `uv lock` โ€” refresh the lockfile without changing pins - `uv lock --upgrade` โ€” upgrade everything After editing `pyproject.toml` (or `uv.lock`) manually, the right order to refresh things is `uv lock` then `uv sync` (the `uv add`/`uv remove` commands above already do both for you). All of these update `pyproject.toml` and `uv.lock`. Commit both files together when changing dependencies. See also the relevant `uv` docs: [`uv sync`](https://docs.astral.sh/uv/reference/cli/#uv-sync), [`uv lock`](https://docs.astral.sh/uv/reference/cli/#uv-lock), [syncing the environment](https://docs.astral.sh/uv/concepts/projects/sync/#syncing-the-environment), and [upgrading locked versions](https://docs.astral.sh/uv/concepts/projects/sync/#upgrading-locked-package-versions). ### Optional: Java for PySpark tests If you want to run PySpark-related tests, you'll also need to have Java installed. Refer to the [Spark documentation](https://spark.apache.org/docs/latest/#downloading) for more information. As an alternative to a system-wide install, `conda` / `pixi` / `mamba` users can pull a compatible JDK from conda-forge ([`openjdk`](https://anaconda.org/conda-forge/openjdk)). ## 1. Setting up your dev environment You can contribute to Narwhals in your local development environment, using `uv`, git and your editor of choice. You can also contribute to Narwhals using [Github Codespaces](https://docs.github.com/en/codespaces/overview) - a development environment that's hosted in the cloud. This way you can easily start to work from your browser without installing git and cloning the repo. Once your environment is set up, the rest of the workflow is the same for everyone, please follow the [Working on your issue](#2-working-on-your-issue) through [Pull requests](#6-pull-requests) sections. ### a. Local development #### 1. Make sure you have git on your machine and a GitHub account Open your terminal and run the following command: ```bash git --version ``` If the output looks like `git version 2.34.1` and you have a personal account on GitHub - you're good to go to the next step. If the terminal output informs about `command not found` you need to [install git](https://docs.github.com/en/get-started/quickstart/set-up-git). If you're new to GitHub, you'll need to create an account on [GitHub.com](https://github.com/) and verify your email address. You should also [check for existing SSH keys](https://docs.github.com/en/authentication/connecting-to-github-with-ssh/checking-for-existing-ssh-keys) and [generate and add a new SSH key](https://docs.github.com/en/authentication/connecting-to-github-with-ssh/generating-a-new-ssh-key-and-adding-it-to-the-ssh-agent) if you don't have one already. #### 2. Fork the repository Go to the [main project page](https://github.com/narwhals-dev/narwhals). Fork the repository by clicking on the fork button. You can find it in the right corner on the top of the page. #### 3. Clone the repository Go to the forked repository on your GitHub account - you'll find it on your account in the tab Repositories. Click on the green `Code` button and then click the `Copy url to clipboard` icon. Open a terminal, choose the directory where you would like to have Narwhals repository and run the following git command: ```bash git clone ``` for example: ```bash git clone git@github.com:YOUR-GITHUB-USERNAME/narwhals.git narwhals-dev ``` You should then navigate to the folder you just created: ```bash cd narwhals-dev ``` #### 4. Add the `upstream` remote and fetch from it ```bash git remote add upstream git@github.com:narwhals-dev/narwhals.git git fetch upstream ``` Check to see the remote has been added with `git remote -v`, you should see something like this: ```bash git remote -v origin git@github.com:YOUR-GITHUB-USERNAME/narwhals.git (fetch) origin git@github.com:YOUR-GITHUB-USERNAME/narwhals.git (push) upstream git@github.com:narwhals-dev/narwhals.git (fetch) upstream git@github.com:narwhals-dev/narwhals.git (push) ``` where `YOUR-GITHUB-USERNAME` will be your GitHub user name. #### 5. Setting up your environment With `uv` already installed (see [Prerequisites](#0-prerequisites)), set up the project by running: ```terminal uv sync --group local-dev ``` This creates a `.venv` and installs the project together with the `local-dev` [dependency group](https://docs.astral.sh/uv/concepts/projects/dependencies/#dependency-groups) (fast-ish core libraries and dev dependencies). The `--group local-dev` flag is what pulls in the dev dependencies: a bare `uv sync` would only install the project and its required dependencies. If you also want to test other libraries like Dask and PySpark, add their extras: ```terminal uv sync --group local-dev --extra dask --extra pyspark ``` You don't need to pin a Python version manually: `uv` will pick a compatible interpreter (downloading one if needed) based on `pyproject.toml`. If you want a specific version, pass `--python 3.12` (or similar) to `uv sync`. You then have two options to run commands inside the project environment: - **Recommended:** prefix commands with `uv run` (e.g. `uv run pytest`). `uv` will re-sync the environment if needed. - Activate the venv (`. .venv/bin/activate` on Linux/macOS, `.\.venv\Scripts\activate` on Windows) and run commands directly (e.g. `pytest`). The pre commit tool ([`prek`](https://prek.j178.dev/)) is installed as part of the `local-dev` dependency group. Run `uv run prek install` to install prek as a git hook. This will automatically format and lint your code before each commit, and it will block the commit if any issues are found. Static typing is run separately from `prek` pre commit hooks, as it's quite slow. Assuming you followed all the instructions above, you can run it with `make typing` (which itself invokes `uv run --group typing ...` under the hood). ### b. Codespaces Codespaces is a great way to work on Narwhals without the need of configuring your local development environment. Every GitHub.com user has a monthly quota of free use of GitHub Codespaces, and you can start working in a codespace without providing any payment details. You'll be informed per email if you'll be close to using 100% of included services. To learn more about it visit [GitHub Docs](https://docs.github.com/en/codespaces/overview) #### 1. Make sure you have GitHub account If you're new to GitHub, you'll need to create an account on [GitHub.com](https://github.com/) and verify your email address. #### 2. Fork the repository Go to the [main project page](https://github.com/narwhals-dev/narwhals). Fork the repository by clicking on the fork button. You can find it in the right corner on the top of the page. #### 3. Create codespace Go to the forked repository on your GitHub account - you'll find it on your account in the tab Repositories. Click on the green `Code` button and navigate to the `Codespaces` tab. Click on the green button `Create codespace on main` - it will open a browser version of VSCode, with the complete repository and git installed. If `uv` is not installed already, install it via the [uv installation instructions](https://docs.astral.sh/uv/getting-started/installation/) before continuing. You can now proceed with [5. Setting up your environment](#5-setting-up-your-environment) from the local development steps above. ## 2. Working on your issue Create a new git branch from the `main` branch in your local repository. Note that your work cannot be merged if the tests below fail. If you add code that should be tested, please add tests. ## 3. Running tests - To run tests: `uv run pytest`. To check coverage: `uv run pytest --cov=src` - To run tests on the doctests, use `uv run pytest src --doctest-modules` - To run unit tests and doctests at the same time, run `uv run pytest src tests --cov=src --doctest-modules` - To run tests multiprocessed, you may also want to use [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) (optional) - To choose which backends to run tests with, you can use the `--constructors` flag: - To only run tests for pandas, Polars, and PyArrow, use `uv run pytest --constructors="pandas,pyarrow,polars[eager]"` - To run tests for all CPU constructors, use `uv run --extra modin --extra pyspark pytest --all-cpu-constructors` - By default, tests run for `pandas,pandas[pyarrow],polars[eager],pyarrow,duckdb,sqlframe,ibis`. You can override this with the `NARWHALS_DEFAULT_CONSTRUCTORS` environment variable. - To run tests using `cudf.pandas`, run `NARWHALS_DEFAULT_CONSTRUCTORS=pandas uv run --extra cudf --module cudf.pandas --module pytest` - To run tests using `polars[gpu]`, run `NARWHALS_POLARS_GPU=1 uv run pytest --constructors="polars[lazy]"` - To run all test with coverage, use `make test-full-coverage` Tip: passing extras (`--extra `) or groups (`--group `) to `uv run` will transparently sync the environment to include those dependencies before running the command. See also [Test Failure Patterns](#test-failure-patterns) in Rules and conventions for the conventions we use to mark xfail/skipif/raises tests. ### Static typing We run `mypy`, `pyright`, and `pyrefly` in CI. All of them are included in `--group typing` and are installed as needed when running `make typing`. Run them with: ```console make typing ``` to verify type completeness / correctness. Note that: - In `_pandas_like`, we type all native objects as if they are pandas ones, though in reality this package is shared between pandas, Modin, and cuDF. - In `_spark_like`, we type all native objects as if they are SQLFrame ones, though in reality this package is shared between SQLFrame and PySpark. ## 4. Writing the doc(strings) If you are adding a new feature or changing an existing one, you should also update the documentation and the docstrings to reflect the changes. Writing the docstring in Narwhals is not an exact science, but we have some high level guidelines (if in doubt just ask us in the PR): - The examples should be clear and to the point. - The examples should import _one_ dataframe library, create a dataframe and exemplify the Narwhals functionality. - We strive for balancing the use of different backend across all our docstrings examples. - There are exceptions to the above rules! Here an example of a docstring: ```python >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table({"foo": [1, 2], "bar": [6.0, 7.0]}) >>> df = nw.from_native(df_native) >>> df.estimated_size() 32 ``` Full discussion at [narwhals#1943](https://github.com/narwhals-dev/narwhals/issues/1943). ## 5. Building the docs To serve the docs locally, run: ```terminal make docs-serve ``` and then open the link provided in a browser. The docs should refresh when you make changes. If they don't, press `ctrl+C`, and then run: ```terminal make docs-clean-serve ``` which rebuilds everything from a clean state (via `make docs-build`) before serving. ## 6. Pull requests When you have resolved your issue, [open a pull request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork) in the Narwhals repository. Please adhere to the following guidelines: 1. Start your pull request title with a [conventional commit](https://www.conventionalcommits.org/) tag. This helps us add your contribution to the right section of the changelog. We use "Type" from the [Angular convention](https://github.com/angular/angular/blob/22b96b9/CONTRIBUTING.md#type). **TLDR**: The PR title should start with any of these abbreviations: `build`, `chore`, `ci`, `depr`, `docs`, `feat`, `fix`, `perf`, `refactor`, `release`, `test`. Add a `!`at the end, if it is a breaking change. For example `refactor!`. 2. This text will end up in the [changelog](https://github.com/narwhals-dev/narwhals/releases). 3. Please follow the instructions in the pull request form and submit. ## How it works If Narwhals looks like underwater unicorn magic to you, then please read [how it works](https://narwhals-dev.github.io/narwhals/how_it_works/). ## Rules and conventions These are non-negotiable and the most common source of review comments. - **Zero dependencies.** Narwhals must never add a runtime dependency. It only uses what the user passes in. - **Never import anything for `isinstance` checks.** Use the functions in [src/narwhals/dependencies.py](src/narwhals/dependencies.py) (e.g. `is_pandas_dataframe`). See the [Import section](#imports) below. - **Never iterate over rows.** Assume infinite rows. Column iteration is acceptable. See the [Dataframe considerations](#general-dataframe-considerations) below. - **Never modify user input data.** Especially with pandas: no inplace operations on user-provided objects. - **100% branch coverage** is enforced by the full-coverage CI job. When a branch is genuinely unreachable (e.g. gated on an unsupported backend version), mark it `# pragma: no cover` with a one-line reason. - **Breaking changes never land in `narwhals.stable.v1` or `narwhals.stable.v2`.** New public APIs land in the main `narwhals` namespace and graduate into the next stable version. See [docs/backcompat.md](docs/backcompat.md), and add an entry to its `main` vs `stable.*` diff when the namespaces diverge. ### General dataframe considerations In general we assume that dataframes are used to store and process columnar data. Therefore: - Iterating over rows in Python is never allowed. Assume that there's an infinite number of rows. - Iterating over columns is acceptable (though native APIs that do the iteration in a low-level language are preferred if possible!). ### Backend-specific considerations - pandas: - Don't use `apply` or `map`. The only place we currently use `apply` is in `group_by` for operations which the pandas API just doesn't support, and even then, it's accompanied by a big warning. - Don't use inplace methods, unless you're creating a new object and are sure it's safe to modify it. In particular, you should never ever modify the user's input data. - Please remember that `assign`, `drop`, `reset_index`, and `rename`, though seemingly harmless, make full copies of the input data. - Instead of `assign`, prefer using `with_columns` at the compliant level. - For `drop` and `reset_index`, use `inplace=True`, so long as you're only modifying a new object which you created. Again, you should never modify user input. This may need updating if/when it's deprecated/removed, but please keep it for older pandas versions https://github.com/pandas-dev/pandas/pull/51466/files. - Instead of `rename`, prefer `alias` at the compliant level. - pandas supports any hashable object as a column name, whereas other libraries tend to only support strings. We tend to just type `: str` in places which accept column names, with the understanding that for pandas, other data types will silently work. - Polars: - Never use `map_elements`. - DuckDB / PySpark / anything lazy-only: - Never assume that your data is ordered in any pre-defined way. - Never materialise your data (only exception: `collect`). - Avoid calling the schema / column names unnecessarily. - DuckDB: In addition to the above: - Use the Python API as much as possible, only falling back to SQL as the last resort for operations not yet supported in their Python API (e.g. `over`). - Use standard SQL constructs where possible instead of non-standard ones such as `GROUP BY ALL` or `EXCLUDE`. ### Test Failure Patterns We aim to use three standard patterns for handling test failures: Note: While we're not currently totally consistent with these patterns, any efforts towards our aim are appreciated and welcome. 1. `requests.applymarker(pytest.mark.xfail)`: Used for features that are planned but not yet supported. ```python def test_future_feature(request): request.applymarker(pytest.mark.xfail) # Test implementation for planned feature ``` 2. `pytest.mark.skipif`: Used when there's a condition under which the test cannot run (e.g., unsupported pandas versions). ```python @pytest.mark.skipif(PANDAS_VERSION < (2, 0), reason="requires pandas 2.0+") def test_version_dependent(): # Test implementation ``` 3. `pytest.raises`: Used for testing that code raises expected exceptions. ```python def test_invalid_input(): with pytest.raises(ValueError, match="expected error message"): # Code that should raise the error ``` Document clear reasons in test comments for any skip/xfail patterns to help maintain test clarity. #### Hypothesis tests We use Hypothesis to generate some random tests, to check for robustness. To keep local test suite times down, not all of these run by default - you can run them by passing the `--runslow` flag to PyTest (e.g. `uv run pytest --runslow`). #### Testing Dask and Modin To keep local development test times down, Dask and Modin are excluded from dev dependencies, and their tests only run in CI. If you re-sync with their extras: ```terminal uv sync --group local-dev --extra dask --extra modin ``` then their tests will run too. #### Testing cuDF We can't currently test in CI against cuDF, but you can test it manually in Kaggle using GPUs. Please follow this [Kaggle notebook](https://www.kaggle.com/code/marcogorelli/testing-cudf-in-narwhals) to run the tests. ### Imports In Narwhals, we are very particular about imports. When it comes to importing heavy third-party libraries (pandas, NumPy, Polars, etc...) please follow these rules: - Never import anything to do `isinstance` checks. Instead, just use the functions in `narwhals.dependencies` (such as `is_pandas_dataframe`); - If you need to import anything, do it in a place where you know that the import is definitely available. For example, NumPy is a required dependency of PyArrow, so it's OK to import NumPy to implement a PyArrow function - however, NumPy should never be imported to implement a Polars function. The only exception is for when there's simply no way around it by definition - for example, `Series.to_numpy` always requires NumPy to be installed. - Don't place a third-party import at the top of a file. Instead, place it in the function where it's used, so that we minimise the chances of it being imported unnecessarily. We're trying to be really lightweight and minimal-overhead, and unnecessary imports can slow things down. ## AI-assisted contributions We do not categorically reject AI-assisted contributions, but we ask for transparency and accountability. The aim is to keep maintainer time focused on review quality, not on triaging machine-generated noise. 1. **Disclose AI usage in the PR description**: when opening a pull request, the template has a dedicated field that must be flagged to disclose whether or not the changes are AI assisted and which tools and models were used to achieve that. Disclosure is for transparency; it does not affect whether your PR is accepted. 2. **You are responsible for every line you submit, regardless of how it was produced**: the disclosure does not transfer responsibility to the tool. Before opening a PR, read through your diff and make sure you can explain and defend each change in review. 3. **Engage with review feedback**: we reserve the right to close, without extended discussion, pull requests where the author does not engage with reviewer comments. **This applies whether or not AI was used.** 4. **First-time contributors** are asked to: - Post a screenshot of the test suite passing locally (not on CI) in the PR description. This is a quick check that your dev environment works and that you have run the code you are proposing. - Keep at most one open pull request at a time, so reviewers can give it proper attention before you open the next one. 5. When interacting in issues, pull requests, discussion, discord, etc., **do not use LLMs to speak for you**, except for translation or grammar edits. Human-to-human communication is foundational to open source communities. ## Claiming issues If you start working on an issue, it's usually a good idea to let others know about this in order to avoid duplicating work. Do: - When you're about to start working on an issue, and have understood the requirements and have some idea of what a solution would involve, comment "I've started working on this". - Push partial work (even if unfinished) to a branch, which you can open in "draft" state. - If someone else has commented that they're working on an issue but hadn't made any public work for 1-2 weeks, it's usually OK to assume that they're no longer working on it. Don't: - Don't claim issues that you intend to work on at a later date. For example, if it's Monday and you see an issue that interests you and you would like to work on it on Sunday, then the correct time to write "I'm working on this" is on Sunday when you start working on it. - Don't ask for permission to work on issues, or to be assigned them. You have permission, we accept (and welcome!) contributions from everyone! ## Behaviour towards other contributors or maintainers Above all else, please assume good intentions and go the extra mile to be super-extra-nice. Some general guidelines: - If in doubt, err on the side of being warm rather than being cold. - If in doubt, put one extra positive emoji than one fewer one. - Never delete or dismiss other maintainers' comments. - Non-maintainers' comments should only be deleted if they are unambiguously spam (e.g. crypto adverts). In cases of rude or abusive behaviour, please contact the project author (`@MarcoGorelli`). - Avoid escalating conflicts. People type harder than they speak, and online discourse is especially difficult. Again, please assume good intentions. ## Happy contributing! Please remember to abide by the code of conduct, else you'll be conducted away from this project. ## Community Calendar We have a community call every 2 weeks, all welcome to attend. [Subscribe to our calendar](https://calendar.google.com/calendar/embed?src=27ff6dc5f598c1d94c1f6e627a1aaae680e2fac88f848bda1f2c7946ae74d5ab%40group.calendar.google.com). narwhals-2.26.0/LICENSE.md000066400000000000000000000020721525000603700150230ustar00rootroot00000000000000MIT License Copyright (c) 2024 to present, Marco Gorelli Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. narwhals-2.26.0/Makefile000066400000000000000000000045651525000603700150700ustar00rootroot00000000000000# Mostly based on polars Makefile # https://github.com/pola-rs/polars/blob/main/py-polars/Makefile .DEFAULT_GOAL := help .PHONY: help help: ## Display this help screen @echo -e "\033[1mAvailable commands:\033[0m" @grep -E '^[a-z.A-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf " \033[36m%-22s\033[0m %s\n", $$1, $$2}' | sort .PHONY: lint lint: ## Run code formatting and linting via ruff prek run ruff-format --all-files prek run ruff-check --all-files .PHONY: typing typing: ## Run type checkers uv run --group typing pyright uv run --group typing mypy uv run --group typing pyrefly check .PHONY: typing-coverage typing-coverage: ## Run type checkers uv run --group typing pyrefly coverage check src/narwhals --public-only .PHONY: docs-dynamic-content docs-dynamic-content: ## Regenerate the dynamic docs pages (API completeness tables, docs/this.md, ...) uv run --group docs --extra dask --extra ibis utils/generate_backend_completeness.py uv run --group docs utils/generate_zen_content.py .PHONY: docs-build docs-build: ## Build the docs from a clean state, failing on warnings $(MAKE) docs-dynamic-content uv run --group docs zensical build --clean --strict .PHONY: docs-serve docs-serve: ## Serve the docs locally $(MAKE) docs-dynamic-content uv run --group docs zensical serve .PHONY: docs-clean-serve docs-clean-serve: ## Rebuild docs from a clean state and serve them locally $(MAKE) docs-build uv run --group docs zensical serve .PHONY: run-ci run-ci: ## Print resolved deps, then run a command via uv. Usage: make run-ci DEPS="" CMD="" [RUN_ONLY=""] uv export --no-annotate --no-hashes $(DEPS) uv run $(DEPS) $(RUN_ONLY) $(CMD) .PHONY: doctest doctest: ## Run doctests make run-ci \ DEPS="--extra pandas --extra dask --group core-tests --group sklearn" \ CMD="pytest src --doctest-modules" .PHONY: test-full-coverage test-full-coverage: ## Run the full test suite with 100% coverage across all constructors as in CI PYTEST_ADDOPTS="--numprocesses=logical" make run-ci \ DEPS="--extra pandas --extra dask --group core-tests --group sklearn --group plugins" \ CMD="pytest tests --cov=src --cov=tests --cov-fail-under=100 --runslow --durations=30 --constructors=pandas,pandas[nullable],pandas[pyarrow],pyarrow,polars[eager],polars[lazy],dask,duckdb,sqlframe" narwhals-2.26.0/README.md000066400000000000000000000313261525000603700147020ustar00rootroot00000000000000# Narwhals

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[![PyPI version](https://badge.fury.io/py/narwhals.svg)](https://badge.fury.io/py/narwhals) [![Downloads](https://static.pepy.tech/badge/narwhals/month)](https://pepy.tech/project/narwhals) [![Trusted publishing](https://img.shields.io/badge/Trusted_publishing-Provides_attestations-bright_green)](https://peps.python.org/pep-0740/) [![PYPI - Types](https://img.shields.io/pypi/types/narwhals)](https://pypi.org/project/narwhals) [![LFX Health Score](https://insights.linuxfoundation.org/api/badge/health-score?project=narwhals-dev-narwhals)](https://insights.linuxfoundation.org/project/narwhals-dev-narwhals) [![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/narwhals-dev/narwhals/badge)](https://securityscorecards.dev/viewer/?uri=github.com/narwhals-dev/narwhals) Extremely lightweight and extensible compatibility layer between dataframe libraries! - **Full API support**: cuDF, Modin, pandas, Polars, PyArrow. - **Lazy-only support**: Dask, DuckDB, Ibis, PySpark, SQLFrame, and Daft via the [narwhals-daft](https://github.com/narwhals-dev/narwhals-daft) plugin. Seamlessly support all, without depending on any! - โœ… **Just use** [a subset of **the Polars API**](https://narwhals-dev.github.io/narwhals/api-reference/), no need to learn anything new - โœ… **Zero dependencies**, Narwhals only uses what the user passes in so your library can stay lightweight - โœ… Separate **lazy** and eager APIs, use **expressions** - โœ… Support pandas' complicated type system and index, without either getting in the way - โœ… **100% branch coverage**, tested against pandas and Polars nightly builds - โœ… **Negligible overhead**, see [overhead](https://narwhals-dev.github.io/narwhals/overhead/) - โœ… Let your IDE help you thanks to **full static typing**, see [typing](https://narwhals-dev.github.io/narwhals/api-reference/typing/) - โœ… **Perfect backwards compatibility policy**, see [stable api](https://narwhals-dev.github.io/narwhals/backcompat/) for how to opt-in Get started! - [Read the documentation](https://narwhals-dev.github.io/narwhals/) - [Chat with us on Discord!](https://discord.gg/V3PqtB4VA4) - [Join our community call](https://calendar.google.com/calendar/embed?src=27ff6dc5f598c1d94c1f6e627a1aaae680e2fac88f848bda1f2c7946ae74d5ab%40group.calendar.google.com) - [Read the contributing guide](https://github.com/narwhals-dev/narwhals/blob/main/CONTRIBUTING.md)
Table of contents - [Narwhals](#narwhals) - [Installation](#installation) - [Usage](#usage) - [Example](#example) - [Scope](#scope) - [Roadmap](#roadmap) - [Used by](#used-by) - [Sponsors and institutional partners](#sponsors-and-institutional-partners) - [Support](#support) - [Appears on](#appears-on) - [Why "Narwhals"?](#why-narwhals)
## Installation - pip (recommended, as it's the most up-to-date) ``` pip install narwhals ``` - conda-forge (also fine, but the latest version may take longer to appear) ``` conda install -c conda-forge narwhals ``` ## Usage There are three steps to writing dataframe-agnostic code using Narwhals: 1. use `narwhals.from_native` to wrap a pandas/Polars/Modin/cuDF/PyArrow DataFrame/LazyFrame in a Narwhals class 2. use the [subset of the Polars API supported by Narwhals](https://narwhals-dev.github.io/narwhals/api-reference/) 3. use `narwhals.to_native` to return an object to the user in its original dataframe flavour. For example: - if you started with pandas, you'll get pandas back - if you started with Polars, you'll get Polars back - if you started with Modin, you'll get Modin back (and compute will be distributed) - if you started with cuDF, you'll get cuDF back (and compute will happen on GPU) - if you started with PyArrow, you'll get PyArrow back

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## Example Narwhals allows you to define dataframe-agnostic functions. For example: ```python import narwhals as nw from narwhals.typing import IntoFrameT def agnostic_function(df_native: IntoFrameT) -> IntoFrameT: return ( nw.from_native(df_native) .with_columns( category=nw.when(nw.col("animal").str.contains("whale")) .then(nw.lit("whale")) .otherwise(nw.lit("other")) ) .to_native() ) ``` You can then pass `pandas.DataFrame`, `polars.DataFrame`, `polars.LazyFrame`, `duckdb.DuckDBPyRelation`, `pyspark.sql.DataFrame`, `pyarrow.Table`, and more, to `agnostic_function`. In each case, no additional dependencies will be required, and computation will stay native to the input library: ```python import duckdb import polars as pl import pandas as pd data = { "animal": ["blue whale", "orca", "dolphin", "humpback whale", "seal"], "length_m": [30.0, 8, 2.5, 17, 2.2], "weight_kg": [150000, 4000, 200, 30000, 85], } print("Polars result") df_pl = pl.DataFrame(data) print(agnostic_function(df_pl)) print("DuckDB result") print(agnostic_function(duckdb.sql("select * from df_pl"))) print("pandas result") df_pd = pd.DataFrame(data) print(agnostic_function(df_pd)) ``` ```terminal Polars result shape: (5, 4) โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ animal โ”† length_m โ”† weight_kg โ”† category โ”‚ โ”‚ --- โ”† --- โ”† --- โ”† --- โ”‚ โ”‚ str โ”† f64 โ”† i64 โ”† str โ”‚ โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก โ”‚ blue whale โ”† 30.0 โ”† 150000 โ”† whale โ”‚ โ”‚ orca โ”† 8.0 โ”† 4000 โ”† other โ”‚ โ”‚ dolphin โ”† 2.5 โ”† 200 โ”† other โ”‚ โ”‚ humpback whale โ”† 17.0 โ”† 30000 โ”† whale โ”‚ โ”‚ seal โ”† 2.2 โ”† 85 โ”† other โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ DuckDB result โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ animal โ”‚ length_m โ”‚ weight_kg โ”‚ category โ”‚ โ”‚ varchar โ”‚ double โ”‚ int64 โ”‚ varchar โ”‚ โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค โ”‚ blue whale โ”‚ 30.0 โ”‚ 150000 โ”‚ whale โ”‚ โ”‚ orca โ”‚ 8.0 โ”‚ 4000 โ”‚ other โ”‚ โ”‚ dolphin โ”‚ 2.5 โ”‚ 200 โ”‚ other โ”‚ โ”‚ humpback whale โ”‚ 17.0 โ”‚ 30000 โ”‚ whale โ”‚ โ”‚ seal โ”‚ 2.2 โ”‚ 85 โ”‚ other โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ pandas result animal length_m weight_kg category 0 blue whale 30.0 150000 whale 1 orca 8.0 4000 other 2 dolphin 2.5 200 other 3 humpback whale 17.0 30000 whale 4 seal 2.2 85 other ``` See the [tutorial](https://narwhals-dev.github.io/narwhals/basics/dataframe/) for several examples! ## Scope - Do you maintain a dataframe-consuming library? - Do you have a specific Polars function in mind that you would like Narwhals to have in order to make your work easier? If you said yes to both, we'd love to hear from you! ## Roadmap See [roadmap discussion on GitHub](https://github.com/narwhals-dev/narwhals/discussions/1370) for an up-to-date plan of future work. ## Used by Join the party! - [altair](https://github.com/vega/altair/) - [bokeh](https://github.com/bokeh/bokeh) - [darts](https://github.com/unit8co/darts) - [fairlearn](https://github.com/fairlearn/fairlearn) - [formulaic](https://github.com/matthewwardrop/formulaic) - [gt-extras](https://github.com/posit-dev/gt-extras) - [hierarchicalforecast](https://github.com/Nixtla/hierarchicalforecast) - [lightgbm](https://github.com/lightgbm-org/LightGBM) - [marimo](https://github.com/marimo-team/marimo) - [metalearners](https://github.com/Quantco/metalearners) - [mosaic](https://github.com/uwdata/mosaic) - [pandera](https://github.com/pandera-dev/pandera) - [panel-graphic-walker](https://github.com/panel-extensions/panel-graphic-walker) - [plotly](https://plotly.com) - [pointblank](https://github.com/posit-dev/pointblank) - [pymarginaleffects](https://github.com/vincentarelbundock/pymarginaleffects) - [pyreadstat](https://github.com/Roche/pyreadstat) - [py-shiny](https://github.com/posit-dev/py-shiny) - [pysummaries](https://github.com/Genentech/pysummaries) - [rio](https://github.com/rio-labs/rio) - [scikit-learn](https://github.com/scikit-learn/scikit-learn) - [scikit-lego](https://github.com/koaning/scikit-lego) - [scikit-playtime](https://github.com/koaning/scikit-playtime) - [tabmat](https://github.com/Quantco/tabmat) - [tea-tasting](https://github.com/e10v/tea-tasting) - [timebasedcv](https://github.com/FBruzzesi/timebasedcv) - [tubular](https://github.com/lvgig/tubular) - [Validoopsie](https://github.com/akmalsoliev/Validoopsie) - [vegafusion](https://github.com/vega/vegafusion) - [wimsey](https://github.com/benrutter/wimsey) Feel free to add your project to the list if it's missing, and/or [chat with us on Discord](https://discord.gg/V3PqtB4VA4) if you'd like any support. ## Sponsors and institutional partners Narwhals is 100% independent, community-driven, and community-owned. We are extremely grateful to the following organisations for having provided some funding / development time: - [Quansight Labs](https://labs.quansight.org) - [Quansight Futures](https://www.qi.ventures) - [OpenTeams](https://www.openteams.com) - [POSSEE initiative](https://possee.org) - [BYU-Idaho](https://www.byui.edu) - [Intella](https://www.intella.tech/) If you contribute to Narwhals on your organization's time, please let us know. We'd be happy to add your employer to this list! ## Support If you'd like to say "thank you", please give us a โญ star โญ. Please contact [hello_narwhals@proton.me](mailto:hello_narwhals@proton.me) if you would like to: - Receive professional support (e.g., if you're using or would like to use Narwhals at your company). - Have any Narwhals fixes / features prioritised. - Commission any Narwhals plugins for new backends. ## Appears on Narwhals has been featured in several talks, podcasts, and blog posts: - [Inspiring Computing Podcast](https://www.inspiringcomputing.com/2107763/episodes/16702460-the-rise-of-narwhals-in-open-source) The Rise of Narwhals in Open-Source - [PyCon DE & PyData 2025](https://youtu.be/DJk782DWcss) How Narwhals is silently bringing pandas, Polars, DuckDB, PyArrow, and more together - [The Python Exchange March 2025](https://youtu.be/TvFWFlK-2po) What Can Narwhals Do for You? - [PyData London 2025](https://youtu.be/r2PxJlO7_QA) How Narwhals brings Polars, DuckDB, PyArrow, & pandas together - [Talk Python to me Podcast](https://youtu.be/FSH7BZ0tuE0) Ahoy, Narwhals are bridging the data science APIs - [Python Bytes Podcast](https://www.youtube.com/live/N7w_ESVW40I?si=y-wN1uCsAuJOKlOT&t=382) Episode 402, topic #2 - [Super Data Science: ML & AI Podcast](https://www.youtube.com/watch?v=TeG4U8R0U8U) Narwhals: For Pandas-to-Polars DataFrame Compatibility - [Sample Space Podcast | probabl](https://youtu.be/8hYdq4sWbbQ?si=WG0QP1CZ6gkFf18b) How Narwhals has many end users ... that never use it directly. - Marco Gorelli - [The Real Python Podcast](https://www.youtube.com/watch?v=w5DFZbFYzCM) Narwhals: Expanding DataFrame Compatibility Between Libraries - [Pycon Lithuania 2024](https://www.youtube.com/watch?v=-mdx7Cn6_6E) Marco Gorelli - DataFrame interoperatiblity - what's been achieved, and what comes next? - [Pycon Italy 2024](https://www.youtube.com/watch?v=3IqUli9XsmQ) How you can write a dataframe-agnostic library - Marco Gorelli - [Polars Blog Post](https://pola.rs/posts/lightweight_plotting/) Polars has a new lightweight plotting backend - [Quansight Labs blog post (w/ Scikit-Lego)](https://labs.quansight.org/blog/scikit-lego-narwhals) How Narwhals and scikit-lego came together to achieve dataframe-agnosticism ## Why "Narwhals"? [Coz they are so awesome](https://youtu.be/ykwqXuMPsoc?si=A-i8LdR38teYsos4). Thanks to [Olha Urdeichuk](https://www.fiverr.com/olhaurdeichuk) for the illustration! narwhals-2.26.0/_typos.toml000066400000000000000000000005411525000603700156300ustar00rootroot00000000000000[default.extend-words] ser = "ser" # Used in Series classes ND = "ND" # Used in NativeNDFrameT ba = "ba" # Used as column name in docstring examples (way too much?) iy = "iy" # Used as column name (once in a test) pn = "pn" # Used in docs: pn = PandasLikeNamespace(...) TYP = "TYP" # Used in flake8 rule [files] extend-exclude = ["tests/data/*"] narwhals-2.26.0/docs/000077500000000000000000000000001525000603700143465ustar00rootroot00000000000000narwhals-2.26.0/docs/api-completeness/000077500000000000000000000000001525000603700176165ustar00rootroot00000000000000narwhals-2.26.0/docs/api-completeness/index.md000066400000000000000000000007271525000603700212550ustar00rootroot00000000000000--- search: exclude: true --- # API Completeness In the following section it is possible to check which method is implemented for which class and backend. !!! info - By design, Polars supports all the methods of the Narwhals API. - "pandas-like" means pandas, cuDF and Modin. - "spark-like" means PySpark (including Spark Connect) and SQLFrame. - Backends provided by [plugins](../extending.md) (such as Daft) are not covered by these tables. narwhals-2.26.0/docs/api-reference/000077500000000000000000000000001525000603700170535ustar00rootroot00000000000000narwhals-2.26.0/docs/api-reference/dataframe.md000066400000000000000000000022611525000603700213220ustar00rootroot00000000000000# `narwhals.DataFrame` ::: narwhals.dataframe.DataFrame handler: python options: members: - __arrow_c_stream__ - __getitem__ - clone - collect_schema - columns - drop - drop_nulls - estimated_size - explode - filter - from_arrow - from_dict - from_dicts - from_numpy - gather_every - get_column - group_by - head - implementation - is_duplicated - is_empty - is_unique - item - iter_columns - iter_rows - join - join_asof - lazy - null_count - pipe - pivot - rename - row - rows - sample - schema - select - shape - sort - tail - to_arrow - to_dict - to_native - to_numpy - to_pandas - to_polars - top_k - unique - unpivot - with_columns - with_row_index - write_csv - write_parquet show_source: false show_bases: false inherited_members: true narwhals-2.26.0/docs/api-reference/dependencies.md000066400000000000000000000015711525000603700220270ustar00rootroot00000000000000# `narwhals.dependencies` ::: narwhals.dependencies handler: python options: members: - get_cudf - get_ibis - get_modin - get_pandas - get_polars - get_pyarrow - is_cudf_dataframe - is_cudf_index - is_cudf_series - is_dask_dataframe - is_ibis_table - is_into_dataframe - is_into_series - is_modin_dataframe - is_modin_index - is_modin_series - is_numpy_array - is_pandas_dataframe - is_pandas_index - is_pandas_like_dataframe - is_pandas_like_index - is_pandas_like_series - is_pandas_series - is_polars_dataframe - is_polars_lazyframe - is_polars_series - is_pyarrow_chunked_array - is_pyarrow_table show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/dtypes.md000066400000000000000000000016361525000603700207130ustar00rootroot00000000000000# `narwhals.dtypes` ::: narwhals.dtypes handler: python options: members: - DType - NumericType - Decimal - FloatType - Float16 - Float32 - Float64 - IntegerType - SignedIntegerType - Int8 - Int16 - Int32 - Int64 - Int128 - UnsignedIntegerType - UInt8 - UInt16 - UInt32 - UInt64 - UInt128 - TemporalType - Date - Datetime - Duration - Time - NestedType - Array - List - Field - Struct - String - Categorical - Enum - Binary - Boolean - Object - Unknown show_root_heading: false show_source: false show_bases: true filters: - "!