test_operators.py

# -*- coding: utf-8 -*-

from __future__ import print_function
from collections import deque
from datetime import datetime
from decimal import Decimal
import operator

import pytest

from numpy import nan
import numpy as np

from pandas.compat import range
from pandas import compat
from pandas import (DataFrame, Series, MultiIndex, Timestamp,
                    date_range)
import pandas.core.common as com
import pandas as pd

from pandas.util.testing import (assert_numpy_array_equal,
                                 assert_series_equal,
                                 assert_frame_equal)

import pandas.util.testing as tm

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


class TestDataFrameUnaryOperators(object):
    # __pos__, __neg__, __inv__

    @pytest.mark.parametrize('df,expected', [
        (pd.DataFrame({'a': [-1, 1]}), pd.DataFrame({'a': [1, -1]})),
        (pd.DataFrame({'a': [False, True]}),
            pd.DataFrame({'a': [True, False]})),
        (pd.DataFrame({'a': pd.Series(pd.to_timedelta([-1, 1]))}),
            pd.DataFrame({'a': pd.Series(pd.to_timedelta([1, -1]))}))
    ])
    def test_neg_numeric(self, df, expected):
        assert_frame_equal(-df, expected)
        assert_series_equal(-df['a'], expected['a'])

    @pytest.mark.parametrize('df, expected', [
        (np.array([1, 2], dtype=object), np.array([-1, -2], dtype=object)),
        ([Decimal('1.0'), Decimal('2.0')], [Decimal('-1.0'), Decimal('-2.0')]),
    ])
    def test_neg_object(self, df, expected):
        # GH#21380
        df = pd.DataFrame({'a': df})
        expected = pd.DataFrame({'a': expected})
        assert_frame_equal(-df, expected)
        assert_series_equal(-df['a'], expected['a'])

    @pytest.mark.parametrize('df', [
        pd.DataFrame({'a': ['a', 'b']}),
        pd.DataFrame({'a': pd.to_datetime(['2017-01-22', '1970-01-01'])}),
    ])
    def test_neg_raises(self, df):
        with pytest.raises(TypeError):
            (- df)
        with pytest.raises(TypeError):
            (- df['a'])

    def test_invert(self):
        _seriesd = tm.getSeriesData()
        df = pd.DataFrame(_seriesd)

        assert_frame_equal(-(df < 0), ~(df < 0))

    @pytest.mark.parametrize('df', [
        pd.DataFrame({'a': [-1, 1]}),
        pd.DataFrame({'a': [False, True]}),
        pd.DataFrame({'a': pd.Series(pd.to_timedelta([-1, 1]))}),
    ])
    def test_pos_numeric(self, df):
        # GH#16073
        assert_frame_equal(+df, df)
        assert_series_equal(+df['a'], df['a'])

    @pytest.mark.parametrize('df', [
        # numpy changing behavior in the future
        pytest.param(pd.DataFrame({'a': ['a', 'b']}),
                     marks=[pytest.mark.filterwarnings("ignore")]),
        pd.DataFrame({'a': np.array([-1, 2], dtype=object)}),
        pd.DataFrame({'a': [Decimal('-1.0'), Decimal('2.0')]}),
    ])
    def test_pos_object(self, df):
        # GH#21380
        assert_frame_equal(+df, df)
        assert_series_equal(+df['a'], df['a'])

    @pytest.mark.parametrize('df', [
        pd.DataFrame({'a': pd.to_datetime(['2017-01-22', '1970-01-01'])}),
    ])
    def test_pos_raises(self, df):
        with pytest.raises(TypeError):
            (+ df)
        with pytest.raises(TypeError):
            (+ df['a'])


class TestDataFrameLogicalOperators(object):
    # &, |, ^

    def test_logical_ops_empty_frame(self):
        # GH#5808
        # empty frames, non-mixed dtype
        df = DataFrame(index=[1])

        result = df & df
        assert_frame_equal(result, df)

        result = df | df
        assert_frame_equal(result, df)

        df2 = DataFrame(index=[1, 2])
        result = df & df2
        assert_frame_equal(result, df2)

        dfa = DataFrame(index=[1], columns=['A'])

        result = dfa & dfa
        assert_frame_equal(result, dfa)

    def test_logical_ops_bool_frame(self):
        # GH#5808
        df1a_bool = DataFrame(True, index=[1], columns=['A'])

