Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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1334 lines
46 KiB
1334 lines
46 KiB
4 years ago
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# Arithmetic tests for DataFrame/Series/Index/Array classes that should
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# behave identically.
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# Specifically for numeric dtypes
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from collections import abc
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from decimal import Decimal
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from itertools import combinations
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import operator
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from typing import Any, List
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import Index, Series, Timedelta, TimedeltaIndex
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import pandas._testing as tm
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from pandas.core import ops
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def adjust_negative_zero(zero, expected):
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"""
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Helper to adjust the expected result if we are dividing by -0.0
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as opposed to 0.0
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"""
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if np.signbit(np.array(zero)).any():
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# All entries in the `zero` fixture should be either
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# all-negative or no-negative.
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assert np.signbit(np.array(zero)).all()
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expected *= -1
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return expected
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# TODO: remove this kludge once mypy stops giving false positives here
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# List comprehension has incompatible type List[PandasObject]; expected List[RangeIndex]
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# See GH#29725
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ser_or_index: List[Any] = [pd.Series, pd.Index]
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lefts: List[Any] = [pd.RangeIndex(10, 40, 10)]
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lefts.extend(
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[
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cls([10, 20, 30], dtype=dtype)
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for dtype in ["i1", "i2", "i4", "i8", "u1", "u2", "u4", "u8", "f2", "f4", "f8"]
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for cls in ser_or_index
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]
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)
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# ------------------------------------------------------------------
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# Comparisons
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class TestNumericComparisons:
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def test_operator_series_comparison_zerorank(self):
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# GH#13006
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result = np.float64(0) > pd.Series([1, 2, 3])
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expected = 0.0 > pd.Series([1, 2, 3])
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tm.assert_series_equal(result, expected)
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result = pd.Series([1, 2, 3]) < np.float64(0)
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expected = pd.Series([1, 2, 3]) < 0.0
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tm.assert_series_equal(result, expected)
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result = np.array([0, 1, 2])[0] > pd.Series([0, 1, 2])
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expected = 0.0 > pd.Series([1, 2, 3])
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tm.assert_series_equal(result, expected)
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def test_df_numeric_cmp_dt64_raises(self):
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# GH#8932, GH#22163
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ts = pd.Timestamp.now()
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df = pd.DataFrame({"x": range(5)})
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msg = (
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"'[<>]' not supported between instances of 'numpy.ndarray' and 'Timestamp'"
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)
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with pytest.raises(TypeError, match=msg):
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df > ts
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with pytest.raises(TypeError, match=msg):
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df < ts
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with pytest.raises(TypeError, match=msg):
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ts < df
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with pytest.raises(TypeError, match=msg):
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ts > df
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assert not (df == ts).any().any()
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assert (df != ts).all().all()
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def test_compare_invalid(self):
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# GH#8058
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# ops testing
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a = pd.Series(np.random.randn(5), name=0)
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b = pd.Series(np.random.randn(5))
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b.name = pd.Timestamp("2000-01-01")
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tm.assert_series_equal(a / b, 1 / (b / a))
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def test_numeric_cmp_string_numexpr_path(self, box):
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# GH#36377, GH#35700
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xbox = box if box is not pd.Index else np.ndarray
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obj = pd.Series(np.random.randn(10 ** 5))
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obj = tm.box_expected(obj, box, transpose=False)
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result = obj == "a"
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expected = pd.Series(np.zeros(10 ** 5, dtype=bool))
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expected = tm.box_expected(expected, xbox, transpose=False)
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tm.assert_equal(result, expected)
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result = obj != "a"
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tm.assert_equal(result, ~expected)
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msg = "Invalid comparison between dtype=float64 and str"
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with pytest.raises(TypeError, match=msg):
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obj < "a"
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# ------------------------------------------------------------------
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# Numeric dtypes Arithmetic with Datetime/Timedelta Scalar
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class TestNumericArraylikeArithmeticWithDatetimeLike:
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# TODO: also check name retentention
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@pytest.mark.parametrize("box_cls", [np.array, pd.Index, pd.Series])
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@pytest.mark.parametrize(
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"left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype),
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)
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def test_mul_td64arr(self, left, box_cls):
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# GH#22390
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right = np.array([1, 2, 3], dtype="m8[s]")
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right = box_cls(right)
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expected = pd.TimedeltaIndex(["10s", "40s", "90s"])
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if isinstance(left, pd.Series) or box_cls is pd.Series:
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expected = pd.Series(expected)
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result = left * right
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tm.assert_equal(result, expected)
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result = right * left
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tm.assert_equal(result, expected)
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# TODO: also check name retentention
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@pytest.mark.parametrize("box_cls", [np.array, pd.Index, pd.Series])
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@pytest.mark.parametrize(
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"left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype),
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)
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def test_div_td64arr(self, left, box_cls):
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# GH#22390
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right = np.array([10, 40, 90], dtype="m8[s]")
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right = box_cls(right)
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expected = pd.TimedeltaIndex(["1s", "2s", "3s"])
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if isinstance(left, pd.Series) or box_cls is pd.Series:
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expected = pd.Series(expected)
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result = right / left
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tm.assert_equal(result, expected)
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result = right // left
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tm.assert_equal(result, expected)
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msg = "Cannot divide"
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with pytest.raises(TypeError, match=msg):
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left / right
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with pytest.raises(TypeError, match=msg):
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left // right
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# TODO: de-duplicate with test_numeric_arr_mul_tdscalar
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def test_ops_series(self):
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# regression test for G#H8813
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td = Timedelta("1 day")
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other = pd.Series([1, 2])
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expected = pd.Series(pd.to_timedelta(["1 day", "2 days"]))
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tm.assert_series_equal(expected, td * other)
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tm.assert_series_equal(expected, other * td)
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# TODO: also test non-nanosecond timedelta64 and Tick objects;
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# see test_numeric_arr_rdiv_tdscalar for note on these failing
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@pytest.mark.parametrize(
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"scalar_td",
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[
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Timedelta(days=1),
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Timedelta(days=1).to_timedelta64(),
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Timedelta(days=1).to_pytimedelta(),
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],
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ids=lambda x: type(x).__name__,
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)
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def test_numeric_arr_mul_tdscalar(self, scalar_td, numeric_idx, box):
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# GH#19333
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index = numeric_idx
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expected = pd.TimedeltaIndex([pd.Timedelta(days=n) for n in range(5)])
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index = tm.box_expected(index, box)
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expected = tm.box_expected(expected, box)
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result = index * scalar_td
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tm.assert_equal(result, expected)
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commute = scalar_td * index
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tm.assert_equal(commute, expected)
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@pytest.mark.parametrize(
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"scalar_td",
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[
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Timedelta(days=1),
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Timedelta(days=1).to_timedelta64(),
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Timedelta(days=1).to_pytimedelta(),
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],
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ids=lambda x: type(x).__name__,
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)
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def test_numeric_arr_mul_tdscalar_numexpr_path(self, scalar_td, box):
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arr = np.arange(2 * 10 ** 4).astype(np.int64)
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obj = tm.box_expected(arr, box, transpose=False)
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expected = arr.view("timedelta64[D]").astype("timedelta64[ns]")
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expected = tm.box_expected(expected, box, transpose=False)
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result = obj * scalar_td
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tm.assert_equal(result, expected)
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result = scalar_td * obj
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tm.assert_equal(result, expected)
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def test_numeric_arr_rdiv_tdscalar(self, three_days, numeric_idx, box):
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index = numeric_idx[1:3]
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expected = TimedeltaIndex(["3 Days", "36 Hours"])
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index = tm.box_expected(index, box)
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expected = tm.box_expected(expected, box)
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result = three_days / index
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tm.assert_equal(result, expected)
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msg = "cannot use operands with types dtype"
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with pytest.raises(TypeError, match=msg):
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index / three_days
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@pytest.mark.parametrize(
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"other",
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[
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pd.Timedelta(hours=31),
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pd.Timedelta(hours=31).to_pytimedelta(),
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pd.Timedelta(hours=31).to_timedelta64(),
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pd.Timedelta(hours=31).to_timedelta64().astype("m8[h]"),
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np.timedelta64("NaT"),
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np.timedelta64("NaT", "D"),
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pd.offsets.Minute(3),
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pd.offsets.Second(0),
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],
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)
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def test_add_sub_timedeltalike_invalid(self, numeric_idx, other, box):
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left = tm.box_expected(numeric_idx, box)
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msg = (
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"unsupported operand type|"
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"Addition/subtraction of integers and integer-arrays|"
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"Instead of adding/subtracting|"
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"cannot use operands with types dtype|"
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"Concatenation operation is not implemented for NumPy arrays"
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)
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with pytest.raises(TypeError, match=msg):
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left + other
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with pytest.raises(TypeError, match=msg):
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other + left
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with pytest.raises(TypeError, match=msg):
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left - other
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with pytest.raises(TypeError, match=msg):
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other - left
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@pytest.mark.parametrize(
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"other",
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[
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pd.Timestamp.now().to_pydatetime(),
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pd.Timestamp.now(tz="UTC").to_pydatetime(),
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pd.Timestamp.now().to_datetime64(),
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pd.NaT,
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],
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)
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@pytest.mark.filterwarnings("ignore:elementwise comp:DeprecationWarning")
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def test_add_sub_datetimelike_invalid(self, numeric_idx, other, box):
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# GH#28080 numeric+datetime64 should raise; Timestamp raises
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# NullFrequencyError instead of TypeError so is excluded.
