test_arithmetic.py

# -*- coding: utf-8 -*-
from collections import deque
from datetime import datetime
import operator

import pytest
import numpy as np

from pandas.compat import range

import pandas as pd
import pandas.util.testing as tm

from pandas.tests.frame.common import _check_mixed_float, _check_mixed_int


# -------------------------------------------------------------------
# Comparisons

class TestFrameComparisons(object):
    # Specifically _not_ flex-comparisons

    def test_comparison_invalid(self):

        def check(df, df2):

            for (x, y) in [(df, df2), (df2, df)]:
                # we expect the result to match Series comparisons for
                # == and !=, inequalities should raise
                result = x == y
                expected = pd.DataFrame({col: x[col] == y[col]
                                         for col in x.columns},
                                        index=x.index, columns=x.columns)
                tm.assert_frame_equal(result, expected)

                result = x != y
                expected = pd.DataFrame({col: x[col] != y[col]
                                         for col in x.columns},
                                        index=x.index, columns=x.columns)
                tm.assert_frame_equal(result, expected)

                with pytest.raises(TypeError):
                    x >= y
                with pytest.raises(TypeError):
                    x > y
                with pytest.raises(TypeError):
                    x < y
                with pytest.raises(TypeError):
                    x <= y

        # GH4968
        # invalid date/int comparisons
        df = pd.DataFrame(np.random.randint(10, size=(10, 1)), columns=['a'])
        df['dates'] = pd.date_range('20010101', periods=len(df))

        df2 = df.copy()
        df2['dates'] = df['a']
        check(df, df2)

        df = pd.DataFrame(np.random.randint(10, size=(10, 2)),
                          columns=['a', 'b'])
        df2 = pd.DataFrame({'a': pd.date_range('20010101', periods=len(df)),
                            'b': pd.date_range('20100101', periods=len(df))})
        check(df, df2)

    def test_timestamp_compare(self):
        # make sure we can compare Timestamps on the right AND left hand side
        # GH#4982
        df = pd. DataFrame({'dates1': pd.date_range('20010101', periods=10),
                            'dates2': pd.date_range('20010102', periods=10),
                            'intcol': np.random.randint(1000000000, size=10),
                            'floatcol': np.random.randn(10),
                            'stringcol': list(tm.rands(10))})
        df.loc[np.random.rand(len(df)) > 0.5, 'dates2'] = pd.NaT
        ops = {'gt': 'lt', 'lt': 'gt', 'ge': 'le', 'le': 'ge', 'eq': 'eq',
               'ne': 'ne'}

        for left, right in ops.items():
            left_f = getattr(operator, left)
            right_f = getattr(operator, right)

            # no nats
            if left in ['eq', 'ne']:
                expected = left_f(df, pd.Timestamp('20010109'))
                result = right_f(pd.Timestamp('20010109'), df)
                tm.assert_frame_equal(result, expected)
            else:
                with pytest.raises(TypeError):
                    left_f(df, pd.Timestamp('20010109'))
                with pytest.raises(TypeError):
                    right_f(pd.Timestamp('20010109'), df)
            # nats
            expected = left_f(df, pd.Timestamp('nat'))
            result = right_f(pd.Timestamp('nat'), df)
            tm.assert_frame_equal(result, expected)

    def test_mixed_comparison(self):
        # GH#13128, GH#22163 != datetime64 vs non-dt64 should be False,
        # not raise TypeError
        # (this appears to be fixed before GH#22163, not sure when)
        df = pd.DataFrame([['1989-08-01', 1], ['1989-08-01', 2]])
        other = pd.DataFrame([['a', 'b'], ['c', 'd']])

        result = df == other
        assert not result.any().any()

        result = df != other
        assert result.all().all()

    def test_df_boolean_comparison_error(self):
        # GH#4576, GH#22880
        # comparing DataFrame against list/tuple with len(obj) matching
        #  len(df.columns) is supported as of GH#22800
        df = pd.DataFrame(np.arange(6).reshape((3, 2)))

        expected = pd.DataFrame([[False, False],
                                 [True, False],
                                 [False, False]])

        result = df == (2, 2)
        tm.assert_frame_equal(result, expected)

        result = df == [2, 2]
        tm.assert_frame_equal(result, expected)

    def test_df_float_none_comparison(self):
        df = pd.DataFrame(np.random.randn(8, 3), index=range(8),
                          columns=['A', 'B', 'C'])