        result = df1a_bool & df1a_bool
        assert_frame_equal(result, df1a_bool)

        result = df1a_bool | df1a_bool
        assert_frame_equal(result, df1a_bool)

    def test_logical_ops_int_frame(self):
        # GH#5808
        df1a_int = DataFrame(1, index=[1], columns=['A'])
        df1a_bool = DataFrame(True, index=[1], columns=['A'])

        result = df1a_int | df1a_bool
        assert_frame_equal(result, df1a_int)

    def test_logical_ops_invalid(self):
        # GH#5808

        df1 = DataFrame(1.0, index=[1], columns=['A'])
        df2 = DataFrame(True, index=[1], columns=['A'])
        with pytest.raises(TypeError):
            df1 | df2

        df1 = DataFrame('foo', index=[1], columns=['A'])
        df2 = DataFrame(True, index=[1], columns=['A'])
        with pytest.raises(TypeError):
            df1 | df2

    def test_logical_operators(self):

        def _check_bin_op(op):
            result = op(df1, df2)
            expected = DataFrame(op(df1.values, df2.values), index=df1.index,
                                 columns=df1.columns)
            assert result.values.dtype == np.bool_
            assert_frame_equal(result, expected)

        def _check_unary_op(op):
            result = op(df1)
            expected = DataFrame(op(df1.values), index=df1.index,
                                 columns=df1.columns)
            assert result.values.dtype == np.bool_
            assert_frame_equal(result, expected)

        df1 = {'a': {'a': True, 'b': False, 'c': False, 'd': True, 'e': True},
               'b': {'a': False, 'b': True, 'c': False,
                     'd': False, 'e': False},
               'c': {'a': False, 'b': False, 'c': True,
                     'd': False, 'e': False},
               'd': {'a': True, 'b': False, 'c': False, 'd': True, 'e': True},
               'e': {'a': True, 'b': False, 'c': False, 'd': True, 'e': True}}

        df2 = {'a': {'a': True, 'b': False, 'c': True, 'd': False, 'e': False},
               'b': {'a': False, 'b': True, 'c': False,
                     'd': False, 'e': False},
               'c': {'a': True, 'b': False, 'c': True, 'd': False, 'e': False},
               'd': {'a': False, 'b': False, 'c': False,
                     'd': True, 'e': False},
               'e': {'a': False, 'b': False, 'c': False,
                     'd': False, 'e': True}}

        df1 = DataFrame(df1)
        df2 = DataFrame(df2)

        _check_bin_op(operator.and_)
        _check_bin_op(operator.or_)
        _check_bin_op(operator.xor)

        # operator.neg is deprecated in numpy >= 1.9
        _check_unary_op(operator.inv)  # TODO: belongs elsewhere

    def test_logical_with_nas(self):
        d = DataFrame({'a': [np.nan, False], 'b': [True, True]})

        # GH4947
        # bool comparisons should return bool
        result = d['a'] | d['b']
        expected = Series([False, True])
        assert_series_equal(result, expected)

        # GH4604, automatic casting here
        result = d['a'].fillna(False) | d['b']
        expected = Series([True, True])
        assert_series_equal(result, expected)

        result = d['a'].fillna(False, downcast=False) | d['b']
        expected = Series([True, True])
        assert_series_equal(result, expected)


class TestDataFrameOperators(TestData):

    @pytest.mark.parametrize('op', [operator.add, operator.sub,
                                    operator.mul, operator.truediv])
    def test_operators_none_as_na(self, op):
        df = DataFrame({"col1": [2, 5.0, 123, None],
                        "col2": [1, 2, 3, 4]}, dtype=object)

        # since filling converts dtypes from object, changed expected to be
        # object
        filled = df.fillna(np.nan)
        result = op(df, 3)
        expected = op(filled, 3).astype(object)
        expected[com.isna(expected)] = None
        assert_frame_equal(result, expected)

        result = op(df, df)
        expected = op(filled, filled).astype(object)
        expected[com.isna(expected)] = None
        assert_frame_equal(result, expected)

        result = op(df, df.fillna(7))
        assert_frame_equal(result, expected)

        result = op(df.fillna(7), df)
        assert_frame_equal(result, expected, check_dtype=False)

