mirror of
https://github.com/langchain-ai/datafusion.git
synced 2026-07-16 12:04:27 -04:00
Jiayu Liu
48 lines
1.6 KiB
Python
48 lines
1.6 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, 4, 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_built_in_aggregation(df):
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col_a = f.col("a")
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col_b = f.col("b")
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df = df.aggregate(
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[],
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[f.max(col_a), f.min(col_a), f.count(col_a), f.approx_distinct(col_b)],
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)
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result = df.collect()[0]
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assert result.column(0) == pa.array([3])
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assert result.column(1) == pa.array([1])
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assert result.column(2) == pa.array([3], type=pa.uint64())
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assert result.column(3) == pa.array([2], type=pa.uint64())
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