        result = df.__eq__(None)
        assert not result.any().any()

    def test_df_string_comparison(self):
        df = pd.DataFrame([{"a": 1, "b": "foo"}, {"a": 2, "b": "bar"}])
        mask_a = df.a > 1
        tm.assert_frame_equal(df[mask_a], df.loc[1:1, :])
        tm.assert_frame_equal(df[-mask_a], df.loc[0:0, :])

        mask_b = df.b == "foo"
        tm.assert_frame_equal(df[mask_b], df.loc[0:0, :])
        tm.assert_frame_equal(df[-mask_b], df.loc[1:1, :])


class TestFrameFlexComparisons(object):
    # TODO: test_bool_flex_frame needs a better name
    def test_bool_flex_frame(self):
        data = np.random.randn(5, 3)
        other_data = np.random.randn(5, 3)
        df = pd.DataFrame(data)
        other = pd.DataFrame(other_data)
        ndim_5 = np.ones(df.shape + (1, 3))

        # Unaligned
        def _check_unaligned_frame(meth, op, df, other):
            part_o = other.loc[3:, 1:].copy()
            rs = meth(part_o)
            xp = op(df, part_o.reindex(index=df.index, columns=df.columns))
            tm.assert_frame_equal(rs, xp)

        # DataFrame
        assert df.eq(df).values.all()
        assert not df.ne(df).values.any()
        for op in ['eq', 'ne', 'gt', 'lt', 'ge', 'le']:
            f = getattr(df, op)
            o = getattr(operator, op)
            # No NAs
            tm.assert_frame_equal(f(other), o(df, other))
            _check_unaligned_frame(f, o, df, other)
            # ndarray
            tm.assert_frame_equal(f(other.values), o(df, other.values))
            # scalar
            tm.assert_frame_equal(f(0), o(df, 0))
            # NAs
            msg = "Unable to coerce to Series/DataFrame"
            tm.assert_frame_equal(f(np.nan), o(df, np.nan))
            with pytest.raises(ValueError, match=msg):
                f(ndim_5)

        # Series
        def _test_seq(df, idx_ser, col_ser):
            idx_eq = df.eq(idx_ser, axis=0)
            col_eq = df.eq(col_ser)
            idx_ne = df.ne(idx_ser, axis=0)
            col_ne = df.ne(col_ser)
            tm.assert_frame_equal(col_eq, df == pd.Series(col_ser))
            tm.assert_frame_equal(col_eq, -col_ne)
            tm.assert_frame_equal(idx_eq, -idx_ne)
            tm.assert_frame_equal(idx_eq, df.T.eq(idx_ser).T)
            tm.assert_frame_equal(col_eq, df.eq(list(col_ser)))
            tm.assert_frame_equal(idx_eq, df.eq(pd.Series(idx_ser), axis=0))
            tm.assert_frame_equal(idx_eq, df.eq(list(idx_ser), axis=0))

            idx_gt = df.gt(idx_ser, axis=0)
            col_gt = df.gt(col_ser)
            idx_le = df.le(idx_ser, axis=0)
            col_le = df.le(col_ser)

            tm.assert_frame_equal(col_gt, df > pd.Series(col_ser))
            tm.assert_frame_equal(col_gt, -col_le)
            tm.assert_frame_equal(idx_gt, -idx_le)
            tm.assert_frame_equal(idx_gt, df.T.gt(idx_ser).T)

            idx_ge = df.ge(idx_ser, axis=0)
            col_ge = df.ge(col_ser)
            idx_lt = df.lt(idx_ser, axis=0)
            col_lt = df.lt(col_ser)
            tm.assert_frame_equal(col_ge, df >= pd.Series(col_ser))
            tm.assert_frame_equal(col_ge, -col_lt)
            tm.assert_frame_equal(idx_ge, -idx_lt)
            tm.assert_frame_equal(idx_ge, df.T.ge(idx_ser).T)

        idx_ser = pd.Series(np.random.randn(5))
        col_ser = pd.Series(np.random.randn(3))
        _test_seq(df, idx_ser, col_ser)

