/pandas/tests/test_take.py
Python | 468 lines | 455 code | 9 blank | 4 comment | 1 complexity | 444eb52978bb9990f8b07e6dc38d827f MD5 | raw file
Possible License(s): BSD-3-Clause, Apache-2.0
- # -*- coding: utf-8 -*-
- from datetime import datetime
- import re
- import numpy as np
- import pytest
- from pandas._libs.tslib import iNaT
- from pandas.compat import long
- import pandas.core.algorithms as algos
- import pandas.util.testing as tm
- @pytest.fixture(params=[True, False])
- def writeable(request):
- return request.param
- # Check that take_nd works both with writeable arrays
- # (in which case fast typed memory-views implementation)
- # and read-only arrays alike.
- @pytest.fixture(params=[
- (np.float64, True),
- (np.float32, True),
- (np.uint64, False),
- (np.uint32, False),
- (np.uint16, False),
- (np.uint8, False),
- (np.int64, False),
- (np.int32, False),
- (np.int16, False),
- (np.int8, False),
- (np.object_, True),
- (np.bool, False),
- ])
- def dtype_can_hold_na(request):
- return request.param
- @pytest.fixture(params=[
- (np.int8, np.int16(127), np.int8),
- (np.int8, np.int16(128), np.int16),
- (np.int32, 1, np.int32),
- (np.int32, 2.0, np.float64),
- (np.int32, 3.0 + 4.0j, np.complex128),
- (np.int32, True, np.object_),
- (np.int32, "", np.object_),
- (np.float64, 1, np.float64),
- (np.float64, 2.0, np.float64),
- (np.float64, 3.0 + 4.0j, np.complex128),
- (np.float64, True, np.object_),
- (np.float64, "", np.object_),
- (np.complex128, 1, np.complex128),
- (np.complex128, 2.0, np.complex128),
- (np.complex128, 3.0 + 4.0j, np.complex128),
- (np.complex128, True, np.object_),
- (np.complex128, "", np.object_),
- (np.bool_, 1, np.object_),
- (np.bool_, 2.0, np.object_),
- (np.bool_, 3.0 + 4.0j, np.object_),
- (np.bool_, True, np.bool_),
- (np.bool_, '', np.object_),
- ])
- def dtype_fill_out_dtype(request):
- return request.param
- class TestTake(object):
- # Standard incompatible fill error.
- fill_error = re.compile("Incompatible type for fill_value")
- def test_1d_with_out(self, dtype_can_hold_na, writeable):
- dtype, can_hold_na = dtype_can_hold_na
- data = np.random.randint(0, 2, 4).astype(dtype)
- data.flags.writeable = writeable
- indexer = [2, 1, 0, 1]
- out = np.empty(4, dtype=dtype)
- algos.take_1d(data, indexer, out=out)
- expected = data.take(indexer)
- tm.assert_almost_equal(out, expected)
- indexer = [2, 1, 0, -1]
- out = np.empty(4, dtype=dtype)
- if can_hold_na:
- algos.take_1d(data, indexer, out=out)
- expected = data.take(indexer)
- expected[3] = np.nan
- tm.assert_almost_equal(out, expected)
- else:
- with pytest.raises(TypeError, match=self.fill_error):
- algos.take_1d(data, indexer, out=out)
- # No Exception otherwise.
- data.take(indexer, out=out)
- def test_1d_fill_nonna(self, dtype_fill_out_dtype):
- dtype, fill_value, out_dtype = dtype_fill_out_dtype
- data = np.random.randint(0, 2, 4).astype(dtype)
- indexer = [2, 1, 0, -1]
- result = algos.take_1d(data, indexer, fill_value=fill_value)
- assert ((result[[0, 1, 2]] == data[[2, 1, 0]]).all())
- assert (result[3] == fill_value)
- assert (result.dtype == out_dtype)
- indexer = [2, 1, 0, 1]
- result = algos.take_1d(data, indexer, fill_value=fill_value)
- assert ((result[[0, 1, 2, 3]] == data[indexer]).all())
- assert (result.dtype == dtype)
- def test_2d_with_out(self, dtype_can_hold_na, writeable):
- dtype, can_hold_na = dtype_can_hold_na
- data = np.random.randint(0, 2, (5, 3)).astype(dtype)
- data.flags.writeable = writeable
- indexer = [2, 1, 0, 1]
- out0 = np.empty((4, 3), dtype=dtype)
- out1 = np.empty((5, 4), dtype=dtype)
- algos.take_nd(data, indexer, out=out0, axis=0)
- algos.take_nd(data, indexer, out=out1, axis=1)
- expected0 = data.take(indexer, axis=0)
- expected1 = data.take(indexer, axis=1)
- tm.assert_almost_equal(out0, expected0)
- tm.assert_almost_equal(out1, expected1)
- indexer = [2, 1, 0, -1]
- out0 = np.empty((4, 3), dtype=dtype)
- out1 = np.empty((5, 4), dtype=dtype)
- if can_hold_na:
- algos.take_nd(data, indexer, out=out0, axis=0)
- algos.take_nd(data, indexer, out=out1, axis=1)
- expected0 = data.take(indexer, axis=0)
- expected1 = data.take(indexer, axis=1)
- expected0[3, :] = np.nan
- expected1[:, 3] = np.nan
- tm.assert_almost_equal(out0, expected0)
- tm.assert_almost_equal(out1, expected1)
- else:
- for i, out in enumerate([out0, out1]):
- with pytest.raises(TypeError, match=self.fill_error):
- algos.take_nd(data, indexer, out=out, axis=i)
- # No Exception otherwise.
