4066 lines
121 KiB
Python
4066 lines
121 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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from collections import OrderedDict
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from collections.abc import Iterable
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import sys
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import weakref
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try:
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import numpy as np
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except ImportError:
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np = None
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import pytest
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import pyarrow as pa
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import pyarrow.compute as pc
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from pyarrow.interchange import from_dataframe
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from pyarrow.vendored.version import Version
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def test_chunked_array_basics():
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data = pa.chunked_array([], type=pa.string())
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assert data.type == pa.string()
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assert data.to_pylist() == []
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data.validate()
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data2 = pa.chunked_array([], type='binary')
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assert data2.type == pa.binary()
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with pytest.raises(ValueError):
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pa.chunked_array([])
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data = pa.chunked_array([
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[1, 2, 3],
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[4, 5, 6],
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[7, 8, 9]
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])
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assert isinstance(data.chunks, list)
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assert all(isinstance(c, pa.lib.Int64Array) for c in data.chunks)
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assert all(isinstance(c, pa.lib.Int64Array) for c in data.iterchunks())
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assert len(data.chunks) == 3
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assert data.get_total_buffer_size() == sum(c.get_total_buffer_size()
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for c in data.iterchunks())
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assert sys.getsizeof(data) >= object.__sizeof__(
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data) + data.get_total_buffer_size()
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assert data.nbytes == 3 * 3 * 8 # 3 items per 3 lists with int64 size(8)
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data.validate()
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wr = weakref.ref(data)
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assert wr() is not None
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del data
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assert wr() is None
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def test_chunked_array_construction():
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arr = pa.chunked_array([
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[1, 2, 3],
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[4, 5, 6],
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[7, 8, 9],
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])
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assert arr.type == pa.int64()
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assert len(arr) == 9
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assert len(arr.chunks) == 3
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arr = pa.chunked_array([
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[1, 2, 3],
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[4., 5., 6.],
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[7, 8, 9],
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])
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assert arr.type == pa.int64()
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assert len(arr) == 9
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assert len(arr.chunks) == 3
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arr = pa.chunked_array([
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[1, 2, 3],
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[4., 5., 6.],
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[7, 8, 9],
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], type=pa.int8())
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assert arr.type == pa.int8()
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assert len(arr) == 9
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assert len(arr.chunks) == 3
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arr = pa.chunked_array([
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[1, 2, 3],
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[]
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])
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assert arr.type == pa.int64()
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assert len(arr) == 3
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assert len(arr.chunks) == 2
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msg = "cannot construct ChunkedArray from empty vector and omitted type"
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with pytest.raises(ValueError, match=msg):
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assert pa.chunked_array([])
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assert pa.chunked_array([], type=pa.string()).type == pa.string()
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assert pa.chunked_array([[]]).type == pa.null()
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assert pa.chunked_array([[]], type=pa.string()).type == pa.string()
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def test_combine_chunks():
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# ARROW-77363
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arr = pa.array([1, 2])
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chunked_arr = pa.chunked_array([arr, arr])
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res = chunked_arr.combine_chunks()
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expected = pa.array([1, 2, 1, 2])
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assert res.equals(expected)
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def test_chunked_array_can_combine_chunks_with_no_chunks():
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# https://issues.apache.org/jira/browse/ARROW-17256
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assert pa.chunked_array([], type=pa.bool_()).combine_chunks() == pa.array(
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[], type=pa.bool_()
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)
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assert pa.chunked_array(
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[pa.array([], type=pa.bool_())], type=pa.bool_()
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).combine_chunks() == pa.array([], type=pa.bool_())
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@pytest.mark.numpy
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def test_chunked_array_to_numpy():
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data = pa.chunked_array([
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[1, 2, 3],
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[4, 5, 6],
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[]
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])
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arr1 = np.asarray(data)
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arr2 = data.to_numpy()
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assert isinstance(arr2, np.ndarray)
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assert arr2.shape == (6,)
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assert np.array_equal(arr1, arr2)
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def test_chunked_array_mismatch_types():
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msg = "chunks must all be same type"
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with pytest.raises(TypeError, match=msg):
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# Given array types are different
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pa.chunked_array([
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pa.array([1, 2, 3]),
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pa.array([1., 2., 3.])
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])
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with pytest.raises(TypeError, match=msg):
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# Given array type is different from explicit type argument
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pa.chunked_array([pa.array([1, 2, 3])], type=pa.float64())
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def test_chunked_array_str():
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data = [
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pa.array([1, 2, 3]),
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pa.array([4, 5, 6])
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]
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data = pa.chunked_array(data)
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assert str(data) == """[
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[
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1,
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2,
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3
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],
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[
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4,
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5,
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6
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]
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]"""
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@pytest.mark.numpy
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def test_chunked_array_getitem():
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data = [
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pa.array([1, 2, 3]),
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pa.array([4, 5, 6])
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]
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data = pa.chunked_array(data)
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assert data[1].as_py() == 2
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assert data[-1].as_py() == 6
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assert data[-6].as_py() == 1
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with pytest.raises(IndexError):
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data[6]
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with pytest.raises(IndexError):
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data[-7]
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# Ensure this works with numpy scalars
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assert data[np.int32(1)].as_py() == 2
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data_slice = data[2:4]
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assert data_slice.to_pylist() == [3, 4]
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data_slice = data[4:-1]
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assert data_slice.to_pylist() == [5]
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data_slice = data[99:99]
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assert data_slice.type == data.type
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assert data_slice.to_pylist() == []
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def test_chunked_array_slice():
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data = [
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pa.array([1, 2, 3]),
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pa.array([4, 5, 6])
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]
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data = pa.chunked_array(data)
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data_slice = data.slice(len(data))
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assert data_slice.type == data.type
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assert data_slice.to_pylist() == []
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data_slice = data.slice(len(data) + 10)
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assert data_slice.type == data.type
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assert data_slice.to_pylist() == []
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table = pa.Table.from_arrays([data], names=["a"])
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table_slice = table.slice(len(table))
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assert len(table_slice) == 0
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table = pa.Table.from_arrays([data], names=["a"])
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table_slice = table.slice(len(table) + 10)
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assert len(table_slice) == 0
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def test_chunked_array_iter():
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data = [
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pa.array([0]),
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pa.array([1, 2, 3]),
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pa.array([4, 5, 6]),
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pa.array([7, 8, 9])
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]
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arr = pa.chunked_array(data)
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for i, j in zip(range(10), arr):
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assert i == j.as_py()
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assert isinstance(arr, Iterable)
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def test_chunked_array_equals():
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def eq(xarrs, yarrs):
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if isinstance(xarrs, pa.ChunkedArray):
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x = xarrs
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else:
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x = pa.chunked_array(xarrs)
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if isinstance(yarrs, pa.ChunkedArray):
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y = yarrs
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else:
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y = pa.chunked_array(yarrs)
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assert x.equals(y)
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assert y.equals(x)
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assert x == y
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assert x != str(y)
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def ne(xarrs, yarrs):
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if isinstance(xarrs, pa.ChunkedArray):
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x = xarrs
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else:
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x = pa.chunked_array(xarrs)
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if isinstance(yarrs, pa.ChunkedArray):
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y = yarrs
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else:
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y = pa.chunked_array(yarrs)
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assert not x.equals(y)
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assert not y.equals(x)
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assert x != y
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eq(pa.chunked_array([], type=pa.int32()),
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pa.chunked_array([], type=pa.int32()))
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ne(pa.chunked_array([], type=pa.int32()),
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pa.chunked_array([], type=pa.int64()))
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a = pa.array([0, 2], type=pa.int32())
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b = pa.array([0, 2], type=pa.int64())
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c = pa.array([0, 3], type=pa.int32())
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d = pa.array([0, 2, 0, 3], type=pa.int32())
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eq([a], [a])
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ne([a], [b])
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eq([a, c], [a, c])
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eq([a, c], [d])
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ne([c, a], [a, c])
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# ARROW-4822
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assert not pa.chunked_array([], type=pa.int32()).equals(None)
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@pytest.mark.parametrize(
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('data', 'typ'),
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[
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([True, False, True, True], pa.bool_()),
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([1, 2, 4, 6], pa.int64()),
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([1.0, 2.5, None], pa.float64()),
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(['a', None, 'b'], pa.string()),
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([], pa.list_(pa.uint8())),
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([[1, 2], [3]], pa.list_(pa.int64())),
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([['a'], None, ['b', 'c']], pa.list_(pa.string())),
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([(1, 'a'), (2, 'c'), None],
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pa.struct([pa.field('a', pa.int64()), pa.field('b', pa.string())]))
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]
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)
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def test_chunked_array_pickle(data, typ, pickle_module):
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arrays = []
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while data:
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arrays.append(pa.array(data[:2], type=typ))
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data = data[2:]
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array = pa.chunked_array(arrays, type=typ)
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array.validate()
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result = pickle_module.loads(pickle_module.dumps(array))
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result.validate()
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assert result.equals(array)
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@pytest.mark.pandas
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def test_chunked_array_to_pandas():
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import pandas as pd
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data = [
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pa.array([-10, -5, 0, 5, 10])
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]
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table = pa.table(data, names=['a'])
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col = table.column(0)
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assert isinstance(col, pa.ChunkedArray)
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series = col.to_pandas()
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assert isinstance(series, pd.Series)
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assert series.shape == (5,)
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assert series[0] == -10
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assert series.name == 'a'
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@pytest.mark.pandas
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def test_chunked_array_to_pandas_preserve_name():
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# https://issues.apache.org/jira/browse/ARROW-7709
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import pandas as pd
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import pandas.testing as tm
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for data in [
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pa.array([1, 2, 3]),
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pa.array(pd.Categorical(["a", "b", "a"])),
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pa.array(pd.date_range("2012", periods=3)),
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pa.array(pd.date_range("2012", periods=3, tz="Europe/Brussels")),
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pa.array([1, 2, 3], pa.timestamp("ms")),
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pa.array([1, 2, 3], pa.timestamp("ms", "Europe/Brussels"))]:
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table = pa.table({"name": data})
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result = table.column("name").to_pandas()
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assert result.name == "name"
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expected = pd.Series(data.to_pandas(), name="name")
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tm.assert_series_equal(result, expected)
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@pytest.mark.pandas
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def test_table_roundtrip_to_pandas_empty_dataframe():
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# https://issues.apache.org/jira/browse/ARROW-10643
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# The conversion should not results in a table with 0 rows if the original
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# DataFrame has a RangeIndex but is empty.
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import pandas as pd
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data = pd.DataFrame(index=pd.RangeIndex(0, 10, 1))
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table = pa.table(data)
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result = table.to_pandas()
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assert table.num_rows == 10
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assert data.shape == (10, 0)
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assert result.shape == (10, 0)
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assert result.index.equals(data.index)
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data = pd.DataFrame(index=pd.RangeIndex(0, 10, 3))
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table = pa.table(data)
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result = table.to_pandas()
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assert table.num_rows == 4
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assert data.shape == (4, 0)
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assert result.shape == (4, 0)
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assert result.index.equals(data.index)
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@pytest.mark.pandas
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def test_recordbatch_roundtrip_to_pandas_empty_dataframe():
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# https://issues.apache.org/jira/browse/ARROW-10643
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# The conversion should not results in a RecordBatch with 0 rows if
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# the original DataFrame has a RangeIndex but is empty.
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import pandas as pd
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data = pd.DataFrame(index=pd.RangeIndex(0, 10, 1))
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batch = pa.RecordBatch.from_pandas(data)
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result = batch.to_pandas()
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assert batch.num_rows == 10
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assert data.shape == (10, 0)
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assert result.shape == (10, 0)
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assert result.index.equals(data.index)
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data = pd.DataFrame(index=pd.RangeIndex(0, 10, 3))
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batch = pa.RecordBatch.from_pandas(data)
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result = batch.to_pandas()
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assert batch.num_rows == 4
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assert data.shape == (4, 0)
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assert result.shape == (4, 0)
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assert result.index.equals(data.index)
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@pytest.mark.pandas
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def test_to_pandas_empty_table():
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# https://issues.apache.org/jira/browse/ARROW-15370
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import pandas as pd
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import pandas.testing as tm
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df = pd.DataFrame({'a': [1, 2], 'b': [0.1, 0.2]})
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table = pa.table(df)
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result = table.schema.empty_table().to_pandas()
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assert result.shape == (0, 2)
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tm.assert_frame_equal(result, df.iloc[:0])
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@pytest.mark.pandas
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@pytest.mark.nopandas
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def test_chunked_array_asarray():
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# ensure this is tested both when pandas is present or not (ARROW-6564)
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data = [
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pa.array([0]),
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pa.array([1, 2, 3])
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]
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chunked_arr = pa.chunked_array(data)
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np_arr = np.asarray(chunked_arr)
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assert np_arr.tolist() == [0, 1, 2, 3]
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assert np_arr.dtype == np.dtype('int64')
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# An optional type can be specified when calling np.asarray
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np_arr = np.asarray(chunked_arr, dtype='str')
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assert np_arr.tolist() == ['0', '1', '2', '3']
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# Types are modified when there are nulls
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data = [
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pa.array([1, None]),
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pa.array([1, 2, 3])
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]
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chunked_arr = pa.chunked_array(data)
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np_arr = np.asarray(chunked_arr)
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elements = np_arr.tolist()
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assert elements[0] == 1.
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assert np.isnan(elements[1])
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assert elements[2:] == [1., 2., 3.]
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assert np_arr.dtype == np.dtype('float64')
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# DictionaryType data will be converted to dense numpy array
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arr = pa.DictionaryArray.from_arrays(
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pa.array([0, 1, 2, 0, 1]), pa.array(['a', 'b', 'c']))
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chunked_arr = pa.chunked_array([arr, arr])
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np_arr = np.asarray(chunked_arr)
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assert np_arr.dtype == np.dtype('object')
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assert np_arr.tolist() == ['a', 'b', 'c', 'a', 'b'] * 2
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def test_chunked_array_flatten():
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ty = pa.struct([pa.field('x', pa.int16()),
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pa.field('y', pa.float32())])
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a = pa.array([(1, 2.5), (3, 4.5), (5, 6.5)], type=ty)
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carr = pa.chunked_array(a)
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x, y = carr.flatten()
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assert x.equals(pa.chunked_array(pa.array([1, 3, 5], type=pa.int16())))
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assert y.equals(pa.chunked_array(pa.array([2.5, 4.5, 6.5],
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type=pa.float32())))
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# Empty column
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a = pa.array([], type=ty)
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carr = pa.chunked_array(a)
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x, y = carr.flatten()
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assert x.equals(pa.chunked_array(pa.array([], type=pa.int16())))
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assert y.equals(pa.chunked_array(pa.array([], type=pa.float32())))
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def test_chunked_array_unify_dictionaries():
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arr = pa.chunked_array([
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pa.array(["foo", "bar", None, "foo"]).dictionary_encode(),
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pa.array(["quux", None, "foo"]).dictionary_encode(),
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])
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assert arr.chunk(0).dictionary.equals(pa.array(["foo", "bar"]))
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assert arr.chunk(1).dictionary.equals(pa.array(["quux", "foo"]))
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arr = arr.unify_dictionaries()
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expected_dict = pa.array(["foo", "bar", "quux"])
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assert arr.chunk(0).dictionary.equals(expected_dict)
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assert arr.chunk(1).dictionary.equals(expected_dict)
|
|
assert arr.to_pylist() == ["foo", "bar", None, "foo", "quux", None, "foo"]
|
|
|
|
|
|
def test_recordbatch_dunder_init():
|
|
with pytest.raises(TypeError, match='RecordBatch'):
|
|
pa.RecordBatch()
|
|
|
|
|
|
def test_chunked_array_c_array_interface():
|
|
class ArrayWrapper:
|
|
def __init__(self, array):
|
|
self.array = array
|
|
|
|
def __arrow_c_array__(self, requested_schema=None):
|
|
return self.array.__arrow_c_array__(requested_schema)
|
|
|
|
data = pa.array([1, 2, 3], pa.int64())
|
|
chunked = pa.chunked_array([data])
|
|
wrapper = ArrayWrapper(data)
|
|
|
|
# Can roundtrip through the wrapper.
|
|
result = pa.chunked_array(wrapper)
|
|
assert result == chunked
|
|
|
|
# Can also import with a type that implementer can cast to.
|
|
result = pa.chunked_array(wrapper, type=pa.int16())
|
|
assert result == chunked.cast(pa.int16())
|
|
|
|
|
|
def test_chunked_array_c_stream_interface():
|
|
class ChunkedArrayWrapper:
|
|
def __init__(self, chunked):
|
|
self.chunked = chunked
|
|
|
|
def __arrow_c_stream__(self, requested_schema=None):
|
|
return self.chunked.__arrow_c_stream__(requested_schema)
|
|
|
|
data = pa.chunked_array([[1, 2, 3], [4, None, 6]])
|
|
wrapper = ChunkedArrayWrapper(data)
|
|
|
|
# Can roundtrip through the wrapper.
|
|
result = pa.chunked_array(wrapper)
|
|
assert result == data
|
|
|
|
# Can also import with a type that implementer can cast to.
|
|
result = pa.chunked_array(wrapper, type=pa.int16())
|
|
assert result == data.cast(pa.int16())
|
|
|
|
|
|
class BatchWrapper:
|
|
def __init__(self, batch):
|
|
self.batch = batch
|
|
|
|
def __arrow_c_array__(self, requested_schema=None):
|
|
return self.batch.__arrow_c_array__(requested_schema)
|
|
|
|
|
|
class BatchDeviceWrapper:
|
|
def __init__(self, batch):
|
|
self.batch = batch
|
|
|
|
def __arrow_c_device_array__(self, requested_schema=None, **kwargs):
|
|
return self.batch.__arrow_c_device_array__(requested_schema, **kwargs)
|
|
|
|
|
|
@pytest.mark.parametrize("wrapper_class", [BatchWrapper, BatchDeviceWrapper])
|
|
def test_recordbatch_c_array_interface(wrapper_class):
|
|
data = pa.record_batch([
|
|
pa.array([1, 2, 3], type=pa.int64())
|
|
], names=['a'])
|
|
wrapper = wrapper_class(data)
|
|
|
|
# Can roundtrip through the wrapper.
|
|
result = pa.record_batch(wrapper)
|
|
assert result == data
|
|
|
|
# Can also import with a schema that implementer can cast to.
|
|
castable_schema = pa.schema([
|
|
pa.field('a', pa.int32())
|
|
])
|
|
result = pa.record_batch(wrapper, schema=castable_schema)
|
|
expected = pa.record_batch([
|
|
pa.array([1, 2, 3], type=pa.int32())
|
|
], names=['a'])
|
|
assert result == expected
|
|
|
|
|
|
def test_recordbatch_c_array_interface_device_unsupported_keyword():
|
|
# For the device-aware version, we raise a specific error for unsupported keywords
|
|
data = pa.record_batch(
|
|
[pa.array([1, 2, 3], type=pa.int64())], names=['a']
|
|
)
|
|
|
|
with pytest.raises(
|
|
NotImplementedError,
|
|
match=r"Received unsupported keyword argument\(s\): \['other'\]"
|
|
):
|
|
data.__arrow_c_device_array__(other="not-none")
|
|
|
|
# but with None value it is ignored
|
|
_ = data.__arrow_c_device_array__(other=None)
|
|
|
|
|
|
@pytest.mark.parametrize("wrapper_class", [BatchWrapper, BatchDeviceWrapper])
|
|
def test_table_c_array_interface(wrapper_class):
|
|
data = pa.record_batch([
|
|
pa.array([1, 2, 3], type=pa.int64())
|
|
], names=['a'])
|
|
wrapper = wrapper_class(data)