        # list/tuple
        _test_seq(df, idx_ser.values, col_ser.values)

        # NA
        df.loc[0, 0] = np.nan
        rs = df.eq(df)
        assert not rs.loc[0, 0]
        rs = df.ne(df)
        assert rs.loc[0, 0]
        rs = df.gt(df)
        assert not rs.loc[0, 0]
        rs = df.lt(df)
        assert not rs.loc[0, 0]
        rs = df.ge(df)
        assert not rs.loc[0, 0]
        rs = df.le(df)
        assert not rs.loc[0, 0]

        # complex
        arr = np.array([np.nan, 1, 6, np.nan])
        arr2 = np.array([2j, np.nan, 7, None])
        df = pd.DataFrame({'a': arr})
        df2 = pd.DataFrame({'a': arr2})
        rs = df.gt(df2)
        assert not rs.values.any()
        rs = df.ne(df2)
        assert rs.values.all()

        arr3 = np.array([2j, np.nan, None])
        df3 = pd.DataFrame({'a': arr3})
        rs = df3.gt(2j)
        assert not rs.values.any()

        # corner, dtype=object
        df1 = pd.DataFrame({'col': ['foo', np.nan, 'bar']})
        df2 = pd.DataFrame({'col': ['foo', datetime.now(), 'bar']})
        result = df1.ne(df2)
        exp = pd.DataFrame({'col': [False, True, False]})
        tm.assert_frame_equal(result, exp)

    def test_flex_comparison_nat(self):
        # GH 15697, GH 22163 df.eq(pd.NaT) should behave like df == pd.NaT,
        # and _definitely_ not be NaN
        df = pd.DataFrame([pd.NaT])

        result = df == pd.NaT
        # result.iloc[0, 0] is a np.bool_ object
        assert result.iloc[0, 0].item() is False

        result = df.eq(pd.NaT)
        assert result.iloc[0, 0].item() is False

        result = df != pd.NaT
        assert result.iloc[0, 0].item() is True

        result = df.ne(pd.NaT)
        assert result.iloc[0, 0].item() is True

    @pytest.mark.parametrize('opname', ['eq', 'ne', 'gt', 'lt', 'ge', 'le'])
    def test_df_flex_cmp_constant_return_types(self, opname):
        # GH 15077, non-empty DataFrame
        df = pd.DataFrame({'x': [1, 2, 3], 'y': [1., 2., 3.]})
        const = 2

        result = getattr(df, opname)(const).get_dtype_counts()
        tm.assert_series_equal(result, pd.Series([2], ['bool']))

    @pytest.mark.parametrize('opname', ['eq', 'ne', 'gt', 'lt', 'ge', 'le'])
    def test_df_flex_cmp_constant_return_types_empty(self, opname):
        # GH 15077 empty DataFrame
        df = pd.DataFrame({'x': [1, 2, 3], 'y': [1., 2., 3.]})
        const = 2

        empty = df.iloc[:0]
        result = getattr(empty, opname)(const).get_dtype_counts()
        tm.assert_series_equal(result, pd.Series([2], ['bool']))


# -------------------------------------------------------------------
# Arithmetic

class TestFrameFlexArithmetic(object):

    def test_df_add_td64_columnwise(self):
        # GH 22534 Check that column-wise addition broadcasts correctly
        dti = pd.date_range('2016-01-01', periods=10)
        tdi = pd.timedelta_range('1ns', periods=10)
        tser = pd.Series(tdi)
        df = pd.DataFrame({0: dti, 1: tdi})

        result = df.add(tser, axis=0)
        expected = pd.DataFrame({0: dti + tdi,
                                 1: tdi + tdi})
        tm.assert_frame_equal(result, expected)

    def test_df_add_flex_filled_mixed_dtypes(self):
        # GH 19611
        dti = pd.date_range('2016-01-01', periods=3)
        ser = pd.Series(['1 Day', 'NaT', '2 Days'], dtype='timedelta64[ns]')
        df = pd.DataFrame({'A': dti, 'B': ser})
        other = pd.DataFrame({'A': ser, 'B': ser})
        fill = pd.Timedelta(days=1).to_timedelta64()
        result = df.add(other, fill_value=fill)

        expected = pd.DataFrame(
            {'A': pd.Series(['2016-01-02', '2016-01-03', '2016-01-05'],
                            dtype='datetime64[ns]'),
             'B': ser * 2})
        tm.assert_frame_equal(result, expected)

    def test_arith_flex_frame(self, all_arithmetic_operators, float_frame,
                              mixed_float_frame):
        # one instance of parametrized fixture
        op = all_arithmetic_operators

        def f(x, y):
            # r-versions not in operator-stdlib; get op without "r" and invert
            if op.startswith('__r'):
                return getattr(operator, op.replace('__r', '__'))(y, x)
            return getattr(operator, op)(x, y)

        result = getattr(float_frame, op)(2 * float_frame)
        expected = f(float_frame, 2 * float_frame)
        tm.assert_frame_equal(result, expected)

