802 lines
23 KiB
Python
802 lines
23 KiB
Python
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import decimal
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import numpy as np
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from numpy import iinfo
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import pytest
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from pandas.compat import is_platform_arm
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import pandas as pd
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from pandas import (
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DataFrame,
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Index,
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Series,
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to_numeric,
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)
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import pandas._testing as tm
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@pytest.fixture(params=[None, "ignore", "raise", "coerce"])
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def errors(request):
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return request.param
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@pytest.fixture(params=[True, False])
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def signed(request):
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return request.param
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@pytest.fixture(params=[lambda x: x, str], ids=["identity", "str"])
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def transform(request):
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return request.param
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@pytest.fixture(params=[47393996303418497800, 100000000000000000000])
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def large_val(request):
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return request.param
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@pytest.fixture(params=[True, False])
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def multiple_elts(request):
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return request.param
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@pytest.fixture(
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params=[
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(lambda x: Index(x, name="idx"), tm.assert_index_equal),
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(lambda x: Series(x, name="ser"), tm.assert_series_equal),
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(lambda x: np.array(Index(x).values), tm.assert_numpy_array_equal),
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]
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)
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def transform_assert_equal(request):
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return request.param
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@pytest.mark.parametrize(
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"input_kwargs,result_kwargs",
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[
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({}, {"dtype": np.int64}),
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({"errors": "coerce", "downcast": "integer"}, {"dtype": np.int8}),
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],
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)
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def test_empty(input_kwargs, result_kwargs):
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# see gh-16302
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ser = Series([], dtype=object)
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result = to_numeric(ser, **input_kwargs)
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expected = Series([], **result_kwargs)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("last_val", ["7", 7])
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def test_series(last_val):
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ser = Series(["1", "-3.14", last_val])
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result = to_numeric(ser)
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expected = Series([1, -3.14, 7])
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"data",
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[
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[1, 3, 4, 5],
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[1.0, 3.0, 4.0, 5.0],
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# Bool is regarded as numeric.
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[True, False, True, True],
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],
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)
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def test_series_numeric(data):
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ser = Series(data, index=list("ABCD"), name="EFG")
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result = to_numeric(ser)
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tm.assert_series_equal(result, ser)
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@pytest.mark.parametrize(
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"data,msg",
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[
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([1, -3.14, "apple"], 'Unable to parse string "apple" at position 2'),
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(
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["orange", 1, -3.14, "apple"],
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'Unable to parse string "orange" at position 0',
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),
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],
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)
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def test_error(data, msg):
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ser = Series(data)
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with pytest.raises(ValueError, match=msg):
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to_numeric(ser, errors="raise")
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@pytest.mark.parametrize(
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"errors,exp_data", [("ignore", [1, -3.14, "apple"]), ("coerce", [1, -3.14, np.nan])]
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)
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def test_ignore_error(errors, exp_data):
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ser = Series([1, -3.14, "apple"])
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result = to_numeric(ser, errors=errors)
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expected = Series(exp_data)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"errors,exp",
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[
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("raise", 'Unable to parse string "apple" at position 2'),
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("ignore", [True, False, "apple"]),
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# Coerces to float.
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("coerce", [1.0, 0.0, np.nan]),
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],
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)
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def test_bool_handling(errors, exp):
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ser = Series([True, False, "apple"])
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if isinstance(exp, str):
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with pytest.raises(ValueError, match=exp):
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to_numeric(ser, errors=errors)
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else:
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result = to_numeric(ser, errors=errors)
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expected = Series(exp)
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tm.assert_series_equal(result, expected)
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def test_list():
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ser = ["1", "-3.14", "7"]
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res = to_numeric(ser)
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expected = np.array([1, -3.14, 7])
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tm.assert_numpy_array_equal(res, expected)
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@pytest.mark.parametrize(
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"data,arr_kwargs",
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[
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([1, 3, 4, 5], {"dtype": np.int64}),
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([1.0, 3.0, 4.0, 5.0], {}),
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# Boolean is regarded as numeric.
