Updated script that can be controled by Nodejs web app
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3911
lib/python3.13/site-packages/pandas/tests/tools/test_to_datetime.py
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3911
lib/python3.13/site-packages/pandas/tests/tools/test_to_datetime.py
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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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import pandas.util._test_decorators as td
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import pandas as pd
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from pandas import (
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ArrowDtype,
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DataFrame,
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Index,
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Series,
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option_context,
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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(
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"infer_string", [False, pytest.param(True, marks=td.skip_if_no("pyarrow"))]
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)
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@pytest.mark.parametrize("last_val", ["7", 7])
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def test_series(last_val, infer_string):
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with option_context("future.infer_string", infer_string):
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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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@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
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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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@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
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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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@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
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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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@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
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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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@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
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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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@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
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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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@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
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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):
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transform, assert_equal = transform_assert_equal
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idx = pd.timedelta_range("1 days", periods=3, freq="D")
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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_period(request, transform_assert_equal):
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transform, assert_equal = transform_assert_equal
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idx = pd.period_range("2011-01", periods=3, freq="M", name="")
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inp = transform(idx)
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if not isinstance(inp, Index):
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request.applymarker(
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pytest.mark.xfail(reason="Missing PeriodDtype support in to_numeric")
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)
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result = to_numeric(inp)
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expected = transform(idx.asi8)
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assert_equal(result, expected)
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@pytest.mark.parametrize(
|
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"errors,expected",
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[
|
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("raise", "Invalid object type at position 0"),
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("ignore", Series([[10.0, 2], 1.0, "apple"])),
|
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("coerce", Series([np.nan, 1.0, np.nan])),
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],
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)
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@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
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def test_non_hashable(errors, expected):
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# see gh-13324
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ser = Series([[10.0, 2], 1.0, "apple"])
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if isinstance(expected, str):
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with pytest.raises(TypeError, match=expected):
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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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tm.assert_series_equal(result, expected)
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def test_downcast_invalid_cast():
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# see gh-13352
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data = ["1", 2, 3]
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invalid_downcast = "unsigned-integer"
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msg = "invalid downcasting method provided"
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with pytest.raises(ValueError, match=msg):
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to_numeric(data, downcast=invalid_downcast)
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def test_errors_invalid_value():
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# see gh-26466
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data = ["1", 2, 3]
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invalid_error_value = "invalid"
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msg = "invalid error value specified"
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with pytest.raises(ValueError, match=msg):
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to_numeric(data, errors=invalid_error_value)
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@pytest.mark.parametrize(
|
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"data",
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[
|
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["1", 2, 3],
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[1, 2, 3],
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np.array(["1970-01-02", "1970-01-03", "1970-01-04"], dtype="datetime64[D]"),
|
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],
|
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)
|
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@pytest.mark.parametrize(
|
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"kwargs,exp_dtype",
|
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[
|
||||
# Basic function tests.
|
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({}, np.int64),
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({"downcast": None}, np.int64),
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# Support below np.float32 is rare and far between.
|
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({"downcast": "float"}, np.dtype(np.float32).char),
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# Basic dtype support.