- data.take(indexer, out=out, axis=i)
- def test_2d_fill_nonna(self, dtype_fill_out_dtype):
- dtype, fill_value, out_dtype = dtype_fill_out_dtype
- data = np.random.randint(0, 2, (5, 3)).astype(dtype)
- indexer = [2, 1, 0, -1]
- result = algos.take_nd(data, indexer, axis=0,
- fill_value=fill_value)
- assert ((result[[0, 1, 2], :] == data[[2, 1, 0], :]).all())
- assert ((result[3, :] == fill_value).all())
- assert (result.dtype == out_dtype)
- result = algos.take_nd(data, indexer, axis=1,
- fill_value=fill_value)
- assert ((result[:, [0, 1, 2]] == data[:, [2, 1, 0]]).all())
- assert ((result[:, 3] == fill_value).all())
- assert (result.dtype == out_dtype)
- indexer = [2, 1, 0, 1]
- result = algos.take_nd(data, indexer, axis=0,
- fill_value=fill_value)
- assert ((result[[0, 1, 2, 3], :] == data[indexer, :]).all())
- assert (result.dtype == dtype)
- result = algos.take_nd(data, indexer, axis=1,
- fill_value=fill_value)
- assert ((result[:, [0, 1, 2, 3]] == data[:, indexer]).all())
- assert (result.dtype == dtype)
- def test_3d_with_out(self, dtype_can_hold_na):
- dtype, can_hold_na = dtype_can_hold_na
- data = np.random.randint(0, 2, (5, 4, 3)).astype(dtype)
- indexer = [2, 1, 0, 1]
- out0 = np.empty((4, 4, 3), dtype=dtype)
- out1 = np.empty((5, 4, 3), dtype=dtype)
- out2 = np.empty((5, 4, 4), dtype=dtype)
- algos.take_nd(data, indexer, out=out0, axis=0)
- algos.take_nd(data, indexer, out=out1, axis=1)
- algos.take_nd(data, indexer, out=out2, axis=2)
- expected0 = data.take(indexer, axis=0)
- expected1 = data.take(indexer, axis=1)
- expected2 = data.take(indexer, axis=2)
- tm.assert_almost_equal(out0, expected0)
- tm.assert_almost_equal(out1, expected1)
- tm.assert_almost_equal(out2, expected2)
- indexer = [2, 1, 0, -1]
- out0 = np.empty((4, 4, 3), dtype=dtype)
- out1 = np.empty((5, 4, 3), dtype=dtype)
- out2 = np.empty((5, 4, 4), dtype=dtype)
- if can_hold_na:
- algos.take_nd(data, indexer, out=out0, axis=0)
- algos.take_nd(data, indexer, out=out1, axis=1)
- algos.take_nd(data, indexer, out=out2, axis=2)
- expected0 = data.take(indexer, axis=0)
- expected1 = data.take(indexer, axis=1)
- expected2 = data.take(indexer, axis=2)
- expected0[3, :, :] = np.nan
- expected1[:, 3, :] = np.nan
- expected2[:, :, 3] = np.nan
- tm.assert_almost_equal(out0, expected0)
- tm.assert_almost_equal(out1, expected1)
- tm.assert_almost_equal(out2, expected2)
- else:
- for i, out in enumerate([out0, out1, out2]):
- with pytest.raises(TypeError, match=self.fill_error):
- algos.take_nd(data, indexer, out=out, axis=i)
- # No Exception otherwise.