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

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

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

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

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

        df = DataFrame(np.random.randint(10, size=(10, 2)), columns=['a', 'b'])
        df2 = DataFrame({'a': date_range('20010101', periods=len(
            df)), 'b': 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
        # GH4982
        df = DataFrame({'dates1': date_range('20010101', periods=10),
                        'dates2': 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, Timestamp('20010109'))
                result = right_f(Timestamp('20010109'), df)
                assert_frame_equal(result, expected)
            else:
                with pytest.raises(TypeError):
                    left_f(df, Timestamp('20010109'))
                with pytest.raises(TypeError):
                    right_f(Timestamp('20010109'), df)
            # nats
            expected = left_f(df, Timestamp('nat'))
            result = right_f(Timestamp('nat'), df)
            assert_frame_equal(result, expected)

    @pytest.mark.parametrize('op,res', [('__eq__', False),
                                        ('__ne__', True)])
    # TODO: not sure what's correct here.
    @pytest.mark.filterwarnings("ignore:elementwise:FutureWarning")
    def test_logical_typeerror_with_non_valid(self, op, res):
        # we are comparing floats vs a string
        result = getattr(self.frame, op)('foo')
        assert bool(result.all().all()) is res

    def test_binary_ops_align(self):

        # test aligning binary ops

        # GH 6681
        index = MultiIndex.from_product([list('abc'),
                                         ['one', 'two', 'three'],
                                         [1, 2, 3]],
                                        names=['first', 'second', 'third'])

        df = DataFrame(np.arange(27 * 3).reshape(27, 3),
                       index=index,
                       columns=['value1', 'value2', 'value3']).sort_index()

        idx = pd.IndexSlice
        for op in ['add', 'sub', 'mul', 'div', 'truediv']:
            opa = getattr(operator, op, None)
            if opa is None:
                continue

            x = Series([1.0, 10.0, 100.0], [1, 2, 3])
            result = getattr(df, op)(x, level='third', axis=0)

            expected = pd.concat([opa(df.loc[idx[:, :, i], :], v)
                                  for i, v in x.iteritems()]).sort_index()
            assert_frame_equal(result, expected)

            x = Series([1.0, 10.0], ['two', 'three'])
            result = getattr(df, op)(x, level='second', axis=0)

            expected = (pd.concat([opa(df.loc[idx[:, i], :], v)
                                   for i, v in x.iteritems()])
                        .reindex_like(df).sort_index())
            assert_frame_equal(result, expected)

        # GH9463 (alignment level of dataframe with series)

        midx = MultiIndex.from_product([['A', 'B'], ['a', 'b']])
        df = DataFrame(np.ones((2, 4), dtype='int64'), columns=midx)
        s = pd.Series({'a': 1, 'b': 2})

        df2 = df.copy()
        df2.columns.names = ['lvl0', 'lvl1']
        s2 = s.copy()
        s2.index.name = 'lvl1'

        # different cases of integer/string level names:
        res1 = df.mul(s, axis=1, level=1)
        res2 = df.mul(s2, axis=1, level=1)
        res3 = df2.mul(s, axis=1, level=1)
        res4 = df2.mul(s2, axis=1, level=1)
        res5 = df2.mul(s, axis=1, level='lvl1')
        res6 = df2.mul(s2, axis=1, level='lvl1')

        exp = DataFrame(np.array([[1, 2, 1, 2], [1, 2, 1, 2]], dtype='int64'),
                        columns=midx)

        for res in [res1, res2]:
            assert_frame_equal(res, exp)

        exp.columns.names = ['lvl0', 'lvl1']
        for res in [res3, res4, res5, res6]:
            assert_frame_equal(res, exp)

    def test_arith_mixed(self):

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

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

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

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

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

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

    @staticmethod
    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:
            assert_series_equal(result[col], op(df[col], 1))

    def test_bool_flex_frame(self):
        data = np.random.randn(5, 3)
        other_data = np.random.randn(5, 3)
        df = DataFrame(data)
        other = 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))
            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
            assert_frame_equal(f(other), o(df, other))
            _check_unaligned_frame(f, o, df, other)
            # ndarray
            assert_frame_equal(f(other.values), o(df, other.values))
            # scalar
            assert_frame_equal(f(0), o(df, 0))
            # NAs
            msg = "Unable to coerce to Series/DataFrame"
            assert_frame_equal(f(np.nan), o(df, np.nan))
            with tm.assert_raises_regex(ValueError, 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)
            assert_frame_equal(col_eq, df == Series(col_ser))
            assert_frame_equal(col_eq, -col_ne)
            assert_frame_equal(idx_eq, -idx_ne)
            assert_frame_equal(idx_eq, df.T.eq(idx_ser).T)
            assert_frame_equal(col_eq, df.eq(list(col_ser)))
            assert_frame_equal(idx_eq, df.eq(Series(idx_ser), axis=0))
            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)