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left = tm.box_expected(numeric_idx, box)
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msg = (
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"unsupported operand type|"
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"Cannot (add|subtract) NaT (to|from) ndarray|"
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"Addition/subtraction of integers and integer-arrays|"
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"Concatenation operation is not implemented for NumPy arrays"
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)
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with pytest.raises(TypeError, match=msg):
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left + other
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with pytest.raises(TypeError, match=msg):
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other + left
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with pytest.raises(TypeError, match=msg):
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left - other
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with pytest.raises(TypeError, match=msg):
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other - left
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# ------------------------------------------------------------------
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# Arithmetic
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class TestDivisionByZero:
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def test_div_zero(self, zero, numeric_idx):
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idx = numeric_idx
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expected = pd.Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64)
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# We only adjust for Index, because Series does not yet apply
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# the adjustment correctly.
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expected2 = adjust_negative_zero(zero, expected)
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result = idx / zero
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tm.assert_index_equal(result, expected2)
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ser_compat = Series(idx).astype("i8") / np.array(zero).astype("i8")
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tm.assert_series_equal(ser_compat, Series(expected))
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def test_floordiv_zero(self, zero, numeric_idx):
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idx = numeric_idx
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expected = pd.Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64)
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# We only adjust for Index, because Series does not yet apply
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# the adjustment correctly.
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expected2 = adjust_negative_zero(zero, expected)
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result = idx // zero
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tm.assert_index_equal(result, expected2)
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ser_compat = Series(idx).astype("i8") // np.array(zero).astype("i8")
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tm.assert_series_equal(ser_compat, Series(expected))
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def test_mod_zero(self, zero, numeric_idx):
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idx = numeric_idx
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expected = pd.Index([np.nan, np.nan, np.nan, np.nan, np.nan], dtype=np.float64)
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result = idx % zero
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tm.assert_index_equal(result, expected)
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ser_compat = Series(idx).astype("i8") % np.array(zero).astype("i8")
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tm.assert_series_equal(ser_compat, Series(result))
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def test_divmod_zero(self, zero, numeric_idx):
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idx = numeric_idx
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exleft = pd.Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64)
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exright = pd.Index([np.nan, np.nan, np.nan, np.nan, np.nan], dtype=np.float64)
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exleft = adjust_negative_zero(zero, exleft)
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result = divmod(idx, zero)
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tm.assert_index_equal(result[0], exleft)
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tm.assert_index_equal(result[1], exright)
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@pytest.mark.parametrize("op", [operator.truediv, operator.floordiv])
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def test_div_negative_zero(self, zero, numeric_idx, op):
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||
|
|
# Check that -1 / -0.0 returns np.inf, not -np.inf
|
||
|
|
if isinstance(numeric_idx, pd.UInt64Index):
|
||
|
|
return
|
||
|
|
idx = numeric_idx - 3
|
||
|
|
|
||
|
|
expected = pd.Index(
|
||
|
|
[-np.inf, -np.inf, -np.inf, np.nan, np.inf], dtype=np.float64
|
||
|
|
)
|
||
|
|
expected = adjust_negative_zero(zero, expected)
|
||
|
|
|
||
|
|
result = op(idx, zero)
|
||
|
|
tm.assert_index_equal(result, expected)
|
||
|
|
|
||
|
|
# ------------------------------------------------------------------
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype1", [np.int64, np.float64, np.uint64])
|
||
|
|
def test_ser_div_ser(self, dtype1, any_real_dtype):
|
||
|
|
# no longer do integer div for any ops, but deal with the 0's
|