|
|
|
|
# Can roundtrip through the wrapper.
|
|
result = pa.table(wrapper)
|
|
expected = pa.Table.from_batches([data])
|
|
assert result == expected
|
|
|
|
# Can also import with a schema that implementer can cast to.
|
|
castable_schema = pa.schema([
|
|
pa.field('a', pa.int32())
|
|
])
|
|
result = pa.table(wrapper, schema=castable_schema)
|
|
expected = pa.table({
|
|
'a': pa.array([1, 2, 3], type=pa.int32())
|
|
})
|
|
assert result == expected
|
|
|
|
|
|
def test_table_c_stream_interface():
|
|
class StreamWrapper:
|
|
def __init__(self, batches):
|
|
self.batches = batches
|
|
|
|
def __arrow_c_stream__(self, requested_schema=None):
|
|
reader = pa.RecordBatchReader.from_batches(
|
|
self.batches[0].schema, self.batches)
|
|
return reader.__arrow_c_stream__(requested_schema)
|
|
|
|
data = [
|
|
pa.record_batch([pa.array([1, 2, 3], type=pa.int64())], names=['a']),
|
|
pa.record_batch([pa.array([4, 5, 6], type=pa.int64())], names=['a'])
|
|
]
|
|
wrapper = StreamWrapper(data)
|
|
|
|
# Can roundtrip through the wrapper.
|
|
result = pa.table(wrapper)
|
|
expected = pa.Table.from_batches(data)
|
|
assert result == expected
|
|
|
|
# Passing schema works if already that schema
|
|
result = pa.table(wrapper, schema=data[0].schema)
|
|
assert result == expected
|
|
|
|
# Passing a different schema will cast
|
|
good_schema = pa.schema([pa.field('a', pa.int32())])
|
|
result = pa.table(wrapper, schema=good_schema)
|
|
assert result == expected.cast(good_schema)
|
|
|
|
# If schema doesn't match, raises NotImplementedError
|
|
with pytest.raises(
|
|
pa.lib.ArrowTypeError, match="Field 0 cannot be cast"
|
|
):
|
|
pa.table(
|
|
wrapper, schema=pa.schema([pa.field('a', pa.list_(pa.int32()))])
|
|
)
|
|
|
|
|
|
def test_recordbatch_itercolumns():
|
|
data = [
|
|
pa.array(range(5), type='int16'),
|
|
pa.array([-10, -5, 0, None, 10], type='int32')
|
|
]
|
|
batch = pa.record_batch(data, ['c0', 'c1'])
|
|
|
|
columns = []
|
|
for col in batch.itercolumns():
|
|
columns.append(col)
|
|
|
|
assert batch.columns == columns
|
|
assert batch == pa.record_batch(columns, names=batch.column_names)
|
|
assert batch != pa.record_batch(columns[1:], names=batch.column_names[1:])
|
|
assert batch != columns
|
|
|
|
|
|
def test_recordbatch_equals():
|
|
data1 = [
|
|
pa.array(range(5), type='int16'),
|
|
pa.array([-10, -5, 0, None, 10], type='int32')
|
|
]
|
|
data2 = [
|
|
pa.array(['a', 'b', 'c']),
|
|
pa.array([['d'], ['e'], ['f']]),
|
|
]
|
|
column_names = ['c0', 'c1']
|
|
|
|
batch = pa.record_batch(data1, column_names)
|
|
assert batch == pa.record_batch(data1, column_names)
|
|
assert batch.equals(pa.record_batch(data1, column_names))
|
|
|
|
assert batch != pa.record_batch(data2, column_names)
|
|
assert not batch.equals(pa.record_batch(data2, column_names))
|
|
|
|
batch_meta = pa.record_batch(data1, names=column_names,
|
|
metadata={'key': 'value'})
|
|
assert batch_meta.equals(batch)
|
|
assert not batch_meta.equals(batch, check_metadata=True)
|
|
|
|
# ARROW-8889
|
|
assert not batch.equals(None)
|
|
assert batch != "foo"
|
|
|
|
|
|
def test_recordbatch_take():
|
|
batch = pa.record_batch(
|
|
[pa.array([1, 2, 3, None, 5]),
|
|
pa.array(['a', 'b', 'c', 'd', 'e'])],
|
|
['f1', 'f2'])
|
|
assert batch.take(pa.array([2, 3])).equals(batch.slice(2, 2))
|
|
assert batch.take(pa.array([2, None])).equals(
|
|
pa.record_batch([pa.array([3, None]), pa.array(['c', None])],
|
|
['f1', 'f2']))
|
|
|
|
|
|
def test_recordbatch_column_sets_private_name():
|
|
# ARROW-6429
|
|
rb = pa.record_batch([pa.array([1, 2, 3, 4])], names=['a0'])
|
|
assert rb[0]._name == 'a0'
|
|
|
|
|
|
def test_recordbatch_from_arrays_validate_schema():
|
|
# ARROW-6263
|
|
arr = pa.array([1, 2])
|
|
schema = pa.schema([pa.field('f0', pa.list_(pa.utf8()))])
|
|
with pytest.raises(NotImplementedError):
|
|
pa.record_batch([arr], schema=schema)
|
|
|
|
|
|
def test_recordbatch_from_arrays_validate_lengths():
|
|
# ARROW-2820
|
|
data = [pa.array([1]), pa.array(["tokyo", "like", "happy"]),
|
|
pa.array(["derek"])]
|
|
|
|
with pytest.raises(ValueError):
|
|
pa.record_batch(data, ['id', 'tags', 'name'])
|
|
|
|
|
|
def test_recordbatch_no_fields():
|
|
batch = pa.record_batch([], [])
|
|
|
|
assert len(batch) == 0
|
|
assert batch.num_rows == 0
|
|
assert batch.num_columns == 0
|
|
|
|
|
|
def test_recordbatch_from_arrays_invalid_names():
|
|
data = [
|
|
pa.array(range(5)),
|
|
pa.array([-10, -5, 0, 5, 10])
|
|
]
|
|
with pytest.raises(ValueError):
|
|
pa.record_batch(data, names=['a', 'b', 'c'])
|
|
|
|
with pytest.raises(ValueError):
|
|
pa.record_batch(data, names=['a'])
|
|
|
|
|
|
def test_recordbatch_empty_metadata():
|
|
data = [
|
|
pa.array(range(5)),
|
|
pa.array([-10, -5, 0, 5, 10])
|
|
]
|
|
|
|
batch = pa.record_batch(data, ['c0', 'c1'])
|
|
assert batch.schema.metadata is None
|
|
|
|
|
|
def test_recordbatch_pickle(pickle_module):
|
|
data = [
|
|
pa.array(range(5), type='int8'),
|
|
pa.array([-10, -5, 0, 5, 10], type='float32')
|
|
]
|
|
fields = [
|
|
pa.field('ints', pa.int8()),
|
|
pa.field('floats', pa.float32()),
|
|
]
|
|
schema = pa.schema(fields, metadata={b'foo': b'bar'})
|
|
batch = pa.record_batch(data, schema=schema)
|
|
|
|
result = pickle_module.loads(pickle_module.dumps(batch))
|
|
assert result.equals(batch)
|
|
assert result.schema == schema
|
|
|
|
|
|
def test_recordbatch_get_field():
|
|
data = [
|
|
pa.array(range(5)),
|
|
pa.array([-10, -5, 0, 5, 10]),
|
|
pa.array(range(5, 10))
|
|
]
|
|
batch = pa.RecordBatch.from_arrays(data, names=('a', 'b', 'c'))
|
|
|
|
assert batch.field('a').equals(batch.schema.field('a'))
|
|
assert batch.field(0).equals(batch.schema.field('a'))
|
|
|
|
with pytest.raises(KeyError):
|
|
batch.field('d')
|
|
|
|
with pytest.raises(TypeError):
|
|
batch.field(None)
|
|
|
|
with pytest.raises(IndexError):
|
|
batch.field(4)
|
|
|
|
|
|
def test_recordbatch_select_column():
|
|
data = [
|
|
pa.array(range(5)),
|
|
pa.array([-10, -5, 0, 5, 10]),
|
|
pa.array(range(5, 10))
|
|
]
|
|
batch = pa.RecordBatch.from_arrays(data, names=('a', 'b', 'c'))
|
|
|
|
assert batch.column('a').equals(batch.column(0))
|
|
|
|
with pytest.raises(
|
|
KeyError, match='Field "d" does not exist in schema'):
|
|
batch.column('d')
|
|
|
|
with pytest.raises(TypeError):
|
|
batch.column(None)
|
|
|
|
with pytest.raises(IndexError):
|
|
batch.column(4)
|
|
|
|
|
|
def test_recordbatch_select():
|
|
a1 = pa.array([1, 2, 3, None, 5])
|
|
a2 = pa.array(['a', 'b', 'c', 'd', 'e'])
|
|
a3 = pa.array([[1, 2], [3, 4], [5, 6], None, [9, 10]])
|
|
batch = pa.record_batch([a1, a2, a3], ['f1', 'f2', 'f3'])
|
|
|
|
# selecting with string names
|
|
result = batch.select(['f1'])
|
|
expected = pa.record_batch([a1], ['f1'])
|
|
assert result.equals(expected)
|
|
|
|
result = batch.select(['f3', 'f2'])
|
|
expected = pa.record_batch([a3, a2], ['f3', 'f2'])
|
|
assert result.equals(expected)
|
|
|
|
# selecting with integer indices
|
|
result = batch.select([0])
|
|
expected = pa.record_batch([a1], ['f1'])
|
|
assert result.equals(expected)
|
|
|
|
result = batch.select([2, 1])
|
|
expected = pa.record_batch([a3, a2], ['f3', 'f2'])
|
|
assert result.equals(expected)
|
|
|
|
# preserve metadata
|
|
batch2 = batch.replace_schema_metadata({"a": "test"})
|
|
result = batch2.select(["f1", "f2"])
|
|
assert b"a" in result.schema.metadata
|
|
|
|
# selecting non-existing column raises
|
|
with pytest.raises(KeyError, match='Field "f5" does not exist'):
|
|
batch.select(['f5'])
|
|
|
|
with pytest.raises(IndexError, match="index out of bounds"):
|
|
batch.select([5])
|
|
|
|
# duplicate selection gives duplicated names in resulting recordbatch
|
|
result = batch.select(['f2', 'f2'])
|
|
expected = pa.record_batch([a2, a2], ['f2', 'f2'])
|
|
assert result.equals(expected)
|
|
|
|
# selection duplicated column raises
|
|
batch = pa.record_batch([a1, a2, a3], ['f1', 'f2', 'f1'])
|
|
with pytest.raises(KeyError, match='Field "f1" exists 2 times'):
|
|
batch.select(['f1'])
|
|
|
|
result = batch.select(['f2'])
|
|
expected = pa.record_batch([a2], ['f2'])
|
|
assert result.equals(expected)
|
|
|
|
|
|
def test_recordbatch_from_struct_array_invalid():
|
|
with pytest.raises(TypeError):
|
|
pa.RecordBatch.from_struct_array(pa.array(range(5)))
|
|
|
|
|
|
def test_recordbatch_from_struct_array():
|
|
struct_array = pa.array(
|
|
[{"ints": 1}, {"floats": 1.0}],
|
|
type=pa.struct([("ints", pa.int32()), ("floats", pa.float32())]),
|
|
)
|
|
result = pa.RecordBatch.from_struct_array(struct_array)
|
|
assert result.equals(pa.RecordBatch.from_arrays(
|
|
[
|
|
pa.array([1, None], type=pa.int32()),
|
|
pa.array([None, 1.0], type=pa.float32()),
|
|
], ["ints", "floats"]
|
|
))
|
|
|
|
|
|
def test_recordbatch_to_struct_array():
|
|
batch = pa.RecordBatch.from_arrays(
|
|
[
|
|
pa.array([1, None], type=pa.int32()),
|
|
pa.array([None, 1.0], type=pa.float32()),
|
|
], ["ints", "floats"]
|
|
)
|
|
result = batch.to_struct_array()
|
|
assert result.equals(pa.array(
|
|
[{"ints": 1}, {"floats": 1.0}],
|
|
type=pa.struct([("ints", pa.int32()), ("floats", pa.float32())]),
|
|
))
|
|
|
|
|
|
def test_table_from_struct_array_invalid():
|
|
with pytest.raises(TypeError, match="Argument 'struct_array' has incorrect type"):
|
|
pa.Table.from_struct_array(pa.array(range(5)))
|
|
|
|
|
|
def test_table_from_struct_array():
|
|
struct_array = pa.array(
|
|
[{"ints": 1}, {"floats": 1.0}],
|
|
type=pa.struct([("ints", pa.int32()), ("floats", pa.float32())]),
|
|
)
|
|
result = pa.Table.from_struct_array(struct_array)
|
|
assert result.equals(pa.Table.from_arrays(
|
|
[
|
|
pa.array([1, None], type=pa.int32()),
|
|
pa.array([None, 1.0], type=pa.float32()),
|
|
], ["ints", "floats"]
|
|
))
|
|
|
|
|
|
def test_table_from_struct_array_chunked_array():
|
|
chunked_struct_array = pa.chunked_array(
|
|
[[{"ints": 1}, {"floats": 1.0}]],
|
|
type=pa.struct([("ints", pa.int32()), ("floats", pa.float32())]),
|
|
)
|
|
result = pa.Table.from_struct_array(chunked_struct_array)
|
|
assert result.equals(pa.Table.from_arrays(
|
|
[
|
|
pa.array([1, None], type=pa.int32()),
|
|
pa.array([None, 1.0], type=pa.float32()),
|
|
], ["ints", "floats"]
|
|
))
|
|
|
|
|
|
def test_table_to_struct_array():
|
|
table = pa.Table.from_arrays(
|
|
[
|
|
pa.array([1, None], type=pa.int32()),
|
|
pa.array([None, 1.0], type=pa.float32()),
|
|
], ["ints", "floats"]
|
|
)
|
|
result = table.to_struct_array()
|
|
assert result.equals(pa.chunked_array(
|
|
[[{"ints": 1}, {"floats": 1.0}]],
|
|
type=pa.struct([("ints", pa.int32()), ("floats", pa.float32())]),
|
|
))
|
|
|
|
|
|
def test_table_to_struct_array_with_max_chunksize():
|
|
table = pa.Table.from_arrays(
|
|
[
|
|
pa.array([1, None], type=pa.int32()),
|
|
pa.array([None, 1.0], type=pa.float32()),
|
|
], ["ints", "floats"]
|
|
)
|
|
result = table.to_struct_array(max_chunksize=1)
|
|
assert result.equals(pa.chunked_array(
|
|
[[{"ints": 1}], [{"floats": 1.0}]],
|
|
type=pa.struct([("ints", pa.int32()), ("floats", pa.float32())]),
|
|
))
|
|
|
|
|
|
def test_table_to_struct_array_for_empty_table():
|
|
table = pa.Table.from_arrays(
|
|
[
|
|
pa.array([], type=pa.int32()),
|
|
pa.array([], type=pa.float32()),
|
|
], ["ints", "floats"]
|
|
)
|
|
result = table.to_struct_array()
|
|
assert result.equals(
|
|
pa.chunked_array(
|
|
[],
|
|
type=pa.struct({"ints": pa.int32(), "floats": pa.float32()}),
|
|
),
|
|
)
|
|
|
|
|
|
def check_tensors(tensor, expected_tensor, type, size):
|
|
assert tensor.equals(expected_tensor)
|
|
assert tensor.size == size
|
|
assert tensor.type == type
|
|
assert tensor.shape == expected_tensor.shape
|
|
assert tensor.strides == expected_tensor.strides
|
|
|
|
|
|
@pytest.mark.numpy
|
|
@pytest.mark.parametrize('typ_str', [
|
|
"uint8", "uint16", "uint32", "uint64",
|
|
"int8", "int16", "int32", "int64",
|
|
"float32", "float64",
|
|
])
|
|
def test_recordbatch_to_tensor_uniform_type(typ_str):
|
|
typ = np.dtype(typ_str)
|
|
arr1 = [1, 2, 3, 4, 5, 6, 7, 8, 9]
|
|
arr2 = [10, 20, 30, 40, 50, 60, 70, 80, 90]
|
|
arr3 = [100, 100, 100, 100, 100, 100, 100, 100, 100]
|
|
batch = pa.RecordBatch.from_arrays(
|
|
[
|
|
pa.array(arr1, type=pa.from_numpy_dtype(typ)),
|
|
pa.array(arr2, type=pa.from_numpy_dtype(typ)),
|
|
pa.array(arr3, type=pa.from_numpy_dtype(typ)),
|
|
], ["a", "b", "c"]
|
|
)
|
|
|
|
result = batch.to_tensor(row_major=False)
|
|
x = np.column_stack([arr1, arr2, arr3]).astype(typ, order="F")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
check_tensors(result, expected, pa.from_numpy_dtype(typ), 27)
|
|
|
|
result = batch.to_tensor()
|
|
x = np.column_stack([arr1, arr2, arr3]).astype(typ, order="C")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
check_tensors(result, expected, pa.from_numpy_dtype(typ), 27)
|
|
|
|
# Test offset
|
|
batch1 = batch.slice(1)
|
|
arr1 = [2, 3, 4, 5, 6, 7, 8, 9]
|
|
arr2 = [20, 30, 40, 50, 60, 70, 80, 90]
|
|
arr3 = [100, 100, 100, 100, 100, 100, 100, 100]
|
|
|
|
result = batch1.to_tensor(row_major=False)
|
|
x = np.column_stack([arr1, arr2, arr3]).astype(typ, order="F")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
check_tensors(result, expected, pa.from_numpy_dtype(typ), 24)
|
|
|
|
result = batch1.to_tensor()
|
|
x = np.column_stack([arr1, arr2, arr3]).astype(typ, order="C")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
check_tensors(result, expected, pa.from_numpy_dtype(typ), 24)
|
|
|
|
batch2 = batch.slice(1, 5)
|
|
arr1 = [2, 3, 4, 5, 6]
|
|
arr2 = [20, 30, 40, 50, 60]
|
|
arr3 = [100, 100, 100, 100, 100]
|
|
|
|
result = batch2.to_tensor(row_major=False)
|
|
x = np.column_stack([arr1, arr2, arr3]).astype(typ, order="F")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
check_tensors(result, expected, pa.from_numpy_dtype(typ), 15)
|
|
|
|
result = batch2.to_tensor()
|
|
x = np.column_stack([arr1, arr2, arr3]).astype(typ, order="C")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
check_tensors(result, expected, pa.from_numpy_dtype(typ), 15)
|
|
|
|
|
|
@pytest.mark.numpy
|
|
def test_recordbatch_to_tensor_uniform_float_16():
|
|
arr1 = [1, 2, 3, 4, 5, 6, 7, 8, 9]
|
|
arr2 = [10, 20, 30, 40, 50, 60, 70, 80, 90]
|
|
arr3 = [100, 100, 100, 100, 100, 100, 100, 100, 100]
|
|
batch = pa.RecordBatch.from_arrays(
|
|
[
|
|
pa.array(np.array(arr1, dtype=np.float16), type=pa.float16()),
|
|
pa.array(np.array(arr2, dtype=np.float16), type=pa.float16()),
|
|
pa.array(np.array(arr3, dtype=np.float16), type=pa.float16()),
|
|
], ["a", "b", "c"]
|
|
)
|
|
|
|
result = batch.to_tensor(row_major=False)
|
|
x = np.column_stack([arr1, arr2, arr3]).astype(np.float16, order="F")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
check_tensors(result, expected, pa.float16(), 27)
|
|
|
|
result = batch.to_tensor()
|
|
x = np.column_stack([arr1, arr2, arr3]).astype(np.float16, order="C")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
check_tensors(result, expected, pa.float16(), 27)
|
|
|
|
|
|
@pytest.mark.numpy
|
|
def test_recordbatch_to_tensor_mixed_type():
|
|
# uint16 + int16 = int32
|
|
arr1 = [1, 2, 3, 4, 5, 6, 7, 8, 9]
|
|
arr2 = [10, 20, 30, 40, 50, 60, 70, 80, 90]
|
|
arr3 = [100, 200, 300, np.nan, 500, 600, 700, 800, 900]
|
|
batch = pa.RecordBatch.from_arrays(
|
|
[
|
|
pa.array(arr1, type=pa.uint16()),
|
|
pa.array(arr2, type=pa.int16()),
|
|
], ["a", "b"]
|
|
)
|
|
|
|
result = batch.to_tensor(row_major=False)
|
|
x = np.column_stack([arr1, arr2]).astype(np.int32, order="F")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
check_tensors(result, expected, pa.int32(), 18)
|
|
|
|
result = batch.to_tensor()
|
|
x = np.column_stack([arr1, arr2]).astype(np.int32, order="C")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
check_tensors(result, expected, pa.int32(), 18)
|
|
|
|
# uint16 + int16 + float32 = float64
|
|
batch = pa.RecordBatch.from_arrays(
|
|
[
|
|
pa.array(arr1, type=pa.uint16()),
|
|
pa.array(arr2, type=pa.int16()),
|
|
pa.array(arr3, type=pa.float32()),
|
|
], ["a", "b", "c"]
|
|
)
|
|
result = batch.to_tensor(row_major=False)
|
|
x = np.column_stack([arr1, arr2, arr3]).astype(np.float64, order="F")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
|
|
np.testing.assert_equal(result.to_numpy(), x)
|
|
assert result.size == 27
|
|
assert result.type == pa.float64()
|
|
assert result.shape == expected.shape
|
|
assert result.strides == expected.strides
|
|
|
|
result = batch.to_tensor()
|
|
x = np.column_stack([arr1, arr2, arr3]).astype(np.float64, order="C")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
|
|
np.testing.assert_equal(result.to_numpy(), x)
|
|
assert result.size == 27
|
|
assert result.type == pa.float64()
|
|
assert result.shape == expected.shape
|
|
assert result.strides == expected.strides
|
|
|
|
|
|
@pytest.mark.numpy
|
|
def test_recordbatch_to_tensor_unsupported_mixed_type_with_float16():
|
|
arr1 = [1, 2, 3, 4, 5, 6, 7, 8, 9]
|
|
arr2 = [10, 20, 30, 40, 50, 60, 70, 80, 90]
|
|
arr3 = [100, 200, 300, 400, 500, 600, 700, 800, 900]
|
|
batch = pa.RecordBatch.from_arrays(
|
|
[
|
|
pa.array(arr1, type=pa.uint16()),
|
|
pa.array(np.array(arr2, dtype=np.float16), type=pa.float16()),
|
|
pa.array(arr3, type=pa.float32()),
|
|
], ["a", "b", "c"]
|
|
)
|
|
|
|
with pytest.raises(
|
|
NotImplementedError,
|
|
match="Casting from or to halffloat is not supported."
