Python pandas.util.testing.assert_panel_equal() Examples

def test_numpy_transpose(self):
        msg = "the 'axes' parameter is not supported"

        s = tm.makeFloatSeries()
        tm.assert_series_equal(np.transpose(s), s)

        with pytest.raises(ValueError, match=msg):
            np.transpose(s, axes=1)

        df = tm.makeTimeDataFrame()
        tm.assert_frame_equal(np.transpose(np.transpose(df)), df)

        with pytest.raises(ValueError, match=msg):
            np.transpose(df, axes=1)

        with catch_warnings(record=True):
            simplefilter("ignore", FutureWarning)
            p = tm.makePanel()
            tm.assert_panel_equal(np.transpose(
                np.transpose(p, axes=(2, 0, 1)),
                axes=(1, 2, 0)), p) 

def test_constructor_resize(self):
        with catch_warnings(record=True):
            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_reindex_axis_style(self):
        panel = Panel(np.random.rand(5, 5, 5))
        expected0 = Panel(panel.values).iloc[[0, 1]]
        expected1 = Panel(panel.values).iloc[:, [0, 1]]
        expected2 = Panel(panel.values).iloc[:, :, [0, 1]]

        result = panel.reindex([0, 1], axis=0)
        assert_panel_equal(result, expected0)

        result = panel.reindex([0, 1], axis=1)
        assert_panel_equal(result, expected1)

        result = panel.reindex([0, 1], axis=2)
        assert_panel_equal(result, expected2)

        result = panel.reindex([0, 1], axis=2)
        assert_panel_equal(result, expected2) 

def test_take(self):
        with catch_warnings(record=True):
            # 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)

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

def test_constructor_dict_mixed(self):
        with catch_warnings(record=True):
            data = {k: v.values for k, v in self.panel.iteritems()}
            result = Panel(data)
            exp_major = Index(np.arange(len(self.panel.major_axis)))
            tm.assert_index_equal(result.major_axis, 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]
            pytest.raises(Exception, Panel, data)

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

def test_swapaxes(self):
        with catch_warnings(record=True):
            result = self.panel.swapaxes('items', 'minor')
            assert result.items is self.panel.minor_axis

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

            result = self.panel.swapaxes('major', 'minor')
            assert result.major_axis is 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)
            assert result.items is self.panel.major_axis

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

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 indices 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)

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

def test_abs(self):

        result = self.panel.abs()
        result2 = abs(self.panel)
        expected = np.abs(self.panel)
        assert_panel_equal(result, expected)
        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)
        assert result.name == 'A'
        assert result2.name == 'A' 

def test_reindex_axis_style(self):
        with catch_warnings(record=True):
            panel = Panel(np.random.rand(5, 5, 5))
            expected0 = Panel(panel.values).iloc[[0, 1]]
            expected1 = Panel(panel.values).iloc[:, [0, 1]]
            expected2 = Panel(panel.values).iloc[:, :, [0, 1]]

            result = panel.reindex([0, 1], axis=0)
            assert_panel_equal(result, expected0)

            result = panel.reindex([0, 1], axis=1)
            assert_panel_equal(result, expected1)

            result = panel.reindex([0, 1], axis=2)
            assert_panel_equal(result, expected2)

            result = panel.reindex([0, 1], axis=2)
            assert_panel_equal(result, expected2) 

def test_constructor_dict_mixed(self):
        data = {k: v.values for k, v in self.panel.iteritems()}
        result = Panel(data)
        exp_major = Index(np.arange(len(self.panel.major_axis)))
        tm.assert_index_equal(result.major_axis, 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]
        pytest.raises(Exception, Panel, data)

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

def test_abs(self):

        with catch_warnings(record=True):
            result = self.panel.abs()
            result2 = abs(self.panel)
            expected = np.abs(self.panel)
            assert_panel_equal(result, expected)
            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)
            assert result.name == 'A'
            assert result2.name == 'A' 

def test_to_frame_mixed(self):
        panel = self.panel.fillna(0)
        panel['str'] = 'foo'
        panel['bool'] = panel['ItemA'] > 0

        lp = panel.to_frame()
        wp = lp.to_panel()
        assert wp['bool'].values.dtype == np.bool_
        # Previously, this was mutating the underlying
        # index and changing its name
        assert_frame_equal(wp['bool'], panel['bool'], check_names=False)

