# Arithmetic tests for DataFrame/Series/Index/Array classes that should # behave identically. # Specifically for numeric dtypes from collections import abc from decimal import Decimal from itertools import combinations import operator from typing import Any, List import numpy as np import pytest import pandas as pd from pandas import Index, Series, Timedelta, TimedeltaIndex import pandas._testing as tm from pandas.core import ops 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 # TODO: remove this kludge once mypy stops giving false positives here # List comprehension has incompatible type List[PandasObject]; expected List[RangeIndex] # See GH#29725 ser_or_index: List[Any] = [pd.Series, pd.Index] lefts: List[Any] = [pd.RangeIndex(10, 40, 10)] lefts.extend( [ cls([10, 20, 30], dtype=dtype) for dtype in ["i1", "i2", "i4", "i8", "u1", "u2", "u4", "u8", "f2", "f4", "f8"] for cls in ser_or_index ] ) # ------------------------------------------------------------------ # Comparisons class TestNumericComparisons: def test_operator_series_comparison_zerorank(self): # GH#13006 result = np.float64(0) > pd.Series([1, 2, 3]) expected = 0.0 > pd.Series([1, 2, 3]) tm.assert_series_equal(result, expected) result = pd.Series([1, 2, 3]) < np.float64(0) expected = pd.Series([1, 2, 3]) < 0.0 tm.assert_series_equal(result, expected) result = np.array([0, 1, 2])[0] > pd.Series([0, 1, 2]) expected = 0.0 > pd.Series([1, 2, 3]) tm.assert_series_equal(result, expected) def test_df_numeric_cmp_dt64_raises(self): # GH#8932, GH#22163 ts = pd.Timestamp.now() df = pd.DataFrame({"x": range(5)}) msg = ( "'[<>]' not supported between instances of 'numpy.ndarray' and 'Timestamp'" ) with pytest.raises(TypeError, match=msg): df > ts with pytest.raises(TypeError, match=msg): df < ts with pytest.raises(TypeError, match=msg): ts < df with pytest.raises(TypeError, match=msg): ts > df assert not (df == ts).any().any() assert (df != ts).all().all() def test_compare_invalid(self): # GH#8058 # ops testing a = pd.Series(np.random.randn(5), name=0) b = pd.Series(np.random.randn(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): # GH#36377, GH#35700 xbox = box if box is not pd.Index else np.ndarray obj = pd.Series(np.random.randn(10 ** 5)) obj = tm.box_expected(obj, box, transpose=False) result = obj == "a" expected = pd.Series(np.zeros(10 ** 5, dtype=bool)) expected = tm.box_expected(expected, xbox, transpose=False) tm.assert_equal(result, expected) 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: # TODO: also check name retentention @pytest.mark.parametrize("box_cls", [np.array, pd.Index, pd.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 = pd.TimedeltaIndex(["10s", "40s", "90s"]) if isinstance(left, pd.Series) or box_cls is pd.Series: expected = pd.Series(expected) result = left * right tm.assert_equal(result, expected) result = right * left tm.assert_equal(result, expected) # TODO: also check name retentention @pytest.mark.parametrize("box_cls", [np.array, pd.Index, pd.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 = pd.TimedeltaIndex(["1s", "2s", "3s"]) if isinstance(left, pd.Series) or box_cls is pd.Series: expected = pd.Series(expected) result = right / left tm.assert_equal(result, expected) result = right // left tm.assert_equal(result, expected) msg = "Cannot divide" with pytest.raises(TypeError, match=msg): left / right with pytest.raises(TypeError, match=msg): left // right # TODO: de-duplicate with test_numeric_arr_mul_tdscalar def test_ops_series(self): # regression test for G#H8813 td = Timedelta("1 day") other = pd.Series([1, 2]) expected = pd.Series(pd.to_timedelta(["1 day", "2 days"])) tm.assert_series_equal(expected, td * other) tm.assert_series_equal(expected, other * td) # TODO: also test non-nanosecond timedelta64 and 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(), ], ids=lambda x: type(x).__name__, ) def test_numeric_arr_mul_tdscalar(self, scalar_td, numeric_idx, box): # GH#19333 index = numeric_idx expected = pd.TimedeltaIndex([pd.Timedelta(days=n) for n in range(5)]) 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__, ) def test_numeric_arr_mul_tdscalar_numexpr_path(self, scalar_td, box): arr = np.arange(2 * 10 ** 4).astype(np.int64) obj = tm.box_expected(arr, box, transpose=False) expected = arr.view("timedelta64[D]").astype("timedelta64[ns]") 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): index = numeric_idx[1:3] expected = TimedeltaIndex(["3 Days", "36 Hours"]) 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", [ pd.Timedelta(hours=31), pd.Timedelta(hours=31).to_pytimedelta(), pd.Timedelta(hours=31).to_timedelta64(), pd.Timedelta(hours=31).to_timedelta64().astype("m8[h]"), np.timedelta64("NaT"), np.timedelta64("NaT", "D"), pd.offsets.Minute(3), pd.offsets.Second(0), ], ) def test_add_sub_timedeltalike_invalid(self, numeric_idx, other, box): left = tm.box_expected(numeric_idx, box) msg = ( "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" ) with pytest.raises(TypeError, match=msg): left + other with pytest.raises(TypeError, match=msg): other + left with pytest.raises(TypeError, match=msg): left - other with pytest.raises(TypeError, match=msg): other - left @pytest.mark.parametrize( "other", [ pd.Timestamp.now().to_pydatetime(), pd.Timestamp.now(tz="UTC").to_pydatetime(), pd.Timestamp.now().to_datetime64(), pd.NaT, ], ) @pytest.mark.filterwarnings("ignore:elementwise comp:DeprecationWarning") def test_add_sub_datetimelike_invalid(self, numeric_idx, other, box): # GH#28080 numeric+datetime64 should raise; Timestamp raises # NullFrequencyError instead of TypeError so is excluded. left = tm.box_expected(numeric_idx, box) msg = ( "unsupported operand type|" "Cannot (add|subtract) NaT (to|from) ndarray|" "Addition/subtraction of integers and integer-arrays|" "Concatenation operation is not implemented for NumPy arrays" ) with pytest.raises(TypeError, match=msg): left + other with pytest.raises(TypeError, match=msg): other + left with pytest.raises(TypeError, match=msg): left - other with pytest.raises(TypeError, match=msg): other - left # ------------------------------------------------------------------ # Arithmetic class TestDivisionByZero: def test_div_zero(self, zero, numeric_idx): idx = numeric_idx expected = pd.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 = pd.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 = pd.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 = pd.Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64) exright = pd.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 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)