import numpy as np
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import pytest
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from pandas import (
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Categorical,
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CategoricalDtype,
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NaT,
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Timestamp,
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array,
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to_datetime,
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)
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import pandas._testing as tm
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class TestAstype:
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def test_astype_str_int_categories_to_nullable_int(self):
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# GH#39616
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dtype = CategoricalDtype([str(i) for i in range(5)])
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codes = np.random.randint(5, size=20)
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arr = Categorical.from_codes(codes, dtype=dtype)
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res = arr.astype("Int64")
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expected = array(codes, dtype="Int64")
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tm.assert_extension_array_equal(res, expected)
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def test_astype_str_int_categories_to_nullable_float(self):
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# GH#39616
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dtype = CategoricalDtype([str(i / 2) for i in range(5)])
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codes = np.random.randint(5, size=20)
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arr = Categorical.from_codes(codes, dtype=dtype)
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res = arr.astype("Float64")
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expected = array(codes, dtype="Float64") / 2
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tm.assert_extension_array_equal(res, expected)
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@pytest.mark.parametrize("ordered", [True, False])
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def test_astype(self, ordered):
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# string
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cat = Categorical(list("abbaaccc"), ordered=ordered)
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result = cat.astype(object)
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expected = np.array(cat)
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tm.assert_numpy_array_equal(result, expected)
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msg = r"Cannot cast object dtype to float64"
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with pytest.raises(ValueError, match=msg):
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cat.astype(float)
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# numeric
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cat = Categorical([0, 1, 2, 2, 1, 0, 1, 0, 2], ordered=ordered)
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result = cat.astype(object)
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expected = np.array(cat, dtype=object)
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tm.assert_numpy_array_equal(result, expected)
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result = cat.astype(int)
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expected = np.array(cat, dtype="int")
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tm.assert_numpy_array_equal(result, expected)
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result = cat.astype(float)
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expected = np.array(cat, dtype=float)
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tm.assert_numpy_array_equal(result, expected)
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@pytest.mark.parametrize("dtype_ordered", [True, False])
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@pytest.mark.parametrize("cat_ordered", [True, False])
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def test_astype_category(self, dtype_ordered, cat_ordered):
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# GH#10696/GH#18593
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data = list("abcaacbab")
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cat = Categorical(data, categories=list("bac"), ordered=cat_ordered)
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# standard categories
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dtype = CategoricalDtype(ordered=dtype_ordered)
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result = cat.astype(dtype)
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expected = Categorical(data, categories=cat.categories, ordered=dtype_ordered)
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tm.assert_categorical_equal(result, expected)
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# non-standard categories
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dtype = CategoricalDtype(list("adc"), dtype_ordered)
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result = cat.astype(dtype)
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expected = Categorical(data, dtype=dtype)
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tm.assert_categorical_equal(result, expected)
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if dtype_ordered is False:
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# dtype='category' can't specify ordered, so only test once
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result = cat.astype("category")
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expected = cat
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tm.assert_categorical_equal(result, expected)
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def test_astype_object_datetime_categories(self):
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# GH#40754
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cat = Categorical(to_datetime(["2021-03-27", NaT]))
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result = cat.astype(object)
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expected = np.array([Timestamp("2021-03-27 00:00:00"), NaT], dtype="object")
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tm.assert_numpy_array_equal(result, expected)
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def test_astype_object_timestamp_categories(self):
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# GH#18024
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cat = Categorical([Timestamp("2014-01-01")])
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result = cat.astype(object)
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expected = np.array([Timestamp("2014-01-01 00:00:00")], dtype="object")
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tm.assert_numpy_array_equal(result, expected)
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