/pandas/tests/test_panel.py

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# pylint: disable=W0612,E1101

from datetime import datetime
import operator
import nose

import numpy as np

from pandas import Series, DataFrame, Index, isnull, notnull, pivot, MultiIndex
from pandas.core.datetools import bday
from pandas.core.frame import group_agg
from pandas.core.panel import Panel
from pandas.core.series import remove_na
import pandas.core.common as com
from pandas import compat
from pandas.compat import range, lrange, StringIO, cPickle, OrderedDict

from pandas.util.testing import (assert_panel_equal,
                                 assert_frame_equal,
                                 assert_series_equal,
                                 assert_almost_equal,
                                 ensure_clean,
                                 assertRaisesRegexp,
                                 makeCustomDataframe as mkdf,
                                 makeMixedDataFrame
    )
import pandas.core.panel as panelm
import pandas.util.testing as tm


class PanelTests(object):
    panel = None

    def test_pickle(self):
        pickled = cPickle.dumps(self.panel)
        unpickled = cPickle.loads(pickled)
        assert_frame_equal(unpickled['ItemA'], self.panel['ItemA'])

    def test_cumsum(self):
        cumsum = self.panel.cumsum()
        assert_frame_equal(cumsum['ItemA'], self.panel['ItemA'].cumsum())

    def not_hashable(self):
        c_empty = Panel()
        c = Panel(Panel([[[1]]]))
        self.assertRaises(TypeError, hash, c_empty)
        self.assertRaises(TypeError, hash, c)


class SafeForLongAndSparse(object):
    _multiprocess_can_split_ = True

    def test_repr(self):
        foo = repr(self.panel)

    def test_copy_names(self):
        for attr in ('major_axis', 'minor_axis'):
            getattr(self.panel, attr).name = None
            cp = self.panel.copy()
            getattr(cp, attr).name = 'foo'
            self.assertIsNone(getattr(self.panel, attr).name)

    def test_iter(self):
        tm.equalContents(list(self.panel), self.panel.items)

    def test_count(self):
        f = lambda s: notnull(s).sum()
        self._check_stat_op('count', f, obj=self.panel, has_skipna=False)

    def test_sum(self):
        self._check_stat_op('sum', np.sum)

    def test_mean(self):
        self._check_stat_op('mean', np.mean)

    def test_prod(self):
        self._check_stat_op('prod', np.prod)

    def test_median(self):
        def wrapper(x):
            if isnull(x).any():
                return np.nan
            return np.median(x)

        self._check_stat_op('median', wrapper)

    def test_min(self):
        self._check_stat_op('min', np.min)

    def test_max(self):
        self._check_stat_op('max', np.max)

    def test_skew(self):
        try:
            from scipy.stats import skew
        except ImportError:
            raise nose.SkipTest("no scipy.stats.skew")

        def this_skew(x):
            if len(x) < 3:
                return np.nan
            return skew(x, bias=False)
        self._check_stat_op('skew', this_skew)

    # def test_mad(self):
    #     f = lambda x: np.abs(x - x.mean()).mean()
    #     self._check_stat_op('mad', f)

    def test_var(self):
        def alt(x):
            if len(x) < 2:
                return np.nan
            return np.var(x, ddof=1)
        self._check_stat_op('var', alt)

    def test_std(self):
        def alt(x):
            if len(x) < 2:
                return np.nan
            return np.std(x, ddof=1)
        self._check_stat_op('std', alt)

    def test_sem(self):
        def alt(x):
            if len(x) < 2:
                return np.nan
            return np.std(x, ddof=1)/np.sqrt(len(x))
        self._check_stat_op('sem', alt)

    # def test_skew(self):
    #     from scipy.stats import skew

    #     def alt(x):
    #         if len(x) < 3:
    #             return np.nan
    #         return skew(x, bias=False)

    #     self._check_stat_op('skew', alt)

    def _check_stat_op(self, name, alternative, obj=None, has_skipna=True):
        if obj is None:
            obj = self.panel

            # # set some NAs
            # obj.ix[5:10] = np.nan
            # obj.ix[15:20, -2:] = np.nan

        f = getattr(obj, name)

        if has_skipna:
            def skipna_wrapper(x):
                nona = remove_na(x)
                if len(nona) == 0:
                    return np.nan
                return alternative(nona)

            def wrapper(x):
                return alternative(np.asarray(x))

            for i in range(obj.ndim):
                result = f(axis=i, skipna=False)
                assert_frame_equal(result, obj.apply(wrapper, axis=i))
        else:
            skipna_wrapper = alternative
            wrapper = alternative

        for i in range(obj.ndim):
            result = f(axis=i)
            assert_frame_equal(result, obj.apply(skipna_wrapper, axis=i))

        self.assertRaises(Exception, f, axis=obj.ndim)


class SafeForSparse(object):
    _multiprocess_can_split_ = True

    @classmethod
    def assert_panel_equal(cls, x, y):
        assert_panel_equal(x, y)

    def test_get_axis(self):
        assert(self.panel._get_axis(0) is self.panel.items)
        assert(self.panel._get_axis(1) is self.panel.major_axis)
        assert(self.panel._get_axis(2) is self.panel.minor_axis)

    def test_set_axis(self):
        new_items = Index(np.arange(len(self.panel.items)))
        new_major = Index(np.arange(len(self.panel.major_axis)))
        new_minor = Index(np.arange(len(self.panel.minor_axis)))

        # ensure propagate to potentially prior-cached items too
        item = self.panel['ItemA']
        self.panel.items = new_items

        if hasattr(self.panel, '_item_cache'):
            self.assertNotIn('ItemA', self.panel._item_cache)
        self.assertIs(self.panel.items, new_items)

        item = self.panel[0]
        self.panel.major_axis = new_major
        self.assertIs(self.panel[0].index, new_major)
        self.assertIs(self.panel.major_axis, new_major)

        item = self.panel[0]
        self.panel.minor_axis = new_minor
        self.assertIs(self.panel[0].columns, new_minor)
        self.assertIs(self.panel.minor_axis, new_minor)

    def test_get_axis_number(self):
        self.assertEqual(self.panel._get_axis_number('items'), 0)
        self.assertEqual(self.panel._get_axis_number('major'), 1)
        self.assertEqual(self.panel._get_axis_number('minor'), 2)

    def test_get_axis_name(self):
        self.assertEqual(self.panel._get_axis_name(0), 'items')
        self.assertEqual(self.panel._get_axis_name(1), 'major_axis')
        self.assertEqual(self.panel._get_axis_name(2), 'minor_axis')

    def test_get_plane_axes(self):
        # what to do here?

