108 lines
3.2 KiB
Python
108 lines
3.2 KiB
Python
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import numpy as np
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import pytest
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from pandas._libs.parsers import ( # type: ignore[attr-defined]
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_maybe_upcast,
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na_values,
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)
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import pandas.util._test_decorators as td
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import pandas as pd
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from pandas import NA
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import pandas._testing as tm
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from pandas.core.arrays import (
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ArrowStringArray,
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BooleanArray,
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FloatingArray,
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IntegerArray,
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StringArray,
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)
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def test_maybe_upcast(any_real_numpy_dtype):
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# GH#36712
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dtype = np.dtype(any_real_numpy_dtype)
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na_value = na_values[dtype]
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arr = np.array([1, 2, na_value], dtype=dtype)
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result = _maybe_upcast(arr, use_dtype_backend=True)
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expected_mask = np.array([False, False, True])
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if issubclass(dtype.type, np.integer):
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expected = IntegerArray(arr, mask=expected_mask)
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else:
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expected = FloatingArray(arr, mask=expected_mask)
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tm.assert_extension_array_equal(result, expected)
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def test_maybe_upcast_no_na(any_real_numpy_dtype):
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# GH#36712
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if any_real_numpy_dtype == "float32":
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pytest.skip()
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arr = np.array([1, 2, 3], dtype=any_real_numpy_dtype)
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result = _maybe_upcast(arr, use_dtype_backend=True)
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expected_mask = np.array([False, False, False])
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if issubclass(np.dtype(any_real_numpy_dtype).type, np.integer):
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expected = IntegerArray(arr, mask=expected_mask)
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else:
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expected = FloatingArray(arr, mask=expected_mask)
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tm.assert_extension_array_equal(result, expected)
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def test_maybe_upcaste_bool():
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# GH#36712
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dtype = np.bool_
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na_value = na_values[dtype]
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arr = np.array([True, False, na_value], dtype="uint8").view(dtype)
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result = _maybe_upcast(arr, use_dtype_backend=True)
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expected_mask = np.array([False, False, True])
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expected = BooleanArray(arr, mask=expected_mask)
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tm.assert_extension_array_equal(result, expected)
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def test_maybe_upcaste_bool_no_nan():
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# GH#36712
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dtype = np.bool_
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arr = np.array([True, False, False], dtype="uint8").view(dtype)
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result = _maybe_upcast(arr, use_dtype_backend=True)
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expected_mask = np.array([False, False, False])
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expected = BooleanArray(arr, mask=expected_mask)
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tm.assert_extension_array_equal(result, expected)
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def test_maybe_upcaste_all_nan():
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# GH#36712
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dtype = np.int64
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na_value = na_values[dtype]
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arr = np.array([na_value, na_value], dtype=dtype)
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result = _maybe_upcast(arr, use_dtype_backend=True)
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expected_mask = np.array([True, True])
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expected = IntegerArray(arr, mask=expected_mask)
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tm.assert_extension_array_equal(result, expected)
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@td.skip_if_no("pyarrow")
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@pytest.mark.parametrize("val", [na_values[np.object_], "c"])
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def test_maybe_upcast_object(val, string_storage):
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# GH#36712
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import pyarrow as pa
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with pd.option_context("mode.string_storage", string_storage):
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arr = np.array(["a", "b", val], dtype=np.object_)
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result = _maybe_upcast(arr, use_dtype_backend=True)
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if string_storage == "python":
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exp_val = "c" if val == "c" else NA
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expected = StringArray(np.array(["a", "b", exp_val], dtype=np.object_))
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else:
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exp_val = "c" if val == "c" else None
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expected = ArrowStringArray(pa.array(["a", "b", exp_val]))
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tm.assert_extension_array_equal(result, expected)
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