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({"downcast": "unsigned"}, np.dtype(np.typecodes["UnsignedInteger"][0])),
|
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],
|
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)
|
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def test_downcast_basic(data, kwargs, exp_dtype):
|
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# 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)
|
||||
|
||||
msg = "errors='ignore' is deprecated"
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
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)
|
||||
|
||||
|
||||
# Warning in 32 bit platforms
|
||||
@pytest.mark.filterwarnings("ignore:invalid value encountered in cast:RuntimeWarning")
|
||||
@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"),
|
||||
],
|
||||
)
|
||||
@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
|
||||
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], dtype="Int64")),
|
||||
(["1", "2", "3"], Series([1, 2, 3], dtype="Int64")),
|
||||
(["1", "2", 3], Series([1, 2, 3], dtype="Int64")),
|
||||
(["1", "2", 3.5], Series([1, 2, 3.5], dtype="Float64")),
|
||||
(["1", None, 3.5], Series([1, np.nan, 3.5], dtype="Float64")),
|
||||
(["1", "2", "3.5"], Series([1, 2, 3.5], dtype="Float64")),
|
||||
],
|
||||
)
|
||||
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)
|
||||
|
||||
|
||||
def test_to_numeric_from_nullable_string_coerce(nullable_string_dtype):
|
||||
# GH#52146
|
||||
values = ["a", "1"]
|
||||
ser = Series(values, dtype=nullable_string_dtype)
|
||||
result = to_numeric(ser, errors="coerce")
|
||||
expected = Series([pd.NA, 1], dtype="Int64")
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
def test_to_numeric_from_nullable_string_ignore(nullable_string_dtype):
|
||||
# GH#52146
|
||||
values = ["a", "1"]
|
||||
ser = Series(values, dtype=nullable_string_dtype)
|
||||
expected = ser.copy()
|
||||
msg = "errors='ignore' is deprecated"
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
result = to_numeric(ser, errors="ignore")
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"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"),
|
||||
([np.iinfo(np.uint64).max - 1, 1], "UInt64", "signed", "UInt64"),
|
||||
([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
|
||||
|
||||
|
||||
@pytest.mark.parametrize("val", [9876543210.0, 2.0**128])
|
||||
def test_to_numeric_large_float_not_downcast_to_float_32(val):
|
||||
# GH 19729
|
||||
expected = Series([val])
|
||||
result = to_numeric(expected, downcast="float")
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"val, dtype", [(1, "Int64"), (1.5, "Float64"), (True, "boolean")]
|
||||
)
|
||||
def test_to_numeric_dtype_backend(val, dtype):
|
||||
# GH#50505
|
||||
ser = Series([val], dtype=object)
|
||||
result = to_numeric(ser, dtype_backend="numpy_nullable")
|
||||
expected = Series([val], dtype=dtype)
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"val, dtype",
|
||||
[
|
||||
(1, "Int64"),
|
||||
(1.5, "Float64"),
|
||||
(True, "boolean"),
|
||||
(1, "int64[pyarrow]"),
|
||||
(1.5, "float64[pyarrow]"),
|
||||
(True, "bool[pyarrow]"),
|
||||
],
|
||||
)
|
||||
def test_to_numeric_dtype_backend_na(val, dtype):
|
||||
# GH#50505
|
||||
if "pyarrow" in dtype:
|
||||
pytest.importorskip("pyarrow")
|
||||
dtype_backend = "pyarrow"
|
||||
else:
|
||||
dtype_backend = "numpy_nullable"
|
||||
ser = Series([val, None], dtype=object)
