test_dataframe.py

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import pyarrow as pa
import pytest

from datafusion import functions as f
from datafusion import DataFrame, ExecutionContext, column, literal, udf


@pytest.fixture
def df():
    ctx = ExecutionContext()

    # create a RecordBatch and a new DataFrame from it
    batch = pa.RecordBatch.from_arrays(
        [pa.array([1, 2, 3]), pa.array([4, 5, 6])],
        names=["a", "b"],
    )

    return ctx.create_dataframe([[batch]])


@pytest.fixture
def struct_df():
    ctx = ExecutionContext()

    # create a RecordBatch and a new DataFrame from it
    batch = pa.RecordBatch.from_arrays(
        [pa.array([{"c": 1}, {"c": 2}, {"c": 3}]), pa.array([4, 5, 6])],
        names=["a", "b"],
    )

    return ctx.create_dataframe([[batch]])


def test_select(df):
    df = df.select(
        column("a") + column("b"),
        column("a") - column("b"),
    )

    # execute and collect the first (and only) batch
    result = df.collect()[0]

    assert result.column(0) == pa.array([5, 7, 9])
    assert result.column(1) == pa.array([-3, -3, -3])


def test_filter(df):
    df = df.select(
        column("a") + column("b"),
        column("a") - column("b"),
    ).filter(column("a") > literal(2))

    # execute and collect the first (and only) batch
    result = df.collect()[0]

    assert result.column(0) == pa.array([9])
    assert result.column(1) == pa.array([-3])


def test_sort(df):
    df = df.sort(column("b").sort(ascending=False))

    table = pa.Table.from_batches(df.collect())
    expected = {"a": [3, 2, 1], "b": [6, 5, 4]}

    assert table.to_pydict() == expected


def test_limit(df):
    df = df.limit(1)

    # execute and collect the first (and only) batch
    result = df.collect()[0]

    assert len(result.column(0)) == 1
    assert len(result.column(1)) == 1


def test_udf(df):
    # is_null is a pa function over arrays
    is_null = udf(
        lambda x: x.is_null(),
        [pa.int64()],
        pa.bool_(),
        volatility="immutable",
    )

    df = df.select(is_null(column("a")))
    result = df.collect()[0].column(0)

    assert result == pa.array([False, False, False])


def test_join():
    ctx = ExecutionContext()

    batch = pa.RecordBatch.from_arrays(
        [pa.array([1, 2, 3]), pa.array([4, 5, 6])],
        names=["a", "b"],
    )
    df = ctx.create_dataframe([[batch]])

    batch = pa.RecordBatch.from_arrays(
        [pa.array([1, 2]), pa.array([8, 10])],
        names=["a", "c"],
    )
    df1 = ctx.create_dataframe([[batch]])

    df = df.join(df1, join_keys=(["a"], ["a"]), how="inner")
    df = df.sort(column("a").sort(ascending=True))
    table = pa.Table.from_batches(df.collect())

    expected = {"a": [1, 2], "c": [8, 10], "b": [4, 5]}
    assert table.to_pydict() == expected


def test_window_lead(df):
    df = df.select(
        column("a"),
        f.alias(
            f.window(
                "lead", [column("b")], order_by=[f.order_by(column("b"))]
            ),
            "a_next",
        ),
    )

    table = pa.Table.from_batches(df.collect())

    expected = {"a": [1, 2, 3], "a_next": [5, 6, None]}
    assert table.to_pydict() == expected


def test_get_dataframe(tmp_path):
    ctx = ExecutionContext()

    path = tmp_path / "test.csv"
    table = pa.Table.from_arrays(
        [
            [1, 2, 3, 4],
            ["a", "b", "c", "d"],
            [1.1, 2.2, 3.3, 4.4],
        ],
        names=["int", "str", "float"],
    )
    pa.csv.write_csv(table, path)

    ctx.register_csv("csv", path)

    df = ctx.table("csv")
    assert isinstance(df, DataFrame)


def test_struct_select(struct_df):
    df = struct_df.select(
        column("a")["c"] + column("b"),
        column("a")["c"] - column("b"),
    )

    # execute and collect the first (and only) batch
    result = df.collect()[0]

    assert result.column(0) == pa.array([5, 7, 9])
    assert result.column(1) == pa.array([-3, -3, -3])


def test_explain(df):
    df = df.select(
        column("a") + column("b"),
        column("a") - column("b"),
    )
    df.explain()