            assert_frame_equal(col_gt, df > Series(col_ser))
            assert_frame_equal(col_gt, -col_le)
            assert_frame_equal(idx_gt, -idx_le)
            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)
            assert_frame_equal(col_ge, df >= Series(col_ser))
            assert_frame_equal(col_ge, -col_lt)
            assert_frame_equal(idx_ge, -idx_lt)
            assert_frame_equal(idx_ge, df.T.ge(idx_ser).T)

        idx_ser = Series(np.random.randn(5))
        col_ser = 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 = DataFrame({'a': arr})
        df2 = 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 = DataFrame({'a': arr3})
        rs = df3.gt(2j)
        assert not rs.values.any()

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

    def test_dti_tz_convert_to_utc(self):
        base = pd.DatetimeIndex(['2011-01-01', '2011-01-02',
                                 '2011-01-03'], tz='UTC')
        idx1 = base.tz_convert('Asia/Tokyo')[:2]
        idx2 = base.tz_convert('US/Eastern')[1:]

        df1 = DataFrame({'A': [1, 2]}, index=idx1)
        df2 = DataFrame({'A': [1, 1]}, index=idx2)
        exp = DataFrame({'A': [np.nan, 3, np.nan]}, index=base)
        assert_frame_equal(df1 + df2, exp)

    def test_arith_non_pandas_object(self):
        df = self.simple

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

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

        val2 = list(df['two'])

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

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

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

    @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 = DataFrame({'A': [1, 1], 'B': [1, 1]})
        expected = DataFrame({'A': [2, 2], 'B': [3, 3]})
        result = df + values
        assert_frame_equal(result, expected)

    def test_combineFrame(self):
        frame_copy = self.frame.reindex(self.frame.index[::2])

        del frame_copy['D']
        frame_copy['C'][:5] = nan

        added = self.frame + frame_copy

        indexer = added['A'].dropna().index
        exp = (self.frame['A'] * 2).copy()

        tm.assert_series_equal(added['A'].dropna(), exp.loc[indexer])

        exp.loc[~exp.index.isin(indexer)] = np.nan
        tm.assert_series_equal(added['A'], exp.loc[added['A'].index])

        assert np.isnan(added['C'].reindex(frame_copy.index)[:5]).all()

        # assert(False)

        assert np.isnan(added['D']).all()

        self_added = self.frame + self.frame
        tm.assert_index_equal(self_added.index, self.frame.index)

        added_rev = frame_copy + self.frame
        assert np.isnan(added['D']).all()
        assert np.isnan(added_rev['D']).all()

        # corner cases

        # empty
        plus_empty = self.frame + self.empty
        assert np.isnan(plus_empty.values).all()

        empty_plus = self.empty + self.frame
        assert np.isnan(empty_plus.values).all()

        empty_empty = self.empty + self.empty
        assert empty_empty.empty

        # out of order
        reverse = self.frame.reindex(columns=self.frame.columns[::-1])

        assert_frame_equal(reverse + self.frame, self.frame * 2)

        # mix vs float64, upcast
        added = self.frame + self.mixed_float
        _check_mixed_float(added, dtype='float64')
        added = self.mixed_float + self.frame
        _check_mixed_float(added, dtype='float64')

        # mix vs mix
        added = self.mixed_float + self.mixed_float2
        _check_mixed_float(added, dtype=dict(C=None))
        added = self.mixed_float2 + self.mixed_float
        _check_mixed_float(added, dtype=dict(C=None))

        # with int
        added = self.frame + self.mixed_int
        _check_mixed_float(added, dtype='float64')

    def test_combineSeries(self):

        # Series
        series = self.frame.xs(self.frame.index[0])

        added = self.frame + series

        for key, s in compat.iteritems(added):
            assert_series_equal(s, self.frame[key] + series[key])

        larger_series = series.to_dict()
        larger_series['E'] = 1
        larger_series = Series(larger_series)
        larger_added = self.frame + larger_series

        for key, s in compat.iteritems(self.frame):
            assert_series_equal(larger_added[key], s + series[key])
        assert 'E' in larger_added
        assert np.isnan(larger_added['E']).all()