^_" - "^__eq__$" inherited_members: false narwhals-2.26.0/docs/api-reference/exceptions.md000066400000000000000000000006331525000603700215600ustar00rootroot00000000000000# `narwhals.exceptions` ::: narwhals.exceptions handler: python options: members: - ColumnNotFoundError - ComputeError - DuplicateError - InvalidIntoExprError - InvalidOperationError - MultiOutputExpressionError - NarwhalsUnstableWarning - ShapeError - UnsupportedDTypeError show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/expr.md000066400000000000000000000023531525000603700203560ustar00rootroot00000000000000# `narwhals.Expr` ::: narwhals.Expr handler: python options: members: - abs - alias - all - any - any_value - cast - ceil - clip - cos - count - cum_count - cum_max - cum_min - cum_prod - cum_sum - diff - drop_nulls - ewm_mean - exp - fill_nan - fill_null - filter - first - floor - is_between - is_close - is_duplicated - is_finite - is_first_distinct - is_in - is_last_distinct - is_nan - is_null - is_unique - kurtosis - last - len - log - map_batches - max - mean - median - min - mode - n_unique - null_count - over - pipe - quantile - rank - replace_strict - rolling_mean - rolling_std - rolling_sum - rolling_var - round - shift - sin - skew - sqrt - std - sum - unique - var show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/expr_cat.md000066400000000000000000000002641525000603700212040ustar00rootroot00000000000000# `narwhals.Expr.cat` ::: narwhals.expr.ExprCatNamespace handler: python options: members: - get_categories show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/expr_dt.md000066400000000000000000000012021525000603700210350ustar00rootroot00000000000000# `narwhals.Expr.dt` ::: narwhals.expr.ExprDateTimeNamespace handler: python options: members: - convert_time_zone - date - day - hour - microsecond - millisecond - minute - month - nanosecond - offset_by - ordinal_day - replace_time_zone - second - timestamp - to_string - total_microseconds - total_milliseconds - total_minutes - total_nanoseconds - total_seconds - truncate - weekday - year show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/expr_list.md000066400000000000000000000004661525000603700214140ustar00rootroot00000000000000# `narwhals.Expr.list` ::: narwhals.expr.ExprListNamespace handler: python options: members: - contains - get - len - max - mean - median - min - sort - sum - unique show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/expr_name.md000066400000000000000000000004121525000603700213500ustar00rootroot00000000000000# `narwhals.Expr.name` ::: narwhals.expr.ExprNameNamespace handler: python options: members: - keep - map - prefix - suffix - to_lowercase - to_uppercase show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/expr_str.md000066400000000000000000000011261525000603700212430ustar00rootroot00000000000000# `narwhals.Expr.str` ::: narwhals.expr.ExprStringNamespace handler: python options: members: - contains - ends_with - head - len_chars - pad_end - pad_start - replace - replace_all - slice - split - starts_with - strip_chars - strip_chars_end - strip_chars_start - tail - to_date - to_datetime - to_lowercase - to_time - to_titlecase - to_uppercase - zfill show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/expr_struct.md000066400000000000000000000002611525000603700217560ustar00rootroot00000000000000# `narwhals.Expr.struct` ::: narwhals.expr.ExprStructNamespace handler: python options: members: - field show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/group_by.md000066400000000000000000000002431525000603700212220ustar00rootroot00000000000000# `narwhals.GroupBy` ::: narwhals.group_by.GroupBy handler: python options: members: - agg show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/implementation.md000066400000000000000000000001631525000603700224220ustar00rootroot00000000000000# `narwhals.Implementation` ::: narwhals.Implementation handler: python options: show_source: false narwhals-2.26.0/docs/api-reference/index.md000066400000000000000000000020041525000603700205000ustar00rootroot00000000000000# API Reference - [Top-level functions](narwhals.md) - [narwhals.DataFrame](dataframe.md) - [narwhals.Expr](expr.md) - [narwhals.Expr.cat](expr_cat.md) - [narwhals.Expr.dt](expr_dt.md) - [narwhals.Expr.list](expr_list.md) - [narwhals.Expr.name](expr_name.md) - [narwhals.Expr.str](expr_str.md) - [narwhals.Expr.struct](expr_struct.md) - [narwhals.GroupBy](group_by.md) - [narwhals.LazyGroupBy](lazy_group_by.md) - [narwhals.LazyFrame](lazyframe.md) - [narwhals.Schema](schema.md) - [narwhals.Series](series.md) - [narwhals.Series.cat](series_cat.md) - [narwhals.Series.dt](series_dt.md) - [narwhals.Series.list](series_list.md) - [narwhals.Series.str](series_str.md) - [narwhals.Series.struct](series_struct.md) - [narwhals.dependencies](dependencies.md) - [narwhals.Implementation](implementation.md) - [narwhals.dtypes](dtypes.md) - [narwhals.exceptions](exceptions.md) - [narwhals.selectors](selectors.md) - [narwhals.sql](sql.md) - [narwhals.testing](testing.md) - [narwhals.typing](typing.md) - [narwhals.utils](utils.md) narwhals-2.26.0/docs/api-reference/lazy_group_by.md000066400000000000000000000002531525000603700222620ustar00rootroot00000000000000# `narwhals.LazyGroupBy` ::: narwhals.group_by.LazyGroupBy handler: python options: members: - agg show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/lazyframe.md000066400000000000000000000013151525000603700213670ustar00rootroot00000000000000# `narwhals.LazyFrame` ::: narwhals.dataframe.LazyFrame handler: python options: members: - collect - collect_schema - columns - drop - drop_nulls - explode - filter - gather_every - group_by - head - implementation - join - join_asof - lazy - pipe - rename - schema - select - sink_parquet - sort - tail - to_native - top_k - unique - unpivot - with_columns - with_row_index show_root_heading: false show_source: false show_bases: false inherited_members: true narwhals-2.26.0/docs/api-reference/narwhals.md000066400000000000000000000022231525000603700212130ustar00rootroot00000000000000# Top-level functions Here are the top-level functions available in Narwhals. ::: narwhals handler: python options: members: - all - all_horizontal - any_horizontal - coalesce - col - concat - concat_str - corr - cov - exclude - format - from_arrow - from_dict - from_dicts - from_native - from_numpy - generate_temporary_column_name - get_native_namespace - is_ordered_categorical - len - list - lit - max - max_horizontal - maybe_align_index - maybe_convert_dtypes - maybe_get_index - maybe_reset_index - maybe_set_index - mean - mean_horizontal - median - min - min_horizontal - narwhalify - new_series - nth - read_csv - read_parquet - scan_csv - scan_parquet - show_versions - struct - sum - sum_horizontal - to_native - to_py_scalar - when show_source: false narwhals-2.26.0/docs/api-reference/schema.md000066400000000000000000000005661525000603700206440ustar00rootroot00000000000000# `narwhals.Schema` ::: narwhals.schema.Schema handler: python options: members: - dtypes - from_arrow - from_native - from_pandas_like - from_polars - len - names - to_arrow - to_pandas - to_polars show_root_heading: false show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/selectors.md000066400000000000000000000010151525000603700213750ustar00rootroot00000000000000# `narwhals.selectors` The following selectors are all supported. In addition, just like in Polars, the following set operations are supported: - set intersection: `&` - set union: `|` - set difference: `-` - complement: `~` ::: narwhals.selectors handler: python options: members: - boolean - by_dtype - categorical - datetime - enum - matches - numeric - string show_root_heading: false show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/series.md000066400000000000000000000035351525000603700206750ustar00rootroot00000000000000# `narwhals.Series` ::: narwhals.series.Series handler: python options: members: - __arrow_c_stream__ - __contains__ - __getitem__ - __iter__ - abs - alias - all - any - any_value - arg_max - arg_min - arg_true - cast - ceil - clip - cos - count - cum_count - cum_max - cum_min - cum_prod - cum_sum - diff - drop_nulls - dtype - ewm_mean - exp - fill_nan - fill_null - filter - first - floor - from_iterable - from_numpy - gather_every - head - hist - implementation - is_between - is_close - is_duplicated - is_empty - is_finite - is_first_distinct - is_in - is_last_distinct - is_nan - is_null - is_sorted - is_unique - item - kurtosis - last - len - log - max - mean - median - min - mode - n_unique - name - null_count - pipe - quantile - rank - rename - replace_strict - rolling_mean - rolling_std - rolling_sum - rolling_var - round - sample - scatter - shape - shift - sin - skew - sort - sqrt - std - sum - tail - to_arrow - to_dummies - to_frame - to_list - to_native - to_numpy - to_pandas - to_polars - unique - value_counts - var - zip_with show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/series_cat.md000066400000000000000000000002721525000603700215170ustar00rootroot00000000000000# `narwhals.Series.cat` ::: narwhals.series.SeriesCatNamespace handler: python options: members: - get_categories show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/series_dt.md000066400000000000000000000012101525000603700213500ustar00rootroot00000000000000# `narwhals.Series.dt` ::: narwhals.series.SeriesDateTimeNamespace handler: python options: members: - convert_time_zone - date - day - hour - microsecond - millisecond - minute - month - nanosecond - offset_by - ordinal_day - replace_time_zone - second - timestamp - to_string - total_microseconds - total_milliseconds - total_minutes - total_nanoseconds - total_seconds - truncate - weekday - year show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/series_list.md000066400000000000000000000004741525000603700217270ustar00rootroot00000000000000# `narwhals.Series.list` ::: narwhals.series.SeriesListNamespace handler: python options: members: - contains - get - len - max - mean - median - min - sort - sum - unique show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/series_str.md000066400000000000000000000011341525000603700215560ustar00rootroot00000000000000# `narwhals.Series.str` ::: narwhals.series.SeriesStringNamespace handler: python options: members: - contains - ends_with - head - len_chars - pad_end - pad_start - replace - replace_all - slice - split - starts_with - strip_chars - strip_chars_end - strip_chars_start - tail - to_date - to_datetime - to_lowercase - to_time - to_titlecase - to_uppercase - zfill show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/series_struct.md000066400000000000000000000002671525000603700223000ustar00rootroot00000000000000# `narwhals.Series.struct` ::: narwhals.series.SeriesStructNamespace handler: python options: members: - field show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/sql.md000066400000000000000000000003601525000603700201730ustar00rootroot00000000000000# `narwhals.sql` ::: narwhals.sql handler: python options: members: - table ::: narwhals.sql.SQLTable handler: python options: members: - to_sql show_source: false show_bases: false narwhals-2.26.0/docs/api-reference/testing.md000066400000000000000000000002261525000603700210520ustar00rootroot00000000000000# `narwhals.testing` ::: narwhals.testing handler: python options: members: - assert_frame_equal - assert_series_equal narwhals-2.26.0/docs/api-reference/typing.md000066400000000000000000000040231525000603700207060ustar00rootroot00000000000000# `narwhals.typing` Narwhals comes fully statically typed. In addition to `nw.DataFrame`, `nw.Expr`, `nw.Series`, `nw.LazyFrame`, we also provide the following type hints: ::: narwhals.typing handler: python options: members: - DataFrameT - Frame - FrameT - IntoDataFrame - IntoDataFrameT - IntoExpr - IntoFrame - IntoFrameT - IntoSeries - IntoSeriesT - IntoBackend - Backend - EagerAllowed - LazyAllowed - IntoDType - IntoSchema - SizeUnit - TimeUnit - AsofJoinStrategy - ClosedInterval - ConcatMethod - FillNullStrategy - JoinStrategy - ModeKeepStrategy - PivotAgg - RankMethod - RollingInterpolationMethod - UniqueKeepStrategy show_source: false show_bases: false ## `nw.narwhalify`, or `nw.from_native`? Although some people find the former more readable, the latter is better at preserving type hints. Here's an example: ```py import polars as pl import narwhals as nw from narwhals.typing import IntoDataFrameT, DataFrameT df = pl.DataFrame({"a": [1, 2, 3]}) def func(df_native: IntoDataFrameT) -> IntoDataFrameT: df = nw.from_native(df_native, eager_only=True) return df.select(b=nw.col("a")).to_native() reveal_type(func(df)) @nw.narwhalify(strict=True) def func_2(df: DataFrameT) -> DataFrameT: return df.select(b=nw.col("a")) reveal_type(func_2(df)) ``` Running `mypy` on it gives: ```console $ mypy t.py t.py:13: note: Revealed type is "polars.dataframe.frame.DataFrame" t.py:21: note: Revealed type is "Any" Success: no issues found in 1 source file ``` In the first case, mypy can infer that `df` is a `polars.DataFrame`. In the second case, it can't. If you want to make the most out of type hints and preserve them as much as possible, we recommend `nw.from_native` and `nw.to_native`. 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~šฐuห8วไๅ–งœฃ๏จŒcบ฿1ฦ๒๑ห๙lqู%Fr†‡ใฎmัgยEDHk๓ฒHj@ึ€ื4 šBŠ‘”3KH[ชC†g4nKศื*ŒhN์JT‰Ÿ†ฐํƒจนงํ0Vrู\m๊` ฅ%iPŠ `}=pฮฒ๖าฺP(ฌ~!g14ๆ"ƒ€ณาWO‘R,ฅf2E>#฿‡%p!W‰[โณB”(b”<:_ว„ตNภ(1mศีญๆTKvฝไ„ฑVึS:า9วพ“พsŒม(22`ค,ศีฺZQƒUมj@Œ ฟ$qฦืต`eซช&ๅ„๗ซฤ#ญ"gฮhญ ข_PJแWO‰A๐9ก7Žมšคฮ5”,+ุMลH)ัด]ุ ไšบฒ+9๒qผc๓?ใะ†็็_ษ)ัถ=บi.P"ฯฟ™๗หeƒ+ฦแNีtิUC‘ถ?ะ?|เื?oข2 ื๋+ป๓฿฿iบ…iชร™ฆ;0 Šฉ๘๘๙ nฯ๑้3iYธฟ‚-‰_~ขniฉ๚=ฎ—}FXWž~๘=~8์Ž|แ๗dภu=อ๓ว'‰Šํ๖hญู๗ธฎๅp:rh:๚ำ™ำงุ,ๅZAณ—h•ฉšใ ฅ• ๛๛๓r“ยaGำIษษ๎tฆ=๎๑๓ฤ๘๖ฦtฟ‘ ๘แ"๒‹qเภbตFkจบQiฆ้ฮ๕ํ†+๋2}ไp~ข;์๑ซว6 ูOuืq๖2ซ—ืJฮ9–5RB\=?‹แ๒ฦ&ฬไธzฮ?‹Yคชุ?=’‹ศ@~ว๘H ‘ถำีปฟ๛kฌ5่E‚*้v{b๙ฎi๊M"ชYฆใํ•ร๑Lณ?ณไ1UMฝ฿oาŸฌ”5h ำ๕…้๚BXรz†ฦ๖Bส• ~ธbwa'™JCˆฌใฤำวDZL‰y™Yๆ™V\ทตฃd†ห…’ ซtว#!ฌจœ$๘‘2ลฯPท„R๋B@ณ„ J -m]QŠd@งuู^่3aYpึb–XŽซภ้hืBญšผ—]ชถจสกŒๆ~ygg๔ftlšญ5%Š’JƒAQีึ(๊บฃ฿๏eG narwhals-2.26.0/docs/backcompat.md000066400000000000000000000241521525000603700170000ustar00rootroot00000000000000# Perfect backwards compatibility policy Narwhals is primarily aimed at library maintainers rather than end users. As such, we need to take stability and backwards compatibility extra-seriously. Our policy is: - If you write code using `narwhals.stable.v1` or `import narwhals.stable.v2`, then we promise to never change or remove any public function you're using. - If we need to make a backwards-incompatible change, it will be pushed into the main `narwhals` namespace (and eventually `narwhals.stable.v3`), leaving `narwhals.stable.v1` and `narwhals.stable.v2` unaffected. - We will maintain `narwhals.stable.v1` and `narwhals.stable.v2` indefinitely, even as `narwhals.stable.v3` and other stable APIs come out. For example, Narwhals version 1.0.0 offers `narwhals.stable.v1`, whereas Narwhals 2.0.0 offers both `narwhals.stable.v1` and `narwhals.stable.v2`. Like this, we enable different packages to be on different Narwhals stable APIs, and for end-users to use all of them in the same project without conflicts nor incompatibilities. ## Background Ever upgraded a package, only to find that it breaks all your tests because of an intentional API change? Did you end up having to litter your code with statements such as the following? ```py if parse_version(pdx.__version__) < parse_version("1.3.0"): df = df.brewbeer() elif parse_version("1.3.0") <= parse_version(pdx.__version__) < parse_version("1.5.0"): df = df.brew_beer() else: df = df.brew_drink("beer") ``` Now imagine multiplying that complexity over all the dataframe libraries you want to support... Narwhals offers a simple solution, inspired by Rust editions. ## Narwhals' Stable API Narwhals implements a subset of the Polars API. What will Narwhals do if/when Polars makes a backwards-incompatible change? Would you need to update your Narwhals code? To understand the solution, let's go through an example. Suppose that, hypothetically, in Polars 2.0, `polars.Expr.cum_sum` was renamed to `polars.Expr.cumulative_sum`. In Narwhals, we have `narwhals.Expr.cum_sum`. Does this mean that Narwhals will also rename its method, and deprecate the old one? The answer is...no! Narwhals offers a `stable` namespace, which allows you to write your code once and forget about it. That is to say, if you write your code like this: === "from/to_native" ```py import narwhals.stable.v2 as nw from narwhals.stable.v2.typing import IntoFrameT def func(df: IntoFrameT) -> IntoFrameT: return nw.from_native(df).with_columns(nw.col("a").cum_sum()).to_native() ``` === "@narwhalify" ```py import narwhals.stable.v2 as nw from narwhals.stable.v2.typing import FrameT @nw.narwhalify def func(df: FrameT) -> FrameT: return df.with_columns(nw.col("a").cum_sum()) ``` then we, in Narwhals, promise that your code will keep working, even in newer versions of Polars after they have renamed their method. Concretely, we would do the following: - `narwhals.stable.v2`: you can keep using `Expr.cum_sum` - `narwhals.stable.v3`: you can only use `Expr.cumulative_sum`, `Expr.cum_sum` will have been removed - `narwhals`: you can only use `Expr.cumulative_sum`, `Expr.cum_sum` will have been removed So, although Narwhals' main API (and `narwhals.stable.v3`) will have introduced a breaking change, users of `narwhals.stable.v2` will have their code unaffected. ### Exceptions Are we really promising perfect backwards compatibility in all cases, without exceptions? Not quite. There are some exceptions, which we'll now list. But we'll never intentionally break your code. Anything currently in `narwhals.stable.v1` or `narwhals.stable.v2` will not be changed or removed in future Narwhals versions. Here are exceptions to our backwards compatibility policy: - Unambiguous bugs. If a function contains what is unambiguously a bug, then we'll fix it, without considering that to be a breaking change. - Radical changes in backends. Suppose that Polars was to remove expressions, or pandas were to remove support for categorical data. At that point, we might need to rethink Narwhals. However, we expect such radical changes to be exceedingly unlikely. - We may consider making some type hints more precise. - Anything labelled "unstable". - We may sometimes need to bump the minimum versions of supported backends. - We will drop support for old python versions, roughly in sync with [their end of life](https://devguide.python.org/versions/#supported-versions) In general, decisions are driven by use-cases, and we conduct a search of public GitHub repositories before making any change. ### `import narwhals as nw`, `import narwhals.stable.v2 as nw`, or `import narwhals.stable.v1 as nw`? Which should you use? In general we recommend: - When prototyping, use `import narwhals as nw`, so you can iterate quickly. - Once you're happy with what you've got and want to release something production-ready and stable, then switch out your `import narwhals as nw` usage for `import narwhals.stable.v2 as nw`. - If you're starting a new project, use either the main Narwhals namespace or `narwhals.stable.v2`. - If your project is already using `narwhals.stable.v1`, and you don't need any of the newer Narwhals features, there's probably no need to switch to `narwhals.stable.v2`, as that would require you to raise the minimum version of Narwhals you support. If you'd like to use `narwhals.stable.v2`, make sure to require at least `narwhals>=2.0`. ## `main` vs `stable.v2` differences - Since Narwhals 2.26: - `Expr.cat.get_categories` and `Series.cat.get_categories` are deprecated in the main Narwhals namespace, as Polars removed their own `cat.get_categories`. To get the distinct values present in a Categorical column, use `unique`; for the fixed category list of an Enum, use its `dtype.categories`. Until they are removed, they are implemented as `unique().drop_nulls().cast(String)`. In `narwhals.stable.v1` and `narwhals.stable.v2` they remain available and keep dispatching to each backend's native implementation. Other than that, everything not labelled "unstable" in the main namespace is also available in `narwhals.stable.v2`. ## `main` vs `stable.v1` differences - Since Narwhals 1.49: - `nw.Expr.arg_max` and `nw.Expr.arg_min` are deprecated from the main Narwhals namespace. Note that the `Series` methods (`Series.arg_max`, `Series.arg_min`) will remain available. - Since Narwhals 1.45: - `nw.any_horizontal` and `nw.all_horizontal` have a `ignore_nulls` keyword. In `narwhals.stable.v1`, it defaults to `False`; since Narwhals 2.0 it is a required argument in the main namespace (and in `narwhals.stable.v2`). - `LazyFrame.with_row_index` requires `order_by` to be specified as it is an order-dependent operation, in the main Narwhals namespace. - Since Narwhals 1.43: - `nw.get_level` is deprecated in the main Narwhals namespace. - Since Narwhals 1.35: - pandas' ordered categoricals get mapped to `nw.Enum` instead of `nw.Categorical`. - `nw.Enum` must be provided `categories` at instantiation. - Since Narwhals 1.29.0, `LazyFrame.gather_every` has been deprecated from the main namespace. - Since Narwhals 1.25.0, `native_namespace` is generally deprecated across the API. Please use `backend` instead. - Since Narwhals 1.24.1, an empty or all-null object-dtype pandas Series is inferred to be of dtype `String`. Previously, it would have been inferred as `Object`. - Since Narwhals 1.23: - Passing an `ibis.Table` to `from_native` returns a `LazyFrame`. In `narwhals.stable.v1`, it returns a `DataFrame` with `level='interchange'`. - `eager_or_interchange_only` has been removed from `from_native` and `narwhalify`. - Order-dependent expressions can no longer be used with `narwhals.LazyFrame`. - The following expressions have been deprecated from the main namespace: `Expr.head`, `Expr.tail`, `Expr.gather_every`, `Expr.sample`, `Expr.arg_true`, `Expr.sort`. - Since Narwhals 1.21, passing a `DuckDBPyRelation` to `from_native` returns a `LazyFrame`. In `narwhals.stable.v1`, it returns a `DataFrame` with `level='interchange'`. - Since Narwhals 1.15, `Series` is generic in the native Series, meaning that you can write: ```py import narwhals as nw import polars as pl s_pl = pl.Series([1, 2, 3]) s = nw.from_native(s_pl, series_only=True) # mypy infers `s.to_native()` to be `polars.Series` reveal_type(s.to_native()) ``` Previously, `Series` was not generic, so in the above example `s.to_native()` would have been inferred as `Any`. - Since Narwhals 1.13.0, the `strict` parameter in `from_native`, `to_native`, and `narwhalify` has been deprecated in favour of `pass_through`. This is because several users expressed confusion/surprise over what `strict=False` did. ```py # v1 syntax: nw.from_native(df, strict=False) # main namespace (and, when we get there, v2) syntax: nw.from_native(df, pass_through=True) ``` If you are using Narwhals>=1.13.0, then we recommend using `pass_through`, as that works consistently across namespaces. In the future: - in the main Narwhals namespace, `strict` will be removed in favour of `pass_through` - in `stable.v1`, we will keep both `strict` and `pass_through` - Since Narwhals 1.9.0, `Datetime` and `Duration` dtypes hash using both `time_unit` and `time_zone`. The effect of this can be seen when placing these dtypes in sets: ```python exec="yes" source="above" session="backcompat" import narwhals.stable.v1 as nw_v1 import narwhals as nw # v1 behaviour: assert nw_v1.Datetime("us") in {nw_v1.Datetime} # main namespace (and, when we get there, v2) behaviour: assert nw.Datetime("us") not in {nw.Datetime} assert nw.Datetime("us") in {nw.Datetime("us")} ``` To check if a dtype is a datetime (regardless of `time_unit` or `time_zone`) we recommend using `==` instead, as that works consistently across namespaces: ```python exec="yes" source="above" session="backcompat" # Recommended assert nw.Datetime("us") == nw.Datetime assert nw_v1.Datetime("us") == nw_v1.Datetime ``` narwhals-2.26.0/docs/basics/000077500000000000000000000000001525000603700156125ustar00rootroot00000000000000narwhals-2.26.0/docs/basics/complete_example.md000066400000000000000000000130101525000603700214520ustar00rootroot00000000000000# Complete example We're going to write a dataframe-agnostic "Standard Scaler". This class will have `fit` and `transform` methods (like `scikit-learn` transformers), and will work agnostically for pandas and Polars. We'll need to write two methods: - `fit`: find the mean and standard deviation for each column from a given training set; - `transform`: scale a given dataset with the mean and standard deviations calculated during `fit`. ## Fit method Unlike the `transform` method, which we'll write below, `fit` cannot stay lazy, as we need to compute concrete values for the means and standard deviations. To be able to get `Series` out of our `DataFrame`, we'll pass `eager_only=True` to `nw.from_native`. This is because Polars doesn't have a concept of lazy `Series`, and so Narwhals doesn't either. We can specify that in the `@nw.narwhalify` decorator by setting `eager_only=True`, and the argument will be propagated to `nw.from_native`. === "from/to_native" ```py from typing import Self import narwhals as nw from narwhals.typing import IntoDataFrameT class StandardScaler: def fit(self, df: IntoDataFrameT) -> Self: df_nw = nw.from_native(df, eager_only=True) self._means = {col: df_nw[col].mean() for col in df_nw.columns} self._std_devs = {col: df_nw[col].std() for col in df_nw.columns} self._columns = df_nw.columns return self ``` === "@narwhalify" ```py from typing import Self import narwhals as nw from narwhals.typing import DataFrameT class StandardScaler: @nw.narwhalify(eager_only=True) def fit(self, df: DataFrameT) -> Self: self._means = {col: df[col].mean() for col in df.columns} self._std_devs = {col: df[col].std() for col in df.columns} self._columns = df.columns return self ``` ## Transform method We're going to take in a dataframe, and return a dataframe of the same type: === "from/to_native" ```py from typing import Self import narwhals as nw from narwhals.typing import IntoFrameT class StandardScaler: ... def transform(self, df: IntoFrameT) -> IntoFrameT: df_nw = nw.from_native(df) return df_nw.with_columns( (nw.col(col) - self._means[col]) / self._std_devs[col] for col in self._columns ).to_native() ``` === "@narwhalify" ```py from typing import Self import narwhals as nw from narwhals.typing import FrameT class StandardScaler: ... @nw.narwhalify def transform(self, df: FrameT) -> FrameT: return df.with_columns( (nw.col(col) - self._means[col]) / self._std_devs[col] for col in self._columns ) ``` Note that all the calculations here can stay lazy if the underlying library permits it, so we don't pass in any extra keyword-arguments such as `eager_only`, we just use the default `eager_only=False`. ## Putting it all together Here is our dataframe-agnostic standard scaler: === "from/to_native" ```py from typing import Self import narwhals as nw from narwhals.typing import IntoDataFrameT from narwhals.typing import IntoFrameT class StandardScaler: def fit(self, df: IntoDataFrameT) -> Self: df_nw = nw.from_native(df, eager_only=True) self._means = {col: df_nw[col].mean() for col in df_nw.columns} self._std_devs = {col: df_nw[col].std() for col in df_nw.columns} self._columns = df_nw.columns return self def transform(self, df: IntoFrameT) -> IntoFrameT: df_nw = nw.from_native(df) return df_nw.with_columns( (nw.col(col) - self._means[col]) / self._std_devs[col] for col in self._columns ).to_native() ``` === "@narwhalify" ```python exec="yes" source="above" session="standard-scaler-example" from typing import Self import narwhals as nw from narwhals.typing import DataFrameT from narwhals.typing import FrameT class StandardScaler: @nw.narwhalify(eager_only=True) def fit(self, df: DataFrameT) -> Self: self._means = {col: df[col].mean() for col in df.columns} self._std_devs = {col: df[col].std() for col in df.columns} self._columns = df.columns return self @nw.narwhalify def transform(self, df: FrameT) -> FrameT: return df.with_columns( (nw.col(col) - self._means[col]) / self._std_devs[col] for col in self._columns ) ``` Next, let's try running it. Notice how, as `transform` doesn't use any eager-only features, we can pass a Polars LazyFrame to it and have it stay lazy! === "pandas" ```python exec="yes" source="material-block" result="python" session="standard-scaler-example" import pandas as pd df_train = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 7]}) df_test = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 7]}) scaler = StandardScaler() scaler.fit(df_train) print(scaler.transform(df_test)) ``` === "Polars" ```python exec="yes" source="material-block" result="python" session="standard-scaler-example" import polars as pl df_train = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 7]}) df_test = pl.LazyFrame({"a": [1, 2, 3], "b": [4, 5, 7]}) scaler = StandardScaler() scaler.fit(df_train) print(scaler.transform(df_test).collect()) ``` narwhals-2.26.0/docs/basics/dataframe.md000066400000000000000000000161261525000603700200660ustar00rootroot00000000000000# DataFrame To write a dataframe-agnostic function, the steps you'll want to follow are: 1. Initialise a Narwhals DataFrame or LazyFrame by passing your dataframe to `nw.from_native`. All the calculations stay lazy if we start with a lazy dataframe - Narwhals will never automatically trigger computation without you asking it to. Note: if you need eager execution, make sure to pass `eager_only=True` to `nw.from_native`. 2. Express your logic using the subset of the Polars API supported by Narwhals. 3. If you need to return a dataframe to the user in its original library, call `nw.to_native`. Steps 1 and 3 are so common that we provide a utility `@nw.narwhalify` decorator, which allows you to only explicitly write step 2. Let's explore this with some simple examples. ## Example 1: descriptive statistics Just like in Polars, we can pass expressions to `DataFrame.select` or `LazyFrame.select`. Make a Python file with the following content: === "from/to_native" ```python exec="yes" source="above" session="df_ex1" import narwhals as nw from narwhals.typing import IntoFrameT def func(df: IntoFrameT) -> IntoFrameT: return ( nw.from_native(df) .select( a_sum=nw.col("a").sum(), a_mean=nw.col("a").mean(), a_std=nw.col("a").std() ) .to_native() ) ``` === "@narwhalify" ```python exec="yes" source="above" session="df_ex1" import narwhals as nw from narwhals.typing import FrameT @nw.narwhalify def func(df: FrameT) -> FrameT: return df.select( a_sum=nw.col("a").sum(), a_mean=nw.col("a").mean(), a_std=nw.col("a").std() ) ``` Let's try it out: === "pandas" ```python exec="yes" source="material-block" result="python" session="df_ex1" import pandas as pd df = pd.DataFrame({"a": [1, 1, 2]}) print(func(df)) ``` === "Polars (eager)" ```python exec="yes" source="material-block" result="python" session="df_ex1" import polars as pl df = pl.DataFrame({"a": [1, 1, 2]}) print(func(df)) ``` === "Polars (lazy)" ```python exec="yes" source="material-block" result="python" session="df_ex1" import polars as pl df = pl.LazyFrame({"a": [1, 1, 2]}) print(func(df).collect()) ``` === "PyArrow" ```python exec="yes" source="material-block" result="python" session="df_ex1" import pyarrow as pa table = pa.table({"a": [1, 1, 2]}) print(func(table)) ``` ## Example 2: group-by and mean Just like in Polars, we can pass expressions to `GroupBy.agg`. Make a Python file with the following content: === "from/to_native" ```python exec="yes" source="above" session="df_ex2" import narwhals as nw from narwhals.typing import IntoFrameT def func(df: IntoFrameT) -> IntoFrameT: return nw.from_native(df).group_by("a").agg(nw.col("b").mean()).sort("a").to_native() ``` === "@narwhalify" ```python exec="yes" source="above" session="df_ex2" import narwhals as nw from narwhals.typing import FrameT @nw.narwhalify def func(df: FrameT) -> FrameT: return df.group_by("a").agg(nw.col("b").mean()).sort("a") ``` Let's try it out: === "pandas" ```python exec="yes" source="material-block" result="python" session="df_ex2" import pandas as pd df = pd.DataFrame({"a": [1, 1, 2], "b": [4, 5, 6]}) print(func(df)) ``` === "Polars (eager)" ```python exec="yes" source="material-block" result="python" session="df_ex2" import polars as pl df = pl.DataFrame({"a": [1, 1, 2], "b": [4, 5, 6]}) print(func(df)) ``` === "Polars (lazy)" ```python exec="yes" source="material-block" result="python" session="df_ex2" import polars as pl df = pl.LazyFrame({"a": [1, 1, 2], "b": [4, 5, 6]}) print(func(df).collect()) ``` === "PyArrow" ```python exec="yes" source="material-block" result="python" session="df_ex2" import pyarrow as pa table = pa.table({"a": [1, 1, 2], "b": [4, 5, 6]}) print(func(table)) ``` ## Example 3: horizontal sum Expressions can be free-standing functions which accept other expressions as inputs. For example, we can compute a horizontal sum using `nw.sum_horizontal`. Make a Python file with the following content: === "from/to_native" ```python exec="yes" source="above" session="df_ex3" import narwhals as nw from narwhals.typing import IntoFrameT def func(df: IntoFrameT) -> IntoFrameT: return ( nw.from_native(df).with_columns(a_plus_b=nw.sum_horizontal("a", "b")).to_native() ) ``` === "@narwhalify" ```python exec="yes" source="above" session="df_ex3" import narwhals as nw from narwhals.typing import FrameT @nw.narwhalify def func(df: FrameT) -> FrameT: return df.with_columns(a_plus_b=nw.sum_horizontal("a", "b")) ``` Let's try it out: === "pandas" ```python exec="yes" source="material-block" result="python" session="df_ex3" import pandas as pd df = pd.DataFrame({"a": [1, 1, 2], "b": [4, 5, 6]}) print(func(df)) ``` === "Polars (eager)" ```python exec="yes" source="material-block" result="python" session="df_ex3" import polars as pl df = pl.DataFrame({"a": [1, 1, 2], "b": [4, 5, 6]}) print(func(df)) ``` === "Polars (lazy)" ```python exec="yes" source="material-block" result="python" session="df_ex3" import polars as pl df = pl.LazyFrame({"a": [1, 1, 2], "b": [4, 5, 6]}) print(func(df).collect()) ``` === "PyArrow" ```python exec="yes" source="material-block" result="python" session="df_ex3" import pyarrow as pa table = pa.table({"a": [1, 1, 2], "b": [4, 5, 6]}) print(func(table)) ``` ## Example 4: multiple inputs `nw.narwhalify` can be used to decorate functions that take multiple inputs as well and return a non dataframe/series-like object. For example, let's compute how many rows are left in a dataframe after filtering it based on a series. Make a Python file with the following content: ```python exec="yes" source="above" session="df_ex4" import narwhals as nw from narwhals.typing import DataFrameT @nw.narwhalify(eager_only=True) def func(df: DataFrameT, s: nw.Series, col_name: str) -> int: return df.filter(nw.col(col_name).is_in(s)).shape[0] ``` We require `eager_only=True` here because lazyframe doesn't support `.shape`. Let's try it out: === "pandas" ```python exec="yes" source="material-block" result="python" session="df_ex4" import pandas as pd df = pd.DataFrame({"a": [1, 1, 2, 2, 3], "b": [4, 5, 6, 7, 8]}) s = pd.Series([1, 3]) print(func(df, s, "a")) ``` === "Polars (eager)" ```python exec="yes" source="material-block" result="python" session="df_ex4" import polars as pl df = pl.DataFrame({"a": [1, 1, 2, 2, 3], "b": [4, 5, 6, 7, 8]}) s = pl.Series([1, 3]) print(func(df, s, "a")) ``` === "PyArrow" ```python exec="yes" source="material-block" result="python" session="df_ex4" import pyarrow as pa table = pa.table({"a": [1, 1, 2, 2, 3], "b": [4, 5, 6, 7, 8]}) a = pa.chunked_array([[1, 3]]) print(func(table, a, "a")) ``` narwhals-2.26.0/docs/basics/dataframe_conversion.md000066400000000000000000000053521525000603700223320ustar00rootroot00000000000000# Conversion between libraries Some library maintainers must apply complex dataframe operations, using methods and functions that may not (yet) be implemented in Narwhals. In such cases, Narwhals can still be highly beneficial, by allowing easy dataframe conversion. ## Dataframe X in, pandas out Imagine that you maintain a library with a function that operates on pandas dataframes to produce automated reports. You want to allow users to supply a dataframe in any format to that function (pandas, Polars, DuckDB, cuDF, Modin, etc.) without adding all those dependencies to your own project and without special-casing each input library's variation of `to_pandas` / `toPandas` / `to_pandas_df` / `df` ... One solution is to use Narwhals as a thin Dataframe ingestion layer, to convert user-supplied dataframe to the format that your library uses internally. Since Narwhals is zero-dependency, this is a much more lightweight solution than including all the dataframe libraries as dependencies, and easier to write than special casing each input library's `to_pandas` method (if it even exists!). To illustrate, we create dataframes in various formats: ```python exec="yes" source="above" session="conversion" import narwhals as nw from narwhals.typing import IntoDataFrame from typing import Any import duckdb import polars as pl import pandas as pd df_polars = pl.DataFrame( { "A": [1, 2, 3, 4, 5], "fruits": ["banana", "banana", "apple", "apple", "banana"], "B": [5, 4, 3, 2, 1], "cars": ["beetle", "audi", "beetle", "beetle", "beetle"], } ) df_pandas = df_polars.to_pandas() df_duckdb = duckdb.sql("SELECT * FROM df_polars") ``` Now, we define a function that can ingest any dataframe type supported by Narwhals, and convert it to a pandas DataFrame for internal use: ```python exec="yes" source="above" session="conversion" result="python" def df_to_pandas(df: IntoDataFrame) -> pd.DataFrame: return nw.from_native(df).to_pandas() print(df_to_pandas(df_polars)) ``` ## Dataframe