        # vs mix float
        result = getattr(mixed_float_frame, op)(2 * mixed_float_frame)
        expected = f(mixed_float_frame, 2 * mixed_float_frame)
        tm.assert_frame_equal(result, expected)
        _check_mixed_float(result, dtype=dict(C=None))

    @pytest.mark.parametrize('op', ['__add__', '__sub__', '__mul__'])
    def test_arith_flex_frame_mixed(self, op, int_frame, mixed_int_frame,
                                    mixed_float_frame):
        f = getattr(operator, op)

        # vs mix int
        result = getattr(mixed_int_frame, op)(2 + mixed_int_frame)
        expected = f(mixed_int_frame, 2 + mixed_int_frame)

        # no overflow in the uint
        dtype = None
        if op in ['__sub__']:
            dtype = dict(B='uint64', C=None)
        elif op in ['__add__', '__mul__']:
            dtype = dict(C=None)
        tm.assert_frame_equal(result, expected)
        _check_mixed_int(result, dtype=dtype)

        # vs mix float
        result = getattr(mixed_float_frame, op)(2 * mixed_float_frame)
        expected = f(mixed_float_frame, 2 * mixed_float_frame)
        tm.assert_frame_equal(result, expected)
        _check_mixed_float(result, dtype=dict(C=None))

        # vs plain int
        result = getattr(int_frame, op)(2 * int_frame)
        expected = f(int_frame, 2 * int_frame)
        tm.assert_frame_equal(result, expected)

    def test_arith_flex_frame_raise(self, all_arithmetic_operators,
                                    float_frame):
        # one instance of parametrized fixture
        op = all_arithmetic_operators

        # Check that arrays with dim >= 3 raise
        for dim in range(3, 6):
            arr = np.ones((1,) * dim)
            msg = "Unable to coerce to Series/DataFrame"
            with pytest.raises(ValueError, match=msg):
                getattr(float_frame, op)(arr)

    def test_arith_flex_frame_corner(self, float_frame):

        const_add = float_frame.add(1)
        tm.assert_frame_equal(const_add, float_frame + 1)

        # corner cases
        result = float_frame.add(float_frame[:0])
        tm.assert_frame_equal(result, float_frame * np.nan)

        result = float_frame[:0].add(float_frame)
        tm.assert_frame_equal(result, float_frame * np.nan)

        with pytest.raises(NotImplementedError, match='fill_value'):
            float_frame.add(float_frame.iloc[0], fill_value=3)

        with pytest.raises(NotImplementedError, match='fill_value'):
            float_frame.add(float_frame.iloc[0], axis='index', fill_value=3)

    def test_arith_flex_series(self, simple_frame):
        df = simple_frame

        row = df.xs('a')
        col = df['two']
        # after arithmetic refactor, add truediv here
        ops = ['add', 'sub', 'mul', 'mod']
        for op in ops:
            f = getattr(df, op)
            op = getattr(operator, op)
            tm.assert_frame_equal(f(row), op(df, row))
            tm.assert_frame_equal(f(col, axis=0), op(df.T, col).T)

        # special case for some reason
        tm.assert_frame_equal(df.add(row, axis=None), df + row)

        # cases which will be refactored after big arithmetic refactor
        tm.assert_frame_equal(df.div(row), df / row)
        tm.assert_frame_equal(df.div(col, axis=0), (df.T / col).T)