        # GH 8704
        # with categorical
        df = panel.to_frame()
        df['category'] = df['str'].astype('category')

        # to_panel
        # TODO: this converts back to object
        p = df.to_panel()
        expected = panel.copy()
        expected['category'] = 'foo'
        assert_panel_equal(p, expected) 

def test_resample_panel_numpy():
    rng = date_range('1/1/2000', '6/30/2000')
    n = len(rng)

    with catch_warnings(record=True):
        panel = Panel(np.random.randn(3, n, 5),
                      items=['one', 'two', 'three'],
                      major_axis=rng,
                      minor_axis=['a', 'b', 'c', 'd', 'e'])

        result = panel.resample('M', axis=1).apply(lambda x: x.mean(1))
        expected = panel.resample('M', axis=1).mean()
        tm.assert_panel_equal(result, expected)

        panel = panel.swapaxes(1, 2)
        result = panel.resample('M', axis=2).apply(lambda x: x.mean(2))
        expected = panel.resample('M', axis=2).mean()
        tm.assert_panel_equal(result, expected) 

def test_multiindex_get(self):
        ind = MultiIndex.from_tuples(
            [('a', 1), ('a', 2), ('b', 1), ('b', 2)],
            names=['first', 'second'])
        wp = Panel(np.random.random((4, 5, 5)),
                   items=ind,
                   major_axis=np.arange(5),
                   minor_axis=np.arange(5))
        f1 = wp['a']
        f2 = wp.loc['a']
        assert_panel_equal(f1, f2)

        assert (f1.items == [1, 2]).all()
        assert (f2.items == [1, 2]).all()

        MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1)],
                               names=['first', 'second']) 

def test_select(self):
        with catch_warnings(record=True):
            p = self.panel

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

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

def test_transpose(self):
        msg = (r"transpose\(\) got multiple values for "
               r"keyword argument 'axes'")
        for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
                  tm.makeObjectSeries()]:
            # calls implementation in pandas/core/base.py
            tm.assert_series_equal(s.transpose(), s)
        for df in [tm.makeTimeDataFrame()]:
            tm.assert_frame_equal(df.transpose().transpose(), df)

        with catch_warnings(record=True):
            simplefilter("ignore", FutureWarning)
            for p in [tm.makePanel()]:
                tm.assert_panel_equal(p.transpose(2, 0, 1)
                                      .transpose(1, 2, 0), p)
                with pytest.raises(TypeError, match=msg):
                    p.transpose(2, 0, 1, axes=(2, 0, 1)) 

def test_take(self):
        indices = [1, 5, -2, 6, 3, -1]
        for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
                  tm.makeObjectSeries()]:
            out = s.take(indices)
            expected = Series(data=s.values.take(indices),
                              index=s.index.take(indices), dtype=s.dtype)
            tm.assert_series_equal(out, expected)
        for df in [tm.makeTimeDataFrame()]:
            out = df.take(indices)
            expected = DataFrame(data=df.values.take(indices, axis=0),
                                 index=df.index.take(indices),
                                 columns=df.columns)
            tm.assert_frame_equal(out, expected)

        indices = [-3, 2, 0, 1]
        with catch_warnings(record=True):
            simplefilter("ignore", FutureWarning)
            for p in [tm.makePanel()]:
                out = p.take(indices)
                expected = Panel(data=p.values.take(indices, axis=0),
                                 items=p.items.take(indices),
                                 major_axis=p.major_axis,
                                 minor_axis=p.minor_axis)
                tm.assert_panel_equal(out, expected) 

def test_to_xarray(self):
        from xarray import DataArray

        with catch_warnings(record=True):
            simplefilter("ignore", FutureWarning)
            p = tm.makePanel()

            result = p.to_xarray()
            assert isinstance(result, DataArray)
            assert len(result.coords) == 3
            assert_almost_equal(list(result.coords.keys()),
                                ['items', 'major_axis', 'minor_axis'])
            assert len(result.dims) == 3

            # idempotency
            assert_panel_equal(result.to_pandas(), p)


# run all the tests, but wrap each in a warning catcher 

def test_panel_join_overlap(self):
        with catch_warnings(record=True):
            panel = tm.makePanel()
            tm.add_nans(panel)

            p1 = panel.loc[['ItemA', 'ItemB', 'ItemC']]
            p2 = panel.loc[['ItemB', 'ItemC']]

