335 行
12 KiB
Python
335 行
12 KiB
Python
from datetime import datetime
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import re
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import numpy as np
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import pytest
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from pandas._libs import iNaT
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import pandas._testing as tm
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import pandas.core.algorithms as algos
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@pytest.fixture(params=[True, False])
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def writeable(request):
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return request.param
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# Check that take_nd works both with writeable arrays
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# (in which case fast typed memory-views implementation)
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# and read-only arrays alike.
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@pytest.fixture(
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params=[
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(np.float64, True),
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(np.float32, True),
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(np.uint64, False),
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(np.uint32, False),
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(np.uint16, False),
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(np.uint8, False),
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(np.int64, False),
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(np.int32, False),
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(np.int16, False),
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(np.int8, False),
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(np.object_, True),
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(np.bool_, False),
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]
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)
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def dtype_can_hold_na(request):
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return request.param
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@pytest.fixture(
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params=[
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(np.int8, np.int16(127), np.int8),
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(np.int8, np.int16(128), np.int16),
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(np.int32, 1, np.int32),
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(np.int32, 2.0, np.float64),
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(np.int32, 3.0 + 4.0j, np.complex128),
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(np.int32, True, np.object_),
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(np.int32, "", np.object_),
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(np.float64, 1, np.float64),
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(np.float64, 2.0, np.float64),
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(np.float64, 3.0 + 4.0j, np.complex128),
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(np.float64, True, np.object_),
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(np.float64, "", np.object_),
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(np.complex128, 1, np.complex128),
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(np.complex128, 2.0, np.complex128),
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(np.complex128, 3.0 + 4.0j, np.complex128),
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(np.complex128, True, np.object_),
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(np.complex128, "", np.object_),
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(np.bool_, 1, np.object_),
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(np.bool_, 2.0, np.object_),
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(np.bool_, 3.0 + 4.0j, np.object_),
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(np.bool_, True, np.bool_),
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(np.bool_, "", np.object_),
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]
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)
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def dtype_fill_out_dtype(request):
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return request.param
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class TestTake:
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# Standard incompatible fill error.
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fill_error = re.compile("Incompatible type for fill_value")
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def test_1d_fill_nonna(self, dtype_fill_out_dtype):
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dtype, fill_value, out_dtype = dtype_fill_out_dtype
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data = np.random.randint(0, 2, 4).astype(dtype)
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indexer = [2, 1, 0, -1]
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result = algos.take_nd(data, indexer, fill_value=fill_value)
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assert (result[[0, 1, 2]] == data[[2, 1, 0]]).all()
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assert result[3] == fill_value
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assert result.dtype == out_dtype
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indexer = [2, 1, 0, 1]
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result = algos.take_nd(data, indexer, fill_value=fill_value)
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assert (result[[0, 1, 2, 3]] == data[indexer]).all()
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assert result.dtype == dtype
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def test_2d_fill_nonna(self, dtype_fill_out_dtype):
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dtype, fill_value, out_dtype = dtype_fill_out_dtype
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data = np.random.randint(0, 2, (5, 3)).astype(dtype)
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indexer = [2, 1, 0, -1]
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result = algos.take_nd(data, indexer, axis=0, fill_value=fill_value)
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assert (result[[0, 1, 2], :] == data[[2, 1, 0], :]).all()
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assert (result[3, :] == fill_value).all()
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assert result.dtype == out_dtype
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result = algos.take_nd(data, indexer, axis=1, fill_value=fill_value)
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assert (result[:, [0, 1, 2]] == data[:, [2, 1, 0]]).all()
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assert (result[:, 3] == fill_value).all()
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assert result.dtype == out_dtype
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indexer = [2, 1, 0, 1]
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result = algos.take_nd(data, indexer, axis=0, fill_value=fill_value)
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assert (result[[0, 1, 2, 3], :] == data[indexer, :]).all()
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assert result.dtype == dtype
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result = algos.take_nd(data, indexer, axis=1, fill_value=fill_value)
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assert (result[:, [0, 1, 2, 3]] == data[:, indexer]).all()
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assert result.dtype == dtype
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def test_3d_fill_nonna(self, dtype_fill_out_dtype):