|
|
):
|
|
batch.to_tensor()
|
|
|
|
|
|
@pytest.mark.numpy
|
|
def test_recordbatch_to_tensor_nan():
|
|
arr1 = [1, 2, 3, 4, np.nan, 6, 7, 8, 9]
|
|
arr2 = [10, 20, 30, 40, 50, 60, 70, np.nan, 90]
|
|
batch = pa.RecordBatch.from_arrays(
|
|
[
|
|
pa.array(arr1, type=pa.float32()),
|
|
pa.array(arr2, type=pa.float32()),
|
|
], ["a", "b"]
|
|
)
|
|
result = batch.to_tensor(row_major=False)
|
|
x = np.column_stack([arr1, arr2]).astype(np.float32, order="F")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
|
|
np.testing.assert_equal(result.to_numpy(), x)
|
|
assert result.size == 18
|
|
assert result.type == pa.float32()
|
|
assert result.shape == expected.shape
|
|
assert result.strides == expected.strides
|
|
|
|
|
|
@pytest.mark.numpy
|
|
def test_recordbatch_to_tensor_null():
|
|
arr1 = [1, 2, 3, 4, None, 6, 7, 8, 9]
|
|
arr2 = [10, 20, 30, 40, 50, 60, 70, None, 90]
|
|
batch = pa.RecordBatch.from_arrays(
|
|
[
|
|
pa.array(arr1, type=pa.int32()),
|
|
pa.array(arr2, type=pa.float32()),
|
|
], ["a", "b"]
|
|
)
|
|
with pytest.raises(
|
|
pa.ArrowTypeError,
|
|
match="Can only convert a RecordBatch with no nulls."
|
|
):
|
|
batch.to_tensor()
|
|
|
|
result = batch.to_tensor(null_to_nan=True, row_major=False)
|
|
x = np.column_stack([arr1, arr2]).astype(np.float64, order="F")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
|
|
np.testing.assert_equal(result.to_numpy(), x)
|
|
assert result.size == 18
|
|
assert result.type == pa.float64()
|
|
assert result.shape == expected.shape
|
|
assert result.strides == expected.strides
|
|
|
|
# int32 -> float64
|
|
batch = pa.RecordBatch.from_arrays(
|
|
[
|
|
pa.array(arr1, type=pa.int32()),
|
|
pa.array(arr2, type=pa.int32()),
|
|
], ["a", "b"]
|
|
)
|
|
|
|
result = batch.to_tensor(null_to_nan=True, row_major=False)
|
|
|
|
np.testing.assert_equal(result.to_numpy(), x)
|
|
assert result.size == 18
|
|
assert result.type == pa.float64()
|
|
assert result.shape == expected.shape
|
|
assert result.strides == expected.strides
|
|
|
|
# int8 -> float32
|
|
batch = pa.RecordBatch.from_arrays(
|
|
[
|
|
pa.array(arr1, type=pa.int8()),
|
|
pa.array(arr2, type=pa.int8()),
|
|
], ["a", "b"]
|
|
)
|
|
|
|
result = batch.to_tensor(null_to_nan=True, row_major=False)
|
|
x = np.column_stack([arr1, arr2]).astype(np.float32, order="F")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
|
|
np.testing.assert_equal(result.to_numpy(), x)
|
|
assert result.size == 18
|
|
assert result.type == pa.float32()
|
|
assert result.shape == expected.shape
|
|
assert result.strides == expected.strides
|
|
|
|
|
|
@pytest.mark.numpy
|
|
def test_recordbatch_to_tensor_empty():
|
|
batch = pa.RecordBatch.from_arrays(
|
|
[
|
|
pa.array([], type=pa.float32()),
|
|
pa.array([], type=pa.float32()),
|
|
], ["a", "b"]
|
|
)
|
|
result = batch.to_tensor()
|
|
|
|
x = np.column_stack([[], []]).astype(np.float32, order="F")
|
|
expected = pa.Tensor.from_numpy(x)
|
|
|
|
assert result.size == expected.size
|
|
assert result.type == pa.float32()
|
|
assert result.shape == expected.shape
|
|
assert result.strides == (4, 4)
|
|
|
|
|
|
def test_recordbatch_to_tensor_unsupported():
|
|
arr1 = [1, 2, 3, 4, 5, 6, 7, 8, 9]
|
|
# Unsupported data type
|
|
arr2 = ["a", "b", "c", "a", "b", "c", "a", "b", "c"]
|
|
batch = pa.RecordBatch.from_arrays(
|
|
[
|
|
pa.array(arr1, type=pa.int32()),
|
|
pa.array(arr2, type=pa.utf8()),
|
|
], ["a", "b"]
|
|
)
|
|
with pytest.raises(
|
|
pa.ArrowTypeError,
|
|
match="DataType is not supported"
|
|
):
|
|
batch.to_tensor()
|
|
|
|
|
|
def _table_like_slice_tests(factory):
|
|
data = [
|
|
pa.array(range(5)),
|
|
pa.array([-10, -5, 0, 5, 10])
|
|
]
|
|
names = ['c0', 'c1']
|
|
|
|
obj = factory(data, names=names)
|
|
|
|
sliced = obj.slice(2)
|
|
assert sliced.num_rows == 3
|
|
|
|
expected = factory([x.slice(2) for x in data], names=names)
|
|
assert sliced.equals(expected)
|
|
|
|
sliced2 = obj.slice(2, 2)
|
|
expected2 = factory([x.slice(2, 2) for x in data], names=names)
|
|
assert sliced2.equals(expected2)
|
|
|
|
# 0 offset
|
|
assert obj.slice(0).equals(obj)
|
|
|
|
# Slice past end of array
|
|
assert len(obj.slice(len(obj))) == 0
|
|
|
|
with pytest.raises(IndexError):
|
|
obj.slice(-1)
|
|
|
|
# Check __getitem__-based slicing
|
|
assert obj.slice(0, 0).equals(obj[:0])
|
|
assert obj.slice(0, 2).equals(obj[:2])
|
|
assert obj.slice(2, 2).equals(obj[2:4])
|
|
assert obj.slice(2, len(obj) - 2).equals(obj[2:])
|
|
assert obj.slice(len(obj) - 2, 2).equals(obj[-2:])
|
|
assert obj.slice(len(obj) - 4, 2).equals(obj[-4:-2])
|
|
|
|
|
|
def test_recordbatch_slice_getitem():
|
|
return _table_like_slice_tests(pa.RecordBatch.from_arrays)
|
|
|
|
|
|
def test_table_slice_getitem():
|
|
return _table_like_slice_tests(pa.table)
|
|
|
|
|
|
@pytest.mark.pandas
|
|
def test_slice_zero_length_table():
|
|
# ARROW-7907: a segfault on this code was fixed after 0.16.0
|
|
table = pa.table({'a': pa.array([], type=pa.timestamp('us'))})
|
|
table_slice = table.slice(0, 0)
|
|
table_slice.to_pandas()
|
|
|
|
table = pa.table({'a': pa.chunked_array([], type=pa.string())})
|
|
table.to_pandas()
|
|
|
|
|
|
@pytest.mark.numpy
|
|
def test_recordbatchlist_schema_equals():
|
|
a1 = np.array([1], dtype='uint32')
|
|
a2 = np.array([4.0, 5.0], dtype='float64')
|
|
batch1 = pa.record_batch([pa.array(a1)], ['c1'])
|
|
batch2 = pa.record_batch([pa.array(a2)], ['c1'])
|
|
|
|
with pytest.raises(pa.ArrowInvalid):
|
|
pa.Table.from_batches([batch1, batch2])
|
|
|
|
|
|
def test_table_column_sets_private_name():
|
|
# ARROW-6429
|
|
t = pa.table([pa.array([1, 2, 3, 4])], names=['a0'])
|
|
assert t[0]._name == 'a0'
|
|
|
|
|
|
def test_table_equals():
|
|
table = pa.Table.from_arrays([], names=[])
|
|
assert table.equals(table)
|
|
|
|
# ARROW-4822
|
|
assert not table.equals(None)
|
|
|
|
other = pa.Table.from_arrays([], names=[], metadata={'key': 'value'})
|
|
assert not table.equals(other, check_metadata=True)
|
|
assert table.equals(other)
|
|
|
|
|
|
def test_table_from_batches_and_schema():
|
|
schema = pa.schema([
|
|
pa.field('a', pa.int64()),
|
|
pa.field('b', pa.float64()),
|
|
])
|
|
batch = pa.record_batch([pa.array([1]), pa.array([3.14])],
|
|
names=['a', 'b'])
|
|
table = pa.Table.from_batches([batch], schema)
|
|
assert table.schema.equals(schema)
|
|
assert table.column(0) == pa.chunked_array([[1]])
|
|
assert table.column(1) == pa.chunked_array([[3.14]])
|
|
|
|
incompatible_schema = pa.schema([pa.field('a', pa.int64())])
|
|
with pytest.raises(pa.ArrowInvalid):
|
|
pa.Table.from_batches([batch], incompatible_schema)
|
|
|
|
incompatible_batch = pa.record_batch([pa.array([1])], ['a'])
|
|
with pytest.raises(pa.ArrowInvalid):
|
|
pa.Table.from_batches([incompatible_batch], schema)
|
|
|
|
|
|
@pytest.mark.pandas
|
|
def test_table_to_batches():
|
|
from pandas.testing import assert_frame_equal
|
|
import pandas as pd
|
|
|
|
df1 = pd.DataFrame({'a': list(range(10))})
|
|
df2 = pd.DataFrame({'a': list(range(10, 30))})
|
|
|
|
batch1 = pa.RecordBatch.from_pandas(df1, preserve_index=False)
|
|
batch2 = pa.RecordBatch.from_pandas(df2, preserve_index=False)
|
|
|
|
table = pa.Table.from_batches([batch1, batch2, batch1])
|
|
|
|
expected_df = pd.concat([df1, df2, df1], ignore_index=True)
|
|
|
|
batches = table.to_batches()
|
|
assert len(batches) == 3
|
|
|
|
assert_frame_equal(pa.Table.from_batches(batches).to_pandas(),
|
|
expected_df)
|
|
|
|
batches = table.to_batches(max_chunksize=15)
|
|
assert list(map(len, batches)) == [10, 15, 5, 10]
|
|
|
|
assert_frame_equal(table.to_pandas(), expected_df)
|
|
assert_frame_equal(pa.Table.from_batches(batches).to_pandas(),
|
|
expected_df)
|
|
|
|
table_from_iter = pa.Table.from_batches(iter([batch1, batch2, batch1]))
|
|
assert table.equals(table_from_iter)
|
|
|
|
with pytest.raises(ValueError):
|
|
table.to_batches(max_chunksize=0)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('cls'),
|
|
[
|
|
(pa.Table),
|
|
(pa.RecordBatch)
|
|
]
|
|
)
|
|
def test_table_basics(cls):
|
|
data = [
|
|
pa.array(range(5), type='int16'),
|
|
pa.array([-10, -5, 0, None, 10], type='int32')
|
|
]
|
|
table = cls.from_arrays(data, names=('a', 'b'))
|
|
table.validate()
|
|
|
|
assert not table.schema.metadata
|
|
assert len(table) == 5
|
|
assert table.num_rows == 5
|
|
assert table.num_columns == len(data)
|
|
assert table.shape == (5, 2)
|
|
# (only the second array has a null bitmap)
|
|
assert table.get_total_buffer_size() == (5 * 2) + (5 * 4 + 1)
|
|
assert table.nbytes == (5 * 2) + (5 * 4 + 1)
|
|
assert sys.getsizeof(table) >= object.__sizeof__(
|
|
table) + table.get_total_buffer_size()
|
|
|
|
pydict = table.to_pydict()
|
|
assert pydict == OrderedDict([
|
|
('a', [0, 1, 2, 3, 4]),
|
|
('b', [-10, -5, 0, None, 10])
|
|
])
|
|
assert isinstance(pydict, dict)
|
|
assert table == cls.from_pydict(pydict, schema=table.schema)
|
|
|
|
with pytest.raises(IndexError):
|
|
# bounds checking
|
|
table[2]
|
|
|
|
columns = []
|
|
for col in table.itercolumns():
|
|
|
|
if cls is pa.Table:
|
|
assert type(col) is pa.ChunkedArray
|
|
|
|
for chunk in col.iterchunks():
|
|
assert chunk is not None
|
|
|
|
with pytest.raises(IndexError):
|
|
col.chunk(-1)
|
|
|
|
with pytest.raises(IndexError):
|
|
col.chunk(col.num_chunks)
|
|
|
|
else:
|
|
assert issubclass(type(col), pa.Array)
|
|
|
|
columns.append(col)
|
|
|
|
assert table.columns == columns
|
|
assert table == cls.from_arrays(columns, names=table.column_names)
|
|
assert table != cls.from_arrays(columns[1:], names=table.column_names[1:])
|
|
assert table != columns
|
|
|
|
# Schema passed explicitly
|
|
schema = pa.schema([pa.field('c0', pa.int16(),
|
|
metadata={'key': 'value'}),
|
|
pa.field('c1', pa.int32())],
|
|
metadata={b'foo': b'bar'})
|
|
table = cls.from_arrays(data, schema=schema)
|
|
assert table.schema == schema
|
|
|
|
wr = weakref.ref(table)
|
|
assert wr() is not None
|
|
del table
|
|
assert wr() is None
|
|
|
|
|
|
def test_table_dunder_init():
|
|
with pytest.raises(TypeError, match='Table'):
|
|
pa.Table()
|
|
|
|
|
|
def test_table_from_arrays_preserves_column_metadata():
|
|
# Added to test https://issues.apache.org/jira/browse/ARROW-3866
|
|
arr0 = pa.array([1, 2])
|
|
arr1 = pa.array([3, 4])
|
|
field0 = pa.field('field1', pa.int64(), metadata=dict(a="A", b="B"))
|
|
field1 = pa.field('field2', pa.int64(), nullable=False)
|
|
table = pa.Table.from_arrays([arr0, arr1],
|
|
schema=pa.schema([field0, field1]))
|
|
assert b"a" in table.field(0).metadata
|
|
assert table.field(1).nullable is False
|
|
|
|
|
|
def test_table_from_arrays_invalid_names():
|
|
data = [
|
|
pa.array(range(5)),
|
|
pa.array([-10, -5, 0, 5, 10])
|
|
]
|
|
with pytest.raises(ValueError):
|
|
pa.Table.from_arrays(data, names=['a', 'b', 'c'])
|
|
|
|
with pytest.raises(ValueError):
|
|
pa.Table.from_arrays(data, names=['a'])
|
|
|
|
|
|
def test_table_from_lists():
|
|
data = [
|
|
list(range(5)),
|
|
[-10, -5, 0, 5, 10]
|
|
]
|
|
|
|
result = pa.table(data, names=['a', 'b'])
|
|
expected = pa.Table.from_arrays(data, names=['a', 'b'])
|
|
assert result.equals(expected)
|
|
|
|
schema = pa.schema([
|
|
pa.field('a', pa.uint16()),
|
|
pa.field('b', pa.int64())
|
|
])
|
|
result = pa.table(data, schema=schema)
|
|
expected = pa.Table.from_arrays(data, schema=schema)
|
|
assert result.equals(expected)
|
|
|
|
|
|
def test_table_pickle(pickle_module):
|
|
data = [
|
|
pa.chunked_array([[1, 2], [3, 4]], type=pa.uint32()),
|
|
pa.chunked_array([["some", "strings", None, ""]], type=pa.string()),
|
|
]
|
|
schema = pa.schema([pa.field('ints', pa.uint32()),
|
|
pa.field('strs', pa.string())],
|
|
metadata={b'foo': b'bar'})
|
|
table = pa.Table.from_arrays(data, schema=schema)
|
|
|
|
result = pickle_module.loads(pickle_module.dumps(table))
|
|
result.validate()
|
|
assert result.equals(table)
|
|
|
|
|
|
def test_table_get_field():
|
|
data = [
|
|
pa.array(range(5)),
|
|
pa.array([-10, -5, 0, 5, 10]),
|
|
pa.array(range(5, 10))
|
|
]
|
|
table = pa.Table.from_arrays(data, names=('a', 'b', 'c'))
|
|
|
|
assert table.field('a').equals(table.schema.field('a'))
|
|
assert table.field(0).equals(table.schema.field('a'))
|
|
|
|
with pytest.raises(KeyError):
|
|
table.field('d')
|
|
|
|
with pytest.raises(TypeError):
|
|
table.field(None)
|
|
|
|
with pytest.raises(IndexError):
|
|
table.field(4)
|
|
|
|
|
|
def test_table_select_column():
|
|
data = [
|
|
pa.array(range(5)),
|
|
pa.array([-10, -5, 0, 5, 10]),
|
|
pa.array(range(5, 10))
|
|
]
|
|
table = pa.Table.from_arrays(data, names=('a', 'b', 'c'))
|
|
|
|
assert table.column('a').equals(table.column(0))
|
|
|
|
with pytest.raises(KeyError,
|
|
match='Field "d" does not exist in schema'):
|
|
table.column('d')
|
|
|
|
with pytest.raises(TypeError):
|
|
table.column(None)
|
|
|
|
with pytest.raises(IndexError):
|
|
table.column(4)
|
|
|
|
|
|
def test_table_column_with_duplicates():
|
|
# ARROW-8209
|
|
table = pa.table([pa.array([1, 2, 3]),
|
|
pa.array([4, 5, 6]),
|
|
pa.array([7, 8, 9])], names=['a', 'b', 'a'])
|
|
|
|
with pytest.raises(KeyError,
|
|
match='Field "a" exists 2 times in schema'):
|
|
table.column('a')
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('cls'),
|
|
[
|
|
(pa.Table),
|
|
(pa.RecordBatch)
|
|
]
|
|
)
|
|
def test_table_add_column(cls):
|
|
data = [
|
|
pa.array(range(5)),
|
|
pa.array([-10, -5, 0, 5, 10]),
|
|
pa.array(range(5, 10))
|
|
]
|
|
table = cls.from_arrays(data, names=('a', 'b', 'c'))
|
|
|
|
new_field = pa.field('d', data[1].type)
|
|
t2 = table.add_column(3, new_field, data[1])
|
|
t3 = table.append_column(new_field, data[1])
|
|
|
|
expected = cls.from_arrays(data + [data[1]],
|
|
names=('a', 'b', 'c', 'd'))