        index, columns = self.panel._get_plane_axes('items')
        index, columns = self.panel._get_plane_axes('major_axis')
        index, columns = self.panel._get_plane_axes('minor_axis')
        index, columns = self.panel._get_plane_axes(0)

    def test_truncate(self):
        dates = self.panel.major_axis
        start, end = dates[1], dates[5]

        trunced = self.panel.truncate(start, end, axis='major')
        expected = self.panel['ItemA'].truncate(start, end)

        assert_frame_equal(trunced['ItemA'], expected)

        trunced = self.panel.truncate(before=start, axis='major')
        expected = self.panel['ItemA'].truncate(before=start)

        assert_frame_equal(trunced['ItemA'], expected)

        trunced = self.panel.truncate(after=end, axis='major')
        expected = self.panel['ItemA'].truncate(after=end)

        assert_frame_equal(trunced['ItemA'], expected)

        # XXX test other axes

    def test_arith(self):
        self._test_op(self.panel, operator.add)
        self._test_op(self.panel, operator.sub)
        self._test_op(self.panel, operator.mul)
        self._test_op(self.panel, operator.truediv)
        self._test_op(self.panel, operator.floordiv)
        self._test_op(self.panel, operator.pow)

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

        self._test_op(self.panel, lambda x, y: x + y)  # panel + 1
        self._test_op(self.panel, lambda x, y: x - y)  # panel - 1
        self._test_op(self.panel, lambda x, y: x * y)  # panel * 1
        self._test_op(self.panel, lambda x, y: x / y)  # panel / 1
        self._test_op(self.panel, lambda x, y: x ** y)  # panel ** 1

        self.assertRaises(Exception, self.panel.__add__, self.panel['ItemA'])

    @staticmethod
    def _test_op(panel, op):
        result = op(panel, 1)
        assert_frame_equal(result['ItemA'], op(panel['ItemA'], 1))

    def test_keys(self):
        tm.equalContents(list(self.panel.keys()), self.panel.items)

    def test_iteritems(self):
        # Test panel.iteritems(), aka panel.iteritems()
        # just test that it works
        for k, v in compat.iteritems(self.panel):
            pass

        self.assertEqual(len(list(compat.iteritems(self.panel))),
                         len(self.panel.items))

    def test_combineFrame(self):
        def check_op(op, name):
            # items
            df = self.panel['ItemA']

            func = getattr(self.panel, name)

            result = func(df, axis='items')

            assert_frame_equal(result['ItemB'], op(self.panel['ItemB'], df))

            # major
            xs = self.panel.major_xs(self.panel.major_axis[0])
            result = func(xs, axis='major')

            idx = self.panel.major_axis[1]

            assert_frame_equal(result.major_xs(idx),
                               op(self.panel.major_xs(idx), xs))

            # minor
            xs = self.panel.minor_xs(self.panel.minor_axis[0])
            result = func(xs, axis='minor')

            idx = self.panel.minor_axis[1]

            assert_frame_equal(result.minor_xs(idx),
                               op(self.panel.minor_xs(idx), xs))
        from pandas import SparsePanel
        ops = ['add', 'sub', 'mul', 'truediv', 'floordiv']
        if not compat.PY3:
            ops.append('div')
        # pow, mod not supported for SparsePanel as flex ops (for now)
        if not isinstance(self.panel, SparsePanel):
            ops.extend(['pow', 'mod'])
        else:
            idx = self.panel.minor_axis[1]
            with assertRaisesRegexp(ValueError, "Simple arithmetic.*scalar"):
                self.panel.pow(self.panel.minor_xs(idx), axis='minor')
            with assertRaisesRegexp(ValueError, "Simple arithmetic.*scalar"):
                self.panel.mod(self.panel.minor_xs(idx), axis='minor')

        for op in ops:
            try:
                check_op(getattr(operator, op), op)
            except:
                com.pprint_thing("Failing operation: %r" % op)
                raise
        if compat.PY3:
            try:
                check_op(operator.truediv, 'div')
            except:
                com.pprint_thing("Failing operation: %r" % name)
                raise

    def test_combinePanel(self):
        result = self.panel.add(self.panel)
        self.assert_panel_equal(result, self.panel * 2)

    def test_neg(self):
        self.assert_panel_equal(-self.panel, self.panel * -1)

    def test_select(self):
        p = self.panel

        # select items
        result = p.select(lambda x: x in ('ItemA', 'ItemC'), axis='items')
        expected = p.reindex(items=['ItemA', 'ItemC'])
        self.assert_panel_equal(result, expected)

        # select major_axis
        result = p.select(lambda x: x >= datetime(2000, 1, 15), axis='major')
        new_major = p.major_axis[p.major_axis >= datetime(2000, 1, 15)]
        expected = p.reindex(major=new_major)
        self.assert_panel_equal(result, expected)

        # select minor_axis
        result = p.select(lambda x: x in ('D', 'A'), axis=2)
        expected = p.reindex(minor=['A', 'D'])
        self.assert_panel_equal(result, expected)

        # corner case, empty thing
        result = p.select(lambda x: x in ('foo',), axis='items')
        self.assert_panel_equal(result, p.reindex(items=[]))

    def test_get_value(self):
        for item in self.panel.items:
            for mjr in self.panel.major_axis[::2]:
                for mnr in self.panel.minor_axis:
                    result = self.panel.get_value(item, mjr, mnr)
                    expected = self.panel[item][mnr][mjr]
                    assert_almost_equal(result, expected)

    def test_abs(self):
        result = self.panel.abs()
        result2 = abs(self.panel)
        expected = np.abs(self.panel)
        self.assert_panel_equal(result, expected)
        self.assert_panel_equal(result2, expected)

        df = self.panel['ItemA']
        result = df.abs()
        result2 = abs(df)
        expected = np.abs(df)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        s = df['A']
        result = s.abs()
        result2 = abs(s)
        expected = np.abs(s)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)


class CheckIndexing(object):