X in, Polars out ### Via PyCapsule Interface Similarly, if your library uses Polars internally, you can convert any user-supplied dataframe which implements `__arrow_c_stream__`: ```python exec="yes" source="above" session="conversion" result="python" def df_to_polars(df_native: Any) -> pl.DataFrame: if hasattr(df_native, "__arrow_c_stream__"): return nw.from_arrow(df_native, backend=pl).to_native() msg = f"Expected object which implements '__arrow_c_stream__' got: {type(df_native)}" raise TypeError(msg) print(df_to_polars(df_duckdb)) ``` It works to pass Polars to `backend` here because Polars supports the [PyCapsule Interface](https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html) for import. narwhals-2.26.0/docs/basics/series.md000066400000000000000000000154551525000603700174400ustar00rootroot00000000000000# Series In [dataframe](dataframe.md), you learned how to write a dataframe-agnostic function. We only used DataFrame methods there - but what if we need to operate on its columns? There are two ways to do this: - Series, e.g. `df.with_columns(df['a']*2)`. - expressions, e.g. `df.with_columns(nw.col('a')*2)`. Expressions are by far the preferred way: - They're inherently lazy and allow for optimisations. - They're the most idiomatic and portable solution. - Series is only available for eager backends. Nonetheless, you might need to write dataframe-agnostic code which accepts user-provided Series. In that case, you'll need to pass `eager_only=True` to `nw.from_native`. ## Example 1: filter based on a column's values This can stay lazy, so we just use expressions: === "from/to_native" ```python exec="yes" source="above" session="series_ex1" import narwhals as nw from narwhals.typing import IntoFrameT def my_func(df: IntoFrameT) -> IntoFrameT: return nw.from_native(df).filter(nw.col("a") > 0).to_native() ``` === "@narwhalify" ```python exec="yes" source="above" session="series_ex1" import narwhals as nw from narwhals.typing import FrameT @nw.narwhalify def my_func(df: FrameT) -> FrameT: return df.filter(nw.col("a") > 0) ``` and call it on either an eager or a lazy dataframe: === "pandas" ```python exec="yes" source="material-block" result="python" session="series_ex1" import pandas as pd df = pd.DataFrame({"a": [-1, 1, 3], "b": [3, 5, -3]}) print(my_func(df)) ``` === "Polars (eager)" ```python exec="yes" source="material-block" result="python" session="series_ex1" import polars as pl df = pl.DataFrame({"a": [-1, 1, 3], "b": [3, 5, -3]}) print(my_func(df)) ``` === "Polars (lazy)" ```python exec="yes" source="material-block" result="python" session="series_ex1" import polars as pl df = pl.LazyFrame({"a": [-1, 1, 3], "b": [3, 5, -3]}) print(my_func(df).collect()) ``` === "PyArrow" ```python exec="yes" source="material-block" result="python" session="series_ex1" import pyarrow as pa table = pa.table({"a": [-1, 1, 3], "b": [3, 5, -3]}) print(my_func(table)) ``` ## Example 2: multiply a column's values by a constant Let's write a dataframe-agnostic function which multiplies the values in column `'a'` by 2. This can also stay lazy, and can use expressions: === "from/to_native" ```python exec="yes" source="above" session="series_ex2" import narwhals as nw from narwhals.typing import IntoFrameT def my_func(df: IntoFrameT) -> IntoFrameT: return nw.from_native(df).with_columns(nw.col("a") * 2).to_native() ``` === "@narwhalify" ```python exec="yes" source="above" session="series_ex2" import narwhals as nw from narwhals.typing import FrameT @nw.narwhalify def my_func(df: FrameT) -> FrameT: return df.with_columns(nw.col("a") * 2) ``` and call it on either an eager or a lazy dataframe: === "pandas" ```python exec="yes" source="material-block" result="python" session="series_ex2" import pandas as pd df = pd.DataFrame({"a": [-1, 1, 3], "b": [3, 5, -3]}) print(my_func(df)) ``` === "Polars (eager)" ```python exec="yes" source="material-block" result="python" session="series_ex2" import polars as pl df = pl.DataFrame({"a": [-1, 1, 3], "b": [3, 5, -3]}) print(my_func(df)) ``` === "Polars (lazy)" ```python exec="yes" source="material-block" result="python" session="series_ex2" import polars as pl df = pl.LazyFrame({"a": [-1, 1, 3], "b": [3, 5, -3]}) print(my_func(df).collect()) ``` === "PyArrow" ```python exec="yes" source="material-block" result="python" session="series_ex2" import pyarrow as pa table = pa.table({"a": [-1, 1, 3], "b": [3, 5, -3]}) print(my_func(table)) ``` Note that column `'a'` was overwritten. If we had wanted to add a new column called `'c'` containing column `'a'`'s values multiplied by 2, we could have used `Expr.alias`: ```python exec="yes" source="above" session="series_ex2.1" import narwhals as nw from narwhals.typing import FrameT @nw.narwhalify def my_func(df: FrameT) -> FrameT: return df.with_columns((nw.col("a") * 2).alias("c")) ``` === "pandas" ```python exec="yes" source="material-block" result="python" session="series_ex2.1" import pandas as pd df = pd.DataFrame({"a": [-1, 1, 3], "b": [3, 5, -3]}) print(my_func(df)) ``` === "Polars (eager)" ```python exec="yes" source="material-block" result="python" session="series_ex2.1" import polars as pl df = pl.DataFrame({"a": [-1, 1, 3], "b": [3, 5, -3]}) print(my_func(df)) ``` === "Polars (lazy)" ```python exec="yes" source="material-block" result="python" session="series_ex2.1" import polars as pl df = pl.LazyFrame({"a": [-1, 1, 3], "b": [3, 5, -3]}) print(my_func(df).collect()) ``` === "PyArrow" ```python exec="yes" source="material-block" result="python" session="series_ex2.1" import pyarrow as pa table = pa.table({"a": [-1, 1, 3], "b": [3, 5, -3]}) print(my_func(table)) ``` ## Example 3: finding the mean of a column as a scalar Now, we want to find the mean of column `'a'`, and we need it as a Python scalar. This means that computation cannot stay lazy - it must execute! Therefore, we'll pass `eager_only=True` to `nw.from_native` (or `nw.narwhalify`), and then, instead of using expressions, we'll extract a `Series`. === "from/to_native" ```python exec="yes" source="above" session="series_ex3" import narwhals as nw from narwhals.typing import IntoDataFrameT def my_func(df: IntoDataFrameT) -> float | None: return nw.from_native(df, eager_only=True)["a"].mean() ``` === "@narwhalify" ```python exec="yes" source="above" session="series_ex3" import narwhals as nw from narwhals.typing import DataFrameT @nw.narwhalify(eager_only=True) def my_func(df: DataFrameT) -> float | None: return df["a"].mean() ``` Now we can call it on an eager dataframe only: === "pandas" ```python exec="yes" source="material-block" result="python" session="series_ex3" import pandas as pd df = pd.DataFrame({"a": [-1, 1, 3], "b": [3, 5, -3]}) print(my_func(df)) ``` === "Polars (eager)" ```python exec="yes" source="material-block" result="python" session="series_ex3" import polars as pl df = pl.DataFrame({"a": [-1, 1, 3], "b": [3, 5, -3]}) print(my_func(df)) ``` === "PyArrow" ```python exec="yes" source="material-block" result="python" session="series_ex3" import pyarrow as pa table = pa.table({"a": [-1, 1, 3], "b": [3, 5, -3]}) print(my_func(table)) ``` Note that, even though the output of our function is not a dataframe nor a series, we can still use `narwhalify`. narwhals-2.26.0/docs/concepts/000077500000000000000000000000001525000603700161645ustar00rootroot00000000000000narwhals-2.26.0/docs/concepts/boolean.md000066400000000000000000000107641525000603700201350ustar00rootroot00000000000000# Boolean columns ## Null preservation Generally speaking, Narwhals operations preserve null values. For example, if you do `nw.col('a')*2`, then: - Values which were non-null get multiplied by 2. - Null values stay null. ```python exec="yes" source="above" session="boolean" import narwhals as nw from narwhals.typing import IntoFrameT data = {"a": [1.4, None, 4.2]} def multiplication(df: IntoFrameT) -> IntoFrameT: return nw.from_native(df).with_columns((nw.col("a") * 2).alias("a*2")).to_native() ``` === "pandas" ```python exec="yes" source="material-block" result="python" session="boolean" import pandas as pd df = pd.DataFrame(data) print(multiplication(df)) ``` === "Polars (eager)" ```python exec="yes" source="material-block" result="python" session="boolean" import polars as pl df = pl.DataFrame(data) print(multiplication(df)) ``` === "PyArrow" ```python exec="yes" source="material-block" result="python" session="boolean" import pyarrow as pa table = pa.table(data) print(multiplication(table)) ``` What do we do, however, when the result column is boolean? For example, `nw.col('a') > 0`? Unfortunately, this is backend-dependent: - for all backends except pandas, null values are preserved - for pandas, this depends on the dtype backend: - for PyArrow dtypes and pandas nullable dtypes, null values are preserved - for the classic NumPy dtypes, null values are typically filled in with `False`. pandas is generally moving towards nullable dtypes, and they [may become the default in the future](https://github.com/pandas-dev/pandas/pull/58988), so we hope that the classical NumPy dtypes not supporting null values will just be a temporary legacy pandas issue which will eventually go away anyway. ```python exec="yes" source="above" session="boolean" from narwhals.typing import FrameT def comparison(df: FrameT) -> FrameT: return nw.from_native(df).with_columns((nw.col("a") > 2).alias("a>2")).to_native() ``` === "pandas" ```python exec="yes" source="material-block" result="python" session="boolean" import pandas as pd df = pd.DataFrame(data) print(comparison(df)) ``` === "Polars (eager)" ```python exec="yes" source="material-block" result="python" session="boolean" import polars as pl df = pl.DataFrame(data) print(comparison(df)) ``` === "PyArrow" ```python exec="yes" source="material-block" result="python" session="boolean" import pyarrow as pa table = pa.table(data) print(comparison(table)) ``` ## Kleene logic Generally speaking, if we have two boolean columns `'a'` and `'b'`, then `nw.col('a') | nw.col('b')` and `nw.col('a') & nw.col('b')` follow Kleene logic. That is to say: | `nw.col('a')` | `nw.col('b')` | `nw.col('a') | nw.col('b')` | `nw.col('a') & nw.col('b')` | |---------------|---------------|-----------------------------|-----------------------------| | True | True | True | True | | True | False | True | False | | True | None | True | None | | False | True | True | False | | False | False | False | False | | False | None | None | False | | None | True | True | None | | None | False | None | False | | None | None | None | None | Here, too, pandas backed by NumPy types differs, as its boolean columns cannot store null values: - For `nw.col('a') | nw.col('b')`, pandas returns `True` if at least one column contains a `True` value, and `False` otherwise. - For `nw.col('a') & nw.col('b')`, pandas returns `True` if both columns contain `True` values, and `False` otherwise. In `any_horizontal` and `all_horizontal` there is an `ignore_nulls` argument, which behaves as follows: - If `True`, then null values are ignored and contribute nothing to the final result. If there are no values, the result is: - `False` for `any_horizontal`. - `True` for `all_horizontal`. - If `False`, then Kleene logic is followed. If using pandas backed by classical NumPy types, then this option is not supported. narwhals-2.26.0/docs/concepts/column_names.md000066400000000000000000000020061525000603700211640ustar00rootroot00000000000000# Column names The majority of backends, as Polars, PyArrow, duckdb and PySpark for example, only allow for string column names. What about pandas-like and Dask backends? ```python exec="yes" source="above" result="python" session="col_names" import pandas as pd df = pd.concat([pd.Series([1, 2], name=0), pd.Series([1, 3], name=0)], axis=1) print(df) ``` Oh...not only does it let us create a dataframe with a column named `0` - it lets us create one with _two_ such columns! What does Narwhals do about this? - In general, non-string column names are supported. In some places where this might create ambiguity (such as `DataFrame.__getitem__` or `DataFrame.select`) we may be strict and only allow passing in column names if they're strings. - If you have a use-case that's failing for non-string column names, please report it to [https://github.com/narwhals-dev/narwhals/issues](https://github.com/narwhals-dev/narwhals/issues) and we'll see if we can support it. - Duplicate column names are ๐Ÿšซ banned ๐Ÿšซ. narwhals-2.26.0/docs/concepts/improve_group_by_operation.md000066400000000000000000000061331525000603700241600ustar00rootroot00000000000000# Avoiding the `UserWarning` when using pandas `group_by` ## Introduction If you have ever experienced the > UserWarning: Found complex group-by expression, which can't be expressed efficiently with the pandas API. If you can, please rewrite your query such that group-by aggregations are simple (e.g. mean, std, min, max, ...) message while using the narwhals `group_by()` method, this is for you. If you haven't, this is also for you as you might experience it and you need to know how to avoid it. Take the following two codes as an example. === "Approach 1" ```python exec="yes" source="above" result="python" session="group_by_warning_1" hl_lines="12" import narwhals as nw import pandas as pd from narwhals.typing import IntoFrameT data = {"a": [1, 2, 3, 4, 5], "b": [5, 4, 3, 2, 1], "c": [10, 20, 30, 40, 50]} df_pd = pd.DataFrame(data) def approach_1(df_native: IntoFrameT) -> IntoFrameT: df = nw.from_native(df_native) df = df.group_by("a").agg(d=(nw.col("b") + nw.col("c")).sum()) return df.to_native() print(approach_1(df_pd)) ``` === "Approach 2" ```python exec="yes" source="above" result="python" session="group_by_warning_2" hl_lines="12" import narwhals as nw import pandas as pd from narwhals.typing import IntoFrameT data = {"a": [1, 2, 3, 4, 5], "b": [5, 4, 3, 2, 1], "c": [10, 20, 30, 40, 50]} df_pd = pd.DataFrame(data) def approach_2(df_native: IntoFrameT) -> IntoFrameT: df = nw.from_native(df_native) df = df.with_columns(d=nw.col("b") + nw.col("c")).group_by("a").agg(nw.sum("d")) return df.to_native() print(approach_2(df_pd)) ``` Both approaches shown above return the exact same result, but Approach 1 is inefficient and returns the warning message we showed at the top. What makes the first approach inefficient and the second approach efficient? It comes down to what the pandas API lets us express. ## Approach 1 The literal pandas translation is: ```py df.groupby("a").apply( lambda df: pd.Series([(df["b"] + df["c"]).sum()], index=["d"]), include_groups=False ) ``` pandas experts immediately recognise the issue: it uses `apply`. Any time you use `apply` in pandas, that's a performance footgun - best to avoid it and use vectorised operations instead. Let's take a look at how "approach 2" gets translated to pandas to see the difference. ## Approach 2 The literal pandas translation is: ```py df.assign(d=lambda df: df["b"] + df["c"]).groupby("a").agg({"d": "sum"}) ``` Because we're using pandas' own API, as opposed to `apply` and a custom `lambda` function, then this is going to be much more efficient! That's why it's preferred. ## Tips for Avoiding the `UserWarning` 1. Decompose complex operations: break down complex transformations into simpler steps. In this case, keep the `.agg` method simple. Compute new columns first, then use these columns in aggregation or other operations. 2. Avoid redundant computations: if an operation (like addition) is used multiple times, compute it once and store the result in a new column. **_Happy grouping!_** ๐Ÿซก narwhals-2.26.0/docs/concepts/null_handling.md000066400000000000000000000131551525000603700213310ustar00rootroot00000000000000# Null/NaN handling ## TL;DR All dataframe tools, except for those which piggy-back off of pandas, make a clear distinction between NaN and null values. !!! tip **We recommend only handling null values in applications and leaving NaN values as an edge case resulting from users having performed undefined mathematical operations.** ## What's the difference? Most data tools except pandas make a clear distinction between: - Null values, representing missing data. - NaN values, resulting from "illegal" mathematical operations like `0/0`. In Narwhals, this is reflected in separate methods for Null/NaN values: | Operation | Null | NaN | | --------- | --------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | is | [`Expr.is_null`](../api-reference/expr.md#narwhals.Expr.is_null) | [`Expr.is_nan`](../api-reference/expr.md#narwhals.Expr.is_nan) | | fill | [`Expr.fill_null`](../api-reference/expr.md#narwhals.Expr.fill_null) | [`Expr.fill_nan`](../api-reference/expr.md#narwhals.Expr.fill_nan) | | drop | [`Expr.drop_nulls`](../api-reference/expr.md#narwhals.Expr.drop_nulls) | *Not yet implemented (See [discussion](https://github.com/narwhals-dev/narwhals/issues/3031#issuecomment-3219910366))*
[`polars.Expr.drop_nans`](https://docs.pola.rs/api/python/stable/reference/expressions/api/polars.Expr.drop_nans.html) | | count | [`Expr.null_count`](../api-reference/expr.md#narwhals.Expr.null_count) | *No upstream equivalent* | In pandas however the concepts are muddied, as different sentinel values represent *missing* [depending on the data type](https://pandas.pydata.org/docs/user_guide/missing_data.html). Check how different tools distinguish them (or don't) in the following example: ```python exec="yes" source="above" session="null_handling" import narwhals as nw import numpy as np from narwhals.typing import IntoFrameT data = {"a": [1.0, 0.0, None]} def check_null_behavior(df: IntoFrameT) -> IntoFrameT: return ( nw.from_native(df) .with_columns(a=nw.col("a") / nw.col("a")) .with_columns(a_is_null=nw.col("a").is_null(), a_is_nan=nw.col("a").is_nan()) ).to_native() ``` === "pandas" ```python exec="yes" source="material-block" result="python" session="null_handling" import pandas as pd df = pd.DataFrame(data) print(check_null_behavior(df)) ``` === "pandas (pyarrow-backed)" ```python exec="yes" source="material-block" result="python" session="null_handling" import pandas as pd df = pd.DataFrame(data).convert_dtypes(dtype_backend="pyarrow") print(check_null_behavior(df)) ``` === "Polars (eager)" ```python exec="yes" source="material-block" result="python" session="null_handling" import polars as pl df = pl.DataFrame(data) print(check_null_behavior(df)) ``` === "PyArrow" ```python exec="yes" source="material-block" result="python" session="null_handling" import pyarrow as pa df = pa.table(data) print(check_null_behavior(df)) ``` Notice how the classic pandas dtypes make no distinction between the concepts, whereas the other libraries do. Note however that discussion on what PyArrow-backed pandas dataframe should do [is ongoing](https://github.com/pandas-dev/pandas/issues/32265). ## NaN comparisons According to the IEEE-754 standard, NaN should compare as not equal to itself, and cannot be compared with other floating point numbers. Python and PyArrow follow these rules: ```python exec="yes" source="above" session="nan-comparisons" result="python" import pyarrow as pa import pyarrow.compute as pc print("Python result:") print(float("nan") == float("nan"), 0.0 == 0.0) print() print("PyArrow result:") arr = pa.array([float("nan"), 0.0]) print(pc.equal(arr, arr)) ``` Polars and DuckDB, however, don't follow this rule, and treat NaN as equal to itself. ```python exec="yes" source="above" session="nan-comparisons" result="python" import polars as pl import duckdb print("Polars result") df = pl.DataFrame({"a": [float("nan"), 0.0]}) print(df.with_columns(a_equals_a=pl.col("a") == pl.col("a"))) print() print("DuckDB result") print(duckdb.sql("from df select a, a == a as a_equals_a")) ``` Furthermore, Polars [excludes NaN values in `max`](https://github.com/pola-rs/polars/issues/23635) whereas DuckDB treats them as larger than any other floating-point value. For all these reasons it bears reiterating that our recommendation is to only handle null values in applications, and leave NaN values as an edge case resulting from users having performed undefined mathematical operations. narwhals-2.26.0/docs/concepts/order_dependence.md000066400000000000000000000046711525000603700220030ustar00rootroot00000000000000# Order-dependence Narwhals has four main public classes: - `Expr`: this is what gets created when you write `nw.col('a')`. - `DataFrame`: in-memory, eager dataframe with a well-defined row order which is preserved across `with_columns` and `select` operations. - `LazyFrame`: a dataframe which makes no assumptions about row-ordering. This allows it to be backed by SQL engines. - `Series`: 1-dimensional in-memory structure with a defined row order. This is what you get if you extract a single column from a `DataFrame`. Row order is important to think about when performing operations which rely on it, such as: - `diff`, `shift`. - `cum_sum`, `cum_min`, ... - `rolling_sum`, `rolling_min`, ... - `is_first_distinct`, `is_last_distinct`. - `first`, `last`. When row-order is defined, as is the case for `DataFrame`, these operations pose no issue. ```python exec="yes" result="python" session="order_dependence" source="above" import narwhals as nw import pandas as pd data = {"a": [1, 3, 4], "i": [0, 1, 2]} df = nw.from_native(pd.DataFrame(data)) print(df.with_columns(a_cum_sum=nw.col("a").cum_sum())) ``` When row order is undefined however, then these operations do not have a defined result. To make them well-defined, you need to follow them with `over` in which you specify `order_by`. For example: - `nw.col('a').cum_sum()` can only be executed by a `DataFrame`. - `nw.col('a').cum_sum().over(order_by="i")` can only be executed by either a `DataFrame` or a `LazyFrame`. ```python exec="yes" result="python" session="order_dependence" source="above" import polars as pl lf = nw.from_native(pl.LazyFrame(data)) result = lf.with_columns(a_cum_sum=nw.col("a").cum_sum().over(order_by="i")) print(result.collect()) ``` When writing an order-dependent function, if you want it to be executable by `LazyFrame` (and not just `DataFrame`), make sure that all order-dependent expressions are followed by `over` with `order_by` specified. If you forget to, don't worry, Narwhals will give you a loud and clear error message. ## Aggregations To make `nw.col('a').first()` valid in the lazy case, you have the choice between writing: - `nw.col('a').first().over(order_by='i')`. - `nw.col('a').first(order_by='i')`. The first produces a new column of the same length as the original dataframe, whereas the other one produces a scalar. If you're using `first` in a group-by context, where you're required to provide aggregations, then we recommend using the latter. narwhals-2.26.0/docs/concepts/pandas_index.md000066400000000000000000000054141525000603700211470ustar00rootroot00000000000000# What about the pandas Index? There are two types of pandas users: - The ones who make full use of the Index's power. - The `.reset_index(drop=True)` ones, who would rather not think about the Index. Narwhals aims to accommodate both! - If you'd rather not think about the Index, then don't worry: it's not part of the Narwhals public API, and you'll never have to worry about resetting the index or about pandas doing funky index alignment for you. - If you want your library to cater to Index powerusers who would be very angry if you reset their beautiful Index on their behalf, then don't worry: Narwhals makes certain promises with regards to the Index. Let's learn about what Narwhals promises. ## 1. Narwhals will preserve your index for common dataframe operations ```python exec="yes" source="above" session="ex1" import narwhals as nw from narwhals.typing import IntoFrameT def my_func(df: IntoFrameT) -> IntoFrameT: df = nw.from_native(df) df = df.with_columns(a_plus_one=nw.col("a") + 1) return nw.to_native(df) ``` Let's start with a dataframe with an Index with values `[7, 8, 9]`. ```python exec="yes" source="material-block" result="python" session="ex1" import pandas as pd df = pd.DataFrame({"a": [2, 1, 3], "b": [3, 5, -3]}, index=[7, 8, 9]) print(my_func(df)) ``` Note how the result still has the original index - Narwhals did not modify it. Narwhals will preserve your original index for most common dataframe operations. However, Narwhals will _not_ preserve the original index for `DataFrame.group_by`, because there, overlapping index and column names raise errors. ## 2. Index alignment follows the left-hand-rule pandas automatically aligns indices for users. For example: ```python exec="yes" source="above" session="ex2" import pandas as pd df_pd = pd.DataFrame({"a": [2, 1, 3], "b": [4, 5, 6]}) s_pd = df_pd["a"].sort_values() df_pd["a_sorted"] = s_pd ``` Reading the code, you might expect that `'a_sorted'` will contain the values `[1, 2, 3]`. **However**, here's what actually happens: ```python exec="yes" source="material-block" session="ex2" result="python" print(df_pd) ``` In other words, pandas' index alignment undid the `sort_values` operation! Narwhals, on the other hand, preserves the index of the left-hand-side argument. Everything else will be inserted positionally, just like Polars would do: ```python exec="yes" source="material-block" session="ex2" result="python" import narwhals as nw df = nw.from_native(df_pd) s = nw.from_native(s_pd, allow_series=True) df = df.with_columns(a_sorted=s.sort()) print(nw.to_native(df)) ``` If you keep these two rules in mind, then Narwhals will both help you avoid Index-related surprises whilst letting you preserve the Index for the subset of your users who consciously make great use of it. narwhals-2.26.0/docs/css/000077500000000000000000000000001525000603700151365ustar00rootroot00000000000000narwhals-2.26.0/docs/css/extra.css000066400000000000000000000032621525000603700167760ustar00rootroot00000000000000.md-typeset ol li, .md-typeset ul li { margin-bottom: 0.1em !important; } .md-main__inner.md-grid { max-width: initial; margin-left: 5vw; } /* API Completeness Table Styles */ .table-container { overflow-x: auto; } article table:not([class]) { width: auto !important; max-width: 100%; table-layout: auto; } article table:not([class]) th, article table:not([class]) td { white-space: nowrap; padding: 0.6rem 1rem; } article table:not([class]) th:first-child, article table:not([class]) td:first-child { min-width: 150px; } /* Column Selector Styles */ .column-selector-button:hover { background: var(--md-primary-fg-color--dark, #303f9f) !important; } .column-selector-button:active { transform: scale(0.98); } .column-selector-dropdown { scrollbar-width: thin; scrollbar-color: var(--md-default-fg-color--lighter, #ccc) transparent; } .column-selector-dropdown::-webkit-scrollbar { width: 8px; } .column-selector-dropdown::-webkit-scrollbar-track { background: transparent; } .column-selector-dropdown::-webkit-scrollbar-thumb { background: var(--md-default-fg-color--lighter, #ccc); border-radius: 4px; } .column-selector-dropdown::-webkit-scrollbar-thumb:hover { background: var(--md-default-fg-color--light, #999); } /* Dark mode adjustments */ [data-md-color-scheme="slate"] .column-selector-dropdown { background: var(--md-default-bg-color, #1e1e1e); border-color: var(--md-default-fg-color--lighter, #444); } [data-md-color-scheme="slate"] .column-selector-button { background: var(--md-primary-fg-color, #5d6cc0); } [data-md-color-scheme="slate"] .column-selector-button:hover { background: var(--md-primary-fg-color--dark, #4051b5) !important; } narwhals-2.26.0/docs/ecosystem.md000066400000000000000000000100471525000603700167050ustar00rootroot00000000000000# Ecosystem ## Used by The following is a non-exhaustive list of libraries and tools that choose to use Narwhals for their dataframe interoperability needs: * [altair](https://github.com/vega/altair/) * [bokeh](https://github.com/bokeh/bokeh) * [darts](https://github.com/unit8co/darts) * [fairlearn](https://github.com/fairlearn/fairlearn) * [formulaic](https://github.com/matthewwardrop/formulaic) * [gt-extras](https://github.com/posit-dev/gt-extras) * [hierarchicalforecast](https://github.com/Nixtla/hierarchicalforecast) * [lightgbm](https://github.com/lightgbm-org/LightGBM) * [marimo](https://github.com/marimo-team/marimo) * [metalearners](https://github.com/Quantco/metalearners) * [mosaic](https://github.com/uwdata/mosaic) * [pandera](https://github.com/pandera-dev/pandera) * [panel-graphic-walker](https://github.com/panel-extensions/panel-graphic-walker) * [plotly](https://github.com/plotly/plotly.py) * [pointblank](https://github.com/posit-dev/pointblank) * [pymarginaleffects](https://github.com/vincentarelbundock/pymarginaleffects) * [pyreadstat](https://github.com/Roche/pyreadstat) * [py-shiny](https://github.com/posit-dev/py-shiny) * [pysummaries](https://github.com/Genentech/pysummaries) * [rio](https://github.com/rio-labs/rio) * [scikit-learn](https://github.com/scikit-learn/scikit-learn) * [scikit-lego](https://github.com/koaning/scikit-lego) * [scikit-playtime](https://github.com/koaning/scikit-playtime) * [tabmat](https://github.com/Quantco/tabmat) * [tea-tasting](https://github.com/e10v/tea-tasting) * [timebasedcv](https://github.com/FBruzzesi/timebasedcv) * [tubular](https://github.com/lvgig/tubular) * [Validoopsie](https://github.com/akmalsoliev/Validoopsie) * [vegafusion](https://github.com/vega/vegafusion) * [wimsey](https://github.com/benrutter/wimsey) If your project is missing from the list, feel free to open a PR to add it. If you would like to chat with us, or if you need any support, please [join our Discord server](https://discord.gg/V3PqtB4VA4). ## Related projects ### Array API Array counterpart to the DataFrame API, see [here](https://data-apis.org/array-api/2022.12/index.html). ### PyCapsule Interface Allows C extension modules to safely share pointers to C data structures with Python code and other C modules, encapsulating the pointer with a name and optional destructor to manage resources and ensure safe access, see [here](https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html) for details. Narwhals supports exporting a DataFrame via the Arrow PyCapsule Interface. See [Universal dataframe support with the Arrow PyCapsule Interface + Narwhals](https://labs.quansight.org/blog/narwhals-pycapsule) for how you can use them together. ### Ibis Pitched as "The portable Dataframe library", Ibis provides a Pythonic frontend to various SQL (as well as Polars LazyFrame) engines. Some differences with Narwhals are: * Narwhals allows you to write "Dataframe X in, Dataframe X out" functions. Ibis allows materialising to pandas, Polars (eager), and PyArrow, but has no way to get back to the input type exactly (e.g. `Enum`s don't round-trip in Ibis) * Narwhals respects input data types as much as possible, Ibis doesn't support Categorical / Enum. * Narwhals separates between lazy and eager APIs, with the eager API providing very fine control over dataframe operations (slicing rows and columns, iterating over rows, getting values out of the dataframe as Python scalars). Ibis is more focused on lazy execution. * Narwhals is extremely lightweight and comes with zero required dependencies, Ibis requires pandas and PyArrow for all backends. * Narwhals uses a subset of the Polars API, Ibis uses its own pandas/dplyr-inspired API. * Ibis currently supports more backends than Narwhals * Narwhals supports pandas and Dask, which Ibis has deprecated support for. Although people often ask about the two tools, we consider them to be very different and not in competition. In particular, **Narwhals supports ibis Tables**, meaning that dataframe-agnostic code written using Narwhals' lazy API also supports Ibis. narwhals-2.26.0/docs/extending.md000066400000000000000000000106531525000603700166620ustar00rootroot00000000000000# Extensions and Plugins !!! warning The extension mechanism in Narwhals is experimental and under development. If anything is not clear, or doesn't work, please do raise an issue or contact us on Discord (see the link on the README). If you would like to make a new library Narwhals-compatible, then there are three ways to go about it: - Open a PR to Narwhals. At this point, however, the bar for including new libraries is very high, so please discuss it with us beforehand. - Make that library Narwhals-compliant. - Make a plugin for that library which makes it Narwhals-compliant. ## Making a library Narwhals-compliant It's possible to integrate your library with Narwhals with zero code changes in Narwhals. To do this, you'll need to make sure that your library complies with the Narwhals protocols found in `narwhals/compliant.py`. For example, you'll need: - A `LazyFrame` class which implements `__narwhals_lazyframe__`. - A `Expr` class which implements `broadcast`. - A `Namespace` class which implements `is_native`. - ... Full details can be found by inspecting the protocols in `narwhals/compliant.py`. ## Creating a Plugin If it's not possible to add extra functions like `__narwhals_namespace__` and others to a dataframe object itself, then another option is to write a plugin. Narwhals itself has the necessary utilities to detect and handle plugins. For this integration to work, any plugin architecture must contain the following: 1. an entrypoint defined in a `pyproject.toml` file: ``` [project.entry-points.'narwhals.plugins'] narwhals- = 'narwhals_' ``` The section name needs to be the same for all plugins; inside it, plugin creators can replace their own library name, for example `narwhals-grizzlies = 'narwhals_grizzlies'` 2. a top-level `__init__.py`ย file containing the following: - `is_native` and `__narwhals_namespace__` functions. - a string constant `NATIVE_PACKAGE` which holds the name of the library for which the plugin is made. `is_native` accepts a native object and returns a boolean indicating whether the native object is a dataframe of the library the plugin was written for. `__narwhals_namespace__` takes the Narwhals version and returns a compliant namespace for the library, i.e. one that complies with theย CompliantNamespaceย protocol. This protocol specifies a `from_native` function, whose input parameter is the Narwhals version and which returns a compliant Narwhals LazyFrame which wraps the native dataframe. Take a look at the `Plugin` protocol in `narwhals/plugins.py` for the signatures. ## IO functions: the namespace contract The Narwhals IO functions (`read_csv`, `scan_csv`, `read_parquet`, `scan_parquet`) dispatch to same-named methods on the compliant namespace. This is a single mechanism, shared by built-in backends and extensions alike: to support these functions, a compliant namespace implements (a subset of): ```py from narwhals.typing import NormalizedPath def read_csv( self, source: NormalizedPath, *, separator: str = ",", **kwds: Any ) -> CompliantDataFrame: ... def scan_csv( self, source: NormalizedPath, *, separator: str = ",", **kwds: Any ) -> CompliantFrame: ... def read_parquet(self, source: NormalizedPath, **kwds: Any) -> CompliantDataFrame: ... def scan_parquet(self, source: NormalizedPath, **kwds: Any) -> CompliantFrame: ... ``` In all cases: - `source` is a plain string at runtime: `NormalizedPath` is a `str` [`NewType`](https://docs.python.org/3/library/typing.html#newtype) tagging that Narwhals has already normalized `Path` and path-like inputs before dispatching to the namespace. - `kwds` are forwarded to the native reader, and it is the namespace's responsibility to translate `separator` into whatever its native CSV reader expects (and to raise if the two conflict). - `read_*` methods are eager-only, so they are only ever called on namespaces of eager backends. `scan_*` methods are called for any backend: lazy namespaces return a compliant LazyFrame, while eager ones may simply read eagerly. Namespaces complying with the `EagerNamespace` protocol only need to implement `read_csv` and `read_parquet`, as they inherit `scan_*` default implementations which fall back to the corresponding `read_*` method. ## Can I see an example? Yes! For a reference plugin, please check out [narwhals-daft](https://github.com/narwhals-dev/narwhals-daft). narwhals-2.26.0/docs/generating_sql.md000066400000000000000000000041301525000603700176700ustar00rootroot00000000000000# Generating SQL Suppose you want to write Polars syntax and translate it to SQL. For example, what's the SQL equivalent to: ```python exec="yes" source="above" session="generating-sql" import narwhals as nw from narwhals.typing import FrameT def avg_monthly_price(df: FrameT) -> FrameT: return ( df.group_by(nw.col("date").dt.truncate("1mo")) .agg(nw.col("price").mean()) .sort("date") ) ``` Narwhals provides you with a `narwhals.sql` module to do just that! !!! info `narwhals.sql` currently requires DuckDB to be installed. ## `narwhals.sql` You can generate SQL directly from DuckDB. ```python exec="yes" source="above" session="generating-sql" result="sql" import narwhals as nw from narwhals.sql import table prices = table("prices", {"date": nw.Date, "price": nw.Float64}) result = ( prices.group_by(nw.col("date").dt.truncate("1mo")) .agg(nw.col("price").mean()) .sort("date") ) print(result.to_sql()) ``` To make it look a bit prettier, you can pass `pretty=True`, but note that this currently requires [sqlparse](https://github.com/andialbrecht/sqlparse) to be installed. ```python exec="yes" source="above" session="generating-sql" result="sql" print(result.to_sql(pretty=True)) ``` Note that the generated SQL follows DuckDB's dialect. To translate it to other dialects, you may want to look into [sqlglot](https://github.com/tobymao/sqlglot), or use one of the solutions below (which also use sqlglot). ## Via Ibis Ibis can generate SQL from the same dataframe-agnostic function: ```py import ibis df = nw.from_native(ibis.table({"date": "date", "price": "double"}, name="prices")) print(ibis.to_sql(avg_monthly_price(df).to_native())) ``` ## Via SQLFrame So can SQLFrame: ```python exec="yes" source="above" session="generating-sql" result="sql" from sqlframe.standalone import StandaloneSession session = StandaloneSession.builder.getOrCreate() session.catalog.add_table("prices", column_mapping={"date": "date", "price": "float"}) df = nw.from_native(session.read.table("prices")) print(avg_monthly_price(df).to_native().sql(dialect="duckdb")) ``` narwhals-2.26.0/docs/how_it_works.md000066400000000000000000000507451525000603700174210ustar00rootroot00000000000000# How it works ## Theory You might think that Narwhals runs on underwater unicorn magic. However, this section exists to reassure you that there's no such thing. There's only one rule you need to understand in order to make sense of Narwhals: > **An expression is a function from a DataFrame to a sequence of Series.