|
||||
result = to_numeric(ser, dtype_backend=dtype_backend)
|
||||
expected = Series([val, pd.NA], dtype=dtype)
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"val, dtype, downcast",
|
||||
[
|
||||
(1, "Int8", "integer"),
|
||||
(1.5, "Float32", "float"),
|
||||
(1, "Int8", "signed"),
|
||||
(1, "int8[pyarrow]", "integer"),
|
||||
(1.5, "float[pyarrow]", "float"),
|
||||
(1, "int8[pyarrow]", "signed"),
|
||||
],
|
||||
)
|
||||
def test_to_numeric_dtype_backend_downcasting(val, dtype, downcast):
|
||||
# GH#50505
|
||||
if "pyarrow" in dtype:
|
||||
pytest.importorskip("pyarrow")
|
||||
dtype_backend = "pyarrow"
|
||||
else:
|
||||
dtype_backend = "numpy_nullable"
|
||||
ser = Series([val, None], dtype=object)
|
||||
result = to_numeric(ser, dtype_backend=dtype_backend, downcast=downcast)
|
||||
expected = Series([val, pd.NA], dtype=dtype)
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"smaller, dtype_backend",
|
||||
[["UInt8", "numpy_nullable"], ["uint8[pyarrow]", "pyarrow"]],
|
||||
)
|
||||
def test_to_numeric_dtype_backend_downcasting_uint(smaller, dtype_backend):
|
||||
# GH#50505
|
||||
if dtype_backend == "pyarrow":
|
||||
pytest.importorskip("pyarrow")
|
||||
ser = Series([1, pd.NA], dtype="UInt64")
|
||||
result = to_numeric(ser, dtype_backend=dtype_backend, downcast="unsigned")
|
||||
expected = Series([1, pd.NA], dtype=smaller)
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"dtype",
|
||||
[
|
||||
"Int64",
|
||||
"UInt64",
|
||||
"Float64",
|
||||
"boolean",
|
||||
"int64[pyarrow]",
|
||||
"uint64[pyarrow]",
|
||||
"float64[pyarrow]",
|
||||
"bool[pyarrow]",
|
||||
],
|
||||
)
|
||||
def test_to_numeric_dtype_backend_already_nullable(dtype):
|
||||
# GH#50505
|
||||
if "pyarrow" in dtype:
|
||||
pytest.importorskip("pyarrow")
|
||||
ser = Series([1, pd.NA], dtype=dtype)
|
||||
result = to_numeric(ser, dtype_backend="numpy_nullable")
|
||||
expected = Series([1, pd.NA], dtype=dtype)
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
def test_to_numeric_dtype_backend_error(dtype_backend):
|
||||
# GH#50505
|
||||
ser = Series(["a", "b", ""])
|
||||
expected = ser.copy()
|
||||
with pytest.raises(ValueError, match="Unable to parse string"):
|
||||
to_numeric(ser, dtype_backend=dtype_backend)
|
||||
|
||||
msg = "errors='ignore' is deprecated"
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
result = to_numeric(ser, dtype_backend=dtype_backend, errors="ignore")
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
result = to_numeric(ser, dtype_backend=dtype_backend, errors="coerce")
|
||||
if dtype_backend == "pyarrow":
|
||||
dtype = "double[pyarrow]"
|
||||
else:
|
||||
dtype = "Float64"
|
||||
expected = Series([np.nan, np.nan, np.nan], dtype=dtype)
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
def test_invalid_dtype_backend():
|
||||
ser = Series([1, 2, 3])
|
||||
msg = (
|
||||
"dtype_backend numpy is invalid, only 'numpy_nullable' and "
|
||||
"'pyarrow' are allowed."
|
||||
)
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
to_numeric(ser, dtype_backend="numpy")
|
||||
|
||||
|
||||
def test_coerce_pyarrow_backend():
|
||||
# GH 52588
|
||||
pa = pytest.importorskip("pyarrow")
|
||||
ser = Series(list("12x"), dtype=ArrowDtype(pa.string()))
|
||||
result = to_numeric(ser, errors="coerce", dtype_backend="pyarrow")
|
||||
expected = Series([1, 2, None], dtype=ArrowDtype(pa.int64()))
|
||||
tm.assert_series_equal(result, expected)