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([True, False, True, True], {}),
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],
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)
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def test_list_numeric(data, arr_kwargs):
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result = to_numeric(data)
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expected = np.array(data, **arr_kwargs)
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tm.assert_numpy_array_equal(result, expected)
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@pytest.mark.parametrize("kwargs", [{"dtype": "O"}, {}])
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def test_numeric(kwargs):
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data = [1, -3.14, 7]
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ser = Series(data, **kwargs)
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result = to_numeric(ser)
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expected = Series(data)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"columns",
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[
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# One column.
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"a",
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# Multiple columns.
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["a", "b"],
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],
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)
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def test_numeric_df_columns(columns):
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# see gh-14827
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df = DataFrame(
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{
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"a": [1.2, decimal.Decimal(3.14), decimal.Decimal("infinity"), "0.1"],
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"b": [1.0, 2.0, 3.0, 4.0],
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}
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)
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expected = DataFrame({"a": [1.2, 3.14, np.inf, 0.1], "b": [1.0, 2.0, 3.0, 4.0]})
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df_copy = df.copy()
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df_copy[columns] = df_copy[columns].apply(to_numeric)
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tm.assert_frame_equal(df_copy, expected)
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@pytest.mark.parametrize(
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"data,exp_data",
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[
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(
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[[decimal.Decimal(3.14), 1.0], decimal.Decimal(1.6), 0.1],
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[[3.14, 1.0], 1.6, 0.1],
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),
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([np.array([decimal.Decimal(3.14), 1.0]), 0.1], [[3.14, 1.0], 0.1]),
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],
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)
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def test_numeric_embedded_arr_likes(data, exp_data):
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# Test to_numeric with embedded lists and arrays
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df = DataFrame({"a": data})
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df["a"] = df["a"].apply(to_numeric)
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expected = DataFrame({"a": exp_data})
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tm.assert_frame_equal(df, expected)
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def test_all_nan():
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ser = Series(["a", "b", "c"])
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result = to_numeric(ser, errors="coerce")
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expected = Series([np.nan, np.nan, np.nan])
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tm.assert_series_equal(result, expected)
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def test_type_check(errors):
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# see gh-11776
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df = DataFrame({"a": [1, -3.14, 7], "b": ["4", "5", "6"]})
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kwargs = {"errors": errors} if errors is not None else {}
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with pytest.raises(TypeError, match="1-d array"):
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to_numeric(df, **kwargs)
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@pytest.mark.parametrize("val", [1, 1.1, 20001])
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def test_scalar(val, signed, transform):
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val = -val if signed else val
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assert to_numeric(transform(val)) == float(val)
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def test_really_large_scalar(large_val, signed, transform, errors):
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# see gh-24910
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kwargs = {"errors": errors} if errors is not None else {}
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val = -large_val if signed else large_val
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val = transform(val)
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val_is_string = isinstance(val, str)
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if val_is_string and errors in (None, "raise"):
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msg = "Integer out of range. at position 0"
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with pytest.raises(ValueError, match=msg):
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to_numeric(val, **kwargs)
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else:
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expected = float(val) if (errors == "coerce" and val_is_string) else val
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tm.assert_almost_equal(to_numeric(val, **kwargs), expected)
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def test_really_large_in_arr(large_val, signed, transform, multiple_elts, errors):
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# see gh-24910
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kwargs = {"errors": errors} if errors is not None else {}
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val = -large_val if signed else large_val
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val = transform(val)
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extra_elt = "string"
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arr = [val] + multiple_elts * [extra_elt]
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val_is_string = isinstance(val, str)
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coercing = errors == "coerce"
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if errors in (None, "raise") and (val_is_string or multiple_elts):
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if val_is_string:
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msg = "Integer out of range. at position 0"
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else:
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msg = 'Unable to parse string "string" at position 1'
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with pytest.raises(ValueError, match=msg):
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to_numeric(arr, **kwargs)
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else:
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result = to_numeric(arr, **kwargs)
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exp_val = float(val) if (coercing and val_is_string) else val
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expected = [exp_val]
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if multiple_elts:
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if coercing:
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expected.append(np.nan)
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exp_dtype = float
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else:
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expected.append(extra_elt)
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exp_dtype = object
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else:
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exp_dtype = float if isinstance(exp_val, (int, float)) else object
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tm.assert_almost_equal(result, np.array(expected, dtype=exp_dtype))
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def test_really_large_in_arr_consistent(large_val, signed, multiple_elts, errors):
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# see gh-24910
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#
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# Even if we discover that we have to hold float, does not mean
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# we should be lenient on subsequent elements that fail to be integer.