- data.take(indexer, out=out, axis=i)
- def test_3d_fill_nonna(self, dtype_fill_out_dtype):
- dtype, fill_value, out_dtype = dtype_fill_out_dtype
- data = np.random.randint(0, 2, (5, 4, 3)).astype(dtype)
- indexer = [2, 1, 0, -1]
- result = algos.take_nd(data, indexer, axis=0,
- fill_value=fill_value)
- assert ((result[[0, 1, 2], :, :] == data[[2, 1, 0], :, :]).all())
- assert ((result[3, :, :] == fill_value).all())
- assert (result.dtype == out_dtype)
- result = algos.take_nd(data, indexer, axis=1,
- fill_value=fill_value)
- assert ((result[:, [0, 1, 2], :] == data[:, [2, 1, 0], :]).all())
- assert ((result[:, 3, :] == fill_value).all())
- assert (result.dtype == out_dtype)
- result = algos.take_nd(data, indexer, axis=2,
- fill_value=fill_value)
- assert ((result[:, :, [0, 1, 2]] == data[:, :, [2, 1, 0]]).all())
- assert ((result[:, :, 3] == fill_value).all())
- assert (result.dtype == out_dtype)
- indexer = [2, 1, 0, 1]
- result = algos.take_nd(data, indexer, axis=0,
- fill_value=fill_value)
- assert ((result[[0, 1, 2, 3], :, :] == data[indexer, :, :]).all())
- assert (result.dtype == dtype)
- result = algos.take_nd(data, indexer, axis=1,
- fill_value=fill_value)
- assert ((result[:, [0, 1, 2, 3], :] == data[:, indexer, :]).all())
- assert (result.dtype == dtype)
- result = algos.take_nd(data, indexer, axis=2,
- fill_value=fill_value)
- assert ((result[:, :, [0, 1, 2, 3]] == data[:, :, indexer]).all())
- assert (result.dtype == dtype)
- def test_1d_other_dtypes(self):
- arr = np.random.randn(10).astype(np.float32)
- indexer = [1, 2, 3, -1]
- result = algos.take_1d(arr, indexer)
- expected = arr.take(indexer)
- expected[-1] = np.nan
- tm.assert_almost_equal(result, expected)
- def test_2d_other_dtypes(self):
- arr = np.random.randn(10, 5).astype(np.float32)
- indexer = [1, 2, 3, -1]
- # axis=0
- result = algos.take_nd(arr, indexer, axis=0)
- expected = arr.take(indexer, axis=0)
- expected[-1] = np.nan
- tm.assert_almost_equal(result, expected)
- # axis=1
- result = algos.take_nd(arr, indexer, axis=1)
- expected = arr.take(indexer, axis=1)
- expected[:, -1] = np.nan
- tm.assert_almost_equal(result, expected)
- def test_1d_bool(self):
- arr = np.array([0, 1, 0], dtype=bool)
- result = algos.take_1d(arr, [0, 2, 2, 1])
- expected = arr.take([0, 2, 2, 1])
- tm.assert_numpy_array_equal(result, expected)
- result = algos.take_1d(arr, [0, 2, -1])
- assert result.dtype == np.object_
- def test_2d_bool(self):
- arr = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 1]], dtype=bool)
- result = algos.take_nd(arr, [0, 2, 2, 1])
- expected = arr.take([0, 2, 2, 1], axis=0)
- tm.assert_numpy_array_equal(result, expected)
- result = algos.take_nd(arr, [0, 2, 2, 1], axis=1)
- expected = arr.take([0, 2, 2, 1], axis=1)
- tm.assert_numpy_array_equal(result, expected)
- result = algos.take_nd(arr, [0, 2, -1])
- assert result.dtype == np.object_
- def test_2d_float32(self):
- arr = np.random.randn(4, 3).astype(np.float32)
- indexer = [0, 2, -1, 1, -1]
- # axis=0
- result = algos.take_nd(arr, indexer, axis=0)
- result2 = np.empty_like(result)
- algos.take_nd(arr, indexer, axis=0, out=result2)
- tm.assert_almost_equal(result, result2)
- expected = arr.take(indexer, axis=0)
- expected[[2, 4], :] = np.nan
- tm.assert_almost_equal(result, expected)
- # this now accepts a float32! # test with float64 out buffer
- out = np.empty((len(indexer), arr.shape[1]), dtype='float32')
- algos.take_nd(arr, indexer, out=out) # it works!