||
|
|
dtype2 = any_real_dtype
|
||
|
|
|
||
|
|
first = Series([3, 4, 5, 8], name="first").astype(dtype1)
|
||
|
|
second = Series([0, 0, 0, 3], name="second").astype(dtype2)
|
||
|
|
|
||
|
|
with np.errstate(all="ignore"):
|
||
|
|
expected = Series(
|
||
|
|
first.values.astype(np.float64) / second.values,
|
||
|
|
dtype="float64",
|
||
|
|
name=None,
|
||
|
|
)
|
||
|
|
expected.iloc[0:3] = np.inf
|
||
|
|
|
||
|
|
result = first / second
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
assert not result.equals(second / first)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype1", [np.int64, np.float64, np.uint64])
|
||
|
|
def test_ser_divmod_zero(self, dtype1, any_real_dtype):
|
||
|
|
# GH#26987
|
||
|
|
dtype2 = any_real_dtype
|
||
|
|
left = pd.Series([1, 1]).astype(dtype1)
|
||
|
|
right = pd.Series([0, 2]).astype(dtype2)
|
||
|
|
|
||
|
|
# GH#27321 pandas convention is to set 1 // 0 to np.inf, as opposed
|
||
|
|
# to numpy which sets to np.nan; patch `expected[0]` below
|
||
|
|
expected = left // right, left % right
|
||
|
|
expected = list(expected)
|
||
|
|
expected[0] = expected[0].astype(np.float64)
|
||
|
|
expected[0][0] = np.inf
|
||
|
|
result = divmod(left, right)
|
||
|
|
|
||
|
|
tm.assert_series_equal(result[0], expected[0])
|
||
|
|
tm.assert_series_equal(result[1], expected[1])
|
||
|
|
|
||
|
|
# rdivmod case
|
||
|
|
result = divmod(left.values, right)
|
||
|
|
tm.assert_series_equal(result[0], expected[0])
|
||
|
|
tm.assert_series_equal(result[1], expected[1])
|
||
|
|
|
||
|
|
def test_ser_divmod_inf(self):
|
||
|
|
left = pd.Series([np.inf, 1.0])
|
||
|
|
right = pd.Series([np.inf, 2.0])
|
||
|
|
|
||
|
|
expected = left // right, left % right
|
||
|
|
result = divmod(left, right)
|
||
|
|
|
||
|
|
tm.assert_series_equal(result[0], expected[0])
|
||
|
|
tm.assert_series_equal(result[1], expected[1])
|
||
|
|
|
||
|
|
# rdivmod case
|
||
|
|
result = divmod(left.values, right)
|
||
|
|
tm.assert_series_equal(result[0], expected[0])
|
||
|
|
tm.assert_series_equal(result[1], expected[1])
|
||
|
|
|
||
|
|
def test_rdiv_zero_compat(self):
|
||
|
|
# GH#8674
|
||
|
|
zero_array = np.array([0] * 5)
|
||
|
|
data = np.random.randn(5)
|
||
|
|
expected = Series([0.0] * 5)
|
||
|
|
|
||
|
|
result = zero_array / Series(data)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = Series(zero_array) / data
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = Series(zero_array) / Series(data)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_div_zero_inf_signs(self):
|
||
|
|
# GH#9144, inf signing
|
||
|
|
ser = Series([-1, 0, 1], name="first")
|
||
|
|
expected = Series([-np.inf, np.nan, np.inf], name="first")
|
||
|
|
|
||
|
|
result = ser / 0
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_rdiv_zero(self):
|
||
|
|
# GH#9144
|
||
|
|
ser = Series([-1, 0, 1], name="first")
|
||
|
|
expected = Series([0.0, np.nan, 0.0], name="first")
|
||
|
|
|
||
|
|
result = 0 / ser
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_floordiv_div(self):
|
||
|
|
# GH#9144
|
||
|
|
ser = Series([-1, 0, 1], name="first")
|
||
|
|
|
||
|
|
result = ser // 0
|
||
|
|
expected = Series([-np.inf, np.nan, np.inf], name="first")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_df_div_zero_df(self):
|
||
|
|
# integer div, but deal with the 0's (GH#9144)
|
||
|
|
df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
|
||
|
|
result = df / df
|
||
|
|
|
||
|
|
first = pd.Series([1.0, 1.0, 1.0, 1.0])
|
||
|
|
second = pd.Series([np.nan, np.nan, np.nan, 1])
|
||
|
|
expected = pd.DataFrame({"first": first, "second": second})
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_df_div_zero_array(self):
|
||
|
|
# integer div, but deal with the 0's (GH#9144)
|
||
|
|
df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
|
||
|
|
|
||
|
|
first = pd.Series([1.0, 1.0, 1.0, 1.0])
|
||
|
|
second = pd.Series([np.nan, np.nan, np.nan, 1])
|
||
|
|
expected = pd.DataFrame({"first": first, "second": second})
|
||
|
|
|
||
|
|
with np.errstate(all="ignore"):
|
||
|
|
arr = df.values.astype("float") / df.values
|
||
|
|
result = pd.DataFrame(arr, index=df.index, columns=df.columns)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_df_div_zero_int(self):
|
||
|
|
# integer div, but deal with the 0's (GH#9144)
|
||
|
|
df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
|
||
|
|
|
||
|
|
result = df / 0
|
||
|
|
expected = pd.DataFrame(np.inf, index=df.index, columns=df.columns)
|
||
|
|
expected.iloc[0:3, 1] = np.nan
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# numpy has a slightly different (wrong) treatment
|
||
|
|
with np.errstate(all="ignore"):
|
||
|
|
arr = df.values.astype("float64") / 0
|
||
|
|
result2 = pd.DataFrame(arr, index=df.index, columns=df.columns)
|
||
|
|
tm.assert_frame_equal(result2, expected)
|
||
|
|
|
||
|
|
def test_df_div_zero_series_does_not_commute(self):
|
||
|
|
# integer div, but deal with the 0's (GH#9144)
|
||
|
|
df = pd.DataFrame(np.random.randn(10, 5))
|
||
|
|
ser = df[0]
|
||
|
|
res = ser / df
|
||
|
|
res2 = df / ser
|
||
|
|
assert not res.fillna(0).equals(res2.fillna(0))
|
||
|
|
|
||
|
|
# ------------------------------------------------------------------
|
||
|
|
# Mod By Zero
|
||
|
|
|
||
|
|
def test_df_mod_zero_df(self):
|
||
|
|
# GH#3590, modulo as ints
|
||
|
|
df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
|
||
|
|
|
||
|
|
# this is technically wrong, as the integer portion is coerced to float
|
||
|
|
# ###
|
||
|
|
first = pd.Series([0, 0, 0, 0], dtype="float64")
|
||
|
|
second = pd.Series([np.nan, np.nan, np.nan, 0])
|
||
|
|
expected = pd.DataFrame({"first": first, "second": second})
|
||
|
|
result = df % df
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_df_mod_zero_array(self):
|
||
|
|
# GH#3590, modulo as ints
|
||
|
|
df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
|
||
|
|
|
||
|
|