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kwargs = {"errors": errors} if errors is not None else {}
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arr = [str(-large_val if signed else large_val)]
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if multiple_elts:
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arr.insert(0, large_val)
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if errors in (None, "raise"):
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index = int(multiple_elts)
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msg = f"Integer out of range. at position {index}"
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with pytest.raises(ValueError, match=msg):
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to_numeric(arr, **kwargs)
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else:
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result = to_numeric(arr, **kwargs)
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if errors == "coerce":
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expected = [float(i) for i in arr]
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exp_dtype = float
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else:
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expected = arr
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exp_dtype = object
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tm.assert_almost_equal(result, np.array(expected, dtype=exp_dtype))
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@pytest.mark.parametrize(
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"errors,checker",
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[
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("raise", 'Unable to parse string "fail" at position 0'),
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("ignore", lambda x: x == "fail"),
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("coerce", lambda x: np.isnan(x)),
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],
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)
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def test_scalar_fail(errors, checker):
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scalar = "fail"
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if isinstance(checker, str):
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with pytest.raises(ValueError, match=checker):
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to_numeric(scalar, errors=errors)
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else:
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assert checker(to_numeric(scalar, errors=errors))
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@pytest.mark.parametrize("data", [[1, 2, 3], [1.0, np.nan, 3, np.nan]])
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def test_numeric_dtypes(data, transform_assert_equal):
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transform, assert_equal = transform_assert_equal
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data = transform(data)
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result = to_numeric(data)
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assert_equal(result, data)
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@pytest.mark.parametrize(
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"data,exp",
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[
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(["1", "2", "3"], np.array([1, 2, 3], dtype="int64")),
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(["1.5", "2.7", "3.4"], np.array([1.5, 2.7, 3.4])),
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],
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)
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def test_str(data, exp, transform_assert_equal):
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transform, assert_equal = transform_assert_equal
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result = to_numeric(transform(data))
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expected = transform(exp)
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assert_equal(result, expected)
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def test_datetime_like(tz_naive_fixture, transform_assert_equal):
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transform, assert_equal = transform_assert_equal
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idx = pd.date_range("20130101", periods=3, tz=tz_naive_fixture)
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result = to_numeric(transform(idx))
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expected = transform(idx.asi8)
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assert_equal(result, expected)
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def test_timedelta(transform_assert_equal):
|
||
|
|
transform, assert_equal = transform_assert_equal
|
||
|
|
idx = pd.timedelta_range("1 days", periods=3, freq="D")
|
||
|
|
|
||
|
|
result = to_numeric(transform(idx))
|
||
|
|
expected = transform(idx.asi8)
|
||
|
|
assert_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_period(request, transform_assert_equal):
|
||
|
|
transform, assert_equal = transform_assert_equal
|
||
|
|
|
||
|
|
idx = pd.period_range("2011-01", periods=3, freq="M", name="")
|
||
|
|
inp = transform(idx)
|
||
|
|
|
||
|
|
if not isinstance(inp, Index):
|
||
|
|
request.node.add_marker(
|
||
|
|
pytest.mark.xfail(reason="Missing PeriodDtype support in to_numeric")
|
||
|
|
)
|
||
|
|
result = to_numeric(inp)
|
||
|
|
expected = transform(idx.asi8)
|
||
|
|
assert_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"errors,expected",
|
||
|
|
[
|
||
|
|
("raise", "Invalid object type at position 0"),
|
||
|
|
("ignore", Series([[10.0, 2], 1.0, "apple"])),
|
||
|
|
("coerce", Series([np.nan, 1.0, np.nan])),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_non_hashable(errors, expected):
|
||
|
|
# see gh-13324
|
||
|
|
ser = Series([[10.0, 2], 1.0, "apple"])
|
||
|
|
|
||
|
|
if isinstance(expected, str):
|
||
|
|
with pytest.raises(TypeError, match=expected):
|
||
|
|
to_numeric(ser, errors=errors)
|
||
|
|
else:
|
||
|
|
result = to_numeric(ser, errors=errors)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_downcast_invalid_cast():
|
||
|
|
# see gh-13352
|
||
|
|
data = ["1", 2, 3]
|
||
|
|
invalid_downcast = "unsigned-integer"
|
||
|
|
msg = "invalid downcasting method provided"
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
to_numeric(data, downcast=invalid_downcast)
|
||
|
|
|
||
|
|
|
||
|
|
def test_errors_invalid_value():
|
||
|
|
# see gh-26466
|
||
|
|
data = ["1", 2, 3]
|
||
|
|
invalid_error_value = "invalid"
|
||
|
|
msg = "invalid error value specified"
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
to_numeric(data, errors=invalid_error_value)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data",
|
||
|
|
[
|
||
|
|
["1", 2, 3],
|
||
|
|
[1, 2, 3],
|
||
|
|
np.array(["1970-01-02", "1970-01-03", "1970-01-04"], dtype="datetime64[D]"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"kwargs,exp_dtype",