    _multiprocess_can_split_ = True

    def test_getitem(self):
        self.assertRaises(Exception, self.panel.__getitem__, 'ItemQ')

    def test_delitem_and_pop(self):
        expected = self.panel['ItemA']
        result = self.panel.pop('ItemA')
        assert_frame_equal(expected, result)
        self.assertNotIn('ItemA', self.panel.items)

        del self.panel['ItemB']
        self.assertNotIn('ItemB', self.panel.items)
        self.assertRaises(Exception, self.panel.__delitem__, 'ItemB')

        values = np.empty((3, 3, 3))
        values[0] = 0
        values[1] = 1
        values[2] = 2

        panel = Panel(values, lrange(3), lrange(3), lrange(3))

        # did we delete the right row?

        panelc = panel.copy()
        del panelc[0]
        assert_frame_equal(panelc[1], panel[1])
        assert_frame_equal(panelc[2], panel[2])

        panelc = panel.copy()
        del panelc[1]
        assert_frame_equal(panelc[0], panel[0])
        assert_frame_equal(panelc[2], panel[2])

        panelc = panel.copy()
        del panelc[2]
        assert_frame_equal(panelc[1], panel[1])
        assert_frame_equal(panelc[0], panel[0])

    def test_setitem(self):
        # LongPanel with one item
        lp = self.panel.filter(['ItemA', 'ItemB']).to_frame()
        with tm.assertRaises(ValueError):
            self.panel['ItemE'] = lp

        # DataFrame
        df = self.panel['ItemA'][2:].filter(items=['A', 'B'])
        self.panel['ItemF'] = df
        self.panel['ItemE'] = df

        df2 = self.panel['ItemF']

        assert_frame_equal(df, df2.reindex(index=df.index,
                                           columns=df.columns))

        # scalar
        self.panel['ItemG'] = 1
        self.panel['ItemE'] = True
        self.assertEqual(self.panel['ItemG'].values.dtype, np.int64)
        self.assertEqual(self.panel['ItemE'].values.dtype, np.bool_)

        # object dtype
        self.panel['ItemQ'] = 'foo'
        self.assertEqual(self.panel['ItemQ'].values.dtype, np.object_)

        # boolean dtype
        self.panel['ItemP'] = self.panel['ItemA'] > 0
        self.assertEqual(self.panel['ItemP'].values.dtype, np.bool_)

        self.assertRaises(TypeError, self.panel.__setitem__, 'foo',
                          self.panel.ix[['ItemP']])

        # bad shape
        p = Panel(np.random.randn(4, 3, 2))
        with tm.assertRaisesRegexp(ValueError,
                                   "shape of value must be \(3, 2\), "
                                   "shape of given object was \(4, 2\)"):
            p[0] = np.random.randn(4, 2)

    def test_setitem_ndarray(self):
        from pandas import date_range, datetools

        timeidx = date_range(start=datetime(2009, 1, 1),
                             end=datetime(2009, 12, 31),
                             freq=datetools.MonthEnd())
        lons_coarse = np.linspace(-177.5, 177.5, 72)
        lats_coarse = np.linspace(-87.5, 87.5, 36)
        P = Panel(items=timeidx, major_axis=lons_coarse,
                  minor_axis=lats_coarse)
        data = np.random.randn(72 * 36).reshape((72, 36))
        key = datetime(2009, 2, 28)
        P[key] = data

        assert_almost_equal(P[key].values, data)

    def test_major_xs(self):
        ref = self.panel['ItemA']

        idx = self.panel.major_axis[5]
        xs = self.panel.major_xs(idx)

        assert_series_equal(xs['ItemA'], ref.xs(idx))

        # not contained
        idx = self.panel.major_axis[0] - bday
        self.assertRaises(Exception, self.panel.major_xs, idx)

    def test_major_xs_mixed(self):
        self.panel['ItemD'] = 'foo'
        xs = self.panel.major_xs(self.panel.major_axis[0])
        self.assertEqual(xs['ItemA'].dtype, np.float64)
        self.assertEqual(xs['ItemD'].dtype, np.object_)

    def test_minor_xs(self):
        ref = self.panel['ItemA']

        idx = self.panel.minor_axis[1]
        xs = self.panel.minor_xs(idx)

        assert_series_equal(xs['ItemA'], ref[idx])

        # not contained
        self.assertRaises(Exception, self.panel.minor_xs, 'E')

    def test_minor_xs_mixed(self):
        self.panel['ItemD'] = 'foo'

        xs = self.panel.minor_xs('D')
        self.assertEqual(xs['ItemA'].dtype, np.float64)
        self.assertEqual(xs['ItemD'].dtype, np.object_)

    def test_xs(self):
        itemA = self.panel.xs('ItemA', axis=0)
        expected = self.panel['ItemA']
        assert_frame_equal(itemA, expected)

        # get a view by default
        itemA_view = self.panel.xs('ItemA', axis=0)
        itemA_view.values[:] = np.nan
        self.assertTrue(np.isnan(self.panel['ItemA'].values).all())

        # mixed-type yields a copy
        self.panel['strings'] = 'foo'
        result = self.panel.xs('D', axis=2)
        self.assertIsNotNone(result.is_copy)

    def test_getitem_fancy_labels(self):
        p = self.panel

        items = p.items[[1, 0]]
        dates = p.major_axis[::2]
        cols = ['D', 'C', 'F']

        # all 3 specified
        assert_panel_equal(p.ix[items, dates, cols],
                           p.reindex(items=items, major=dates, minor=cols))

        # 2 specified
        assert_panel_equal(p.ix[:, dates, cols],
                           p.reindex(major=dates, minor=cols))

        assert_panel_equal(p.ix[items, :, cols],
                           p.reindex(items=items, minor=cols))

        assert_panel_equal(p.ix[items, dates, :],
                           p.reindex(items=items, major=dates))