        # no upcast needed
        added = self.mixed_float + series
        _check_mixed_float(added)

        # vs mix (upcast) as needed
        added = self.mixed_float + series.astype('float32')
        _check_mixed_float(added, dtype=dict(C=None))
        added = self.mixed_float + series.astype('float16')
        _check_mixed_float(added, dtype=dict(C=None))

        # these raise with numexpr.....as we are adding an int64 to an
        # uint64....weird vs int

        # added = self.mixed_int + (100*series).astype('int64')
        # _check_mixed_int(added, dtype = dict(A = 'int64', B = 'float64', C =
        # 'int64', D = 'int64'))
        # added = self.mixed_int + (100*series).astype('int32')
        # _check_mixed_int(added, dtype = dict(A = 'int32', B = 'float64', C =
        # 'int32', D = 'int64'))

        # TimeSeries
        ts = self.tsframe['A']

        # 10890
        # we no longer allow auto timeseries broadcasting
        # and require explicit broadcasting
        added = self.tsframe.add(ts, axis='index')

        for key, col in compat.iteritems(self.tsframe):
            result = col + ts
            assert_series_equal(added[key], result, check_names=False)
            assert added[key].name == key
            if col.name == ts.name:
                assert result.name == 'A'
            else:
                assert result.name is None

        smaller_frame = self.tsframe[:-5]
        smaller_added = smaller_frame.add(ts, axis='index')

        tm.assert_index_equal(smaller_added.index, self.tsframe.index)

        smaller_ts = ts[:-5]
        smaller_added2 = self.tsframe.add(smaller_ts, axis='index')
        assert_frame_equal(smaller_added, smaller_added2)

        # length 0, result is all-nan
        result = self.tsframe.add(ts[:0], axis='index')
        expected = DataFrame(np.nan, index=self.tsframe.index,
                             columns=self.tsframe.columns)
        assert_frame_equal(result, expected)

        # Frame is all-nan
        result = self.tsframe[:0].add(ts, axis='index')
        expected = DataFrame(np.nan, index=self.tsframe.index,
                             columns=self.tsframe.columns)
        assert_frame_equal(result, expected)

        # empty but with non-empty index
        frame = self.tsframe[:1].reindex(columns=[])
        result = frame.mul(ts, axis='index')
        assert len(result) == len(ts)

    def test_combineFunc(self):
        result = self.frame * 2
        tm.assert_numpy_array_equal(result.values, self.frame.values * 2)

        # vs mix
        result = self.mixed_float * 2
        for c, s in compat.iteritems(result):
            tm.assert_numpy_array_equal(
                s.values, self.mixed_float[c].values * 2)
        _check_mixed_float(result, dtype=dict(C=None))

        result = self.empty * 2
        assert result.index is self.empty.index
        assert len(result.columns) == 0

    def test_comparisons(self):
        df1 = tm.makeTimeDataFrame()
        df2 = tm.makeTimeDataFrame()

        row = self.simple.xs('a')
        ndim_5 = np.ones(df1.shape + (1, 1, 1))

        def test_comp(func):
            result = func(df1, df2)
            tm.assert_numpy_array_equal(result.values,
                                        func(df1.values, df2.values))
            with tm.assert_raises_regex(ValueError,
                                        'Wrong number of dimensions'):
                func(df1, ndim_5)

            result2 = func(self.simple, row)
            tm.assert_numpy_array_equal(result2.values,
                                        func(self.simple.values, row.values))

            result3 = func(self.frame, 0)
            tm.assert_numpy_array_equal(result3.values,
                                        func(self.frame.values, 0))

            with tm.assert_raises_regex(ValueError,
                                        'Can only compare identically'
                                        '-labeled DataFrame'):
                func(self.simple, self.simple[:2])

        test_comp(operator.eq)
        test_comp(operator.ne)
        test_comp(operator.lt)
        test_comp(operator.gt)
        test_comp(operator.ge)
        test_comp(operator.le)

    def test_comparison_protected_from_errstate(self):
        missing_df = tm.makeDataFrame()
        missing_df.iloc[0]['A'] = np.nan
        with np.errstate(invalid='ignore'):
            expected = missing_df.values < 0
        with np.errstate(invalid='raise'):
            result = (missing_df < 0).values
        tm.assert_numpy_array_equal(result, expected)

    def test_boolean_comparison(self):