** For example, `nw.col('a')` means "given a dataframe `df`, give me the Series `'a'` from `df`". Translating this to pandas syntax, we get: ```python exec="yes" source="above" def col_a(df): return [df.loc[:, "a"]] ``` Let's step up the complexity. How about `nw.col('a')+1`? We already know what the `nw.col('a')` part looks like, so we just need to add `1` to each of its outputs: ```python exec="yes" source="above" def col_a(df): return [df.loc[:, "a"]] def col_a_plus_1(df): return [x + 1 for x in col_a(df)] ``` Expressions can return multiple Series - for example, `nw.col('a', 'b')` translates to: ```python exec="yes" source="above" def col_a_b(df): return [df.loc[:, "a"], df.loc[:, "b"]] ``` Expressions can also take multiple columns as input - for example, `nw.sum_horizontal('a', 'b')` translates to: ```python exec="yes" source="above" def sum_horizontal_a_b(df): return [df.loc[:, "a"] + df.loc[:, "b"]] ``` Note that although an expression may have multiple columns as input, those columns must all have been derived from the same dataframe. This last sentence was quite important, you might want to re-read it to make sure it sunk in. By itself, an expression doesn't produce a value. It only produces a value once you give it to a DataFrame context. What happens to the value(s) it produces depends on which context you hand it to: - `DataFrame.select`: produce a DataFrame with only the result of the given expression - `DataFrame.with_columns`: produce a DataFrame like the current one, but also with the result of the given expression - `DataFrame.filter`: evaluate the given expression(s), combine them with `&` (via `nw.all_horizontal`), and only keep rows where the result is `True`. The combined predicate must preserve length and produce a single output. Now let's turn our attention to the implementation. ## pandas implementation The pandas namespace (`pd`) isn't Narwhals-compliant, as the pandas API is very different from Polars'. So...Narwhals implements a `PandasLikeNamespace`, which includes the top-level Polars functions included in the Narwhals API: ```python exec="yes" source="above" result="python" session="pandas_impl" import narwhals as nw from narwhals._pandas_like.namespace import PandasLikeNamespace from narwhals.utils import Implementation, Version pn = PandasLikeNamespace(implementation=Implementation.PANDAS, version=Version.MAIN) print(nw.col("a")._to_compliant_expr(pn)) ``` The result from the last line above is the same as we'd get from `pn.col('a')`, and it's a `narwhals._pandas_like.expr.PandasLikeExpr` object, which we'll call `PandasLikeExpr` for short. `PandasLikeExpr` has a `_call` method which expects a `PandasLikeDataFrame` as input. Recall from above that an expression is a function from a dataframe to a sequence of series. The `_call` method gives us that function! Let's see it in action. Note: the following examples use `PandasLikeDataFrame` and `PandasLikeSeries`. These are backed by actual `pandas.DataFrame`s and `pandas.Series` respectively and are Narwhals-compliant. We can access the underlying pandas objects via the `native` property (`PandasLikeDataFrame.native` / `PandasLikeSeries.native`, backed by the `_native_frame` / `_native_series` attributes). ```python exec="yes" result="python" session="pandas_impl" source="above" import narwhals as nw from narwhals._pandas_like.namespace import PandasLikeNamespace from narwhals._pandas_like.dataframe import PandasLikeDataFrame from narwhals.utils import Implementation, Version import pandas as pd pn = PandasLikeNamespace(implementation=Implementation.PANDAS, version=Version.MAIN) df_pd = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) df = PandasLikeDataFrame( df_pd, implementation=Implementation.PANDAS, version=Version.MAIN, validate_column_names=True, ) expression = pn.col("a") + 1 result = expression._call(df) print(f"length of result: {len(result)}\n") print("native series of first value of result: ") print([x.native for x in result][0]) ``` So indeed, our expression did what it said on the tin - it took some dataframe, took column 'a', and added 1 to it. If you search for `def _reuse_series` in `narwhals/_compliant/expr.py`, you'll see that that's all expressions do for eager backends in Narwhals: whenever `Series.foo` is already defined, `EagerExpr.foo` is derived from it by mapping it over the sequence of Series. They just keep rigorously applying the definition of expression. It may look like there should be significant overhead to doing it this way - but really, it's just a few Python calls which get unwinded. From timing tests I've done, there's no detectable difference - in fact, because the Narwhals API guards against misusing the pandas API, it's likely that running pandas via Narwhals will in general be more efficient than running pandas directly. Further attempts at demystifying Narwhals, refactoring code so it's clearer, and explaining this section better are 110% welcome. ## Polars and other implementations Other implementations are similar to the above: they define their own Narwhals-compliant objects. So, all-in-all, there are a couple of layers here: - `nw.DataFrame` is backed by a Narwhals-compliant Dataframe, such as: - `narwhals._pandas_like.dataframe.PandasLikeDataFrame` - `narwhals._arrow.dataframe.ArrowDataFrame` - `narwhals._polars.dataframe.PolarsDataFrame` - each Narwhals-compliant DataFrame is backed by a native Dataframe, for example: - `narwhals._pandas_like.dataframe.PandasLikeDataFrame` is backed by a pandas DataFrame - `narwhals._arrow.dataframe.ArrowDataFrame` is backed by a PyArrow Table - `narwhals._polars.dataframe.PolarsDataFrame` is backed by a Polars DataFrame The same holds for `nw.LazyFrame`, which is backed by a Narwhals-compliant LazyFrame such as `narwhals._duckdb.dataframe.DuckDBLazyFrame`, `narwhals._spark_like.dataframe.SparkLikeLazyFrame`, `narwhals._dask.dataframe.DaskLazyFrame`, or `narwhals._ibis.dataframe.IbisLazyFrame`. Each implementation defines its own objects in subfolders such as `narwhals._pandas_like`, `narwhals._arrow`, `narwhals._polars`, `narwhals._duckdb`, `narwhals._spark_like`, `narwhals._dask`, and `narwhals._ibis`, whereas the top-level modules such as `narwhals.dataframe` and `narwhals.series` coordinate how to dispatch the Narwhals API to each backend. Protocols and shared base classes live in `narwhals._compliant`, and SQL-generation helpers shared by the SQL backends live in `narwhals._sql`. Backends can also live outside the Narwhals repository entirely - see [Extensions and Plugins](extending.md). ## Mapping from API to implementations If an end user executes some Narwhals code, such as ```py df.select(nw.col("a") + 1) ``` then how does that get mapped to the underlying dataframe's native API? Let's walk through this example to see. Things generally go through a couple of layers: - The user calls some top-level Narwhals API. - The Narwhals API forwards the call to a Narwhals-compliant dataframe wrapper, such as - `PandasLikeDataFrame` / `ArrowDataFrame` / `PolarsDataFrame` / ... - `PandasLikeSeries` / `ArrowSeries` / `PolarsSeries` / ... - `PandasLikeExpr` / `ArrowExpr` / `PolarsExpr` / ... - The dataframe wrapper forwards the call to the underlying library, e.g.: - `PandasLikeDataFrame` forwards the call to the underlying pandas/Modin/cuDF dataframe. - `ArrowDataFrame` forwards the call to the underlying PyArrow table. - `PolarsDataFrame` forwards the call to the underlying Polars DataFrame. The way you access the Narwhals-compliant wrapper depends on the object: - `narwhals.DataFrame` and `narwhals.LazyFrame`: use the `._compliant_frame` attribute. - `narwhals.Series`: use the `._compliant_series` attribute. - `narwhals.Expr`: call the `._to_compliant_expr` method, and pass to it the Narwhals-compliant namespace associated with the given backend. ๐Ÿ›‘ BUT WAIT! What's a Narwhals-compliant namespace? Each backend is expected to implement a Narwhals-compliant namespace (`PandasLikeNamespace`, `ArrowNamespace`, `PolarsNamespace`). These can be used to interact with the Narwhals-compliant Dataframe and Series objects described above - let's work through the motivating example to see how. ```python exec="yes" session="pandas_api_mapping" source="above" import narwhals as nw from narwhals._pandas_like.namespace import PandasLikeNamespace from narwhals.utils import Implementation, Version import pandas as pd pn = PandasLikeNamespace(implementation=Implementation.PANDAS, version=Version.MAIN) df_pd = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) df = nw.from_native(df_pd) df.select(nw.col("a") + 1) ``` The first thing `narwhals.DataFrame.select` does is to parse each input expression to end up with a compliant expression for the given backend, and it does so by passing a Narwhals-compliant namespace to `nw.Expr._to_compliant_expr`: ```python exec="yes" result="python" session="pandas_api_mapping" source="above" pn = PandasLikeNamespace(implementation=Implementation.PANDAS, version=Version.MAIN) expr = (nw.col("a") + 1)._to_compliant_expr(pn) print(expr) ``` If we then extract a Narwhals-compliant dataframe from `df` by calling `._compliant_frame`, we get a `PandasLikeDataFrame` - and that's an object which we can pass `expr` to! ```python exec="yes" session="pandas_api_mapping" source="above" df_compliant = df._compliant_frame result = df_compliant.select(expr) ``` We can then view the underlying pandas Dataframe which was produced by accessing `.native`: ```python exec="yes" result="python" session="pandas_api_mapping" source="above" print(result.native) ``` which is the same as we'd have obtained by just using the Narwhals API directly: ```python exec="yes" result="python" session="pandas_api_mapping" source="above" print(nw.to_native(df.select(nw.col("a") + 1))) ``` ## Group-by Group-by is probably one of Polars' most significant innovations (on the syntax side) with respect to pandas. We can write something like ```py df: pl.DataFrame df.group_by("a").agg((pl.col("c") > pl.col("b").mean()).max()) ``` To do this in pandas, we need to either use `GroupBy.apply` (sloooow), or do some crazy manual optimisations to get it to work. In Narwhals, here's what we do: - if somebody uses a simple group-by aggregation (e.g. `df.group_by('a').agg(nw.col('b').mean())`), then on the pandas side we translate it to a native aggregation on the `GroupBy` object: ```py df: pd.DataFrame df.groupby("a")["b"].mean() ``` Each aggregation is evaluated this way and the results are concatenated horizontally. See `AggExpr._getitem_aggs` in `narwhals/_pandas_like/group_by.py`. - if somebody passes a complex group-by aggregation, then we use `apply` and raise a `UserWarning`, warning users of the performance penalty and advising them to refactor their code so that the aggregation they perform ends up being a simple one. See [Avoiding the `UserWarning` when using pandas `group_by`](concepts/improve_group_by_operation.md). ## Nodes If we have a Narwhals expression, we can look at the operations which make it up by accessing `_nodes`: ```python exec="yes" result="python" session="pandas_impl" source="above" import narwhals as nw expr = nw.col("a").abs().std(ddof=1) + nw.col("b") print(expr._nodes) ``` Each node represents an operation. Here, we have 4 operations: 1. Given some dataframe, select column `'a'`. 2. Take its absolute value. 3. Take its standard deviation, with `ddof=1`. 4. Add column `'b'` to the result. Let's take a look at a couple of these nodes. Let's start with the third one: ```python exec="yes" result="python" session="pandas_impl" source="above" print(expr._nodes[2].as_dict()) ``` This tells us a few things: - We're performing an aggregation. - The name of the function is `'std'`. This will be looked up in the compliant object. - It takes keyword arguments `ddof=1`. - We'll look at `exprs`, `str_as_lit`, and `allow_multi_output` later. In order for the evaluation to succeed, then `PandasLikeExpr` must have a `std` method defined on it, which takes a `ddof` argument. And this is what the `CompliantExpr` Protocol is for: so long as a backend's implementation complies with the protocol, then Narwhals will be able to unpack a `ExprNode` and turn it into a valid call. Let's take a look at the fourth node: ```python exec="yes" result="python" session="pandas_impl" source="above" print(expr._nodes[3].as_dict()) ``` Note how now, the `exprs` attribute is populated. Indeed, we are adding another expression: `col('b')`. The `exprs` parameter holds arguments which are either expressions, or should be interpreted as expressions. The `str_as_lit` parameter tells us whether string literals should be interpreted as literals (e.g. `lit('foo')`) or columns (e.g. `col('foo')`). Finally `allow_multi_output` tells us whether multi-output expressions (more on this in the next section) are allowed to appear in `exprs`. Note that the expression in `exprs` also has its own nodes: ```python exec="yes" result="python" session="pandas_impl" source="above" print(expr._nodes[3].exprs[0]._nodes) ``` It's nodes all the way down! ## Expression Metadata Let's try printing out some compliant expressions' metadata to see what it shows us: ```python exec="yes" result="python" session="pandas_impl" source="above" import narwhals as nw print(nw.col("a")._to_compliant_expr(pn)._metadata) print(nw.col("a").mean()._to_compliant_expr(pn)._metadata) print(nw.col("a").mean().over("b")._to_compliant_expr(pn)._metadata) ``` This section is all about making sense of what that all means, what the rules are, and what it enables. Here's a brief description of each piece of metadata: - `expansion_kind`: How and whether the expression expands to multiple outputs. This can be one of: - `ExpansionKind.SINGLE`: Only produces a single output. For example, `nw.col('a')`, or `nw.sum_horizontal(nw.all())`. - `ExpansionKind.MULTI_NAMED`: Produces multiple outputs which were explicitly requested, for example `nw.col('a', 'b')` or `nw.nth(0, 1)`. - `ExpansionKind.MULTI_UNNAMED`: Produces multiple outputs which depend on which columns the input dataframe happens to have. For example, `nw.all()` or `nw.selectors.numeric()`. Unlike `MULTI_NAMED`, these skip group-by keys when expanded in a group-by context, so `df.group_by('a').agg(nw.all().sum())` does not try to aggregate `'a'`. - `has_windows`: Whether the expression already contains an `over(...)` statement. - `n_orderable_ops`: How many order-dependent operations the expression contains. Examples: - `nw.col('a')` contains 0 orderable operations. - `nw.col('a').diff()` contains 1 orderable operation. - `nw.col('a').diff().shift()` contains 2 orderable operations. - `is_elementwise`: Whether it preserves length and operates on each row independently of the rows around it (e.g. `abs`, `is_null`, `round`, ...). - `preserves_length`: Whether the output of the expression is the same length as the dataframe it gets evaluated on. - `is_scalar_like`: Whether the output of the expression is always length-1. - `is_literal`: Whether the expression doesn't depend on any column but instead only on literal values, like `nw.lit(1)`. - `nodes`: Tuple of operations which this expression applies when evaluated (see [Nodes](#nodes) above). ### Chaining Say we have `expr.expr_method()`. How does `expr`'s `ExprMetadata` change? This depends on `expr_method`. Details can be found in `narwhals/_expression_parsing`, in the `ExprMetadata.with_*` methods. #### Binary operations (e.g. `nw.col('a') + nw.col('b')`) How do expression kinds change under binary operations? For example, if we do `expr1 + expr2`, then what can we say about the output kind? The rules are: - If one changes the input length (e.g. `Expr.drop_nulls`), then: - if the other is scalar-like, then the output also changes length. - else, we raise an error. - If one preserves length and the other is scalar-like, then the output preserves length (because of broadcasting). - If one is scalar-like but not literal and the other is scalar-like, the output is scalar-like but not literal. For n-ary operations such as `nw.sum_horizontal`, the above logic is extended across inputs. For example, `nw.sum_horizontal(expr1, expr2, expr3)` is `LITERAL` if all of `expr1`, `expr2`, and `expr3` are. ### "You open a window to another window to another window to another window" When working with `DataFrame`s, row order is well-defined, as the dataframes are assumed to be eager and in-memory. Therefore, `n_orderable_ops` is disregarded. When working with `LazyFrame`s, on the other hand, row order is undefined. Therefore, when evaluating an expression, `n_orderable_ops` must be exactly zero - if it's not, it means that the expression depends on physical row order, which is not allowed for `LazyFrame`s. The way that `n_orderable_ops` can change is: - Orderable window functions like `diff` and `rolling_mean` increase `n_orderable_ops` by 1. - If an orderable window function is immediately followed by `over(order_by=...)`, then `n_orderable_ops` is decreased by 1. This is the only way that `n_orderable_ops` can decrease. ## Broadcasting When performing comparisons between columns and aggregations or scalars, we operate as if the aggregation or scalar was broadcasted to the length of the whole column. For example, if we have a dataframe with values `{'a': [1, 2, 3]}` and do `nw.col('a') - nw.col('a').mean()`, then each value from column `'a'` will have its mean subtracted from it, and we will end up with values `[-1, 0, 1]`. Different libraries do broadcasting differently. SQL-like libraries require an empty window function for expressions (e.g. `a - sum(a) over ()`), Polars does its own broadcasting of length-1 Series, and pandas does its own broadcasting of scalars. Narwhals triggers a broadcast in these situations: - In `select` when some values preserve length and others don't, e.g. `df.select('a', nw.col('b').mean())`. - In `with_columns`, all new columns get broadcasted to the length of the dataframe. - In n-ary operations between expressions, such as `nw.col('a') + nw.col('a').mean()`. Each backend is then responsible for doing its own broadcasting, as defined in each `CompliantExpr.broadcast` method. ## Elementwise push-down SQL is picky about `over` operations. For example: - `sum(a) over (partition by b)` is valid. - `sum(abs(a)) over (partition by b)` is valid. - `abs(sum(a)) over (partition by b)` is not valid. In Polars, however, all three of - `pl.col('a').sum().over('b')` is valid. - `pl.col('a').abs().sum().over('b')` is valid. - `pl.col('a').sum().abs().over('b')` is valid. How can we retain Polars' level of flexibility when translating to SQL engines? The answer is: by rewriting expressions. Specifically, we push down `over` nodes past elementwise ones. To see this, let's try printing the Narwhals equivalent of the last expression above (the one that SQL rejects): ```python exec="yes" result="python" session="pushdown" source="above" import narwhals as nw print(nw.col("a").sum().abs().over("b")) ``` Note how Narwhals automatically inserted the `over` operation _before_ the `abs` one. In other words, instead of doing - `sum` -> `abs` -> `over` it did - `sum` -> `over` -> `abs` thus allowing the expression to be valid for SQL engines! This is what we refer to as "pushing down `over` nodes". The idea is: - Elementwise operations operate row-by-row and don't depend on the rows around them. - An `over` node partitions or orders a computation. - Therefore, an elementwise operation followed by an `over` operation is the same as doing the `over` operation followed by that same elementwise operation! Note that the pushdown also applies to any arguments to the elementwise operation. For example, if we have ```py (nw.col("a").sum() + nw.col("b").sum()).over("c") ``` then `+` is an elementwise operation and so can be swapped with `over`. We just need to take care to apply the `over` operation to all the arguments of `+`, so that we end up with ```py nw.col("a").sum().over("c") + nw.col("b").sum().over("c") ``` !!! info In general, query optimisation is out-of-scope for Narwhals. We consider this expression rewrite acceptable because: - It's simple. - It allows us to evaluate operations which otherwise wouldn't be allowed for certain backends. narwhals-2.26.0/docs/index.md000066400000000000000000000043101525000603700157750ustar00rootroot00000000000000# Narwhals ![Narwhals Logo](assets/image.png) [![PyPI version](https://badge.fury.io/py/narwhals.svg)](https://badge.fury.io/py/narwhals) [![Downloads](https://static.pepy.tech/badge/narwhals/month)](https://pepy.tech/project/narwhals) [![Trusted publishing](https://img.shields.io/badge/Trusted_publishing-Provides_attestations-bright_green)](https://peps.python.org/pep-0740/) [![PYPI - Types](https://img.shields.io/pypi/types/narwhals)](https://pypi.org/project/narwhals) [![LFX Health Score](https://insights.linuxfoundation.org/api/badge/health-score?project=narwhals-dev-narwhals)](https://insights.linuxfoundation.org/project/narwhals-dev-narwhals) [![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/narwhals-dev/narwhals/badge)](https://securityscorecards.dev/viewer/?uri=github.com/narwhals-dev/narwhals) Extremely lightweight and extensible compatibility layer between dataframe libraries! - **Full API support**: cuDF, Modin, pandas, Polars, PyArrow. - **Lazy-only support**: Dask, DuckDB, Ibis, PySpark, SQLFrame, and Daft via the [narwhals-daft](https://github.com/narwhals-dev/narwhals-daft) plugin. Seamlessly support all, without depending on any! - โœ… **Just use** [a subset of **the Polars API**](./api-reference/index.md), no need to learn anything new - โœ… **Zero dependencies**, Narwhals only uses what the user passes in so your library can stay lightweight - โœ… Separate **lazy** and eager APIs, use **expressions** - โœ… Support pandas' complicated type system and index, without either getting in the way - โœ… **100% branch coverage**, tested against pandas and Polars nightly builds - โœ… **Negligible overhead**, see [overhead](overhead.md) - โœ… Let your IDE help you thanks to **full static typing**, see [`narwhals.typing`](./api-reference/typing.md) - โœ… **Perfect backwards compatibility policy**, see [stable api](backcompat.md) for how to opt-in ## Who's this for? Anyone wishing to write a library/application/service which consumes dataframes, and wishing to make it completely dataframe-agnostic. Let's get started! ## Roadmap See [roadmap discussion on GitHub](https://github.com/narwhals-dev/narwhals/discussions/1370) for an up-to-date plan of future work. narwhals-2.26.0/docs/installation.md000066400000000000000000000131151525000603700173720ustar00rootroot00000000000000# Installation and quick start ## Installation Narwhals requires **Python 3.10+** and has no required dependencies: it only ever uses the dataframe libraries you pass into it. Pick the workflow that matches how you manage your project's dependencies. === "uv" If you're starting a new project with [uv](https://docs.astral.sh/uv/), add Narwhals to it with: ```terminal uv add narwhals ``` This will create a virtual environment (if one doesn't already exist), add `narwhals` to your `pyproject.toml`, and update `uv.lock`. To install Narwhals into an existing virtual environment without touching `pyproject.toml`, use: ```terminal uv pip install narwhals ``` === "pip" First, [create and activate](https://docs.python.org/3/library/venv.html) a Python 3.10+ virtual environment, then run: ```terminal python -m pip install narwhals ``` Unlike `uv add` or `poetry add`, `pip install` does not touch `pyproject.toml`. If you're working on a project, you'll need to record the dependency there yourself. === "Poetry" From within a [Poetry](https://python-poetry.org/) project, run: ```terminal poetry add narwhals ``` This will add `narwhals` to your `pyproject.toml` and update `poetry.lock`. ### Installing with extras Narwhals exposes optional extras that pull in a specific backend at a version known to be compatible. These are convenience pins, not requirements: if you already have the backend installed (or want to manage its version yourself), you can skip them. Available extras include `pandas`, `polars`, `pyarrow`, `modin`, `dask`, `duckdb`, `ibis`, `pyspark`, `pyspark-connect`, `sqlframe`, `sql`, and `cudf` (Linux only). For the authoritative list, see `[project.optional-dependencies]` in [`pyproject.toml`](https://github.com/narwhals-dev/narwhals/blob/main/pyproject.toml). Specify one or more extras in square brackets, for example: === "uv" ```terminal uv add "narwhals[polars,pyarrow]" ``` === "pip" ```terminal python -m pip install "narwhals[polars,pyarrow]" ``` === "Poetry" ```terminal poetry add "narwhals[polars,pyarrow]" ``` !!! warning "Library authors: prefer keeping backends out of your direct dependencies" `uv add "narwhals[polars,pyarrow]"` adds the extras to `[project.dependencies]`. If you publish your project, **every consumer is then forced to install polars and pyarrow**, which defeats Narwhals' "support all, depend on none" design. If you're building a library (rather than an application), keep `narwhals` as your runtime dependency and pin the backends only for development: === "uv" ```terminal uv add narwhals uv add --group dev "narwhals[polars,pyarrow]" ``` === "Poetry" ```terminal poetry add narwhals poetry add --group dev "narwhals[polars,pyarrow]" ``` Dependency groups are not shipped in your distribution metadata, so consumers receive only `narwhals` and bring their own backend. Alternatively, re-expose the backends as your library's **own optional extras**, so consumers opt in explicitly (e.g. `pip install your-library[polars]`): ```toml [project.optional-dependencies] polars = ["narwhals[polars]"] pyarrow = ["narwhals[pyarrow]"] ``` ### Verifying the installation To verify the installation, start the Python REPL and execute: ```python exec="yes" source="above" session="quickstart" result="python" import narwhals print(narwhals.__version__) ``` If you see the version number, then the installation was successful! ## Quick start ### Optional companion libraries Narwhals has no required dataframe dependencies, but to follow along with the examples below you'll want at least one of: - [pandas](https://pandas.pydata.org/docs/getting_started/install.html) - [Polars](https://pola-rs.github.io/polars/user-guide/installation/) - [PyArrow](https://arrow.apache.org/docs/python/install.html) ### Simple example Create a Python file `t.py` with the following content: ```python exec="yes" source="above" session="quickstart" result="python" from __future__ import annotations import pandas as pd import polars as pl import pyarrow as pa import narwhals as nw from narwhals.typing import IntoFrame def agnostic_get_columns(df_native: IntoFrame) -> list[str]: df = nw.from_native(df_native) column_names = df.columns return column_names data = {"a": [1, 2, 3], "b": [4, 5, 6]} df_pandas = pd.DataFrame(data) df_polars = pl.DataFrame(data) table_pa = pa.table(data) print("pandas output") print(agnostic_get_columns(df_pandas)) print("Polars output") print(agnostic_get_columns(df_polars)) print("PyArrow output") print(agnostic_get_columns(table_pa)) ``` If you run `python t.py` then your output should look like the above. !!! tip "Running with `uv` (no install needed)" If you have [uv](https://docs.astral.sh/uv/), you can skip the environment setup entirely by declaring [inline script dependencies](https://docs.astral.sh/uv/guides/scripts/#declaring-script-dependencies) at the top of `t.py`: ```python # /// script # dependencies = ["narwhals[pandas,polars,pyarrow]"] # /// ``` Then run it with `uv run t.py`. This is the simplest possible example of a dataframe-agnostic function - as we'll soon see, we can do much more advanced things. Let's learn about what you just did, and what Narwhals can do for you! !!! info These examples are using pandas, Polars, and PyArrow, however Narwhals supports other dataframe libraries (See [the home page](index.md) for supported libraries). narwhals-2.26.0/docs/javascripts/000077500000000000000000000000001525000603700166775ustar00rootroot00000000000000narwhals-2.26.0/docs/javascripts/extra.js000066400000000000000000000041721525000603700203640ustar00rootroot00000000000000/** * Copied from Ruff https://github.com/astral-sh/ruff/blob/924741cb11a68ed037899f9db1bea6969c48385e/docs/js/extra.js * * @author Astral Software Inc. * @license MIT */ function cleanupClipboardText(targetSelector) { const targetElement = document.querySelector(targetSelector); // exclude "Generic Prompt" and "Generic Output" spans from copy const excludedClasses = ["gp", "go"]; const clipboardText = Array.from(targetElement.childNodes) .filter( (node) => !excludedClasses.some((className) => node?.classList?.contains(className), ), ) .map((node) => node.textContent) .filter((s) => s !== ""); return clipboardText.join("").trim(); } // Sets copy text to attributes lazily using an Intersection Observer. function setCopyText() { // The `data-clipboard-text` attribute allows for customized content in the copy // See: https://www.npmjs.com/package/clipboard#copy-text-from-attribute const attr = "clipboardText"; // all "copy" buttons whose target selector is a element const elements = document.querySelectorAll( 'button[data-clipboard-target$="code"]', ); if (elements.length === 0) { return; } const observer = new IntersectionObserver((entries) => { entries.forEach((entry) => { // target in the viewport that have not been patched if ( entry.intersectionRatio > 0 && entry.target.dataset[attr] === undefined ) { entry.target.dataset[attr] = cleanupClipboardText( entry.target.dataset.clipboardTarget, ); } }); }); elements.forEach((elt) => { observer.observe(elt); }); } // Using the document$ observable is particularly important if you are using instant loading since // it will not result in a page refresh in the browser // See `How to integrate with third-party JavaScript libraries` guideline: // https://squidfunk.github.io/mkdocs-material/customization/?h=javascript#additional-javascript document$.subscribe(function () { setCopyText(); }); narwhals-2.26.0/docs/javascripts/katex.js000066400000000000000000000005061525000603700203520ustar00rootroot00000000000000document$.subscribe(({ body }) => { renderMathInElement(body, { delimiters: [ { left: "$$", right: "$$", display: true }, { left: "$", right: "$", display: false }, { left: "\\(", right: "\\)", display: false }, { left: "\\[", right: "\\]", display: true } ], }) }) narwhals-2.26.0/docs/javascripts/table.js000066400000000000000000000215531525000603700203320ustar00rootroot00000000000000document$.subscribe(function() { // Only run on api-completeness pages var currentPath = window.location.pathname if (!currentPath.includes('/api-completeness/')) { return } var tables = document.querySelectorAll("article table:not([class])") tables.forEach(function(table) { new Tablesort(table) addColumnSelector(table) addMethodFilter(table) }) }) function addColumnSelector(table) { var thead = table.querySelector('thead') var headerRow = thead.querySelector('tr') var headers = Array.from(headerRow.querySelectorAll('th')) // Skip if table only has one column (Method column) if (headers.length <= 1) return // Get backend columns (all except first "Method" column) var backendColumns = headers.slice(1).map(function(th, index) { return { name: th.textContent.trim(), index: index + 1, visible: true } }) // Sort backends alphabetically backendColumns.sort(function(a, b) { return a.name.localeCompare(b.name) }) // Create container for the dropdown var container = document.createElement('div') container.className = 'column-selector-container' container.style.cssText = 'margin: 16px 0; position: relative; display: inline-block;' // Create the dropdown button var button = document.createElement('button') button.className = 'column-selector-button' button.textContent = 'Select Backends' button.style.cssText = ` padding: 8px 16px; background: var(--md-primary-fg-color, #4051b5); color: white; border: none; border-radius: 6px; cursor: pointer; font-size: 14px; font-weight: 500; display: flex; align-items: center; gap: 8px; transition: background 0.2s; ` // Add arrow icon var arrow = document.createElement('span') arrow.textContent = 'โ–ผ' arrow.style.cssText = 'font-size: 10px; transition: transform 0.2s;' button.appendChild(arrow) // Create dropdown menu var dropdown = document.createElement('div') dropdown.className = 'column-selector-dropdown' dropdown.style.cssText = ` position: absolute; top: 100%; left: 0; margin-top: 4px; background: var(--md-default-bg-color, white); border: 1px solid var(--md-default-fg-color--lightest, #e0e0e0); border-radius: 6px; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15); min-width: 200px; max-height: 300px; overflow-y: auto; display: none; z-index: 1000; ` // Add "Select All" / "Deselect All" buttons var selectAllContainer = document.createElement('div') selectAllContainer.style.cssText = ` padding: 8px 12px; border-bottom: 1px solid var(--md-default-fg-color--lightest, #e0e0e0); display: flex; gap: 8px; ` var selectAllBtn = document.createElement('button') selectAllBtn.textContent = 'Select All' selectAllBtn.style.cssText = ` flex: 1; padding: 4px 8px; font-size: 12px; border: 1px solid var(--md-default-fg-color--lighter, #ccc); border-radius: 4px; background: var(--md-default-bg-color, white); color: var(--md-default-fg-color, black); cursor: pointer; transition: background 0.2s; ` var deselectAllBtn = document.createElement('button') deselectAllBtn.textContent = 'Clear All' deselectAllBtn.style.cssText = selectAllBtn.style.cssText selectAllContainer.appendChild(selectAllBtn) selectAllContainer.appendChild(deselectAllBtn) dropdown.appendChild(selectAllContainer) // Create checkboxes for each backend backendColumns.forEach(function(column) { var label = document.createElement('label') label.style.cssText = ` display: flex; align-items: center; padding: 8px 12px; cursor: pointer; transition: background 0.2s; user-select: none; ` label.addEventListener('mouseenter', function() { label.style.background = 'var(--md-default-fg-color--lightest, #f5f5f5)' }) label.addEventListener('mouseleave', function() { label.style.background = 'transparent' }) var checkbox = document.createElement('input') checkbox.type = 'checkbox' checkbox.checked = true checkbox.style.cssText = 'margin-right: 8px; cursor: pointer;' checkbox.dataset.columnIndex = column.index var span = document.createElement('span') span.textContent = column.name span.style.cssText = 'font-size: 14px; color: var(--md-default-fg-color, black);' label.appendChild(checkbox) label.appendChild(span) dropdown.appendChild(label) // Handle column visibility toggle checkbox.addEventListener('change', function() { toggleColumn(table, column.index, checkbox.checked) updateButtonText() }) }) // Toggle dropdown visibility button.addEventListener('click', function(e) { e.stopPropagation() var isVisible = dropdown.style.display === 'block' dropdown.style.display = isVisible ? 'none' : 'block' arrow.style.transform = isVisible ? 