|
||
|
|
[
|
||
|
|
# Basic function tests.
|
||
|
|
({}, np.int64),
|
||
|
|
({"downcast": None}, np.int64),
|
||
|
|
# Support below np.float32 is rare and far between.
|
||
|
|
({"downcast": "float"}, np.dtype(np.float32).char),
|
||
|
|
# Basic dtype support.
|
||
|
|
({"downcast": "unsigned"}, np.dtype(np.typecodes["UnsignedInteger"][0])),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_downcast_basic(data, kwargs, exp_dtype):
|
||
|
|
# see gh-13352
|
||
|
|
result = to_numeric(data, **kwargs)
|
||
|
|
expected = np.array([1, 2, 3], dtype=exp_dtype)
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("signed_downcast", ["integer", "signed"])
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data",
|
||
|
|
[
|
||
|
|
["1", 2, 3],
|
||
|
|
[1, 2, 3],
|
||
|
|
np.array(["1970-01-02", "1970-01-03", "1970-01-04"], dtype="datetime64[D]"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_signed_downcast(data, signed_downcast):
|
||
|
|
# see gh-13352
|
||
|
|
smallest_int_dtype = np.dtype(np.typecodes["Integer"][0])
|
||
|
|
expected = np.array([1, 2, 3], dtype=smallest_int_dtype)
|
||
|
|
|
||
|
|
res = to_numeric(data, downcast=signed_downcast)
|
||
|
|
tm.assert_numpy_array_equal(res, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ignore_downcast_invalid_data():
|
||
|
|
# If we can't successfully cast the given
|
||
|
|
# data to a numeric dtype, do not bother
|
||
|
|
# with the downcast parameter.
|
||
|
|
data = ["foo", 2, 3]
|
||
|
|
expected = np.array(data, dtype=object)
|
||
|
|
|
||
|
|
res = to_numeric(data, errors="ignore", downcast="unsigned")
|
||
|
|
tm.assert_numpy_array_equal(res, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ignore_downcast_neg_to_unsigned():
|
||
|
|
# Cannot cast to an unsigned integer
|
||
|
|
# because we have a negative number.
|
||
|
|
data = ["-1", 2, 3]
|
||
|
|
expected = np.array([-1, 2, 3], dtype=np.int64)
|
||
|
|
|
||
|
|
res = to_numeric(data, downcast="unsigned")
|
||
|
|
tm.assert_numpy_array_equal(res, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("downcast", ["integer", "signed", "unsigned"])
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data,expected",
|
||
|
|
[
|
||
|
|
(["1.1", 2, 3], np.array([1.1, 2, 3], dtype=np.float64)),
|
||
|
|
(
|
||
|
|
[10000.0, 20000, 3000, 40000.36, 50000, 50000.00],
|
||
|
|
np.array(
|
||
|
|
[10000.0, 20000, 3000, 40000.36, 50000, 50000.00], dtype=np.float64
|
||
|
|
),
|
||
|
|
),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_ignore_downcast_cannot_convert_float(data, expected, downcast):