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dtype, fill_value, out_dtype = dtype_fill_out_dtype
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data = np.random.randint(0, 2, (5, 4, 3)).astype(dtype)
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indexer = [2, 1, 0, -1]
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result = algos.take_nd(data, indexer, axis=0, fill_value=fill_value)
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assert (result[[0, 1, 2], :, :] == data[[2, 1, 0], :, :]).all()
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assert (result[3, :, :] == fill_value).all()
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assert result.dtype == out_dtype
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result = algos.take_nd(data, indexer, axis=1, fill_value=fill_value)
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assert (result[:, [0, 1, 2], :] == data[:, [2, 1, 0], :]).all()
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assert (result[:, 3, :] == fill_value).all()
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assert result.dtype == out_dtype
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result = algos.take_nd(data, indexer, axis=2, fill_value=fill_value)
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assert (result[:, :, [0, 1, 2]] == data[:, :, [2, 1, 0]]).all()
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assert (result[:, :, 3] == fill_value).all()
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assert result.dtype == out_dtype
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indexer = [2, 1, 0, 1]
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result = algos.take_nd(data, indexer, axis=0, fill_value=fill_value)
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assert (result[[0, 1, 2, 3], :, :] == data[indexer, :, :]).all()
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assert result.dtype == dtype
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result = algos.take_nd(data, indexer, axis=1, fill_value=fill_value)
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assert (result[:, [0, 1, 2, 3], :] == data[:, indexer, :]).all()
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assert result.dtype == dtype
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result = algos.take_nd(data, indexer, axis=2, fill_value=fill_value)
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assert (result[:, :, [0, 1, 2, 3]] == data[:, :, indexer]).all()
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assert result.dtype == dtype
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def test_1d_other_dtypes(self):
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arr = np.random.randn(10).astype(np.float32)
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indexer = [1, 2, 3, -1]
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result = algos.take_nd(arr, indexer)
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expected = arr.take(indexer)
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expected[-1] = np.nan
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tm.assert_almost_equal(result, expected)
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def test_2d_other_dtypes(self):
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arr = np.random.randn(10, 5).astype(np.float32)
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indexer = [1, 2, 3, -1]
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# axis=0
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result = algos.take_nd(arr, indexer, axis=0)
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expected = arr.take(indexer, axis=0)
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expected[-1] = np.nan
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tm.assert_almost_equal(result, expected)
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# axis=1
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result = algos.take_nd(arr, indexer, axis=1)
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expected = arr.take(indexer, axis=1)
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expected[:, -1] = np.nan
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tm.assert_almost_equal(result, expected)
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def test_1d_bool(self):
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arr = np.array([0, 1, 0], dtype=bool)
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result = algos.take_nd(arr, [0, 2, 2, 1])
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expected = arr.take([0, 2, 2, 1])
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tm.assert_numpy_array_equal(result, expected)
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result = algos.take_nd(arr, [0, 2, -1])
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assert result.dtype == np.object_
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def test_2d_bool(self):
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arr = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 1]], dtype=bool)
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result = algos.take_nd(arr, [0, 2, 2, 1])
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expected = arr.take([0, 2, 2, 1], axis=0)
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tm.assert_numpy_array_equal(result, expected)
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result = algos.take_nd(arr, [0, 2, 2, 1], axis=1)
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expected = arr.take([0, 2, 2, 1], axis=1)
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tm.assert_numpy_array_equal(result, expected)
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result = algos.take_nd(arr, [0, 2, -1])
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assert result.dtype == np.object_
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def test_2d_float32(self):
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arr = np.random.randn(4, 3).astype(np.float32)
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indexer = [0, 2, -1, 1, -1]
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# axis=0
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result = algos.take_nd(arr, indexer, axis=0)
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expected = arr.take(indexer, axis=0)
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expected[[2, 4], :] = np.nan
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tm.assert_almost_equal(result, expected)
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# axis=1
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result = algos.take_nd(arr, indexer, axis=1)
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expected = arr.take(indexer, axis=1)
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expected[:, [2, 4]] = np.nan
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tm.assert_almost_equal(result, expected)
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def test_2d_datetime64(self):
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# 2005/01/01 - 2006/01/01
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arr = np.random.randint(11_045_376, 11_360_736, (5, 3)) * 100_000_000_000
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arr = arr.view(dtype="datetime64[ns]")
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indexer = [0, 2, -1, 1, -1]
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# axis=0
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result = algos.take_nd(arr, indexer, axis=0)
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expected = arr.take(indexer, axis=0)
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expected.view(np.int64)[[2, 4], :] = iNaT
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tm.assert_almost_equal(result, expected)
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result = algos.take_nd(arr, indexer, axis=0, fill_value=datetime(2007, 1, 1))