|
|
assert t2.equals(expected)
|
|
assert t3.equals(expected)
|
|
|
|
t4 = table.add_column(0, new_field, data[1])
|
|
expected = cls.from_arrays([data[1]] + data,
|
|
names=('d', 'a', 'b', 'c'))
|
|
assert t4.equals(expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('cls'),
|
|
[
|
|
(pa.Table),
|
|
(pa.RecordBatch)
|
|
]
|
|
)
|
|
def test_table_set_column(cls):
|
|
data = [
|
|
pa.array(range(5)),
|
|
pa.array([-10, -5, 0, 5, 10]),
|
|
pa.array(range(5, 10))
|
|
]
|
|
table = cls.from_arrays(data, names=('a', 'b', 'c'))
|
|
|
|
new_field = pa.field('d', data[1].type)
|
|
t2 = table.set_column(0, new_field, data[1])
|
|
|
|
expected_data = list(data)
|
|
expected_data[0] = data[1]
|
|
expected = cls.from_arrays(expected_data,
|
|
names=('d', 'b', 'c'))
|
|
assert t2.equals(expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('cls'),
|
|
[
|
|
(pa.Table),
|
|
(pa.RecordBatch)
|
|
]
|
|
)
|
|
def test_table_drop_columns(cls):
|
|
""" drop one or more columns given labels"""
|
|
a = pa.array(range(5))
|
|
b = pa.array([-10, -5, 0, 5, 10])
|
|
c = pa.array(range(5, 10))
|
|
|
|
table = cls.from_arrays([a, b, c], names=('a', 'b', 'c'))
|
|
t2 = table.drop_columns(['a', 'b'])
|
|
t3 = table.drop_columns('a')
|
|
|
|
exp_t2 = cls.from_arrays([c], names=('c',))
|
|
assert exp_t2.equals(t2)
|
|
exp_t3 = cls.from_arrays([b, c], names=('b', 'c',))
|
|
assert exp_t3.equals(t3)
|
|
|
|
# -- raise KeyError if column not in Table
|
|
with pytest.raises(KeyError, match="Column 'd' not found"):
|
|
table.drop_columns(['d'])
|
|
|
|
|
|
def test_table_drop():
|
|
""" verify the alias of drop_columns is working"""
|
|
a = pa.array(range(5))
|
|
b = pa.array([-10, -5, 0, 5, 10])
|
|
c = pa.array(range(5, 10))
|
|
|
|
table = pa.Table.from_arrays([a, b, c], names=('a', 'b', 'c'))
|
|
t2 = table.drop(['a', 'b'])
|
|
t3 = table.drop('a')
|
|
|
|
exp_t2 = pa.Table.from_arrays([c], names=('c',))
|
|
assert exp_t2.equals(t2)
|
|
exp_t3 = pa.Table.from_arrays([b, c], names=('b', 'c',))
|
|
assert exp_t3.equals(t3)
|
|
|
|
# -- raise KeyError if column not in Table
|
|
with pytest.raises(KeyError, match="Column 'd' not found"):
|
|
table.drop(['d'])
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('cls'),
|
|
[
|
|
(pa.Table),
|
|
(pa.RecordBatch)
|
|
]
|
|
)
|
|
def test_table_remove_column(cls):
|
|
data = [
|
|
pa.array(range(5)),
|
|
pa.array([-10, -5, 0, 5, 10]),
|
|
pa.array(range(5, 10))
|
|
]
|
|
table = cls.from_arrays(data, names=('a', 'b', 'c'))
|
|
|
|
t2 = table.remove_column(0)
|
|
t2.validate()
|
|
expected = cls.from_arrays(data[1:], names=('b', 'c'))
|
|
assert t2.equals(expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('cls'),
|
|
[
|
|
(pa.Table),
|
|
(pa.RecordBatch)
|
|
]
|
|
)
|
|
def test_table_remove_column_empty(cls):
|
|
# ARROW-1865
|
|
data = [
|
|
pa.array(range(5)),
|
|
]
|
|
table = cls.from_arrays(data, names=['a'])
|
|
|
|
t2 = table.remove_column(0)
|
|
t2.validate()
|
|
assert len(t2) == len(table)
|
|
|
|
t3 = t2.add_column(0, table.field(0), table[0])
|
|
t3.validate()
|
|
assert t3.equals(table)
|
|
|
|
|
|
def test_empty_table_with_names():
|
|
# ARROW-13784
|
|
data = []
|
|
names = ["a", "b"]
|
|
message = (
|
|
'Length of names [(]2[)] does not match length of arrays [(]0[)]')
|
|
with pytest.raises(ValueError, match=message):
|
|
pa.Table.from_arrays(data, names=names)
|
|
|
|
|
|
def test_empty_table():
|
|
table = pa.table([])
|
|
|
|
assert table.column_names == []
|
|
assert table.equals(pa.Table.from_arrays([], []))
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('cls'),
|
|
[
|
|
(pa.Table),
|
|
(pa.RecordBatch)
|
|
]
|
|
)
|
|
def test_table_rename_columns(cls):
|
|
data = [
|
|
pa.array(range(5)),
|
|
pa.array([-10, -5, 0, 5, 10]),
|
|
pa.array(range(5, 10))
|
|
]
|
|
table = cls.from_arrays(data, names=['a', 'b', 'c'])
|
|
assert table.column_names == ['a', 'b', 'c']
|
|
|
|
expected = cls.from_arrays(data, names=['eh', 'bee', 'sea'])
|
|
|
|
# Testing with list
|
|
t2 = table.rename_columns(['eh', 'bee', 'sea'])
|
|
t2.validate()
|
|
assert t2.column_names == ['eh', 'bee', 'sea']
|
|
assert t2.equals(expected)
|
|
|
|
# Testing with tuple
|
|
t3 = table.rename_columns(('eh', 'bee', 'sea'))
|
|
t3.validate()
|
|
assert t3.column_names == ['eh', 'bee', 'sea']
|
|
assert t3.equals(expected)
|
|
|
|
message = "names must be a list or dict not <class 'str'>"
|
|
with pytest.raises(TypeError, match=message):
|
|
table.rename_columns('not a list')
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('cls'),
|
|
[
|
|
(pa.Table),
|
|
(pa.RecordBatch)
|
|
]
|
|
)
|
|
def test_table_rename_columns_mapping(cls):
|
|
data = [
|
|
pa.array(range(5)),
|
|
pa.array([-10, -5, 0, 5, 10]),
|
|
pa.array(range(5, 10))
|
|
]
|
|
table = cls.from_arrays(data, names=['a', 'b', 'c'])
|
|
assert table.column_names == ['a', 'b', 'c']
|
|
|
|
expected = cls.from_arrays(data, names=['eh', 'b', 'sea'])
|
|
t1 = table.rename_columns({'a': 'eh', 'c': 'sea'})
|
|
t1.validate()
|
|
assert t1 == expected
|
|
|
|
# Test renaming duplicate column names
|
|
table = cls.from_arrays(data, names=['a', 'a', 'c'])
|
|
expected = cls.from_arrays(data, names=['eh', 'eh', 'sea'])
|
|
t2 = table.rename_columns({'a': 'eh', 'c': 'sea'})
|
|
t2.validate()
|
|
assert t2 == expected
|
|
|
|
# Test column not found
|
|
with pytest.raises(KeyError, match=r"Column 'd' not found"):
|
|
table.rename_columns({'a': 'eh', 'd': 'sea'})
|
|
|
|
|
|
def test_table_flatten():
|
|
ty1 = pa.struct([pa.field('x', pa.int16()),
|
|
pa.field('y', pa.float32())])
|
|
ty2 = pa.struct([pa.field('nest', ty1)])
|
|
a = pa.array([(1, 2.5), (3, 4.5)], type=ty1)
|
|
b = pa.array([((11, 12.5),), ((13, 14.5),)], type=ty2)
|
|
c = pa.array([False, True], type=pa.bool_())
|
|
|
|
table = pa.Table.from_arrays([a, b, c], names=['a', 'b', 'c'])
|
|
t2 = table.flatten()
|
|
t2.validate()
|
|
expected = pa.Table.from_arrays([
|
|
pa.array([1, 3], type=pa.int16()),
|
|
pa.array([2.5, 4.5], type=pa.float32()),
|
|
pa.array([(11, 12.5), (13, 14.5)], type=ty1),
|
|
c],
|
|
names=['a.x', 'a.y', 'b.nest', 'c'])
|
|
assert t2.equals(expected)
|
|
|
|
|
|
def test_table_combine_chunks():
|
|
batch1 = pa.record_batch([pa.array([1]), pa.array(["a"])],
|
|
names=['f1', 'f2'])
|
|
batch2 = pa.record_batch([pa.array([2]), pa.array(["b"])],
|
|
names=['f1', 'f2'])
|
|
table = pa.Table.from_batches([batch1, batch2])
|
|
combined = table.combine_chunks()
|
|
combined.validate()
|
|
assert combined.equals(table)
|
|
for c in combined.columns:
|
|
assert c.num_chunks == 1
|
|
|
|
|
|
def test_table_unify_dictionaries():
|
|
batch1 = pa.record_batch([
|
|
pa.array(["foo", "bar", None, "foo"]).dictionary_encode(),
|
|
pa.array([123, 456, 456, 789]).dictionary_encode(),
|
|
pa.array([True, False, None, None])], names=['a', 'b', 'c'])
|
|
batch2 = pa.record_batch([
|
|
pa.array(["quux", "foo", None, "quux"]).dictionary_encode(),
|
|
pa.array([456, 789, 789, None]).dictionary_encode(),
|
|
pa.array([False, None, None, True])], names=['a', 'b', 'c'])
|
|
|
|
table = pa.Table.from_batches([batch1, batch2])
|
|
table = table.replace_schema_metadata({b"key1": b"value1"})
|
|
assert table.column(0).chunk(0).dictionary.equals(
|
|
pa.array(["foo", "bar"]))
|
|
assert table.column(0).chunk(1).dictionary.equals(
|
|
pa.array(["quux", "foo"]))
|
|
assert table.column(1).chunk(0).dictionary.equals(
|
|
pa.array([123, 456, 789]))
|
|
assert table.column(1).chunk(1).dictionary.equals(
|
|
pa.array([456, 789]))
|
|
|
|
table = table.unify_dictionaries(pa.default_memory_pool())
|
|
expected_dict_0 = pa.array(["foo", "bar", "quux"])
|
|
expected_dict_1 = pa.array([123, 456, 789])
|
|
assert table.column(0).chunk(0).dictionary.equals(expected_dict_0)
|
|
assert table.column(0).chunk(1).dictionary.equals(expected_dict_0)
|
|
assert table.column(1).chunk(0).dictionary.equals(expected_dict_1)
|
|
assert table.column(1).chunk(1).dictionary.equals(expected_dict_1)
|
|
|
|
assert table.to_pydict() == {
|
|
'a': ["foo", "bar", None, "foo", "quux", "foo", None, "quux"],
|
|
'b': [123, 456, 456, 789, 456, 789, 789, None],
|
|
'c': [True, False, None, None, False, None, None, True],
|
|
}
|
|
assert table.schema.metadata == {b"key1": b"value1"}
|
|
|
|
|
|
def test_table_maps_as_pydicts():
|
|
arrays = [
|
|
pa.array(
|
|
[{'x': 1, 'y': 2}, {'z': 3}],
|
|
type=pa.map_(pa.string(), pa.int32())
|
|
)
|
|
]
|
|
table = pa.Table.from_arrays(arrays, names=['a'])
|
|
|
|
table_dict = table.to_pydict(maps_as_pydicts="strict")
|
|
assert 'a' in table_dict
|
|
column_list = table_dict['a']
|
|
assert len(column_list) == 2
|
|
assert column_list == [{'x': 1, 'y': 2}, {'z': 3}]
|
|
|
|
table_list = table.to_pylist(maps_as_pydicts="strict")
|
|
assert len(table_list) == 2
|
|
assert table_list == [{'a': {'x': 1, 'y': 2}}, {'a': {'z': 3}}]
|
|
|
|
|
|
def test_concat_tables():
|
|
data = [
|
|
list(range(5)),
|
|
[-10., -5., 0., 5., 10.]
|
|
]
|
|
data2 = [
|
|
list(range(5, 10)),
|
|
[1., 2., 3., 4., 5.]
|
|
]
|
|
|
|
t1 = pa.Table.from_arrays([pa.array(x) for x in data],
|
|
names=('a', 'b'))
|
|
t2 = pa.Table.from_arrays([pa.array(x) for x in data2],
|
|
names=('a', 'b'))
|
|
|
|
result = pa.concat_tables([t1, t2])
|
|
result.validate()
|
|
assert len(result) == 10
|
|
|
|
expected = pa.Table.from_arrays([pa.array(x + y)
|
|
for x, y in zip(data, data2)],
|
|
names=('a', 'b'))
|
|
|
|
assert result.equals(expected)
|
|
|
|
|
|
def test_concat_tables_permissive():
|
|
t1 = pa.Table.from_arrays([list(range(10))], names=('a',))
|
|
t2 = pa.Table.from_arrays([list(('a', 'b', 'c'))], names=('a',))
|
|
|
|
with pytest.raises(
|
|
pa.ArrowTypeError,
|
|
match="Unable to merge: Field a has incompatible types: int64 vs string"):
|
|
_ = pa.concat_tables([t1, t2], promote_options="permissive")
|
|
|
|
|
|
def test_concat_tables_invalid_option():
|
|
t = pa.Table.from_arrays([list(range(10))], names=('a',))
|
|
|
|
with pytest.raises(ValueError, match="Invalid promote options: invalid"):
|
|
pa.concat_tables([t, t], promote_options="invalid")
|
|
|
|
|
|
def test_concat_tables_none_table():
|
|
# ARROW-11997
|
|
with pytest.raises(AttributeError):
|
|
pa.concat_tables([None])
|
|
|
|
|
|
@pytest.mark.pandas
|
|
def test_concat_tables_with_different_schema_metadata():
|
|
import pandas as pd
|
|
|
|
schema = pa.schema([
|
|
pa.field('a', pa.string()),
|
|
pa.field('b', pa.string()),
|
|
])
|
|
|
|
values = list('abcdefgh')
|
|
df1 = pd.DataFrame({'a': values, 'b': values})
|
|
df2 = pd.DataFrame({'a': [np.nan] * 8, 'b': values})
|
|
|
|
table1 = pa.Table.from_pandas(df1, schema=schema, preserve_index=False)
|
|
table2 = pa.Table.from_pandas(df2, schema=schema, preserve_index=False)
|
|
assert table1.schema.equals(table2.schema)
|
|
assert not table1.schema.equals(table2.schema, check_metadata=True)
|
|
|
|
table3 = pa.concat_tables([table1, table2])
|
|
assert table1.schema.equals(table3.schema, check_metadata=True)
|
|
assert table2.schema.equals(table3.schema)
|
|
|
|
|
|
def test_concat_tables_with_promote_option():
|
|
t1 = pa.Table.from_arrays(
|
|
[pa.array([1, 2], type=pa.int64())], ["int64_field"])
|
|
t2 = pa.Table.from_arrays(
|
|
[pa.array([1.0, 2.0], type=pa.float32())], ["float_field"])
|
|
|
|
with pytest.warns(FutureWarning):
|
|
result = pa.concat_tables([t1, t2], promote=True)
|
|
|
|
assert result.equals(pa.Table.from_arrays([
|
|
pa.array([1, 2, None, None], type=pa.int64()),
|
|
pa.array([None, None, 1.0, 2.0], type=pa.float32()),
|
|
], ["int64_field", "float_field"]))
|
|
|
|
t1 = pa.Table.from_arrays(
|
|
[pa.array([1, 2], type=pa.int64())], ["f"])
|
|
t2 = pa.Table.from_arrays(
|
|
[pa.array([1, 2], type=pa.float32())], ["f"])
|
|
|
|
with pytest.raises(pa.ArrowInvalid, match="Schema at index 1 was different:"):
|
|
with pytest.warns(FutureWarning):
|
|
pa.concat_tables([t1, t2], promote=False)
|
|
|
|
|
|
def test_concat_tables_with_promotion():
|
|
t1 = pa.Table.from_arrays(
|
|
[pa.array([1, 2], type=pa.int64())], ["int64_field"])
|
|
t2 = pa.Table.from_arrays(
|
|
[pa.array([1.0, 2.0], type=pa.float32())], ["float_field"])
|
|
|
|
result = pa.concat_tables([t1, t2], promote_options="default")
|
|
|
|
assert result.equals(pa.Table.from_arrays([
|
|
pa.array([1, 2, None, None], type=pa.int64()),
|
|
pa.array([None, None, 1.0, 2.0], type=pa.float32()),
|
|
], ["int64_field", "float_field"]))
|
|
|
|
t3 = pa.Table.from_arrays(
|
|
[pa.array([1, 2], type=pa.int32())], ["int64_field"])
|
|
result = pa.concat_tables(
|
|
[t1, t3], promote_options="permissive")
|
|
assert result.equals(pa.Table.from_arrays([
|
|
pa.array([1, 2, 1, 2], type=pa.int64()),
|
|
], ["int64_field"]))
|
|
|
|
|
|
def test_concat_tables_with_promotion_error():
|
|
t1 = pa.Table.from_arrays(
|
|
[pa.array([1, 2], type=pa.int64())], ["f"])
|
|
t2 = pa.Table.from_arrays(
|
|
[pa.array([1, 2], type=pa.float32())], ["f"])
|
|
|
|
with pytest.raises(pa.ArrowTypeError, match="Unable to merge:"):
|
|
pa.concat_tables([t1, t2], promote_options="default")
|
|
|
|
|
|
def test_table_negative_indexing():
|
|
data = [
|
|
pa.array(range(5)),
|
|
pa.array([-10, -5, 0, 5, 10]),
|
|
pa.array([1.0, 2.0, 3.0, 4.0, 5.0]),
|
|
pa.array(['ab', 'bc', 'cd', 'de', 'ef']),
|
|
]
|
|
table = pa.Table.from_arrays(data, names=tuple('abcd'))
|
|
|
|
assert table[-1].equals(table[3])
|
|
assert table[-2].equals(table[2])
|
|
assert table[-3].equals(table[1])
|
|
assert table[-4].equals(table[0])
|
|
|
|
with pytest.raises(IndexError):
|
|
table[-5]
|
|
|
|
with pytest.raises(IndexError):
|
|
table[4]
|
|
|
|
|
|
def test_concat_batches():
|
|
data = [
|
|
list(range(5)),
|
|
[-10., -5., 0., 5., 10.]