|
||
|
|
# Cannot cast to an integer (signed or unsigned)
|
||
|
|
# because we have a float number.
|
||
|
|
res = to_numeric(data, downcast=downcast)
|
||
|
|
tm.assert_numpy_array_equal(res, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"downcast,expected_dtype",
|
||
|
|
[("integer", np.int16), ("signed", np.int16), ("unsigned", np.uint16)],
|
||
|
|
)
|
||
|
|
def test_downcast_not8bit(downcast, expected_dtype):
|
||
|
|
# the smallest integer dtype need not be np.(u)int8
|
||
|
|
data = ["256", 257, 258]
|
||
|
|
|
||
|
|
expected = np.array([256, 257, 258], dtype=expected_dtype)
|
||
|
|
res = to_numeric(data, downcast=downcast)
|
||
|
|
tm.assert_numpy_array_equal(res, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"dtype,downcast,min_max",
|
||
|
|
[
|
||
|
|
("int8", "integer", [iinfo(np.int8).min, iinfo(np.int8).max]),
|
||
|
|
("int16", "integer", [iinfo(np.int16).min, iinfo(np.int16).max]),
|
||
|
|
("int32", "integer", [iinfo(np.int32).min, iinfo(np.int32).max]),
|
||
|
|
("int64", "integer", [iinfo(np.int64).min, iinfo(np.int64).max]),
|
||
|
|
("uint8", "unsigned", [iinfo(np.uint8).min, iinfo(np.uint8).max]),
|
||
|
|
("uint16", "unsigned", [iinfo(np.uint16).min, iinfo(np.uint16).max]),
|
||
|
|
("uint32", "unsigned", [iinfo(np.uint32).min, iinfo(np.uint32).max]),
|
||
|
|
("uint64", "unsigned", [iinfo(np.uint64).min, iinfo(np.uint64).max]),
|
||
|
|
("int16", "integer", [iinfo(np.int8).min, iinfo(np.int8).max + 1]),
|
||
|
|
("int32", "integer", [iinfo(np.int16).min, iinfo(np.int16).max + 1]),
|
||
|
|
("int64", "integer", [iinfo(np.int32).min, iinfo(np.int32).max + 1]),
|
||
|
|
("int16", "integer", [iinfo(np.int8).min - 1, iinfo(np.int16).max]),
|
||
|
|
("int32", "integer", [iinfo(np.int16).min - 1, iinfo(np.int32).max]),
|
||
|
|
("int64", "integer", [iinfo(np.int32).min - 1, iinfo(np.int64).max]),
|
||
|
|
("uint16", "unsigned", [iinfo(np.uint8).min, iinfo(np.uint8).max + 1]),
|
||
|
|
("uint32", "unsigned", [iinfo(np.uint16).min, iinfo(np.uint16).max + 1]),
|
||
|
|
("uint64", "unsigned", [iinfo(np.uint32).min, iinfo(np.uint32).max + 1]),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_downcast_limits(dtype, downcast, min_max):
|
||
|
|
# see gh-14404: test the limits of each downcast.
|
||
|
|
series = to_numeric(Series(min_max), downcast=downcast)
|
||
|
|
assert series.dtype == dtype
|
||
|
|
|
||
|
|
|
||
|
|
def test_downcast_float64_to_float32():
|
||
|
|
# GH-43693: Check float64 preservation when >= 16,777,217
|
||
|
|
series = Series([16777217.0, np.finfo(np.float64).max, np.nan], dtype=np.float64)
|
||
|
|
result = to_numeric(series, downcast="float")
|
||
|
|
|
||
|
|
assert series.dtype == result.dtype
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"ser,expected",
|
||
|
|
[
|
||
|
|
(
|
||
|
|
Series([0, 9223372036854775808]),
|
||
|
|
Series([0, 9223372036854775808], dtype=np.uint64),
|
||
|
|
)
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_downcast_uint64(ser, expected):
|
||
|
|
# see gh-14422:
|
||
|
|
# BUG: to_numeric doesn't work uint64 numbers
|
||
|
|
|
||
|
|
result = to_numeric(ser, downcast="unsigned")
|
||
|
|
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data,exp_data",
|
||
|
|
[
|
||
|
|
(
|
||
|
|
[200, 300, "", "NaN", 30000000000000000000],
|
||
|
|
[200, 300, np.nan, np.nan, 30000000000000000000],
|
||
|
|
),
|
||
|
|
(
|
||
|
|
["12345678901234567890", "1234567890", "ITEM"],
|
||
|
|
[12345678901234567890, 1234567890, np.nan],
|
||
|
|
),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_coerce_uint64_conflict(data, exp_data):