            # Expected index is
            #
            # ItemA, ItemB_p1, ItemC_p1, ItemB_p2, ItemC_p2
            joined = p1.join(p2, lsuffix='_p1', rsuffix='_p2')
            p1_suf = p1.loc[['ItemB', 'ItemC']].add_suffix('_p1')
            p2_suf = p2.loc[['ItemB', 'ItemC']].add_suffix('_p2')
            no_overlap = panel.loc[['ItemA']]
            expected = no_overlap.join(p1_suf.join(p2_suf))
            tm.assert_panel_equal(joined, expected) 

def test_panel_assignment(self):

        with catch_warnings(record=True):
            # GH3777
            wp = Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
                       major_axis=date_range('1/1/2000', periods=5),
                       minor_axis=['A', 'B', 'C', 'D'])
            wp2 = Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
                        major_axis=date_range('1/1/2000', periods=5),
                        minor_axis=['A', 'B', 'C', 'D'])

            # TODO: unused?
            # expected = wp.loc[['Item1', 'Item2'], :, ['A', 'B']]

            with pytest.raises(NotImplementedError):
                wp.loc[['Item1', 'Item2'], :, ['A', 'B']] = wp2.loc[
                    ['Item1', 'Item2'], :, ['A', 'B']]

            # to_assign = wp2.loc[['Item1', 'Item2'], :, ['A', 'B']]
            # wp.loc[['Item1', 'Item2'], :, ['A', 'B']] = to_assign
            # result = wp.loc[['Item1', 'Item2'], :, ['A', 'B']]
            # tm.assert_panel_equal(result,expected) 

def test_panel_groupby():
    panel = tm.makePanel()
    tm.add_nans(panel)
    grouped = panel.groupby({'ItemA': 0, 'ItemB': 0, 'ItemC': 1},
                            axis='items')
    agged = grouped.mean()
    agged2 = grouped.agg(lambda x: x.mean('items'))

    tm.assert_panel_equal(agged, agged2)

    tm.assert_index_equal(agged.items, Index([0, 1]))

    grouped = panel.groupby(lambda x: x.month, axis='major')
    agged = grouped.mean()

    exp = Index(sorted(list(set(panel.major_axis.month))))
    tm.assert_index_equal(agged.major_axis, exp)

    grouped = panel.groupby({'A': 0, 'B': 0, 'C': 1, 'D': 1},
                            axis='minor')
    agged = grouped.mean()
    tm.assert_index_equal(agged.minor_axis, Index([0, 1])) 

def test_update_filtered(self):
        pan = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
                      [1.5, np.nan, 3.],
                      [1.5, np.nan, 3.]],
                     [[1.5, np.nan, 3.], [1.5, np.nan, 3.],
                      [1.5, np.nan, 3.],
                      [1.5, np.nan, 3.]]])

        other = Panel(
            [[[3.6, 2., np.nan], [np.nan, np.nan, 7]]], items=[1])

        pan.update(other, filter_func=lambda x: x > 2)

        expected = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
                           [1.5, np.nan, 3.], [1.5, np.nan, 3.]],
                          [[1.5, np.nan, 3], [1.5, np.nan, 7],
                           [1.5, np.nan, 3.], [1.5, np.nan, 3.]]])

        assert_panel_equal(pan, expected) 

def test_update_nooverwrite(self):
        pan = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
                      [1.5, np.nan, 3.],
                      [1.5, np.nan, 3.]],
                     [[1.5, np.nan, 3.], [1.5, np.nan, 3.],
                      [1.5, np.nan, 3.],
                      [1.5, np.nan, 3.]]])

        other = Panel(
            [[[3.6, 2., np.nan], [np.nan, np.nan, 7]]], items=[1])

        pan.update(other, overwrite=False)

        expected = Panel([[[1.5, np.nan, 3], [1.5, np.nan, 3],
                           [1.5, np.nan, 3.], [1.5, np.nan, 3.]],
                          [[1.5, 2., 3.], [1.5, np.nan, 3.],
                           [1.5, np.nan, 3.],
                           [1.5, np.nan, 3.]]])

