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:
- Dask SQL uses DataFusion's Python bindings for SQL parsing, query planning, and logical plan optimizations, and then transpiles the logical plan to Dask operations for execution.
- DataFusion Ballista is a distributed SQL query engine that extends DataFusion's Python bindings for distributed use cases.
It is also possible to use these Python bindings directly for DataFrame and SQL operations, but you may find that Polars and DuckDB are more suitable for this use case, since they have more of an end-user focus and are more actively maintained than these Python bindings.
- Execute queries using SQL or DataFrames against CSV, Parquet, and JSON data sources.
- Queries are optimized using DataFusion's query optimizer.
- Execute user-defined Python code from SQL.
- Exchange data with Pandas and other DataFrame libraries that support PyArrow.
- Serialize and deserialize query plans in Substrait format.
- Experimental support for transpiling SQL queries to DataFrame calls with Polars, Pandas, and cuDF.
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:
It is possible to configure runtime (memory and disk settings) and configuration settings when creating a context.
runtime = (
RuntimeConfig()
.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)See examples for more information.
- Query a Parquet file using SQL
- Query a Parquet file using the DataFrame API
- Run a SQL query and store the results in a Pandas DataFrame
- Run a SQL query with a Python user-defined function (UDF)
- Run a SQL query with a Python user-defined aggregation function (UDAF)
- Query PyArrow Data
- Create dataframe
- Export dataframe
pip install datafusion
# or
python -m pip install datafusionconda install -c conda-forge datafusionYou 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 Conda or Pip. Both approaches should offer the same experience. Multiple approaches are only offered to appease developer preference. Bootstrapping for both Conda and Pip are as follows.
Bootstrap (Conda):
# fetch this repo
git clone [email protected]:apache/datafusion-python.git
# create the conda environment for dev
conda env create -f ./conda/environments/datafusion-dev.yaml -n datafusion-dev
# activate the conda environment
conda activate datafusion-devOr alternatively, if you are on an OS that supports CUDA Toolkit, you can use -f ./conda/environments/datafusion-cuda-dev.yaml.
Bootstrap (Pip):
# fetch this repo
git clone [email protected]:apache/datafusion-python.git
# 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 (for Python 3.8+)
python -m pip install -r requirements.inThe tests rely on test data in git submodules.
git submodule init
git submodule updateWhenever rust code changes (your changes or via git pull):
# make sure you activate the venv using "source venv/bin/activate" first
maturin develop
python -m pytestarrow-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 ARROW_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
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.shTo change test dependencies, change the requirements.in and run
# install pip-tools (this can be done only once), also consider running in venv
python -m pip install pip-tools
python -m piptools compile --generate-hashes -o requirements-310.txtTo update dependencies, run with -U
python -m piptools compile -U --generate-hashes -o requirements-310.txtMore details here