        # broadcasting issue in GH 7325
        df = pd.DataFrame(np.arange(3 * 2).reshape((3, 2)), dtype='int64')
        expected = pd.DataFrame([[np.nan, np.inf], [1.0, 1.5], [1.0, 1.25]])
        result = df.div(df[0], axis='index')
        tm.assert_frame_equal(result, expected)

        df = pd.DataFrame(np.arange(3 * 2).reshape((3, 2)), dtype='float64')
        expected = pd.DataFrame([[np.nan, np.inf], [1.0, 1.5], [1.0, 1.25]])
        result = df.div(df[0], axis='index')
        tm.assert_frame_equal(result, expected)

    def test_arith_flex_zero_len_raises(self):
        # GH 19522 passing fill_value to frame flex arith methods should
        # raise even in the zero-length special cases
        ser_len0 = pd.Series([])
        df_len0 = pd.DataFrame([], columns=['A', 'B'])
        df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])

        with pytest.raises(NotImplementedError, match='fill_value'):
            df.add(ser_len0, fill_value='E')

        with pytest.raises(NotImplementedError, match='fill_value'):
            df_len0.sub(df['A'], axis=None, fill_value=3)


class TestFrameArithmetic(object):
    def test_df_add_2d_array_rowlike_broadcasts(self):
        # GH#23000
        arr = np.arange(6).reshape(3, 2)
        df = pd.DataFrame(arr, columns=[True, False], index=['A', 'B', 'C'])

        rowlike = arr[[1], :]  # shape --> (1, ncols)
        assert rowlike.shape == (1, df.shape[1])

        expected = pd.DataFrame([[2, 4],
                                 [4, 6],
                                 [6, 8]],
                                columns=df.columns, index=df.index,
                                # specify dtype explicitly to avoid failing
                                # on 32bit builds
                                dtype=arr.dtype)
        result = df + rowlike
        tm.assert_frame_equal(result, expected)
        result = rowlike + df
        tm.assert_frame_equal(result, expected)

    def test_df_add_2d_array_collike_broadcasts(self):
        # GH#23000
        arr = np.arange(6).reshape(3, 2)
        df = pd.DataFrame(arr, columns=[True, False], index=['A', 'B', 'C'])

        collike = arr[:, [1]]  # shape --> (nrows, 1)
        assert collike.shape == (df.shape[0], 1)

        expected = pd.DataFrame([[1, 2],
                                 [5, 6],
                                 [9, 10]],
                                columns=df.columns, index=df.index,
                                # specify dtype explicitly to avoid failing
                                # on 32bit builds
                                dtype=arr.dtype)
        result = df + collike
        tm.assert_frame_equal(result, expected)
        result = collike + df
        tm.assert_frame_equal(result, expected)

    def test_df_arith_2d_array_rowlike_broadcasts(self,
                                                  all_arithmetic_operators):
        # GH#23000
        opname = all_arithmetic_operators

        arr = np.arange(6).reshape(3, 2)
        df = pd.DataFrame(arr, columns=[True, False], index=['A', 'B', 'C'])

        rowlike = arr[[1], :]  # shape --> (1, ncols)
        assert rowlike.shape == (1, df.shape[1])

        exvals = [getattr(df.loc['A'], opname)(rowlike.squeeze()),
                  getattr(df.loc['B'], opname)(rowlike.squeeze()),
                  getattr(df.loc['C'], opname)(rowlike.squeeze())]

        expected = pd.DataFrame(exvals, columns=df.columns, index=df.index)

        if opname in ['__rmod__', '__rfloordiv__']:
            # exvals will have dtypes [f8, i8, i8] so expected will be
            #   all-f8, but the DataFrame operation will return mixed dtypes
            # use exvals[-1].dtype instead of "i8" for compat with 32-bit
            # systems/pythons
            expected[False] = expected[False].astype(exvals[-1].dtype)

        result = getattr(df, opname)(rowlike)
        tm.assert_frame_equal(result, expected)

    def test_df_arith_2d_array_collike_broadcasts(self,
                                                  all_arithmetic_operators):
        # GH#23000
        opname = all_arithmetic_operators

        arr = np.arange(6).reshape(3, 2)
        df = pd.DataFrame(arr, columns=[True, False], index=['A', 'B', 'C'])

        collike = arr[:, [1]]  # shape --> (nrows, 1)
        assert collike.shape == (df.shape[0], 1)

        exvals = {True: getattr(df[True], opname)(collike.squeeze()),
                  False: getattr(df[False], opname)(collike.squeeze())}

        dtype = None
        if opname in ['__rmod__', '__rfloordiv__']:
            # Series ops may return mixed int/float dtypes in cases where
            #   DataFrame op will return all-float.  So we upcast `expected`
            dtype = np.common_type(*[x.values for x in exvals.values()])

        expected = pd.DataFrame(exvals, columns=df.columns, index=df.index,
                                dtype=dtype)

        result = getattr(df, opname)(collike)
        tm.assert_frame_equal(result, expected)

    def test_df_bool_mul_int(self):
        # GH 22047, GH 22163 multiplication by 1 should result in int dtype,
        # not object dtype
        df = pd.DataFrame([[False, True], [False, False]])
        result = df * 1

