mirror of
https://github.com/langchain-ai/datafusion.git
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QP Hou
113 lines
3.0 KiB
Python
113 lines
3.0 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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import pyarrow as pa
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import pytest
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from datafusion import ExecutionContext
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from datafusion import functions as f
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@pytest.fixture
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def df():
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ctx = ExecutionContext()
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# create a RecordBatch and a new DataFrame from it
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batch = pa.RecordBatch.from_arrays(
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[pa.array([1, 2, 3]), pa.array([4, 5, 6])],
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names=["a", "b"],
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)
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return ctx.create_dataframe([[batch]])
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def test_select(df):
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df = df.select(
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f.col("a") + f.col("b"),
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f.col("a") - f.col("b"),
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)
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# execute and collect the first (and only) batch
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result = df.collect()[0]
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assert result.column(0) == pa.array([5, 7, 9])
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assert result.column(1) == pa.array([-3, -3, -3])
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def test_filter(df):
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df = df.select(
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f.col("a") + f.col("b"),
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f.col("a") - f.col("b"),
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).filter(f.col("a") > f.lit(2))
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# execute and collect the first (and only) batch
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result = df.collect()[0]
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assert result.column(0) == pa.array([9])
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assert result.column(1) == pa.array([-3])
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def test_sort(df):
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df = df.sort([f.col("b").sort(ascending=False)])
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table = pa.Table.from_batches(df.collect())
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expected = {"a": [3, 2, 1], "b": [6, 5, 4]}
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assert table.to_pydict() == expected
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def test_limit(df):
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df = df.limit(1)
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# execute and collect the first (and only) batch
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result = df.collect()[0]
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assert len(result.column(0)) == 1
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assert len(result.column(1)) == 1
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def test_udf(df):
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# is_null is a pa function over arrays
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udf = f.udf(lambda x: x.is_null(), [pa.int64()], pa.bool_())
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df = df.select(udf(f.col("a")))
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result = df.collect()[0].column(0)
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assert result == pa.array([False, False, False])
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def test_join():
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ctx = ExecutionContext()
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batch = pa.RecordBatch.from_arrays(
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[pa.array([1, 2, 3]), pa.array([4, 5, 6])],
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names=["a", "b"],
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)
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df = ctx.create_dataframe([[batch]])
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batch = pa.RecordBatch.from_arrays(
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[pa.array([1, 2]), pa.array([8, 10])],
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names=["a", "c"],
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)
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df1 = ctx.create_dataframe([[batch]])
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df = df.join(df1, join_keys=(["a"], ["a"]), how="inner")
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df = df.sort([f.col("a").sort(ascending=True)])
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table = pa.Table.from_batches(df.collect())
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expected = {"a": [1, 2], "c": [8, 10], "b": [4, 5]}
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assert table.to_pydict() == expected
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