|
||
|
|
# see gh-17007 and gh-17125
|
||
|
|
#
|
||
|
|
# Still returns float despite the uint64-nan conflict,
|
||
|
|
# which would normally force the casting to object.
|
||
|
|
result = to_numeric(Series(data), errors="coerce")
|
||
|
|
expected = Series(exp_data, dtype=float)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"errors,exp",
|
||
|
|
[
|
||
|
|
("ignore", Series(["12345678901234567890", "1234567890", "ITEM"])),
|
||
|
|
("raise", "Unable to parse string"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_non_coerce_uint64_conflict(errors, exp):
|
||
|
|
# see gh-17007 and gh-17125
|
||
|
|
#
|
||
|
|
# For completeness.
|
||
|
|
ser = Series(["12345678901234567890", "1234567890", "ITEM"])
|
||
|
|
|
||
|
|
if isinstance(exp, str):
|
||
|
|
with pytest.raises(ValueError, match=exp):
|
||
|
|
to_numeric(ser, errors=errors)
|
||
|
|
else:
|
||
|
|
result = to_numeric(ser, errors=errors)
|
||
|
|
tm.assert_series_equal(result, ser)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dc1", ["integer", "float", "unsigned"])
|
||
|
|
@pytest.mark.parametrize("dc2", ["integer", "float", "unsigned"])
|
||
|
|
def test_downcast_empty(dc1, dc2):
|
||
|
|
# GH32493
|
||
|
|
|
||
|
|
tm.assert_numpy_array_equal(
|
||
|
|
to_numeric([], downcast=dc1),
|
||
|
|
to_numeric([], downcast=dc2),
|
||
|
|
check_dtype=False,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def test_failure_to_convert_uint64_string_to_NaN():
|
||
|
|
# GH 32394
|
||
|
|
result = to_numeric("uint64", errors="coerce")
|
||
|
|
assert np.isnan(result)
|
||
|
|
|
||
|
|
ser = Series([32, 64, np.nan])
|
||
|
|
result = to_numeric(Series(["32", "64", "uint64"]), errors="coerce")
|
||
|
|
tm.assert_series_equal(result, ser)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"strrep",
|
||
|
|
[
|
||
|
|
"243.164",
|
||
|
|
"245.968",
|
||
|
|
"249.585",
|
||
|
|
"259.745",
|
||
|
|
"265.742",
|
||
|
|
"272.567",
|
||
|
|
"279.196",
|
||
|
|
"280.366",
|
||
|
|
"275.034",
|
||
|
|
"271.351",
|
||
|
|
"272.889",
|
||
|
|
"270.627",
|
||
|
|
"280.828",
|
||
|
|
"290.383",
|
||
|
|
"308.153",
|
||
|
|
"319.945",
|
||
|
|
"336.0",
|
||
|
|
"344.09",
|
||
|
|
"351.385",
|
||
|
|
"356.178",
|
||
|
|
"359.82",
|
||
|
|
"361.03",
|
||
|
|
"367.701",
|
||
|
|
"380.812",
|
||
|
|
"387.98",
|
||
|
|
"391.749",
|
||
|
|
"391.171",
|
||
|
|
"385.97",
|
||
|
|
"385.345",
|
||
|
|
"386.121",
|
||
|
|
"390.996",
|
||
|
|
"399.734",
|
||
|
|
"413.073",
|
||
|
|
"421.532",
|
||
|
|
"430.221",
|
||
|
|
"437.092",
|
||
|
|
"439.746",
|
||
|
|
"446.01",
|
||
|
|
"451.191",
|
||
|
|
"460.463",
|
||
|
|
"469.779",
|
||
|
|
"472.025",
|
||
|
|
"479.49",
|
||
|
|
"474.864",
|
||
|
|
"467.54",
|
||
|
|
"471.978",
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_precision_float_conversion(strrep):
|
||
|
|
# GH 31364
|
||
|
|
result = to_numeric(strrep)
|
||
|
|
|
||
|
|
assert result == float(strrep)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"values, expected",
|
||
|
|
[
|
||
|
|
(["1", "2", None], Series([1, 2, np.nan])),
|
||
|
|