|
||||
@ -0,0 +1,72 @@
|
||||
from datetime import time
|
||||
import locale
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas.compat import PY311
|
||||
|
||||
from pandas import Series
|
||||
import pandas._testing as tm
|
||||
from pandas.core.tools.times import to_time
|
||||
|
||||
# The tests marked with this are locale-dependent.
|
||||
# They pass, except when the machine locale is zh_CN or it_IT.
|
||||
fails_on_non_english = pytest.mark.xfail(
|
||||
locale.getlocale()[0] in ("zh_CN", "it_IT"),
|
||||
reason="fail on a CI build with LC_ALL=zh_CN.utf8/it_IT.utf8",
|
||||
strict=False,
|
||||
)
|
||||
|
||||
|
||||
class TestToTime:
|
||||
@pytest.mark.parametrize(
|
||||
"time_string",
|
||||
[
|
||||
"14:15",
|
||||
"1415",
|
||||
pytest.param("2:15pm", marks=fails_on_non_english),
|
||||
pytest.param("0215pm", marks=fails_on_non_english),
|
||||
"14:15:00",
|
||||
"141500",
|
||||
pytest.param("2:15:00pm", marks=fails_on_non_english),
|
||||
pytest.param("021500pm", marks=fails_on_non_english),
|
||||
time(14, 15),
|
||||
],
|
||||
)
|
||||
def test_parsers_time(self, time_string):
|
||||
# GH#11818
|
||||
assert to_time(time_string) == time(14, 15)
|
||||
|
||||
def test_odd_format(self):
|
||||
new_string = "14.15"
|
||||
msg = r"Cannot convert arg \['14\.15'\] to a time"
|
||||
if not PY311:
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
to_time(new_string)
|
||||
assert to_time(new_string, format="%H.%M") == time(14, 15)
|
||||
|
||||
def test_arraylike(self):
|
||||
arg = ["14:15", "20:20"]
|
||||
expected_arr = [time(14, 15), time(20, 20)]
|
||||
assert to_time(arg) == expected_arr
|
||||
assert to_time(arg, format="%H:%M") == expected_arr
|
||||
assert to_time(arg, infer_time_format=True) == expected_arr
|
||||
assert to_time(arg, format="%I:%M%p", errors="coerce") == [None, None]
|
||||
|
||||
msg = "errors='ignore' is deprecated"
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
res = to_time(arg, format="%I:%M%p", errors="ignore")
|
||||
tm.assert_numpy_array_equal(res, np.array(arg, dtype=np.object_))
|
||||
|
||||
msg = "Cannot convert.+to a time with given format"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
to_time(arg, format="%I:%M%p", errors="raise")
|
||||
|
||||
tm.assert_series_equal(
|
||||
to_time(Series(arg, name="test")), Series(expected_arr, name="test")
|
||||
)
|
||||
|
||||
res = to_time(np.array(arg))
|
||||
assert isinstance(res, list)
|
||||
assert res == expected_arr
|
||||
@ -0,0 +1,340 @@
|
||||
from datetime import (
|
||||
time,
|
||||
timedelta,
|
||||
)
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas.compat import IS64
|
||||
from pandas.errors import OutOfBoundsTimedelta
|
||||
|
||||
import pandas as pd
|
||||
from pandas import (
|
||||
Series,
|
||||
TimedeltaIndex,
|
||||
isna,
|
||||
to_timedelta,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
from pandas.core.arrays import TimedeltaArray
|
||||
|
||||
|
||||
class TestTimedeltas:
|
||||
def test_to_timedelta_dt64_raises(self):
|
||||
# Passing datetime64-dtype data to TimedeltaIndex is no longer
|
||||
# supported GH#29794
|
||||
msg = r"dtype datetime64\[ns\] cannot be converted to timedelta64\[ns\]"
|
||||
|
||||
ser = Series([pd.NaT])
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
to_timedelta(ser)
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
ser.to_frame().apply(to_timedelta)
|
||||
|
||||
@pytest.mark.parametrize("readonly", [True, False])
|
||||
def test_to_timedelta_readonly(self, readonly):
|
||||
# GH#34857
|
||||
arr = np.array([], dtype=object)
|
||||
if readonly:
|
||||
arr.setflags(write=False)
|
||||
result = to_timedelta(arr)
|
||||
expected = to_timedelta([])
|
||||