|
|
]
|
|
data2 = [
|
|
list(range(5, 10)),
|
|
[1., 2., 3., 4., 5.]
|
|
]
|
|
|
|
t1 = pa.RecordBatch.from_arrays([pa.array(x) for x in data],
|
|
names=('a', 'b'))
|
|
t2 = pa.RecordBatch.from_arrays([pa.array(x) for x in data2],
|
|
names=('a', 'b'))
|
|
|
|
result = pa.concat_batches([t1, t2])
|
|
result.validate()
|
|
assert len(result) == 10
|
|
|
|
expected = pa.RecordBatch.from_arrays([pa.array(x + y)
|
|
for x, y in zip(data, data2)],
|
|
names=('a', 'b'))
|
|
|
|
assert result.equals(expected)
|
|
|
|
|
|
def test_concat_batches_different_schema():
|
|
t1 = pa.RecordBatch.from_arrays(
|
|
[pa.array([1, 2], type=pa.int64())], ["f"])
|
|
t2 = pa.RecordBatch.from_arrays(
|
|
[pa.array([1, 2], type=pa.float32())], ["f"])
|
|
|
|
with pytest.raises(pa.ArrowInvalid,
|
|
match="not match index 0 recordbatch schema"):
|
|
pa.concat_batches([t1, t2])
|
|
|
|
|
|
def test_concat_batches_none_batches():
|
|
# ARROW-11997
|
|
with pytest.raises(AttributeError):
|
|
pa.concat_batches([None])
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('cls'),
|
|
[
|
|
(pa.Table),
|
|
(pa.RecordBatch)
|
|
]
|
|
)
|
|
def test_table_cast_to_incompatible_schema(cls):
|
|
data = [
|
|
pa.array(range(5)),
|
|
pa.array([-10, -5, 0, 5, 10]),
|
|
]
|
|
table = cls.from_arrays(data, names=tuple('ab'))
|
|
|
|
target_schema1 = pa.schema([
|
|
pa.field('A', pa.int32()),
|
|
pa.field('b', pa.int16()),
|
|
])
|
|
target_schema2 = pa.schema([
|
|
pa.field('a', pa.int32()),
|
|
])
|
|
|
|
if cls is pa.Table:
|
|
cls_name = 'table'
|
|
else:
|
|
cls_name = 'record batch'
|
|
message = ("Target schema's field names are not matching the "
|
|
f"{cls_name}'s field names:.*")
|
|
|
|
with pytest.raises(ValueError, match=message):
|
|
table.cast(target_schema1)
|
|
with pytest.raises(ValueError, match=message):
|
|
table.cast(target_schema2)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('cls'),
|
|
[
|
|
(pa.Table),
|
|
(pa.RecordBatch)
|
|
]
|
|
)
|
|
def test_table_safe_casting(cls):
|
|
data = [
|
|
pa.array(range(5), type=pa.int64()),
|
|
pa.array([-10, -5, 0, 5, 10], type=pa.int32()),
|
|
pa.array([1.0, 2.0, 3.0, 4.0, 5.0], type=pa.float64()),
|
|
pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string())
|
|
]
|
|
table = cls.from_arrays(data, names=tuple('abcd'))
|
|
|
|
expected_data = [
|
|
pa.array(range(5), type=pa.int32()),
|
|
pa.array([-10, -5, 0, 5, 10], type=pa.int16()),
|
|
pa.array([1, 2, 3, 4, 5], type=pa.int64()),
|
|
pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string())
|
|
]
|
|
expected_table = cls.from_arrays(expected_data, names=tuple('abcd'))
|
|
|
|
target_schema = pa.schema([
|
|
pa.field('a', pa.int32()),
|
|
pa.field('b', pa.int16()),
|
|
pa.field('c', pa.int64()),
|
|
pa.field('d', pa.string())
|
|
])
|
|
casted_table = table.cast(target_schema)
|
|
|
|
assert casted_table.equals(expected_table)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('cls'),
|
|
[
|
|
(pa.Table),
|
|
(pa.RecordBatch)
|
|
]
|
|
)
|
|
def test_table_unsafe_casting(cls):
|
|
data = [
|
|
pa.array(range(5), type=pa.int64()),
|
|
pa.array([-10, -5, 0, 5, 10], type=pa.int32()),
|
|
pa.array([1.1, 2.2, 3.3, 4.4, 5.5], type=pa.float64()),
|
|
pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string())
|
|
]
|
|
table = cls.from_arrays(data, names=tuple('abcd'))
|
|
|
|
expected_data = [
|
|
pa.array(range(5), type=pa.int32()),
|
|
pa.array([-10, -5, 0, 5, 10], type=pa.int16()),
|
|
pa.array([1, 2, 3, 4, 5], type=pa.int64()),
|
|
pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string())
|
|
]
|
|
expected_table = cls.from_arrays(expected_data, names=tuple('abcd'))
|
|
|
|
target_schema = pa.schema([
|
|
pa.field('a', pa.int32()),
|
|
pa.field('b', pa.int16()),
|
|
pa.field('c', pa.int64()),
|
|
pa.field('d', pa.string())
|
|
])
|
|
|
|
with pytest.raises(pa.ArrowInvalid, match='truncated'):
|
|
table.cast(target_schema)
|
|
|
|
casted_table = table.cast(target_schema, safe=False)
|
|
assert casted_table.equals(expected_table)
|
|
|
|
|
|
@pytest.mark.numpy
|
|
def test_invalid_table_construct():
|
|
array = np.array([0, 1], dtype=np.uint8)
|
|
u8 = pa.uint8()
|
|
arrays = [pa.array(array, type=u8), pa.array(array[1:], type=u8)]
|
|
|
|
with pytest.raises(pa.lib.ArrowInvalid):
|
|
pa.Table.from_arrays(arrays, names=["a1", "a2"])
|
|
|
|
|
|
@pytest.mark.parametrize('data, klass', [
|
|
((['', 'foo', 'bar'], [4.5, 5, None]), list),
|
|
((['', 'foo', 'bar'], [4.5, 5, None]), pa.array),
|
|
(([[''], ['foo', 'bar']], [[4.5], [5., None]]), pa.chunked_array),
|
|
])
|
|
def test_from_arrays_schema(data, klass):
|
|
data = [klass(data[0]), klass(data[1])]
|
|
schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float32())])
|
|
|
|
table = pa.Table.from_arrays(data, schema=schema)
|
|
assert table.num_columns == 2
|
|
assert table.num_rows == 3
|
|
assert table.schema == schema
|
|
|
|
# length of data and schema not matching
|
|
schema = pa.schema([('strs', pa.utf8())])
|
|
with pytest.raises(ValueError):
|
|
pa.Table.from_arrays(data, schema=schema)
|
|
|
|
# with different but compatible schema
|
|
schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float32())])
|
|
table = pa.Table.from_arrays(data, schema=schema)
|
|
assert pa.types.is_float32(table.column('floats').type)
|
|
assert table.num_columns == 2
|
|
assert table.num_rows == 3
|
|
assert table.schema == schema
|
|
|
|
# with different and incompatible schema
|
|
schema = pa.schema([('strs', pa.utf8()), ('floats', pa.timestamp('s'))])
|
|
with pytest.raises((NotImplementedError, TypeError)):
|
|
pa.Table.from_pydict(data, schema=schema)
|
|
|
|
# Cannot pass both schema and metadata / names
|
|
with pytest.raises(ValueError):
|
|
pa.Table.from_arrays(data, schema=schema, names=['strs', 'floats'])
|
|
|
|
with pytest.raises(ValueError):
|
|
pa.Table.from_arrays(data, schema=schema, metadata={b'foo': b'bar'})
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('cls'),
|
|
[
|
|
(pa.Table),
|
|
(pa.RecordBatch)
|
|
]
|
|
)
|
|
def test_table_from_pydict(cls):
|
|
table = cls.from_pydict({})
|
|
assert table.num_columns == 0
|
|
assert table.num_rows == 0
|
|
assert table.schema == pa.schema([])
|
|
assert table.to_pydict() == {}
|
|
|
|
schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float64())])
|
|
|
|
# With lists as values
|
|
data = OrderedDict([('strs', ['', 'foo', 'bar']),
|
|
('floats', [4.5, 5, None])])
|
|
table = cls.from_pydict(data)
|
|
assert table.num_columns == 2
|
|
assert table.num_rows == 3
|
|
assert table.schema == schema
|
|
assert table.to_pydict() == data
|
|
|
|
# With metadata and inferred schema
|
|
metadata = {b'foo': b'bar'}
|
|
schema = schema.with_metadata(metadata)
|
|
table = cls.from_pydict(data, metadata=metadata)
|
|
assert table.schema == schema
|
|
assert table.schema.metadata == metadata
|
|
assert table.to_pydict() == data
|
|
|
|
# With explicit schema
|
|
table = cls.from_pydict(data, schema=schema)
|
|
assert table.schema == schema
|
|
assert table.schema.metadata == metadata
|
|
assert table.to_pydict() == data
|
|
|
|
# Cannot pass both schema and metadata
|
|
with pytest.raises(ValueError):
|
|
cls.from_pydict(data, schema=schema, metadata=metadata)
|
|
|
|
# Non-convertible values given schema
|
|
with pytest.raises(TypeError):
|
|
cls.from_pydict({'c0': [0, 1, 2]},
|
|
schema=pa.schema([("c0", pa.string())]))
|
|
|
|
# Missing schema fields from the passed mapping
|
|
with pytest.raises(KeyError, match="doesn\'t contain.* c, d"):
|
|
cls.from_pydict(
|
|
{'a': [1, 2, 3], 'b': [3, 4, 5]},
|
|
schema=pa.schema([
|
|
('a', pa.int64()),
|
|
('c', pa.int32()),
|
|
('d', pa.int16())
|
|
])
|
|
)
|
|
|
|
# Passed wrong schema type
|
|
with pytest.raises(TypeError):
|
|
cls.from_pydict({'a': [1, 2, 3]}, schema={})
|
|
|
|
|
|
@pytest.mark.parametrize('data, klass', [
|
|
((['', 'foo', 'bar'], [4.5, 5, None]), pa.array),
|
|
(([[''], ['foo', 'bar']], [[4.5], [5., None]]), pa.chunked_array),
|
|
])
|
|
def test_table_from_pydict_arrow_arrays(data, klass):
|
|
data = OrderedDict([('strs', klass(data[0])), ('floats', klass(data[1]))])
|
|
schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float64())])
|
|
|
|
# With arrays as values
|
|
table = pa.Table.from_pydict(data)
|
|
assert table.num_columns == 2
|
|
assert table.num_rows == 3
|
|
assert table.schema == schema
|
|
|
|
# With explicit (matching) schema
|
|
table = pa.Table.from_pydict(data, schema=schema)
|
|
assert table.num_columns == 2
|
|
assert table.num_rows == 3
|
|
assert table.schema == schema
|
|
|
|
# with different but compatible schema
|
|
schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float32())])
|
|
table = pa.Table.from_pydict(data, schema=schema)
|
|
assert pa.types.is_float32(table.column('floats').type)
|
|
assert table.num_columns == 2
|
|
assert table.num_rows == 3
|
|
assert table.schema == schema
|
|
|
|
# with different and incompatible schema
|
|
schema = pa.schema([('strs', pa.utf8()), ('floats', pa.timestamp('s'))])
|
|
with pytest.raises((NotImplementedError, TypeError)):
|
|
pa.Table.from_pydict(data, schema=schema)
|
|
|
|
|
|
@pytest.mark.parametrize('data, klass', [
|
|
((['', 'foo', 'bar'], [4.5, 5, None]), list),
|
|
((['', 'foo', 'bar'], [4.5, 5, None]), pa.array),
|
|
(([[''], ['foo', 'bar']], [[4.5], [5., None]]), pa.chunked_array),
|
|
])
|
|
def test_table_from_pydict_schema(data, klass):
|
|
# passed schema is source of truth for the columns
|
|
|
|
data = OrderedDict([('strs', klass(data[0])), ('floats', klass(data[1]))])
|
|
|
|
# schema has columns not present in data -> error
|
|
schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float64()),
|
|
('ints', pa.int64())])
|
|
with pytest.raises(KeyError, match='ints'):
|
|
pa.Table.from_pydict(data, schema=schema)
|
|
|
|
# data has columns not present in schema -> ignored
|
|
schema = pa.schema([('strs', pa.utf8())])
|
|
table = pa.Table.from_pydict(data, schema=schema)
|
|
assert table.num_columns == 1
|
|
assert table.schema == schema
|
|
assert table.column_names == ['strs']
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('cls'),
|
|
[
|
|
(pa.Table),
|
|
(pa.RecordBatch)
|
|
]
|
|
)
|
|
def test_table_from_pylist(cls):
|
|
table = cls.from_pylist([])
|
|
assert table.num_columns == 0
|
|
assert table.num_rows == 0
|
|
assert table.schema == pa.schema([])
|
|
assert table.to_pylist() == []
|
|
|
|
schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float64())])
|
|
|
|
# With lists as values
|
|
data = [{'strs': '', 'floats': 4.5},
|
|
{'strs': 'foo', 'floats': 5},
|
|
{'strs': 'bar', 'floats': None}]
|
|
table = cls.from_pylist(data)
|
|
assert table.num_columns == 2
|
|
assert table.num_rows == 3
|
|
assert table.schema == schema
|
|
assert table.to_pylist() == data
|
|
|
|
# With metadata and inferred schema
|
|
metadata = {b'foo': b'bar'}
|
|
schema = schema.with_metadata(metadata)
|
|
table = cls.from_pylist(data, metadata=metadata)
|
|
assert table.schema == schema
|
|
assert table.schema.metadata == metadata
|
|
assert table.to_pylist() == data
|
|
|
|
# With explicit schema
|
|
table = cls.from_pylist(data, schema=schema)
|
|
assert table.schema == schema
|
|
assert table.schema.metadata == metadata
|
|
assert table.to_pylist() == data
|
|
|
|
# Cannot pass both schema and metadata
|
|
with pytest.raises(ValueError):
|
|
cls.from_pylist(data, schema=schema, metadata=metadata)
|
|
|
|
# Non-convertible values given schema
|
|
with pytest.raises(TypeError):
|
|
cls.from_pylist([{'c0': 0}, {'c0': 1}, {'c0': 2}],
|
|
schema=pa.schema([("c0", pa.string())]))
|
|
|
|
# Missing schema fields in the passed mapping translate to None
|
|
schema = pa.schema([('a', pa.int64()),
|
|
('c', pa.int32()),
|
|
('d', pa.int16())
|
|
])
|
|
table = cls.from_pylist(
|
|
[{'a': 1, 'b': 3}, {'a': 2, 'b': 4}, {'a': 3, 'b': 5}],
|
|
schema=schema
|
|
)
|
|
data = [{'a': 1, 'c': None, 'd': None},
|
|
{'a': 2, 'c': None, 'd': None},
|
|
{'a': 3, 'c': None, 'd': None}]
|
|
assert table.schema == schema
|
|
assert table.to_pylist() == data
|
|
|
|
# Passed wrong schema type
|
|
with pytest.raises(TypeError):
|
|
cls.from_pylist([{'a': 1}, {'a': 2}, {'a': 3}], schema={})
|
|
|
|
# If the dictionaries of rows are not same length
|
|
data = [{'strs': '', 'floats': 4.5},
|
|
{'floats': 5},
|
|
{'strs': 'bar'}]
|
|
data2 = [{'strs': '', 'floats': 4.5},
|
|
{'strs': None, 'floats': 5},
|
|
{'strs': 'bar', 'floats': None}]
|
|
table = cls.from_pylist(data)
|
|
assert table.num_columns == 2
|
|
assert table.num_rows == 3
|
|
assert table.to_pylist() == data2
|
|
|
|
data = [{'strs': ''},
|
|
{'strs': 'foo', 'floats': 5},
|
|
{'floats': None}]
|
|
data2 = [{'strs': ''},
|
|
{'strs': 'foo'},
|
|
{'strs': None}]
|
|
table = cls.from_pylist(data)
|
|
assert table.num_columns == 1
|
|
assert table.num_rows == 3
|
|
assert table.to_pylist() == data2
|
|
|
|
|
|
@pytest.mark.pandas
|
|
def test_table_from_pandas_schema():
|
|
# passed schema is source of truth for the columns
|
|
import pandas as pd
|
|
|
|
df = pd.DataFrame(OrderedDict([('strs', ['', 'foo', 'bar']),
|
|
('floats', [4.5, 5, None])]))
|
|
|
|
# with different but compatible schema
|
|
schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float32())])
|
|
table = pa.Table.from_pandas(df, schema=schema)
|
|
assert pa.types.is_float32(table.column('floats').type)
|
|
assert table.schema.remove_metadata() == schema
|
|
|
|
# with different and incompatible schema
|
|
schema = pa.schema([('strs', pa.utf8()), ('floats', pa.timestamp('s'))])
|
|
with pytest.raises((NotImplementedError, TypeError)):
|
|
pa.Table.from_pandas(df, schema=schema)
|
|
|
|
# schema has columns not present in data -> error
|
|
schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float64()),
|
|
('ints', pa.int64())])
|
|
with pytest.raises(KeyError, match='ints'):
|
|
pa.Table.from_pandas(df, schema=schema)
|
|
|
|
# data has columns not present in schema -> ignored
|
|
schema = pa.schema([('strs', pa.utf8())])
|
|
table = pa.Table.from_pandas(df, schema=schema)
|
|
assert table.num_columns == 1
|
|
assert table.schema.remove_metadata() == schema
|
|
assert table.column_names == ['strs']
|
|
|
|
|
|
@pytest.mark.pandas
|
|
def test_table_factory_function():
|
|
import pandas as pd
|
|
|
|
# Put in wrong order to make sure that lines up with schema
|
|
d = OrderedDict([('b', ['a', 'b', 'c']), ('a', [1, 2, 3])])
|
|
|
|
d_explicit = {'b': pa.array(['a', 'b', 'c'], type='string'),
|
|
'a': pa.array([1, 2, 3], type='int32')}
|
|
|
|
schema = pa.schema([('a', pa.int32()), ('b', pa.string())])
|
|
|
|
df = pd.DataFrame(d)
|
|
table1 = pa.table(df)
|
|
table2 = pa.Table.from_pandas(df)
|
|
assert table1.equals(table2)
|
|
table1 = pa.table(df, schema=schema)
|
|
table2 = pa.Table.from_pandas(df, schema=schema)
|
|
assert table1.equals(table2)
|
|
|
|
table1 = pa.table(d_explicit)
|
|
table2 = pa.Table.from_pydict(d_explicit)
|
|
assert table1.equals(table2)
|
|
|
|
# schema coerces type
|
|
table1 = pa.table(d, schema=schema)
|
|
table2 = pa.Table.from_pydict(d, schema=schema)
|
|
assert table1.equals(table2)
|
|
|
|
|
|
def test_table_factory_function_args():
|
|
# from_pydict not accepting names:
|
|
with pytest.raises(ValueError):
|
|
pa.table({'a': [1, 2, 3]}, names=['a'])
|
|
|
|
# backwards compatibility for schema as first positional argument
|
|
schema = pa.schema([('a', pa.int32())])
|
|
table = pa.table({'a': pa.array([1, 2, 3], type=pa.int64())}, schema)
|
|
assert table.column('a').type == pa.int32()
|
|
|
|
# from_arrays: accept both names and schema as positional first argument
|
|
data = [pa.array([1, 2, 3], type='int64')]
|
|
names = ['a']
|
|
table = pa.table(data, names)
|
|
assert table.column_names == names
|
|
schema = pa.schema([('a', pa.int64())])
|
|
table = pa.table(data, schema)
|
|
assert table.column_names == names
|
|
|
|
|
|
@pytest.mark.pandas
|
|
def test_table_factory_function_args_pandas():
|
|
import pandas as pd
|
|
|
|
# from_pandas not accepting names or metadata:
|
|
with pytest.raises(ValueError):
|
|
pa.table(pd.DataFrame({'a': [1, 2, 3]}), names=['a'])
|
|
|
|
with pytest.raises(ValueError):
|
|
pa.table(pd.DataFrame({'a': [1, 2, 3]}), metadata={b'foo': b'bar'})
|
|
|
|
# backwards compatibility for schema as first positional argument
|
|
schema = pa.schema([('a', pa.int32())])
|
|
table = pa.table(pd.DataFrame({'a': [1, 2, 3]}), schema)
|
|
assert table.column('a').type == pa.int32()
|
|
|
|
|
|
def test_factory_functions_invalid_input():
|
|
with pytest.raises(TypeError, match="Expected pandas DataFrame, python"):
|
|
pa.table("invalid input")
|
|
|
|
with pytest.raises(TypeError, match="Expected pandas DataFrame"):
|
|
pa.record_batch("invalid input")
|
|
|
|
|
|
def test_table_repr_to_string():
|
|
# Schema passed explicitly
|
|
schema = pa.schema([pa.field('c0', pa.int16(),
|
|
metadata={'key': 'value'}),
|
|
pa.field('c1', pa.int32())],
|
|
metadata={b'foo': b'bar'})
|
|
|
|
tab = pa.table([pa.array([1, 2, 3, 4], type='int16'),
|
|
pa.array([10, 20, 30, 40], type='int32')], schema=schema)
|
|
assert str(tab) == """pyarrow.Table
|
|
c0: int16
|
|
c1: int32
|
|
----
|
|
c0: [[1,2,3,4]]
|
|
c1: [[10,20,30,40]]"""
|
|
|
|
assert tab.to_string(show_metadata=True) == """\
|
|
pyarrow.Table
|
|
c0: int16
|
|
-- field metadata --
|
|
key: 'value'
|
|
c1: int32
|
|
-- schema metadata --
|
|
foo: 'bar'"""
|
|
|
|
assert tab.to_string(preview_cols=5) == """\
|
|
pyarrow.Table
|
|
c0: int16
|
|
c1: int32
|
|
----
|
|
c0: [[1,2,3,4]]
|
|
c1: [[10,20,30,40]]"""
|
|
|
|
assert tab.to_string(preview_cols=1) == """\
|
|
pyarrow.Table
|
|
c0: int16
|
|
c1: int32
|
|
----
|
|
c0: [[1,2,3,4]]
|
|
..."""