        # GH 4576
        # boolean comparisons with a tuple/list give unexpected results
        df = DataFrame(np.arange(6).reshape((3, 2)))
        b = np.array([2, 2])
        b_r = np.atleast_2d([2, 2])
        b_c = b_r.T
        lst = [2, 2, 2]
        tup = tuple(lst)

        # gt
        expected = DataFrame([[False, False], [False, True], [True, True]])
        result = df > b
        assert_frame_equal(result, expected)

        result = df.values > b
        assert_numpy_array_equal(result, expected.values)

        result = df > lst
        assert_frame_equal(result, expected)

        result = df > tup
        assert_frame_equal(result, expected)

        result = df > b_r
        assert_frame_equal(result, expected)

        result = df.values > b_r
        assert_numpy_array_equal(result, expected.values)

        with pytest.raises(ValueError):
            df > b_c

        with pytest.raises(ValueError):
            df.values > b_c

        # ==
        expected = DataFrame([[False, False], [True, False], [False, False]])
        result = df == b
        assert_frame_equal(result, expected)

        result = df == lst
        assert_frame_equal(result, expected)

        result = df == tup
        assert_frame_equal(result, expected)

        result = df == b_r
        assert_frame_equal(result, expected)

        result = df.values == b_r
        assert_numpy_array_equal(result, expected.values)

        with pytest.raises(ValueError):
            df == b_c

        assert df.values.shape != b_c.shape

        # with alignment
        df = DataFrame(np.arange(6).reshape((3, 2)),
                       columns=list('AB'), index=list('abc'))
        expected.index = df.index
        expected.columns = df.columns

        result = df == lst
        assert_frame_equal(result, expected)

        result = df == tup
        assert_frame_equal(result, expected)

    def test_combine_generic(self):
        df1 = self.frame
        df2 = self.frame.loc[self.frame.index[:-5], ['A', 'B', 'C']]

        combined = df1.combine(df2, np.add)
        combined2 = df2.combine(df1, np.add)
        assert combined['D'].isna().all()
        assert combined2['D'].isna().all()

        chunk = combined.loc[combined.index[:-5], ['A', 'B', 'C']]
        chunk2 = combined2.loc[combined2.index[:-5], ['A', 'B', 'C']]

        exp = self.frame.loc[self.frame.index[:-5],
                             ['A', 'B', 'C']].reindex_like(chunk) * 2
        assert_frame_equal(chunk, exp)
        assert_frame_equal(chunk2, exp)

    def test_inplace_ops_alignment(self):

        # inplace ops / ops alignment
        # GH 8511

        columns = list('abcdefg')
        X_orig = DataFrame(np.arange(10 * len(columns))
                           .reshape(-1, len(columns)),
                           columns=columns, index=range(10))
        Z = 100 * X_orig.iloc[:, 1:-1].copy()
        block1 = list('bedcf')
        subs = list('bcdef')

        # add
        X = X_orig.copy()
        result1 = (X[block1] + Z).reindex(columns=subs)

        X[block1] += Z
        result2 = X.reindex(columns=subs)

        X = X_orig.copy()
        result3 = (X[block1] + Z[block1]).reindex(columns=subs)

        X[block1] += Z[block1]
        result4 = X.reindex(columns=subs)

        assert_frame_equal(result1, result2)
        assert_frame_equal(result1, result3)
        assert_frame_equal(result1, result4)

        # sub
        X = X_orig.copy()
        result1 = (X[block1] - Z).reindex(columns=subs)

        X[block1] -= Z
        result2 = X.reindex(columns=subs)

        X = X_orig.copy()
        result3 = (X[block1] - Z[block1]).reindex(columns=subs)

        X[block1] -= Z[block1]
        result4 = X.reindex(columns=subs)

        assert_frame_equal(result1, result2)
        assert_frame_equal(result1, result3)
        assert_frame_equal(result1, result4)

    def test_inplace_ops_identity(self):

        # GH 5104
        # make sure that we are actually changing the object
        s_orig = Series([1, 2, 3])
        df_orig = DataFrame(np.random.randint(0, 5, size=10).reshape(-1, 5))

