mirror of
https://github.com/langchain-ai/datafusion.git
synced 2026-07-18 13:15:59 -04:00
Andy Grove
116 lines
3.7 KiB
Python
116 lines
3.7 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 unittest
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import pyarrow
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import datafusion
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f = datafusion.functions
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class TestCase(unittest.TestCase):
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def _prepare(self):
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ctx = datafusion.ExecutionContext()
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# create a RecordBatch and a new DataFrame from it
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batch = pyarrow.RecordBatch.from_arrays(
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[pyarrow.array([1, 2, 3]), pyarrow.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(self):
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df = self._prepare()
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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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self.assertEqual(result.column(0), pyarrow.array([5, 7, 9]))
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self.assertEqual(result.column(1), pyarrow.array([-3, -3, -3]))
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def test_filter(self):
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df = self._prepare()
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df = df \
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.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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.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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self.assertEqual(result.column(0), pyarrow.array([9]))
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self.assertEqual(result.column(1), pyarrow.array([-3]))
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def test_limit(self):
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df = self._prepare()
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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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self.assertEqual(len(result.column(0)), 1)
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self.assertEqual(len(result.column(1)), 1)
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def test_udf(self):
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df = self._prepare()
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# is_null is a pyarrow function over arrays
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udf = f.udf(lambda x: x.is_null(), [pyarrow.int64()], pyarrow.bool_())
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df = df.select(udf(f.col("a")))
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self.assertEqual(df.collect()[0].column(0), pyarrow.array([False, False, False]))
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def test_join(self):
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ctx = datafusion.ExecutionContext()
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batch = pyarrow.RecordBatch.from_arrays(
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[pyarrow.array([1, 2, 3]), pyarrow.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 = pyarrow.RecordBatch.from_arrays(
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[pyarrow.array([1, 2]), pyarrow.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, on="a", how="inner")
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# execute and collect the first (and only) batch
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batch = df.collect()[0]
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if batch.column(0) == pyarrow.array([1, 2]):
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self.assertEqual(batch.column(0), pyarrow.array([1, 2]))
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self.assertEqual(batch.column(1), pyarrow.array([8, 10]))
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self.assertEqual(batch.column(2), pyarrow.array([4, 5]))
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else:
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self.assertEqual(batch.column(0), pyarrow.array([2, 1]))
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self.assertEqual(batch.column(1), pyarrow.array([10, 8]))
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self.assertEqual(batch.column(2), pyarrow.array([5, 4]))
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