'rotate(0deg)' : 'rotate(180deg)' }) // Close dropdown when clicking outside document.addEventListener('click', function(e) { if (!container.contains(e.target)) { dropdown.style.display = 'none' arrow.style.transform = 'rotate(0deg)' } }) // Select all handler selectAllBtn.addEventListener('click', function(e) { e.stopPropagation() var checkboxes = dropdown.querySelectorAll('input[type="checkbox"]') checkboxes.forEach(function(cb) { cb.checked = true toggleColumn(table, parseInt(cb.dataset.columnIndex), true) }) updateButtonText() }) // Deselect all handler deselectAllBtn.addEventListener('click', function(e) { e.stopPropagation() var checkboxes = dropdown.querySelectorAll('input[type="checkbox"]') checkboxes.forEach(function(cb) { cb.checked = false toggleColumn(table, parseInt(cb.dataset.columnIndex), false) }) updateButtonText() }) function updateButtonText() { var checkboxes = Array.from(dropdown.querySelectorAll('input[type="checkbox"]')) var checkedCount = checkboxes.filter(function(cb) { return cb.checked }).length var totalCount = checkboxes.length if (checkedCount === 0) { button.firstChild.textContent = 'Select Backends' } else if (checkedCount === totalCount) { button.firstChild.textContent = 'All Backends Selected' } else { button.firstChild.textContent = checkedCount + ' of ' + totalCount + ' Selected' } } container.appendChild(button) container.appendChild(dropdown) // Wrap table in a container for better layout control var tableContainer = document.createElement('div') tableContainer.className = 'table-container' table.parentNode.insertBefore(tableContainer, table) tableContainer.appendChild(table) // Insert button container before the table container tableContainer.parentNode.insertBefore(container, tableContainer) } function toggleColumn(table, columnIndex, visible) { var rows = table.querySelectorAll('tr') rows.forEach(function(row) { var cells = row.querySelectorAll('th, td') if (cells[columnIndex]) { cells[columnIndex].style.display = visible ? '' : 'none' } }) } function addMethodFilter(table) { var thead = table.querySelector('thead') var headerRow = thead.querySelector('tr') var headers = headerRow.querySelectorAll('th') // Create filter input row var filterRow = document.createElement('tr') filterRow.className = 'filter-row' headers.forEach(function(header, index) { var th = document.createElement('th') // Add input only to first column (Method column) if (index === 0) { var input = document.createElement('input') input.type = 'text' input.placeholder = 'Filter methods...' input.style.width = '100%' input.style.padding = '4px' input.style.boxSizing = 'border-box' input.style.border = '1px solid #ccc' input.style.borderRadius = '4px' input.addEventListener('input', function(e) { filterTable(table, e.target.value) }) th.appendChild(input) } filterRow.appendChild(th) }) thead.appendChild(filterRow) } function filterTable(table, filterValue) { var tbody = table.querySelector('tbody') var rows = tbody.querySelectorAll('tr') var lowerFilterValue = filterValue.toLowerCase() rows.forEach(function(row) { var firstCell = row.querySelector('td') var cellText = firstCell.textContent.toLowerCase() var originalText = firstCell.textContent if (cellText.includes(lowerFilterValue)) { row.style.display = '' // Highlight matching text if (lowerFilterValue) { var regex = new RegExp('(' + escapeRegex(filterValue) + ')', 'gi') firstCell.innerHTML = originalText.replace(regex, '$1') } else { firstCell.textContent = originalText } } else { row.style.display = 'none' } }) } function escapeRegex(string) { return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&') } narwhals-2.26.0/docs/overhead.md000066400000000000000000000044041525000603700164670ustar00rootroot00000000000000# Overhead Narwhals converts Polars syntax to non-Polars dataframes. So, what's the overhead of running "pandas" vs "pandas via Narwhals"? Based on experiments we've done, the answer is: it's negligible. Sometimes it's even negative, because of how careful we are in Narwhals to avoid unnecessary copies and index resets. Here are timings from the TPC-H queries, comparing running pandas directly vs running pandas via Narwhals: [Complete code to reproduce](https://www.kaggle.com/code/marcogorelli/narwhals-vs-pandas-overhead-tpc-h-s2). This comparison is also run as a regression test in CI, see [`tpch/tests/overhead_test.py`](https://github.com/narwhals-dev/narwhals/blob/main/tpch/tests/overhead_test.py): it fails if pandas via Narwhals ever becomes meaningfully slower than native pandas for the same TPC-H queries. ## Plotly's story One big difference between Plotly v5 and Plotly v6 is the handling of non-pandas inputs: - In v5, Plotly would convert non-pandas inputs to pandas. - In v6, Plotly operates on non-pandas inputs natively (via Narwhals). We expected that this would bring a noticeable performance benefit for non-pandas inputs, but that there may be some slight overhead for pandas. Instead, we observed that things got noticeably faster for both non-pandas inputs and for pandas ones! - Polars plots got 3x, and sometimes even more than 10x, faster. - pandas plots were typically no slower, but sometimes ~20% faster. Full details on [Plotly's write-up](https://plotly.com/blog/chart-smarter-not-harder-universal-dataframe-support/). ## Overhead for DuckDB, PySpark, and other lazy backends For lazy backends, Narwhals respects the backends' laziness and always keeps everything lazy. Narwhals never evaluates a full query unless you ask it to (with `.collect()`). In order to mimic Polars' behaviour, there are some places where Narwhals does need to inspect dataframes' schemas, such as: - joins - selectors - `nth` - `concat` with `how='vertical'` - `unique` This is typically cheap (as it does not require reading a full dataset into memory and can often just be done from metadata alone) but it's not free, especially if your data lives on the cloud. To minimise the overhead, when Narwhals needs to evaluate schemas or column names, it makes sure to cache them. narwhals-2.26.0/docs/resources.md000066400000000000000000000036471525000603700167140ustar00rootroot00000000000000# Resources ## Sponsors and institutional partners Narwhals is 100% independent, community-driven, and community-owned. We are extremely grateful to the following organisations for having provided some funding / development time: - [Quansight Labs](https://labs.quansight.org) - [Quansight Futures](https://www.qi.ventures) - [OpenTeams](https://www.openteams.com) - [POSSEE initiative](https://possee.org) - [BYU-Idaho](https://www.byui.edu) - [Intella](https://www.intella.tech/) If you contribute to Narwhals on your organization's time, please let us know. We'd be happy to add your employer to this list! ## Appears on Narwhals has been featured in several talks, podcasts, and blog posts: - [Talk Python to me Podcast](https://youtu.be/FSH7BZ0tuE0) Ahoy, Narwhals are bridging the data science APIs - [Python Bytes Podcast](https://www.youtube.com/live/N7w_ESVW40I?si=y-wN1uCsAuJOKlOT&t=382) Episode 402, topic #2 - [Super Data Science: ML & AI Podcast](https://www.youtube.com/watch?v=TeG4U8R0U8U) Narwhals: For Pandas-to-Polars DataFrame Compatibility - [Sample Space Podcast | probabl](https://youtu.be/8hYdq4sWbbQ?si=WG0QP1CZ6gkFf18b) How Narwhals has many end users ... that never use it directly. - Marco Gorelli - [The Real Python Podcast](https://www.youtube.com/watch?v=w5DFZbFYzCM) Narwhals: Expanding DataFrame Compatibility Between Libraries - [Pycon Lithuania](https://www.youtube.com/watch?v=-mdx7Cn6_6E) Marco Gorelli - DataFrame interoperatiblity - what's been achieved, and what comes next? - [Pycon Italy](https://www.youtube.com/watch?v=3IqUli9XsmQ) How you can write a dataframe-agnostic library - Marco Gorelli - [Polars Blog Post](https://pola.rs/posts/lightweight_plotting/) Polars has a new lightweight plotting backend - [Quansight Labs blog post (w/ Scikit-Lego)](https://labs.quansight.org/blog/scikit-lego-narwhals) How Narwhals and scikit-lego came together to achieve dataframe-agnosticism narwhals-2.26.0/docs/security.md000066400000000000000000000051631525000603700165440ustar00rootroot00000000000000# Security ## Reporting a Vulnerability If you believe you have found a security vulnerability in Narwhals, please report it privately rather than opening a public issue. We support two channels: * **GitHub Private Vulnerability Reporting**: use the [Report a vulnerability](https://github.com/narwhals-dev/narwhals/security/advisories/new) form on our Security tab. * **Email**: send details to `hello_narwhals@proton.me`. Please include a description of the issue, reproduction steps, the affected version(s), and any proof-of-concept code. Do not include data that is itself sensitive. ### Disclosure process and timeline * We acknowledge receipt of vulnerability reports within 7 days. * We aim to provide an initial assessment (accepted, more info needed, or declined) within 30 days. * For confirmed vulnerabilities we target a fix and coordinated disclosure within 90 days of the initial report. We will keep the reporter informed of progress and may request an embargo extension for complex issues. * After a fix is released, we publish a GitHub Security Advisory and credit the reporter unless they prefer to remain anonymous. We follow good-faith vulnerability disclosure: researchers acting in line with this policy will not be pursued under DMCA, CFAA, or equivalent local statutes. ## Security practices Given that Narwhals can only work if people trust it, we recognise the importance of following good security practices. Here are some practices we follow: * We publish to PyPI via trusted publishing and are PEP740-compliant. * We don't use `pull_request_target` in any CI job. * The release CI job can only be triggered for tag pushes, and only Narwhals members with release permissions (see below) can push tags. * All members of `narwhals-dev` are required to have two-factor authentication enabled. * There are no binary or opaque files in the Narwhals repository. * Release permissions are only given to people who satisfy all of the following: * Have met the original author in real life on multiple days. * Have made significant contributions to Narwhals. * Give off good vibes. This is hard to rigorously define, but it's there so we can refuse anyone who, despite satisfying the above two criteria, we don't feel like we can trust. On top of those criteria, we cap the number of people with release permissions at 5. Even if someone satisfies all of the above, if the cap is already reached then we will not be adding any more (though you may still be added to `narwhals-dev` and get permission to merge pull requests which you believe are ready). The cap is currently reached. narwhals-2.26.0/docs/why.md000066400000000000000000000027061525000603700155040ustar00rootroot00000000000000# Why? You may think that pandas, Polars, and all dataframe libraries are quite similar. But are they really? For example, do the following produce the same output? ```py import pandas as pd import polars as pl print(3 in pd.Series([1, 2, 3])) print(3 in pl.Series([1, 2, 3])) ``` Try it out and see ๐Ÿ˜‰ Spoiler alert: they don't. pandas checks if `3` is in the index, Polars checks if it's in the values. For another example, try running the code below - note how the outputs have different column names after the join! ```py pd_df_left = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) pd_df_right = pd.DataFrame({"a": [1, 2, 3], "c": [4, 5, 6]}) pd_left_merge = pd_df_left.merge(pd_df_right, left_on="b", right_on="c", how="left") pl_df_left = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) pl_df_right = pl.DataFrame({"a": [1, 2, 3], "c": [4, 5, 6]}) pl_left_merge = pl_df_left.join(pl_df_right, left_on="b", right_on="c", how="left") print(pd_left_merge.columns) print(pl_left_merge.columns) ``` There are several such subtle differences between the libraries. **Writing dataframe-agnostic code is hard!** But by having a unified, simple, and predictable API, you can focus on behaviour rather than on subtle implementation differences. Furthermore, both pandas and Polars frequently deprecate behaviour. Narwhals handles this for you by testing against nightly builds of both libraries and handling backwards compatibility internally (so you don't have to!). narwhals-2.26.0/docstring_template.mustache000066400000000000000000000003551525000603700210430ustar00rootroot00000000000000{{! narwhals docstring template }} {{summaryPlaceholder}}. {{#parametersExist}} Arguments: {{#args}} {{var}}: {{descriptionPlaceholder}} {{/args}} {{#kwargs}} {{var}}: {{descriptionPlaceholder}} {{/kwargs}} {{/parametersExist}} narwhals-2.26.0/packages/000077500000000000000000000000001525000603700151745ustar00rootroot00000000000000narwhals-2.26.0/packages/test-plugin/000077500000000000000000000000001525000603700174475ustar00rootroot00000000000000narwhals-2.26.0/packages/test-plugin/pyproject.toml000066400000000000000000000003701525000603700223630ustar00rootroot00000000000000[build-system] requires = ["uv_build>=0.11.0,<0.12.0"] build-backend = "uv_build" [project] name = "test_plugin" version = "0.1.0" requires-python = ">=3.10" dependencies = [] [project.entry-points.'narwhals.plugins'] test-plugin = 'test_plugin' narwhals-2.26.0/packages/test-plugin/src/000077500000000000000000000000001525000603700202365ustar00rootroot00000000000000narwhals-2.26.0/packages/test-plugin/src/test_plugin/000077500000000000000000000000001525000603700225735ustar00rootroot00000000000000narwhals-2.26.0/packages/test-plugin/src/test_plugin/__init__.py000066400000000000000000000011011525000603700246750ustar00rootroot00000000000000from __future__ import annotations from typing import TYPE_CHECKING if TYPE_CHECKING: from typing_extensions import TypeIs from narwhals.utils import Version from test_plugin.dataframe import DictFrame from test_plugin.namespace import DictNamespace def __narwhals_namespace__(version: Version) -> DictNamespace: # noqa: N807 from test_plugin.namespace import DictNamespace return DictNamespace(version=version) def is_native(native_object: object) -> TypeIs[DictFrame]: return isinstance(native_object, dict) NATIVE_PACKAGE = "builtins" narwhals-2.26.0/packages/test-plugin/src/test_plugin/dataframe.py000066400000000000000000000041771525000603700251020ustar00rootroot00000000000000from __future__ import annotations from typing import TYPE_CHECKING, Any from narwhals._utils import ( Implementation, ValidateBackendVersion, Version, not_implemented, ) from narwhals.typing import CompliantLazyFrame if TYPE_CHECKING: from typing import TypeAlias from typing_extensions import Self from narwhals import LazyFrame # noqa: F401 DictFrame: TypeAlias = dict[str, list[Any]] class DictLazyFrame( CompliantLazyFrame[Any, "DictFrame", "LazyFrame[DictFrame]"], # type: ignore[type-var] ValidateBackendVersion, ): _implementation = Implementation.UNKNOWN def __init__(self, native_dataframe: DictFrame, *, version: Version) -> None: self._native_frame: DictFrame = native_dataframe self._version = version def __narwhals_lazyframe__(self) -> Self: return self @property def columns(self) -> list[str]: # pragma: no cover return list(self._native_frame.keys()) _with_native = not_implemented() def _with_version(self, version: Version) -> Self: return self.__class__(self._native_frame, version=version) # Dunders __narwhals_namespace__ = not_implemented() __native_namespace__ = not_implemented() # Properties schema: Any = not_implemented() # Static _is_native = not_implemented() # Helpers _iter_columns = not_implemented() # Functions aggregate = not_implemented() collect = not_implemented() collect_schema = not_implemented() drop = not_implemented() drop_nulls = not_implemented() explode = not_implemented() filter = not_implemented() from_native = not_implemented() group_by = not_implemented() head = not_implemented() join = not_implemented() join_asof = not_implemented() rename = not_implemented() select = not_implemented() simple_select = not_implemented() sink_parquet = not_implemented() sort = not_implemented() tail = not_implemented() to_narwhals = not_implemented() unique = not_implemented() unpivot = not_implemented() with_columns = not_implemented() with_row_index = not_implemented() narwhals-2.26.0/packages/test-plugin/src/test_plugin/namespace.py000066400000000000000000000026241525000603700251050ustar00rootroot00000000000000from __future__ import annotations from typing import TYPE_CHECKING, Any from narwhals._compliant import CompliantNamespace from narwhals._utils import not_implemented from test_plugin.dataframe import DictFrame, DictLazyFrame if TYPE_CHECKING: from narwhals.utils import Version class DictNamespace(CompliantNamespace[DictLazyFrame, Any]): def __init__(self, *, version: Version) -> None: self._version = version def from_native(self, native_object: DictFrame) -> DictLazyFrame: return DictLazyFrame(native_object, version=self._version) is_native: Any = not_implemented() _expr: Any = not_implemented() _implementation: Any = not_implemented() corr: Any = not_implemented() cov: Any = not_implemented() len: Any = not_implemented() list: Any = not_implemented() lit: Any = not_implemented() all_horizontal: Any = not_implemented() any_horizontal: Any = not_implemented() sum_horizontal: Any = not_implemented() mean_horizontal: Any = not_implemented() min_horizontal: Any = not_implemented() max_horizontal: Any = not_implemented() concat: Any = not_implemented() when: Any = not_implemented() concat_str: Any = not_implemented() selectors: Any = not_implemented() coalesce: Any = not_implemented() struct: Any = not_implemented() scan_csv: Any = not_implemented() scan_parquet: Any = not_implemented() narwhals-2.26.0/packages/test-plugin/src/test_plugin/py.typed000066400000000000000000000000001525000603700242600ustar00rootroot00000000000000narwhals-2.26.0/pyproject.toml000066400000000000000000000404211525000603700163330ustar00rootroot00000000000000# Reference: https://packaging.python.org/en/latest/guides/writing-pyproject-toml [build-system] requires = ["uv_build>=0.11.0,<0.13.0"] build-backend = "uv_build" [project] name = "narwhals" version = "2.26.0" dependencies = [] requires-python = ">=3.10" authors = [ { name = "Marco Gorelli", email = "hello_narwhals@proton.me" }, ] license = "MIT" license-files = ["LICENSE.md"] description = "Extremely lightweight compatibility layer between dataframe libraries" readme = "README.md" keywords = ["dataframes", "interoperability", "pandas", "polars", "pyarrow", "dask", "modin", "cudf"] classifiers = [ "Development Status :: 5 - Production/Stable", "Operating System :: OS Independent", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", "Programming Language :: Python :: 3.14", "Programming Language :: Python :: Free Threading :: 2 - Beta", "Typing :: Typed", ] [project.urls] Homepage = "https://github.com/narwhals-dev/narwhals" Documentation = "https://narwhals-dev.github.io/narwhals/" Repository = "https://github.com/narwhals-dev/narwhals" "Bug Tracker" = "https://github.com/narwhals-dev/narwhals/issues" [project.optional-dependencies] # These should be aligned with MIN_VERSIONS in narwhals/utils.py # Exception: modin, because `modin.__version__` isn't aligned with # `modin.pandas.__version__`. The latter is the one that we make # API decisions based on, so that's the one we track internally. # We have yet to determine the minimum Modin version we support # https://github.com/narwhals-dev/narwhals/issues/817 pandas = ["pandas>=1.3.4"] modin = ["modin>=0.22.0"] cudf = ["cudf-cu12>=24.10.0; sys_platform == 'linux'"] pyarrow = ["pyarrow>=13.0.0"] pyspark = ["pyspark>=3.5.0"] pyspark-connect = ["pyspark[connect]>=3.5.0"] polars = ["polars>=0.20.4"] dask = ["dask[dataframe]>=2024.8"] duckdb = ["duckdb>=1.1"] ibis = [ "ibis-framework>=6.0.0", "packaging>=21.3", # runtime import at ibis/backends/duckdb and others "pyarrow_hotfix>=0.7", # runtime import anytime pyarrow is imported ] sqlframe = ["sqlframe>=3.22.0,!=3.39.3"] sql = ["narwhals[duckdb]", "sqlparse>=0.5.5"] [dependency-groups] core = [ "pandas>=1.3.4", "duckdb>=1.5", "narwhals[duckdb,polars,pyarrow,sqlframe,sql]" ] plugins = [ "test-plugin", ] tests = [ "covdefaults", "pytest>=9.0.3", "pytest-cov", "pytest-env", "pytest-randomly", "pytest-xdist", "hypothesis>=6.0.0", ] # NOTE: `plugins` is deliberately NOT included within `tests`. #ย `test-plugin` is a workspace member, so pulling it into `tests` makes the group unresolvable # outside the workspace (e.g. `pip install --group tests` from an unpacked sdist, #ย as hit by downstream packagers). `tests/plugins_test.py` uses `importorskip`, #ย so it skips gracefully when the plugin is absent. #ย CI jobs that measure `tests/` coverage add `--group plugins` explicitly. #ย See https://github.com/narwhals-dev/narwhals/pull/3664 and https://github.com/narwhals-dev/narwhals/pull/3665 sklearn = [ "scikit-learn", ] typing = [ #ย Type checkers "mypy", "pyrefly", "pyright", # Dependencies "hypothesis>=6.0.0", "pytest>=9.0.3", "pandas-stubs>=2.3.0.250703", "pyarrow-stubs>=19.2", "rich>=12.4.4", #ย type import to resolve ibis "typing-extensions>=4.15.0", "narwhals[dask,duckdb,ibis,pandas,polars,pyarrow,sql,sqlframe]", {include-group = "plugins"}, ] docs = [ "jinja2>=3.1.6", "markdown-exec[ansi]>=1.12.1", "mkdocs-autorefs>=1.4.4", "mkdocstrings-python>=2.0.3", "pandas>=1.3.4", "narwhals[duckdb,pyarrow,polars,sql,sqlframe]", "ruff", "zensical>=0.0.38" ] dev = [ "prek", "ruff", {include-group = "tests"}, ] dev-core = [ {include-group = "dev"}, {include-group = "core"}, ] core-tests = [ {include-group = "core"}, {include-group = "tests"}, ] local-dev = [ {include-group = "dev"}, {include-group = "core"}, {include-group = "docs"}, {include-group = "plugins"}, ] extreme-minimum-versions = [ "setuptools", "tzdata", "pandas==1.3.4", "polars==0.20.4", "numpy==1.21.4", "pyarrow==13.0.0", "pyarrow-stubs<17", "scipy==1.7.3", "scikit-learn==1.1.0", "duckdb==1.1", {include-group = "tests"}, ] extreme-pretty-old-versions = [ "setuptools", "tzdata", "pandas==1.4.1", "polars==0.20.4", "numpy==1.22.0", "pyarrow==14.0.0", "pyarrow-stubs<17", "scipy==1.8.0", "scikit-learn==1.1.0", "duckdb==1.2", {include-group = "tests"}, ] extreme-not-so-old-versions = [ "setuptools", "tzdata", "pandas==2.0.3", "polars==0.20.8", "numpy==1.24.4", "pyarrow==15.0.0", "pyarrow-stubs<17", "scipy==1.8.0", "scikit-learn==1.3.0", "duckdb==1.3", "dask[dataframe]==2024.10", {include-group = "tests"}, ] min-pyspark-version = [ "pyspark==3.5.0", {include-group = "core-tests"}, ] [tool.uv.build-backend] # The `src/narwhals` module, pyproject.toml, README and LICENSE are shipped by # default. We only need to add `tests/` to the sdist; `/**` recurses into # subdirectories (a bare `*` would not, dropping e.g. `tests/data/`). #ย See: https://docs.astral.sh/uv/reference/settings/#build-backend_source-exclude #ย > Glob expressions which files and directories to additionally include in the source distribution. source-include = ["tests/**"] [tool.uv] # NOTE: When updating these conflicts, every entry in a single inner list is #ย treated as mutually exclusive with every other entry in the same list. # See https://docs.astral.sh/uv/reference/settings/#conflicts. conflicts = [ # TODO: Remove these conflict when Modin and/or CuDF will support pandas v3.0+ #ย In the meanwhile, this is needed to run tests with pandas v3.0+ [ { extra = "cudf" }, { extra = "modin" }, { extra = "pandas" }, ], # The `extreme-*` groups pin mutually incompatible pyarrow/pandas/numpy # versions, and all pin `pyarrow-stubs<17` while `typing` pins ==19.2. `cudf` # is here because old pandas/numpy forces cudf-cu12==24.10.x, whose # nvidia-stub build backend fails on non-linux platforms during lock. [ { extra = "cudf" }, { group = "typing" }, { group = "extreme-minimum-versions" }, { group = "extreme-not-so-old-versions" }, { group = "extreme-pretty-old-versions" }, ], # `dask[dataframe]>=2024.8` transitively requires pyarrow>=16 / pandas>=2, # and `modin>=0.22` pins pandas==1.5.3, both incompatible with the older two # `extreme-*` groups. `extreme-not-so-old-versions` is omitted: it already # pins dask==2024.10 and pandas==2.0.3, which are compatible. [ { extra = "cudf" }, { extra = "dask" }, { extra = "modin" }, { group = "extreme-minimum-versions" }, { group = "extreme-pretty-old-versions" }, ], # `core` resolves modern pandas/duckdb/polars/etc., conflicting with every # `extreme-*` group's old version pins. [ { group = "core" }, { group = "extreme-minimum-versions" }, { group = "extreme-not-so-old-versions" }, { group = "extreme-pretty-old-versions" }, ], ] [tool.uv.workspace] members = ["packages/*"] [tool.uv.sources] test-plugin = { workspace = true } [tool.ruff] line-length = 90 fix = true target-version = "py310" extend-exclude = ["**/this.py"] [tool.ruff.lint] preview = true explicit-preview-rules = true typing-modules = ["narwhals._typing_compat"] extend-safe-fixes = [ "C419", # unnecessary-comprehension-in-call "C420", # unnecessary-dict-comprehension-for-iterable "EM101", # raw-string-in-exception "EM102", # f-string-in-exception "PLR6201", # literal-membership "TC", # flake8-type-checking "UP", # pyupgrade "PLW1514", # unspecified-encoding ] extend-select = [ "PLR0914", # too-many-locals "PLR0916", # too-many-boolean-expressions "PLR1702", # too-many-nested-blocks "PLR6201", # literal-membership "PLW1514", # unspecified-encoding ] select = ["ALL"] ignore = [ "A001", # builtin-variable-shadowing "A004", # builtin-import-shadowing "ARG002", # unused-method-argument "ANN401", # any-type "CPY001", #ย missing-copyright-notice "COM812", # missing-trailing-comma "D100", # undocumented-public-module "D101", # undocumented-public-class "D102", # undocumented-public-method "D103", # undocumented-public-function "D104", # undocumented-public-package "D105", # undocumented-magic-method "D107", # undocumented-public-init "DTZ001", # call-datetime-without-tzinfo "E501", # line-too-long "FBT003", # boolean-positional-value-in-call (We enforce at definition site when it is a flag, not a value (e.g. `lit(False)`)) "FIX", # flake8-fixme "PD010", # pandas-use-of-dot-pivot-or-unstack "PLC0415", # `import` should be at the top-level of a file "PLR0913", # too-many-arguments "PLR2004", # magic-value-comparison "PLW1641", # Object does not implement `__hash__` method "SLF001", # private-member-access "TD003", # missing-todo-link "TD004", # missing-todo-colon ] [tool.ruff.lint.flake8-builtins] builtins-ignorelist = ["format"] [tool.ruff.lint.pep8-naming] extend-ignore-names = [ "Series", "DataFrame", "_Window", "F", "_F", ] [tool.ruff.lint.per-file-ignores] "src/narwhals/expr_dt.py" = ["RUF002"] "src/narwhals/typing.py" = [ "PYI042", # snake-case-type-alias (catches `_1DArray`) ] "src/narwhals/_*/utils.py" = [ "PLR0911", # too-many-return-statements ] "tests/*" = [ "S101", "C901", # complex-structure "PLR0912", # too-many-branches "PLR0916", # too-many-boolean-expressions "PLR0917", # too-many-positional-arguments ] "tpch/tests/*" = ["S101"] "tpch/execute/*" = ["T201"] "tpch/queries/*" = ["PLR0917"] "tpch/notebooks/*" = [ "ANN001", "ANN201", "EM101", "EXE002", "PTH123", "T203", "TRY003", ] "utils/*" = ["S311", "T201"] "utils/bump_version.py" = ["S603", "S607"] [tool.ruff.lint.pydocstyle] convention = "google" [tool.ruff.lint.pylint] max-nested-blocks = 4 [tool.ruff.lint.isort] required-imports = ["from __future__ import annotations"] combine-as-imports = true split-on-trailing-comma = false [tool.ruff.format] docstring-code-format = true skip-magic-trailing-comma = true [tool.pytest.ini_options] norecursedirs = ['*.egg', '.*', '_darcs', 'build', 'CVS', 'dist', 'node_modules', 'venv', '{arch}', 'src/narwhals/_*'] testpaths = ["tests"] pythonpath = ["src"] filterwarnings = [ "error", 'ignore:.*defaulting to pandas implementation:Warning:modin', 'ignore:.*implementation has mismatches with pandas:Warning:modin', 'ignore:.*You are using pyarrow version', # Deprecation raised by DuckDB 'ignore:.*fetch_arrow_table\(\) is deprecated:DeprecationWarning:sqlframe', 'ignore:.*fetch_arrow_table\(\) is deprecated:DeprecationWarning:ibis', # https://discuss.python.org/t/types-uniontype-was-merged-with-wrong-class-not-even-a-class/102275/31 "ignore:.*'_UnionGenericAlias' is deprecated:DeprecationWarning", # This warning was temporarily raised by pandas but then reverted. 'ignore:.*Passing a BlockManager to DataFrame:DeprecationWarning', # This warning was temporarily raised by Polars but then reverted. 'ignore:.*The default coalesce behavior of left join will change:DeprecationWarning', 'ignore: unclosed =2.5 deprecates generic-unit timedeltas; pandas (>=2.3) trips this from # `pd.Timedelta(...)`. The warning is attributed to the caller's module (e.g. test # code), so it cannot be scoped to `pandas`; keep it unscoped for this exact message. "ignore:.*'generic' unit for NumPy timedelta is deprecated.*:DeprecationWarning", # Warning raised when calling PandasLikeNamespace.from_arrow with old pyarrow "ignore:.*is_sparse is deprecated and will be removed in a future version.*:DeprecationWarning:pyarrow", "ignore:.*invalid value encountered in cast:RuntimeWarning:pandas", "ignore:.*The behavior of DataFrame concatenation with empty or all-NA entries is deprecated:FutureWarning", # raised internally by Dask "ignore:.*Downcasting behavior in Series and DataFrame methods 'where', 'mask', and 'clip' is deprecated.*:FutureWarning:dask", "ignore:.*inplace keyword .* is deprecated*:Warning:dask", #ย raise internally by polars "ignore:.*interchange protocol is deprecated.*:DeprecationWarning" ] xfail_strict = true markers = ["slow: marks tests as slow (deselect with '-m \"not slow\"')"] env = [ "MODIN_ENGINE=python", "PYARROW_IGNORE_TIMEZONE=1", "TZ=UTC", ] [tool.coverage.run] plugins = ["covdefaults"] [tool.coverage.report] fail_under = 80 # This is just for local development, in CI we set it to 100 omit = [ "src/narwhals/typing.py", "src/narwhals/compliant.py", "src/narwhals/stable/v1/typing.py", "src/narwhals/stable/v2/typing.py", "src/narwhals/this.py", "src/narwhals/_arrow/typing.py", "src/narwhals/_duckdb/typing.py", "src/narwhals/_spark_like/typing.py", # We run Ibis in a separate CI job to the coverage one, to avoid their tight dependency pins. "src/narwhals/_ibis/*", # Remove after finishing eager sub-protocols "src/narwhals/_compliant/namespace.py", "tests/ibis_test.py", ] exclude_also = [ "if sys.version_info() <", "if .* is Implementation.(CUDF|MODIN|PYSPARK|PYSPARK_CONNECT|IBIS)", "if .*.is_(cudf|modin|pyspark|pyspark_connect|ibis)", 'if "(cudf|modin|pyspark|ibis)" in', "if not .*.is_pandas", "if is_ibis_table", "if MYPY", "except ModuleNotFoundError", 'request.applymarker\(pytest.mark.xfail', 'backend_version <', '.*._backend_version\(\) <', 'BACKEND_VERSION <', 'if ".*" in str\(constructor', 'pytest.skip\(', 'assert_never\(', 'PANDAS_VERSION < \(' ] [tool.mypy] files = ["src", "tests", "packages/test-plugin"] pretty = true strict = true allow_redefinition = true # https://mypy.readthedocs.io/en/stable/command_line.html#cmdoption-mypy-allow-redefinition [[tool.mypy.overrides]] module = [ "cudf.*", "cupy.*", "dask.*", "dask_expr.*", # https://github.com/ibis-project/ibis/issues/6844 "ibis.*", "joblib.*", "modin.*", "numpy.*", "pyspark.*", "sklearn.*", "sqlparse.*", ] # TODO: remove follow_imports follow_imports = "skip" ignore_missing_imports = true [[tool.mypy.overrides]] module = [ "narwhals._expression_parsing", "narwhals._utils", "narwhals._pandas_like.*", "narwhals._ibis.*", "narwhals._arrow.*", "narwhals._dask.*", "narwhals._spark_like.*", ] warn_return_any = false [tool.pyright] pythonPlatform = "All" # NOTE (`pyarrow-stubs` do unsafe `TypeAlias` and `TypeVar` imports) # pythonVersion = "3.10" reportMissingTypeArgument = "error" reportIncompatibleMethodOverride = "error" reportMissingImports = "none" reportMissingModuleSource = "none" reportPrivateImportUsage = "none" reportUnusedExpression = "none" # handled by (https://docs.astral.sh/ruff/rules/unused-variable/) typeCheckingMode = "basic" include = ["src", "tests", "packages/test-plugin"] ignore = [ "../.venv/", "../../../**/Lib", # stdlib "../../../**/typeshed*" # typeshed-fallback ] defineConstant = { "MYPY" = false } [tool.pyrefly] search-path = ["src", "."] project-includes = ["tests"] ignore-missing-imports = [ "cudf.*", "cupy.*", "dask.*", "dask_expr.*", "joblib.*", "modin.*", "pyspark.*", "sklearn.*", "sqlparse.*", ] narwhals-2.26.0/renovate.json000066400000000000000000000024611525000603700161370ustar00rootroot00000000000000{ "$schema": "https://docs.renovatebot.com/renovate-schema.json", "extends": ["config:recommended", ":dependencyDashboard"], "enabledManagers": ["pep621", "github-actions"], "labels": ["dependencies"], "lockFileMaintenance": { "enabled": true, "schedule": ["* 0-5 * * *"] }, "packageRules": [ { "description": "Never edit pyproject.toml. Disable every dependency version update; uv.lock is refreshed wholesale by lockFileMaintenance, the equivalent of `uv lock -U` (latest versions allowed by the pyproject constraints).", "matchManagers": ["pep621"], "matchUpdateTypes": ["major", "minor", "patch", "pin", "digest"], "enabled": false }, { "description": "Replaces the github-actions ecosystem previously handled by .github/dependabot.yml, which opened one PR per action. Every action is bumped in a single weekly PR instead. `minimumReleaseAge` is the supply-chain cooldown dependabot expressed as `cooldown.default-days`, and `pinDigests` keeps the `@ # vX.Y.Z` form that zizmor's `unpinned-uses` audit requires.", "matchManagers": ["github-actions"], "groupName": "github actions", "commitMessagePrefix": "skip changelog(deps):", "minimumReleaseAge": "7 days", "pinDigests": true, "schedule": ["* 0-5 * * 2"] } ] } narwhals-2.26.0/src/000077500000000000000000000000001525000603700142055ustar00rootroot00000000000000narwhals-2.26.0/src/narwhals/000077500000000000000000000000001525000603700160245ustar00rootroot00000000000000narwhals-2.26.0/src/narwhals/__init__.py000066400000000000000000000065641525000603700201500ustar00rootroot00000000000000from __future__ import annotations import typing as _t from narwhals import dependencies, dtypes, exceptions, selectors from narwhals._utils import ( Implementation, generate_temporary_column_name, is_ordered_categorical, maybe_align_index, maybe_convert_dtypes, maybe_get_index, maybe_reset_index, maybe_set_index, ) from narwhals.dataframe import DataFrame, LazyFrame from narwhals.dtypes import ( Array, Binary, Boolean, Categorical, Date, Datetime, Decimal, Duration, Enum, Field, Float16, Float32, Float64, Int8, Int16, Int32, Int64, Int128, List, Object, String, Struct, Time, UInt8, UInt16, UInt32, UInt64, UInt128, Unknown, ) from narwhals.expr import Expr from narwhals.functions import ( all_ as all, all_horizontal, any_horizontal, coalesce, col, concat, concat_str, corr, cov, exclude, format, from_arrow, from_dict, from_dicts, from_numpy, len_ as len, list_ as list, lit, max, max_horizontal, mean, mean_horizontal, median, min, min_horizontal, new_series, nth, read_csv, read_parquet, scan_csv, scan_parquet, show_versions, struct, sum, sum_horizontal, when, ) from narwhals.schema import Schema from narwhals.series import Series from narwhals.translate import ( from_native, get_native_namespace, narwhalify, to_native, to_py_scalar, ) __version__: str __all__ = [ "Array", "Binary", "Boolean", "Categorical", "DataFrame", "Date", "Datetime", "Decimal", "Duration", "Enum", "Expr", "Field", "Float16", "Float32", "Float64", "Implementation", "Int8", "Int16", "Int32", "Int64", "Int128", "LazyFrame", "List", "Object", "Schema", "Series", "String", "Struct", "Time", "UInt8", "UInt16", "UInt32", "UInt64", "UInt128", "Unknown", "all", "all_horizontal", "any_horizontal", "coalesce", "col", "concat", "concat_str", "corr", "cov", "dependencies", "dtypes", "exceptions", "exclude", "format", "from_arrow", "from_dict", "from_dicts", "from_native", "from_numpy", "generate_temporary_column_name", "get_native_namespace", "is_ordered_categorical", "len", "list", "lit", "max", "max_horizontal", "maybe_align_index", "maybe_convert_dtypes", "maybe_get_index", "maybe_reset_index", "maybe_set_index", "mean", "mean_horizontal", "median", "min", "min_horizontal", "narwhalify", "new_series", "nth", "read_csv", "read_parquet", "scan_csv", "scan_parquet", "selectors", "show_versions", "struct", "sum", "sum_horizontal", "to_native", "to_py_scalar", "when", ] if not _t.TYPE_CHECKING: def __getattr__(name: str) -> _t.Any: if name == "__version__": global __version__ # noqa: PLW0603 from importlib import metadata __version__ = metadata.version(__name__) return __version__ msg = f"module {__name__!r} has no attribute {name!r}" raise AttributeError(msg) else: # pragma: no cover ... narwhals-2.26.0/src/narwhals/_arrow/000077500000000000000000000000001525000603700173155ustar00rootroot00000000000000narwhals-2.26.0/src/narwhals/_arrow/__init__.py000066400000000000000000000000001525000603700214140ustar00rootroot00000000000000narwhals-2.26.0/src/narwhals/_arrow/dataframe.py000066400000000000000000000753701525000603700216270ustar00rootroot00000000000000from __future__ import annotations from collections.abc import Collection, Iterator, Mapping, Sequence from typing import TYPE_CHECKING, Any, Final, Literal, cast, overload import pyarrow as pa import pyarrow.compute as pc from narwhals._arrow.series import ArrowSeries from narwhals._arrow.utils import ( arange, chunked_array, concat_tables, narwhals_to_native_dtype, native_to_narwhals_dtype, repeat, sort_indices, sortable_table, ) from narwhals._compliant import EagerDataFrame from narwhals._utils import ( Implementation, check_column_names_are_unique, convert_str_slice_to_int_slice, generate_temporary_column_name, not_implemented, parse_columns_to_drop, scale_bytes, supports_arrow_c_stream, ) from narwhals.dependencies import is_numpy_array_1d from narwhals.exceptions import ShapeError if TYPE_CHECKING: from collections.abc import Iterable from io import BytesIO from pathlib import Path from types import ModuleType from typing import TypeAlias import pandas as pd import polars as pl from typing_extensions import Self, TypeIs from narwhals._arrow.expr import ArrowExpr from narwhals._arrow.group_by import ArrowGroupBy from narwhals._arrow.namespace import ArrowNamespace from narwhals._arrow.typing import ( # type: ignore[attr-defined] ChunkedArrayAny, NullPlacement, Order, ) from narwhals._compliant.typing import CompliantDataFrameAny, CompliantLazyFrameAny from narwhals._spark_like.utils import SparkSession from narwhals._translate import IntoArrowTable from narwhals._typing import _EagerAllowedImpl, _LazyAllowedImpl from narwhals._utils import Version, _LimitedContext from narwhals.dtypes import DType from narwhals.typing import ( IntoDType, IntoSchema, JoinStrategy, SizedMultiIndexSelector, SizedMultiNameSelector, SizeUnit, UniqueKeepStrategy, _1DArray, _2DArray, _SliceIndex, _SliceName, ) JoinType: TypeAlias = Literal[ "left semi", "right semi", "left anti", "right anti", "inner", "left outer", "right outer", "full outer", ] MYPY: Final = False class ArrowDataFrame( EagerDataFrame["ArrowSeries", "ArrowExpr", "pa.Table", "ChunkedArrayAny"] ): _implementation = Implementation.PYARROW def __init__( self, native_dataframe: pa.Table, *, version: Version, validate_column_names: bool, validate_backend_version: bool = False, ) -> None: if validate_column_names: check_column_names_are_unique(native_dataframe.column_names) if validate_backend_version: self._validate_backend_version() self._native_frame = native_dataframe self._version = version @classmethod def from_arrow(cls, data: IntoArrowTable, /, *, context: _LimitedContext) -> Self: backend_version = context._implementation._backend_version() if cls._is_native(data): native = data elif backend_version >= (14,) or isinstance(data, Collection): native = pa.table(data) elif supports_arrow_c_stream(data): # pragma: no cover msg = f"'pyarrow>=14.0.0' is required for `from_arrow` for object of type {type(data).__name__!r}." raise ModuleNotFoundError(msg) else: # pragma: no cover msg = f"`from_arrow` is not supported for object of type {type(data).__name__!r}." raise TypeError(msg) return cls.from_native(native, context=context) @classmethod def from_dict( cls, data: Mapping[str, Any], /, *, context: _LimitedContext, schema: Mapping[str, IntoDType | None] | None, ) -> Self: if not schema and not data: return cls.from_native(pa.table({}), context=context) if not schema: return cls.from_native(pa.table(data), context=context) if not any(dtype is None for dtype in schema.values()): from narwhals.schema import Schema pa_schema = Schema(cast("IntoSchema", schema)).to_arrow() if pa_schema and not data: native = pa_schema.empty_table() else: native = pa.Table.from_pydict(data, schema=pa_schema) return cls.from_native(native, context=context) if context._implementation._backend_version() < (14,): msg = "Passing `None` dtype in `from_dict` requires PyArrow>=14" raise NotImplementedError(msg) version = context._version # NOTE: stubs don't allow `ChunkedArray` values, but `pa.table` accepts them arrays: Mapping[str, Any] = { name: chunked_array( [data[name] if data else []], narwhals_to_native_dtype(nw_dtype, version=version) if nw_dtype is not None else None, ) for name, nw_dtype in schema.items() } return cls.from_native(pa.table(arrays), context=context) @classmethod def from_dicts( cls, data: Sequence[Mapping[str, Any]], /, *, context: _LimitedContext, schema: Mapping[str, IntoDType | None] | None, ) -> Self: from narwhals.schema import Schema if schema and any(dtype is None for dtype in schema.values()): msg = "`from_dicts` with `schema` where any dtype is `None` is not supported for PyArrow." raise NotImplementedError(msg) pa_schema = ( Schema(cast("IntoSchema", schema)).to_arrow() if schema is not None else schema ) if pa_schema and not data: native = pa_schema.empty_table() else: native = pa.Table.from_pylist(data, schema=pa_schema) return cls.from_native(native, context=context) @staticmethod def _is_native(obj: pa.Table | Any) -> TypeIs[pa.Table]: return isinstance(obj, pa.Table) @classmethod def from_native(cls, data: pa.Table, /, *, context: _LimitedContext) -> Self: return cls(data, version=context._version, validate_column_names=True) @classmethod def from_numpy( cls, data: _2DArray, /, *, context: _LimitedContext, schema: Mapping[str, IntoDType] | Sequence[str] | None, ) -> Self: from narwhals.schema import Schema arrays = [pa.array(val) for val in data.T] if isinstance(schema, (Mapping, Schema)): native = pa.Table.from_arrays(arrays, schema=Schema(schema).to_arrow()) else: native = pa.Table.from_arrays(arrays, cls._numpy_column_names(data, schema)) return cls.from_native(native, context=context) def __narwhals_namespace__(self) -> ArrowNamespace: from narwhals._arrow.namespace import ArrowNamespace return ArrowNamespace(version=self._version) def __native_namespace__(self) -> ModuleType: if self._implementation is Implementation.PYARROW: return self._implementation.to_native_namespace() msg = f"Expected pyarrow, got: {type(self._implementation)}" # pragma: no cover raise AssertionError(msg) def __narwhals_dataframe__(self) -> Self: return self def __narwhals_lazyframe__(self) -> Self: return self def _with_version(self, version: Version) -> Self: return self.