Why Gemfury?
Push, build, and install
RubyGems
npm packages
Python packages
Maven artifacts
PHP packages
Go Modules
Bower components
Debian packages
RPM packages
NuGet packages
/ tests / test_take.py
from datetime import datetime
import re
import numpy as np
import pytest
from pandas._libs.tslib import iNaT
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:
# 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(11045376, 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)
# GH#26976 make sure we validate along the correct axis
with pytest.raises(IndexError, match="indices are out-of-bounds"):
algos.take(arr, [0, 3], axis=1, allow_fill=True, fill_value=0)
class TestExtensionTake:
# 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, 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)