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301 lines
10 KiB
301 lines
10 KiB
"""
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Tests for 2D compatibility.
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"""
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import numpy as np
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import pytest
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from pandas._libs.missing import is_matching_na
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import pandas as pd
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from pandas.core.arrays.integer import INT_STR_TO_DTYPE
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from pandas.tests.extension.base.base import BaseExtensionTests
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class Dim2CompatTests(BaseExtensionTests):
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def test_transpose(self, data):
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arr2d = data.repeat(2).reshape(-1, 2)
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shape = arr2d.shape
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assert shape[0] != shape[-1] # otherwise the rest of the test is useless
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assert arr2d.T.shape == shape[::-1]
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def test_frame_from_2d_array(self, data):
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arr2d = data.repeat(2).reshape(-1, 2)
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df = pd.DataFrame(arr2d)
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expected = pd.DataFrame({0: arr2d[:, 0], 1: arr2d[:, 1]})
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self.assert_frame_equal(df, expected)
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def test_swapaxes(self, data):
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arr2d = data.repeat(2).reshape(-1, 2)
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result = arr2d.swapaxes(0, 1)
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expected = arr2d.T
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self.assert_extension_array_equal(result, expected)
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def test_delete_2d(self, data):
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arr2d = data.repeat(3).reshape(-1, 3)
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# axis = 0
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result = arr2d.delete(1, axis=0)
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expected = data.delete(1).repeat(3).reshape(-1, 3)
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self.assert_extension_array_equal(result, expected)
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# axis = 1
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result = arr2d.delete(1, axis=1)
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expected = data.repeat(2).reshape(-1, 2)
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self.assert_extension_array_equal(result, expected)
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def test_take_2d(self, data):
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arr2d = data.reshape(-1, 1)
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result = arr2d.take([0, 0, -1], axis=0)
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expected = data.take([0, 0, -1]).reshape(-1, 1)
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self.assert_extension_array_equal(result, expected)
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def test_repr_2d(self, data):
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# this could fail in a corner case where an element contained the name
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res = repr(data.reshape(1, -1))
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assert res.count(f"<{type(data).__name__}") == 1
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res = repr(data.reshape(-1, 1))
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assert res.count(f"<{type(data).__name__}") == 1
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def test_reshape(self, data):
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arr2d = data.reshape(-1, 1)
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assert arr2d.shape == (data.size, 1)
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assert len(arr2d) == len(data)
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arr2d = data.reshape((-1, 1))
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assert arr2d.shape == (data.size, 1)
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assert len(arr2d) == len(data)
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with pytest.raises(ValueError):
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data.reshape((data.size, 2))
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with pytest.raises(ValueError):
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data.reshape(data.size, 2)
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def test_getitem_2d(self, data):
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arr2d = data.reshape(1, -1)
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result = arr2d[0]
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self.assert_extension_array_equal(result, data)
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with pytest.raises(IndexError):
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arr2d[1]
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with pytest.raises(IndexError):
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arr2d[-2]
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result = arr2d[:]
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self.assert_extension_array_equal(result, arr2d)
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result = arr2d[:, :]
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self.assert_extension_array_equal(result, arr2d)
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result = arr2d[:, 0]
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expected = data[[0]]
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self.assert_extension_array_equal(result, expected)
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# dimension-expanding getitem on 1D
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result = data[:, np.newaxis]
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self.assert_extension_array_equal(result, arr2d.T)
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def test_iter_2d(self, data):
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arr2d = data.reshape(1, -1)
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objs = list(iter(arr2d))
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assert len(objs) == arr2d.shape[0]
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for obj in objs:
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assert isinstance(obj, type(data))
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assert obj.dtype == data.dtype
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assert obj.ndim == 1
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assert len(obj) == arr2d.shape[1]
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def test_tolist_2d(self, data):
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arr2d = data.reshape(1, -1)
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result = arr2d.tolist()
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expected = [data.tolist()]
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assert isinstance(result, list)
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assert all(isinstance(x, list) for x in result)
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assert result == expected
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def test_concat_2d(self, data):
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left = type(data)._concat_same_type([data, data]).reshape(-1, 2)
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right = left.copy()
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# axis=0
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result = left._concat_same_type([left, right], axis=0)
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expected = data._concat_same_type([data] * 4).reshape(-1, 2)
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self.assert_extension_array_equal(result, expected)
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# axis=1
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result = left._concat_same_type([left, right], axis=1)
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assert result.shape == (len(data), 4)
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self.assert_extension_array_equal(result[:, :2], left)
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self.assert_extension_array_equal(result[:, 2:], right)
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# axis > 1 -> invalid
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msg = "axis 2 is out of bounds for array of dimension 2"
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with pytest.raises(ValueError, match=msg):
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left._concat_same_type([left, right], axis=2)
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@pytest.mark.parametrize("method", ["backfill", "pad"])
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def test_fillna_2d_method(self, data_missing, method):
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arr = data_missing.repeat(2).reshape(2, 2)
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assert arr[0].isna().all()
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assert not arr[1].isna().any()
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result = arr.fillna(method=method)
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expected = data_missing.fillna(method=method).repeat(2).reshape(2, 2)
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self.assert_extension_array_equal(result, expected)
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@pytest.mark.parametrize("method", ["mean", "median", "var", "std", "sum", "prod"])
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def test_reductions_2d_axis_none(self, data, method):
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arr2d = data.reshape(1, -1)
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err_expected = None
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err_result = None
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try:
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expected = getattr(data, method)()
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except Exception as err:
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# if the 1D reduction is invalid, the 2D reduction should be as well
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err_expected = err
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try:
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result = getattr(arr2d, method)(axis=None)
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except Exception as err2:
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err_result = err2
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else:
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result = getattr(arr2d, method)(axis=None)
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if err_result is not None or err_expected is not None:
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assert type(err_result) == type(err_expected)
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return
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assert is_matching_na(result, expected) or result == expected
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@pytest.mark.parametrize("method", ["mean", "median", "var", "std", "sum", "prod"])
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def test_reductions_2d_axis0(self, data, method):
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arr2d = data.reshape(1, -1)
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kwargs = {}
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if method == "std":
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# pass ddof=0 so we get all-zero std instead of all-NA std
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kwargs["ddof"] = 0
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try:
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result = getattr(arr2d, method)(axis=0, **kwargs)
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except Exception as err:
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try:
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getattr(data, method)()
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except Exception as err2:
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assert type(err) == type(err2)
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return
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else:
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raise AssertionError("Both reductions should raise or neither")
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def get_reduction_result_dtype(dtype):
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# windows and 32bit builds will in some cases have int32/uint32
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# where other builds will have int64/uint64.