__class__(self.native, version=version, validate_column_names=False) def _with_native(self, df: pa.Table, *, validate_column_names: bool = True) -> Self: return self.__class__( df, version=self._version, validate_column_names=validate_column_names ) @property def shape(self) -> tuple[int, int]: return self.native.shape def __len__(self) -> int: return len(self.native) def row(self, index: int) -> tuple[Any, ...]: return tuple(self.native.take([index]).to_pylist()[0].values()) @overload def rows(self, *, named: Literal[True]) -> list[dict[str, Any]]: ... @overload def rows(self, *, named: Literal[False]) -> list[tuple[Any, ...]]: ... @overload def rows(self, *, named: bool) -> list[tuple[Any, ...]] | list[dict[str, Any]]: ... def rows(self, *, named: bool) -> list[tuple[Any, ...]] | list[dict[str, Any]]: if not named: return list(self.iter_rows(named=False, buffer_size=512)) # type: ignore[return-value] return self.native.to_pylist() def iter_columns(self) -> Iterator[ArrowSeries]: for name, series in zip(self.columns, self.native.itercolumns(), strict=True): yield ArrowSeries.from_native(series, context=self, name=name) _iter_columns = iter_columns def iter_rows( self, *, named: bool, buffer_size: int ) -> Iterator[tuple[Any, ...]] | Iterator[dict[str, Any]]: df = self.native num_rows = df.num_rows if not named: for i in range(0, num_rows, buffer_size): rows = df[i : i + buffer_size].to_pydict().values() yield from zip(*rows, strict=True) else: for i in range(0, num_rows, buffer_size): yield from df[i : i + buffer_size].to_pylist() def get_column(self, name: str) -> ArrowSeries: if not isinstance(name, str): msg = f"Expected str, got: {type(name)}" raise TypeError(msg) return ArrowSeries.from_native(self.native[name], context=self, name=name) def __array__(self, dtype: Any, *, copy: bool | None) -> _2DArray: return self.native.__array__(dtype, copy=copy) def _gather(self, rows: SizedMultiIndexSelector[ChunkedArrayAny]) -> Self: if len(rows) == 0: return self._with_native(self.native.slice(0, 0)) if self._backend_version < (18,) and isinstance(rows, tuple): rows = list(rows) return self._with_native(self.native.take(rows)) def _gather_slice(self, rows: _SliceIndex | range) -> Self: start = rows.start or 0 stop = rows.stop if rows.stop is not None else len(self.native) if start < 0: start = len(self.native) + start if stop < 0: stop = len(self.native) + stop if rows.step is not None and rows.step != 1: msg = "Slicing with step is not supported on PyArrow tables" raise NotImplementedError(msg) return self._with_native(self.native.slice(start, stop - start)) def _select_slice_name(self, columns: _SliceName) -> Self: start, stop, step = convert_str_slice_to_int_slice(columns, self.columns) return self._with_native(self.native.select(self.columns[start:stop:step])) def _select_slice_index(self, columns: _SliceIndex | range) -> Self: return self._with_native( self.native.select(self.columns[columns.start : columns.stop : columns.step]) ) def _select_multi_index( self, columns: SizedMultiIndexSelector[ChunkedArrayAny] ) -> Self: selector: Sequence[int] if isinstance(columns, pa.ChunkedArray): # TODO @dangotbanned: Fix upstream with `pa.ChunkedArray.to_pylist(self) -> list[Any]:` selector = cast("Sequence[int]", columns.to_pylist()) # TODO @dangotbanned: Fix upstream, it is actually much narrower # **Doesn't accept `ndarray`** elif is_numpy_array_1d(columns): selector = columns.tolist() elif MYPY: selector = columns # type: ignore[assignment] else: selector = columns return self._with_native(self.native.select(selector)) def _select_multi_name( self, columns: SizedMultiNameSelector[ChunkedArrayAny] ) -> Self: selector: Sequence[str] | _1DArray if isinstance(columns, pa.ChunkedArray): # TODO @dangotbanned: Fix upstream with `pa.ChunkedArray.to_pylist(self) -> list[Any]:` selector = cast("Sequence[str]", columns.to_pylist()) else: selector = columns # NOTE: Fixed in https://github.com/zen-xu/pyarrow-stubs/pull/221 return self._with_native(self.native.select(selector)) # pyright: ignore[reportArgumentType] @property def schema(self) -> dict[str, DType]: return { field.name: native_to_narwhals_dtype(field.type, self._version) for field in self.native.schema } def collect_schema(self) -> dict[str, DType]: return self.schema def estimated_size(self, unit: SizeUnit) -> int | float: sz = self.native.nbytes return scale_bytes(sz, unit) explode = not_implemented() @property def columns(self) -> list[str]: return self.native.column_names def simple_select(self, *column_names: str) -> Self: return self._with_native( self.native.select(list(column_names)), validate_column_names=False ) def select(self, *exprs: ArrowExpr) -> Self: new_series = self._evaluate_exprs(*exprs) if not new_series: # return empty dataframe, like Polars does return self._with_native( self.native.__class__.from_arrays([]), validate_column_names=False ) names = [s.name for s in new_series] align = new_series[0]._align_full_broadcast reshaped = align(*new_series) df = pa.Table.from_arrays([s.native for s in reshaped], names=names) return self._with_native(df, validate_column_names=True) def _extract_comparand(self, other: ArrowSeries) -> ChunkedArrayAny: length = len(self) if not other._broadcast: if (len_other := len(other)) != length: msg = f"Expected object of length {length}, got: {len_other}." raise ShapeError(msg) return other.native value = other.native[0] return pa.chunked_array([pa.repeat(value, length)]) def with_columns(self, *exprs: ArrowExpr) -> Self: # NOTE: We use a faux-mutable variable and repeatedly "overwrite" (native_frame) # All `pyarrow` data is immutable, so this is fine native_frame = self.native new_columns = self._evaluate_exprs(*exprs) columns = self.columns for col_value in new_columns: col_name = col_value.name column = self._extract_comparand(col_value) native_frame = ( native_frame.set_column(columns.index(col_name), col_name, column=column) if col_name in columns else native_frame.append_column(col_name, column=column) ) return self._with_native(native_frame, validate_column_names=False) def group_by( self, keys: Sequence[str] | Sequence[ArrowExpr], *, drop_null_keys: bool ) -> ArrowGroupBy: from narwhals._arrow.group_by import ArrowGroupBy return ArrowGroupBy(self, keys, drop_null_keys=drop_null_keys) def join( self, other: Self, *, how: JoinStrategy, left_on: Sequence[str] | None, right_on: Sequence[str] | None, suffix: str, ) -> Self: how_to_join_map: dict[str, JoinType] = { "anti": "left anti", "semi": "left semi", "inner": "inner", "left": "left outer", "full": "full outer", } if how == "cross": plx = self.__narwhals_namespace__() key_token = generate_temporary_column_name( n_bytes=8, columns=[*self.columns, *other.columns] ) return self._with_native( self.with_columns(plx.lit(0, None).alias(key_token).broadcast()) .native.join( other.with_columns( plx.lit(0, None).alias(key_token).broadcast() ).native, keys=key_token, right_keys=key_token, join_type="inner", right_suffix=suffix, ) .drop([key_token]) ) coalesce_keys = how != "full" # polars full join does not coalesce keys return self._with_native( self.native.join( other.native, keys=left_on or [], # type: ignore[arg-type] right_keys=right_on, # type: ignore[arg-type] join_type=how_to_join_map[how], right_suffix=suffix, coalesce_keys=coalesce_keys, ) ) join_asof = not_implemented() def drop(self, columns: Sequence[str], *, strict: bool) -> Self: to_drop = parse_columns_to_drop(self, columns, strict=strict) return self._with_native(self.native.drop(to_drop), validate_column_names=False) def drop_nulls(self, subset: Sequence[str] | None) -> Self: if subset is None: return self._with_native(self.native.drop_null(), validate_column_names=False) plx = self.__narwhals_namespace__() mask = ~plx.any_horizontal(plx.col(*subset).is_null(), ignore_nulls=True) return self.filter(mask) def sort(self, *by: str, descending: bool | Sequence[bool], nulls_last: bool) -> Self: if isinstance(descending, bool): order: Order = "descending" if descending else "ascending" sorting: list[tuple[str, Order]] = [(key, order) for key in by] else: sorting = [ (key, "descending" if is_descending else "ascending") for key, is_descending in zip(by, descending, strict=True) ] null_placement: NullPlacement = "at_end" if nulls_last else "at_start" # NOTE: sort on `sortable_table`, take from the original, so that # dictionary-encoded (categorical) keys keep their encoding in the output. indices = sort_indices(sortable_table(self.native, by), sorting, null_placement) return self._with_native(self.native.take(indices), validate_column_names=False) def top_k(self, k: int, *, by: Iterable[str], reverse: bool | Sequence[bool]) -> Self: if isinstance(reverse, bool): order: Order = "ascending" if reverse else "descending" sorting: list[tuple[str, Order]] = [(key, order) for key in by] else: sorting = [ (key, "ascending" if is_ascending else "descending") for key, is_ascending in zip(by, reverse, strict=True) ] keys = [name for name, _ in sorting] table = sortable_table(self.native, keys) indices = pc.select_k_unstable(table, k, sorting) # type: ignore[call-overload] return self._with_native(self.native.take(indices), validate_column_names=False) def to_pandas(self) -> pd.DataFrame: return self.native.to_pandas() def to_polars(self) -> pl.DataFrame: import polars as pl # ignore-banned-import return pl.from_arrow(self.native) # type: ignore[return-value] def to_numpy(self, dtype: Any = None, *, copy: bool | None = None) -> _2DArray: import numpy as np # ignore-banned-import arr: Any = np.column_stack([col.to_numpy() for col in self.native.columns]) return arr @overload def to_dict(self, *, as_series: Literal[True]) -> dict[str, ArrowSeries]: ... @overload def to_dict(self, *, as_series: Literal[False]) -> dict[str, list[Any]]: ... def to_dict( self, *, as_series: bool ) -> dict[str, ArrowSeries] | dict[str, list[Any]]: it = self.iter_columns() if as_series: return {ser.name: ser for ser in it} return {ser.name: ser.to_list() for ser in it} def with_row_index(self, name: str, order_by: Sequence[str] | None) -> Self: plx = self.__narwhals_namespace__() data = arange(0, len(self), 1) if order_by is None: row_index = plx._expr._from_series( plx._series.from_iterable(data, context=self, name=name) ) return self.select(row_index, plx.all()) sorting: list[tuple[str, Order]] = [(name, "ascending") for name in order_by] indices = sort_indices(sortable_table(self.native, order_by), sorting, "at_start") if self._backend_version < (20,): new_col = data.take(pc.sort_indices(indices)) else: new_col = pc.scatter(data, indices.cast(pa.int64())) # type: ignore[attr-defined] return self._with_native(self.native.add_column(0, name, new_col)) def filter(self, predicate: ArrowExpr) -> Self: mask_native = self._evaluate_single_output_expr(predicate).native return self._with_native( self.native.filter(mask_native), validate_column_names=False ) def head(self, n: int) -> Self: df = self.native if n >= 0: return self._with_native(df.slice(0, n), validate_column_names=False) num_rows = df.num_rows return self._with_native( df.slice(0, max(0, num_rows + n)), validate_column_names=False ) def tail(self, n: int) -> Self: df = self.native if n >= 0: num_rows = df.num_rows return self._with_native( df.slice(max(0, num_rows - n)), validate_column_names=False ) return self._with_native(df.slice(abs(n)), validate_column_names=False) def lazy( self, backend: _LazyAllowedImpl | None = None, *, session: SparkSession | None = None, ) -> CompliantLazyFrameAny: if backend is None: return self if backend is Implementation.DUCKDB: import duckdb # ignore-banned-import from narwhals._duckdb.dataframe import DuckDBLazyFrame _df = self.native return DuckDBLazyFrame( duckdb.table("_df"), validate_backend_version=True, version=self._version ) if backend is Implementation.POLARS: import polars as pl # ignore-banned-import from narwhals._polars.dataframe import PolarsLazyFrame return PolarsLazyFrame( cast("pl.DataFrame", pl.from_arrow(self.native)).lazy(), validate_backend_version=True, version=self._version, ) if backend is Implementation.DASK: import dask.dataframe as dd # ignore-banned-import from narwhals._dask.dataframe import DaskLazyFrame return DaskLazyFrame( dd.from_pandas(self.native.to_pandas()), validate_backend_version=True, version=self._version, ) if backend is Implementation.IBIS: import ibis # ignore-banned-import from narwhals._ibis.dataframe import IbisLazyFrame return IbisLazyFrame( ibis.memtable(self.native, columns=self.columns), validate_backend_version=True, version=self._version, ) if backend.is_spark_like(): from narwhals._spark_like.dataframe import SparkLikeLazyFrame if session is None: msg = "Spark like backends require `session` to be not None." raise ValueError(msg) return SparkLikeLazyFrame._from_compliant_dataframe( self, session=session, implementation=backend, version=self._version ) raise AssertionError # pragma: no cover def collect( self, backend: _EagerAllowedImpl | None, **kwargs: Any ) -> CompliantDataFrameAny: if backend is Implementation.PYARROW or backend is None: return ArrowDataFrame( self.native, version=self._version, validate_column_names=False ) if backend is Implementation.PANDAS: from narwhals._pandas_like.dataframe import PandasLikeDataFrame return PandasLikeDataFrame( self.native.to_pandas(), implementation=Implementation.PANDAS, validate_backend_version=True, version=self._version, validate_column_names=False, ) if backend is Implementation.POLARS: import polars as pl # ignore-banned-import from narwhals._polars.dataframe import PolarsDataFrame return PolarsDataFrame( cast("pl.DataFrame", pl.from_arrow(self.native)), validate_backend_version=True, version=self._version, ) msg = f"Unsupported `backend` value: {backend}" # pragma: no cover raise AssertionError(msg) # pragma: no cover def clone(self) -> Self: return self._with_native(self.native, validate_column_names=False) def item(self, row: int | None, column: int | str | None) -> Any: from narwhals._arrow.series import maybe_extract_py_scalar if row is None and column is None: if (shape := self.shape) != (1, 1): msg = ( 'can only call `.item()` without "row" or "column" values if the ' f"DataFrame has a single element; shape={shape!r}" ) raise ValueError(msg) return maybe_extract_py_scalar(self.native[0][0], return_py_scalar=True) if row is None or column is None: msg = "cannot call `.item()` with only one of `row` or `column`" raise ValueError(msg) _col = self.columns.index(column) if isinstance(column, str) else column return maybe_extract_py_scalar(self.native[_col][row], return_py_scalar=True) def rename(self, mapping: Mapping[str, str]) -> Self: names: dict[str, str] | list[str] if self._backend_version >= (17,): names = cast("dict[str, str]", mapping) else: # pragma: no cover names = [mapping.get(c, c) for c in self.columns] return self._with_native(self.native.rename_columns(names)) def write_parquet(self, file: str | Path | BytesIO) -> None: import pyarrow.parquet as pp pp.write_table(self.native, file) @overload def write_csv(self, file: None) -> str: ... @overload def write_csv(self, file: str | Path | BytesIO) -> None: ... def write_csv(self, file: str | Path | BytesIO | None) -> str | None: import pyarrow.csv as pa_csv if file is None: csv_buffer = pa.BufferOutputStream() pa_csv.write_csv(self.native, csv_buffer) return csv_buffer.getvalue().to_pybytes().decode() pa_csv.write_csv(self.native, file) return None def is_unique(self) -> ArrowSeries: import numpy as np # ignore-banned-import col_token = generate_temporary_column_name(n_bytes=8, columns=self.columns) row_index = pa.array(np.arange(len(self))) keep_idx = ( self.native.append_column(col_token, row_index) .group_by(self.columns) .aggregate([(col_token, "min"), (col_token, "max")]) ) native = pa.chunked_array( [ pc.and_( pc.is_in(row_index, keep_idx[f"{col_token}_min"]), pc.is_in(row_index, keep_idx[f"{col_token}_max"]), ) ] ) return ArrowSeries.from_native(native, context=self) def unique( self, subset: Sequence[str] | None, *, keep: UniqueKeepStrategy, maintain_order: bool | None = None, order_by: Sequence[str] | None, ) -> Self: # The param `maintain_order` is only here for compatibility with the Polars API # and has no effect on the output. import numpy as np # ignore-banned-import if subset and (error := self._check_columns_exist(subset)): raise error subset = list(subset or self.columns) if keep in {"any", "first", "last"}: from narwhals._arrow.group_by import ArrowGroupBy agg_func = ArrowGroupBy._REMAP_UNIQUE[keep] col_token = generate_temporary_column_name(n_bytes=8, columns=self.columns) if order_by and maintain_order: idx_token = generate_temporary_column_name( n_bytes=8, columns=[*self.columns, col_token] ) df = ( self.with_row_index(idx_token, order_by=None) .sort(*order_by, nulls_last=False, descending=False) .unique(subset=subset, keep=keep, maintain_order=False, order_by=None) ) return df.sort(idx_token, descending=False, nulls_last=False).drop( [idx_token], strict=False ) if order_by: native = self.sort(*order_by, nulls_last=False, descending=False).native else: native = self.native keep_idx_native = ( native.append_column(col_token, pa.array(np.arange(len(self)))) .group_by(subset) .aggregate([(col_token, agg_func)]) .column(f"{col_token}_{agg_func}") ) return self._with_native( native.take(keep_idx_native), validate_column_names=False ) keep_idx = self.simple_select(*subset).is_unique() plx = self.__narwhals_namespace__() return self.filter(plx._expr._from_series(keep_idx)) def gather_every(self, n: int, offset: int) -> Self: return self._with_native(self.native[offset::n], validate_column_names=False) def to_arrow(self) -> pa.Table: return self.native def sample(self, n: int, *, with_replacement: bool, seed: int | None) -> Self: import numpy as np # ignore-banned-import rng = np.random.default_rng(seed=seed) indices = rng.choice(np.arange(n), size=n, replace=with_replacement) return self._with_native( self.native.take(pa.array(indices, type=pa.uint64())), validate_column_names=False, ) def unpivot( self, on: Sequence[str] | None, index: Sequence[str] | None, variable_name: str, value_name: str, ) -> Self: # TODO(Unassigned): Even with promote_options="permissive", pyarrow does not # upcast numeric to non-numeric (e.g. string) datatypes n = len(self) index = [] if index is None else list(index) on_ = (c for c in self.columns if c not in index) if on is None else iter(on) index_cols = self.native.select(index) column = self.native.column tables = ( index_cols.append_column(variable_name, repeat(name, n)).append_column( value_name, column(name) ) for name in on_ ) return self._with_native(concat_tables(tables, "permissive")) pivot = not_implemented() narwhals-2.26.0/src/narwhals/_arrow/expr.py000066400000000000000000000223471525000603700206550ustar00rootroot00000000000000from __future__ import annotations from typing import TYPE_CHECKING, Any, cast import pyarrow as pa import pyarrow.compute as pc from narwhals._arrow.series import ArrowSeries from narwhals._compliant import EagerExpr from narwhals._expression_parsing import evaluate_nodes, evaluate_output_names_and_aliases from narwhals._utils import ( Implementation, generate_temporary_column_name, not_implemented, ) from narwhals.functions import col as nw_col if TYPE_CHECKING: from collections.abc import Sequence from typing_extensions import Self from narwhals._arrow.dataframe import ArrowDataFrame from narwhals._arrow.namespace import ArrowNamespace from narwhals._compliant.typing import AliasNames, EvalNames, EvalSeries from narwhals._utils import Version, _LimitedContext class ArrowExpr(EagerExpr["ArrowDataFrame", ArrowSeries]): _implementation: Implementation = Implementation.PYARROW def __init__( self, call: EvalSeries[ArrowDataFrame, ArrowSeries], *, evaluate_output_names: EvalNames[ArrowDataFrame], alias_output_names: AliasNames | None, version: Version, implementation: Implementation = Implementation.PYARROW, ) -> None: self._call = call self._evaluate_output_names = evaluate_output_names self._alias_output_names = alias_output_names self._version = version @classmethod def from_column_names( cls: type[Self], evaluate_column_names: EvalNames[ArrowDataFrame], /, *, context: _LimitedContext, ) -> Self: def func(df: ArrowDataFrame) -> list[ArrowSeries]: try: return [ ArrowSeries( df.native[column_name], name=column_name, version=df._version ) for column_name in evaluate_column_names(df) ] except KeyError as e: if error := df._check_columns_exist(evaluate_column_names(df)): raise error from e raise return cls( func, evaluate_output_names=evaluate_column_names, alias_output_names=None, version=context._version, ) @classmethod def from_column_indices(cls, *column_indices: int, context: _LimitedContext) -> Self: def func(df: ArrowDataFrame) -> list[ArrowSeries]: tbl = df.native cols = df.columns return [ ArrowSeries.from_native(tbl[i], name=cols[i], context=df) for i in column_indices ] return cls( func, evaluate_output_names=cls._eval_names_indices(column_indices), alias_output_names=None, version=context._version, ) def __narwhals_namespace__(self) -> ArrowNamespace: from narwhals._arrow.namespace import ArrowNamespace return ArrowNamespace(version=self._version) def _reuse_series_extra_kwargs( self, *, returns_scalar: bool = False ) -> dict[str, Any]: return {"_return_py_scalar": False} if returns_scalar else {} def _over_without_partition_by(self, order_by: Sequence[str]) -> Self: # e.g. `nw.col('a').cum_sum().order_by(key)` # which we can always easily support, as it doesn't require grouping. assert order_by # noqa: S101 meta = self._metadata def func(df: ArrowDataFrame) -> Sequence[ArrowSeries]: token = generate_temporary_column_name(8, df.columns) df = df.with_row_index(token, order_by=None).sort( *order_by, descending=False, nulls_last=False ) results = self(df.drop([token], strict=True)) if meta is not None and meta.is_scalar_like: # We need to broadcast the results to the original size, since # `over` is a length-preserving operation. size = len(df) return [s._with_native(pa.repeat(s.item(), size)) for s in results] # TODO(marco): is there a way to do this efficiently without # doing 2 sorts? Here we're sorting the dataframe and then # again calling `sort_indices`. `ArrowSeries.scatter` would also sort. sorting_indices = pc.sort_indices(df.get_column(token).native) return [s._with_native(s.native.take(sorting_indices)) for s in results] return self.__class__( func, evaluate_output_names=self._evaluate_output_names, alias_output_names=self._alias_output_names, version=self._version, ) def over(self, partition_by: Sequence[str], order_by: Sequence[str]) -> Self: if not partition_by: assert order_by # noqa: S101 return self._over_without_partition_by(order_by) # We have something like prev.leaf().over(...) (e.g. `nw.col('a').sum().over('b')`), where: # - `prev` must be elementwise (in the example: `nw.col('a')`) # - `leaf` must be an aggregation (in the example: `sum`) # # We first evaluate `prev` as-is, and then evaluate `leaf().over(...)`` by doing a `group_by`. meta = self._metadata if partition_by and ( not meta.current_node.kind.is_scalar_like or (meta.prev is not None and not meta.prev.is_elementwise) ): msg = ( "Only elementary aggregations are supported for `.over` in PyArrow backend " "when `partition_by` is specified.\n\n" "Please see: " "https://narwhals-dev.github.io/narwhals/concepts/improve_group_by_operation/" ) raise NotImplementedError(msg) nodes = list(reversed(list(self._metadata.iter_nodes_reversed()))) def func(df: ArrowDataFrame) -> Sequence[ArrowSeries]: # noqa: PLR0914 plx = self.__narwhals_namespace__() if meta.prev is not None: df = df.with_columns(cast("ArrowExpr", evaluate_nodes(nodes[:-1], plx))) _, aliases = evaluate_output_names_and_aliases(self, df, []) leaf_ce = cast( "ArrowExpr", nw_col(*aliases)._append_node(nodes[-1])._to_compliant_expr(plx), ) else: _, aliases = evaluate_output_names_and_aliases(self, df, []) leaf_ce = self if order_by: df = df.sort(*order_by, descending=False, nulls_last=False) if overlap := set(aliases).intersection(partition_by): # E.g. `df.select(nw.all().sum().over('a'))`. This is well-defined, # we just don't support it yet. msg = ( f"Column names {overlap} appear in both expression output names and in `over` keys.\n" "This is not yet supported." ) raise NotImplementedError(msg) partition_tbl = df.simple_select(*partition_by) has_nulls = [ca.null_count > 0 for ca in partition_tbl.native.columns] if not any(has_nulls): tmp = df.group_by(partition_by, drop_null_keys=False).agg(leaf_ce) tmp = partition_tbl.join( tmp, how="left", left_on=partition_by, right_on=partition_by, suffix="_right", ) return [tmp.get_column(alias) for alias in aliases] tbl_native, current_cols = df.native, df.columns plx = self.__narwhals_namespace__() group_keys: list[str] = [] encoded_cols: list[ArrowExpr] = [] for col_name, has_null in zip(partition_tbl.columns, has_nulls, strict=False): if not has_null: group_keys.append(col_name) else: tmp_name = generate_temporary_column_name(8, current_cols) group_keys.append(tmp_name) current_cols.append(tmp_name) indices = plx._series.from_native( ( tbl_native.column(col_name) .dictionary_encode("encode") .combine_chunks() .indices # type: ignore[attr-defined] ), context=plx, name=tmp_name, ) encoded_cols.append(plx._expr._from_series(indices)) tbl_encoded = df.with_columns(*encoded_cols) windowed = tbl_encoded.group_by(group_keys, drop_null_keys=False).agg(leaf_ce) ret = tbl_encoded.simple_select(*group_keys).join( windowed, left_on=group_keys, right_on=group_keys, how="inner", suffix="_right", ) return [ret.get_column(alias) for alias in aliases] return self.__class__( func, evaluate_output_names=self._evaluate_output_names, alias_output_names=self._alias_output_names, version=self._version, ) ewm_mean = not_implemented() narwhals-2.26.0/src/narwhals/_arrow/group_by.py000066400000000000000000000217441525000603700215250ustar00rootroot00000000000000from __future__ import annotations import collections from typing import TYPE_CHECKING, Any, ClassVar import pyarrow as pa import pyarrow.compute as pc from narwhals._arrow.utils import ( BACKEND_VERSION, cast_to_comparable_string_types, extract_py_scalar, ) from narwhals._compliant import EagerGroupBy from narwhals._expression_parsing import evaluate_output_names_and_aliases from narwhals._utils import generate_temporary_column_name, requires if TYPE_CHECKING: from collections.abc import Iterator, Mapping, Sequence from narwhals._arrow.dataframe import ArrowDataFrame from narwhals._arrow.expr import ArrowExpr from narwhals._arrow.typing import ( # type: ignore[attr-defined] AggregateOptions, Aggregation, Incomplete, ) from narwhals._compliant.typing import NarwhalsAggregation from narwhals.typing import UniqueKeepStrategy class ArrowGroupBy(EagerGroupBy["ArrowDataFrame", "ArrowExpr", "Aggregation"]): _REMAP_AGGS: ClassVar[Mapping[NarwhalsAggregation, Aggregation]] = { "sum": "sum", "mean": "mean", "median": "approximate_median", "max": "max", "min": "min", "std": "stddev", "var": "variance", "len": "count", "n_unique": "count_distinct", "count": "count", "all": "all", "any": "any", "first": "first", "last": "last", "any_value": "first", } _REMAP_UNIQUE: ClassVar[Mapping[UniqueKeepStrategy, Aggregation]] = { "any": "min", "first": "min", "last": "max", } _OPTION_COUNT_ALL: ClassVar[frozenset[NarwhalsAggregation]] = frozenset( ("len", "n_unique") ) _OPTION_COUNT_VALID: ClassVar[frozenset[NarwhalsAggregation]] = frozenset(("count",)) _OPTION_ORDERED: ClassVar[frozenset[NarwhalsAggregation]] = frozenset( ("first", "last", "any_value") ) _OPTION_VARIANCE: ClassVar[frozenset[NarwhalsAggregation]] = frozenset(("std", "var")) _OPTION_SCALAR: ClassVar[frozenset[NarwhalsAggregation]] = frozenset( ("any", "all", "sum") ) def __init__( self, df: ArrowDataFrame, keys: Sequence[ArrowExpr] | Sequence[str], /, *, drop_null_keys: bool, ) -> None: self._df = df frame, self._keys, self._output_key_names = self._parse_keys(df, keys=keys) self._compliant_frame = frame.drop_nulls(self._keys) if drop_null_keys else frame self._drop_null_keys = drop_null_keys def _configure_agg( self, expr: ArrowExpr, / ) -> tuple[Aggregation, AggregateOptions | None]: option: AggregateOptions | None = None function_name = self._leaf_name(expr) kwargs = self._kwargs(expr) if function_name in self._OPTION_VARIANCE: ddof = kwargs["ddof"] option = pc.VarianceOptions(ddof=ddof) elif function_name in self._OPTION_COUNT_ALL: option = pc.CountOptions(mode="all") elif function_name in self._OPTION_COUNT_VALID: option = pc.CountOptions(mode="only_valid") elif function_name in self._OPTION_SCALAR: option = pc.ScalarAggregateOptions(min_count=0) elif function_name in self._OPTION_ORDERED: ignore_nulls = kwargs.get("ignore_nulls", False) option = pc.ScalarAggregateOptions(skip_nulls=ignore_nulls) return self._remap_expr_name(function_name), option def _configure_grouped(self, *exprs: ArrowExpr) -> pa.TableGroupBy: order_by = () use_threads = True for expr in exprs: md = next(expr._metadata.op_nodes_reversed()) if md.name not in self._OPTION_ORDERED: continue # [pyarrow-36709]: https://github.com/apache/arrow/issues/36709 use_threads = False if _current_order_by := md.kwargs.get("order_by", ()): if order_by and _current_order_by != order_by: msg = f"Only one `order_by` can be specified in `group_by`. Found both {order_by} and {_current_order_by}." raise NotImplementedError(msg) order_by = _current_order_by if not use_threads and BACKEND_VERSION < (14,): # pragma: no cover msg = ( f"Using `first/last` in a `group_by().agg(...)` context is only available in 'pyarrow>=14.0.0', " f"found version {requires._unparse_version(BACKEND_VERSION)!r}.\n\n" f"See https://github.com/apache/arrow/issues/36709" ) raise NotImplementedError(msg) if order_by: return pa.TableGroupBy( self.compliant.sort(*order_by, descending=False, nulls_last=False).native, self._keys, use_threads=use_threads, ) if not use_threads: return pa.TableGroupBy(self.compliant.native, self._keys, use_threads=False) # TODO(unassigned): combine with `return` above once PyArrow 15 is the minimum. return pa.TableGroupBy(self.compliant.native, self._keys) def agg(self, *exprs: ArrowExpr) -> ArrowDataFrame: self._ensure_all_simple(exprs) grouped = self._configure_grouped(*exprs) aggs: list[tuple[str, Aggregation, AggregateOptions | None]] = [] expected_pyarrow_column_names: list[str] = self._keys.copy() new_column_names: list[str] = self._keys.copy() exclude = (*self._keys, *self._output_key_names) for expr in exprs: output_names, aliases = evaluate_output_names_and_aliases( expr, self.compliant, exclude ) md = expr._metadata op_nodes_reversed = list(md.op_nodes_reversed()) if len(op_nodes_reversed) == 1: # e.g. `agg(nw.len())` if op_nodes_reversed[0].name != "len": # pragma: no cover msg = "Safety assertion failed, please report a bug to https://github.com/narwhals-dev/narwhals/issues" raise AssertionError(msg) new_column_names.append(aliases[0]) expected_pyarrow_column_names.append(f"{self._keys[0]}_count") aggs.append((self._keys[0], "count", pc.CountOptions(mode="all"))) continue function_name, option = self._configure_agg(expr) new_column_names.extend(aliases) expected_pyarrow_column_names.extend( [f"{output_name}_{function_name}" for output_name in output_names] ) aggs.extend( [(output_name, function_name, option) for output_name in output_names] ) result_simple = grouped.aggregate(aggs) # Rename columns, being very careful expected_old_names_indices: dict[str, list[int]] = collections.defaultdict(list) for idx, item in enumerate(expected_pyarrow_column_names): expected_old_names_indices[item].append(idx) if not ( set(result_simple.column_names) == set(expected_pyarrow_column_names) and len(result_simple.column_names) == len(expected_pyarrow_column_names) ): # pragma: no cover msg = ( f"Safety assertion failed, expected {expected_pyarrow_column_names} " f"got {result_simple.column_names}, " "please report a bug at https://github.com/narwhals-dev/narwhals/issues" ) raise AssertionError(msg) index_map: list[int] = [ expected_old_names_indices[item].pop(0) for item in result_simple.column_names ] new_column_names = [new_column_names[i] for i in index_map] result_simple = result_simple.rename_columns(new_column_names) return self.compliant._with_native(result_simple).rename( dict(zip(self._keys, self._output_key_names, strict=False)) ) def __iter__(self) -> Iterator[tuple[Any, ArrowDataFrame]]: col_token = generate_temporary_column_name( n_bytes=8, columns=self.compliant.columns ) null_token: str = "__null_token_value__" # noqa: S105 table = self.compliant.native it, separator_scalar = cast_to_comparable_string_types( *(table[key] for key in self._keys), separator="" ) # NOTE: stubs indicate `separator` must also be a `ChunkedArray` # Reality: `str` is fine concat_str: Incomplete = pc.binary_join_element_wise key_values = concat_str( *it, separator_scalar, null_handling="replace", null_replacement=null_token ) table = table.add_column(i=0, field_=col_token, column=key_values) for v in pc.unique(key_values): t = self.compliant._with_native( table.filter(pc.equal(table[col_token], v)).drop([col_token]) ) row = t.simple_select(*self._keys).row(0) yield ( tuple(extract_py_scalar(el) for el in row), t.simple_select(*self._df.columns), ) narwhals-2.26.0/src/narwhals/_arrow/namespace.py000066400000000000000000000355671525000603700216430ustar00rootroot00000000000000from __future__ import annotations import operator from functools import reduce from itertools import chain from typing import TYPE_CHECKING, Any, Literal, cast import pyarrow as pa import pyarrow.compute as pc from narwhals._arrow.dataframe import ArrowDataFrame from narwhals._arrow.expr import ArrowExpr from narwhals._arrow.selectors import ArrowSelectorNamespace from narwhals._arrow.series import ArrowSeries from narwhals._arrow.utils import build_list_array, cast_to_comparable_string_types from narwhals._compliant import EagerNamespace from narwhals._expression_parsing import ( combine_alias_output_names, combine_evaluate_output_names, ) from narwhals._utils import Implementation if TYPE_CHECKING: from collections.abc import Iterator, Sequence from narwhals._arrow.typing import ChunkedArrayAny, Incomplete, ScalarAny from narwhals._utils import Version from narwhals.typing import ( CorrelationMethod, IntoDType, NormalizedPath, PythonLiteral, ) class ArrowNamespace( EagerNamespace[ArrowDataFrame, ArrowSeries, ArrowExpr, pa.Table, "ChunkedArrayAny"] ): _implementation = Implementation.PYARROW @property def _dataframe(self) -> type[ArrowDataFrame]: return ArrowDataFrame @property def _expr(self) -> type[ArrowExpr]: return ArrowExpr @property def _series(self) -> type[ArrowSeries]: return ArrowSeries def __init__(self, *, version: Version) -> None: self._version = version def read_csv( self, source: NormalizedPath, *, separator: str = ",", **kwds: Any ) -> ArrowDataFrame: from pyarrow import csv if (parse_options := kwds.get("parse_options")) is not None: if cast("csv.ParseOptions", parse_options).delimiter != separator: msg = ( "`separator` and `parse_options.delimiter` do not match: " f"`separator`={separator} and `delimiter`={parse_options.delimiter}." ) raise TypeError(msg) else: kwds["parse_options"] = csv.ParseOptions(delimiter=separator) return self._dataframe.from_native(csv.read_csv(source, **kwds), context=self) def read_parquet(self, source: NormalizedPath, **kwds: Any) -> ArrowDataFrame: from pyarrow import parquet as pq return self._dataframe.from_native(pq.read_table(source, **kwds), context=self) def extract_native( self, *series: ArrowSeries ) -> Iterator[ChunkedArrayAny | ScalarAny]: return (s.native[0] if s._broadcast else s.native for s in series) def len(self) -> ArrowExpr: # coverage bug? this is definitely hit return self._expr( # pragma: no cover lambda df: [ ArrowSeries.from_iterable([len(df.native)], name="len", context=self) ], evaluate_output_names=lambda _df: ["len"], alias_output_names=None, version=self._version, ) def lit(self, value: PythonLiteral, dtype: IntoDType | None) -> ArrowExpr: def _lit_arrow_series(_: ArrowDataFrame) -> ArrowSeries: arrow_series = ArrowSeries.from_iterable( data=[value], name="literal", context=self ) if dtype: return arrow_series.cast(dtype) return arrow_series return self._expr( lambda df: [_lit_arrow_series(df)], evaluate_output_names=lambda _df: ["literal"], alias_output_names=None, version=self._version, ) def all_horizontal(self, *exprs: ArrowExpr, ignore_nulls: bool) -> ArrowExpr: def func(df: ArrowDataFrame) -> list[ArrowSeries]: series: Iterator[ArrowSeries] = chain.from_iterable(e(df) for e in exprs) if ignore_nulls: series = (s.fill_null(True, None, None) for s in series) return [reduce(operator.and_, series)] return self._expr._from_callable( func=func, evaluate_output_names=combine_evaluate_output_names(*exprs), alias_output_names=combine_alias_output_names(*exprs), context=self, ) def any_horizontal(self, *exprs: ArrowExpr, ignore_nulls: bool) -> ArrowExpr: def func(df: ArrowDataFrame) -> list[ArrowSeries]: series: Iterator[ArrowSeries] = chain.from_iterable(e(df) for e in exprs) if ignore_nulls: series = (s.fill_null(False, None, None) for s in series) return [reduce(operator.or_, series)] return self._expr._from_callable( func=func, evaluate_output_names=combine_evaluate_output_names(*exprs), alias_output_names=combine_alias_output_names(*exprs), context=self, ) def sum_horizontal(self, *exprs: ArrowExpr) -> ArrowExpr: def func(df: ArrowDataFrame) -> list[ArrowSeries]: it = chain.from_iterable(expr(df) for expr