        # only 1
        assert_panel_equal(p.ix[items, :, :],
                           p.reindex(items=items))

        assert_panel_equal(p.ix[:, dates, :],
                           p.reindex(major=dates))

        assert_panel_equal(p.ix[:, :, cols],
                           p.reindex(minor=cols))

    def test_getitem_fancy_slice(self):
        pass

    def test_getitem_fancy_ints(self):
        p = self.panel

        # #1603
        result = p.ix[:, -1, :]
        expected = p.ix[:, p.major_axis[-1], :]
        assert_frame_equal(result, expected)

    def test_getitem_fancy_xs(self):
        p = self.panel
        item = 'ItemB'

        date = p.major_axis[5]
        col = 'C'

        # get DataFrame
        # item
        assert_frame_equal(p.ix[item], p[item])
        assert_frame_equal(p.ix[item, :], p[item])
        assert_frame_equal(p.ix[item, :, :], p[item])

        # major axis, axis=1
        assert_frame_equal(p.ix[:, date], p.major_xs(date))
        assert_frame_equal(p.ix[:, date, :], p.major_xs(date))

        # minor axis, axis=2
        assert_frame_equal(p.ix[:, :, 'C'], p.minor_xs('C'))

        # get Series
        assert_series_equal(p.ix[item, date], p[item].ix[date])
        assert_series_equal(p.ix[item, date, :], p[item].ix[date])
        assert_series_equal(p.ix[item, :, col], p[item][col])
        assert_series_equal(p.ix[:, date, col], p.major_xs(date).ix[col])

    def test_getitem_fancy_xs_check_view(self):
        item = 'ItemB'
        date = self.panel.major_axis[5]
        col = 'C'

        # make sure it's always a view
        NS = slice(None, None)

        # DataFrames
        comp = assert_frame_equal
        self._check_view(item, comp)
        self._check_view((item, NS), comp)
        self._check_view((item, NS, NS), comp)
        self._check_view((NS, date), comp)
        self._check_view((NS, date, NS), comp)
        self._check_view((NS, NS, 'C'), comp)

        # Series
        comp = assert_series_equal
        self._check_view((item, date), comp)
        self._check_view((item, date, NS), comp)
        self._check_view((item, NS, 'C'), comp)
        self._check_view((NS, date, 'C'), comp)

    def test_ix_setitem_slice_dataframe(self):
        a = Panel(items=[1, 2, 3], major_axis=[11, 22, 33],
                  minor_axis=[111, 222, 333])
        b = DataFrame(np.random.randn(2, 3), index=[111, 333],
                      columns=[1, 2, 3])

        a.ix[:, 22, [111, 333]] = b

        assert_frame_equal(a.ix[:, 22, [111, 333]], b)

    def test_ix_align(self):
        from pandas import Series
        b = Series(np.random.randn(10))
        b.sort()
        df_orig = Panel(np.random.randn(3, 10, 2))
        df = df_orig.copy()

        df.ix[0, :, 0] = b
        assert_series_equal(df.ix[0, :, 0].reindex(b.index), b)

        df = df_orig.swapaxes(0, 1)
        df.ix[:, 0, 0] = b
        assert_series_equal(df.ix[:, 0, 0].reindex(b.index), b)

        df = df_orig.swapaxes(1, 2)
        df.ix[0, 0, :] = b
        assert_series_equal(df.ix[0, 0, :].reindex(b.index), b)

    def test_ix_frame_align(self):
        from pandas import DataFrame
        df = DataFrame(np.random.randn(2, 10))
        df.sort_index(inplace=True)
        p_orig = Panel(np.random.randn(3, 10, 2))

        p = p_orig.copy()
        p.ix[0, :, :] = df
        out = p.ix[0, :, :].T.reindex(df.index, columns=df.columns)
        assert_frame_equal(out, df)

        p = p_orig.copy()
        p.ix[0] = df
        out = p.ix[0].T.reindex(df.index, columns=df.columns)
        assert_frame_equal(out, df)

        p = p_orig.copy()
        p.ix[0, [0, 1, 3, 5], -2:] = df
        out = p.ix[0, [0, 1, 3, 5], -2:]
        assert_frame_equal(out, df.T.reindex([0, 1, 3, 5], p.minor_axis[-2:]))

        # GH3830, panel assignent by values/frame
        for dtype in ['float64','int64']:

            panel = Panel(np.arange(40).reshape((2,4,5)), items=['a1','a2'], dtype=dtype)
            df1 = panel.iloc[0]
            df2 = panel.iloc[1]

            tm.assert_frame_equal(panel.loc['a1'], df1)
            tm.assert_frame_equal(panel.loc['a2'], df2)

            # Assignment by Value Passes for 'a2'
            panel.loc['a2'] = df1.values
            tm.assert_frame_equal(panel.loc['a1'], df1)
            tm.assert_frame_equal(panel.loc['a2'], df1)

            # Assignment by DataFrame Ok w/o loc 'a2'
            panel['a2'] = df2
            tm.assert_frame_equal(panel.loc['a1'], df1)
            tm.assert_frame_equal(panel.loc['a2'], df2)

            # Assignment by DataFrame Fails for 'a2'
            panel.loc['a2'] = df2
            tm.assert_frame_equal(panel.loc['a1'], df1)
            tm.assert_frame_equal(panel.loc['a2'], df2)

    def _check_view(self, indexer, comp):
        cp = self.panel.copy()
        obj = cp.ix[indexer]
        obj.values[:] = 0
        self.assertTrue((obj.values == 0).all())
        comp(cp.ix[indexer].reindex_like(obj), obj)

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

        result = d['ItemA'] | d['ItemB']
        expected = DataFrame({'a': [np.nan, True]})
        assert_frame_equal(result, expected)

        # this is autodowncasted here
        result = d['ItemA'].fillna(False) | d['ItemB']
        expected = DataFrame({'a': [True, True]})
        assert_frame_equal(result, expected)

    def test_neg(self):
        # what to do?
        assert_panel_equal(-self.panel, -1 * self.panel)

    def test_invert(self):
        assert_panel_equal(-(self.panel < 0), ~(self.panel < 0))

    def test_comparisons(self):
        p1 = tm.makePanel()
        p2 = tm.makePanel()

        tp = p1.reindex(items=p1.items + ['foo'])
        df = p1[p1.items[0]]

        def test_comp(func):

            # versus same index
            result = func(p1, p2)
            self.assert_numpy_array_equal(result.values,
                                          func(p1.values, p2.values))

