import numpy as np import pytest from pandas.core.dtypes.common import is_integer_dtype import pandas as pd import pandas._testing as tm arrays = [pd.array([1, 2, 3, None], dtype=dtype) for dtype in tm.ALL_INT_EA_DTYPES] arrays += [ pd.array([0.141, -0.268, 5.895, None], dtype=dtype) for dtype in tm.FLOAT_EA_DTYPES ] @pytest.fixture(params=arrays, ids=[a.dtype.name for a in arrays]) def data(request): """ Fixture returning parametrized 'data' array with different integer and floating point types """ return request.param @pytest.fixture() def numpy_dtype(data): """ Fixture returning numpy dtype from 'data' input array. """ # For integer dtype, the numpy conversion must be done to float if is_integer_dtype(data): numpy_dtype = float else: numpy_dtype = data.dtype.type return numpy_dtype def test_round(data, numpy_dtype): # No arguments result = data.round() expected = pd.array( np.round(data.to_numpy(dtype=numpy_dtype, na_value=None)), dtype=data.dtype ) tm.assert_extension_array_equal(result, expected) # Decimals argument result = data.round(decimals=2) expected = pd.array( np.round(data.to_numpy(dtype=numpy_dtype, na_value=None), decimals=2), dtype=data.dtype, ) tm.assert_extension_array_equal(result, expected) def test_tolist(data): result = data.tolist() expected = list(data) tm.assert_equal(result, expected)