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import numpy as np import pytest import pandas as pd import pandas._testing as tm from pandas.core.arrays import FloatingArray @pytest.mark.parametrize("box", [True, False], ids=["series", "array"]) def test_to_numpy(box): con = pd.Series if box else pd.array # default (with or without missing values) -> object dtype arr = con([0.1, 0.2, 0.3], dtype="Float64") result = arr.to_numpy() expected = np.array([0.1, 0.2, 0.3], dtype="float64") tm.assert_numpy_array_equal(result, expected) arr = con([0.1, 0.2, None], dtype="Float64") result = arr.to_numpy() expected = np.array([0.1, 0.2, np.nan], dtype="float64") tm.assert_numpy_array_equal(result, expected) @pytest.mark.parametrize("box", [True, False], ids=["series", "array"]) def test_to_numpy_float(box): con = pd.Series if box else pd.array # no missing values -> can convert to float, otherwise raises arr = con([0.1, 0.2, 0.3], dtype="Float64") result = arr.to_numpy(dtype="float64") expected = np.array([0.1, 0.2, 0.3], dtype="float64") tm.assert_numpy_array_equal(result, expected) arr = con([0.1, 0.2, None], dtype="Float64") result = arr.to_numpy(dtype="float64") expected = np.array([0.1, 0.2, np.nan], dtype="float64") tm.assert_numpy_array_equal(result, expected) result = arr.to_numpy(dtype="float64", na_value=np.nan) expected = np.array([0.1, 0.2, np.nan], dtype="float64") tm.assert_numpy_array_equal(result, expected) @pytest.mark.parametrize("box", [True, False], ids=["series", "array"]) def test_to_numpy_int(box): con = pd.Series if box else pd.array # no missing values -> can convert to int, otherwise raises arr = con([1.0, 2.0, 3.0], dtype="Float64") result = arr.to_numpy(dtype="int64") expected = np.array([1, 2, 3], dtype="int64") tm.assert_numpy_array_equal(result, expected) arr = con([1.0, 2.0, None], dtype="Float64") with pytest.raises(ValueError, match="cannot convert to 'int64'-dtype"): result = arr.to_numpy(dtype="int64") # automatic casting (floors the values) arr = con([0.1, 0.9, 1.1], dtype="Float64") result = arr.to_numpy(dtype="int64") expected = np.array([0, 0, 1], dtype="int64") tm.assert_numpy_array_equal(result, expected) @pytest.mark.parametrize("box", [True, False], ids=["series", "array"]) def test_to_numpy_na_value(box): con = pd.Series if box else pd.array arr = con([0.0, 1.0, None], dtype="Float64") result = arr.to_numpy(dtype=object, na_value=None) expected = np.array([0.0, 1.0, None], dtype="object") tm.assert_numpy_array_equal(result, expected) result = arr.to_numpy(dtype=bool, na_value=False) expected = np.array([False, True, False], dtype="bool") tm.assert_numpy_array_equal(result, expected) result = arr.to_numpy(dtype="int64", na_value=-99) expected = np.array([0, 1, -99], dtype="int64") tm.assert_numpy_array_equal(result, expected) def test_to_numpy_na_value_with_nan(): # array with both NaN and NA -> only fill NA with `na_value` arr = FloatingArray(np.array([0.0, np.nan, 0.0]), np.array([False, False, True])) result = arr.to_numpy(dtype="float64", na_value=-1) expected = np.array([0.0, np.nan, -1.0], dtype="float64") tm.assert_numpy_array_equal(result, expected) @pytest.mark.parametrize("dtype", ["float64", "float32", "int32", "int64", "bool"]) @pytest.mark.parametrize("box", [True, False], ids=["series", "array"]) def test_to_numpy_dtype(box, dtype): con = pd.Series if box else pd.array arr = con([0.0, 1.0], dtype="Float64") result = arr.to_numpy(dtype=dtype) expected = np.array([0, 1], dtype=dtype) tm.assert_numpy_array_equal(result, expected) @pytest.mark.parametrize("dtype", ["int32", "int64", "bool"]) @pytest.mark.parametrize("box", [True, False], ids=["series", "array"]) def test_to_numpy_na_raises(box, dtype): con = pd.Series if box else pd.array arr = con([0.0, 1.0, None], dtype="Float64") with pytest.raises(ValueError, match=dtype): arr.to_numpy(dtype=dtype) @pytest.mark.parametrize("box", [True, False], ids=["series", "array"]) def test_to_numpy_string(box, dtype): con = pd.Series if box else pd.array arr = con([0.0, 1.0, None], dtype="Float64") result = arr.to_numpy(dtype="str") expected = np.array([0.0, 1.0, pd.NA], dtype=f"{tm.ENDIAN}U32") tm.assert_numpy_array_equal(result, expected) def test_to_numpy_copy(): # to_numpy can be zero-copy if no missing values arr = pd.array([0.1, 0.2, 0.3], dtype="Float64") result = arr.to_numpy(dtype="float64") result[0] = 10 tm.assert_extension_array_equal(arr, pd.array([10, 0.2, 0.3], dtype="Float64")) arr = pd.array([0.1, 0.2, 0.3], dtype="Float64") result = arr.to_numpy(dtype="float64", copy=True) result[0] = 10 tm.assert_extension_array_equal(arr, pd.array([0.1, 0.2, 0.3], dtype="Float64"))
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