in exprs) series = (s.fill_null(0, strategy=None, limit=None) for s in it) return [reduce(operator.add, series)] return self._expr._from_callable( func=func, evaluate_output_names=combine_evaluate_output_names(*exprs), alias_output_names=combine_alias_output_names(*exprs), context=self, ) def mean_horizontal(self, *exprs: ArrowExpr) -> ArrowExpr: int_64 = self._version.dtypes.Int64() def func(df: ArrowDataFrame) -> list[ArrowSeries]: expr_results = tuple(chain.from_iterable(expr(df) for expr in exprs)) series = [s.fill_null(0, strategy=None, limit=None) for s in expr_results] non_na = [1 - s.is_null().cast(int_64) for s in expr_results] return [reduce(operator.add, series) / reduce(operator.add, non_na)] return self._expr._from_callable( func=func, evaluate_output_names=combine_evaluate_output_names(*exprs), alias_output_names=combine_alias_output_names(*exprs), context=self, ) def min_horizontal(self, *exprs: ArrowExpr) -> ArrowExpr: def func(df: ArrowDataFrame) -> list[ArrowSeries]: init_series, *series = tuple(chain.from_iterable(expr(df) for expr in exprs)) native_series = reduce( pc.min_element_wise, [s.native for s in series], init_series.native ) return [ ArrowSeries(native_series, name=init_series.name, version=self._version) ] return self._expr._from_callable( func=func, evaluate_output_names=combine_evaluate_output_names(*exprs), alias_output_names=combine_alias_output_names(*exprs), context=self, ) def max_horizontal(self, *exprs: ArrowExpr) -> ArrowExpr: def func(df: ArrowDataFrame) -> list[ArrowSeries]: init_series, *series = tuple(chain.from_iterable(expr(df) for expr in exprs)) native_series = reduce( pc.max_element_wise, [s.native for s in series], init_series.native ) return [ ArrowSeries(native_series, name=init_series.name, version=self._version) ] return self._expr._from_callable( func=func, evaluate_output_names=combine_evaluate_output_names(*exprs), alias_output_names=combine_alias_output_names(*exprs), context=self, ) def _concat_diagonal(self, dfs: Sequence[pa.Table], /) -> pa.Table: if self._backend_version >= (14,): return pa.concat_tables(dfs, promote_options="default") return pa.concat_tables(dfs, promote=True) # pragma: no cover def _concat_horizontal(self, dfs: Sequence[pa.Table], /) -> pa.Table: names = list(chain.from_iterable(df.column_names for df in dfs)) arrays = tuple(chain.from_iterable(df.itercolumns() for df in dfs)) return pa.Table.from_arrays(arrays, names=names) def _concat_vertical(self, dfs: Sequence[pa.Table], /) -> pa.Table: cols_0 = dfs[0].column_names for i, df in enumerate(dfs[1:], start=1): if (cols_current := df.column_names) != cols_0: msg = ( "unable to vstack, column names don't match:\n" f" - dataframe 0: {cols_0}\n" f" - dataframe {i}: {cols_current}\n" ) raise TypeError(msg) return pa.concat_tables(dfs) @property def selectors(self) -> ArrowSelectorNamespace: return ArrowSelectorNamespace.from_namespace(self) def concat_str( self, *exprs: ArrowExpr, separator: str, ignore_nulls: bool ) -> ArrowExpr: def func(df: ArrowDataFrame) -> list[ArrowSeries]: series = list(chain.from_iterable(expr(df) for expr in exprs)) name = series[0].name null_handling: Literal["skip", "emit_null"] = ( "skip" if ignore_nulls else "emit_null" ) it, separator_scalar = cast_to_comparable_string_types( *self.extract_native(*series), separator=separator ) # NOTE: stubs indicate `separator` must also be a `ChunkedArray` # Reality: `str` is fine concat_str: Incomplete = pc.binary_join_element_wise compliant = self._series( concat_str(*it, separator_scalar, null_handling=null_handling), name=name, version=self._version, ) return [compliant] return self._expr._from_callable( func=func, evaluate_output_names=combine_evaluate_output_names(*exprs), alias_output_names=combine_alias_output_names(*exprs), context=self, ) def coalesce(self, *exprs: ArrowExpr) -> ArrowExpr: def func(df: ArrowDataFrame) -> list[ArrowSeries]: align = self._series._align_full_broadcast init_series, *series = align(*chain.from_iterable(expr(df) for expr in exprs)) return [ ArrowSeries( pc.coalesce(init_series.native, *(s.native for s in series)), name=init_series.name, version=self._version, ) ] return self._expr._from_callable( func=func, evaluate_output_names=combine_evaluate_output_names(*exprs), alias_output_names=combine_alias_output_names(*exprs), context=self, ) def struct(self, *exprs: ArrowExpr) -> ArrowExpr: def func(df: ArrowDataFrame) -> list[ArrowSeries]: series = tuple(chain.from_iterable(expr(df) for expr in exprs)) name = series[0].name struct_array = pc.make_struct( *(s.native if len(s) > 1 else s.native[0] for s in series), field_names=tuple(s.name for s in series), ) result = pa.chunked_array([struct_array]) return [ArrowSeries(result, name=name, version=self._version)] return self._expr._from_callable( func=func, evaluate_output_names=combine_evaluate_output_names(*exprs), alias_output_names=combine_alias_output_names(*exprs), context=self, ) def list(self, *exprs: ArrowExpr) -> ArrowExpr: def func(df: ArrowDataFrame) -> list[ArrowSeries]: series = tuple(chain.from_iterable(expr(df) for expr in exprs)) # pa.concat_arrays requires Array, not ChunkedArray result = build_list_array([s.native.combine_chunks() for s in series]) return [ArrowSeries(result, name=series[0].name, version=self._version)] return self._expr._from_callable( func=func, evaluate_output_names=combine_evaluate_output_names(*exprs), alias_output_names=combine_alias_output_names(*exprs), context=self, ) def _if_then_else( self, when: ChunkedArrayAny, then: ChunkedArrayAny, otherwise: ChunkedArrayAny | None = None, ) -> ChunkedArrayAny: otherwise = pa.nulls(len(when), then.type) if otherwise is None else otherwise return pc.if_else(when.fill_null(pa.scalar(False)), then, otherwise) def corr(self, a: ArrowExpr, b: ArrowExpr, *, method: CorrelationMethod) -> ArrowExpr: if method != "pearson": msg = "Only 'pearson' correlation is supported for Pyarrow." raise NotImplementedError(msg) def func(df: ArrowDataFrame) -> list[ArrowSeries]: a_series = df._evaluate_single_output_expr(a) arr1 = a_series.native arr2 = df._evaluate_single_output_expr(b).native valid = pc.and_(pc.is_valid(arr1), pc.is_valid(arr2)) arr1 = pc.filter(arr1, valid) arr2 = pc.filter(arr2, valid) dev1 = pc.subtract(arr1, pc.mean(arr1)) dev2 = pc.subtract(arr2, pc.mean(arr2)) covariance = pc.mean(pc.multiply(dev1, dev2)) std1 = pc.stddev(arr1) std2 = pc.stddev(arr2) correlation = pc.divide(covariance, pc.multiply(std1, std2)) return [ ArrowSeries.from_iterable( data=[correlation], name=a_series.name, context=self ) ] return self._expr._from_callable( func=func, evaluate_output_names=combine_evaluate_output_names(a, b), alias_output_names=combine_alias_output_names(a, b), context=self, ) def cov(self, a: ArrowExpr, b: ArrowExpr, *, ddof: int) -> ArrowExpr: def func(df: ArrowDataFrame) -> list[ArrowSeries]: a_series = df._evaluate_single_output_expr(a) arr1 = a_series.native arr2 = df._evaluate_single_output_expr(b).native valid = pc.and_(pc.is_valid(arr1), pc.is_valid(arr2)) arr1 = pc.filter(arr1, valid) arr2 = pc.filter(arr2, valid) n = len(arr1) if n - ddof <= 0: # Matches pandas/Polars. A single valid pair with ddof=0 falls # through and yields 0.0, as the deviations are then zero. covariance = pa.scalar(None, type=pa.float64()) else: dev1 = pc.subtract(arr1, pc.mean(arr1)) dev2 = pc.subtract(arr2, pc.mean(arr2)) numerator = pc.sum(pc.multiply(dev1, dev2)) covariance = pc.divide(numerator, pa.scalar(n - ddof)) return [ ArrowSeries.from_iterable( data=[covariance], name=a_series.name, context=self ) ] return self._expr._from_callable( func=func, evaluate_output_names=combine_evaluate_output_names(a, b), alias_output_names=combine_alias_output_names(a, b), context=self, ) narwhals-2.26.0/src/narwhals/_arrow/selectors.py000066400000000000000000000015621525000603700216760ustar00rootroot00000000000000from __future__ import annotations from typing import TYPE_CHECKING from narwhals._arrow.expr import ArrowExpr from narwhals._compliant import CompliantSelector, EagerSelectorNamespace if TYPE_CHECKING: from narwhals._arrow.dataframe import ArrowDataFrame # noqa: F401 from narwhals._arrow.series import ArrowSeries # noqa: F401 class ArrowSelectorNamespace(EagerSelectorNamespace["ArrowDataFrame", "ArrowSeries"]): @property def _selector(self) -> type[ArrowSelector]: return ArrowSelector class ArrowSelector(CompliantSelector["ArrowDataFrame", "ArrowSeries"], ArrowExpr): # type: ignore[misc] def _to_expr(self) -> ArrowExpr: return ArrowExpr( self._call, evaluate_output_names=self._evaluate_output_names, alias_output_names=self._alias_output_names, version=self._version, ) narwhals-2.26.0/src/narwhals/_arrow/series.py000066400000000000000000001326051525000603700211700ustar00rootroot00000000000000from __future__ import annotations from typing import TYPE_CHECKING, Any, Literal, cast, overload import pyarrow as pa import pyarrow.compute as pc from narwhals._arrow.series_cat import ArrowSeriesCatNamespace from narwhals._arrow.series_dt import ArrowSeriesDateTimeNamespace from narwhals._arrow.series_list import ArrowSeriesListNamespace from narwhals._arrow.series_str import ArrowSeriesStringNamespace from narwhals._arrow.series_struct import ArrowSeriesStructNamespace from narwhals._arrow.utils import ( arange, cast_for_truediv, chunked_array, extract_native, floordiv_compat, is_array_or_scalar, lit, narwhals_to_native_dtype, native_to_narwhals_dtype, nulls_like, pad_series, sortable, zeros, ) from narwhals._compliant import EagerSeries, EagerSeriesHist from narwhals._typing_compat import assert_never from narwhals._utils import ( NO_DEFAULT, Implementation, generate_temporary_column_name, is_list_of, not_implemented, ) from narwhals.dependencies import is_numpy_array_1d from narwhals.exceptions import InvalidOperationError, ShapeError if TYPE_CHECKING: from collections.abc import Callable, Iterable, Iterator, Sequence from types import ModuleType from typing import TypeAlias import pandas as pd import polars as pl from typing_extensions import Self, TypeIs from narwhals._arrow.dataframe import ArrowDataFrame from narwhals._arrow.namespace import ArrowNamespace from narwhals._arrow.typing import ( # type: ignore[attr-defined] ArrayAny, ArrayOrChunkedArray, ArrayOrScalar, ChunkedArrayAny, Incomplete, NullPlacement, Order, ScalarAny, TieBreaker, _AsPyType, _BasicDataType, ) from narwhals._compliant.series import HistData from narwhals._typing import NoDefault from narwhals._utils import Version, _LimitedContext from narwhals.dtypes import DType from narwhals.typing import ( ClosedInterval, FillNullStrategy, Into1DArray, IntoDType, ModeKeepStrategy, NonNestedLiteral, PythonLiteral, RankMethod, RollingInterpolationMethod, SizedMultiIndexSelector, _1DArray, _2DArray, _SliceIndex, ) ArrowHistData: TypeAlias = ( "HistData[ChunkedArrayAny, list[ScalarAny] | pa.Int64Array | list[float]]" ) # TODO @dangotbanned: move into `_arrow.utils` # Lots of modules are importing inline @overload def maybe_extract_py_scalar( value: pa.Scalar[_BasicDataType[_AsPyType]], return_py_scalar: bool, # noqa: FBT001 ) -> _AsPyType: ... @overload def maybe_extract_py_scalar( value: pa.Scalar[pa.StructType], return_py_scalar: bool, # noqa: FBT001 ) -> list[dict[str, Any]]: ... @overload def maybe_extract_py_scalar( value: pa.Scalar[pa.ListType[_BasicDataType[_AsPyType]]], return_py_scalar: bool, # noqa: FBT001 ) -> list[_AsPyType]: ... @overload def maybe_extract_py_scalar( value: pa.Scalar[Any] | Any, return_py_scalar: bool, # noqa: FBT001 ) -> Any: ... def maybe_extract_py_scalar(value: Any, return_py_scalar: bool) -> Any: # noqa: FBT001 if TYPE_CHECKING: return value.as_py() if return_py_scalar: return getattr(value, "as_py", lambda: value)() return value class ArrowSeries(EagerSeries["ChunkedArrayAny"]): _implementation = Implementation.PYARROW def __init__( self, native_series: ChunkedArrayAny, *, name: str, version: Version ) -> None: self._name = name self._native_series: ChunkedArrayAny = native_series self._version = version self._broadcast = False @property def native(self) -> ChunkedArrayAny: return self._native_series def _with_version(self, version: Version) -> Self: return self.__class__(self.native, name=self._name, version=version) def _with_native( self, series: ArrayOrScalar, *, preserve_broadcast: bool = False ) -> Self: result = self.from_native(chunked_array(series), name=self.name, context=self) if preserve_broadcast: result._broadcast = self._broadcast return result def _with_binary(self, op: Callable[..., ArrayOrScalar], other: Any) -> Self: ser, other_native = extract_native(self, other) preserve_broadcast = self._broadcast and getattr(other, "_broadcast", True) return self._with_native( op(ser, other_native), preserve_broadcast=preserve_broadcast ).alias(self.name) def _with_binary_right(self, op: Callable[..., ArrayOrScalar], other: Any) -> Self: return self._with_binary(lambda x, y: op(y, x), other).alias(self.name) @classmethod def from_iterable( cls, data: Iterable[Any], *, context: _LimitedContext, name: str = "", dtype: IntoDType | None = None, ) -> Self: version = context._version if dtype is not None: dtype_pa: pa.DataType | None = narwhals_to_native_dtype(dtype, version) if is_array_or_scalar(data): data = data.cast(dtype_pa) dtype_pa = None native = data if cls._is_native(data) else chunked_array([data], dtype_pa) else: native = chunked_array([data]) return cls.from_native(native, context=context, name=name) def _from_scalar(self, value: Any) -> Self: if hasattr(value, "as_py"): value = value.as_py() return super()._from_scalar(value) @staticmethod def _is_native(obj: ChunkedArrayAny | Any) -> TypeIs[ChunkedArrayAny]: return isinstance(obj, pa.ChunkedArray) @classmethod def from_native( cls, data: ChunkedArrayAny, /, *, context: _LimitedContext, name: str = "" ) -> Self: return cls(data, version=context._version, name=name) @classmethod def from_numpy(cls, data: Into1DArray, /, *, context: _LimitedContext) -> Self: return cls.from_iterable( data if is_numpy_array_1d(data) else [data], context=context ) @classmethod def _align_full_broadcast(cls, *series: Self) -> Sequence[Self]: lengths = [len(s) for s in series] target_length = max( length for length, s in zip(lengths, series, strict=False) if not s._broadcast ) fast_path = all(_len == target_length for _len in lengths) if fast_path: return series reshaped = [] for s in series: if s._broadcast: compliant = s._with_native(pa.repeat(s.native[0], target_length)) elif (actual_len := len(s)) != target_length: msg = f"Expected object of length {target_length}, got {actual_len}." raise ShapeError(msg) else: compliant = s reshaped.append(compliant) return reshaped def __narwhals_namespace__(self) -> ArrowNamespace: from narwhals._arrow.namespace import ArrowNamespace return ArrowNamespace(version=self._version) def __eq__(self, other: object) -> Self: # type: ignore[override] return self._with_binary(pc.equal, other) def __ne__(self, other: object) -> Self: # type: ignore[override] return self._with_binary(pc.not_equal, other) def __ge__(self, other: Any) -> Self: return self._with_binary(pc.greater_equal, other) def __gt__(self, other: Any) -> Self: return self._with_binary(pc.greater, other) def __le__(self, other: Any) -> Self: return self._with_binary(pc.less_equal, other) def __lt__(self, other: Any) -> Self: return self._with_binary(pc.less, other) def __and__(self, other: Any) -> Self: return self._with_binary(pc.and_kleene, other) def __rand__(self, other: Any) -> Self: return self._with_binary_right(pc.and_kleene, other) def __or__(self, other: Any) -> Self: return self._with_binary_right(pc.or_kleene, other) def __ror__(self, other: Any) -> Self: return self._with_binary_right(pc.or_kleene, other) def __add__(self, other: Any) -> Self: return self._with_binary(pc.add, other) def __radd__(self, other: Any) -> Self: return self._with_binary_right(pc.add, other) def __sub__(self, other: Any) -> Self: return self._with_binary(pc.subtract, other) def __rsub__(self, other: Any) -> Self: return self._with_binary_right(pc.subtract, other) def __mul__(self, other: Any) -> Self: return self._with_binary(pc.multiply, other) def __rmul__(self, other: Any) -> Self: return self._with_binary_right(pc.multiply, other) def __pow__(self, other: Any) -> Self: return self._with_binary(pc.power, other) def __rpow__(self, other: Any) -> Self: return self._with_binary_right(pc.power, other) def __floordiv__(self, other: Any) -> Self: return self._with_binary(floordiv_compat, other) def __rfloordiv__(self, other: Any) -> Self: return self._with_binary_right(floordiv_compat, other) def __truediv__(self, other: Any) -> Self: return self._with_binary(lambda x, y: pc.divide(*cast_for_truediv(x, y)), other) def __rtruediv__(self, other: Any) -> Self: return self._with_binary_right( lambda x, y: pc.divide(*cast_for_truediv(x, y)), other ) def __mod__(self, other: Any) -> Self: preserve_broadcast = self._broadcast and getattr(other, "_broadcast", True) floor_div = (self // other).native ser, other = extract_native(self, other) res = pc.subtract(ser, pc.multiply(floor_div, other)) return self._with_native(res, preserve_broadcast=preserve_broadcast) def __rmod__(self, other: Any) -> Self: preserve_broadcast = self._broadcast and getattr(other, "_broadcast", True) floor_div = (other // self).native ser, other = extract_native(self, other) res = pc.subtract(other, pc.multiply(floor_div, ser)) return self._with_native(res, preserve_broadcast=preserve_broadcast) def __invert__(self) -> Self: return self._with_native(pc.invert(self.native), preserve_broadcast=True) def __neg__(self) -> Self: return self._with_native(pc.negate(self.native), preserve_broadcast=True) @property def _type(self) -> pa.DataType: return self.native.type def len(self, *, _return_py_scalar: bool = True) -> int: return maybe_extract_py_scalar(len(self.native), _return_py_scalar) def filter(self, predicate: ArrowSeries | list[bool | None]) -> Self: other_native: Any if not is_list_of(predicate, bool): _, other_native = extract_native(self, predicate) else: other_native = predicate return self._with_native(self.native.filter(other_native)) def first(self, *, _return_py_scalar: bool = True) -> PythonLiteral: result = self.native[0] if len(self.native) else None return maybe_extract_py_scalar(result, _return_py_scalar) def last(self, *, _return_py_scalar: bool = True) -> PythonLiteral: ca = self.native result = ca[height - 1] if (height := len(ca)) else None return maybe_extract_py_scalar(result, _return_py_scalar) def mean(self, *, _return_py_scalar: bool = True) -> float: return maybe_extract_py_scalar(pc.mean(self.native), _return_py_scalar) def median(self, *, _return_py_scalar: bool = True) -> float: if not self.dtype.is_numeric(): msg = "`median` operation not supported for non-numeric input type." raise InvalidOperationError(msg) return maybe_extract_py_scalar( pc.quantile(self.native, q=0.5, interpolation="linear")[0], _return_py_scalar ) def min(self, *, _return_py_scalar: bool = True) -> Any: return maybe_extract_py_scalar(pc.min(self.native), _return_py_scalar) def max(self, *, _return_py_scalar: bool = True) -> Any: return maybe_extract_py_scalar(pc.max(self.native), _return_py_scalar) def arg_min(self, *, _return_py_scalar: bool = True) -> int: index_min = pc.index(self.native, pc.min(self.native)) return maybe_extract_py_scalar(index_min, _return_py_scalar) def arg_max(self, *, _return_py_scalar: bool = True) -> int: index_max = pc.index(self.native, pc.max(self.native)) return maybe_extract_py_scalar(index_max, _return_py_scalar) def sum(self, *, _return_py_scalar: bool = True) -> float: return maybe_extract_py_scalar( pc.sum(self.native, min_count=0), _return_py_scalar ) def drop_nulls(self) -> Self: return self._with_native(self.native.drop_null()) def shift(self, n: int) -> Self: if n == 0: return self._with_native(self.native) length = len(self.native) # Cap the number of null padding values at the length of the series: # shifting by at least the length must still return a series of the # same length (all null), and must not desync the array lengths that # rolling windows rely on. null_array = nulls_like(min(abs(n), length), self) if abs(n) >= length: # Every value is shifted out, so the result is all null; return it # directly and skip the slice and concat. return self._with_native(null_array) if n > 0: arrays = [null_array, *self.native[:-n].chunks] else: arrays = [*self.native[-n:].chunks, null_array] return self._with_native(pa.concat_arrays(arrays)) def std(self, *, ddof: int, _return_py_scalar: bool = True) -> float: return maybe_extract_py_scalar( pc.stddev(self.native, ddof=ddof), _return_py_scalar ) def var(self, *, ddof: int, _return_py_scalar: bool = True) -> float: return maybe_extract_py_scalar( pc.variance(self.native, ddof=ddof), _return_py_scalar ) def skew(self, *, _return_py_scalar: bool = True) -> float | None: ser_not_null = self.native.drop_null() if len(ser_not_null) == 0: return None if len(ser_not_null) == 1: return float("nan") if len(ser_not_null) == 2: return 0.0 m = pc.subtract(ser_not_null, pc.mean(ser_not_null)) m2 = pc.mean(pc.power(m, lit(2))) m3 = pc.mean(pc.power(m, lit(3))) biased_population_skewness = pc.divide(m3, pc.power(m2, lit(1.5))) return maybe_extract_py_scalar(biased_population_skewness, _return_py_scalar) def kurtosis(self, *, _return_py_scalar: bool = True) -> float | None: ser_not_null = self.native.drop_null() if len(ser_not_null) == 0: return None if len(ser_not_null) == 1: return float("nan") m = pc.subtract(ser_not_null, pc.mean(ser_not_null)) m2 = pc.mean(pc.power(m, lit(2))) m4 = pc.mean(pc.power(m, lit(4))) k = pc.subtract(pc.divide(m4, pc.power(m2, lit(2))), lit(3)) return maybe_extract_py_scalar(k, _return_py_scalar) def count(self, *, _return_py_scalar: bool = True) -> int: return maybe_extract_py_scalar(pc.count(self.native), _return_py_scalar) def n_unique(self, *, _return_py_scalar: bool = True) -> int: return maybe_extract_py_scalar( pc.count(self.native.unique(), mode="all"), _return_py_scalar ) def __native_namespace__(self) -> ModuleType: if self._implementation is Implementation.PYARROW: return self._implementation.to_native_namespace() msg = f"Expected pyarrow, got: {type(self._implementation)}" # pragma: no cover raise AssertionError(msg) @property def name(self) -> str: return self._name def _gather(self, rows: SizedMultiIndexSelector[ChunkedArrayAny]) -> Self: if len(rows) == 0: return self._with_native(self.native.slice(0, 0)) if self._backend_version < (18,) and isinstance(rows, tuple): rows = list(rows) return self._with_native(self.native.take(rows)) def _gather_slice(self, rows: _SliceIndex | range) -> Self: start = rows.start or 0 stop = rows.stop if rows.stop is not None else len(self.native) if start < 0: start = len(self.native) + start if stop < 0: stop = len(self.native) + stop if rows.step is not None and rows.step != 1: msg = "Slicing with step is not supported on PyArrow tables" raise NotImplementedError(msg) return self._with_native(self.native.slice(start, stop - start)) def scatter(self, indices: Self, values: Self) -> Self: mask = pc.is_in(arange(start=0, end=len(self), step=1), indices.native) sorted_indices = pc.sort_indices(indices.native) replacements = values.native.take(sorted_indices).combine_chunks() return self._with_native(pc.replace_with_mask(self.native, mask, replacements)) def to_list(self) -> list[Any]: return self.native.to_pylist() def __array__(self, dtype: Any = None, *, copy: bool | None = None) -> _1DArray: return self.native.__array__(dtype=dtype, copy=copy) def to_numpy(self, dtype: Any = None, *, copy: bool | None = None) -> _1DArray: return self.native.to_numpy() def alias(self, name: str) -> Self: ret = self.__class__(self.native, name=name, version=self._version) ret._broadcast = self._broadcast return ret @property def dtype(self) -> DType: return native_to_narwhals_dtype(self.native.type, self._version) def abs(self) -> Self: return self._with_native(pc.abs(self.native)) def cum_sum(self, *, reverse: bool) -> Self: cum_sum = pc.cumulative_sum result = ( cum_sum(self.native, skip_nulls=True) if not reverse else cum_sum(self.native[::-1], skip_nulls=True)[::-1] ) return self._with_native(result) def round(self, decimals: int) -> Self: return self._with_native( pc.round(self.native, decimals, round_mode="half_towards_infinity") ) def floor(self) -> Self: return self._with_native(pc.floor(self.native)) def ceil(self) -> Self: return self._with_native(pc.ceil(self.native)) def diff(self) -> Self: return self._with_native(pc.pairwise_diff(self.native.combine_chunks())) def any(self, *, _return_py_scalar: bool = True) -> bool: return maybe_extract_py_scalar( pc.any(self.native, min_count=0), _return_py_scalar ) def all(self, *, _return_py_scalar: bool = True) -> bool: return maybe_extract_py_scalar( pc.all(self.native, min_count=0), _return_py_scalar ) def is_between( self, lower_bound: Any, upper_bound: Any, closed: ClosedInterval ) -> Self: _, lower_bound = extract_native(self, lower_bound) _, upper_bound = extract_native(self, upper_bound) if closed == "left": ge = pc.greater_equal(self.native, lower_bound) lt = pc.less(self.native, upper_bound) res = pc.and_kleene(ge, lt) elif closed == "right": gt = pc.greater(self.native, lower_bound) le = pc.less_equal(self.native, upper_bound) res = pc.and_kleene(gt, le) elif closed == "none": gt = pc.greater(self.native, lower_bound) lt = pc.less(self.native, upper_bound) res = pc.and_kleene(gt, lt) elif closed == "both": ge = pc.greater_equal(self.native, lower_bound) le = pc.less_equal(self.native, upper_bound) res = pc.and_kleene(ge, le) else: assert_never(closed) return self._with_native(res) def is_null(self) -> Self: return self._with_native(self.native.is_null(), preserve_broadcast=True) def is_nan(self) -> Self: return self._with_native(pc.is_nan(self.native), preserve_broadcast=True) def cast(self, dtype: IntoDType) -> Self: data_type = narwhals_to_native_dtype(dtype, self._version) return self._with_native(pc.cast(self.native, data_type), preserve_broadcast=True) def null_count(self, *, _return_py_scalar: bool = True) -> int: return maybe_extract_py_scalar(self.native.null_count, _return_py_scalar) def head(self, n: int) -> Self: if n >= 0: return self._with_native(self.native.slice(0, n)) num_rows = len(self) return self._with_native(self.native.slice(0, max(0, num_rows + n))) def tail(self, n: int) -> Self: if n >= 0: num_rows = len(self) return self._with_native(self.native.slice(max(0, num_rows - n))) return self._with_native(self.native.slice(abs(n))) def is_in(self, other: Any) -> Self: if self._is_native(other): value_set: ArrayOrChunkedArray = other else: value_set = pa.array(other) native = self.native is_in = pc.is_in(native, value_set=value_set) return self._with_native(pc.if_else(native.is_null(), None, is_in)) def arg_true(self) -> Self: import numpy as np # ignore-banned-import res = np.flatnonzero(self.native) return self.from_iterable(res, name=self.name, context=self) def item(self, index: int | None = None) -> Any: if index is None: if len(self) != 1: msg = ( "can only call '.item()' if the Series is of length 1," f" or an explicit index is provided (Series is of length {len(self)})" ) raise ValueError(msg) return maybe_extract_py_scalar(self.native[0], return_py_scalar=True) return maybe_extract_py_scalar(self.native[index], return_py_scalar=True) def value_counts( self, *, sort: bool, parallel: bool, name: str | None, normalize: bool ) -> ArrowDataFrame: """Parallel is unused, exists for compatibility.""" from narwhals._arrow.dataframe import ArrowDataFrame index_name_ = "index" if self._name is None else self._name value_name_ = name or ("proportion" if normalize else "count") val_counts = pc.value_counts(self.native) values = val_counts.field("values") counts = val_counts.field("counts") if normalize: counts = pc.divide(*cast_for_truediv(counts, pc.sum(counts))) arrays = values, counts val_count = pa.Table.from_arrays(arrays, names=[index_name_, value_name_]) if sort: val_count = val_count.sort_by([(value_name_, "descending")]) return ArrowDataFrame( val_count, version=self._version, validate_column_names=True ) def zip_with(self, mask: Self, other: Self) -> Self: cond = mask.native.combine_chunks() return self._with_native(pc.if_else(cond, self.native, other.native)) def sample(self, n: int, *, with_replacement: bool, seed: int | None) -> Self: import numpy as np # ignore-banned-import rng = np.random.default_rng(seed=seed) indices = rng.choice(np.arange(n), size=n, replace=with_replacement) return self._with_native(self.native.take(pa.array(indices, type=pa.uint64()))) def fill_nan(self, value: float | None) -> Self: result = pc.if_else(pc.is_nan(self.native), value, self.native) return self._with_native(result, preserve_broadcast=True) def fill_null( self, value: Self | NonNestedLiteral, strategy: FillNullStrategy | None, limit: int | None, ) -> Self: def fill_null_forward_limit(arr: ChunkedArrayAny, limit: int) -> ArrayAny: # this algorithm first finds the indices of the valid values to fill all the null value positions # then it calculates the distance of each new index and the original index # if the distance is equal to or less than the limit and the original value is null, it is replaced sentinel = lit(-1) is_not_null = pc.is_valid(arr) index = arange(0, len(arr), 1) index_not_null = pc.cumulative_max(pc.if_else(is_not_null, index, sentinel)) # NOTE: The correction here is for nulls at either end of the array # They should be preserved when the `strategy` would need an out-of-bounds index not_oob = pc.not_equal(index_not_null, sentinel) index_not_null = pc.if_else(not_oob, index_not_null, None) beyond_limit = pc.greater(pc.subtract(index, index_not_null), lit(limit)) return pc.if_else( pc.or_(is_not_null, beyond_limit), arr, arr.take(index_not_null) ) native = self.native if value is not None: _, native_value = extract_native(self, value) series: ArrayOrScalar = pc.fill_null(native, native_value) elif limit is None: series = ( pc.fill_null_forward(native) if strategy == "forward" else pc.fill_null_backward(native) ) else: series = ( fill_null_forward_limit(native, limit) if strategy == "forward" else fill_null_forward_limit(native[::-1], limit)[::-1] ) return self._with_native(series, preserve_broadcast=True) def to_frame(self) -> ArrowDataFrame: from narwhals._arrow.dataframe import ArrowDataFrame df = pa.Table.from_arrays([self.native], names=[self.name]) return ArrowDataFrame(df, version=self._version, validate_column_names=False) def to_pandas(self) -> pd.Series[Any]: import pandas as pd # ignore-banned-import() # NOTE: `pandas-stubs` doesn't know `pd.Series` accepts arrow-native input return pd.Series(self.native, name=self.name) # pyright: ignore[reportCallIssue, reportArgumentType] def to_polars(self) -> pl.Series: import polars as pl # ignore-banned-import return cast("pl.Series", pl.from_arrow(self.native)) def is_unique(self) -> ArrowSeries: return self.to_frame().is_unique().alias(self.name) def is_first_distinct(self) -> Self: import numpy as np # ignore-banned-import row_number = pa.array(np.arange(len(self))) col_token = generate_temporary_column_name(n_bytes=8, columns=[self.name]) first_distinct_index = ( pa.Table.from_arrays([self.native], names=[self.name]) .append_column(col_token, row_number) .group_by(self.name) .aggregate([(col_token, "min")]) .column(f"{col_token}_min") ) return self._with_native(pc.is_in(row_number, first_distinct_index)) def is_last_distinct(self) -> Self: import numpy as np # ignore-banned-import row_number = pa.array(np.arange(len(self))) col_token = generate_temporary_column_name(n_bytes=8, columns=[self.name]) last_distinct_index = ( pa.Table.from_arrays([self.native], names=[self.name]) .append_column(col_token, row_number) .group_by(self.name) .aggregate([(col_token, "max")]) .column(f"{col_token}_max") ) return self._with_native(pc.is_in(row_number, last_distinct_index)) def is_sorted(self, *, descending: bool) -> bool: if not isinstance(descending, bool): msg = f"argument 'descending' should be boolean, found {type(descending)}" raise TypeError(msg) if descending: result = pc.all(pc.greater_equal(self.native[:-1], self.native[1:])) else: result = pc.all(pc.less_equal(self.native[:-1], self.native[1:])) return maybe_extract_py_scalar(result, return_py_scalar=True) def unique(self, *, maintain_order: bool = True) -> Self: # TODO(marco): `pc.unique` seems to always maintain order, is that guaranteed? return self._with_native(self.native.unique()) def replace_strict( self, default: Any | NoDefault, old: Sequence[Any], new: Sequence[Any], *, return_dtype: IntoDType | None, ) -> Self: # https://stackoverflow.com/a/79111029/4451315 idxs = pc.index_in(self.native, pa.array(old)) # Tracks which values were found in the mapping (even if mapped to None) was_matched = pc.is_valid(idxs) result_native = pc.take(pa.array(new), idxs) if return_dtype is not None: result_native = result_native.cast( narwhals_to_native_dtype(return_dtype, self._version) ) result = self._with_native(result_native) if default is NO_DEFAULT: # Check that all non-null input values were matched # (result may have more nulls if mapping contains {value: None}) unmatched_mask = pc.and_(pc.is_valid(self.native), pc.invert(was_matched)) if maybe_extract_py_scalar( pc.any(unmatched_mask, min_count=0), return_py_scalar=True ): unmatched_values = ( self.filter(self._with_native(unmatched_mask)) .unique(maintain_order=False) .to_list() ) msg = ( "replace_strict did not replace all non-null values.\n\n" f"The following did not get replaced: {unmatched_values}" ) raise InvalidOperationError(msg) else: result_native, default = extract_native(result, default) # Only fill with default where the value wasn't matched (not where result is null due to mapping) # If was_matched, keep result.native; otherwise use default result = self._with_native(pc.if_else(was_matched, result.native, default)) return result def sort(self, *, descending: bool, nulls_last: bool) -> Self: order: Order = "descending" if descending else "ascending" null_placement: NullPlacement = "at_end" if nulls_last else "at_start" sorted_indices = pc.array_sort_indices( sortable(self.native), order=order, null_placement=null_placement ) return self._with_native(self.native.take(sorted_indices)) def to_dummies(self, *, separator: str, drop_first: bool) -> ArrowDataFrame: import numpy as np # ignore-banned-import from narwhals._arrow.dataframe import ArrowDataFrame name = self._name # NOTE: stub is missing attributes (https://arrow.apache.org/docs/python/generated/pyarrow.DictionaryArray.html) da: Incomplete = self.native.combine_chunks().dictionary_encode("encode") columns: _2DArray = np.zeros((len(da.dictionary), len(da)), np.int8) columns[da.indices, np.arange(len(da))] = 1 null_col_pa, null_col_pl = f"{name}{separator}None", f"{name}{separator}null" cols = [ {null_col_pa: null_col_pl}.get( f"{name}{separator}{v}", f"{name}{separator}{v}" ) for v in da.dictionary ] output_order = ( [ null_col_pl, *sorted([c for c in cols if c != null_col_pl])[int(drop_first) :], ] if null_col_pl in cols else sorted(cols)[int(drop_first) :] ) return ArrowDataFrame( pa.Table.from_arrays(columns, names=cols), version=self._version, validate_column_names=True, ).simple_select(*output_order) def quantile( self, quantile: float, interpolation: RollingInterpolationMethod, *, _return_py_scalar: bool = True, ) -> float: return maybe_extract_py_scalar( pc.quantile(self.native, q=quantile, interpolation=interpolation)[0], _return_py_scalar, ) def gather_every(self, n: int, offset: int = 0) -> Self: return self._with_native(self.native[offset::n]) def clip(self, lower_bound: Self, upper_bound: Self) -> Self: _, lower = extract_native(self, lower_bound) _, upper = extract_native(self, upper_bound) return self._with_native( pc.max_element_wise( pc.min_element_wise(self.native, upper, skip_nulls=False), lower, skip_nulls=False, ) ) def clip_lower(self, lower_bound: Self) -> Self: _, lower = extract_native(self, lower_bound) return self._with_native( pc.max_element_wise(self.native, lower, skip_nulls=False) ) def clip_upper(self, upper_bound: Self) -> Self: _, upper = extract_native(self, upper_bound) return self._with_native( pc.min_element_wise(self.native, upper, skip_nulls=False) ) def to_arrow(self) -> ArrayAny: return self.native.combine_chunks() def mode(self, *, keep: ModeKeepStrategy) -> ArrowSeries: plx = self.