tm.assert_index_equal(result, expected)
|
||||
|
||||
def test_to_timedelta_null(self):
|
||||
result = to_timedelta(["", ""])
|
||||
assert isna(result).all()
|
||||
|
||||
def test_to_timedelta_same_np_timedelta64(self):
|
||||
# pass thru
|
||||
result = to_timedelta(np.array([np.timedelta64(1, "s")]))
|
||||
expected = pd.Index(np.array([np.timedelta64(1, "s")]))
|
||||
tm.assert_index_equal(result, expected)
|
||||
|
||||
def test_to_timedelta_series(self):
|
||||
# Series
|
||||
expected = Series([timedelta(days=1), timedelta(days=1, seconds=1)])
|
||||
result = to_timedelta(Series(["1d", "1days 00:00:01"]))
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
def test_to_timedelta_units(self):
|
||||
# with units
|
||||
result = TimedeltaIndex(
|
||||
[np.timedelta64(0, "ns"), np.timedelta64(10, "s").astype("m8[ns]")]
|
||||
)
|
||||
expected = to_timedelta([0, 10], unit="s")
|
||||
tm.assert_index_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"dtype, unit",
|
||||
[
|
||||
["int64", "s"],
|
||||
["int64", "m"],
|
||||
["int64", "h"],
|
||||
["timedelta64[s]", "s"],
|
||||
["timedelta64[D]", "D"],
|
||||
],
|
||||
)
|
||||
def test_to_timedelta_units_dtypes(self, dtype, unit):
|
||||
# arrays of various dtypes
|
||||
arr = np.array([1] * 5, dtype=dtype)
|
||||
result = to_timedelta(arr, unit=unit)
|
||||
exp_dtype = "m8[ns]" if dtype == "int64" else "m8[s]"
|
||||
expected = TimedeltaIndex([np.timedelta64(1, unit)] * 5, dtype=exp_dtype)
|
||||
tm.assert_index_equal(result, expected)
|
||||
|
||||
def test_to_timedelta_oob_non_nano(self):
|
||||
arr = np.array([pd.NaT._value + 1], dtype="timedelta64[m]")
|
||||
|
||||
msg = (
|
||||
"Cannot convert -9223372036854775807 minutes to "
|
||||
r"timedelta64\[s\] without overflow"
|
||||
)
|
||||
with pytest.raises(OutOfBoundsTimedelta, match=msg):
|
||||
to_timedelta(arr)
|
||||
|
||||
with pytest.raises(OutOfBoundsTimedelta, match=msg):
|
||||
TimedeltaIndex(arr)
|
||||
|
||||
with pytest.raises(OutOfBoundsTimedelta, match=msg):
|
||||
TimedeltaArray._from_sequence(arr, dtype="m8[s]")
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"arg", [np.arange(10).reshape(2, 5), pd.DataFrame(np.arange(10).reshape(2, 5))]
|
||||
)
|
||||
@pytest.mark.parametrize("errors", ["ignore", "raise", "coerce"])
|
||||
@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
|
||||
def test_to_timedelta_dataframe(self, arg, errors):
|
||||
# GH 11776
|
||||
with pytest.raises(TypeError, match="1-d array"):
|
||||
to_timedelta(arg, errors=errors)
|
||||
|
||||
def test_to_timedelta_invalid_errors(self):
|
||||
# bad value for errors parameter
|
||||
msg = "errors must be one of"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
to_timedelta(["foo"], errors="never")
|
||||
|
||||
@pytest.mark.parametrize("arg", [[1, 2], 1])
|
||||
def test_to_timedelta_invalid_unit(self, arg):
|
||||
# these will error
|
||||
msg = "invalid unit abbreviation: foo"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
to_timedelta(arg, unit="foo")
|
||||
|
||||
def test_to_timedelta_time(self):
|
||||
# time not supported ATM
|
||||
msg = (
|
||||
"Value must be Timedelta, string, integer, float, timedelta or convertible"
|
||||
)
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
to_timedelta(time(second=1))
|
||||
assert to_timedelta(time(second=1), errors="coerce") is pd.NaT
|
||||
|
||||
def test_to_timedelta_bad_value(self):
|
||||
msg = "Could not convert 'foo' to NumPy timedelta"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
to_timedelta(["foo", "bar"])
|
||||
|
||||
def test_to_timedelta_bad_value_coerce(self):
|
||||
tm.assert_index_equal(
|
||||
TimedeltaIndex([pd.NaT, pd.NaT]),
|
||||
to_timedelta(["foo", "bar"], errors="coerce"),
|
