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197 lines
6.0 KiB
197 lines
6.0 KiB
import numpy as np
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import pytest
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from pandas.compat import np_version_under1p20
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import pandas as pd
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import pandas._testing as tm
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from pandas.core.arrays import FloatingArray
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from pandas.core.arrays.floating import (
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Float32Dtype,
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Float64Dtype,
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)
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def test_uses_pandas_na():
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a = pd.array([1, None], dtype=Float64Dtype())
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assert a[1] is pd.NA
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def test_floating_array_constructor():
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values = np.array([1, 2, 3, 4], dtype="float64")
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mask = np.array([False, False, False, True], dtype="bool")
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result = FloatingArray(values, mask)
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expected = pd.array([1, 2, 3, np.nan], dtype="Float64")
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tm.assert_extension_array_equal(result, expected)
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tm.assert_numpy_array_equal(result._data, values)
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tm.assert_numpy_array_equal(result._mask, mask)
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msg = r".* should be .* numpy array. Use the 'pd.array' function instead"
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with pytest.raises(TypeError, match=msg):
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FloatingArray(values.tolist(), mask)
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with pytest.raises(TypeError, match=msg):
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FloatingArray(values, mask.tolist())
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with pytest.raises(TypeError, match=msg):
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FloatingArray(values.astype(int), mask)
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msg = r"__init__\(\) missing 1 required positional argument: 'mask'"
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with pytest.raises(TypeError, match=msg):
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FloatingArray(values)
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def test_floating_array_disallows_float16():
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# GH#44715
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arr = np.array([1, 2], dtype=np.float16)
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mask = np.array([False, False])
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msg = "FloatingArray does not support np.float16 dtype"
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with pytest.raises(TypeError, match=msg):
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FloatingArray(arr, mask)
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def test_floating_array_disallows_Float16_dtype(request):
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# GH#44715
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if np_version_under1p20:
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# https://github.com/numpy/numpy/issues/20512
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mark = pytest.mark.xfail(reason="numpy does not raise on np.dtype('Float16')")
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request.node.add_marker(mark)
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with pytest.raises(TypeError, match="data type 'Float16' not understood"):
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pd.array([1.0, 2.0], dtype="Float16")
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def test_floating_array_constructor_copy():
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values = np.array([1, 2, 3, 4], dtype="float64")
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mask = np.array([False, False, False, True], dtype="bool")
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result = FloatingArray(values, mask)
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assert result._data is values
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assert result._mask is mask
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result = FloatingArray(values, mask, copy=True)
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assert result._data is not values
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assert result._mask is not mask
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def test_to_array():
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result = pd.array([0.1, 0.2, 0.3, 0.4])
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expected = pd.array([0.1, 0.2, 0.3, 0.4], dtype="Float64")
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tm.assert_extension_array_equal(result, expected)
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@pytest.mark.parametrize(
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"a, b",
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[
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([1, None], [1, pd.NA]),
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([None], [pd.NA]),
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([None, np.nan], [pd.NA, pd.NA]),
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([1, np.nan], [1, pd.NA]),
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([np.nan], [pd.NA]),
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],
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)
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def test_to_array_none_is_nan(a, b):
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result = pd.array(a, dtype="Float64")
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expected = pd.array(b, dtype="Float64")
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tm.assert_extension_array_equal(result, expected)
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def test_to_array_mixed_integer_float():
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result = pd.array([1, 2.0])
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expected = pd.array([1.0, 2.0], dtype="Float64")
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tm.assert_extension_array_equal(result, expected)
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result = pd.array([1, None, 2.0])
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expected = pd.array([1.0, None, 2.0], dtype="Float64")
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tm.assert_extension_array_equal(result, expected)
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@pytest.mark.parametrize(
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"values",
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[
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["foo", "bar"],
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["1", "2"],
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"foo",
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1,
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1.0,
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pd.date_range("20130101", periods=2),
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np.array(["foo"]),
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[[1, 2], [3, 4]],
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[np.nan, {"a": 1}],
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# GH#44514 all-NA case used to get quietly swapped out before checking ndim
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np.array([pd.NA] * 6, dtype=object).reshape(3, 2),
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],
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)
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def test_to_array_error(values):
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# error in converting existing arrays to FloatingArray
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msg = "|".join(
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[
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"cannot be converted to a FloatingDtype",
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"values must be a 1D list-like",
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"Cannot pass scalar",
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]
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)
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with pytest.raises((TypeError, ValueError), match=msg):
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pd.array(values, dtype="Float64")
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def test_to_array_inferred_dtype():
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# if values has dtype -> respect it
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result = pd.array(np.array([1, 2], dtype="float32"))
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assert result.dtype == Float32Dtype()
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# if values have no dtype -> always float64
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result = pd.array([1.0, 2.0])
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assert result.dtype == Float64Dtype()
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def test_to_array_dtype_keyword():
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result = pd.array([1, 2], dtype="Float32")
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assert result.dtype == Float32Dtype()
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# if values has dtype -> override it
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result = pd.array(np.array([1, 2], dtype="float32"), dtype="Float64")
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assert result.dtype == Float64Dtype()
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def test_to_array_integer():
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result = pd.array([1, 2], dtype="Float64")
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expected = pd.array([1.0, 2.0], dtype="Float64")
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tm.assert_extension_array_equal(result, expected)
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# for integer dtypes, the itemsize is not preserved
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# TODO can we specify "floating" in general?
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result = pd.array(np.array([1, 2], dtype="int32"), dtype="Float64")
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assert result.dtype == Float64Dtype()
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@pytest.mark.parametrize(
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"bool_values, values, target_dtype, expected_dtype",
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[
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([False, True], [0, 1], Float64Dtype(), Float64Dtype()),
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([False, True], [0, 1], "Float64", Float64Dtype()),
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([False, True, np.nan], [0, 1, np.nan], Float64Dtype(), Float64Dtype()),
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],
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)
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def test_to_array_bool(bool_values, values, target_dtype, expected_dtype):
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result = pd.array(bool_values, dtype=target_dtype)
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assert result.dtype == expected_dtype
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expected = pd.array(values, dtype=target_dtype)
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tm.assert_extension_array_equal(result, expected)
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def test_series_from_float(data):
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# construct from our dtype & string dtype
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dtype = data.dtype
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# from float
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expected = pd.Series(data)
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result = pd.Series(data.to_numpy(na_value=np.nan, dtype="float"), dtype=str(dtype))
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tm.assert_series_equal(result, expected)
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# from list
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expected = pd.Series(data)
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result = pd.Series(np.array(data).tolist(), dtype=str(dtype))
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tm.assert_series_equal(result, expected)
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