        # no dtype change
        s = s_orig.copy()
        s2 = s
        s += 1
        assert_series_equal(s, s2)
        assert_series_equal(s_orig + 1, s)
        assert s is s2
        assert s._data is s2._data

        df = df_orig.copy()
        df2 = df
        df += 1
        assert_frame_equal(df, df2)
        assert_frame_equal(df_orig + 1, df)
        assert df is df2
        assert df._data is df2._data

        # dtype change
        s = s_orig.copy()
        s2 = s
        s += 1.5
        assert_series_equal(s, s2)
        assert_series_equal(s_orig + 1.5, s)

        df = df_orig.copy()
        df2 = df
        df += 1.5
        assert_frame_equal(df, df2)
        assert_frame_equal(df_orig + 1.5, df)
        assert df is df2
        assert df._data is df2._data

        # mixed dtype
        arr = np.random.randint(0, 10, size=5)
        df_orig = DataFrame({'A': arr.copy(), 'B': 'foo'})
        df = df_orig.copy()
        df2 = df
        df['A'] += 1
        expected = DataFrame({'A': arr.copy() + 1, 'B': 'foo'})
        assert_frame_equal(df, expected)
        assert_frame_equal(df2, expected)
        assert df._data is df2._data

        df = df_orig.copy()
        df2 = df
        df['A'] += 1.5
        expected = DataFrame({'A': arr.copy() + 1.5, 'B': 'foo'})
        assert_frame_equal(df, expected)
        assert_frame_equal(df2, expected)
        assert df._data is df2._data

    @pytest.mark.parametrize('op', ['add', 'and', 'div', 'floordiv', 'mod',
                                    'mul', 'or', 'pow', 'sub', 'truediv',
                                    'xor'])
    def test_inplace_ops_identity2(self, op):

        if compat.PY3 and op == 'div':
            return

        df = DataFrame({'a': [1., 2., 3.],
                        'b': [1, 2, 3]})

        operand = 2
        if op in ('and', 'or', 'xor'):
            # cannot use floats for boolean ops
            df['a'] = [True, False, True]

        df_copy = df.copy()
        iop = '__i{}__'.format(op)
        op = '__{}__'.format(op)

        # no id change and value is correct
        getattr(df, iop)(operand)
        expected = getattr(df_copy, op)(operand)
        assert_frame_equal(df, expected)
        expected = id(df)
        assert id(df) == expected

    def test_alignment_non_pandas(self):
        index = ['A', 'B', 'C']
        columns = ['X', 'Y', 'Z']
        df = pd.DataFrame(np.random.randn(3, 3), index=index, columns=columns)

        align = pd.core.ops._align_method_FRAME
        for val in [[1, 2, 3], (1, 2, 3), np.array([1, 2, 3], dtype=np.int64),
                    range(1, 4)]:

            tm.assert_series_equal(align(df, val, 'index'),
                                   Series([1, 2, 3], index=df.index))
            tm.assert_series_equal(align(df, val, 'columns'),
                                   Series([1, 2, 3], index=df.columns))

        # length mismatch
        msg = 'Unable to coerce to Series, length must be 3: given 2'
        for val in [[1, 2], (1, 2), np.array([1, 2]), range(1, 3)]:

            with tm.assert_raises_regex(ValueError, msg):
                align(df, val, 'index')

            with tm.assert_raises_regex(ValueError, msg):
                align(df, val, 'columns')

        val = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
        tm.assert_frame_equal(align(df, val, 'index'),
                              DataFrame(val, index=df.index,
                                        columns=df.columns))
        tm.assert_frame_equal(align(df, val, 'columns'),
                              DataFrame(val, index=df.index,
                                        columns=df.columns))

        # shape mismatch
        msg = 'Unable to coerce to DataFrame, shape must be'
        val = np.array([[1, 2, 3], [4, 5, 6]])
        with tm.assert_raises_regex(ValueError, msg):
            align(df, val, 'index')

        with tm.assert_raises_regex(ValueError, msg):
            align(df, val, 'columns')

        val = np.zeros((3, 3, 3))
        with pytest.raises(ValueError):
            align(df, val, 'index')
        with pytest.raises(ValueError):
            align(df, val, 'columns')

    def test_no_warning(self, all_arithmetic_operators):
        df = pd.DataFrame({"A": [0., 0.], "B": [0., None]})
        b = df['B']
        with tm.assert_produces_warning(None):
            getattr(df, all_arithmetic_operators)(b, 0)