datafusion 51.0.0
Build and run queries against data
License: Apache-2.0
Requires Python: >=3.10
Keywords: datafusion, dataframe, rust, query-engine

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# DataFusion in Python

[![Python test](https://github.com/apache/datafusion-python/actions/workflows/test.yaml/badge.svg)](https://github.com/apache/datafusion-python/actions/workflows/test.yaml)
[![Python Release Build](https://github.com/apache/datafusion-python/actions/workflows/build.yml/badge.svg)](https://github.com/apache/datafusion-python/actions/workflows/build.yml)

This is a Python library that binds to [Apache Arrow](https://arrow.apache.org/) in-memory query engine [DataFusion](https://github.com/apache/datafusion).

DataFusion's Python bindings can be used as a foundation for building new data systems in Python. Here are some examples:

- [Dask SQL](https://github.com/dask-contrib/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](https://github.com/apache/datafusion-ballista) is a distributed SQL query engine that extends
  DataFusion's Python bindings for distributed use cases.
- [DataFusion Ray](https://github.com/apache/datafusion-ray) is another distributed query engine that uses
  DataFusion's Python bindings.

## Features

- 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.

For tips on tuning parallelism, see
[Maximizing CPU Usage](docs/source/user-guide/configuration.rst#maximizing-cpu-usage)
in the configuration guide.

## Example Usage

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:

- https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page

```python
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:

![Chart](examples/chart.png)

## Registering a DataFrame as a View

You can use SessionContext's `register_view` method to convert a DataFrame into a view and register it with the context.

```python
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:

```python
[{'a': [3, 4, 5], 'b': [30, 40, 50]}]
```

## Configuration

It is possible to configure runtime (memory and disk settings) and configuration settings when creating a context.

```python
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](https://arrow.apache.org/datafusion-python/#api-reference) for more information.

Printing the context will show the current configuration settings.

```python
print(ctx)
```

## Extensions

For information about how to extend DataFusion Python, please see the extensions page of the
[online documentation](https://datafusion.apache.org/python/).

## More Examples

See [examples](examples/README.md) for more information.

### Executing Queries with DataFusion

- [Query a Parquet file using SQL](https://github.com/apache/datafusion-python/blob/main/examples/sql-parquet.py)
- [Query a Parquet file using the DataFrame API](https://github.com/apache/datafusion-python/blob/main/examples/dataframe-parquet.py)
- [Run a SQL query and store the results in a Pandas DataFrame](https://github.com/apache/datafusion-python/blob/main/examples/sql-to-pandas.py)
- [Run a SQL query with a Python user-defined function (UDF)](https://github.com/apache/datafusion-python/blob/main/examples/sql-using-python-udf.py)
- [Run a SQL query with a Python user-defined aggregation function (UDAF)](https://github.com/apache/datafusion-python/blob/main/examples/sql-using-python-udaf.py)
- [Query PyArrow Data](https://github.com/apache/datafusion-python/blob/main/examples/query-pyarrow-data.py)
- [Create dataframe](https://github.com/apache/datafusion-python/blob/main/examples/import.py)
- [Export dataframe](https://github.com/apache/datafusion-python/blob/main/examples/export.py)

### Running User-Defined Python Code

- [Register a Python UDF with DataFusion](https://github.com/apache/datafusion-python/blob/main/examples/python-udf.py)
- [Register a Python UDAF with DataFusion](https://github.com/apache/datafusion-python/blob/main/examples/python-udaf.py)

### Substrait Support

- [Serialize query plans using Substrait](https://github.com/apache/datafusion-python/blob/main/examples/substrait.py)

## How to install

### uv

```bash
uv add datafusion
```

### Pip

```bash
pip install datafusion
# or
python -m pip install datafusion
```

### Conda

```bash
conda install -c conda-forge datafusion
```

You can verify the installation by running:

```python
>>> import datafusion
>>> datafusion.__version__
'0.6.0'
```

## How to develop

This assumes that you have rust and cargo installed. We use the workflow recommended by [pyo3](https://github.com/PyO3/pyo3) and [maturin](https://github.com/PyO3/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](https://protobuf.dev/installation/) 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`

```bash
# 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`):

```bash
# 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.

```bash
git submodule update --init
```

Whenever rust code changes (your changes or via `git pull`):

```bash
# 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:

```bash
uv run --no-project maturin develop --uv
uv --no-project pytest .
```

### Running & Installing pre-commit hooks

`datafusion-python` takes advantage of [pre-commit](https://pre-commit.com/) 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.

## Running linters without using pre-commit

There are scripts in `ci/scripts` for running Rust and Python linters.

```shell
./ci/scripts/python_lint.sh
./ci/scripts/rust_clippy.sh
./ci/scripts/rust_fmt.sh
./ci/scripts/rust_toml_fmt.sh
```

## How to update dependencies

To change test dependencies, change the `pyproject.toml` and run

```bash
uv sync --dev --no-install-package datafusion
```