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| import decimal
|
| import numpy as np
| import pytest
|
| from pandas.core.dtypes.cast import maybe_downcast_to_dtype
|
| from pandas import (
| Series,
| Timedelta,
| )
| import pandas._testing as tm
|
|
| @pytest.mark.parametrize(
| "arr,dtype,expected",
| [
| (
| np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995]),
| "infer",
| np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995]),
| ),
| (
| np.array([8.0, 8.0, 8.0, 8.0, 8.9999999999995]),
| "infer",
| np.array([8, 8, 8, 8, 9], dtype=np.int64),
| ),
| (
| np.array([8.0, 8.0, 8.0, 8.0, 9.0000000000005]),
| "infer",
| np.array([8, 8, 8, 8, 9], dtype=np.int64),
| ),
| (
| # This is a judgement call, but we do _not_ downcast Decimal
| # objects
| np.array([decimal.Decimal(0.0)]),
| "int64",
| np.array([decimal.Decimal(0.0)]),
| ),
| (
| # GH#45837
| np.array([Timedelta(days=1), Timedelta(days=2)], dtype=object),
| "infer",
| np.array([1, 2], dtype="m8[D]").astype("m8[ns]"),
| ),
| # TODO: similar for dt64, dt64tz, Period, Interval?
| ],
| )
| def test_downcast(arr, expected, dtype):
| result = maybe_downcast_to_dtype(arr, dtype)
| tm.assert_numpy_array_equal(result, expected)
|
|
| def test_downcast_booleans():
| # see gh-16875: coercing of booleans.
| ser = Series([True, True, False])
| result = maybe_downcast_to_dtype(ser, np.dtype(np.float64))
|
| expected = ser
| tm.assert_series_equal(result, expected)
|
|
| def test_downcast_conversion_no_nan(any_real_numpy_dtype):
| dtype = any_real_numpy_dtype
| expected = np.array([1, 2])
| arr = np.array([1.0, 2.0], dtype=dtype)
|
| result = maybe_downcast_to_dtype(arr, "infer")
| tm.assert_almost_equal(result, expected, check_dtype=False)
|
|
| def test_downcast_conversion_nan(float_numpy_dtype):
| dtype = float_numpy_dtype
| data = [1.0, 2.0, np.nan]
|
| expected = np.array(data, dtype=dtype)
| arr = np.array(data, dtype=dtype)
|
| result = maybe_downcast_to_dtype(arr, "infer")
| tm.assert_almost_equal(result, expected)
|
|
| def test_downcast_conversion_empty(any_real_numpy_dtype):
| dtype = any_real_numpy_dtype
| arr = np.array([], dtype=dtype)
| result = maybe_downcast_to_dtype(arr, np.dtype("int64"))
| tm.assert_numpy_array_equal(result, np.array([], dtype=np.int64))
|
|
| @pytest.mark.parametrize("klass", [np.datetime64, np.timedelta64])
| def test_datetime_likes_nan(klass):
| dtype = klass.__name__ + "[ns]"
| arr = np.array([1, 2, np.nan])
|
| exp = np.array([1, 2, klass("NaT")], dtype)
| res = maybe_downcast_to_dtype(arr, dtype)
| tm.assert_numpy_array_equal(res, exp)
|
|
