Add AI skill to check current repository against upstream APIs (#1460) * Initial commit for skill to check upstream repo * Add instructions on using the check-upstream skill * Add FFI type coverage and implementation pattern to check-upstream skill Document the full FFI type pipeline (Rust PyO3 wrapper → Protocol type → Python wrapper → ABC base class → exports → example) and catalog which upstream datafusion-ffi types are supported, which have been evaluated as not needing direct exposure, and how to check for new gaps. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Update check-upstream skill to include FFI types as a checkable area Add "ffi types" to the argument-hint and description so users can invoke the skill with `/check-upstream ffi types`. Also add pipeline verification step to ensure each supported FFI type has the full end-to-end chain (PyO3 wrapper, Protocol, Python wrapper with type hints, ABC, exports). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Move FFI Types section alongside other areas to check Section 7 (FFI Types) was incorrectly placed after the Output Format and Implementation Pattern sections. Move it to sit after Section 6 (SessionContext Methods), consistent with the other checkable areas. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Replace static FFI type list with dynamic discovery instruction The supported FFI types list would go stale as new types are added. Replace it with a grep instruction to discover them at check time, keeping only the "evaluated and not requiring exposure" list which captures rationale not derivable from code. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Make Python API the source of truth for upstream coverage checks Functions exposed in Python (e.g., as aliases of other Rust bindings) were being falsely reported as missing because they lacked a dedicated #[pyfunction] in Rust. The user-facing API is the Python layer, so coverage should be measured there. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add exclusion list for DataFrame methods already covered by Python API show_limit is covered by DataFrame.show() and with_param_values is covered by SessionContext.sql(param_values=...), so neither needs separate exposure. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Move skills to .ai/skills/ for tool-agnostic discoverability Moves the canonical skill definitions from .claude/skills/ to .ai/skills/ and replaces .claude/skills with a symlink, so Claude Code still discovers them while other AI agents can find them in a tool-neutral location. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add AGENTS.md for tool-agnostic agent instructions with CLAUDE.md symlink AGENTS.md points agents to .ai/skills/ for skill discovery. CLAUDE.md symlinks to it so Claude Code picks it up as project instructions. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Make README upstream coverage section tool-agnostic Remove Claude Code references and update skill path from .claude/skills/ to .ai/skills/ to match the new tool-neutral directory structure. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add GitHub issue lookup step to check-upstream skill When gaps are identified, search open issues at apache/datafusion-python before reporting. Existing issues are linked in the report rather than duplicated. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Require Python test coverage in issues created by check-upstream skill Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add license text --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This is a Python library that binds to Apache Arrow in-memory query engine DataFusion.
DataFusion's Python bindings can be used as a foundation for building new data systems in Python. Here are some examples:
For tips on tuning parallelism, see Maximizing CPU Usage in the configuration guide.
The following example demonstrates running a SQL query against a Parquet file using DataFusion, storing the results in a Pandas DataFrame, and then plotting a chart.
The Parquet file used in this example can be downloaded from the following page:
from datafusion import SessionContext # Create a DataFusion context ctx = SessionContext() # Register table with context ctx.register_parquet('taxi', 'yellow_tripdata_2021-01.parquet') # Execute SQL df = ctx.sql("select passenger_count, count(*) " "from taxi " "where passenger_count is not null " "group by passenger_count " "order by passenger_count") # convert to Pandas pandas_df = df.to_pandas() # create a chart fig = pandas_df.plot(kind="bar", title="Trip Count by Number of Passengers").get_figure() fig.savefig('chart.png')
This produces the following chart:
You can use SessionContext's register_view method to convert a DataFrame into a view and register it with the context.
from datafusion import SessionContext, col, literal # Create a DataFusion context ctx = SessionContext() # Create sample data data = {"a": [1, 2, 3, 4, 5], "b": [10, 20, 30, 40, 50]} # Create a DataFrame from the dictionary df = ctx.from_pydict(data, "my_table") # Filter the DataFrame (for example, keep rows where a > 2) df_filtered = df.filter(col("a") > literal(2)) # Register the dataframe as a view with the context ctx.register_view("view1", df_filtered) # Now run a SQL query against the registered view df_view = ctx.sql("SELECT * FROM view1") # Collect the results results = df_view.collect() # Convert results to a list of dictionaries for display result_dicts = [batch.to_pydict() for batch in results] print(result_dicts)
This will output:
[{'a': [3, 4, 5], 'b': [30, 40, 50]}]
It is possible to configure runtime (memory and disk settings) and configuration settings when creating a context.
