Python pandas.Panel方法代碼示例

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import Panel [as 別名]
def load_data(file_path):
    d = binarywave.load(file_path)
    dat = d["wave"]["wData"]
    if dat.shape[-1] not in [4, 6]:
        print ("invalid file")
        return
    labels = (
        [
            "Height",
            "Amplitude 1",
            "Amplitude 2",
            "Phase 1",
            "Phase 2",
            "Resonance Frequency",
        ]
        if dat.shape[-1] == 6
        else ["Height", "Amplitude", "Phase", "z sensor"]
    )
    return Panel(dat, minor_axis=labels).transpose(2, 0, 1).to_frame() 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import Panel [as 別名]
def load_RSM(filename):
    om, tt, psd = xu.io.getxrdml_map(filename)
    om = np.deg2rad(om)
    tt = np.deg2rad(tt)
    wavelength = 1.54056

    q_y = (1 / wavelength) * (np.cos(tt) - np.cos(2 * om - tt))
    q_x = (1 / wavelength) * (np.sin(tt) - np.sin(2 * om - tt))

    xi = np.linspace(np.min(q_x), np.max(q_x), 100)
    yi = np.linspace(np.min(q_y), np.max(q_y), 100)
    psd[psd < 1] = 1
    data_grid = griddata(
        (q_x, q_y), psd, (xi[None, :], yi[:, None]), fill_value=1, method="cubic"
    )
    nx, ny = data_grid.shape

    range_values = [np.min(q_x), np.max(q_x), np.min(q_y), np.max(q_y)]
    output_data = (
        Panel(np.log(data_grid).reshape(nx, ny, 1), minor_axis=["RSM"])
        .transpose(2, 0, 1)
        .to_frame()
    )

    return range_values, output_data 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import Panel [as 別名]
def test_panel_concat_buglet(self, sort):
        # #2257
        def make_panel():
            index = 5
            cols = 3

            def df():
                return DataFrame(np.random.randn(index, cols),
                                 index=["I%s" % i for i in range(index)],
                                 columns=["C%s" % i for i in range(cols)])
            return Panel({"Item%s" % x: df() for x in ['A', 'B', 'C']})

        panel1 = make_panel()
        panel2 = make_panel()

        panel2 = panel2.rename(major_axis={x: "%s_1" % x
                                           for x in panel2.major_axis})

        panel3 = panel2.rename(major_axis=lambda x: '%s_1' % x)
        panel3 = panel3.rename(minor_axis=lambda x: '%s_1' % x)

        # it works!
        concat([panel1, panel3], axis=1, verify_integrity=True, sort=sort) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import Panel [as 別名]
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) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import Panel [as 別名]
def test_to_panel_expanddim(self):
        # GH 9762

        class SubclassedFrame(DataFrame):

            @property
            def _constructor_expanddim(self):
                return SubclassedPanel

        class SubclassedPanel(Panel):
            pass

        index = MultiIndex.from_tuples([(0, 0), (0, 1), (0, 2)])
        df = SubclassedFrame({'X': [1, 2, 3], 'Y': [4, 5, 6]}, index=index)
        result = df.to_panel()
        assert isinstance(result, SubclassedPanel)
        expected = SubclassedPanel([[[1, 2, 3]], [[4, 5, 6]]],
                                   items=['X', 'Y'], major_axis=[0],
                                   minor_axis=[0, 1, 2],
                                   dtype='int64')
        tm.assert_panel_equal(result, expected) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import Panel [as 別名]
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) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import Panel [as 別名]
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) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import Panel [as 別名]
def test_binary_ops_docs(self):
        from pandas import DataFrame, Panel
        op_map = {'add': '+',
                  'sub': '-',
                  'mul': '*',
                  'mod': '%',
                  'pow': '**',
                  'truediv': '/',
                  'floordiv': '//'}
        for op_name in ['add', 'sub', 'mul', 'mod', 'pow', 'truediv',
                        'floordiv']:
            for klass in [Series, DataFrame, Panel]:
                operand1 = klass.__name__.lower()
                operand2 = 'other'
                op = op_map[op_name]
                expected_str = ' '.join([operand1, op, operand2])
                assert expected_str in getattr(klass, op_name).__doc__

