# Arithmetic tests for DataFrame/Series/Index/Array classes that should # behave identically. # Specifically for numeric dtypes from __future__ import annotations from collections import abc from datetime import timedelta from decimal import Decimal import operator import numpy as np import pytest import pandas as pd from pandas import ( Index, RangeIndex, Series, Timedelta, TimedeltaIndex, array, ) import pandas._testing as tm from pandas.core import ops from pandas.core.computation import expressions as expr from pandas.tests.arithmetic.common import ( assert_invalid_addsub_type, assert_invalid_comparison, ) @pytest.fixture(params=[Index, Series, tm.to_array]) def box_pandas_1d_array(request): """ Fixture to test behavior for Index, Series and tm.to_array classes """ return request.param def adjust_negative_zero(zero, expected): """ Helper to adjust the expected result if we are dividing by -0.0 as opposed to 0.0 """ if np.signbit(np.array(zero)).any(): # All entries in the `zero` fixture should be either # all-negative or no-negative. assert np.signbit(np.array(zero)).all() expected *= -1 return expected def compare_op(series, other, op): left = np.abs(series) if op in (ops.rpow, operator.pow) else series right = np.abs(other) if op in (ops.rpow, operator.pow) 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) # TODO: remove this kludge once mypy stops giving false positives here # List comprehension has incompatible type List[PandasObject]; expected List[RangeIndex] # See GH#29725 _ldtypes = ["i1", "i2", "i4", "i8", "u1", "u2", "u4", "u8", "f2", "f4", "f8"] lefts: list[Index | Series] = [RangeIndex(10, 40, 10)] lefts.extend([Series([10, 20, 30], dtype=dtype) for dtype in _ldtypes]) lefts.extend([Index([10, 20, 30], dtype=dtype) for dtype in _ldtypes if dtype != "f2"]) # ------------------------------------------------------------------ # Comparisons class TestNumericComparisons: def test_operator_series_comparison_zerorank(self): # GH#13006 result = np.float64(0) > Series([1, 2, 3]) expected = 0.0 > Series([1, 2, 3]) tm.assert_series_equal(result, expected) result = Series([1, 2, 3]) < np.float64(0) expected = Series([1, 2, 3]) < 0.0 tm.assert_series_equal(result, expected) result = np.array([0, 1, 2])[0] > Series([0, 1, 2]) expected = 0.0 > Series([1, 2, 3]) tm.assert_series_equal(result, expected) def test_df_numeric_cmp_dt64_raises(self, box_with_array, fixed_now_ts): # GH#8932, GH#22163 ts = fixed_now_ts obj = np.array(range(5)) obj = tm.box_expected(obj, box_with_array) assert_invalid_comparison(obj, ts, box_with_array) def test_compare_invalid(self): # GH#8058 # ops testing a = Series(np.random.default_rng(2).standard_normal(5), name=0) b = Series(np.random.default_rng(2).standard_normal(5)) b.name = pd.Timestamp("2000-01-01") tm.assert_series_equal(a / b, 1 / (b / a)) def test_numeric_cmp_string_numexpr_path(self, box_with_array, monkeypatch): # GH#36377, GH#35700 box = box_with_array xbox = box if box is not Index else np.ndarray obj = Series(np.random.default_rng(2).standard_normal(51)) obj = tm.box_expected(obj, box, transpose=False) with monkeypatch.context() as m: m.setattr(expr, "_MIN_ELEMENTS", 50) result = obj == "a" expected = Series(np.zeros(51, dtype=bool)) expected = tm.box_expected(expected, xbox, transpose=False) tm.assert_equal(result, expected) with monkeypatch.context() as m: m.setattr(expr, "_MIN_ELEMENTS", 50) result = obj != "a" tm.assert_equal(result, ~expected) msg = "Invalid comparison between dtype=float64 and str" with pytest.raises(TypeError, match=msg): obj < "a" # ------------------------------------------------------------------ # Numeric dtypes Arithmetic with Datetime/Timedelta Scalar class TestNumericArraylikeArithmeticWithDatetimeLike: @pytest.mark.parametrize("box_cls", [np.array, Index, Series]) @pytest.mark.parametrize( "left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype) ) def test_mul_td64arr(self, left, box_cls): # GH#22390 right = np.array([1, 2, 3], dtype="m8[s]") right = box_cls(right) expected = TimedeltaIndex(["10s", "40s", "90s"], dtype=right.dtype) if isinstance(left, Series) or box_cls is Series: expected = Series(expected) assert expected.dtype == right.dtype result = left * right tm.assert_equal(result, expected) result = right * left tm.assert_equal(result, expected) @pytest.mark.parametrize("box_cls", [np.array, Index, Series]) @pytest.mark.parametrize( "left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype) ) def test_div_td64arr(self, left, box_cls): # GH#22390 right = np.array([10, 40, 90], dtype="m8[s]") right = box_cls(right) expected = TimedeltaIndex(["1s", "2s", "3s"], dtype=right.dtype) if isinstance(left, Series) or box_cls is Series: expected = Series(expected) assert expected.dtype == right.dtype result = right / left tm.assert_equal(result, expected) result = right // left tm.assert_equal(result, expected) # (true_) needed for min-versions build 2022-12-26 msg = "ufunc '(true_)?divide' cannot use operands with types" with pytest.raises(TypeError, match=msg): left / right msg = "ufunc 'floor_divide' cannot use operands with types" with pytest.raises(TypeError, match=msg): left // right # TODO: also test Tick objects; # see test_numeric_arr_rdiv_tdscalar for note on these failing @pytest.mark.parametrize( "scalar_td", [ Timedelta(days=1), Timedelta(days=1).to_timedelta64(), Timedelta(days=1).to_pytimedelta(), Timedelta(days=1).to_timedelta64().astype("timedelta64[s]"), Timedelta(days=1).to_timedelta64().astype("timedelta64[ms]"), ], ids=lambda x: type(x).__name__, ) def test_numeric_arr_mul_tdscalar(self, scalar_td, numeric_idx, box_with_array): # GH#19333 box = box_with_array index = numeric_idx expected = TimedeltaIndex([Timedelta(days=n) for n in range(len(index))]) if isinstance(scalar_td, np.timedelta64): dtype = scalar_td.dtype expected = expected.astype(dtype) elif type(scalar_td) is timedelta: expected = expected.astype("m8[us]") index = tm.box_expected(index, box) expected = tm.box_expected(expected, box) result = index * scalar_td tm.assert_equal(result, expected) commute = scalar_td * index tm.assert_equal(commute, expected) @pytest.mark.parametrize( "scalar_td", [ Timedelta(days=1), Timedelta(days=1).to_timedelta64(), Timedelta(days=1).to_pytimedelta(), ], ids=lambda x: type(x).__name__, ) @pytest.mark.parametrize("dtype", [np.int64, np.float64]) def test_numeric_arr_mul_tdscalar_numexpr_path( self, dtype, scalar_td, box_with_array ): # GH#44772 for the float64 case box = box_with_array arr_i8 = np.arange(2 * 10**4).astype(np.int64, copy=False) arr = arr_i8.astype(dtype, copy=False) obj = tm.box_expected(arr, box, transpose=False) expected = arr_i8.view("timedelta64[D]").astype("timedelta64[ns]") if type(scalar_td) is timedelta: expected = expected.astype("timedelta64[us]") expected = tm.box_expected(expected, box, transpose=False) result = obj * scalar_td tm.assert_equal(result, expected) result = scalar_td * obj tm.assert_equal(result, expected) def test_numeric_arr_rdiv_tdscalar(self, three_days, numeric_idx, box_with_array): box = box_with_array index = numeric_idx[1:3] expected = TimedeltaIndex(["3 Days", "36 Hours"]) if isinstance(three_days, np.timedelta64): dtype = three_days.dtype if dtype < np.dtype("m8[s]"): # i.e. resolution is lower -> use lowest supported resolution dtype = np.dtype("m8[s]") expected = expected.astype(dtype) elif type(three_days) is timedelta: expected = expected.astype("m8[us]") index = tm.box_expected(index, box) expected = tm.box_expected(expected, box) result = three_days / index tm.assert_equal(result, expected) msg = "cannot use operands with types dtype" with pytest.raises(TypeError, match=msg): index / three_days @pytest.mark.parametrize( "other", [ Timedelta(hours=31), Timedelta(hours=31).to_pytimedelta(), Timedelta(hours=31).to_timedelta64(), Timedelta(hours=31).to_timedelta64().astype("m8[h]"), np.timedelta64("NaT"), np.timedelta64("NaT", "D"), pd.offsets.Minute(3), pd.offsets.Second(0), # GH#28080 numeric+datetimelike should raise; Timestamp used # to raise NullFrequencyError but that behavior was removed in 1.0 pd.Timestamp("2021-01-01", tz="Asia/Tokyo"), pd.Timestamp("2021-01-01"), pd.Timestamp("2021-01-01").to_pydatetime(), pd.Timestamp("2021-01-01", tz="UTC").to_pydatetime(), pd.Timestamp("2021-01-01").to_datetime64(), np.datetime64("NaT", "ns"), pd.NaT, ], ids=repr, ) def test_add_sub_datetimedeltalike_invalid( self, numeric_idx, other, box_with_array ): box = box_with_array left = tm.box_expected(numeric_idx, box) msg = "|".join( [ "unsupported operand type", "Addition/subtraction of integers and integer-arrays", "Instead of adding/subtracting", "cannot use operands with types dtype", "Concatenation operation is not implemented for NumPy arrays", "Cannot (add|subtract) NaT (to|from) ndarray", # pd.array vs np.datetime64 case r"operand type\(s\) all returned NotImplemented from __array_ufunc__", "can only perform ops with numeric values", "cannot subtract DatetimeArray from ndarray", # pd.Timedelta(1) + Index([0, 1, 2]) "Cannot add or subtract Timedelta from integers", ] ) assert_invalid_addsub_type(left, other, msg) # ------------------------------------------------------------------ # Arithmetic class TestDivisionByZero: def test_div_zero(self, zero, numeric_idx): idx = numeric_idx expected = Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64) # We only adjust for Index, because Series does not yet apply # the adjustment correctly. expected2 = adjust_negative_zero(zero, expected) result = idx / zero tm.assert_index_equal(result, expected2) ser_compat = Series(idx).astype("i8") / np.array(zero).astype("i8") tm.assert_series_equal(ser_compat, Series(expected)) def test_floordiv_zero(self, zero, numeric_idx): idx = numeric_idx expected = Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64) # We only adjust for Index, because Series does not yet apply # the adjustment correctly. expected2 = adjust_negative_zero(zero, expected) result = idx // zero tm.assert_index_equal(result, expected2) ser_compat = Series(idx).astype("i8") // np.array(zero).astype("i8") tm.assert_series_equal(ser_compat, Series(expected)) def test_mod_zero(self, zero, numeric_idx): idx = numeric_idx expected = Index([np.nan, np.nan, np.nan, np.nan, np.nan], dtype=np.float64) result = idx % zero tm.assert_index_equal(result, expected) ser_compat = Series(idx).astype("i8") % np.array(zero).astype("i8") tm.assert_series_equal(ser_compat, Series(result)) def test_divmod_zero(self, zero, numeric_idx): idx = numeric_idx exleft = Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64) exright = Index([np.nan, np.nan, np.nan, np.nan, np.nan], dtype=np.float64) exleft = adjust_negative_zero(zero, exleft) result = divmod(idx, zero) tm.assert_index_equal(result[0], exleft) tm.assert_index_equal(result[1], exright) @pytest.mark.parametrize("op", [operator.truediv, operator.floordiv]) def test_div_negative_zero(self, zero, numeric_idx, op): # Check that -1 / -0.0 returns np.inf, not -np.inf if numeric_idx.dtype == np.uint64: pytest.skip(f"Not relevant for {numeric_idx.dtype}") idx = numeric_idx - 3 expected = 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, switch_numexpr_min_elements, dtype1, any_real_numpy_dtype, ): # no longer do integer div for any