runtime = ( RuntimeEnvBuilder() .with_disk_manager_os() .with_fair_spill_pool(10000000) ) config = ( SessionConfig() .with_create_default_catalog_and_schema(True) .with_default_catalog_and_schema("foo", "bar") .with_target_partitions(8) .with_information_schema(True) .with_repartition_joins(False) .with_repartition_aggregations(False) .with_repartition_windows(False) .with_parquet_pruning(False) .set("datafusion.execution.parquet.pushdown_filters", "true") ) ctx = SessionContext(config, runtime)
Refer to the API documentation for more information.
Printing the context will show the current configuration settings.
print(ctx)
For information about how to extend DataFusion Python, please see the extensions page of the online documentation.
See examples for more information.
uv add datafusion
pip install datafusion # or python -m pip install datafusion
conda install -c conda-forge datafusion
You can verify the installation by running:
>>> import datafusion >>> datafusion.__version__ '0.6.0'
This assumes that you have rust and cargo installed. We use the workflow recommended by pyo3 and maturin. The Maturin tools used in this workflow can be installed either via uv or pip. Both approaches should offer the same experience. It is recommended to use uv since it has significant performance improvements over pip.
Currently for protobuf support either protobuf or cmake must be installed.
Bootstrap (uv):
By default uv will attempt to build the datafusion python package. For our development we prefer to build manually. This means that when creating your virtual environment using uv sync you need to pass in the additional --no-install-package datafusion and for uv run commands the additional parameter --no-project
# fetch this repo git clone git@github.com:apache/datafusion-python.git # cd to the repo root cd datafusion-python/ # create the virtual environment uv sync --dev --no-install-package datafusion # activate the environment source .venv/bin/activate
Bootstrap (pip):
# fetch this repo git clone git@github.com:apache/datafusion-python.git # cd to the repo root cd datafusion-python/ # prepare development environment (used to build wheel / install in development) python3 -m venv .venv # activate the venv source .venv/bin/activate # update pip itself if necessary python -m pip install -U pip # install dependencies python -m pip install -r pyproject.toml
The tests rely on test data in git submodules.
git submodule update --init
Whenever rust code changes (your changes or via git pull):
# make sure you activate the venv using "source venv/bin/activate" first maturin develop --uv python -m pytest
Alternatively if you are using uv you can do the following without needing to activate the virtual environment:
uv run --no-project maturin develop --uv uv run --no-project pytest .
To run the FFI tests within the examples folder, after you have built datafusion-python with the previous commands:
cd examples/datafusion-ffi-example uv run --no-project maturin develop --uv uv run --no-project pytest python/tests/_test_*py
datafusion-python takes advantage of pre-commit to assist developers with code linting to help reduce the number of commits that ultimately fail in CI due to linter errors. Using the pre-commit hooks is optional for the developer but certainly helpful for keeping PRs clean and concise.
Our pre-commit hooks can be installed by running pre-commit install, which will install the configurations in your DATAFUSION_PYTHON_ROOT/.github directory and run each time you perform a commit, failing to complete the commit if an offending lint is found allowing you to make changes locally before pushing.
The pre-commit hooks can also be run adhoc without installing them by simply running pre-commit run --all-files.
NOTE: the current pre-commit hooks require docker, and cmake. See note on protobuf above.
There are scripts in ci/scripts for running Rust and Python linters.
./ci/scripts/python_lint.sh ./ci/scripts/rust_clippy.sh ./ci/scripts/rust_fmt.sh ./ci/scripts/rust_toml_fmt.sh
This project includes an AI agent skill for auditing which features from the upstream Apache DataFusion Rust library are not yet exposed in these Python bindings. This is useful when adding missing functions, auditing API coverage, or ensuring parity with upstream.
The skill accepts an optional area argument:
scalar functions aggregate functions window functions dataframe session context ffi types all
If no argument is provided, it defaults to checking all areas. The skill will fetch the upstream DataFusion documentation, compare it against the functions and methods exposed in this project, and produce a coverage report listing what is currently exposed and what is missing.
The skill definition lives in .ai/skills/check-upstream/SKILL.md and follows the Agent Skills open standard. It can be used by any AI coding agent that supports skill discovery, or followed manually.
To change test dependencies, change the pyproject.toml and run
uv sync --dev --no-install-package datafusion