||||
)
|
||||
|
||||
tm.assert_index_equal(
|
||||
TimedeltaIndex(["1 day", pd.NaT, "1 min"]),
|
||||
to_timedelta(["1 day", "bar", "1 min"], errors="coerce"),
|
||||
)
|
||||
|
||||
def test_to_timedelta_invalid_errors_ignore(self):
|
||||
# gh-13613: these should not error because errors='ignore'
|
||||
msg = "errors='ignore' is deprecated"
|
||||
invalid_data = "apple"
|
||||
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
result = to_timedelta(invalid_data, errors="ignore")
|
||||
assert invalid_data == result
|
||||
|
||||
invalid_data = ["apple", "1 days"]
|
||||
expected = np.array(invalid_data, dtype=object)
|
||||
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
result = to_timedelta(invalid_data, errors="ignore")
|
||||
tm.assert_numpy_array_equal(expected, result)
|
||||
|
||||
invalid_data = pd.Index(["apple", "1 days"])
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
result = to_timedelta(invalid_data, errors="ignore")
|
||||
tm.assert_index_equal(invalid_data, result)
|
||||
|
||||
invalid_data = Series(["apple", "1 days"])
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
result = to_timedelta(invalid_data, errors="ignore")
|
||||
tm.assert_series_equal(invalid_data, result)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"val, errors",
|
||||
[
|
||||
("1M", True),
|
||||
("1 M", True),
|
||||
("1Y", True),
|
||||
("1 Y", True),
|
||||
("1y", True),
|
||||
("1 y", True),
|
||||
("1m", False),
|
||||
("1 m", False),
|
||||
("1 day", False),
|
||||
("2day", False),
|
||||
],
|
||||
)
|
||||
def test_unambiguous_timedelta_values(self, val, errors):
|
||||
# GH36666 Deprecate use of strings denoting units with 'M', 'Y', 'm' or 'y'
|
||||
# in pd.to_timedelta
|
||||
msg = "Units 'M', 'Y' and 'y' do not represent unambiguous timedelta"
|
||||
if errors:
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
to_timedelta(val)
|
||||
else:
|
||||
# check it doesn't raise
|
||||
to_timedelta(val)
|
||||
|
||||
def test_to_timedelta_via_apply(self):
|
||||
# GH 5458
|
||||
expected = Series([np.timedelta64(1, "s")])
|
||||
result = Series(["00:00:01"]).apply(to_timedelta)
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
result = Series([to_timedelta("00:00:01")])
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
def test_to_timedelta_inference_without_warning(self):
|
||||
# GH#41731 inference produces a warning in the Series constructor,
|
||||
# but _not_ in to_timedelta
|
||||
vals = ["00:00:01", pd.NaT]
|
||||
with tm.assert_produces_warning(None):
|
||||
result = to_timedelta(vals)
|
||||
|
||||
expected = TimedeltaIndex([pd.Timedelta(seconds=1), pd.NaT])
|
||||
tm.assert_index_equal(result, expected)
|
||||
|
||||
def test_to_timedelta_on_missing_values(self):
|
||||
# GH5438
|
||||
timedelta_NaT = np.timedelta64("NaT")
|
||||
|
||||
actual = to_timedelta(Series(["00:00:01", np.nan]))
|
||||
expected = Series(
|
||||
[np.timedelta64(1000000000, "ns"), timedelta_NaT],
|
||||
dtype=f"{tm.ENDIAN}m8[ns]",
|
||||
)
|
||||
tm.assert_series_equal(actual, expected)
|
||||
|
||||
ser = Series(["00:00:01", pd.NaT], dtype="m8[ns]")
|
||||
actual = to_timedelta(ser)
|
||||
tm.assert_series_equal(actual, expected)
|
||||
|
||||
@pytest.mark.parametrize("val", [np.nan, pd.NaT, pd.NA])
|
||||
def test_to_timedelta_on_missing_values_scalar(self, val):
|
||||
actual = to_timedelta(val)
|
||||
assert actual._value == np.timedelta64("NaT").astype("int64")
|
||||
|
||||
@pytest.mark.parametrize("val", [np.nan, pd.NaT, pd.NA])
|
||||
def test_to_timedelta_on_missing_values_list(self, val):
|
||||
actual = to_timedelta([val])
|
||||
assert actual[0]._value == np.timedelta64("NaT").astype("int64")
|
||||
|
||||