|
|
|
|
|
|
def test_table_repr_to_string_ellipsis():
|
|
# Schema passed explicitly
|
|
schema = pa.schema([pa.field('c0', pa.int16(),
|
|
metadata={'key': 'value'}),
|
|
pa.field('c1', pa.int32())],
|
|
metadata={b'foo': b'bar'})
|
|
|
|
tab = pa.table([pa.array([1, 2, 3, 4]*10, type='int16'),
|
|
pa.array([10, 20, 30, 40]*10, type='int32')],
|
|
schema=schema)
|
|
assert str(tab) == """pyarrow.Table
|
|
c0: int16
|
|
c1: int32
|
|
----
|
|
c0: [[1,2,3,4,1,...,4,1,2,3,4]]
|
|
c1: [[10,20,30,40,10,...,40,10,20,30,40]]"""
|
|
|
|
|
|
def test_record_batch_repr_to_string():
|
|
# Schema passed explicitly
|
|
schema = pa.schema([pa.field('c0', pa.int16(),
|
|
metadata={'key': 'value'}),
|
|
pa.field('c1', pa.int32())],
|
|
metadata={b'foo': b'bar'})
|
|
|
|
batch = pa.record_batch([pa.array([1, 2, 3, 4], type='int16'),
|
|
pa.array([10, 20, 30, 40], type='int32')],
|
|
schema=schema)
|
|
assert str(batch) == """pyarrow.RecordBatch
|
|
c0: int16
|
|
c1: int32
|
|
----
|
|
c0: [1,2,3,4]
|
|
c1: [10,20,30,40]"""
|
|
|
|
assert batch.to_string(show_metadata=True) == """\
|
|
pyarrow.RecordBatch
|
|
c0: int16
|
|
-- field metadata --
|
|
key: 'value'
|
|
c1: int32
|
|
-- schema metadata --
|
|
foo: 'bar'"""
|
|
|
|
assert batch.to_string(preview_cols=5) == """\
|
|
pyarrow.RecordBatch
|
|
c0: int16
|
|
c1: int32
|
|
----
|
|
c0: [1,2,3,4]
|
|
c1: [10,20,30,40]"""
|
|
|
|
assert batch.to_string(preview_cols=1) == """\
|
|
pyarrow.RecordBatch
|
|
c0: int16
|
|
c1: int32
|
|
----
|
|
c0: [1,2,3,4]
|
|
..."""
|
|
|
|
|
|
def test_record_batch_repr_to_string_ellipsis():
|
|
# Schema passed explicitly
|
|
schema = pa.schema([pa.field('c0', pa.int16(),
|
|
metadata={'key': 'value'}),
|
|
pa.field('c1', pa.int32())],
|
|
metadata={b'foo': b'bar'})
|
|
|
|
batch = pa.record_batch([pa.array([1, 2, 3, 4]*10, type='int16'),
|
|
pa.array([10, 20, 30, 40]*10, type='int32')],
|
|
schema=schema)
|
|
assert str(batch) == """pyarrow.RecordBatch
|
|
c0: int16
|
|
c1: int32
|
|
----
|
|
c0: [1,2,3,4,1,2,3,4,1,2,...,3,4,1,2,3,4,1,2,3,4]
|
|
c1: [10,20,30,40,10,20,30,40,10,20,...,30,40,10,20,30,40,10,20,30,40]"""
|
|
|
|
|
|
def test_table_function_unicode_schema():
|
|
col_a = "äääh"
|
|
col_b = "öööf"
|
|
|
|
# Put in wrong order to make sure that lines up with schema
|
|
d = OrderedDict([(col_b, ['a', 'b', 'c']), (col_a, [1, 2, 3])])
|
|
|
|
schema = pa.schema([(col_a, pa.int32()), (col_b, pa.string())])
|
|
|
|
result = pa.table(d, schema=schema)
|
|
assert result[0].chunk(0).equals(pa.array([1, 2, 3], type='int32'))
|
|
assert result[1].chunk(0).equals(pa.array(['a', 'b', 'c'], type='string'))
|
|
|
|
|
|
def test_table_take_vanilla_functionality():
|
|
table = pa.table(
|
|
[pa.array([1, 2, 3, None, 5]),
|
|
pa.array(['a', 'b', 'c', 'd', 'e'])],
|
|
['f1', 'f2'])
|
|
|
|
assert table.take(pa.array([2, 3])).equals(table.slice(2, 2))
|
|
|
|
|
|
def test_table_take_null_index():
|
|
table = pa.table(
|
|
[pa.array([1, 2, 3, None, 5]),
|
|
pa.array(['a', 'b', 'c', 'd', 'e'])],
|
|
['f1', 'f2'])
|
|
|
|
result_with_null_index = pa.table(
|
|
[pa.array([1, None]),
|
|
pa.array(['a', None])],
|
|
['f1', 'f2'])
|
|
|
|
assert table.take(pa.array([0, None])).equals(result_with_null_index)
|
|
|
|
|
|
def test_table_take_non_consecutive():
|
|
table = pa.table(
|
|
[pa.array([1, 2, 3, None, 5]),
|
|
pa.array(['a', 'b', 'c', 'd', 'e'])],
|
|
['f1', 'f2'])
|
|
|
|
result_non_consecutive = pa.table(
|
|
[pa.array([2, None]),
|
|
pa.array(['b', 'd'])],
|
|
['f1', 'f2'])
|
|
|
|
assert table.take(pa.array([1, 3])).equals(result_non_consecutive)
|
|
|
|
|
|
def test_table_select():
|
|
a1 = pa.array([1, 2, 3, None, 5])
|
|
a2 = pa.array(['a', 'b', 'c', 'd', 'e'])
|
|
a3 = pa.array([[1, 2], [3, 4], [5, 6], None, [9, 10]])
|
|
table = pa.table([a1, a2, a3], ['f1', 'f2', 'f3'])
|
|
|
|
# selecting with string names
|
|
result = table.select(['f1'])
|
|
expected = pa.table([a1], ['f1'])
|
|
assert result.equals(expected)
|
|
|
|
result = table.select(['f3', 'f2'])
|
|
expected = pa.table([a3, a2], ['f3', 'f2'])
|
|
assert result.equals(expected)
|
|
|
|
# selecting with integer indices
|
|
result = table.select([0])
|
|
expected = pa.table([a1], ['f1'])
|
|
assert result.equals(expected)
|
|
|
|
result = table.select([2, 1])
|
|
expected = pa.table([a3, a2], ['f3', 'f2'])
|
|
assert result.equals(expected)
|
|
|
|
# preserve metadata
|
|
table2 = table.replace_schema_metadata({"a": "test"})
|
|
result = table2.select(["f1", "f2"])
|
|
assert b"a" in result.schema.metadata
|
|
|
|
# selecting non-existing column raises
|
|
with pytest.raises(KeyError, match='Field "f5" does not exist'):
|
|
table.select(['f5'])
|
|
|
|
with pytest.raises(IndexError, match="index out of bounds"):
|
|
table.select([5])
|
|
|
|
# duplicate selection gives duplicated names in resulting table
|
|
result = table.select(['f2', 'f2'])
|
|
expected = pa.table([a2, a2], ['f2', 'f2'])
|
|
assert result.equals(expected)
|
|
|
|
# selection duplicated column raises
|
|
table = pa.table([a1, a2, a3], ['f1', 'f2', 'f1'])
|
|
with pytest.raises(KeyError, match='Field "f1" exists 2 times'):
|
|
table.select(['f1'])
|
|
|
|
result = table.select(['f2'])
|
|
expected = pa.table([a2], ['f2'])
|
|
assert result.equals(expected)
|
|
|
|
|
|
@pytest.mark.acero
|
|
def test_table_group_by():
|
|
def sorted_by_keys(d):
|
|
# Ensure a guaranteed order of keys for aggregation results.
|
|
if "keys2" in d:
|
|
keys = tuple(zip(d["keys"], d["keys2"]))
|
|
else:
|
|
keys = d["keys"]
|
|
sorted_keys = sorted(keys)
|
|
sorted_d = {"keys": sorted(d["keys"])}
|
|
for entry in d:
|
|
if entry == "keys":
|
|
continue
|
|
values = dict(zip(keys, d[entry]))
|
|
for k in sorted_keys:
|
|
sorted_d.setdefault(entry, []).append(values[k])
|
|
return sorted_d
|
|
|
|
table = pa.table([
|
|
pa.array(["a", "a", "b", "b", "c"]),
|
|
pa.array(["X", "X", "Y", "Z", "Z"]),
|
|
pa.array([1, 2, 3, 4, 5]),
|
|
pa.array([10, 20, 30, 40, 50])
|
|
], names=["keys", "keys2", "values", "bigvalues"])
|
|
|
|
r = table.group_by("keys").aggregate([
|
|
("values", "hash_sum")
|
|
])
|
|
assert sorted_by_keys(r.to_pydict()) == {
|
|
"keys": ["a", "b", "c"],
|
|
"values_sum": [3, 7, 5]
|
|
}
|
|
|
|
r = table.group_by("keys").aggregate([
|
|
("values", "hash_sum"),
|
|
("values", "hash_count")
|
|
])
|
|
assert sorted_by_keys(r.to_pydict()) == {
|
|
"keys": ["a", "b", "c"],
|
|
"values_sum": [3, 7, 5],
|
|
"values_count": [2, 2, 1]
|
|
}
|
|
|
|
# Test without hash_ prefix
|
|
r = table.group_by("keys").aggregate([
|
|
("values", "sum")
|
|
])
|
|
assert sorted_by_keys(r.to_pydict()) == {
|
|
"keys": ["a", "b", "c"],
|
|
"values_sum": [3, 7, 5]
|
|
}
|
|
|
|
r = table.group_by("keys").aggregate([
|
|
("values", "max"),
|
|
("bigvalues", "sum")
|
|
])
|
|
assert sorted_by_keys(r.to_pydict()) == {
|
|
"keys": ["a", "b", "c"],
|
|
"values_max": [2, 4, 5],
|
|
"bigvalues_sum": [30, 70, 50]
|
|
}
|
|
|
|
r = table.group_by("keys").aggregate([
|
|
("bigvalues", "max"),
|
|
("values", "sum")
|
|
])
|
|
assert sorted_by_keys(r.to_pydict()) == {
|
|
"keys": ["a", "b", "c"],
|
|
"values_sum": [3, 7, 5],
|
|
"bigvalues_max": [20, 40, 50]
|
|
}
|
|
|
|
r = table.group_by(["keys", "keys2"]).aggregate([
|
|
("values", "sum")
|
|
])
|
|
assert sorted_by_keys(r.to_pydict()) == {
|
|
"keys": ["a", "b", "b", "c"],
|
|
"keys2": ["X", "Y", "Z", "Z"],
|
|
"values_sum": [3, 3, 4, 5]
|
|
}
|
|
|
|
# Test many arguments
|
|
r = table.group_by("keys").aggregate([
|
|
("values", "max"),
|
|
("bigvalues", "sum"),
|
|
("bigvalues", "max"),
|
|
([], "count_all"),
|
|
("values", "sum")
|
|
])
|
|
assert sorted_by_keys(r.to_pydict()) == {
|
|
"keys": ["a", "b", "c"],
|
|
"values_max": [2, 4, 5],
|
|
"bigvalues_sum": [30, 70, 50],
|
|
"bigvalues_max": [20, 40, 50],
|
|
"count_all": [2, 2, 1],
|
|
"values_sum": [3, 7, 5]
|
|
}
|
|
|
|
table_with_nulls = pa.table([
|
|
pa.array(["a", "a", "a"]),
|
|
pa.array([1, None, None])
|
|
], names=["keys", "values"])
|
|
|
|
r = table_with_nulls.group_by(["keys"]).aggregate([
|
|
("values", "count", pc.CountOptions(mode="all"))
|
|
])
|
|
assert r.to_pydict() == {
|
|
"keys": ["a"],
|
|
"values_count": [3]
|
|
}
|
|
|
|
r = table_with_nulls.group_by(["keys"]).aggregate([
|
|
("values", "count", pc.CountOptions(mode="only_null"))
|
|
])
|
|
assert r.to_pydict() == {
|
|
"keys": ["a"],
|
|
"values_count": [2]
|
|
}
|
|
|
|
r = table_with_nulls.group_by(["keys"]).aggregate([
|
|
("values", "count", pc.CountOptions(mode="only_valid"))
|
|
])
|
|
assert r.to_pydict() == {
|
|
"keys": ["a"],
|
|
"values_count": [1]
|
|
}
|
|
|
|
r = table_with_nulls.group_by(["keys"]).aggregate([
|
|
([], "count_all"), # nullary count that takes no parameters
|
|
("values", "count", pc.CountOptions(mode="only_valid"))
|
|
])
|
|
assert r.to_pydict() == {
|
|
"keys": ["a"],
|
|
"count_all": [3],
|
|
"values_count": [1]
|
|
}
|
|
|
|
r = table_with_nulls.group_by(["keys"]).aggregate([
|
|
([], "count_all")
|
|
])
|
|
assert r.to_pydict() == {
|
|
"keys": ["a"],
|
|
"count_all": [3]
|
|
}
|
|
|
|
table = pa.table({
|
|
'keys': ['a', 'b', 'a', 'b', 'a', 'b'],
|
|
'values': range(6)})
|
|
table_with_chunks = pa.Table.from_batches(
|
|
table.to_batches(max_chunksize=3))
|
|
r = table_with_chunks.group_by('keys').aggregate([('values', 'sum')])
|
|
assert sorted_by_keys(r.to_pydict()) == {
|
|
"keys": ["a", "b"],
|
|
"values_sum": [6, 9]
|
|
}
|
|
|
|
|
|
@pytest.mark.acero
|
|
def test_table_group_by_first():
|
|
# "first" is an ordered aggregation -> requires to specify use_threads=False
|
|
table1 = pa.table({'a': [1, 2, 3, 4], 'b': ['a', 'b'] * 2})
|
|
table2 = pa.table({'a': [1, 2, 3, 4], 'b': ['b', 'a'] * 2})
|
|
table = pa.concat_tables([table1, table2])
|
|
|
|
with pytest.raises(NotImplementedError):
|
|
table.group_by("b").aggregate([("a", "first")])
|
|
|
|
result = table.group_by("b", use_threads=False).aggregate([("a", "first")])
|
|
expected = pa.table({"b": ["a", "b"], "a_first": [1, 2]})
|
|
assert result.equals(expected)
|
|
|
|
|
|
@pytest.mark.acero
|
|
def test_table_group_by_pivot_wider():
|
|
table = pa.table({'group': [1, 2, 3, 1, 2, 3],
|
|
'key': ['h', 'h', 'h', 'w', 'w', 'w'],
|
|
'value': [10, 20, 30, 40, 50, 60]})
|
|
|
|
with pytest.raises(ValueError, match='accepts 3 arguments but 2 passed'):
|
|
table.group_by("group").aggregate([("key", "pivot_wider")])
|
|
|
|
# GH-45739: calling hash_pivot_wider without options shouldn't crash
|
|
# (even though it's not very useful as key_names=[])
|
|
result = table.group_by("group").aggregate([(("key", "value"), "pivot_wider")])
|
|
expected = pa.table({'group': [1, 2, 3],
|
|
'key_value_pivot_wider': [{}, {}, {}]})
|
|
assert result.equals(expected)
|
|
|
|
options = pc.PivotWiderOptions(key_names=('h', 'w'))
|
|
result = table.group_by("group").aggregate(
|
|
[(("key", "value"), "pivot_wider", options)])
|
|
expected = pa.table(
|
|
{'group': [1, 2, 3],
|
|
'key_value_pivot_wider': [
|
|
{'h': 10, 'w': 40}, {'h': 20, 'w': 50}, {'h': 30, 'w': 60}]})
|
|
assert result.equals(expected)
|
|
|
|
|
|
def test_table_to_recordbatchreader():
|
|
table = pa.Table.from_pydict({'x': [1, 2, 3]})
|
|
reader = table.to_reader()
|
|
assert table.schema == reader.schema
|
|
assert table == reader.read_all()
|
|
|
|
reader = table.to_reader(max_chunksize=2)
|
|
assert reader.read_next_batch().num_rows == 2
|
|
assert reader.read_next_batch().num_rows == 1
|
|
|
|
|
|
@pytest.mark.acero
|
|
def test_table_join():
|
|
t1 = pa.table({
|
|
"colA": [1, 2, 6],
|
|
"col2": ["a", "b", "f"]
|
|
})
|
|
|
|
t2 = pa.table({
|
|
"colB": [99, 2, 1],
|
|
"col3": ["Z", "B", "A"]
|
|
})
|
|
|
|
result = t1.join(t2, "colA", "colB")
|
|
assert result.combine_chunks() == pa.table({
|
|
"colA": [1, 2, 6],
|
|
"col2": ["a", "b", "f"],
|
|
"col3": ["A", "B", None]
|
|
})
|
|
|
|
result = t1.join(t2, "colA", "colB", join_type="full outer")
|
|
assert result.combine_chunks().sort_by("colA") == pa.table({
|
|
"colA": [1, 2, 6, 99],
|
|
"col2": ["a", "b", "f", None],
|
|
"col3": ["A", "B", None, "Z"]
|
|
})
|
|
|
|
|
|
@pytest.mark.acero
|
|
def test_table_join_unique_key():
|
|
t1 = pa.table({
|
|
"colA": [1, 2, 6],
|
|
"col2": ["a", "b", "f"]
|
|
})
|
|
|
|
t2 = pa.table({
|
|
"colA": [99, 2, 1],
|
|
"col3": ["Z", "B", "A"]
|
|
})
|
|
|
|
result = t1.join(t2, "colA")
|
|
assert result.combine_chunks() == pa.table({
|
|
"colA": [1, 2, 6],
|
|
"col2": ["a", "b", "f"],
|
|
"col3": ["A", "B", None]
|
|
})
|
|
|
|
result = t1.join(t2, "colA", join_type="full outer", right_suffix="_r")
|
|
assert result.combine_chunks().sort_by("colA") == pa.table({
|
|
"colA": [1, 2, 6, 99],
|
|
"col2": ["a", "b", "f", None],
|
|
"col3": ["A", "B", None, "Z"]
|
|
})
|
|
|
|
|
|
@pytest.mark.acero
|
|
def test_table_join_collisions():
|
|
t1 = pa.table({
|
|
"colA": [1, 2, 6],
|
|
"colB": [10, 20, 60],
|
|
"colVals": ["a", "b", "f"]
|
|
})
|
|
|
|
t2 = pa.table({
|
|
"colA": [99, 2, 1],
|
|
"colB": [99, 20, 10],
|
|
"colVals": ["Z", "B", "A"]
|
|
})
|
|
|
|
result = t1.join(t2, "colA", join_type="full outer")
|
|
assert result.combine_chunks().sort_by("colA") == pa.table([
|
|
[1, 2, 6, 99],
|
|
[10, 20, 60, None],
|
|
["a", "b", "f", None],
|
|
[10, 20, None, 99],
|
|
["A", "B", None, "Z"],
|
|
], names=["colA", "colB", "colVals", "colB", "colVals"])
|
|
|
|
|
|
@pytest.mark.acero
|
|
@pytest.mark.parametrize('cls', [(pa.Table), (pa.RecordBatch)])
|
|
def test_table_filter_expression(cls):
|
|
t1 = cls.from_pydict({
|
|
"colA": [1, 2, 3, 6],
|
|
"colB": [10, 20, None, 60],
|
|
"colVals": ["a", "b", "c", "f"]
|
|
})
|
|
|
|
result = t1.filter(pc.field("colB") < 50)
|
|
assert result == cls.from_pydict({
|
|
"colA": [1, 2],
|
|
"colB": [10, 20],
|
|
"colVals": ["a", "b"]
|
|
})
|
|
|
|
|
|
@pytest.mark.acero
|
|
def test_table_filter_expression_chunks():