# this is technically wrong, as the integer portion is coerced to float
|
||
|
|
# ###
|
||
|
|
first = pd.Series([0, 0, 0, 0], dtype="float64")
|
||
|
|
second = pd.Series([np.nan, np.nan, np.nan, 0])
|
||
|
|
expected = pd.DataFrame({"first": first, "second": second})
|
||
|
|
|
||
|
|
# numpy has a slightly different (wrong) treatment
|
||
|
|
with np.errstate(all="ignore"):
|
||
|
|
arr = df.values % df.values
|
||
|
|
result2 = pd.DataFrame(arr, index=df.index, columns=df.columns, dtype="float64")
|
||
|
|
result2.iloc[0:3, 1] = np.nan
|
||
|
|
tm.assert_frame_equal(result2, expected)
|
||
|
|
|
||
|
|
def test_df_mod_zero_int(self):
|
||
|
|
# GH#3590, modulo as ints
|
||
|
|
df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
|
||
|
|
|
||
|
|
result = df % 0
|
||
|
|
expected = pd.DataFrame(np.nan, index=df.index, columns=df.columns)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# numpy has a slightly different (wrong) treatment
|
||
|
|
with np.errstate(all="ignore"):
|
||
|
|
arr = df.values.astype("float64") % 0
|
||
|
|
result2 = pd.DataFrame(arr, index=df.index, columns=df.columns)
|
||
|
|
tm.assert_frame_equal(result2, expected)
|
||
|
|
|
||
|
|
def test_df_mod_zero_series_does_not_commute(self):
|
||
|
|
# GH#3590, modulo as ints
|
||
|
|
# not commutative with series
|
||
|
|
df = pd.DataFrame(np.random.randn(10, 5))
|
||
|
|
ser = df[0]
|
||
|
|
res = ser % df
|
||
|
|
res2 = df % ser
|
||
|
|
assert not res.fillna(0).equals(res2.fillna(0))
|
||
|
|
|
||
|
|
|
||
|
|
class TestMultiplicationDivision:
|
||
|
|
# __mul__, __rmul__, __div__, __rdiv__, __floordiv__, __rfloordiv__
|
||
|
|
# for non-timestamp/timedelta/period dtypes
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"box",
|
||
|
|
[
|
||
|
|
pytest.param(
|
||
|
|
pd.Index,
|
||
|
|
marks=pytest.mark.xfail(
|
||
|
|
reason="Index.__div__ always raises", raises=TypeError
|
||
|
|
),
|
||
|
|
),
|
||
|
|
pd.Series,
|
||
|
|
pd.DataFrame,
|
||
|
|
],
|
||
|
|
ids=lambda x: x.__name__,
|
||
|
|
)
|
||
|
|
def test_divide_decimal(self, box):
|
||
|
|
# resolves issue GH#9787
|
||
|
|
ser = Series([Decimal(10)])
|
||
|
|
expected = Series([Decimal(5)])
|
||
|
|
|
||
|
|
ser = tm.box_expected(ser, box)
|
||
|
|
expected = tm.box_expected(expected, box)
|
||
|
|
|
||
|
|
result = ser / Decimal(2)
|
||
|
|
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
result = ser // Decimal(2)
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
def test_div_equiv_binop(self):
|
||
|
|
# Test Series.div as well as Series.__div__
|
||
|
|
# float/integer issue
|
||
|
|
# GH#7785
|
||
|
|
first = Series([1, 0], name="first")
|
||
|
|
second = Series([-0.01, -0.02], name="second")
|
||
|
|
expected = Series([-0.01, -np.inf])
|
||
|
|
|
||
|
|
result = second.div(first)
|
||
|
|
tm.assert_series_equal(result, expected, check_names=False)
|
||
|
|
|
||
|
|
result = second / first
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_div_int(self, numeric_idx):
|
||
|
|
idx = numeric_idx
|
||
|
|
result = idx / 1
|
||
|
|
expected = idx.astype("float64")
|
||
|
|
tm.assert_index_equal(result, expected)
|
||
|
|
|
||
|
|
result = idx / 2
|
||
|
|
expected = Index(idx.values / 2)
|
||
|
|
tm.assert_index_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("op", [operator.mul, ops.rmul, operator.floordiv])
|
||
|
|
def test_mul_int_identity(self, op, numeric_idx, box_with_array):
|
||
|
|
idx = numeric_idx
|
||
|
|
idx = tm.box_expected(idx, box_with_array)
|
||
|
|
|
||
|
|
result = op(idx, 1)
|
||
|
|
tm.assert_equal(result, idx)
|
||
|
|
|
||
|
|
def test_mul_int_array(self, numeric_idx):
|
||
|
|
idx = numeric_idx
|
||
|
|
didx = idx * idx
|
||
|
|
|
||
|
|
result = idx * np.array(5, dtype="int64")
|
||
|
|
tm.assert_index_equal(result, idx * 5)
|
||
|
|
|
||
|
|
arr_dtype = "uint64" if isinstance(idx, pd.UInt64Index) else "int64"
|
||
|
|
result = idx * np.arange(5, dtype=arr_dtype)
|
||
|
|
tm.assert_index_equal(result, didx)
|
||
|
|
|
||
|
|
def test_mul_int_series(self, numeric_idx):
|
||
|
|
idx = numeric_idx
|
||
|
|
didx = idx * idx
|
||
|
|
|
||
|
|
arr_dtype = "uint64" if isinstance(idx, pd.UInt64Index) else "int64"
|
||
|
|
result = idx * Series(np.arange(5, dtype=arr_dtype))
|
||
|
|
tm.assert_series_equal(result, Series(didx))
|
||
|
|
|
||
|
|
def test_mul_float_series(self, numeric_idx):
|
||
|
|
idx = numeric_idx
|
||
|
|
rng5 = np.arange(5, dtype="float64")
|
||
|
|
|
||
|
|
result = idx * Series(rng5 + 0.1)
|
||
|
|
expected = Series(rng5 * (rng5 + 0.1))
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_mul_index(self, numeric_idx):
|
||
|
|
# in general not true for RangeIndex
|
||
|
|
idx = numeric_idx
|
||
|
|
if not isinstance(idx, pd.RangeIndex):
|
||
|
|
result = idx * idx
|
||
|
|
tm.assert_index_equal(result, idx ** 2)
|
||
|
|
|
||
|
|
def test_mul_datelike_raises(self, numeric_idx):
|
||
|
|
idx = numeric_idx
|
||
|
|
msg = "cannot perform __rmul__ with this index type"
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
idx * pd.date_range("20130101", periods=5)
|
||
|
|
|
||
|
|
def test_mul_size_mismatch_raises(self, numeric_idx):
|
||
|
|
idx = numeric_idx
|
||
|
|
msg = "operands could not be broadcast together"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
idx * idx[0:3]
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
idx * np.array([1, 2])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("op", [operator.pow, ops.rpow])
|
||
|
|
def test_pow_float(self, op, numeric_idx, box_with_array):
|
||
|
|
# test power calculations both ways, GH#14973
|
||
|
|
box = box_with_array
|
||
|
|
idx = numeric_idx
|
||
|
|
expected = pd.Float64Index(op(idx.values, 2.0))
|
||
|
|
|
||
|
|
idx = tm.box_expected(idx, box)
|
||
|
|
expected = tm.box_expected(expected, box)
|
||
|
|
|
||
|
|
result = op(idx, 2.0)
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
def test_modulo(self, numeric_idx, box_with_array):
|
||
|
|
# GH#9244
|
||
|
|
box = box_with_array
|
||
|
|
idx = numeric_idx
|