            # versus non-indexed same objs
            self.assertRaises(Exception, func, p1, tp)

            # versus different objs
            self.assertRaises(Exception, func, p1, df)

            # versus scalar
            result3 = func(self.panel, 0)
            self.assert_numpy_array_equal(result3.values,
                                          func(self.panel.values, 0))

        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_get_value(self):
        for item in self.panel.items:
            for mjr in self.panel.major_axis[::2]:
                for mnr in self.panel.minor_axis:
                    result = self.panel.get_value(item, mjr, mnr)
                    expected = self.panel[item][mnr][mjr]
                    assert_almost_equal(result, expected)
        with tm.assertRaisesRegexp(TypeError,
                                   "There must be an argument for each axis"):
            self.panel.get_value('a')

    def test_set_value(self):
        for item in self.panel.items:
            for mjr in self.panel.major_axis[::2]:
                for mnr in self.panel.minor_axis:
                    self.panel.set_value(item, mjr, mnr, 1.)
                    assert_almost_equal(self.panel[item][mnr][mjr], 1.)

        # resize
        res = self.panel.set_value('ItemE', 'foo', 'bar', 1.5)
        tm.assert_isinstance(res, Panel)
        self.assertIsNot(res, self.panel)
        self.assertEqual(res.get_value('ItemE', 'foo', 'bar'), 1.5)

        res3 = self.panel.set_value('ItemE', 'foobar', 'baz', 5)
        self.assertTrue(com.is_float_dtype(res3['ItemE'].values))
        with tm.assertRaisesRegexp(TypeError,
                                   "There must be an argument for each axis"
                                   " plus the value provided"):
            self.panel.set_value('a')

_panel = tm.makePanel()
tm.add_nans(_panel)


class TestPanel(tm.TestCase, PanelTests, CheckIndexing,
                SafeForLongAndSparse,
                SafeForSparse):
    _multiprocess_can_split_ = True

    @classmethod
    def assert_panel_equal(cls, x, y):
        assert_panel_equal(x, y)

    def setUp(self):
        import warnings
        warnings.filterwarnings(action='ignore', category=FutureWarning)

        self.panel = _panel.copy()
        self.panel.major_axis.name = None
        self.panel.minor_axis.name = None
        self.panel.items.name = None

    def test_panel_warnings(self):
        with tm.assert_produces_warning(FutureWarning):
            shifted1 = self.panel.shift(lags=1)

        with tm.assert_produces_warning(False):
            shifted2 = self.panel.shift(periods=1)

        tm.assert_panel_equal(shifted1, shifted2)

        with tm.assert_produces_warning(False):
            shifted3 = self.panel.shift()

        tm.assert_panel_equal(shifted1, shifted3)

    def test_constructor(self):
        # with BlockManager
        wp = Panel(self.panel._data)
        self.assertIs(wp._data, self.panel._data)

        wp = Panel(self.panel._data, copy=True)
        self.assertIsNot(wp._data, self.panel._data)
        assert_panel_equal(wp, self.panel)

        # strings handled prop
        wp = Panel([[['foo', 'foo', 'foo', ],
                     ['foo', 'foo', 'foo']]])
        self.assertEqual(wp.values.dtype, np.object_)

        vals = self.panel.values

        # no copy
        wp = Panel(vals)
        self.assertIs(wp.values, vals)

        # copy
        wp = Panel(vals, copy=True)
        self.assertIsNot(wp.values, vals)

    def test_constructor_cast(self):
        zero_filled = self.panel.fillna(0)

        casted = Panel(zero_filled._data, dtype=int)
        casted2 = Panel(zero_filled.values, dtype=int)

        exp_values = zero_filled.values.astype(int)
        assert_almost_equal(casted.values, exp_values)
        assert_almost_equal(casted2.values, exp_values)

        casted = Panel(zero_filled._data, dtype=np.int32)
        casted2 = Panel(zero_filled.values, dtype=np.int32)

        exp_values = zero_filled.values.astype(np.int32)
        assert_almost_equal(casted.values, exp_values)
        assert_almost_equal(casted2.values, exp_values)

        # can't cast
        data = [[['foo', 'bar', 'baz']]]
        self.assertRaises(ValueError, Panel, data, dtype=float)

    def test_constructor_empty_panel(self):
        empty = Panel()
        self.assertEqual(len(empty.items), 0)
        self.assertEqual(len(empty.major_axis), 0)
        self.assertEqual(len(empty.minor_axis), 0)

    def test_constructor_observe_dtype(self):
        # GH #411
        panel = Panel(items=lrange(3), major_axis=lrange(3),
                      minor_axis=lrange(3), dtype='O')
        self.assertEqual(panel.values.dtype, np.object_)

    def test_constructor_dtypes(self):
        # GH #797

        def _check_dtype(panel, dtype):
            for i in panel.items:
                self.assertEqual(panel[i].values.dtype.name, dtype)

        # only nan holding types allowed here
        for dtype in ['float64','float32','object']:
            panel = Panel(items=lrange(2),major_axis=lrange(10),minor_axis=lrange(5),dtype=dtype)
            _check_dtype(panel,dtype)

        for dtype in ['float64','float32','int64','int32','object']:
            panel = Panel(np.array(np.random.randn(2,10,5),dtype=dtype),items=lrange(2),major_axis=lrange(10),minor_axis=lrange(5),dtype=dtype)
            _check_dtype(panel,dtype)

        for dtype in ['float64','float32','int64','int32','object']:
            panel = Panel(np.array(np.random.randn(2,10,5),dtype='O'),items=lrange(2),major_axis=lrange(10),minor_axis=lrange(5),dtype=dtype)
            _check_dtype(panel,dtype)

        for dtype in ['float64','float32','int64','int32','object']:
            panel = Panel(np.random.randn(2,10,5),items=lrange(2),major_axis=lrange(10),minor_axis=lrange(5),dtype=dtype)
            _check_dtype(panel,dtype)

    def test_constructor_fails_with_not_3d_input(self):
        with tm.assertRaisesRegexp(ValueError,
                                   "The number of dimensions required is 3"):
            Panel(np.random.randn(10, 2))

    def test_consolidate(self):
        self.assertTrue(self.panel._data.is_consolidated())

        self.panel['foo'] = 1.
        self.assertFalse(self.panel._data.is_consolidated())

        panel = self.panel.consolidate()
        self.assertTrue(panel._data.is_consolidated())

    def test_ctor_dict(self):
        itema = self.panel['ItemA']
        itemb = self.panel['ItemB']

        d = {'A': itema, 'B': itemb[5:]}
        d2 = {'A': itema._series, 'B': itemb[5:]._series}
        d3 = {'A': None,
              'B': DataFrame(itemb[5:]._series),
              'C': DataFrame(itema._series)}

        wp = Panel.from_dict(d)
        wp2 = Panel.from_dict(d2)  # nested Dict
        wp3 = Panel.from_dict(d3)
        self.assertTrue(wp.major_axis.equals(self.panel.major_axis))
        assert_panel_equal(wp, wp2)

