feat: expose arrow_field, arrow_try_cast, cast_to_type, with_metadata (#1568) * feat: expose arrow_field, arrow_try_cast, cast_to_type, with_metadata Adds Python bindings for five scalar functions from datafusion::functions::expr_fn that were not previously surfaced: - arrow_field: returns a struct describing an expression's Arrow field (name, data_type, nullable, metadata). - arrow_try_cast: like arrow_cast but yields NULL on cast failure. - cast_to_type / try_cast_to_type: casts a value to the type of a reference expression. These are exposed as a single Python entry point cast_to_type(value, type_ref, *, try_cast=False); the kwarg switches between the strict and try variants. - with_metadata: attach Arrow field metadata; the inverse of arrow_metadata. Accepts a dict[str, str] for ergonomics. Updates skills/datafusion_python/SKILL.md to list the new functions and documents the cast_to_type kwarg behavior. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * refactor: collapse try_cast_to_type into cast_to_type kwarg The previous commit exposed cast_to_type and try_cast_to_type as two separate pyo3 bindings and unified them in the Python wrapper via a try_cast kwarg. That left try_cast_to_type in datafusion._internal without a matching public Python name, breaking test_datafusion_missing_exports. Move the dispatch into the rust binding: cast_to_type now takes a try_cast kwarg and selects between functions::expr_fn::cast_to_type and try_cast_to_type internally. Only one pyo3 binding is registered, so the wrapper-coverage check passes and the Python entrypoint is unchanged. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat: accept pyarrow DataType in arrow_try_cast Mirrors arrow_cast: arrow_try_cast now accepts `pa.DataType` in addition to `str` and `Expr`. Adds `Expr.try_cast(pa.DataType)` PyO3 binding for the pyarrow-type routing path. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix: guard with_metadata against empty dict and empty keys Empty `metadata` dict now returns the input expression unchanged (previously bubbled an opaque DataFusion error about minimum arg count). Empty keys raise `ValueError` to match the docstring contract. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: assert full struct shape in arrow_field doctest Previous doctest set metadata on the input field but only checked the name — the metadata setup was dead. Now the example asserts the full returned struct (name, data_type, nullable, metadata) so the demo shows what the function actually produces. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * test: add unit tests for arrow_try_cast, arrow_field, cast_to_type, with_metadata Mirrors the existing test_arrow_cast pattern. Covers: - arrow_try_cast: string-syntax, pa.DataType, and null-on-failure paths - arrow_field: full returned struct shape (name, data_type, nullable, metadata) - cast_to_type: type-from-expr happy path and try_cast=True null behavior - with_metadata: round-trip through arrow_metadata, empty-dict no-op, and empty-key ValueError Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * test: parameterize arrow cast / try_cast tests Folds the previous four cast tests (arrow_cast + arrow_try_cast × str + pyarrow target type) into a single parameterized test that runs both functions across all five target-type variants. Collapses the two cast_to_type tests (happy path + try_cast=True) into one parameterized test, and parameterizes arrow_try_cast null-on-failure over both target-type syntaxes. 7 test functions, 19 cases — net less code, same coverage. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: point cast_to_type at arrow_cast for static target types Adds a one-line cross-reference so users with a known target type reach for arrow_cast / arrow_try_cast instead of building a sentinel expression to feed cast_to_type. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * refactor: split cast_to_type into cast_to_type and try_cast_to_type Replace the try_cast bool flag with separate cast_to_type and try_cast_to_type functions, matching upstream DataFusion and the arrow_cast / arrow_try_cast pair. Also drop the redundant data_type parametrization on test_arrow_try_cast_null_on_failure, since the str-vs-pyarrow distinction is already covered by test_arrow_cast_variants. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.7 (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 project ships a SKILL.md that teaches AI coding assistants how to write idiomatic DataFusion Python. It follows the Agent Skills open standard.
Preferred: npx skills add apache/datafusion-python — installs the skill in Claude Code, Cursor, Windsurf, Cline, Codex, Copilot, Gemini CLI, and other supported agents.
Manual: paste this line into your project's AGENTS.md / CLAUDE.md:
For DataFusion Python code, see https://github.com/apache/datafusion-python/blob/main/skills/datafusion_python/SKILL.md
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