||
|
|
expected = Index(idx.values % 2)
|
||
|
|
|
||
|
|
idx = tm.box_expected(idx, box)
|
||
|
|
expected = tm.box_expected(expected, box)
|
||
|
|
|
||
|
|
result = idx % 2
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
def test_divmod_scalar(self, numeric_idx):
|
||
|
|
idx = numeric_idx
|
||
|
|
|
||
|
|
result = divmod(idx, 2)
|
||
|
|
with np.errstate(all="ignore"):
|
||
|
|
div, mod = divmod(idx.values, 2)
|
||
|
|
|
||
|
|
expected = Index(div), Index(mod)
|
||
|
|
for r, e in zip(result, expected):
|
||
|
|
tm.assert_index_equal(r, e)
|
||
|
|
|
||
|
|
def test_divmod_ndarray(self, numeric_idx):
|
||
|
|
idx = numeric_idx
|
||
|
|
other = np.ones(idx.values.shape, dtype=idx.values.dtype) * 2
|
||
|
|
|
||
|
|
result = divmod(idx, other)
|
||
|
|
with np.errstate(all="ignore"):
|
||
|
|
div, mod = divmod(idx.values, other)
|
||
|
|
|
||
|
|
expected = Index(div), Index(mod)
|
||
|
|
for r, e in zip(result, expected):
|
||
|
|
tm.assert_index_equal(r, e)
|
||
|
|
|
||
|
|
def test_divmod_series(self, numeric_idx):
|
||
|
|
idx = numeric_idx
|
||
|
|
other = np.ones(idx.values.shape, dtype=idx.values.dtype) * 2
|
||
|
|
|
||
|
|
result = divmod(idx, Series(other))
|
||
|
|
with np.errstate(all="ignore"):
|
||
|
|
div, mod = divmod(idx.values, other)
|
||
|
|
|
||
|
|
expected = Series(div), Series(mod)
|
||
|
|
for r, e in zip(result, expected):
|
||
|
|
tm.assert_series_equal(r, e)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("other", [np.nan, 7, -23, 2.718, -3.14, np.inf])
|
||
|
|
def test_ops_np_scalar(self, other):
|
||
|
|
vals = np.random.randn(5, 3)
|
||
|
|
f = lambda x: pd.DataFrame(
|
||
|
|
x, index=list("ABCDE"), columns=["jim", "joe", "jolie"]
|
||
|
|
)
|
||
|
|
|
||
|
|
df = f(vals)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(df / np.array(other), f(vals / other))
|
||
|
|
tm.assert_frame_equal(np.array(other) * df, f(vals * other))
|
||
|
|
tm.assert_frame_equal(df + np.array(other), f(vals + other))
|
||
|
|
tm.assert_frame_equal(np.array(other) - df, f(other - vals))
|
||
|
|
|
||
|
|
# TODO: This came from series.test.test_operators, needs cleanup
|
||
|
|
def test_operators_frame(self):
|
||
|
|
# rpow does not work with DataFrame
|
||
|
|
ts = tm.makeTimeSeries()
|
||
|
|
ts.name = "ts"
|
||
|
|
|
||
|
|
df = pd.DataFrame({"A": ts})
|
||
|
|
|
||
|
|
tm.assert_series_equal(ts + ts, ts + df["A"], check_names=False)
|
||
|
|
tm.assert_series_equal(ts ** ts, ts ** df["A"], check_names=False)
|
||
|
|
tm.assert_series_equal(ts < ts, ts < df["A"], check_names=False)
|
||
|
|
tm.assert_series_equal(ts / ts, ts / df["A"], check_names=False)
|
||
|
|
|
||
|
|
# TODO: this came from tests.series.test_analytics, needs cleanup and
|
||
|
|
# de-duplication with test_modulo above
|
||
|
|
def test_modulo2(self):
|
||
|
|
with np.errstate(all="ignore"):
|
||
|
|
|
||
|
|
# GH#3590, modulo as ints
|
||
|
|
p = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
|
||
|
|
result = p["first"] % p["second"]
|
||
|
|
expected = Series(p["first"].values % p["second"].values, dtype="float64")
|
||
|
|
expected.iloc[0:3] = np.nan
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = p["first"] % 0
|
||
|
|
expected = Series(np.nan, index=p.index, name="first")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
p = p.astype("float64")
|
||
|
|
result = p["first"] % p["second"]
|
||
|
|
expected = Series(p["first"].values % p["second"].values)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
p = p.astype("float64")
|
||
|
|
result = p["first"] % p["second"]
|
||
|
|
result2 = p["second"] % p["first"]
|
||
|
|
assert not result.equals(result2)
|
||
|
|
|
||
|
|
def test_modulo_zero_int(self):
|
||
|
|
# GH#9144
|
||
|
|
with np.errstate(all="ignore"):
|
||
|
|
s = Series([0, 1])
|
||
|
|
|
||
|
|
result = s % 0
|
||
|
|
expected = Series([np.nan, np.nan])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = 0 % s
|
||
|
|
expected = Series([np.nan, 0.0])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
class TestAdditionSubtraction:
|
||
|
|
# __add__, __sub__, __radd__, __rsub__, __iadd__, __isub__
|
||
|
|
# for non-timestamp/timedelta/period dtypes
|
||
|
|
|
||
|
|
# TODO: This came from series.test.test_operators, needs cleanup
|
||
|
|
def test_arith_ops_df_compat(self):
|
||
|
|
# GH#1134
|
||
|
|
s1 = pd.Series([1, 2, 3], index=list("ABC"), name="x")
|
||
|
|
s2 = pd.Series([2, 2, 2], index=list("ABD"), name="x")
|
||
|
|
|
||
|
|
exp = pd.Series([3.0, 4.0, np.nan, np.nan], index=list("ABCD"), name="x")
|
||
|
|
tm.assert_series_equal(s1 + s2, exp)
|
||
|
|
tm.assert_series_equal(s2 + s1, exp)
|
||
|
|
|
||
|
|
exp = pd.DataFrame({"x": [3.0, 4.0, np.nan, np.nan]}, index=list("ABCD"))
|
||
|
|
tm.assert_frame_equal(s1.to_frame() + s2.to_frame(), exp)
|
||
|
|
tm.assert_frame_equal(s2.to_frame() + s1.to_frame(), exp)
|
||
|
|
|
||
|
|
# different length
|
||
|
|
s3 = pd.Series([1, 2, 3], index=list("ABC"), name="x")
|
||
|
|
s4 = pd.Series([2, 2, 2, 2], index=list("ABCD"), name="x")
|
||
|
|
|
||
|
|
exp = pd.Series([3, 4, 5, np.nan], index=list("ABCD"), name="x")
|
||
|
|
tm.assert_series_equal(s3 + s4, exp)
|
||
|
|
tm.assert_series_equal(s4 + s3, exp)
|
||
|
|
|
||
|
|
exp = pd.DataFrame({"x": [3, 4, 5, np.nan]}, index=list("ABCD"))
|
||
|
|
tm.assert_frame_equal(s3.to_frame() + s4.to_frame(), exp)
|
||
|
|
tm.assert_frame_equal(s4.to_frame() + s3.to_frame(), exp)
|
||
|
|
|
||
|
|
# TODO: This came from series.test.test_operators, needs cleanup
|
||
|
|
def test_series_frame_radd_bug(self):
|
||
|
|
# GH#353
|
||
|
|
vals = pd.Series(tm.rands_array(5, 10))
|
||
|
|
result = "foo_" + vals
|
||
|
|
expected = vals.map(lambda x: "foo_" + x)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
frame = pd.DataFrame({"vals": vals})
|
||
|
|
result = "foo_" + frame
|
||
|
|
expected = pd.DataFrame({"vals": vals.map(lambda x: "foo_" + x)})
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
ts = tm.makeTimeSeries()
|
||
|
|