(["1", "2", "3"], Series([1, 2, 3])),
|
||
|
|
(["1", "2", 3], Series([1, 2, 3])),
|
||
|
|
(["1", "2", 3.5], Series([1, 2, 3.5])),
|
||
|
|
(["1", None, 3.5], Series([1, np.nan, 3.5])),
|
||
|
|
(["1", "2", "3.5"], Series([1, 2, 3.5])),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_to_numeric_from_nullable_string(values, nullable_string_dtype, expected):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/37262
|
||
|
|
s = Series(values, dtype=nullable_string_dtype)
|
||
|
|
result = to_numeric(s)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
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||
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|
"data, input_dtype, downcast, expected_dtype",
|
||
|
|
(
|
||
|
|
([1, 1], "Int64", "integer", "Int8"),
|
||
|
|
([1.0, pd.NA], "Float64", "integer", "Int8"),
|
||
|
|
([1.0, 1.1], "Float64", "integer", "Float64"),
|
||
|
|
([1, pd.NA], "Int64", "integer", "Int8"),
|
||
|
|
([450, 300], "Int64", "integer", "Int16"),
|
||
|
|
([1, 1], "Float64", "integer", "Int8"),
|
||
|
|
([np.iinfo(np.int64).max - 1, 1], "Int64", "integer", "Int64"),
|
||
|
|
([1, 1], "Int64", "signed", "Int8"),
|
||
|
|
([1.0, 1.0], "Float32", "signed", "Int8"),
|
||
|
|
([1.0, 1.1], "Float64", "signed", "Float64"),
|
||
|
|
([1, pd.NA], "Int64", "signed", "Int8"),
|
||
|
|
([450, -300], "Int64", "signed", "Int16"),
|
||
|
|
pytest.param(
|
||
|
|
[np.iinfo(np.uint64).max - 1, 1],
|
||
|
|
"UInt64",
|
||
|
|
"signed",
|
||
|
|
"UInt64",
|
||
|
|
marks=pytest.mark.xfail(not is_platform_arm(), reason="GH38798"),
|
||
|
|
),
|
||
|
|
([1, 1], "Int64", "unsigned", "UInt8"),
|
||
|
|
([1.0, 1.0], "Float32", "unsigned", "UInt8"),
|
||
|
|
([1.0, 1.1], "Float64", "unsigned", "Float64"),
|
||
|
|
([1, pd.NA], "Int64", "unsigned", "UInt8"),
|
||
|
|
([450, -300], "Int64", "unsigned", "Int64"),
|
||
|
|
([-1, -1], "Int32", "unsigned", "Int32"),
|
||
|
|
([1, 1], "Float64", "float", "Float32"),
|
||
|
|
([1, 1.1], "Float64", "float", "Float32"),
|
||
|
|
([1, 1], "Float32", "float", "Float32"),
|
||
|
|
([1, 1.1], "Float32", "float", "Float32"),
|
||
|
|
),
|
||
|
|
)
|
||
|
|
def test_downcast_nullable_numeric(data, input_dtype, downcast, expected_dtype):
|
||
|
|
arr = pd.array(data, dtype=input_dtype)
|
||
|
|
result = to_numeric(arr, downcast=downcast)
|
||
|
|
expected = pd.array(data, dtype=expected_dtype)
|
||
|
|
tm.assert_extension_array_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_downcast_nullable_mask_is_copied():
|
||
|
|
# GH38974
|
||
|
|
|
||
|
|
arr = pd.array([1, 2, pd.NA], dtype="Int64")
|
||
|
|
|
||
|
|
result = to_numeric(arr, downcast="integer")
|
||
|
|
expected = pd.array([1, 2, pd.NA], dtype="Int8")
|
||
|
|
tm.assert_extension_array_equal(result, expected)
|
||
|
|
|
||
|
|
arr[1] = pd.NA # should not modify result
|
||
|
|
tm.assert_extension_array_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_to_numeric_scientific_notation():
|
||
|
|
# GH 15898
|
||
|
|
result = to_numeric("1.7e+308")
|
||
|
|
expected = np.float64(1.7e308)
|
||
|
|
assert result == expected
|