import numpy as np
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import pytest
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import pandas as pd
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import pandas._testing as tm
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def test_astype():
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# with missing values
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arr = pd.array([0.1, 0.2, None], dtype="Float64")
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with pytest.raises(ValueError, match="cannot convert NA to integer"):
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arr.astype("int64")
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with pytest.raises(ValueError, match="cannot convert float NaN to bool"):
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arr.astype("bool")
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result = arr.astype("float64")
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expected = np.array([0.1, 0.2, np.nan], dtype="float64")
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tm.assert_numpy_array_equal(result, expected)
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# no missing values
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arr = pd.array([0.0, 1.0, 0.5], dtype="Float64")
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result = arr.astype("int64")
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expected = np.array([0, 1, 0], dtype="int64")
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tm.assert_numpy_array_equal(result, expected)
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result = arr.astype("bool")
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expected = np.array([False, True, True], dtype="bool")
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tm.assert_numpy_array_equal(result, expected)
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def test_astype_to_floating_array():
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# astype to FloatingArray
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arr = pd.array([0.0, 1.0, None], dtype="Float64")
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result = arr.astype("Float64")
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tm.assert_extension_array_equal(result, arr)
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result = arr.astype(pd.Float64Dtype())
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tm.assert_extension_array_equal(result, arr)
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result = arr.astype("Float32")
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expected = pd.array([0.0, 1.0, None], dtype="Float32")
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tm.assert_extension_array_equal(result, expected)
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def test_astype_to_boolean_array():
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# astype to BooleanArray
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arr = pd.array([0.0, 1.0, None], dtype="Float64")
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result = arr.astype("boolean")
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expected = pd.array([False, True, None], dtype="boolean")
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tm.assert_extension_array_equal(result, expected)
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result = arr.astype(pd.BooleanDtype())
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tm.assert_extension_array_equal(result, expected)
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def test_astype_to_integer_array():
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# astype to IntegerArray
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arr = pd.array([0.0, 1.5, None], dtype="Float64")
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result = arr.astype("Int64")
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expected = pd.array([0, 1, None], dtype="Int64")
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tm.assert_extension_array_equal(result, expected)
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def test_astype_str():
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a = pd.array([0.1, 0.2, None], dtype="Float64")
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expected = np.array(["0.1", "0.2", "<NA>"], dtype="U32")
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tm.assert_numpy_array_equal(a.astype(str), expected)
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tm.assert_numpy_array_equal(a.astype("str"), expected)
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def test_astype_copy():
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arr = pd.array([0.1, 0.2, None], dtype="Float64")
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orig = pd.array([0.1, 0.2, None], dtype="Float64")
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# copy=True -> ensure both data and mask are actual copies
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result = arr.astype("Float64", copy=True)
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assert result is not arr
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assert not tm.shares_memory(result, arr)
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result[0] = 10
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tm.assert_extension_array_equal(arr, orig)
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result[0] = pd.NA
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tm.assert_extension_array_equal(arr, orig)
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# copy=False
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result = arr.astype("Float64", copy=False)
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assert result is arr
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assert np.shares_memory(result._data, arr._data)
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assert np.shares_memory(result._mask, arr._mask)
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result[0] = 10
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assert arr[0] == 10
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result[0] = pd.NA
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assert arr[0] is pd.NA
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# astype to different dtype -> always needs a copy -> even with copy=False
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# we need to ensure that also the mask is actually copied
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arr = pd.array([0.1, 0.2, None], dtype="Float64")
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orig = pd.array([0.1, 0.2, None], dtype="Float64")
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result = arr.astype("Float32", copy=False)
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assert not tm.shares_memory(result, arr)
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result[0] = 10
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tm.assert_extension_array_equal(arr, orig)
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result[0] = pd.NA
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tm.assert_extension_array_equal(arr, orig)
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def test_astype_object(dtype):
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arr = pd.array([1.0, pd.NA], dtype=dtype)
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result = arr.astype(object)
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expected = np.array([1.0, pd.NA], dtype=object)
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tm.assert_numpy_array_equal(result, expected)
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# check exact element types
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assert isinstance(result[0], float)
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assert result[1] is pd.NA
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def test_Float64_conversion():
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# GH#40729
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testseries = pd.Series(["1", "2", "3", "4"], dtype="object")
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result = testseries.astype(pd.Float64Dtype())
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expected = pd.Series([1.0, 2.0, 3.0, 4.0], dtype=pd.Float64Dtype())
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tm.assert_series_equal(result, expected)
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