        assert_panel_equal(pan, expected) 

def test_panel_aggregation():
    ind = pd.date_range('1/1/2000', periods=100)
    data = np.random.randn(2, len(ind), 4)

    wp = Panel(data, items=['Item1', 'Item2'], major_axis=ind,
               minor_axis=['A', 'B', 'C', 'D'])

    tg = TimeGrouper('M', axis=1)
    _, grouper, _ = tg._get_grouper(wp)
    bingrouped = wp.groupby(grouper)
    binagg = bingrouped.mean()

    def f(x):
        assert (isinstance(x, Panel))
        return x.mean(1)

    result = bingrouped.agg(f)
    tm.assert_panel_equal(result, binagg) 

def test_update(self):
        pan = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
                      [1.5, np.nan, 3.],
                      [1.5, np.nan, 3.]],
                     [[1.5, np.nan, 3.], [1.5, np.nan, 3.],
                      [1.5, np.nan, 3.],
                      [1.5, np.nan, 3.]]])

        other = Panel(
            [[[3.6, 2., np.nan], [np.nan, np.nan, 7]]], items=[1])

        pan.update(other)

        expected = Panel([[[1.5, np.nan, 3.], [1.5, np.nan, 3.],
                           [1.5, np.nan, 3.], [1.5, np.nan, 3.]],
                          [[3.6, 2., 3], [1.5, np.nan, 7],
                           [1.5, np.nan, 3.],
                           [1.5, np.nan, 3.]]])

        assert_panel_equal(pan, expected) 

def test_numpy_round(self):
        values = [[[-3.2, 2.2], [0, -4.8213], [3.123, 123.12],
                   [-1566.213, 88.88], [-12, 94.5]],
                  [[-5.82, 3.5], [6.21, -73.272], [-9.087, 23.12],
                   [272.212, -99.99], [23, -76.5]]]
        evalues = [[[float(np.around(i)) for i in j] for j in k]
                   for k in values]
        p = Panel(values, items=['Item1', 'Item2'],
                  major_axis=date_range('1/1/2000', periods=5),
                  minor_axis=['A', 'B'])
        expected = Panel(evalues, items=['Item1', 'Item2'],
                         major_axis=date_range('1/1/2000', periods=5),
                         minor_axis=['A', 'B'])
        result = np.round(p)
        assert_panel_equal(expected, result)

        msg = "the 'out' parameter is not supported"
        with pytest.raises(ValueError, match=msg):
            np.round(p, out=p)

    # removing Panel before NumPy enforces, so just ignore 

def test_panel_setitem(self):

        with catch_warnings(record=True):
            # GH 7763
            # loc and setitem have setting differences
            np.random.seed(0)
            index = range(3)
            columns = list('abc')

            panel = Panel({'A': DataFrame(np.random.randn(3, 3),
                                          index=index, columns=columns),
                           'B': DataFrame(np.random.randn(3, 3),
                                          index=index, columns=columns),
                           'C': DataFrame(np.random.randn(3, 3),
                                          index=index, columns=columns)})

            replace = DataFrame(np.eye(3, 3), index=range(3), columns=columns)
            expected = Panel({'A': replace, 'B': replace, 'C': replace})

            p = panel.copy()
            for idx in list('ABC'):
                p[idx] = replace
            tm.assert_panel_equal(p, expected)

            p = panel.copy()
            for idx in list('ABC'):
                p.loc[idx, :, :] = replace
            tm.assert_panel_equal(p, expected) 

def test_reindex_like(self):
        with catch_warnings(record=True):
            # 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_isna_isnull(self, isna_f):
        assert not isna_f(1.)
        assert isna_f(None)
        assert isna_f(np.NaN)
        assert float('nan')
        assert not isna_f(np.inf)
        assert not isna_f(-np.inf)

        # series
        for s in [tm.makeFloatSeries(), tm.makeStringSeries(),
                  tm.makeObjectSeries(), tm.makeTimeSeries(),
                  tm.makePeriodSeries()]:
            assert isinstance(isna_f(s), Series)

        # frame
        for df in [tm.makeTimeDataFrame(), tm.makePeriodFrame(),
                   tm.makeMixedDataFrame()]:
            result = isna_f(df)
            expected = df.apply(isna_f)
            tm.assert_frame_equal(result, expected)

        # panel
        with catch_warnings(record=True):
            simplefilter("ignore", FutureWarning)
            for p in [tm.makePanel(), tm.makePeriodPanel(),
                      tm.add_nans(tm.makePanel())]:
                result = isna_f(p)
                expected = p.apply(isna_f)
                tm.assert_panel_equal(result, expected) 

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