@pytest.mark.xfail(not IS64, reason="Floating point error")
|
||||
def test_to_timedelta_float(self):
|
||||
# https://github.com/pandas-dev/pandas/issues/25077
|
||||
arr = np.arange(0, 1, 1e-6)[-10:]
|
||||
result = to_timedelta(arr, unit="s")
|
||||
expected_asi8 = np.arange(999990000, 10**9, 1000, dtype="int64")
|
||||
tm.assert_numpy_array_equal(result.asi8, expected_asi8)
|
||||
|
||||
def test_to_timedelta_coerce_strings_unit(self):
|
||||
arr = np.array([1, 2, "error"], dtype=object)
|
||||
result = to_timedelta(arr, unit="ns", errors="coerce")
|
||||
expected = to_timedelta([1, 2, pd.NaT], unit="ns")
|
||||
tm.assert_index_equal(result, expected)
|
||||
|
||||
def test_to_timedelta_ignore_strings_unit(self):
|
||||
arr = np.array([1, 2, "error"], dtype=object)
|
||||
msg = "errors='ignore' is deprecated"
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
result = to_timedelta(arr, unit="ns", errors="ignore")
|
||||
tm.assert_numpy_array_equal(result, arr)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"expected_val, result_val", [[timedelta(days=2), 2], [None, None]]
|
||||
)
|
||||
def test_to_timedelta_nullable_int64_dtype(self, expected_val, result_val):
|
||||
# GH 35574
|
||||
expected = Series([timedelta(days=1), expected_val])
|
||||
result = to_timedelta(Series([1, result_val], dtype="Int64"), unit="days")
|
||||
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("input", "expected"),
|
||||
[
|
||||
("8:53:08.71800000001", "8:53:08.718"),
|
||||
("8:53:08.718001", "8:53:08.718001"),
|
||||
("8:53:08.7180000001", "8:53:08.7180000001"),
|
||||
("-8:53:08.71800000001", "-8:53:08.718"),
|
||||
("8:53:08.7180000089", "8:53:08.718000008"),
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize("func", [pd.Timedelta, to_timedelta])
|
||||
def test_to_timedelta_precision_over_nanos(self, input, expected, func):
|
||||
# GH: 36738
|
||||
expected = pd.Timedelta(expected)
|
||||
result = func(input)
|
||||
assert result == expected
|
||||
|
||||
def test_to_timedelta_zerodim(self, fixed_now_ts):
|
||||
# ndarray.item() incorrectly returns int for dt64[ns] and td64[ns]
|
||||
dt64 = fixed_now_ts.to_datetime64()
|
||||
arg = np.array(dt64)
|
||||
|
||||
msg = (
|
||||
"Value must be Timedelta, string, integer, float, timedelta "
|
||||
"or convertible, not datetime64"
|
||||
)
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
to_timedelta(arg)
|
||||
|
||||
arg2 = arg.view("m8[ns]")
|
||||
result = to_timedelta(arg2)
|
||||
assert isinstance(result, pd.Timedelta)
|
||||
assert result._value == dt64.view("i8")
|
||||
|
||||
def test_to_timedelta_numeric_ea(self, any_numeric_ea_dtype):
|
||||
# GH#48796
|
||||
ser = Series([1, pd.NA], dtype=any_numeric_ea_dtype)
|
||||
result = to_timedelta(ser)
|
||||
expected = Series([pd.Timedelta(1, unit="ns"), pd.NaT])
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
def test_to_timedelta_fraction(self):
|
||||
result = to_timedelta(1.0 / 3, unit="h")
|
||||
expected = pd.Timedelta("0 days 00:19:59.999999998")
|
||||
assert result == expected
|
||||
|
||||
|
||||
def test_from_numeric_arrow_dtype(any_numeric_ea_dtype):
|
||||
# GH 52425
|
||||
pytest.importorskip("pyarrow")
|
||||
ser = Series([1, 2], dtype=f"{any_numeric_ea_dtype.lower()}[pyarrow]")
|
||||
result = to_timedelta(ser)
|
||||
expected = Series([1, 2], dtype="timedelta64[ns]")
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("unit", ["ns", "ms"])
|
||||
def test_from_timedelta_arrow_dtype(unit):
|
||||
# GH 54298
|
||||
pytest.importorskip("pyarrow")
|
||||
expected = Series([timedelta(1)], dtype=f"duration[{unit}][pyarrow]")
|
||||
result = to_timedelta(expected)
|
||||
tm.assert_series_equal(result, expected)
|
||||
Reference in New Issue
Block a user