__narwhals_namespace__() col_token = generate_temporary_column_name(n_bytes=8, columns=[self.name]) counts = self.value_counts( name=col_token, normalize=False, sort=False, parallel=False ) result = counts.filter( plx.col(col_token) == plx.col(col_token).max().broadcast() ).get_column(self.name) return result.head(1) if keep == "any" else result def is_finite(self) -> Self: return self._with_native(pc.is_finite(self.native)) def cum_count(self, *, reverse: bool) -> Self: dtypes = self._version.dtypes return (~self.is_null()).cast(dtypes.UInt32()).cum_sum(reverse=reverse) def cum_min(self, *, reverse: bool) -> Self: result = ( pc.cumulative_min(self.native, skip_nulls=True) if not reverse else pc.cumulative_min(self.native[::-1], skip_nulls=True)[::-1] ) return self._with_native(result) def cum_max(self, *, reverse: bool) -> Self: result = ( pc.cumulative_max(self.native, skip_nulls=True) if not reverse else pc.cumulative_max(self.native[::-1], skip_nulls=True)[::-1] ) return self._with_native(result) def cum_prod(self, *, reverse: bool) -> Self: result = ( pc.cumulative_prod(self.native, skip_nulls=True) if not reverse else pc.cumulative_prod(self.native[::-1], skip_nulls=True)[::-1] ) return self._with_native(result) def rolling_sum(self, window_size: int, *, min_samples: int, center: bool) -> Self: min_samples = min_samples if min_samples is not None else window_size padded_series, offset = pad_series(self, window_size=window_size, center=center) cum_sum = padded_series.cum_sum(reverse=False).fill_null( value=None, strategy="forward", limit=None ) rolling_sum = ( cum_sum - cum_sum.shift(window_size).fill_null(value=0, strategy=None, limit=None) if window_size != 0 else cum_sum ) valid_count = padded_series.cum_count(reverse=False) count_in_window = valid_count - valid_count.shift(window_size).fill_null( value=0, strategy=None, limit=None ) result = self._with_native( pc.if_else((count_in_window >= min_samples).native, rolling_sum.native, None) ) return result._gather_slice(slice(offset, None)) def rolling_mean(self, window_size: int, *, min_samples: int, center: bool) -> Self: min_samples = min_samples if min_samples is not None else window_size padded_series, offset = pad_series(self, window_size=window_size, center=center) cum_sum = padded_series.cum_sum(reverse=False).fill_null( value=None, strategy="forward", limit=None ) rolling_sum = ( cum_sum - cum_sum.shift(window_size).fill_null(value=0, strategy=None, limit=None) if window_size != 0 else cum_sum ) valid_count = padded_series.cum_count(reverse=False) count_in_window = valid_count - valid_count.shift(window_size).fill_null( value=0, strategy=None, limit=None ) result = ( self._with_native( pc.if_else( (count_in_window >= min_samples).native, rolling_sum.native, None ) ) / count_in_window ) return result._gather_slice(slice(offset, None)) def rolling_var( self, window_size: int, *, min_samples: int, center: bool, ddof: int ) -> Self: min_samples = min_samples if min_samples is not None else window_size padded_series, offset = pad_series(self, window_size=window_size, center=center) cum_sum = padded_series.cum_sum(reverse=False).fill_null( value=None, strategy="forward", limit=None ) rolling_sum = ( cum_sum - cum_sum.shift(window_size).fill_null(value=0, strategy=None, limit=None) if window_size != 0 else cum_sum ) cum_sum_sq = ( pow(padded_series, 2) .cum_sum(reverse=False) .fill_null(value=None, strategy="forward", limit=None) ) rolling_sum_sq = ( cum_sum_sq - cum_sum_sq.shift(window_size).fill_null(value=0, strategy=None, limit=None) if window_size != 0 else cum_sum_sq ) valid_count = padded_series.cum_count(reverse=False) count_in_window = valid_count - valid_count.shift(window_size).fill_null( value=0, strategy=None, limit=None ) result = self._with_native( pc.if_else( (count_in_window >= min_samples).native, (rolling_sum_sq - (rolling_sum**2 / count_in_window)).native, None, ) ) / self._with_native(pc.max_element_wise((count_in_window - ddof).native, 0)) return result._gather_slice(slice(offset, None, None)) def rolling_std( self, window_size: int, *, min_samples: int, center: bool, ddof: int ) -> Self: return ( self.rolling_var( window_size=window_size, min_samples=min_samples, center=center, ddof=ddof ) ** 0.5 ) def rank(self, method: RankMethod, *, descending: bool) -> Self: if method == "average": msg = ( "`rank` with `method='average' is not supported for pyarrow backend. " "The available methods are {'min', 'max', 'dense', 'ordinal'}." ) raise ValueError(msg) sort_keys: Order = "descending" if descending else "ascending" tiebreaker: TieBreaker = "first" if method == "ordinal" else method native_series: ArrayOrChunkedArray sortable_series = sortable(self.native) if self._backend_version < (14, 0, 0): # pragma: no cover native_series = sortable_series.combine_chunks() else: native_series = sortable_series null_mask = pc.is_null(native_series) rank = pc.rank(native_series, sort_keys=sort_keys, tiebreaker=tiebreaker) result = pc.if_else(null_mask, lit(None, rank.type), rank) return self._with_native(result) def hist_from_bins( self, bins: list[float], *, include_breakpoint: bool ) -> ArrowDataFrame: return ( _ArrowHist.from_series(self, include_breakpoint=include_breakpoint) .with_bins(bins) .to_frame() ) def hist_from_bin_count( self, bin_count: int, *, include_breakpoint: bool ) -> ArrowDataFrame: return ( _ArrowHist.from_series(self, include_breakpoint=include_breakpoint) .with_bin_count(bin_count) .to_frame() ) def __iter__(self) -> Iterator[Any]: for x in self.native: yield maybe_extract_py_scalar(x, return_py_scalar=True) def __contains__(self, other: Any) -> bool: from pyarrow import ( ArrowInvalid, # ignore-banned-imports ArrowNotImplementedError, # ignore-banned-imports ArrowTypeError, # ignore-banned-imports ) try: other_ = lit(other) if other is not None else lit(None, type=self._type) return maybe_extract_py_scalar( pc.is_in(other_, self.native), return_py_scalar=True ) except (ArrowInvalid, ArrowNotImplementedError, ArrowTypeError) as exc: msg = f"Unable to compare other of type {type(other)} with series of type {self.dtype}." raise InvalidOperationError(msg) from exc def log(self, base: float) -> Self: return self._with_native(pc.logb(self.native, lit(base))) def exp(self) -> Self: return self._with_native(pc.exp(self.native)) def sqrt(self) -> Self: return self._with_native(pc.sqrt(self.native)) def sin(self) -> Self: return self._with_native(pc.sin(self.native)) def cos(self) -> Self: return self._with_native(pc.cos(self.native)) def any_value( self, *, ignore_nulls: bool, _return_py_scalar: bool = True ) -> PythonLiteral: return self.drop_nulls().first() if ignore_nulls else self.first() @property def dt(self) -> ArrowSeriesDateTimeNamespace: return ArrowSeriesDateTimeNamespace(self) @property def cat(self) -> ArrowSeriesCatNamespace: return ArrowSeriesCatNamespace(self) @property def str(self) -> ArrowSeriesStringNamespace: return ArrowSeriesStringNamespace(self) @property def list(self) -> ArrowSeriesListNamespace: return ArrowSeriesListNamespace(self) @property def struct(self) -> ArrowSeriesStructNamespace: return ArrowSeriesStructNamespace(self) ewm_mean = not_implemented() class _ArrowHist( EagerSeriesHist["ChunkedArrayAny", "list[ScalarAny] | pa.Int64Array | list[float]"] ): _series: ArrowSeries def to_frame(self) -> ArrowDataFrame: # NOTE: Constructor typing is too strict for `TypedDict` table: Incomplete = pa.Table.from_pydict from_native = self._series.__narwhals_namespace__()._dataframe.from_native return from_native(table(self._data), context=self._series) # NOTE: *Could* be handled at narwhals-level def is_empty_series(self) -> bool: # NOTE: `ChunkedArray.combine_chunks` returns the concrete array type # Stubs say `Array[pa.BooleanScalar]`, which is missing properties # https://github.com/zen-xu/pyarrow-stubs/blob/6bedee748bc74feb8513b24bf43d64b24c7fddc8/pyarrow-stubs/__lib_pxi/array.pyi#L2395-L2399 is_null = self.native.is_null(nan_is_null=True) arr = cast("pa.BooleanArray", is_null.combine_chunks()) return arr.false_count == 0 # NOTE: *Could* be handled at narwhals-level, **iff** we add `nw.repeat`, `nw.linear_space` # See https://github.com/narwhals-dev/narwhals/pull/2839#discussion_r2215630696 def series_empty(self, arg: int | list[float], /) -> ArrowHistData: count = self._zeros(arg) if self._breakpoint: return {"breakpoint": self._calculate_breakpoint(arg), "count": count} return {"count": count} def _zeros(self, arg: int | list[float], /) -> pa.Int64Array: return zeros(arg) if isinstance(arg, int) else zeros(len(arg) - 1) def _linear_space( self, start: float, end: float, num_samples: int, *, closed: Literal["both", "none"] = "both", ) -> _1DArray: from numpy import linspace # ignore-banned-import return linspace(start=start, stop=end, num=num_samples, endpoint=closed == "both") def _calculate_bins(self, bin_count: int) -> _1DArray: """Prepare bins for histogram calculation from bin_count.""" d = pc.min_max(self.native) lower, upper = d["min"].as_py(), d["max"].as_py() if lower == upper: lower -= 0.5 upper += 0.5 return self._linear_space(lower, upper, bin_count + 1) def _calculate_hist(self, bins: list[float] | _1DArray) -> ArrowHistData: ser = self.native # NOTE: `mypy` refuses to resolve `ndarray.__getitem__` # Previously annotated as `list[float]`, but # - wasn't accurate to how we implemented it # - `pa.scalar` overloads fail to match on `float | np.float64` (but runtime is fine) bins = cast("list[float]", bins) # Handle single bin case if len(bins) == 2: is_between_bins = pc.and_( pc.greater_equal(ser, lit(bins[0])), pc.less_equal(ser, lit(bins[1])) ) count = pc.sum(is_between_bins.cast(pa.uint8())) if self._breakpoint: return {"breakpoint": [bins[-1]], "count": [count]} return {"count": [count]} # Handle multiple bins import numpy as np # ignore-banned-import bin_indices = np.searchsorted(bins, ser, side="left") # lowest bin is inclusive bin_indices = pc.if_else(pc.equal(ser, lit(bins[0])), 1, bin_indices) # Align unique categories and counts appropriately obs_cats, obs_counts = np.unique(bin_indices, return_counts=True) obj_cats = np.arange(1, len(bins)) counts = np.zeros_like(obj_cats) counts[np.isin(obj_cats, obs_cats)] = obs_counts[np.isin(obs_cats, obj_cats)] if self._breakpoint: return {"breakpoint": bins[1:], "count": counts} return {"count": counts} narwhals-2.26.0/src/narwhals/_arrow/series_cat.py000066400000000000000000000012561525000603700220140ustar00rootroot00000000000000from __future__ import annotations from typing import TYPE_CHECKING import pyarrow as pa from narwhals._arrow.utils import ArrowSeriesNamespace from narwhals._compliant.any_namespace import CatNamespace if TYPE_CHECKING: from narwhals._arrow.series import ArrowSeries from narwhals._arrow.typing import Incomplete class ArrowSeriesCatNamespace(ArrowSeriesNamespace, CatNamespace["ArrowSeries"]): def get_categories(self) -> ArrowSeries: # NOTE: Should be `list[pa.DictionaryArray]`, but `DictionaryArray` has no attributes chunks: Incomplete = self.native.chunks return self.with_native(pa.concat_arrays(x.dictionary for x in chunks).unique()) narwhals-2.26.0/src/narwhals/_arrow/series_dt.py000066400000000000000000000214321525000603700216520ustar00rootroot00000000000000from __future__ import annotations from typing import TYPE_CHECKING, Any, ClassVar, cast import pyarrow as pa import pyarrow.compute as pc from narwhals._arrow.utils import UNITS_DICT, ArrowSeriesNamespace, floordiv_compat, lit from narwhals._compliant.any_namespace import DateTimeNamespace from narwhals._constants import ( MS_PER_MINUTE, MS_PER_SECOND, NS_PER_MICROSECOND, NS_PER_MILLISECOND, NS_PER_MINUTE, NS_PER_SECOND, SECONDS_PER_DAY, SECONDS_PER_MINUTE, US_PER_MINUTE, US_PER_SECOND, ) from narwhals._duration import Interval if TYPE_CHECKING: from collections.abc import Callable, Mapping from typing import TypeAlias from narwhals._arrow.series import ArrowSeries from narwhals._arrow.typing import ChunkedArrayAny, ScalarAny from narwhals.dtypes import Datetime from narwhals.typing import TimeUnit UnitCurrent: TypeAlias = TimeUnit UnitTarget: TypeAlias = TimeUnit BinOpBroadcast: TypeAlias = Callable[[ChunkedArrayAny, ScalarAny], ChunkedArrayAny] IntoRhs: TypeAlias = int class ArrowSeriesDateTimeNamespace( ArrowSeriesNamespace, DateTimeNamespace["ArrowSeries"] ): _TIMESTAMP_DATE_FACTOR: ClassVar[Mapping[TimeUnit, int]] = { "ns": NS_PER_SECOND, "us": US_PER_SECOND, "ms": MS_PER_SECOND, "s": 1, } _TIMESTAMP_DATETIME_OP_FACTOR: ClassVar[ Mapping[tuple[UnitCurrent, UnitTarget], tuple[BinOpBroadcast, IntoRhs]] ] = { ("ns", "us"): (floordiv_compat, 1_000), ("ns", "ms"): (floordiv_compat, 1_000_000), ("us", "ns"): (pc.multiply, NS_PER_MICROSECOND), ("us", "ms"): (floordiv_compat, 1_000), ("ms", "ns"): (pc.multiply, NS_PER_MILLISECOND), ("ms", "us"): (pc.multiply, 1_000), ("s", "ns"): (pc.multiply, NS_PER_SECOND), ("s", "us"): (pc.multiply, US_PER_SECOND), ("s", "ms"): (pc.multiply, MS_PER_SECOND), } @property def unit(self) -> TimeUnit: # NOTE: Unsafe (native). return cast("pa.TimestampType[TimeUnit, Any]", self.native.type).unit @property def time_zone(self) -> str | None: # NOTE: Unsafe (narwhals). return cast("Datetime", self.compliant.dtype).time_zone def to_string(self, format: str) -> ArrowSeries: # PyArrow differs from other libraries in that %S also prints out # the fractional part of the second...:'( # https://arrow.apache.org/docs/python/generated/pyarrow.compute.strftime.html format = format.replace("%S.%f", "%S").replace("%S%.f", "%S") return self.with_native(pc.strftime(self.native, format)) def replace_time_zone(self, time_zone: str | None) -> ArrowSeries: if time_zone is not None: result = pc.assume_timezone(pc.local_timestamp(self.native), time_zone) else: result = pc.local_timestamp(self.native) return self.with_native(result) def convert_time_zone(self, time_zone: str) -> ArrowSeries: ser = self.replace_time_zone("UTC") if self.time_zone is None else self.compliant return self.with_native(ser.native.cast(pa.timestamp(self.unit, time_zone))) def timestamp(self, time_unit: TimeUnit) -> ArrowSeries: ser = self.compliant dtypes = ser._version.dtypes if isinstance(ser.dtype, dtypes.Datetime): current = ser.dtype.time_unit s_cast = self.native.cast(pa.int64()) if current == time_unit: result = s_cast elif item := self._TIMESTAMP_DATETIME_OP_FACTOR.get((current, time_unit)): fn, factor = item result = fn(s_cast, lit(factor)) else: # pragma: no cover msg = f"unexpected time unit {current}, please report an issue at https://github.com/narwhals-dev/narwhals" raise AssertionError(msg) return self.with_native(result) if isinstance(ser.dtype, dtypes.Date): time_s = pc.multiply(self.native.cast(pa.int32()), lit(SECONDS_PER_DAY)) factor = self._TIMESTAMP_DATE_FACTOR[time_unit] return self.with_native(pc.multiply(time_s, lit(factor))) msg = "Input should be either of Date or Datetime type" raise TypeError(msg) def date(self) -> ArrowSeries: return self.with_native(self.native.cast(pa.date32())) def year(self) -> ArrowSeries: return self.with_native(pc.year(self.native)) def month(self) -> ArrowSeries: return self.with_native(pc.month(self.native)) def day(self) -> ArrowSeries: return self.with_native(pc.day(self.native)) def hour(self) -> ArrowSeries: return self.with_native(pc.hour(self.native)) def minute(self) -> ArrowSeries: return self.with_native(pc.minute(self.native)) def second(self) -> ArrowSeries: return self.with_native(pc.second(self.native)) def millisecond(self) -> ArrowSeries: return self.with_native(pc.millisecond(self.native)) def microsecond(self) -> ArrowSeries: arr = self.native result = pc.add(pc.multiply(pc.millisecond(arr), lit(1000)), pc.microsecond(arr)) return self.with_native(result) def nanosecond(self) -> ArrowSeries: result = pc.add( pc.multiply(self.microsecond().native, lit(1000)), pc.nanosecond(self.native) ) return self.with_native(result) def ordinal_day(self) -> ArrowSeries: return self.with_native(pc.day_of_year(self.native)) def weekday(self) -> ArrowSeries: return self.with_native(pc.day_of_week(self.native, count_from_zero=False)) def total_minutes(self) -> ArrowSeries: unit_to_minutes_factor = { "s": SECONDS_PER_MINUTE, "ms": MS_PER_MINUTE, "us": US_PER_MINUTE, "ns": NS_PER_MINUTE, } factor = lit(unit_to_minutes_factor[self.unit], type=pa.int64()) return self.with_native(pc.divide(self.native, factor).cast(pa.int64())) def total_seconds(self) -> ArrowSeries: unit_to_seconds_factor = { "s": 1, "ms": MS_PER_SECOND, "us": US_PER_SECOND, "ns": NS_PER_SECOND, } factor = lit(unit_to_seconds_factor[self.unit], type=pa.int64()) return self.with_native(pc.divide(self.native, factor).cast(pa.int64())) def total_milliseconds(self) -> ArrowSeries: unit_to_milli_factor = { "s": 1e3, # seconds "ms": 1, # milli "us": 1e3, # micro "ns": 1e6, # nano } factor = lit(unit_to_milli_factor[self.unit], type=pa.int64()) if self.unit == "s": return self.with_native(pc.multiply(self.native, factor).cast(pa.int64())) return self.with_native(pc.divide(self.native, factor).cast(pa.int64())) def total_microseconds(self) -> ArrowSeries: unit_to_micro_factor = { "s": 1e6, # seconds "ms": 1e3, # milli "us": 1, # micro "ns": 1e3, # nano } factor = lit(unit_to_micro_factor[self.unit], type=pa.int64()) if self.unit in {"s", "ms"}: return self.with_native(pc.multiply(self.native, factor).cast(pa.int64())) return self.with_native(pc.divide(self.native, factor).cast(pa.int64())) def total_nanoseconds(self) -> ArrowSeries: unit_to_nano_factor = { "s": NS_PER_SECOND, "ms": NS_PER_MILLISECOND, "us": NS_PER_MICROSECOND, "ns": 1, } factor = lit(unit_to_nano_factor[self.unit], type=pa.int64()) return self.with_native(pc.multiply(self.native, factor).cast(pa.int64())) def truncate(self, every: str) -> ArrowSeries: interval = Interval.parse(every) return self.with_native( pc.floor_temporal(self.native, interval.multiple, UNITS_DICT[interval.unit]) ) def offset_by(self, by: str) -> ArrowSeries: interval = Interval.parse_no_constraints(by) native = self.native if interval.unit in {"y", "q", "mo"}: msg = f"Offsetting by {interval.unit} is not yet supported for pyarrow." raise NotImplementedError(msg) dtype = self.compliant.dtype datetime_dtype = self.version.dtypes.Datetime if interval.unit == "d" and isinstance(dtype, datetime_dtype) and dtype.time_zone: offset: pa.DurationScalar[Any] = lit(interval.to_timedelta()) native_naive = pc.local_timestamp(native) result = pc.assume_timezone(pc.add(native_naive, offset), dtype.time_zone) return self.with_native(result) if interval.unit == "ns": # pragma: no cover offset = lit(interval.multiple, pa.duration("ns")) else: offset = lit(interval.to_timedelta()) return self.with_native(pc.add(native, offset)) narwhals-2.26.0/src/narwhals/_arrow/series_list.py000066400000000000000000000026651525000603700222250ustar00rootroot00000000000000from __future__ import annotations from typing import TYPE_CHECKING import pyarrow as pa import pyarrow.compute as pc from narwhals._arrow.utils import ArrowSeriesNamespace, list_agg, list_sort from narwhals._compliant.any_namespace import ListNamespace from narwhals._utils import not_implemented if TYPE_CHECKING: from narwhals._arrow.series import ArrowSeries class ArrowSeriesListNamespace(ArrowSeriesNamespace, ListNamespace["ArrowSeries"]): def len(self) -> ArrowSeries: return self.with_native(pc.list_value_length(self.native).cast(pa.uint32())) def get(self, index: int) -> ArrowSeries: return self.with_native(pc.list_element(self.native, index)) def min(self) -> ArrowSeries: return self.with_native(list_agg(self.native, "min")) def max(self) -> ArrowSeries: return self.with_native(list_agg(self.native, "max")) def mean(self) -> ArrowSeries: return self.with_native(list_agg(self.native, "mean")) def median(self) -> ArrowSeries: return self.with_native(list_agg(self.native, "approximate_median")) def sum(self) -> ArrowSeries: return self.with_native(list_agg(self.native, "sum")) def sort(self, *, descending: bool, nulls_last: bool) -> ArrowSeries: return self.with_native( list_sort(self.native, descending=descending, nulls_last=nulls_last) ) unique = not_implemented() contains = not_implemented() narwhals-2.26.0/src/narwhals/_arrow/series_str.py000066400000000000000000000146111525000603700220540ustar00rootroot00000000000000from __future__ import annotations from typing import TYPE_CHECKING import pyarrow as pa import pyarrow.compute as pc from narwhals._arrow.utils import ( ArrowSeriesNamespace, extract_native, lit, parse_datetime_format, parse_time_format, ) from narwhals._compliant.any_namespace import StringNamespace from narwhals._utils import parse_str_strip_chars if TYPE_CHECKING: from narwhals._arrow.series import ArrowSeries from narwhals._arrow.typing import Incomplete class ArrowSeriesStringNamespace(ArrowSeriesNamespace, StringNamespace["ArrowSeries"]): def len_chars(self) -> ArrowSeries: return self.with_native(pc.utf8_length(self.native)) def replace( self, value: ArrowSeries, pattern: str, *, literal: bool, n: int ) -> ArrowSeries: fn = pc.replace_substring if literal else pc.replace_substring_regex _, value_native = extract_native(self.compliant, value) if not isinstance(value_native, pa.StringScalar): msg = "PyArrow backed `.str.replace` only supports str replacement values" raise TypeError(msg) arr = fn( self.native, pattern, replacement=value_native.as_py(), max_replacements=n ) return self.with_native(arr) def replace_all( self, value: ArrowSeries, pattern: str, *, literal: bool ) -> ArrowSeries: return self.replace(value, pattern, literal=literal, n=-1) def strip_chars(self, characters: str | None) -> ArrowSeries: return self.with_native( pc.utf8_trim(self.native, parse_str_strip_chars(characters)) ) def strip_chars_start(self, characters: str) -> ArrowSeries: return self.with_native(pc.utf8_ltrim(self.native, characters)) def strip_chars_end(self, characters: str) -> ArrowSeries: return self.with_native(pc.utf8_rtrim(self.native, characters)) def starts_with(self, prefix: ArrowSeries) -> ArrowSeries: _, prefix_native = extract_native(self.compliant, prefix) if not isinstance(prefix_native, pa.StringScalar): msg = "`.str.starts_with` only supports str prefix values for pyarrow backend" raise TypeError(msg) return self.with_native( pc.equal( self.slice(0, len(prefix_native.as_py())).native, lit(prefix_native.as_py()), ) ) def ends_with(self, suffix: ArrowSeries) -> ArrowSeries: _, suffix_native = extract_native(self.compliant, suffix) if not isinstance(suffix_native, pa.StringScalar): msg = "`.str.ends_with` only supports str suffix values for pyarrow backend" raise TypeError(msg) return self.with_native( pc.equal( self.slice(-len(suffix_native.as_py()), None).native, lit(suffix_native.as_py()), ) ) def contains(self, pattern: ArrowSeries, *, literal: bool) -> ArrowSeries: _, pattern_native = extract_native(self.compliant, pattern) if not isinstance(pattern_native, pa.StringScalar): msg = "`.str.contains` only supports str pattern values for pyarrow backend" raise TypeError(msg) fn = pc.match_substring if literal else pc.match_substring_regex return self.with_native(fn(self.native, pattern_native.as_py())) def slice(self, offset: int, length: int | None) -> ArrowSeries: stop = offset + length if length is not None else None return self.with_native( pc.utf8_slice_codeunits(self.native, start=offset, stop=stop) ) def split(self, by: str) -> ArrowSeries: split_series = pc.split_pattern(self.native, by) # type: ignore[call-overload] return self.with_native(split_series) def to_datetime(self, format: str | None) -> ArrowSeries: format = parse_datetime_format(self.native) if format is None else format timestamp_array = pc.strptime(self.native, format=format, unit="us") return self.with_native(timestamp_array) def to_date(self, format: str | None) -> ArrowSeries: return self.to_datetime(format=format).dt.date() def to_time(self, format: str | None) -> ArrowSeries: format = parse_time_format(self.native) if format is None else format timestamp_array = pc.strptime(self.native, format=format, unit="us") nw_time_dtype = self.version.dtypes.Time() return self.with_native(timestamp_array).cast(nw_time_dtype) def to_uppercase(self) -> ArrowSeries: return self.with_native(pc.utf8_upper(self.native)) def to_lowercase(self) -> ArrowSeries: return self.with_native(pc.utf8_lower(self.native)) def to_titlecase(self) -> ArrowSeries: return self.with_native(pc.utf8_title(self.native)) def zfill(self, width: int) -> ArrowSeries: binary_join: Incomplete = pc.binary_join_element_wise native = self.native hyphen, plus = lit("-"), lit("+") first_char, remaining_chars = ( self.slice(0, 1).native, self.slice(1, None).native, ) # Conditions less_than_width = pc.less(pc.utf8_length(native), lit(width)) starts_with_hyphen = pc.equal(first_char, hyphen) starts_with_plus = pc.equal(first_char, plus) conditions = pc.make_struct( pc.and_(starts_with_hyphen, less_than_width), pc.and_(starts_with_plus, less_than_width), less_than_width, ) # Cases padded_remaining_chars = pc.utf8_lpad(remaining_chars, width - 1, padding="0") result = pc.case_when( conditions, binary_join( pa.repeat(hyphen, len(native)), padded_remaining_chars, "" ), # starts with hyphen and less than width binary_join( pa.repeat(plus, len(native)), padded_remaining_chars, "" ), # starts with plus and less than width pc.utf8_lpad(native, width=width, padding="0"), # less than width native, ) return self.with_native(result) def pad_start(self, length: int, fill_char: str) -> ArrowSeries: return self.with_native( pc.utf8_lpad(self.native, width=length, padding=fill_char) ) def pad_end(self, length: int, fill_char: str) -> ArrowSeries: return self.with_native( pc.utf8_rpad(self.native, width=length, padding=fill_char) ) narwhals-2.26.0/src/narwhals/_arrow/series_struct.py000066400000000000000000000007701525000603700225710ustar00rootroot00000000000000from __future__ import annotations from typing import TYPE_CHECKING import pyarrow.compute as pc from narwhals._arrow.utils import ArrowSeriesNamespace from narwhals._compliant.any_namespace import StructNamespace if TYPE_CHECKING: from narwhals._arrow.series import ArrowSeries class ArrowSeriesStructNamespace(ArrowSeriesNamespace, StructNamespace["ArrowSeries"]): def field(self, name: str) -> ArrowSeries: return self.with_native(pc.struct_field(self.native, name)).alias(name) narwhals-2.26.0/src/narwhals/_arrow/typing.py000066400000000000000000000047771525000603700212200ustar00rootroot00000000000000from __future__ import annotations # pragma: no cover from typing import ( TYPE_CHECKING, # pragma: no cover Any, # pragma: no cover TypeVar, # pragma: no cover ) if TYPE_CHECKING: from typing import Generic, Literal, TypeAlias import pyarrow as pa from pyarrow.__lib_pxi.table import ( AggregateOptions, # noqa: F401 Aggregation, # noqa: F401 ) from pyarrow._stubs_typing import ( # pyright: ignore[reportMissingModuleSource] # pyright: ignore[reportMissingModuleSource] # pyright: ignore[reportMissingModuleSource] Indices, Mask, # noqa: F401 Order, # noqa: F401 ) from narwhals._arrow.expr import ArrowExpr from narwhals._arrow.series import ArrowSeries IntoArrowExpr: TypeAlias = "ArrowExpr | ArrowSeries" TieBreaker: TypeAlias = Literal["min", "max", "first", "dense"] NullPlacement: TypeAlias = Literal["at_start", "at_end"] NativeIntervalUnit: TypeAlias = Literal[ "year", "quarter", "month", "week", "day", "hour", "minute", "second", "millisecond", "microsecond", "nanosecond", ] PromoteOptions: TypeAlias = Literal["none", "default", "permissive"] ChunkedArrayAny: TypeAlias = pa.ChunkedArray[Any] ArrayAny: TypeAlias = pa.Array[Any] ArrayOrChunkedArray: TypeAlias = "ArrayAny | ChunkedArrayAny" ScalarAny: TypeAlias = pa.Scalar[Any] ArrayOrScalar: TypeAlias = "ArrayOrChunkedArray | ScalarAny" class DictionaryArrayAny(pa.Array[Any]): """`pa.DictionaryArray`, declaring the attributes the stubs are missing. https://arrow.apache.org/docs/python/generated/pyarrow.DictionaryArray.html """ @property def dictionary(self) -> ArrayAny: ... @property def indices(self) -> Indices: ... ArrayOrScalarT1 = TypeVar("ArrayOrScalarT1", ArrayAny, ChunkedArrayAny, ScalarAny) ArrayOrScalarT2 = TypeVar("ArrayOrScalarT2", ArrayAny, ChunkedArrayAny, ScalarAny) _AsPyType = TypeVar("_AsPyType") class _BasicDataType(pa.DataType, Generic[_AsPyType]): ... Incomplete: TypeAlias = Any # pragma: no cover """ Marker for working code that fails on the stubs. Common issues: - Annotated for `Array`, but not `ChunkedArray` - Relies on typing information that the stubs don't provide statically - Missing attributes - Incorrect return types - Inconsistent use of generic/concrete types - `_clone_signature` used on signatures that are not identical """ narwhals-2.26.0/src/narwhals/_arrow/utils.py000066400000000000000000000601631525000603700210350ustar00rootroot00000000000000from __future__ import annotations from functools import lru_cache from typing import TYPE_CHECKING, Any, cast import pyarrow as pa import pyarrow.compute as pc from narwhals._compliant import EagerSeriesNamespace from narwhals._utils import Implementation, Version, isinstance_or_issubclass from narwhals.exceptions import ColumnNotFoundError if TYPE_CHECKING: from collections.abc import Iterable, Iterator, Mapping, Sequence from typing import Literal, TypeAlias from typing_extensions import TypeIs from narwhals._arrow.series import ArrowSeries from narwhals._arrow.typing import ( # type: ignore[attr-defined] ArrayAny, ArrayOrScalar, ArrayOrScalarT1, ArrayOrScalarT2, ChunkedArrayAny, DictionaryArrayAny, Incomplete, NativeIntervalUnit, NullPlacement, Order, PromoteOptions, ScalarAny, ) from narwhals._duration import IntervalUnit from narwhals.dtypes import DType from narwhals.typing import IntoDType, PythonLiteral # NOTE: stubs don't allow for `ChunkedArray[StructArray]` # Intended to represent the `.chunks` property storing `list[pa.StructArray]` ChunkedArrayStructArray: TypeAlias = ChunkedArrayAny def is_timestamp(t: Any) -> TypeIs[pa.TimestampType[Any, Any]]: ... def is_duration(t: Any) -> TypeIs[pa.DurationType[Any]]: ... def is_list(t: Any) -> TypeIs[pa.ListType[Any]]: ... def is_large_list(t: Any) -> TypeIs[pa.LargeListType[Any]]: ... def is_fixed_size_list(t: Any) -> TypeIs[pa.FixedSizeListType[Any, Any]]: ... def is_dictionary(t: Any) -> TypeIs[pa.DictionaryType[Any, Any, Any]]: ... def extract_regex( strings: ChunkedArrayAny, /, pattern: str, *, options: Any = None, memory_pool: Any = None, ) -> ChunkedArrayStructArray: ... else: from pyarrow.compute import extract_regex from pyarrow.types import ( is_dictionary, is_duration, is_fixed_size_list, is_large_list, is_list, is_timestamp, ) BACKEND_VERSION = Implementation.PYARROW._backend_version() """Static backend version for `pyarrow`.""" UNITS_DICT: Mapping[IntervalUnit, NativeIntervalUnit] = { "y": "year", "q": "quarter", "mo": "month", "d": "day", "h": "hour", "m": "minute", "s": "second", "ms": "millisecond", "us": "microsecond", "ns": "nanosecond", } lit = pa.scalar """Alias for `pyarrow.scalar`.""" def extract_py_scalar(value: Any, /) -> Any: from narwhals._arrow.series import maybe_extract_py_scalar return maybe_extract_py_scalar(value, return_py_scalar=True) def is_array_or_scalar(obj: Any) -> TypeIs[ArrayOrScalar]: """Return True for any base `pyarrow` container.""" return isinstance(obj, (pa.ChunkedArray, pa.Array, pa.Scalar)) def arange(start: int, end: int, step: int) -> ArrayAny: if BACKEND_VERSION < (21,): import numpy as np # ignore-banned-import return pa.array(np.arange(start, end, step)) # NOTE: Added in https://github.com/apache/arrow/pull/46778 return pa.arange(start, end, step) # type: ignore[attr-defined] def build_list_array(arrays: list[pa.Array[Any]]) -> pa.ChunkedArray[Any]: # This works by using concat_arrays to vertically stack the arrays. # Then we use take to grab the data by index and to horizontally # pack the arrays. n = len(arrays[0]) num_cols = len(arrays) i = arange(0, n, 1) j = arange(0, num_cols, 1) k = arange(0, n * num_cols, 1) row_idx = cast("pa.Int64Array", pc.divide(k, lit(num_cols))) col_idx = cast("pa.Int64Array", pc.subtract(k, pc.multiply(row_idx, lit(num_cols)))) i_rep = pc.take(i, row_idx) j_tile = pc.take(j, col_idx) indices = cast("pa.Int64Array", pc.add(pc.multiply(j_tile, lit(n)), i_rep)) interleaved = pc.take(pa.concat_arrays(arrays), indices) offsets = cast("pa.Int64Array", arange(0, n * num_cols + 1, num_cols)) # Typing suggests only allow Int32Array is valid for offsets, but Int64Array is fine. return pa.chunked_array([pa.ListArray.from_arrays(offsets, interleaved)]) # type: ignore[call-overload] def chunked_array( arr: ArrayOrScalar | list[Iterable[Any]], dtype: pa.DataType | None = None, / ) -> ChunkedArrayAny: if isinstance(arr, pa.ChunkedArray): return arr # NOTE: stubs only overload on *concrete* types, so a dynamic `pa.DataType` never matches ca: Incomplete = pa.chunked_array return ca(arr, dtype) if isinstance(arr, list) else ca([arr], dtype) def nulls_like(n: int, series: ArrowSeries) -> ArrayAny: """Create a strongly-typed Array instance with all elements null. Uses the type of `series`, without upseting `mypy`. """ return pa.nulls(n, series.native.type) def repeat( value: PythonLiteral | ScalarAny, n: int, /, dtype: pa.DataType | None = None ) -> ArrayAny: """Create an Array instance whose slots are the given scalar. *Optionally*, casting to `dtype` **before** repeating `n` times. """ lit_: Incomplete = lit return pa.repeat(lit_(value, type=dtype), n) def zeros(n: int, /) -> pa.Int64Array: return pa.repeat(0, n) def native_to_narwhals_dtype(dtype: pa.DataType, version: Version) -> DType: if isinstance(dtype, pa.ExtensionType): return version.dtypes.Unknown() return native_non_extension_to_narwhals_dtype(dtype, version) @lru_cache(maxsize=16) def native_non_extension_to_narwhals_dtype(dtype: pa.DataType, version: Version) -> DType: # noqa: C901, PLR0912 dtypes = version.dtypes if pa.types.is_int64(dtype): return dtypes.Int64() if pa.types.is_int32(dtype): return dtypes.Int32() if pa.types.is_int16(dtype): return dtypes.Int16() if pa.types.is_int8(dtype): return dtypes.Int8() if pa.types.is_uint64(dtype): return dtypes.UInt64() if pa.types.is_uint32(dtype): return dtypes.UInt32() if pa.types.is_uint16(dtype): return dtypes.UInt16() if pa.types.is_uint8(dtype): return dtypes.UInt8() if pa.types.is_boolean(dtype): return dtypes.Boolean() if pa.types.is_float64(dtype): return dtypes.Float64() if pa.types.is_float32(dtype): return dtypes.Float32() if pa.types.is_float16(dtype): return dtypes.Float16() # bug in coverage? it shows `31->exit` (where `31` is currently the line number of # the next line), even though both when the if condition is true and false are covered if ( # pragma: no cover pa.types.is_string(dtype) or pa.types.is_large_string(dtype) or getattr(pa.types, "is_string_view", lambda _: False)(dtype) ): return dtypes.String() if pa.types.is_date32(dtype): return dtypes.Date() if is_timestamp(dtype): return dtypes.Datetime(time_unit=dtype.unit, time_zone=dtype.tz) if is_duration(dtype): return dtypes.Duration(time_unit=dtype.unit) if pa.types.is_dictionary(dtype): return dtypes.Categorical() if pa.types.is_struct(dtype): return dtypes.Struct( [ dtypes.Field( dtype.field(i).name, native_to_narwhals_dtype(dtype.field(i).type, version), ) for i in range(dtype.num_fields) ] ) if is_list(dtype) or is_large_list(dtype): return dtypes.List(native_to_narwhals_dtype(dtype.value_type, version)) if is_fixed_size_list(dtype): return dtypes.Array( native_to_narwhals_dtype(dtype.value_type, version), dtype.list_size ) if pa.types.is_decimal128(dtype): return dtypes.Decimal(precision=dtype.precision, scale=dtype.scale) if pa.types.is_time32(dtype) or pa.types.is_time64(dtype): return dtypes.Time() if pa.types.is_binary(dtype): return dtypes.Binary() return dtypes.Unknown() # pragma: no cover dtypes = Version.MAIN.dtypes NW_TO_PA_DTYPES: Mapping[type[DType], pa.DataType] = { dtypes.Float64: pa.float64(), dtypes.Float32: pa.float32(), dtypes.Float16: pa.float16(), dtypes.Binary: pa.binary(), dtypes.String: pa.string(), dtypes.Boolean: pa.bool_(), dtypes.Categorical: pa.dictionary(pa.uint32(), pa.string()), dtypes.Date: pa.date32(), dtypes.Time: pa.time64("ns"), dtypes.Int8: pa.int8(), dtypes.Int16: pa.int16(), dtypes.Int32: pa.int32(), dtypes.Int64: pa.int64(), dtypes.UInt8: pa.uint8(), dtypes.UInt16: pa.uint16(), dtypes.UInt32: pa.uint32(), dtypes.UInt64: pa.uint64(), } UNSUPPORTED_DTYPES = (dtypes.Object, dtypes.Enum) def narwhals_to_native_dtype(dtype: IntoDType, version: Version) -> pa.DataType: dtypes = version.dtypes base_type = dtype.base_type() if pa_type := NW_TO_PA_DTYPES.get(base_type): return pa_type if isinstance_or_issubclass(dtype, dtypes.Datetime): unit = dtype.time_unit return pa.timestamp(unit, tz) if (tz := dtype.time_zone) else pa.timestamp(unit) if isinstance_or_issubclass(dtype, dtypes.Duration): return pa.duration(dtype.time_unit) if isinstance_or_issubclass(dtype, dtypes.List): return pa.list_(value_type=narwhals_to_native_dtype(dtype.inner, version=version)) if isinstance_or_issubclass(dtype, dtypes.Struct): return pa.struct( [ (field.name, narwhals_to_native_dtype(field.dtype, version=version)) for field in dtype.fields ] ) if isinstance_or_issubclass(dtype, dtypes.Array): inner = narwhals_to_native_dtype(dtype.inner, version=version) list_size = dtype.size return pa.list_(inner, list_size=list_size) if isinstance_or_issubclass(dtype, dtypes.Decimal): return pa.decimal128(precision=dtype.precision, scale=dtype.scale) if issubclass(base_type, UNSUPPORTED_DTYPES): msg = f"Converting to {base_type.__name__} dtype is not supported for PyArrow." raise NotImplementedError(msg) msg = f"Unknown dtype: {dtype}" # pragma: no cover raise AssertionError(msg) def extract_native( lhs: ArrowSeries, rhs: ArrowSeries | PythonLiteral | ScalarAny ) -> tuple[ChunkedArrayAny, ChunkedArrayAny | ScalarAny]: """Extract native objects in binary operation. If the comparison isn't supported, return `NotImplemented` so that the "right-hand-side" operation (e.g. `__radd__`) can be tried. If one of the two sides has a `_broadcast` flag, then extract the scalar underneath it so that PyArrow can do its own broadcasting. """ from narwhals._arrow.series import ArrowSeries if rhs is None: # pragma: no cover return lhs.native, lit(None, type=lhs._type) if isinstance(rhs, ArrowSeries): if lhs._broadcast and not rhs._broadcast: return lhs.native[0], rhs.native if rhs._broadcast: return lhs.native, rhs.native[0] return lhs.native, rhs.native if isinstance(rhs, list): msg = "Expected Series or scalar, got list." raise TypeError(msg) return lhs.native, rhs if isinstance(rhs, pa.Scalar) else lit(rhs) def floordiv_compat(left: ArrayOrScalar, right: ArrayOrScalar, /) -> Any: # The following lines are adapted from pandas' pyarrow implementation. # Ref: https://github.com/pandas-dev/pandas/blob/262fcfbffcee5c3116e86a951d8b693f90411e68/pandas/core/arrays/arrow/array.py#L124-L154 # We modify it to add `safe_mask` so as to align with Polars' behaviour when dividing by zero. safe_mask = pc.not_equal(right, lit(0, type=right.type)) safe_right = pc.if_else(safe_mask, right, lit(1, type=right.type)) # Dummy value non_safe_result = lit(None, type=left.type) if pa.types.is_integer(left.type) and pa.types.is_integer(right.type): divided = pc.if_else( safe_mask, pc.divide_checked(left, safe_right), non_safe_result ) # TODO @dangotbanned: Use a `TypeVar` in guards # Narrowing to a `Union` isn't interacting well with the rest of the stubs # https://github.com/zen-xu/pyarrow-stubs/pull/215 if pa.types.is_signed_integer(divided.type): div_type = cast("pa._lib.Int64Type", divided.type) has_remainder = pc.not_equal(pc.multiply(divided, right), left) has_one_negative_operand = pc.less( pc.bit_wise_xor(left, right), lit(0, div_type) ) result = pc.if_else( pc.and_(has_remainder, has_one_negative_operand), pc.subtract(divided, lit(1, div_type)), divided, ) else: result = divided # pragma: no cover result = result.cast(left.type) else: divided = pc.if_else(safe_mask, pc.divide(left, safe_right), non_safe_result) result = pc.floor(divided) return result def cast_for_truediv( arrow_array: ArrayOrScalarT1, pa_object: ArrayOrScalarT2 ) -> tuple[ArrayOrScalarT1, ArrayOrScalarT2]: # Lifted from: # https://github.com/pandas-dev/pandas/blob/262fcfbffcee5c3116e86a951d8b693f90411e68/pandas/core/arrays/arrow/array.py#L108-L122 # Ensure int / int -> float mirroring Python/Numpy behavior # as pc.divide_checked(int, int) -> int if pa.types.is_integer(arrow_array.type) and pa.types.is_integer(pa_object.type): # GH: 56645. # noqa: ERA001 # https://github.com/apache/arrow/issues/35563 return arrow_array.cast(pa.float64(), safe=False), pa_object.cast( pa.float64(), safe=False ) return arrow_array, pa_object # Regex for date, time, separator and timezone components DATE_RE = r"(?P\d{1,4}[-/.]\d{1,2}[-/.]\d{1,4}|\d{8})" SEP_RE = r"(?P\s|T)" TIME_RE = r"(?P