|
|
t1 = pa.table({
|
|
"colA": [1, 2, 6],
|
|
"colB": [10, 20, 60],
|
|
"colVals": ["a", "b", "f"]
|
|
})
|
|
t2 = pa.table({
|
|
"colA": [99, 2, 1],
|
|
"colB": [99, 20, 10],
|
|
"colVals": ["Z", "B", "A"]
|
|
})
|
|
|
|
t3 = pa.concat_tables([t1, t2])
|
|
|
|
result = t3.filter(pc.field("colA") < 10)
|
|
assert result.combine_chunks() == pa.table({
|
|
"colA": [1, 2, 6, 2, 1],
|
|
"colB": [10, 20, 60, 20, 10],
|
|
"colVals": ["a", "b", "f", "B", "A"]
|
|
})
|
|
|
|
|
|
@pytest.mark.acero
|
|
def test_table_join_many_columns():
|
|
t1 = pa.table({
|
|
"colA": [1, 2, 6],
|
|
"col2": ["a", "b", "f"]
|
|
})
|
|
|
|
t2 = pa.table({
|
|
"colB": [99, 2, 1],
|
|
"col3": ["Z", "B", "A"],
|
|
"col4": ["Z", "B", "A"],
|
|
"col5": ["Z", "B", "A"],
|
|
"col6": ["Z", "B", "A"],
|
|
"col7": ["Z", "B", "A"]
|
|
})
|
|
|
|
result = t1.join(t2, "colA", "colB")
|
|
assert result.combine_chunks() == pa.table({
|
|
"colA": [1, 2, 6],
|
|
"col2": ["a", "b", "f"],
|
|
"col3": ["A", "B", None],
|
|
"col4": ["A", "B", None],
|
|
"col5": ["A", "B", None],
|
|
"col6": ["A", "B", None],
|
|
"col7": ["A", "B", None]
|
|
})
|
|
|
|
result = t1.join(t2, "colA", "colB", join_type="full outer")
|
|
assert result.combine_chunks().sort_by("colA") == pa.table({
|
|
"colA": [1, 2, 6, 99],
|
|
"col2": ["a", "b", "f", None],
|
|
"col3": ["A", "B", None, "Z"],
|
|
"col4": ["A", "B", None, "Z"],
|
|
"col5": ["A", "B", None, "Z"],
|
|
"col6": ["A", "B", None, "Z"],
|
|
"col7": ["A", "B", None, "Z"],
|
|
})
|
|
|
|
|
|
@pytest.mark.dataset
|
|
def test_table_join_asof():
|
|
t1 = pa.Table.from_pydict({
|
|
"colA": [1, 1, 5, 6, 7],
|
|
"col2": ["a", "b", "a", "b", "f"]
|
|
})
|
|
|
|
t2 = pa.Table.from_pydict({
|
|
"colB": [2, 9, 15],
|
|
"col3": ["a", "b", "g"],
|
|
"colC": [1., 3., 5.]
|
|
})
|
|
|
|
r = t1.join_asof(
|
|
t2, on="colA", by="col2", tolerance=1,
|
|
right_on="colB", right_by="col3",
|
|
)
|
|
assert r.combine_chunks() == pa.table({
|
|
"colA": [1, 1, 5, 6, 7],
|
|
"col2": ["a", "b", "a", "b", "f"],
|
|
"colC": [1., None, None, None, None],
|
|
})
|
|
|
|
|
|
@pytest.mark.dataset
|
|
def test_table_join_asof_multiple_by():
|
|
t1 = pa.table({
|
|
"colA": [1, 2, 6],
|
|
"colB": [10, 20, 60],
|
|
"on": [1, 2, 3],
|
|
})
|
|
|
|
t2 = pa.table({
|
|
"colB": [99, 20, 10],
|
|
"colVals": ["Z", "B", "A"],
|
|
"colA": [99, 2, 1],
|
|
"on": [2, 3, 4],
|
|
})
|
|
|
|
result = t1.join_asof(
|
|
t2, on="on", by=["colA", "colB"], tolerance=1
|
|
)
|
|
assert result.sort_by("colA") == pa.table({
|
|
"colA": [1, 2, 6],
|
|
"colB": [10, 20, 60],
|
|
"on": [1, 2, 3],
|
|
"colVals": [None, "B", None],
|
|
})
|
|
|
|
|
|
@pytest.mark.dataset
|
|
def test_table_join_asof_empty_by():
|
|
t1 = pa.table({
|
|
"on": [1, 2, 3],
|
|
})
|
|
|
|
t2 = pa.table({
|
|
"colVals": ["Z", "B", "A"],
|
|
"on": [2, 3, 4],
|
|
})
|
|
|
|
result = t1.join_asof(
|
|
t2, on="on", by=[], tolerance=1
|
|
)
|
|
assert result == pa.table({
|
|
"on": [1, 2, 3],
|
|
"colVals": ["Z", "Z", "B"],
|
|
})
|
|
|
|
|
|
@pytest.mark.dataset
|
|
def test_table_join_asof_collisions():
|
|
t1 = pa.table({
|
|
"colA": [1, 2, 6],
|
|
"colB": [10, 20, 60],
|
|
"on": [1, 2, 3],
|
|
"colVals": ["a", "b", "f"]
|
|
})
|
|
|
|
t2 = pa.table({
|
|
"colB": [99, 20, 10],
|
|
"colVals": ["Z", "B", "A"],
|
|
"colUniq": [100, 200, 300],
|
|
"colA": [99, 2, 1],
|
|
"on": [2, 3, 4],
|
|
})
|
|
|
|
msg = (
|
|
"Columns {'colVals'} present in both tables. "
|
|
"AsofJoin does not support column collisions."
|
|
)
|
|
with pytest.raises(ValueError, match=msg):
|
|
t1.join_asof(
|
|
t2, on="on", by=["colA", "colB"], tolerance=1,
|
|
right_on="on", right_by=["colA", "colB"],
|
|
)
|
|
|
|
|
|
@pytest.mark.dataset
|
|
def test_table_join_asof_by_length_mismatch():
|
|
t1 = pa.table({
|
|
"colA": [1, 2, 6],
|
|
"colB": [10, 20, 60],
|
|
"on": [1, 2, 3],
|
|
})
|
|
|
|
t2 = pa.table({
|
|
"colVals": ["Z", "B", "A"],
|
|
"colUniq": [100, 200, 300],
|
|
"colA": [99, 2, 1],
|
|
"on": [2, 3, 4],
|
|
})
|
|
|
|
msg = "inconsistent size of by-key across inputs"
|
|
with pytest.raises(pa.lib.ArrowInvalid, match=msg):
|
|
t1.join_asof(
|
|
t2, on="on", by=["colA", "colB"], tolerance=1,
|
|
right_on="on", right_by=["colA"],
|
|
)
|
|
|
|
|
|
@pytest.mark.dataset
|
|
def test_table_join_asof_by_type_mismatch():
|
|
t1 = pa.table({
|
|
"colA": [1, 2, 6],
|
|
"on": [1, 2, 3],
|
|
})
|
|
|
|
t2 = pa.table({
|
|
"colVals": ["Z", "B", "A"],
|
|
"colUniq": [100, 200, 300],
|
|
"colA": [99., 2., 1.],
|
|
"on": [2, 3, 4],
|
|
})
|
|
|
|
msg = "Expected by-key type int64 but got double for field colA in input 1"
|
|
with pytest.raises(pa.lib.ArrowInvalid, match=msg):
|
|
t1.join_asof(
|
|
t2, on="on", by=["colA"], tolerance=1,
|
|
right_on="on", right_by=["colA"],
|
|
)
|
|
|
|
|
|
@pytest.mark.dataset
|
|
def test_table_join_asof_on_type_mismatch():
|
|
t1 = pa.table({
|
|
"colA": [1, 2, 6],
|
|
"on": [1, 2, 3],
|
|
})
|
|
|
|
t2 = pa.table({
|
|
"colVals": ["Z", "B", "A"],
|
|
"colUniq": [100, 200, 300],
|
|
"colA": [99, 2, 1],
|
|
"on": [2., 3., 4.],
|
|
})
|
|
|
|
msg = "Expected on-key type int64 but got double for field on in input 1"
|
|
with pytest.raises(pa.lib.ArrowInvalid, match=msg):
|
|
t1.join_asof(
|
|
t2, on="on", by=["colA"], tolerance=1,
|
|
right_on="on", right_by=["colA"],
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('cls'),
|
|
[
|
|
(pa.Table),
|
|
(pa.RecordBatch)
|
|
]
|
|
)
|
|
def test_table_cast_invalid(cls):
|
|
# Casting a nullable field to non-nullable should be invalid!
|
|
table = cls.from_pydict({'a': [None, 1], 'b': [None, True]})
|
|
new_schema = pa.schema([pa.field("a", "int64", nullable=True),
|
|
pa.field("b", "bool", nullable=False)])
|
|
with pytest.raises(ValueError):
|
|
table.cast(new_schema)
|
|
|
|
table = cls.from_pydict({'a': [None, 1], 'b': [False, True]})
|
|
assert table.cast(new_schema).schema == new_schema
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('cls'),
|
|
[
|
|
(pa.Table),
|
|
(pa.RecordBatch)
|
|
]
|
|
)
|
|
def test_table_sort_by(cls):
|
|
table = cls.from_arrays([
|
|
pa.array([3, 1, 4, 2, 5]),
|
|
pa.array(["b", "a", "b", "a", "c"]),
|
|
], names=["values", "keys"])
|
|
|
|
assert table.sort_by("values").to_pydict() == {
|
|
"keys": ["a", "a", "b", "b", "c"],
|
|
"values": [1, 2, 3, 4, 5]
|
|
}
|
|
|
|
assert table.sort_by([("values", "descending")]).to_pydict() == {
|
|
"keys": ["c", "b", "b", "a", "a"],
|
|
"values": [5, 4, 3, 2, 1]
|
|
}
|
|
|
|
tab = cls.from_arrays([
|
|
pa.array([5, 7, 7, 35], type=pa.int64()),
|
|
pa.array(["foo", "car", "bar", "foobar"])
|
|
], names=["a", "b"])
|
|
|
|
sorted_tab = tab.sort_by([("a", "descending")])
|
|
sorted_tab_dict = sorted_tab.to_pydict()
|
|
assert sorted_tab_dict["a"] == [35, 7, 7, 5]
|
|
assert sorted_tab_dict["b"] == ["foobar", "car", "bar", "foo"]
|
|
|
|
sorted_tab = tab.sort_by([("a", "ascending")])
|
|
sorted_tab_dict = sorted_tab.to_pydict()
|
|
assert sorted_tab_dict["a"] == [5, 7, 7, 35]
|
|
assert sorted_tab_dict["b"] == ["foo", "car", "bar", "foobar"]
|
|
|
|
|
|
@pytest.mark.numpy
|
|
@pytest.mark.parametrize("constructor", [pa.table, pa.record_batch])
|
|
def test_numpy_asarray(constructor):
|
|
table = constructor([[1, 2, 3], [4.0, 5.0, 6.0]], names=["a", "b"])
|
|
result = np.asarray(table)
|
|
expected = np.array([[1, 4], [2, 5], [3, 6]], dtype="float64")
|
|
np.testing.assert_allclose(result, expected)
|
|
|
|
result = np.asarray(table, dtype="int32")
|
|
np.testing.assert_allclose(result, expected)
|
|
assert result.dtype == "int32"
|
|
|
|
# no columns
|
|
table2 = table.select([])
|
|
result = np.asarray(table2)
|
|
expected = np.empty((3, 0))
|
|
np.testing.assert_allclose(result, expected)
|
|
assert result.dtype == "float64"
|
|
result = np.asarray(table2, dtype="int32")
|
|
np.testing.assert_allclose(result, expected)
|
|
assert result.dtype == "int32"
|
|
|
|
# no rows
|
|
table3 = table.slice(0, 0)
|
|
result = np.asarray(table3)
|
|
expected = np.empty((0, 2))
|
|
np.testing.assert_allclose(result, expected)
|
|
assert result.dtype == "float64"
|
|
result = np.asarray(table3, dtype="int32")
|
|
np.testing.assert_allclose(result, expected)
|
|
assert result.dtype == "int32"
|
|
|
|
|
|
@pytest.mark.numpy
|
|
@pytest.mark.parametrize("constructor", [pa.table, pa.record_batch])
|
|
def test_numpy_array_protocol(constructor):
|
|
table = constructor([[1, 2, 3], [4.0, 5.0, 6.0]], names=["a", "b"])
|
|
expected = np.array([[1, 4], [2, 5], [3, 6]], dtype="float64")
|
|
|
|
if Version(np.__version__) < Version("2.0.0.dev0"):
|
|
# copy keyword is not strict and not passed down to __array__
|
|
result = np.array(table, copy=False)
|
|
np.testing.assert_array_equal(result, expected)
|
|
else:
|
|
# starting with numpy 2.0, the copy=False keyword is assumed to be strict
|
|
with pytest.raises(ValueError, match="Unable to avoid a copy"):
|
|
np.array(table, copy=False)
|
|
|
|
|
|
@pytest.mark.acero
|
|
def test_invalid_non_join_column():
|
|
NUM_ITEMS = 30
|
|
t1 = pa.Table.from_pydict({
|
|
'id': range(NUM_ITEMS),
|
|
'array_column': [[z for z in range(3)] for x in range(NUM_ITEMS)],
|
|
})
|
|
t2 = pa.Table.from_pydict({
|
|
'id': range(NUM_ITEMS),
|
|
'value': [x for x in range(NUM_ITEMS)]
|
|
})
|
|
|
|
# check as left table
|
|
with pytest.raises(pa.lib.ArrowInvalid) as excinfo:
|
|
t1.join(t2, 'id', join_type='inner')
|
|
exp_error_msg = "Data type list<item: int64> is not supported " \
|
|
+ "in join non-key field array_column"
|
|
assert exp_error_msg in str(excinfo.value)
|
|
|
|
# check as right table
|
|
with pytest.raises(pa.lib.ArrowInvalid) as excinfo:
|
|
t2.join(t1, 'id', join_type='inner')
|
|
assert exp_error_msg in str(excinfo.value)
|
|
|
|
|
|
@pytest.fixture
|
|
def cuda_context():
|
|
cuda = pytest.importorskip("pyarrow.cuda")
|
|
return cuda.Context(0)
|
|
|
|
|
|
@pytest.fixture
|
|
def schema():
|
|
return pa.schema([pa.field('c0', pa.int32()), pa.field('c1', pa.int32())])
|
|
|
|
|
|
@pytest.fixture
|
|
def cpu_arrays(schema):
|
|
return [pa.array([1, 2, 3, 4, 5], schema.field(0).type),
|
|
pa.array([-10, -5, 0, None, 10], schema.field(1).type)]
|
|
|
|
|
|
@pytest.fixture
|
|
def cuda_arrays(cuda_context, cpu_arrays):
|
|
return [arr.copy_to(cuda_context.memory_manager) for arr in cpu_arrays]
|
|
|
|
|
|
@pytest.fixture
|
|
def cpu_chunked_array(cpu_arrays):
|
|
chunked_array = pa.chunked_array(cpu_arrays)
|
|
assert chunked_array.is_cpu is True
|
|
return chunked_array
|
|
|
|
|
|
@pytest.fixture
|
|
def cuda_chunked_array(cuda_arrays):
|
|
chunked_array = pa.chunked_array(cuda_arrays)
|
|
assert chunked_array.is_cpu is False
|
|
return chunked_array
|
|
|
|
|
|
@pytest.fixture
|
|
def cpu_and_cuda_chunked_array(cpu_arrays, cuda_arrays):
|
|
chunked_array = pa.chunked_array(cpu_arrays + cuda_arrays)
|
|
assert chunked_array.is_cpu is False
|
|
return chunked_array
|
|
|
|
|
|
@pytest.fixture
|
|
def cpu_recordbatch(cpu_arrays, schema):
|
|
return pa.record_batch(cpu_arrays, schema=schema)
|
|
|
|
|
|
@pytest.fixture
|
|
def cuda_recordbatch(cuda_context, cpu_recordbatch):
|
|
return cpu_recordbatch.copy_to(cuda_context.memory_manager)
|
|
|
|
|
|
@pytest.fixture
|
|
def cpu_table(schema, cpu_chunked_array):
|
|
return pa.table([cpu_chunked_array, cpu_chunked_array], schema=schema)
|
|
|
|
|
|
@pytest.fixture
|
|
def cuda_table(schema, cuda_chunked_array):
|
|
return pa.table([cuda_chunked_array, cuda_chunked_array], schema=schema)
|
|
|
|
|
|
@pytest.fixture
|
|
def cpu_and_cuda_table(schema, cpu_chunked_array, cuda_chunked_array):
|
|
return pa.table([cpu_chunked_array, cuda_chunked_array], schema=schema)
|
|
|
|
|
|
def test_chunked_array_non_cpu(cuda_context, cpu_chunked_array, cuda_chunked_array,
|
|
cpu_and_cuda_chunked_array):
|
|
# type test
|
|
assert cuda_chunked_array.type == cpu_chunked_array.type
|
|
|
|
# length() test
|
|
assert cuda_chunked_array.length() == cpu_chunked_array.length()
|
|
|
|
# str() test
|
|
assert str(cuda_chunked_array) == str(cpu_chunked_array)
|
|
|
|
# repr() test
|
|
assert str(cuda_chunked_array) in repr(cuda_chunked_array)
|
|
|
|
# validate() test
|
|
cuda_chunked_array.validate()
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.validate(full=True)
|
|
|
|
# null_count test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.null_count
|
|
|
|
# nbytes() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.nbytes
|
|
|
|
# get_total_buffer_size() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.get_total_buffer_size()
|
|
|
|
# getitem() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array[0]
|
|
|
|
# is_null() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.is_null()
|
|
|
|
# is_nan() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.is_nan()
|
|
|
|
# is_valid() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.is_valid()
|
|
|
|
# fill_null() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.fill_null(0)
|
|
|
|
# equals() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array == cuda_chunked_array
|
|
|
|
# to_pandas() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.to_pandas()
|
|
|
|
# to_numpy() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.to_numpy()
|
|
|
|
# __array__() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.__array__()
|
|
|
|
# cast() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.cast()
|
|
|
|
# dictionary_encode() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.dictionary_encode()
|
|
|
|
# flatten() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.flatten()
|
|
|
|
# combine_chunks() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.combine_chunks()
|
|
|
|
# unique() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.unique()
|
|
|
|
# value_counts() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.value_counts()
|