ts.name = "ts"
|
||
|
|
|
||
|
|
# really raise this time
|
||
|
|
now = pd.Timestamp.now().to_pydatetime()
|
||
|
|
msg = "unsupported operand type"
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
now + ts
|
||
|
|
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
ts + now
|
||
|
|
|
||
|
|
# TODO: This came from series.test.test_operators, needs cleanup
|
||
|
|
def test_datetime64_with_index(self):
|
||
|
|
# arithmetic integer ops with an index
|
||
|
|
ser = pd.Series(np.random.randn(5))
|
||
|
|
expected = ser - ser.index.to_series()
|
||
|
|
result = ser - ser.index
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# GH#4629
|
||
|
|
# arithmetic datetime64 ops with an index
|
||
|
|
ser = pd.Series(
|
||
|
|
pd.date_range("20130101", periods=5),
|
||
|
|
index=pd.date_range("20130101", periods=5),
|
||
|
|
)
|
||
|
|
expected = ser - ser.index.to_series()
|
||
|
|
result = ser - ser.index
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
msg = "cannot subtract period"
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
# GH#18850
|
||
|
|
result = ser - ser.index.to_period()
|
||
|
|
|
||
|
|
df = pd.DataFrame(
|
||
|
|
np.random.randn(5, 2), index=pd.date_range("20130101", periods=5)
|
||
|
|
)
|
||
|
|
df["date"] = pd.Timestamp("20130102")
|
||
|
|
df["expected"] = df["date"] - df.index.to_series()
|
||
|
|
df["result"] = df["date"] - df.index
|
||
|
|
tm.assert_series_equal(df["result"], df["expected"], check_names=False)
|
||
|
|
|
||
|
|
# TODO: taken from tests.frame.test_operators, needs cleanup
|
||
|
|
def test_frame_operators(self, float_frame):
|
||
|
|
frame = float_frame
|
||
|
|
frame2 = pd.DataFrame(float_frame, columns=["D", "C", "B", "A"])
|
||
|
|
|
||
|
|
garbage = np.random.random(4)
|
||
|
|
colSeries = pd.Series(garbage, index=np.array(frame.columns))
|
||
|
|
|
||
|
|
idSum = frame + frame
|
||
|
|
seriesSum = frame + colSeries
|
||
|
|
|
||
|
|
for col, series in idSum.items():
|
||
|
|
for idx, val in series.items():
|
||
|
|
origVal = frame[col][idx] * 2
|
||
|
|
if not np.isnan(val):
|
||
|
|
assert val == origVal
|
||
|
|
else:
|
||
|
|
assert np.isnan(origVal)
|
||
|
|
|
||
|
|
for col, series in seriesSum.items():
|
||
|
|
for idx, val in series.items():
|
||
|
|
origVal = frame[col][idx] + colSeries[col]
|
||
|
|
if not np.isnan(val):
|
||
|
|
assert val == origVal
|
||
|
|
else:
|
||
|
|
assert np.isnan(origVal)
|
||
|
|
|
||
|
|
added = frame2 + frame2
|
||
|
|
expected = frame2 * 2
|
||
|
|
tm.assert_frame_equal(added, expected)
|
||
|
|
|
||
|
|
df = pd.DataFrame({"a": ["a", None, "b"]})
|
||
|
|
tm.assert_frame_equal(df + df, pd.DataFrame({"a": ["aa", np.nan, "bb"]}))
|
||
|
|
|
||
|
|
# Test for issue #10181
|
||
|
|
for dtype in ("float", "int64"):
|
||
|
|
frames = [
|
||
|
|
pd.DataFrame(dtype=dtype),
|
||
|
|
pd.DataFrame(columns=["A"], dtype=dtype),
|
||
|
|
pd.DataFrame(index=[0], dtype=dtype),
|
||
|
|
]
|
||
|
|
for df in frames:
|
||
|
|
assert (df + df).equals(df)
|
||
|
|
tm.assert_frame_equal(df + df, df)
|
||
|
|
|
||
|
|
# TODO: taken from tests.series.test_operators; needs cleanup
|
||
|
|
def test_series_operators(self):
|
||
|
|
def _check_op(series, other, op, pos_only=False):
|
||
|
|
left = np.abs(series) if pos_only else series
|
||
|
|
right = np.abs(other) if pos_only else other
|
||
|
|
|
||
|
|
cython_or_numpy = op(left, right)
|
||
|
|
python = left.combine(right, op)
|
||
|
|
if isinstance(other, Series) and not other.index.equals(series.index):
|
||
|
|
python.index = python.index._with_freq(None)
|
||
|
|
tm.assert_series_equal(cython_or_numpy, python)
|
||
|
|
|
||
|
|
def check(series, other):
|
||
|
|
simple_ops = ["add", "sub", "mul", "truediv", "floordiv", "mod"]
|
||
|
|
|
||
|
|
for opname in simple_ops:
|
||
|
|
_check_op(series, other, getattr(operator, opname))
|
||
|
|
|
||
|
|
_check_op(series, other, operator.pow, pos_only=True)
|
||
|
|
|
||
|
|
_check_op(series, other, ops.radd)
|
||
|
|
_check_op(series, other, ops.rsub)
|
||
|
|
_check_op(series, other, ops.rtruediv)
|
||
|
|
_check_op(series, other, ops.rfloordiv)
|
||
|
|
_check_op(series, other, ops.rmul)
|
||
|
|
_check_op(series, other, ops.rpow, pos_only=True)
|
||
|
|
_check_op(series, other, ops.rmod)
|
||
|
|
|
||
|
|
tser = tm.makeTimeSeries().rename("ts")
|
||
|
|
check(tser, tser * 2)
|
||
|
|
check(tser, tser[::2])
|
||
|
|
check(tser, 5)
|
||
|
|
|
||
|
|
def check_comparators(series, other):
|
||
|
|
_check_op(series, other, operator.gt)
|
||
|
|
_check_op(series, other, operator.ge)
|
||
|
|
_check_op(series, other, operator.eq)
|
||
|
|
_check_op(series, other, operator.lt)
|
||
|
|
_check_op(series, other, operator.le)
|
||
|
|
|
||
|
|
check_comparators(tser, 5)
|
||
|
|
check_comparators(tser, tser + 1)
|
||
|
|
|
||
|
|
# TODO: taken from tests.series.test_operators; needs cleanup
|
||
|
|
def test_divmod(self):
|
||
|
|
def check(series, other):
|
||
|
|
results = divmod(series, other)
|
||
|
|
if isinstance(other, abc.Iterable) and len(series) != len(other):
|
||
|
|
# if the lengths don't match, this is the test where we use
|
||
|
|
# `tser[::2]`. Pad every other value in `other_np` with nan.
|
||
|
|
other_np = []
|
||
|
|
for n in other:
|
||
|
|
other_np.append(n)
|
||
|
|
other_np.append(np.nan)
|
||
|
|
else:
|
||
|
|
other_np = other
|
||
|
|
other_np = np.asarray(other_np)
|
||
|
|
with np.errstate(all="ignore"):
|
||
|
|
expecteds = divmod(series.values, np.asarray(other_np))
|
||
|
|
|
||
|
|
for result, expected in zip(results, expecteds):
|
||
|
|
# check the values, name, and index separately
|
||
|
|
tm.assert_almost_equal(np.asarray(result), expected)
|
||
|
|
|
||
|
|
assert result.name == series.name
|
||
|
|
tm.assert_index_equal(result.index, series.index._with_freq(None))
|
||
|
|
|
||
|
|
tser = tm.makeTimeSeries().rename("ts")
|
||
|
|
check(tser, tser * 2)
|
||
|
|
check(tser, tser[::2])
|
||
|
|
check(tser, 5)
|
||
|
|
|
||
|
|
def test_series_divmod_zero(self):