- # axis=1
- result = algos.take_nd(arr, indexer, axis=1)
- result2 = np.empty_like(result)
- algos.take_nd(arr, indexer, axis=1, out=result2)
- tm.assert_almost_equal(result, result2)
- expected = arr.take(indexer, axis=1)
- expected[:, [2, 4]] = np.nan
- tm.assert_almost_equal(result, expected)
- def test_2d_datetime64(self):
- # 2005/01/01 - 2006/01/01
- arr = np.random.randint(
- long(11045376), long(11360736), (5, 3)) * 100000000000
- arr = arr.view(dtype='datetime64[ns]')
- indexer = [0, 2, -1, 1, -1]
- # axis=0
- result = algos.take_nd(arr, indexer, axis=0)
- result2 = np.empty_like(result)
- algos.take_nd(arr, indexer, axis=0, out=result2)
- tm.assert_almost_equal(result, result2)
- expected = arr.take(indexer, axis=0)
- expected.view(np.int64)[[2, 4], :] = iNaT
- tm.assert_almost_equal(result, expected)
- result = algos.take_nd(arr, indexer, axis=0,
- fill_value=datetime(2007, 1, 1))
- result2 = np.empty_like(result)
- algos.take_nd(arr, indexer, out=result2, axis=0,
- fill_value=datetime(2007, 1, 1))
- tm.assert_almost_equal(result, result2)
- expected = arr.take(indexer, axis=0)
- expected[[2, 4], :] = datetime(2007, 1, 1)
- tm.assert_almost_equal(result, expected)
- # axis=1
- result = algos.take_nd(arr, indexer, axis=1)
- result2 = np.empty_like(result)
- algos.take_nd(arr, indexer, axis=1, out=result2)
- tm.assert_almost_equal(result, result2)
- expected = arr.take(indexer, axis=1)
- expected.view(np.int64)[:, [2, 4]] = iNaT
- tm.assert_almost_equal(result, expected)
- result = algos.take_nd(arr, indexer, axis=1,
- fill_value=datetime(2007, 1, 1))
- result2 = np.empty_like(result)
- algos.take_nd(arr, indexer, out=result2, axis=1,
- fill_value=datetime(2007, 1, 1))
- tm.assert_almost_equal(result, result2)
- expected = arr.take(indexer, axis=1)
- expected[:, [2, 4]] = datetime(2007, 1, 1)
- tm.assert_almost_equal(result, expected)
- def test_take_axis_0(self):
- arr = np.arange(12).reshape(4, 3)
- result = algos.take(arr, [0, -1])
- expected = np.array([[0, 1, 2], [9, 10, 11]])
- tm.assert_numpy_array_equal(result, expected)
- # allow_fill=True
- result = algos.take(arr, [0, -1], allow_fill=True, fill_value=0)
- expected = np.array([[0, 1, 2], [0, 0, 0]])
- tm.assert_numpy_array_equal(result, expected)
- def test_take_axis_1(self):
- arr = np.arange(12).reshape(4, 3)
- result = algos.take(arr, [0, -1], axis=1)
- expected = np.array([[0, 2], [3, 5], [6, 8], [9, 11]])
- tm.assert_numpy_array_equal(result, expected)
- # allow_fill=True
- result = algos.take(arr, [0, -1], axis=1, allow_fill=True,
- fill_value=0)
- expected = np.array([[0, 0], [3, 0], [6, 0], [9, 0]])
- tm.assert_numpy_array_equal(result, expected)
- class TestExtensionTake(object):
- # The take method found in pd.api.extensions
- def test_bounds_check_large(self):
- arr = np.array([1, 2])
- with pytest.raises(IndexError):
- algos.take(arr, [2, 3], allow_fill=True)
- with pytest.raises(IndexError):
- algos.take(arr, [2, 3], allow_fill=False)
- def test_bounds_check_small(self):
- arr = np.array([1, 2, 3], dtype=np.int64)
- indexer = [0, -1, -2]
- with pytest.raises(ValueError):
- algos.take(arr, indexer, allow_fill=True)
- result = algos.take(arr, indexer)
- expected = np.array([1, 3, 2], dtype=np.int64)
- tm.assert_numpy_array_equal(result, expected)
- @pytest.mark.parametrize('allow_fill', [True, False])
- def test_take_empty(self, allow_fill):
- arr = np.array([], dtype=np.int64)
- # empty take is ok
- result = algos.take(arr, [], allow_fill=allow_fill)
- tm.assert_numpy_array_equal(arr, result)
- with pytest.raises(IndexError):
- algos.take(arr, [0], allow_fill=allow_fill)
- def test_take_na_empty(self):
- result = algos.take(np.array([]), [-1, -1], allow_fill=True,
- fill_value=0.0)
- expected = np.array([0., 0.])
- tm.assert_numpy_array_equal(result, expected)
- def test_take_coerces_list(self):
- arr = [1, 2, 3]
- result = algos.take(arr, [0, 0])
- expected = np.array([1, 1])
- tm.assert_numpy_array_equal(result, expected)