ops, but deal with the 0's dtype2 = any_real_numpy_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 if first.dtype == "int64" and second.dtype == "float32": # when using numexpr, the casting rules are slightly different # and int64/float32 combo results in float32 instead of float64 if expr.USE_NUMEXPR and switch_numexpr_min_elements == 0: expected = expected.astype("float32") 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_numpy_dtype): # GH#26987 dtype2 = any_real_numpy_dtype left = Series([1, 1]).astype(dtype1) right = 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 = Series([np.inf, 1.0]) right = 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.default_rng(2).standard_normal(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 = Series([1.0, 1.0, 1.0, 1.0]) second = 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 = Series([1.0, 1.0, 1.0, 1.0]) second = 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.default_rng(2).standard_normal((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, using_array_manager): # 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 = Series([0, 0, 0, 0]) if not using_array_manager: # INFO(ArrayManager) BlockManager doesn't preserve dtype per column # while ArrayManager performs op column-wisedoes and thus preserves # dtype if possible first = first.astype("float64") second = Series([np.nan, np.nan, np.nan, 0]) expected = pd.DataFrame({"first": first, "second": second}) result = df % df tm.assert_frame_equal(result, expected) # GH#38939 If we dont pass copy=False, df is consolidated and # result["first"] is float64 instead of int64 df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]}, copy=False) first = Series([0, 0, 0, 0], dtype="int64") second = 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 = Series([0, 0, 0, 0], dtype="float64") second = 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.default_rng(2).standard_normal((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 def test_divide_decimal(self, box_with_array): # resolves issue GH#9787 box = box_with_array 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 idx.dtype == np.uint64 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 idx.dtype == np.uint64 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): idx = numeric_idx 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 = Index(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.default_rng(2).standard_normal((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 @pytest.mark.parametrize( "first, second, expected", [ ( Series([1, 2, 3], index=list("ABC"), name="x"), Series([2, 2, 2], index=list("ABD"), name="x"), Series([3.0, 4.0, np.nan, np.nan], index=list("ABCD"), name="x"), ), ( Series([1, 2, 3], index=list("ABC"), name="x"), Series([2, 2, 2, 2], index=list("ABCD"), name="x"), Series([3, 4, 5, np.nan], index=list("ABCD"), name="x"), ), ], ) def test_add_series(self, first, second, expected): # GH#1134 tm.assert_series_equal(first + second, expected) tm.assert_series_equal(second + first, expected) @pytest.mark.parametrize( "first, second, expected", [ ( pd.DataFrame({"x": [1, 2, 3]}, index=list("ABC")), pd.DataFrame({"x": [2, 2, 2]}, index=list("ABD")), pd.DataFrame({"x": [3.0, 4.0, np.nan, np.nan]}, index=list("ABCD")), ), ( pd.DataFrame({"x": [1, 2, 3]}, index=list("ABC")), pd.DataFrame({"x": [2, 2, 2, 2]}, index=list("ABCD")), pd.DataFrame({"x": [3, 4, 5, np.nan]}, index=list("ABCD")), ), ], ) def test_add_frames(self, first, second, expected): # GH#1134 tm.assert_frame_equal(first + second, expected) tm.assert_frame_equal(second + first, expected) # TODO: This came from series.test.test_operators, needs cleanup def test_series_frame_radd_bug(self, fixed_now_ts): # GH#353 vals = Series(tm.makeStringIndex()) 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 fix_now = fixed_now_ts.to_pydatetime() msg = "|".join( [ "unsupported operand type", # wrong error message, see https://github.com/numpy/numpy/issues/18832 "Concatenation operation", ] ) with pytest.raises(TypeError, match=msg): fix_now + ts with pytest.raises(TypeError, match=msg): ts + fix_now # TODO: This came from series.test.test_operators, needs cleanup def test_datetime64_with_index(self): # arithmetic integer ops with an index ser = Series(np.random.default_rng(2).standard_normal(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 = 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 PeriodArray from DatetimeArray" with pytest.raises(TypeError, match=msg): # GH#18850 result = ser - ser.index.to_period() df = pd.DataFrame( np.random.default_rng(2).standard_normal((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 garbage = np.random.default_rng(2).random(4) colSeries = 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) def test_frame_operators_col_align(self, float_frame): frame2 = pd.DataFrame(float_frame, columns=["D", "C", "B", "A"]) added = frame2 + frame2 expected = frame2 * 2 tm.assert_frame_equal(added, expected) def test_frame_operators_none_to_nan(self): df = pd.DataFrame({"a": ["a", None, "b"]}) tm.assert_frame_equal(df + df, pd.DataFrame({"a": ["aa", np.nan, "bb"]})) @pytest.mark.parametrize("dtype", ("float", "int64")) def test_frame_operators_empty_like(self, dtype): # Test for issue #10181 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) @pytest.mark.parametrize( "func", [lambda x: x * 2, lambda x: x[::2], lambda x: 5], ids=["multiply", "slice", "constant"], ) def test_series_operators_arithmetic(self, all_arithmetic_functions, func): op = all_arithmetic_functions series = tm.makeTimeSeries().rename("ts") other = func(series) compare_op(series, other, op) @pytest.mark.parametrize( "func", [lambda x: x + 1, lambda x: 5], ids=["add", "constant"] ) def test_series_operators_compare(self, comparison_op, func): op = comparison_op series = tm.makeTimeSeries().rename("ts") other = func(series) compare_op(series, other, op) @pytest.mark.parametrize( "func", [lambda x: x * 2, lambda x: x[::2], lambda x: 5], ids=["multiply", "slice", "constant"], ) def test_divmod(self, func): series = tm.makeTimeSeries().rename("ts") other = func(series) 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)) 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 = Series([np.inf] * len(tser), index=tser.index, name="ts") exp2 = 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: # TODO: add more dtypes @pytest.mark.parametrize("holder", [Index, RangeIndex, Series]) @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64]) def test_ufunc_compat(self, holder, dtype): box = Series if holder is Series else Index if holder is RangeIndex: if dtype != np.int64: pytest.skip(f"dtype {dtype} not relevant for RangeIndex") idx = RangeIndex(0, 5, name="foo") else: idx = holder(np.arange(5, dtype=dtype), name="foo") result = np.sin(idx) expected = box(np.sin(np.arange(5, dtype=dtype)), name="foo") tm.assert_equal(result, expected) # TODO: add more dtypes @pytest.mark.parametrize("holder", [Index, Series]) @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64]) def test_ufunc_coercions(self, holder, dtype): idx = holder([1, 2, 3, 4, 5], dtype=dtype, name="x") box = Series if holder is Series else Index result = np.sqrt(idx) assert result.dtype == "f8" and isinstance(result, box) exp = Index(np.sqrt(np.array([1, 2, 3, 4, 5], dtype=np.float64)), 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 = Index([0.5, 1.0, 1.5, 2.0, 2.5], dtype=np.float64, 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 = Index([3.0, 