|
||
|
|
# Check that divmod uses pandas convention for division by zero,
|
||
|
|
# which does not match numpy.
|
||
|
|
# pandas convention has
|
||
|
|
# 1/0 == np.inf
|
||
|
|
# -1/0 == -np.inf
|
||
|
|
# 1/-0.0 == -np.inf
|
||
|
|
# -1/-0.0 == np.inf
|
||
|
|
tser = tm.makeTimeSeries().rename("ts")
|
||
|
|
other = tser * 0
|
||
|
|
|
||
|
|
result = divmod(tser, other)
|
||
|
|
exp1 = pd.Series([np.inf] * len(tser), index=tser.index, name="ts")
|
||
|
|
exp2 = pd.Series([np.nan] * len(tser), index=tser.index, name="ts")
|
||
|
|
tm.assert_series_equal(result[0], exp1)
|
||
|
|
tm.assert_series_equal(result[1], exp2)
|
||
|
|
|
||
|
|
|
||
|
|
class TestUFuncCompat:
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"holder",
|
||
|
|
[pd.Int64Index, pd.UInt64Index, pd.Float64Index, pd.RangeIndex, pd.Series],
|
||
|
|
)
|
||
|
|
def test_ufunc_compat(self, holder):
|
||
|
|
box = pd.Series if holder is pd.Series else pd.Index
|
||
|
|
|
||
|
|
if holder is pd.RangeIndex:
|
||
|
|
idx = pd.RangeIndex(0, 5)
|
||
|
|
else:
|
||
|
|
idx = holder(np.arange(5, dtype="int64"))
|
||
|
|
result = np.sin(idx)
|
||
|
|
expected = box(np.sin(np.arange(5, dtype="int64")))
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"holder", [pd.Int64Index, pd.UInt64Index, pd.Float64Index, pd.Series]
|
||
|
|
)
|
||
|
|
def test_ufunc_coercions(self, holder):
|
||
|
|
idx = holder([1, 2, 3, 4, 5], name="x")
|
||
|
|
box = pd.Series if holder is pd.Series else pd.Index
|
||
|
|
|
||
|
|
result = np.sqrt(idx)
|
||
|
|
assert result.dtype == "f8" and isinstance(result, box)
|
||
|
|
exp = pd.Float64Index(np.sqrt(np.array([1, 2, 3, 4, 5])), name="x")
|
||
|
|
exp = tm.box_expected(exp, box)
|
||
|
|
tm.assert_equal(result, exp)
|
||
|
|
|
||
|
|
result = np.divide(idx, 2.0)
|
||
|
|
assert result.dtype == "f8" and isinstance(result, box)
|
||
|
|
exp = pd.Float64Index([0.5, 1.0, 1.5, 2.0, 2.5], name="x")
|
||
|
|
exp = tm.box_expected(exp, box)
|
||
|
|
tm.assert_equal(result, exp)
|
||
|
|
|
||
|
|
# _evaluate_numeric_binop
|
||
|
|
result = idx + 2.0
|
||
|
|
assert result.dtype == "f8" and isinstance(result, box)
|
||
|
|
exp = pd.Float64Index([3.0, 4.0, 5.0, 6.0, 7.0], name="x")
|
||
|
|
exp = tm.box_expected(exp, box)
|
||
|
|
tm.assert_equal(result, exp)
|
||
|
|
|
||
|
|
result = idx - 2.0
|
||
|
|
assert result.dtype == "f8" and isinstance(result, box)
|
||
|
|
exp = pd.Float64Index([-1.0, 0.0, 1.0, 2.0, 3.0], name="x")
|
||
|
|
exp = tm.box_expected(exp, box)
|
||
|
|
tm.assert_equal(result, exp)
|
||
|
|
|
||
|
|
result = idx * 1.0
|
||
|
|
assert result.dtype == "f8" and isinstance(result, box)
|
||
|
|
exp = pd.Float64Index([1.0, 2.0, 3.0, 4.0, 5.0], name="x")
|
||
|
|
exp = tm.box_expected(exp, box)
|
||
|
|
tm.assert_equal(result, exp)
|
||
|
|
|
||
|
|
result = idx / 2.0
|
||
|
|
assert result.dtype == "f8" and isinstance(result, box)
|
||
|
|
exp = pd.Float64Index([0.5, 1.0, 1.5, 2.0, 2.5], name="x")
|
||
|
|
exp = tm.box_expected(exp, box)
|
||
|
|
tm.assert_equal(result, exp)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"holder", [pd.Int64Index, pd.UInt64Index, pd.Float64Index, pd.Series]
|
||
|
|
)
|
||
|
|
def test_ufunc_multiple_return_values(self, holder):
|
||
|
|
obj = holder([1, 2, 3], name="x")
|
||
|
|
box = pd.Series if holder is pd.Series else pd.Index
|
||
|
|
|
||
|
|
result = np.modf(obj)
|
||
|
|
assert isinstance(result, tuple)
|
||
|
|
exp1 = pd.Float64Index([0.0, 0.0, 0.0], name="x")
|
||
|
|
exp2 = pd.Float64Index([1.0, 2.0, 3.0], name="x")
|
||
|
|
tm.assert_equal(result[0], tm.box_expected(exp1, box))
|
||
|
|
tm.assert_equal(result[1], tm.box_expected(exp2, box))
|
||
|
|
|
||
|
|
def test_ufunc_at(self):
|
||
|
|
s = pd.Series([0, 1, 2], index=[1, 2, 3], name="x")
|
||
|
|
np.add.at(s, [0, 2], 10)
|
||
|
|
expected = pd.Series([10, 1, 12], index=[1, 2, 3], name="x")
|
||
|
|
tm.assert_series_equal(s, expected)
|
||
|
|
|
||
|
|
|
||
|
|
class TestObjectDtypeEquivalence:
|
||
|
|
# Tests that arithmetic operations match operations executed elementwise
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", [None, object])
|
||
|
|
def test_numarr_with_dtype_add_nan(self, dtype, box_with_array):
|
||
|
|
box = box_with_array
|
||
|
|
ser = pd.Series([1, 2, 3], dtype=dtype)
|
||
|
|
expected = pd.Series([np.nan, np.nan, np.nan], dtype=dtype)
|
||
|
|
|
||
|
|
ser = tm.box_expected(ser, box)
|
||
|
|
expected = tm.box_expected(expected, box)
|
||
|
|
|
||
|
|
result = np.nan + ser
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
result = ser + np.nan
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", [None, object])
|
||
|
|
def test_numarr_with_dtype_add_int(self, dtype, box_with_array):
|
||
|
|
box = box_with_array
|
||
|
|
ser = pd.Series([1, 2, 3], dtype=dtype)
|
||
|
|
expected = pd.Series([2, 3, 4], dtype=dtype)
|
||
|
|
|
||
|
|
ser = tm.box_expected(ser, box)
|
||
|
|
expected = tm.box_expected(expected, box)
|
||
|
|
|
||
|
|
result = 1 + ser
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
result = ser + 1
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
# TODO: moved from tests.series.test_operators; needs cleanup
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"op",
|
||
|
|
[operator.add, operator.sub, operator.mul, operator.truediv, operator.floordiv],
|
||
|
|
)
|
||
|
|
def test_operators_reverse_object(self, op):
|
||
|
|
# GH#56
|
||
|
|
arr = pd.Series(np.random.randn(10), index=np.arange(10), dtype=object)
|
||
|
|
|
||
|
|
result = op(1.0, arr)
|
||
|
|
expected = op(1.0, arr.astype(float))
|
||
|
|
tm.assert_series_equal(result.astype(float), expected)
|
||
|
|
|
||
|
|
|
||
|
|
class TestNumericArithmeticUnsorted:
|
||
|
|
# Tests in this class have been moved from type-specific test modules
|
||
|
|
# but not yet sorted, parametrized, and de-duplicated
|
||
|
|
|
||
|
|
def check_binop(self, ops, scalars, idxs):
|
||
|
|
for op in ops:
|
||
|
|
for a, b in combinations(idxs, 2):
|
||
|
|
result = op(a, b)
|
||
|
|
expected = op(pd.Int64Index(a), pd.Int64Index(b))
|
||
|
|
tm.assert_index_equal(result, expected)
|
||
|
|
for idx in idxs:
|
||
|
|
for scalar in scalars:
|
||
|
|
result = op(idx, scalar)
|
||
|
|
expected = op(pd.Int64Index(idx), scalar)
|
||
|
|
tm.assert_index_equal(result, expected)
|
||
|
|
|
||
|
|
def test_binops(self):
|
||
|
|
ops = [
|
||
|
|
operator.add,
|
||
|
|
operator.sub,
|
||
|
|
operator.mul,
|
||
|
|
operator.floordiv,
|
||
|
|
operator.truediv,
|
||
|
|
]
|
||
|
|
scalars = [-1, 1, 2]
|
||
|
|
idxs = [
|
||
|
|
pd.RangeIndex(0, 10, 1),
|
||
|
|
pd.RangeIndex(0, 20, 2),
|
||
|
|
pd.RangeIndex(-10, 10, 2),
|
||
|
|
pd.RangeIndex(5, -5, -1),
|
||
|
|
]
|
||
|
|
self.check_binop(ops, scalars, idxs)
|
||
|
|
|
||
|
|
def test_binops_pow(self):
|
||
|
|
# numpy does not allow powers of negative integers so test separately
|
||
|
|
# https://github.com/numpy/numpy/pull/8127
|
||
|
|
ops = [pow]
|
||
|
|
scalars = [1, 2]
|
||
|
|
idxs = [pd.RangeIndex(0, 10, 1), pd.RangeIndex(0, 20, 2)]
|
||
|
|
self.check_binop(ops, scalars, idxs)