        # On appveyor this comes back as np.int32 instead of np.int64,
        # so we check dtype.kind instead of just dtype
        kinds = result.dtypes.apply(lambda x: x.kind)
        assert (kinds == 'i').all()

        result = 1 * df
        kinds = result.dtypes.apply(lambda x: x.kind)
        assert (kinds == 'i').all()

    def test_td64_df_add_int_frame(self):
        # GH#22696 Check that we don't dispatch to numpy implementation,
        # which treats int64 as m8[ns]
        tdi = pd.timedelta_range('1ns', periods=3)
        df = tdi.to_frame()
        other = pd.DataFrame([1, 2, 3], index=tdi)  # indexed like `df`
        with pytest.raises(TypeError):
            df + other
        with pytest.raises(TypeError):
            other + df
        with pytest.raises(TypeError):
            df - other
        with pytest.raises(TypeError):
            other - df

    def test_arith_mixed(self):

        left = pd.DataFrame({'A': ['a', 'b', 'c'],
                             'B': [1, 2, 3]})

        result = left + left
        expected = pd.DataFrame({'A': ['aa', 'bb', 'cc'],
                                 'B': [2, 4, 6]})
        tm.assert_frame_equal(result, expected)

    def test_arith_getitem_commute(self):
        df = pd.DataFrame({'A': [1.1, 3.3], 'B': [2.5, -3.9]})

        def _test_op(df, op):
            result = op(df, 1)

            if not df.columns.is_unique:
                raise ValueError("Only unique columns supported by this test")

            for col in result.columns:
                tm.assert_series_equal(result[col], op(df[col], 1))

        _test_op(df, operator.add)
        _test_op(df, operator.sub)
        _test_op(df, operator.mul)
        _test_op(df, operator.truediv)
        _test_op(df, operator.floordiv)
        _test_op(df, operator.pow)

        _test_op(df, lambda x, y: y + x)
        _test_op(df, lambda x, y: y - x)
        _test_op(df, lambda x, y: y * x)
        _test_op(df, lambda x, y: y / x)
        _test_op(df, lambda x, y: y ** x)

        _test_op(df, lambda x, y: x + y)
        _test_op(df, lambda x, y: x - y)
        _test_op(df, lambda x, y: x * y)
        _test_op(df, lambda x, y: x / y)
        _test_op(df, lambda x, y: x ** y)

    @pytest.mark.parametrize('values', [[1, 2], (1, 2), np.array([1, 2]),
                                        range(1, 3), deque([1, 2])])
    def test_arith_alignment_non_pandas_object(self, values):
        # GH#17901
        df = pd.DataFrame({'A': [1, 1], 'B': [1, 1]})
        expected = pd.DataFrame({'A': [2, 2], 'B': [3, 3]})
        result = df + values
        tm.assert_frame_equal(result, expected)

    def test_arith_non_pandas_object(self):
        df = pd.DataFrame(np.arange(1, 10, dtype='f8').reshape(3, 3),
                          columns=['one', 'two', 'three'],
                          index=['a', 'b', 'c'])

        val1 = df.xs('a').values
        added = pd.DataFrame(df.values + val1,
                             index=df.index, columns=df.columns)
        tm.assert_frame_equal(df + val1, added)

        added = pd.DataFrame((df.values.T + val1).T,
                             index=df.index, columns=df.columns)
        tm.assert_frame_equal(df.add(val1, axis=0), added)

        val2 = list(df['two'])

        added = pd.DataFrame(df.values + val2,
                             index=df.index, columns=df.columns)
        tm.assert_frame_equal(df + val2, added)

        added = pd.DataFrame((df.values.T + val2).T, index=df.index,
                             columns=df.columns)
        tm.assert_frame_equal(df.add(val2, axis='index'), added)

        val3 = np.random.rand(*df.shape)
        added = pd.DataFrame(df.values + val3,
                             index=df.index, columns=df.columns)
        tm.assert_frame_equal(df.add(val3), added)