        # intersect
        wp = Panel.from_dict(d, intersect=True)
        self.assertTrue(wp.major_axis.equals(itemb.index[5:]))

        # use constructor
        assert_panel_equal(Panel(d), Panel.from_dict(d))
        assert_panel_equal(Panel(d2), Panel.from_dict(d2))
        assert_panel_equal(Panel(d3), Panel.from_dict(d3))

        # a pathological case
        d4 = {'A': None, 'B': None}
        wp4 = Panel.from_dict(d4)
        assert_panel_equal(Panel(d4), Panel(items=['A', 'B']))

        # cast
        dcasted = dict((k, v.reindex(wp.major_axis).fillna(0))
                       for k, v in compat.iteritems(d))
        result = Panel(dcasted, dtype=int)
        expected = Panel(dict((k, v.astype(int))
                              for k, v in compat.iteritems(dcasted)))
        assert_panel_equal(result, expected)

        result = Panel(dcasted, dtype=np.int32)
        expected = Panel(dict((k, v.astype(np.int32))
                              for k, v in compat.iteritems(dcasted)))
        assert_panel_equal(result, expected)

    def test_constructor_dict_mixed(self):
        data = dict((k, v.values) for k, v in compat.iteritems(self.panel))
        result = Panel(data)
        exp_major = Index(np.arange(len(self.panel.major_axis)))
        self.assertTrue(result.major_axis.equals(exp_major))

        result = Panel(data, items=self.panel.items,
                       major_axis=self.panel.major_axis,
                       minor_axis=self.panel.minor_axis)
        assert_panel_equal(result, self.panel)

        data['ItemC'] = self.panel['ItemC']
        result = Panel(data)
        assert_panel_equal(result, self.panel)

        # corner, blow up
        data['ItemB'] = data['ItemB'][:-1]
        self.assertRaises(Exception, Panel, data)

        data['ItemB'] = self.panel['ItemB'].values[:, :-1]
        self.assertRaises(Exception, Panel, data)

    def test_ctor_orderedDict(self):
        keys = list(set(np.random.randint(0,5000,100)))[:50] # unique random int  keys
        d = OrderedDict([(k,mkdf(10,5)) for k in keys])
        p = Panel(d)
        self.assertTrue(list(p.items) == keys)

        p = Panel.from_dict(d)
        self.assertTrue(list(p.items) == keys)

    def test_constructor_resize(self):
        data = self.panel._data
        items = self.panel.items[:-1]
        major = self.panel.major_axis[:-1]
        minor = self.panel.minor_axis[:-1]

        result = Panel(data, items=items, major_axis=major,
                       minor_axis=minor)
        expected = self.panel.reindex(items=items, major=major, minor=minor)
        assert_panel_equal(result, expected)

        result = Panel(data, items=items, major_axis=major)
        expected = self.panel.reindex(items=items, major=major)
        assert_panel_equal(result, expected)

        result = Panel(data, items=items)
        expected = self.panel.reindex(items=items)
        assert_panel_equal(result, expected)

        result = Panel(data, minor_axis=minor)
        expected = self.panel.reindex(minor=minor)
        assert_panel_equal(result, expected)

    def test_from_dict_mixed_orient(self):
        df = tm.makeDataFrame()
        df['foo'] = 'bar'

        data = {'k1': df,
                'k2': df}

        panel = Panel.from_dict(data, orient='minor')

        self.assertEqual(panel['foo'].values.dtype, np.object_)
        self.assertEqual(panel['A'].values.dtype, np.float64)

    def test_constructor_error_msgs(self):

        def testit():
            Panel(np.random.randn(3,4,5), lrange(4), lrange(5), lrange(5))
        assertRaisesRegexp(ValueError, "Shape of passed values is \(3, 4, 5\), indices imply \(4, 5, 5\)", testit)

        def testit():
            Panel(np.random.randn(3,4,5), lrange(5), lrange(4), lrange(5))
        assertRaisesRegexp(ValueError, "Shape of passed values is \(3, 4, 5\), indices imply \(5, 4, 5\)", testit)

        def testit():
            Panel(np.random.randn(3,4,5), lrange(5), lrange(5), lrange(4))
        assertRaisesRegexp(ValueError, "Shape of passed values is \(3, 4, 5\), indices imply \(5, 5, 4\)", testit)

    def test_conform(self):
        df = self.panel['ItemA'][:-5].filter(items=['A', 'B'])
        conformed = self.panel.conform(df)

        assert(conformed.index.equals(self.panel.major_axis))
        assert(conformed.columns.equals(self.panel.minor_axis))

    def test_convert_objects(self):

        # GH 4937
        p = Panel(dict(A = dict(a = ['1','1.0'])))
        expected = Panel(dict(A = dict(a = [1,1.0])))
        result = p.convert_objects(convert_numeric='force')
        assert_panel_equal(result, expected)

    def test_dtypes(self):

        result = self.panel.dtypes
        expected = Series(np.dtype('float64'),index=self.panel.items)
        assert_series_equal(result, expected)

    def test_apply(self):
        # GH1148

        from pandas import Series,DataFrame

        # ufunc
        applied = self.panel.apply(np.sqrt)
        self.assertTrue(assert_almost_equal(applied.values,
                                            np.sqrt(self.panel.values)))

        # ufunc same shape
        result = self.panel.apply(lambda x: x*2, axis='items')
        expected = self.panel*2
        assert_panel_equal(result, expected)
        result = self.panel.apply(lambda x: x*2, axis='major_axis')
        expected = self.panel*2
        assert_panel_equal(result, expected)
        result = self.panel.apply(lambda x: x*2, axis='minor_axis')
        expected = self.panel*2
        assert_panel_equal(result, expected)