|
|
|
# filter() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.filter([True, False, True, False, True])
|
|
|
|
# index() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.index(5)
|
|
|
|
# slice() test
|
|
cuda_chunked_array.slice(2, 2)
|
|
|
|
# take() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.take([1])
|
|
|
|
# drop_null() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.drop_null()
|
|
|
|
# sort() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.sort()
|
|
|
|
# unify_dictionaries() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.unify_dictionaries()
|
|
|
|
# num_chunks test
|
|
assert cuda_chunked_array.num_chunks == cpu_chunked_array.num_chunks
|
|
|
|
# chunks test
|
|
assert len(cuda_chunked_array.chunks) == len(cpu_chunked_array.chunks)
|
|
|
|
# chunk() test
|
|
chunk = cuda_chunked_array.chunk(0)
|
|
assert chunk.device_type == pa.DeviceAllocationType.CUDA
|
|
|
|
# to_pylist() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.to_pylist()
|
|
|
|
# __arrow_c_stream__() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.__arrow_c_stream__()
|
|
|
|
# __reduce__() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_chunked_array.__reduce__()
|
|
|
|
|
|
def verify_cuda_recordbatch(batch, expected_schema):
|
|
batch.validate()
|
|
assert batch.device_type == pa.DeviceAllocationType.CUDA
|
|
assert batch.is_cpu is False
|
|
assert batch.num_columns == len(expected_schema.names)
|
|
assert batch.column_names == expected_schema.names
|
|
assert str(batch) in repr(batch)
|
|
for c in batch.columns:
|
|
assert c.device_type == pa.DeviceAllocationType.CUDA
|
|
assert batch.schema == expected_schema
|
|
|
|
|
|
def test_recordbatch_non_cpu(cuda_context, cpu_recordbatch, cuda_recordbatch,
|
|
cuda_arrays, schema):
|
|
verify_cuda_recordbatch(cuda_recordbatch, expected_schema=schema)
|
|
N = cuda_recordbatch.num_rows
|
|
|
|
# shape test
|
|
assert cuda_recordbatch.shape == (5, 2)
|
|
|
|
# columns() test
|
|
assert len(cuda_recordbatch.columns) == 2
|
|
|
|
# add_column(), set_column() test
|
|
for fn in [cuda_recordbatch.add_column, cuda_recordbatch.set_column]:
|
|
col = pa.array([-2, -1, 0, 1, 2], pa.int8()
|
|
).copy_to(cuda_context.memory_manager)
|
|
new_batch = fn(2, 'c2', col)
|
|
verify_cuda_recordbatch(
|
|
new_batch, expected_schema=schema.append(pa.field('c2', pa.int8())))
|
|
err_msg = ("Got column on device <DeviceAllocationType.CPU: 1>, "
|
|
"but expected <DeviceAllocationType.CUDA: 2>.")
|
|
with pytest.raises(TypeError, match=err_msg):
|
|
fn(2, 'c2', [1] * N)
|
|
|
|
# remove_column() test
|
|
new_batch = cuda_recordbatch.remove_column(1)
|
|
verify_cuda_recordbatch(new_batch, expected_schema=schema.remove(1))
|
|
|
|
# drop_columns() test
|
|
new_batch = cuda_recordbatch.drop_columns(['c1'])
|
|
verify_cuda_recordbatch(new_batch, expected_schema=schema.remove(1))
|
|
empty_batch = cuda_recordbatch.drop_columns(['c0', 'c1'])
|
|
assert len(empty_batch.columns) == 0
|
|
assert empty_batch.device_type == pa.DeviceAllocationType.CUDA
|
|
|
|
# select() test
|
|
new_batch = cuda_recordbatch.select(['c0'])
|
|
verify_cuda_recordbatch(new_batch, expected_schema=schema.remove(1))
|
|
|
|
# cast() test
|
|
new_schema = pa.schema([pa.field('c0', pa.int64()), pa.field('c1', pa.int64())])
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_recordbatch.cast(new_schema)
|
|
|
|
# drop_null() test
|
|
null_col = pa.array([1] * N, mask=[True, False, True, False, True]).copy_to(
|
|
cuda_context.memory_manager)
|
|
cuda_recordbatch_with_nulls = cuda_recordbatch.add_column(2, 'c2', null_col)
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_recordbatch_with_nulls.drop_null()
|
|
|
|
# filter() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_recordbatch.filter([True] * N)
|
|
|
|
# take() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_recordbatch.take([0])
|
|
|
|
# sort_by() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_recordbatch.sort_by('c0')
|
|
|
|
# field() test
|
|
assert cuda_recordbatch.field(0) == schema.field(0)
|
|
assert cuda_recordbatch.field(1) == schema.field(1)
|
|
|
|
# equals() test
|
|
new_batch = cpu_recordbatch.copy_to(cuda_context.memory_manager)
|
|
with pytest.raises(NotImplementedError):
|
|
assert cuda_recordbatch.equals(new_batch) is True
|
|
|
|
# from_arrays() test
|
|
new_batch = pa.RecordBatch.from_arrays(cuda_arrays, ['c0', 'c1'])
|
|
verify_cuda_recordbatch(new_batch, expected_schema=schema)
|
|
assert new_batch.copy_to(pa.default_cpu_memory_manager()).equals(cpu_recordbatch)
|
|
|
|
# from_pydict() test
|
|
new_batch = pa.RecordBatch.from_pydict({'c0': cuda_arrays[0], 'c1': cuda_arrays[1]})
|
|
verify_cuda_recordbatch(new_batch, expected_schema=schema)
|
|
assert new_batch.copy_to(pa.default_cpu_memory_manager()).equals(cpu_recordbatch)
|
|
|
|
# from_struct_array() test
|
|
fields = [schema.field(i) for i in range(len(schema.names))]
|
|
struct_array = pa.StructArray.from_arrays(cuda_arrays, fields=fields)
|
|
with pytest.raises(NotImplementedError):
|
|
pa.RecordBatch.from_struct_array(struct_array)
|
|
|
|
# nbytes test
|
|
with pytest.raises(NotImplementedError):
|
|
assert cuda_recordbatch.nbytes
|
|
|
|
# get_total_buffer_size() test
|
|
with pytest.raises(NotImplementedError):
|
|
assert cuda_recordbatch.get_total_buffer_size()
|
|
|
|
# to_pydict() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_recordbatch.to_pydict()
|
|
|
|
# to_pylist() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_recordbatch.to_pylist()
|
|
|
|
# to_pandas() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_recordbatch.to_pandas()
|
|
|
|
# to_tensor() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_recordbatch.to_tensor()
|
|
|
|
# to_struct_array() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_recordbatch.to_struct_array()
|
|
|
|
# serialize() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_recordbatch.serialize()
|
|
|
|
# slice() test
|
|
new_batch = cuda_recordbatch.slice(1, 3)
|
|
verify_cuda_recordbatch(new_batch, expected_schema=schema)
|
|
assert new_batch.num_rows == 3
|
|
cpu_batch = new_batch.copy_to(pa.default_cpu_memory_manager())
|
|
assert cpu_batch == cpu_recordbatch.slice(1, 3)
|
|
|
|
# replace_schema_metadata() test
|
|
new_batch = cuda_recordbatch.replace_schema_metadata({b'key': b'value'})
|
|
verify_cuda_recordbatch(new_batch, expected_schema=schema)
|
|
assert new_batch.schema.metadata == {b'key': b'value'}
|
|
|
|
# rename_columns() test
|
|
new_batch = cuda_recordbatch.rename_columns(['col0', 'col1'])
|
|
expected_schema = pa.schema(
|
|
[pa.field('col0', schema.field(0).type),
|
|
pa.field('col1', schema.field(1).type)])
|
|
verify_cuda_recordbatch(new_batch, expected_schema=expected_schema)
|
|
|
|
# validate() test
|
|
cuda_recordbatch.validate()
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_recordbatch.validate(full=True)
|
|
|
|
# __array__() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_recordbatch.__array__()
|
|
|
|
# __arrow_c_array__() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_recordbatch.__arrow_c_array__()
|
|
|
|
# __arrow_c_stream__() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_recordbatch.__arrow_c_stream__()
|
|
|
|
# __dataframe__() test
|
|
with pytest.raises(NotImplementedError):
|
|
from_dataframe(cuda_recordbatch.__dataframe__())
|
|
|
|
|
|
def verify_cuda_table(table, expected_schema):
|
|
table.validate()
|
|
assert table.is_cpu is False
|
|
assert table.num_columns == len(expected_schema.names)
|
|
assert table.column_names == expected_schema.names
|
|
assert str(table) in repr(table)
|
|
for c in table.columns:
|
|
assert c.is_cpu is False
|
|
for chunk in c.iterchunks():
|
|
assert chunk.is_cpu is False
|
|
assert chunk.device_type == pa.DeviceAllocationType.CUDA
|
|
assert table.schema == expected_schema
|
|
|
|
|
|
def test_table_non_cpu(cuda_context, cpu_table, cuda_table,
|
|
cuda_arrays, cuda_recordbatch, schema):
|
|
verify_cuda_table(cuda_table, expected_schema=schema)
|
|
N = cuda_table.num_rows
|
|
|
|
# shape test
|
|
assert cuda_table.shape == (10, 2)
|
|
|
|
# columns() test
|
|
assert len(cuda_table.columns) == 2
|
|
|
|
# add_column(), set_column() test
|
|
for fn in [cuda_table.add_column, cuda_table.set_column]:
|
|
cpu_col = pa.array([1] * N, pa.int8())
|
|
cuda_col = cpu_col.copy_to(cuda_context.memory_manager)
|
|
new_table = fn(2, 'c2', cuda_col)
|
|
verify_cuda_table(new_table, expected_schema=schema.append(
|
|
pa.field('c2', pa.int8())))
|
|
new_table = fn(2, 'c2', cpu_col)
|
|
assert new_table.is_cpu is False
|
|
assert new_table.column(0).is_cpu is False
|
|
assert new_table.column(1).is_cpu is False
|
|
assert new_table.column(2).is_cpu is True
|
|
|
|
# remove_column() test
|
|
new_table = cuda_table.remove_column(1)
|
|
verify_cuda_table(new_table, expected_schema=schema.remove(1))
|
|
|
|
# drop_columns() test
|
|
new_table = cuda_table.drop_columns(['c1'])
|
|
verify_cuda_table(new_table, expected_schema=schema.remove(1))
|
|
new_table = cuda_table.drop_columns(['c0', 'c1'])
|
|
assert len(new_table.columns) == 0
|
|
assert new_table.is_cpu
|
|
|
|
# select() test
|
|
new_table = cuda_table.select(['c0'])
|
|
verify_cuda_table(new_table, expected_schema=schema.remove(1))
|
|
|
|
# cast() test
|
|
new_schema = pa.schema([pa.field('c0', pa.int64()), pa.field('c1', pa.int64())])
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.cast(new_schema)
|
|
|
|
# drop_null() test
|
|
null_col = pa.array([1] * N, mask=[True] * N).copy_to(cuda_context.memory_manager)
|
|
cuda_table_with_nulls = cuda_table.add_column(2, 'c2', null_col)
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table_with_nulls.drop_null()
|
|
|
|
# filter() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.filter([True] * N)
|
|
|
|
# take() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.take([0])
|
|
|
|
# sort_by() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.sort_by('c0')
|
|
|
|
# field() test
|
|
assert cuda_table.field(0) == schema.field(0)
|
|
assert cuda_table.field(1) == schema.field(1)
|
|
|
|
# equals() test
|
|
with pytest.raises(NotImplementedError):
|
|
assert cuda_table.equals(cpu_table)
|
|
|
|
# from_arrays() test
|
|
new_table = pa.Table.from_arrays(cuda_arrays, ['c0', 'c1'])
|
|
verify_cuda_table(new_table, expected_schema=schema)
|
|
|
|
# from_pydict() test
|
|
new_table = pa.Table.from_pydict({'c0': cuda_arrays[0], 'c1': cuda_arrays[1]})
|
|
verify_cuda_table(new_table, expected_schema=schema)
|
|
|
|
# from_struct_array() test
|
|
fields = [schema.field(i) for i in range(len(schema.names))]
|
|
struct_array = pa.StructArray.from_arrays(cuda_arrays, fields=fields)
|
|
with pytest.raises(NotImplementedError):
|
|
pa.Table.from_struct_array(struct_array)
|
|
|
|
# from_batches() test
|
|
new_table = pa.Table.from_batches([cuda_recordbatch, cuda_recordbatch], schema)
|
|
verify_cuda_table(new_table, expected_schema=schema)
|
|
|
|
# nbytes test
|
|
with pytest.raises(NotImplementedError):
|
|
assert cuda_table.nbytes
|
|
|
|
# get_total_buffer_size() test
|
|
with pytest.raises(NotImplementedError):
|
|
assert cuda_table.get_total_buffer_size()
|
|
|
|
# to_pydict() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.to_pydict()
|
|
|
|
# to_pylist() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.to_pylist()
|
|
|
|
# to_pandas() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.to_pandas()
|
|
|
|
# to_struct_array() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.to_struct_array()
|
|
|
|
# to_batches() test
|
|
batches = cuda_table.to_batches(max_chunksize=5)
|
|
for batch in batches:
|
|
# GH-44049
|
|
with pytest.raises(AssertionError):
|
|
verify_cuda_recordbatch(batch, expected_schema=schema)
|
|
|
|
# to_reader() test
|
|
reader = cuda_table.to_reader(max_chunksize=5)
|
|
for batch in reader:
|
|
# GH-44049
|
|
with pytest.raises(AssertionError):
|
|
verify_cuda_recordbatch(batch, expected_schema=schema)
|
|
|
|
# slice() test
|
|
new_table = cuda_table.slice(1, 3)
|
|
verify_cuda_table(new_table, expected_schema=schema)
|
|
assert new_table.num_rows == 3
|
|
|
|
# replace_schema_metadata() test
|
|
new_table = cuda_table.replace_schema_metadata({b'key': b'value'})
|
|
verify_cuda_table(new_table, expected_schema=schema)
|
|
assert new_table.schema.metadata == {b'key': b'value'}
|
|
|
|
# rename_columns() test
|
|
new_table = cuda_table.rename_columns(['col0', 'col1'])
|
|
expected_schema = pa.schema(
|
|
[pa.field('col0', schema.field(0).type),
|
|
pa.field('col1', schema.field(1).type)])
|
|
verify_cuda_table(new_table, expected_schema=expected_schema)
|
|
|
|
# validate() test
|
|
cuda_table.validate()
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.validate(full=True)
|
|
|
|
# flatten() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.flatten()
|
|
|
|
# combine_chunks() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.flatten()
|
|
|
|
# unify_dictionaries() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.unify_dictionaries()
|
|
|
|
# group_by() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.group_by('c0')
|
|
|
|
# join() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.join(cuda_table, 'c0')
|
|
|
|
# join_asof() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.join_asof(cuda_table, 'c0', 'c0', 0)
|
|
|
|
# __array__() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.__array__()
|
|
|
|
# __arrow_c_stream__() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.__arrow_c_stream__()
|
|
|
|
# __dataframe__() test
|
|
with pytest.raises(NotImplementedError):
|
|
from_dataframe(cuda_table.__dataframe__())
|
|
|
|
# __reduce__() test
|
|
with pytest.raises(NotImplementedError):
|
|
cuda_table.__reduce__()
|