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expected = arr.take(indexer, axis=0)
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expected[[2, 4], :] = datetime(2007, 1, 1)
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tm.assert_almost_equal(result, expected)
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# axis=1
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result = algos.take_nd(arr, indexer, axis=1)
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expected = arr.take(indexer, axis=1)
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expected.view(np.int64)[:, [2, 4]] = iNaT
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tm.assert_almost_equal(result, expected)
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result = algos.take_nd(arr, indexer, axis=1, fill_value=datetime(2007, 1, 1))
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expected = arr.take(indexer, axis=1)
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expected[:, [2, 4]] = datetime(2007, 1, 1)
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tm.assert_almost_equal(result, expected)
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def test_take_axis_0(self):
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arr = np.arange(12).reshape(4, 3)
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result = algos.take(arr, [0, -1])
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expected = np.array([[0, 1, 2], [9, 10, 11]])
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tm.assert_numpy_array_equal(result, expected)
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# allow_fill=True
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result = algos.take(arr, [0, -1], allow_fill=True, fill_value=0)
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expected = np.array([[0, 1, 2], [0, 0, 0]])
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tm.assert_numpy_array_equal(result, expected)
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def test_take_axis_1(self):
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arr = np.arange(12).reshape(4, 3)
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result = algos.take(arr, [0, -1], axis=1)
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expected = np.array([[0, 2], [3, 5], [6, 8], [9, 11]])
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tm.assert_numpy_array_equal(result, expected)
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# allow_fill=True
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result = algos.take(arr, [0, -1], axis=1, allow_fill=True, fill_value=0)
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expected = np.array([[0, 0], [3, 0], [6, 0], [9, 0]])
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tm.assert_numpy_array_equal(result, expected)
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# GH#26976 make sure we validate along the correct axis
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with pytest.raises(IndexError, match="indices are out-of-bounds"):
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algos.take(arr, [0, 3], axis=1, allow_fill=True, fill_value=0)
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def test_take_non_hashable_fill_value(self):
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arr = np.array([1, 2, 3])
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indexer = np.array([1, -1])
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with pytest.raises(ValueError, match="fill_value must be a scalar"):
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algos.take(arr, indexer, allow_fill=True, fill_value=[1])
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# with object dtype it is allowed
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arr = np.array([1, 2, 3], dtype=object)
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result = algos.take(arr, indexer, allow_fill=True, fill_value=[1])
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expected = np.array([2, [1]], dtype=object)
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tm.assert_numpy_array_equal(result, expected)
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class TestExtensionTake:
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# The take method found in pd.api.extensions
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def test_bounds_check_large(self):
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arr = np.array([1, 2])
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msg = "indices are out-of-bounds"
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with pytest.raises(IndexError, match=msg):
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algos.take(arr, [2, 3], allow_fill=True)
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msg = "index 2 is out of bounds for( axis 0 with)? size 2"
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with pytest.raises(IndexError, match=msg):
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algos.take(arr, [2, 3], allow_fill=False)
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def test_bounds_check_small(self):
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arr = np.array([1, 2, 3], dtype=np.int64)
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indexer = [0, -1, -2]
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msg = r"'indices' contains values less than allowed \(-2 < -1\)"
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with pytest.raises(ValueError, match=msg):
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algos.take(arr, indexer, allow_fill=True)
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result = algos.take(arr, indexer)
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expected = np.array([1, 3, 2], dtype=np.int64)
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tm.assert_numpy_array_equal(result, expected)
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@pytest.mark.parametrize("allow_fill", [True, False])
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def test_take_empty(self, allow_fill):
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arr = np.array([], dtype=np.int64)
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# empty take is ok
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result = algos.take(arr, [], allow_fill=allow_fill)
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tm.assert_numpy_array_equal(arr, result)
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msg = "|".join(
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[
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"cannot do a non-empty take from an empty axes.",
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"indices are out-of-bounds",
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]
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)
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with pytest.raises(IndexError, match=msg):
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algos.take(arr, [0], allow_fill=allow_fill)
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def test_take_na_empty(self):
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result = algos.take(np.array([]), [-1, -1], allow_fill=True, fill_value=0.0)
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expected = np.array([0.0, 0.0])
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tm.assert_numpy_array_equal(result, expected)
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def test_take_coerces_list(self):
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arr = [1, 2, 3]
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result = algos.take(arr, [0, 0])
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expected = np.array([1, 1])
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tm.assert_numpy_array_equal(result, expected)
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