        # reduction to DataFrame
        result = self.panel.apply(lambda x: x.dtype, axis='items')
        expected = DataFrame(np.dtype('float64'),index=self.panel.major_axis,columns=self.panel.minor_axis)
        assert_frame_equal(result,expected)
        result = self.panel.apply(lambda x: x.dtype, axis='major_axis')
        expected = DataFrame(np.dtype('float64'),index=self.panel.minor_axis,columns=self.panel.items)
        assert_frame_equal(result,expected)
        result = self.panel.apply(lambda x: x.dtype, axis='minor_axis')
        expected = DataFrame(np.dtype('float64'),index=self.panel.major_axis,columns=self.panel.items)
        assert_frame_equal(result,expected)

        # reductions via other dims
        expected = self.panel.sum(0)
        result = self.panel.apply(lambda x: x.sum(), axis='items')
        assert_frame_equal(result,expected)
        expected = self.panel.sum(1)
        result = self.panel.apply(lambda x: x.sum(), axis='major_axis')
        assert_frame_equal(result,expected)
        expected = self.panel.sum(2)
        result = self.panel.apply(lambda x: x.sum(), axis='minor_axis')
        assert_frame_equal(result,expected)

        # pass kwargs
        result = self.panel.apply(lambda x, y: x.sum() + y, axis='items', y=5)
        expected = self.panel.sum(0) + 5
        assert_frame_equal(result,expected)

    def test_apply_slabs(self):

        # same shape as original
        result = self.panel.apply(lambda x: x*2, axis = ['items','major_axis'])
        expected = (self.panel*2).transpose('minor_axis','major_axis','items')
        assert_panel_equal(result,expected)
        result = self.panel.apply(lambda x: x*2, axis = ['major_axis','items'])
        assert_panel_equal(result,expected)

        result = self.panel.apply(lambda x: x*2, axis = ['items','minor_axis'])
        expected = (self.panel*2).transpose('major_axis','minor_axis','items')
        assert_panel_equal(result,expected)
        result = self.panel.apply(lambda x: x*2, axis = ['minor_axis','items'])
        assert_panel_equal(result,expected)

        result = self.panel.apply(lambda x: x*2, axis = ['major_axis','minor_axis'])
        expected = self.panel*2
        assert_panel_equal(result,expected)
        result = self.panel.apply(lambda x: x*2, axis = ['minor_axis','major_axis'])
        assert_panel_equal(result,expected)

        # reductions
        result = self.panel.apply(lambda x: x.sum(0), axis = ['items','major_axis'])
        expected = self.panel.sum(1).T
        assert_frame_equal(result,expected)

        result = self.panel.apply(lambda x: x.sum(1), axis = ['items','major_axis'])
        expected = self.panel.sum(0)
        assert_frame_equal(result,expected)

        # transforms
        f = lambda x: ((x.T-x.mean(1))/x.std(1)).T

        # make sure that we don't trigger any warnings
        with tm.assert_produces_warning(False):
            result = self.panel.apply(f, axis = ['items','major_axis'])
            expected = Panel(dict([ (ax,f(self.panel.loc[:,:,ax])) for ax in self.panel.minor_axis ]))
            assert_panel_equal(result,expected)

        result = self.panel.apply(f, axis = ['major_axis','minor_axis'])
        expected = Panel(dict([ (ax,f(self.panel.loc[ax])) for ax in self.panel.items ]))
        assert_panel_equal(result,expected)

        result = self.panel.apply(f, axis = ['minor_axis','items'])
        expected = Panel(dict([ (ax,f(self.panel.loc[:,ax])) for ax in self.panel.major_axis ]))
        assert_panel_equal(result,expected)

        # with multi-indexes
        # GH7469
        index = MultiIndex.from_tuples([('one', 'a'), ('one', 'b'), ('two', 'a'), ('two', 'b')])
        dfa = DataFrame(np.array(np.arange(12, dtype='int64')).reshape(4,3), columns=list("ABC"), index=index)
        dfb = DataFrame(np.array(np.arange(10, 22, dtype='int64')).reshape(4,3), columns=list("ABC"), index=index)
        p = Panel({'f':dfa, 'g':dfb})
        result = p.apply(lambda x: x.sum(), axis=0)
        expected = p.sum(0)
        assert_frame_equal(result,expected)

    def test_reindex(self):
        ref = self.panel['ItemB']

        # items
        result = self.panel.reindex(items=['ItemA', 'ItemB'])
        assert_frame_equal(result['ItemB'], ref)

        # major
        new_major = list(self.panel.major_axis[:10])
        result = self.panel.reindex(major=new_major)
        assert_frame_equal(result['ItemB'], ref.reindex(index=new_major))

        # raise exception put both major and major_axis
        self.assertRaises(Exception, self.panel.reindex,
                          major_axis=new_major, major=new_major)

        # minor
        new_minor = list(self.panel.minor_axis[:2])
        result = self.panel.reindex(minor=new_minor)
        assert_frame_equal(result['ItemB'], ref.reindex(columns=new_minor))

        # this ok
        result = self.panel.reindex()
        assert_panel_equal(result,self.panel)
        self.assertFalse(result is self.panel)

        # with filling
        smaller_major = self.panel.major_axis[::5]
        smaller = self.panel.reindex(major=smaller_major)

        larger = smaller.reindex(major=self.panel.major_axis,
                                 method='pad')

        assert_frame_equal(larger.major_xs(self.panel.major_axis[1]),
                           smaller.major_xs(smaller_major[0]))

        # don't necessarily copy
        result = self.panel.reindex(major=self.panel.major_axis, copy=False)
        assert_panel_equal(result,self.panel)
        self.assertTrue(result is self.panel)

    def test_reindex_multi(self):

        # with and without copy full reindexing
        result = self.panel.reindex(items=self.panel.items,
                                    major=self.panel.major_axis,
                                    minor=self.panel.minor_axis,
                                    copy = False)

        self.assertIs(result.items, self.panel.items)
        self.assertIs(result.major_axis, self.panel.major_axis)
        self.assertIs(result.minor_axis, self.panel.minor_axis)

        result = self.panel.reindex(items=self.panel.items,
                                    major=self.panel.major_axis,
                                    minor=self.panel.minor_axis,
                                    copy = False)
        assert_panel_equal(result,self.panel)