4.0, 5.0, 6.0, 7.0], dtype=np.float64, 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 = Index([-1.0, 0.0, 1.0, 2.0, 3.0], dtype=np.float64, 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 = Index([1.0, 2.0, 3.0, 4.0, 5.0], dtype=np.float64, 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 = Index([0.5, 1.0, 1.5, 2.0, 2.5], dtype=np.float64, name="x") exp = tm.box_expected(exp, box) tm.assert_equal(result, exp) # TODO: add more dtypes @pytest.mark.parametrize("holder", [Index, Series]) @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64]) def test_ufunc_multiple_return_values(self, holder, dtype): obj = holder([1, 2, 3], dtype=dtype, name="x") box = Series if holder is Series else Index result = np.modf(obj) assert isinstance(result, tuple) exp1 = Index([0.0, 0.0, 0.0], dtype=np.float64, name="x") exp2 = Index([1.0, 2.0, 3.0], dtype=np.float64, 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 = Series([0, 1, 2], index=[1, 2, 3], name="x") np.add.at(s, [0, 2], 10) expected = 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 = Series([1, 2, 3], dtype=dtype) expected = 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 = Series([1, 2, 3], dtype=dtype) expected = 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 = Series( np.random.default_rng(2).standard_normal(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 @pytest.mark.parametrize( "op", [ operator.add, operator.sub, operator.mul, operator.floordiv, operator.truediv, ], ) @pytest.mark.parametrize( "idx1", [ RangeIndex(0, 10, 1), RangeIndex(0, 20, 2), RangeIndex(-10, 10, 2), RangeIndex(5, -5, -1), ], ) @pytest.mark.parametrize( "idx2", [ RangeIndex(0, 10, 1), RangeIndex(0, 20, 2), RangeIndex(-10, 10, 2), RangeIndex(5, -5, -1), ], ) def test_binops_index(self, op, idx1, idx2): idx1 = idx1._rename("foo") idx2 = idx2._rename("bar") result = op(idx1, idx2) expected = op(Index(idx1.to_numpy()), Index(idx2.to_numpy())) tm.assert_index_equal(result, expected, exact="equiv") @pytest.mark.parametrize( "op", [ operator.add, operator.sub, operator.mul, operator.floordiv, operator.truediv, ], ) @pytest.mark.parametrize( "idx", [ RangeIndex(0, 10, 1), RangeIndex(0, 20, 2), RangeIndex(-10, 10, 2), RangeIndex(5, -5, -1), ], ) @pytest.mark.parametrize("scalar", [-1, 1, 2]) def test_binops_index_scalar(self, op, idx, scalar): result = op(idx, scalar) expected = op(Index(idx.to_numpy()), scalar) tm.assert_index_equal(result, expected, exact="equiv") @pytest.mark.parametrize("idx1", [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)]) @pytest.mark.parametrize("idx2", [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)]) def test_binops_index_pow(self, idx1, idx2): # numpy does not allow powers of negative integers so test separately # https://github.com/numpy/numpy/pull/8127 idx1 = idx1._rename("foo") idx2 = idx2._rename("bar") result = pow(idx1, idx2) expected = pow(Index(idx1.to_numpy()), Index(idx2.to_numpy())) tm.assert_index_equal(result, expected, exact="equiv") @pytest.mark.parametrize("idx", [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)]) @pytest.mark.parametrize("scalar", [1, 2]) def test_binops_index_scalar_pow(self, idx, scalar): # numpy does not allow powers of negative integers so test separately # https://github.com/numpy/numpy/pull/8127 result = pow(idx, scalar) expected = pow(Index(idx.to_numpy()), scalar) tm.assert_index_equal(result, expected, exact="equiv") # TODO: divmod? @pytest.mark.parametrize( "op", [ operator.add, operator.sub, operator.mul, operator.floordiv, operator.truediv, operator.pow, operator.mod, ], ) def test_arithmetic_with_frame_or_series(self, op): # check that we return NotImplemented when operating with Series # or DataFrame index = RangeIndex(5) other = Series(np.random.default_rng(2).standard_normal(5)) expected = op(Series(index), other) result = op(index, other) tm.assert_series_equal(result, expected) other = pd.DataFrame(np.random.default_rng(2).standard_normal((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 = RangeIndex(0, 10, 2) result = idx * 2 expected = RangeIndex(0, 20, 4) tm.assert_index_equal(result, expected, exact=True) result = idx + 2 expected = RangeIndex(2, 12, 2) tm.assert_index_equal(result, expected, exact=True) result = idx - 2 expected = RangeIndex(-2, 8, 2) tm.assert_index_equal(result, expected, exact=True) result = idx / 2 expected = RangeIndex(0, 5, 1).astype("float64") tm.assert_index_equal(result, expected, exact=True) result = idx / 4 expected = 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 = RangeIndex(0, 1000, 2) result = idx**2 expected = Index(idx._values) ** 2 tm.assert_index_equal(Index(result.values), expected, exact=True) @pytest.mark.parametrize( "idx, div, expected", [ # TODO: add more dtypes (RangeIndex(0, 1000, 2), 2, RangeIndex(0, 500, 1)), (RangeIndex(-99, -201, -3), -3, RangeIndex(33, 67, 1)), ( RangeIndex(0, 1000, 1), 2, Index(RangeIndex(0, 1000, 1)._values) // 2, ), ( RangeIndex(0, 100, 1), 2.0, Index(RangeIndex(0, 100, 1)._values) // 2.0, ), (RangeIndex(0), 50, RangeIndex(0)), (RangeIndex(2, 4, 2), 3, RangeIndex(0, 1, 1)), (RangeIndex(-5, -10, -6), 4, RangeIndex(-2, -1, 1)), (RangeIndex(-100, -200, 3), 2, RangeIndex(0)), ], ) def test_numeric_compat2_floordiv(self, idx, div, expected): # __floordiv__ 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 = Index([10, 11, 12], dtype=dtype) result = index + delta expected = Index(index.values + delta, dtype=dtype) tm.assert_index_equal(result, expected) # this subtraction used to fail result = index - delta expected = 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.default_rng(2).standard_normal((10, 4)), index=list("abcdefghjk"), columns=list("ABCX"), ) with tm.assert_produces_warning(None): pdf1.div(pdf2, fill_value=0) @pytest.mark.parametrize( "data, expected_data", [([0, 1, 2], [0, 2, 4])], ) def test_integer_array_add_list_like( box_pandas_1d_array, box_1d_array, data, expected_data ): # GH22606 Verify operators with IntegerArray and list-likes arr = array(data, dtype="Int64") container = box_pandas_1d_array(arr) left = container + box_1d_array(data) right = box_1d_array(data) + container if Series in [box_1d_array, box_pandas_1d_array]: cls = Series elif Index in [box_1d_array, box_pandas_1d_array]: cls = Index else: cls = array expected = cls(expected_data, dtype="Int64") tm.assert_equal(left, expected) tm.assert_equal(right, expected) def test_sub_multiindex_swapped_levels(): # GH 9952 df = pd.DataFrame( {"a": np.random.default_rng(2).standard_normal(6)}, index=pd.MultiIndex.from_product( [["a", "b"], [0, 1, 2]], names=["levA", "levB"] ), ) df2 = df.copy() df2.index = df2.index.swaplevel(0, 1) result = df - df2 expected = pd.DataFrame([0.0] * 6, columns=["a"], index=df.index) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize("power", [1, 2, 5]) @pytest.mark.parametrize("string_size", [0, 1, 2, 5]) def test_empty_str_comparison(power, string_size): # GH 37348 a = np.array(range(10**power)) right = pd.DataFrame(a, dtype=np.int64) left = " " * string_size result = right == left expected = pd.DataFrame(np.zeros(right.shape, dtype=bool)) tm.assert_frame_equal(result, expected) def test_series_add_sub_with_UInt64(): # GH 22023 series1 = Series([1, 2, 3]) series2 = Series([2, 1, 3], dtype="UInt64") result = series1 + series2 expected = Series([3, 3, 6], dtype="Float64") tm.assert_series_equal(result, expected) result = series1 - series2 expected = Series([-1, 1, 0], dtype="Float64") tm.assert_series_equal(result, expected)