                # reverse version of the binary ops
                expected_str = ' '.join([operand2, op, operand1])
                assert expected_str in getattr(klass, 'r' + op_name).__doc__ 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import Panel [as 別名]
def test_drop_labels_or_levels_series(df):

    # Make series with L1 as index
    s = df.set_index('L1').L2
    assert_levels_dropped(s, ['L1'], axis=0)

    with tm.assert_raises_regex(ValueError, "not valid labels or levels"):
        s._drop_labels_or_levels('L4', axis=0)

    # Make series with L1 and L2 as index
    s = df.set_index(['L1', 'L2']).L3
    assert_levels_dropped(s, ['L1', 'L2'], axis=0)

    with tm.assert_raises_regex(ValueError, "not valid labels or levels"):
        s._drop_labels_or_levels('L4', axis=0)


# Panel
# ----- 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import Panel [as 別名]
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']]

            def f():
                wp.loc[['Item1', 'Item2'], :, ['A', 'B']] = wp2.loc[
                    ['Item1', 'Item2'], :, ['A', 'B']]

            pytest.raises(NotImplementedError, f)

            # 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) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import Panel [as 別名]
def test_to_panel_expanddim(self):
        # GH 9762

        with catch_warnings(record=True):
            class SubclassedFrame(DataFrame):

                @property
                def _constructor_expanddim(self):
                    return SubclassedPanel

            class SubclassedPanel(Panel):
                pass

            index = MultiIndex.from_tuples([(0, 0), (0, 1), (0, 2)])
            df = SubclassedFrame({'X': [1, 2, 3], 'Y': [4, 5, 6]}, index=index)
            result = df.to_panel()
            assert isinstance(result, SubclassedPanel)
            expected = SubclassedPanel([[[1, 2, 3]], [[4, 5, 6]]],
                                       items=['X', 'Y'], major_axis=[0],
                                       minor_axis=[0, 1, 2],
                                       dtype='int64')
            tm.assert_panel_equal(result, expected) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import Panel [as 別名]
def test_resample_panel(self):
        rng = date_range('1/1/2000', '6/30/2000')
        n = len(rng)

        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)

        def p_apply(panel, f):
            result = {}
            for item in panel.items:
                result[item] = f(panel[item])
            return Panel(result, items=panel.items)

        expected = p_apply(panel, lambda x: x.resample('M'))
        tm.assert_panel_equal(result, expected)

        panel2 = panel.swapaxes(1, 2)
        result = panel2.resample('M', axis=2)
        expected = p_apply(panel2, lambda x: x.resample('M', axis=1))
        tm.assert_panel_equal(result, expected) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import Panel [as 別名]
def test_resample_panel_numpy(self):
        rng = date_range('1/1/2000', '6/30/2000')
        n = len(rng)

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

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

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import Panel [as 別名]
def test_panel_aggregation(self):
        ind = pd.date_range('1/1/2000', periods=100)
        data = np.random.randn(2, len(ind), 4)
        wp = pd.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) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import Panel [as 別名]
def _dl_mult_symbols(symbols, start, end, chunksize, retry_count, pause,
                     method):
    stocks = {}
    for sym_group in _in_chunks(symbols, chunksize):
        for sym in sym_group:
            try:
                stocks[sym] = method(sym, start, end, retry_count, pause)
            except IOError:
                warnings.warn('Failed to read symbol: {0!r}, replacing with '
                              'NaN.'.format(sym), SymbolWarning)
                stocks[sym] = np.nan

    try:
        return Panel(stocks).swapaxes('items', 'minor')
    except AttributeError:
        # cannot construct a panel with just 1D nans indicating no data
        raise RemoteDataError("No data fetched using "
                              "{0!r}".format(method.__name__))