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if dtype.itemsize == 8:
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return dtype
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elif dtype.kind in "ib":
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return INT_STR_TO_DTYPE[np.dtype(int).name]
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else:
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# i.e. dtype.kind == "u"
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return INT_STR_TO_DTYPE[np.dtype(np.uint).name]
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if method in ["mean", "median", "sum", "prod"]:
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# std and var are not dtype-preserving
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expected = data
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if method in ["sum", "prod"] and data.dtype.kind in "iub":
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dtype = get_reduction_result_dtype(data.dtype)
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expected = data.astype(dtype)
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if data.dtype.kind == "b" and method in ["sum", "prod"]:
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# We get IntegerArray instead of BooleanArray
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pass
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else:
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assert type(expected) == type(data), type(expected)
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assert dtype == expected.dtype
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self.assert_extension_array_equal(result, expected)
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elif method == "std":
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self.assert_extension_array_equal(result, data - data)
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# punt on method == "var"
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@pytest.mark.parametrize("method", ["mean", "median", "var", "std", "sum", "prod"])
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def test_reductions_2d_axis1(self, data, method):
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arr2d = data.reshape(1, -1)
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try:
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result = getattr(arr2d, method)(axis=1)
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except Exception as err:
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try:
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getattr(data, method)()
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except Exception as err2:
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assert type(err) == type(err2)
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return
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else:
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raise AssertionError("Both reductions should raise or neither")
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# not necessarily type/dtype-preserving, so weaker assertions
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assert result.shape == (1,)
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expected_scalar = getattr(data, method)()
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res = result[0]
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assert is_matching_na(res, expected_scalar) or res == expected_scalar
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class NDArrayBacked2DTests(Dim2CompatTests):
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# More specific tests for NDArrayBackedExtensionArray subclasses
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def test_copy_order(self, data):
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# We should be matching numpy semantics for the "order" keyword in 'copy'
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arr2d = data.repeat(2).reshape(-1, 2)
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assert arr2d._ndarray.flags["C_CONTIGUOUS"]
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res = arr2d.copy()
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assert res._ndarray.flags["C_CONTIGUOUS"]
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res = arr2d[::2, ::2].copy()
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assert res._ndarray.flags["C_CONTIGUOUS"]
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res = arr2d.copy("F")
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assert not res._ndarray.flags["C_CONTIGUOUS"]
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assert res._ndarray.flags["F_CONTIGUOUS"]
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res = arr2d.copy("K")
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assert res._ndarray.flags["C_CONTIGUOUS"]
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res = arr2d.T.copy("K")
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assert not res._ndarray.flags["C_CONTIGUOUS"]
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assert res._ndarray.flags["F_CONTIGUOUS"]
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# order not accepted by numpy
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msg = r"order must be one of 'C', 'F', 'A', or 'K' \(got 'Q'\)"
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with pytest.raises(ValueError, match=msg):
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arr2d.copy("Q")
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# neither contiguity
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arr_nc = arr2d[::2]
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assert not arr_nc._ndarray.flags["C_CONTIGUOUS"]
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assert not arr_nc._ndarray.flags["F_CONTIGUOUS"]
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assert arr_nc.copy()._ndarray.flags["C_CONTIGUOUS"]
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assert not arr_nc.copy()._ndarray.flags["F_CONTIGUOUS"]
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assert arr_nc.copy("C")._ndarray.flags["C_CONTIGUOUS"]
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assert not arr_nc.copy("C")._ndarray.flags["F_CONTIGUOUS"]
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assert not arr_nc.copy("F")._ndarray.flags["C_CONTIGUOUS"]
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assert arr_nc.copy("F")._ndarray.flags["F_CONTIGUOUS"]
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assert arr_nc.copy("K")._ndarray.flags["C_CONTIGUOUS"]
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assert not arr_nc.copy("K")._ndarray.flags["F_CONTIGUOUS"]
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