|
||
|
|
|
||
|
|
# TODO: mod, divmod?
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"op",
|
||
|
|
[
|
||
|
|
operator.add,
|
||
|
|
operator.sub,
|
||
|
|
operator.mul,
|
||
|
|
operator.floordiv,
|
||
|
|
operator.truediv,
|
||
|
|
operator.pow,
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_arithmetic_with_frame_or_series(self, op):
|
||
|
|
# check that we return NotImplemented when operating with Series
|
||
|
|
# or DataFrame
|
||
|
|
index = pd.RangeIndex(5)
|
||
|
|
other = pd.Series(np.random.randn(5))
|
||
|
|
|
||
|
|
expected = op(pd.Series(index), other)
|
||
|
|
result = op(index, other)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
other = pd.DataFrame(np.random.randn(2, 5))
|
||
|
|
expected = op(pd.DataFrame([index, index]), other)
|
||
|
|
result = op(index, other)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_numeric_compat2(self):
|
||
|
|
# validate that we are handling the RangeIndex overrides to numeric ops
|
||
|
|
# and returning RangeIndex where possible
|
||
|
|
|
||
|
|
idx = pd.RangeIndex(0, 10, 2)
|
||
|
|
|
||
|
|
result = idx * 2
|
||
|
|
expected = pd.RangeIndex(0, 20, 4)
|
||
|
|
tm.assert_index_equal(result, expected, exact=True)
|
||
|
|
|
||
|
|
result = idx + 2
|
||
|
|
expected = pd.RangeIndex(2, 12, 2)
|
||
|
|
tm.assert_index_equal(result, expected, exact=True)
|
||
|
|
|
||
|
|
result = idx - 2
|
||
|
|
expected = pd.RangeIndex(-2, 8, 2)
|
||
|
|
tm.assert_index_equal(result, expected, exact=True)
|
||
|
|
|
||
|
|
result = idx / 2
|
||
|
|
expected = pd.RangeIndex(0, 5, 1).astype("float64")
|
||
|
|
tm.assert_index_equal(result, expected, exact=True)
|
||
|
|
|
||
|
|
result = idx / 4
|
||
|
|
expected = pd.RangeIndex(0, 10, 2) / 4
|
||
|
|
tm.assert_index_equal(result, expected, exact=True)
|
||
|
|
|
||
|
|
result = idx // 1
|
||
|
|
expected = idx
|
||
|
|
tm.assert_index_equal(result, expected, exact=True)
|
||
|
|
|
||
|
|
# __mul__
|
||
|
|
result = idx * idx
|
||
|
|
expected = Index(idx.values * idx.values)
|
||
|
|
tm.assert_index_equal(result, expected, exact=True)
|
||
|
|
|
||
|
|
# __pow__
|
||
|
|
idx = pd.RangeIndex(0, 1000, 2)
|
||
|
|
result = idx ** 2
|
||
|
|
expected = idx._int64index ** 2
|
||
|
|
tm.assert_index_equal(Index(result.values), expected, exact=True)
|
||
|
|
|
||
|
|
# __floordiv__
|
||
|
|
cases_exact = [
|
||
|
|
(pd.RangeIndex(0, 1000, 2), 2, pd.RangeIndex(0, 500, 1)),
|
||
|
|
(pd.RangeIndex(-99, -201, -3), -3, pd.RangeIndex(33, 67, 1)),
|
||
|
|
(pd.RangeIndex(0, 1000, 1), 2, pd.RangeIndex(0, 1000, 1)._int64index // 2),
|
||
|
|
(
|
||
|
|
pd.RangeIndex(0, 100, 1),
|
||
|
|
2.0,
|
||
|
|
pd.RangeIndex(0, 100, 1)._int64index // 2.0,
|
||
|
|
),
|
||
|
|
(pd.RangeIndex(0), 50, pd.RangeIndex(0)),
|
||
|
|
(pd.RangeIndex(2, 4, 2), 3, pd.RangeIndex(0, 1, 1)),
|
||
|
|
(pd.RangeIndex(-5, -10, -6), 4, pd.RangeIndex(-2, -1, 1)),
|
||
|
|
(pd.RangeIndex(-100, -200, 3), 2, pd.RangeIndex(0)),
|
||
|
|
]
|
||
|
|
for idx, div, expected in cases_exact:
|
||
|
|
tm.assert_index_equal(idx // div, expected, exact=True)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", [np.int64, np.float64])
|
||
|
|
@pytest.mark.parametrize("delta", [1, 0, -1])
|
||
|
|
def test_addsub_arithmetic(self, dtype, delta):
|
||
|
|
# GH#8142
|
||
|
|
delta = dtype(delta)
|
||
|
|
index = pd.Index([10, 11, 12], dtype=dtype)
|
||
|
|
result = index + delta
|
||
|
|
expected = pd.Index(index.values + delta, dtype=dtype)
|
||
|
|
tm.assert_index_equal(result, expected)
|
||
|
|
|
||
|
|
# this subtraction used to fail
|
||
|
|
result = index - delta
|
||
|
|
expected = pd.Index(index.values - delta, dtype=dtype)
|
||
|
|
tm.assert_index_equal(result, expected)
|
||
|
|
|
||
|
|
tm.assert_index_equal(index + index, 2 * index)
|
||
|
|
tm.assert_index_equal(index - index, 0 * index)
|
||
|
|
assert not (index - index).empty
|
||
|
|
|
||
|
|
|
||
|
|
def test_fill_value_inf_masking():
|
||
|
|
# GH #27464 make sure we mask 0/1 with Inf and not NaN
|
||
|
|
df = pd.DataFrame({"A": [0, 1, 2], "B": [1.1, None, 1.1]})
|
||
|
|
|
||
|
|
other = pd.DataFrame({"A": [1.1, 1.2, 1.3]}, index=[0, 2, 3])
|
||
|
|
|
||
|
|
result = df.rfloordiv(other, fill_value=1)
|
||
|
|
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
{"A": [np.inf, 1.0, 0.0, 1.0], "B": [0.0, np.nan, 0.0, np.nan]}
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_dataframe_div_silenced():
|
||
|
|
# GH#26793
|
||
|
|
pdf1 = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"A": np.arange(10),
|
||
|
|
"B": [np.nan, 1, 2, 3, 4] * 2,
|
||
|
|
"C": [np.nan] * 10,
|
||
|
|
"D": np.arange(10),
|
||
|
|
},
|
||
|
|
index=list("abcdefghij"),
|
||
|
|
columns=list("ABCD"),
|
||
|
|
)
|
||
|
|
pdf2 = pd.DataFrame(
|
||
|
|
np.random.randn(10, 4), index=list("abcdefghjk"), columns=list("ABCX")
|
||
|
|
)
|
||
|
|
with tm.assert_produces_warning(None):
|
||
|
|
pdf1.div(pdf2, fill_value=0)
|