        # multi-axis indexing consistency
        # GH 5900
        df = DataFrame(np.random.randn(4,3))
        p = Panel({ 'Item1' : df })
        expected = Panel({ 'Item1' : df })
        expected['Item2'] = np.nan

        items = ['Item1','Item2']
        major_axis = np.arange(4)
        minor_axis = np.arange(3)

        results = []
        results.append(p.reindex(items=items, major_axis=major_axis, copy=True))
        results.append(p.reindex(items=items, major_axis=major_axis, copy=False))
        results.append(p.reindex(items=items, minor_axis=minor_axis, copy=True))
        results.append(p.reindex(items=items, minor_axis=minor_axis, copy=False))
        results.append(p.reindex(items=items, major_axis=major_axis, minor_axis=minor_axis, copy=True))
        results.append(p.reindex(items=items, major_axis=major_axis, minor_axis=minor_axis, copy=False))

        for i, r in enumerate(results):
            assert_panel_equal(expected,r)

    def test_reindex_like(self):
        # reindex_like
        smaller = self.panel.reindex(items=self.panel.items[:-1],
                                     major=self.panel.major_axis[:-1],
                                     minor=self.panel.minor_axis[:-1])
        smaller_like = self.panel.reindex_like(smaller)
        assert_panel_equal(smaller, smaller_like)

    def test_take(self):
        # axis == 0
        result = self.panel.take([2, 0, 1], axis=0)
        expected = self.panel.reindex(items=['ItemC', 'ItemA', 'ItemB'])
        assert_panel_equal(result, expected)

        # axis >= 1
        result = self.panel.take([3, 0, 1, 2], axis=2)
        expected = self.panel.reindex(minor=['D', 'A', 'B', 'C'])
        assert_panel_equal(result, expected)

        # neg indicies ok
        expected = self.panel.reindex(minor=['D', 'D', 'B', 'C'])
        result = self.panel.take([3, -1, 1, 2], axis=2)
        assert_panel_equal(result, expected)

        self.assertRaises(Exception, self.panel.take, [4, 0, 1, 2], axis=2)

    def test_sort_index(self):
        import random

        ritems = list(self.panel.items)
        rmajor = list(self.panel.major_axis)
        rminor = list(self.panel.minor_axis)
        random.shuffle(ritems)
        random.shuffle(rmajor)
        random.shuffle(rminor)

        random_order = self.panel.reindex(items=ritems)
        sorted_panel = random_order.sort_index(axis=0)
        assert_panel_equal(sorted_panel, self.panel)

        # descending
        random_order = self.panel.reindex(items=ritems)
        sorted_panel = random_order.sort_index(axis=0, ascending=False)
        assert_panel_equal(sorted_panel,
                           self.panel.reindex(items=self.panel.items[::-1]))

        random_order = self.panel.reindex(major=rmajor)
        sorted_panel = random_order.sort_index(axis=1)
        assert_panel_equal(sorted_panel, self.panel)

        random_order = self.panel.reindex(minor=rminor)
        sorted_panel = random_order.sort_index(axis=2)
        assert_panel_equal(sorted_panel, self.panel)

    def test_fillna(self):
        filled = self.panel.fillna(0)
        self.assertTrue(np.isfinite(filled.values).all())

        filled = self.panel.fillna(method='backfill')
        assert_frame_equal(filled['ItemA'],
                           self.panel['ItemA'].fillna(method='backfill'))

        panel = self.panel.copy()
        panel['str'] = 'foo'

        filled = panel.fillna(method='backfill')
        assert_frame_equal(filled['ItemA'],
                           panel['ItemA'].fillna(method='backfill'))

        empty = self.panel.reindex(items=[])
        filled = empty.fillna(0)
        assert_panel_equal(filled, empty)

        self.assertRaises(ValueError, self.panel.fillna)
        self.assertRaises(ValueError, self.panel.fillna, 5, method='ffill')

        self.assertRaises(TypeError, self.panel.fillna, [1, 2])
        self.assertRaises(TypeError, self.panel.fillna, (1, 2))

        # limit not implemented when only value is specified
        p = Panel(np.random.randn(3,4,5))
        p.iloc[0:2,0:2,0:2] = np.nan
        self.assertRaises(NotImplementedError, lambda : p.fillna(999,limit=1))

    def test_ffill_bfill(self):
        assert_panel_equal(self.panel.ffill(),
                           self.panel.fillna(method='ffill'))
        assert_panel_equal(self.panel.bfill(),
                           self.panel.fillna(method='bfill'))

    def test_truncate_fillna_bug(self):
        # #1823
        result = self.panel.truncate(before=None, after=None, axis='items')

        # it works!
        result.fillna(value=0.0)

    def test_swapaxes(self):
        result = self.panel.swapaxes('items', 'minor')
        self.assertIs(result.items, self.panel.minor_axis)

        result = self.panel.swapaxes('items', 'major')
        self.assertIs(result.items, self.panel.major_axis)

        result = self.panel.swapaxes('major', 'minor')
        self.assertIs(result.major_axis, self.panel.minor_axis)

        panel = self.panel.copy()
        result = panel.swapaxes('major', 'minor')
        panel.values[0, 0, 1] = np.nan
        expected = panel.swapaxes('major', 'minor')
        assert_panel_equal(result, expected)

        # this should also work
        result = self.panel.swapaxes(0, 1)
        self.assertIs(result.items, self.panel.major_axis)

        # this works, but return a copy
        result = self.panel.swapaxes('items', 'items')
        assert_panel_equal(self.panel,result)
        self.assertNotEqual(id(self.panel), id(result))

    def test_transpose(self):
        result = self.panel.transpose('minor', 'major', 'items')
        expected = self.panel.swapaxes('items', 'minor')
        assert_panel_equal(result, expected)

        # test kwargs
        result = self.panel.transpose(items='minor', major='major',
                                      minor='items')
        expected = self.panel.swapaxes('items', 'minor')
        assert_panel_equal(result, expected)

        # text mixture of args
        result = self.panel.transpose('minor', major='major', minor='items')
        expected = self.p…