Python pandas.Panel() Examples

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() 

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) 

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_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_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) 

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 vAppendHdf(self, sKey, gData, gMetaData=None):
        oHdfStore = self.oHdfStore
        if oHdfStore is None: return
        
        if gData is None:
            # we need to check if the key exists
            pass
            # drop through
        # assert sCat in ['recipe', 'feed'], "ERROR: unrecognized category: " +sCat
        elif type(gData) in [pandas.Series, pandas.DataFrame, pandas.Panel]:
            self.oFd.write("INFO: HDF putting " +sKey +'\n')
            oHdfStore.put('/' +sKey, gData,
                          format='table',
                          data_columns=True)
            oHdfStore.flush()
        else:
            self.oFd.write("ERROR: unsupported datatype for %s: %r \n" % \
                           (sKey, type(gData),))
            return
        
        if gMetaData is None:
            return
        if type(gMetaData) in [dict, OrderedDict]:
            # o = getattr(getattr(oHdfStore.root, sCat), sInst)
            o = oHdfStore.get_node('/'+sKey)
            o._v_attrs.metadata = [gMetaData]
            oHdfStore.flush() 

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) 

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_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_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_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) 

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__ 

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
# ----- 

def get_panel(self):
        """
        Return a Panel consisting of two DataFrames, each consisting
        of two Series, each created using get_series.

        :return: Panel
        :rtype: pandas.Panel
        """
        data1 = {'seriesOne': self.get_series(0, 5),
                 'seriesTwo': self.get_series(0, 5, 10)}
        frame1 = pd.DataFrame(data1)
        data2 = {'seriesThree': self.get_series(6, 10),
                 'seriesFour': self.get_series(6, 10, 10)}
        frame2 = pd.DataFrame(data2)
        data = {'frameOne': frame1, 'frameTwo': frame2}
        return pd.Panel(data) 

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) 

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) 

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__)) 

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) 

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) 

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) 

def coins_snapshot(broker='hb', code='btc', size='5'):
    """
            获取实时快照数据
    params:
    broker:hb,ok,chbtc
    code:btc,ltc,eth,etc,bcc
    size:<150
    return Panel: asks,bids
    """
    try:
        js = _get_data(URL[broker]['snapshot'] % (code, size))
        if js is None:
            return js
        if broker == 'hb':
            timestr = js['ts']
            timestr = int2time(timestr / 1000)
        if broker == 'ok':
            timestr = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) 
        if broker == 'chbtc':
            timestr = js['timestamp']
            timestr = int2time(timestr)
        asks = pd.DataFrame(js['asks'], columns = ['price', 'vol'])
        bids = pd.DataFrame(js['bids'], columns = ['price', 'vol'])
        asks['time'] = timestr
        bids['time'] = timestr
        djs = {"asks": asks, "bids": bids}
        pf = pd.Panel(djs)
        return pf
    except Exception:
        print(traceback.print_exc()) 

def __init__(self):
        """Initialize parameters of the Interactive Brokers price handler
        object.
        """
        super(InteractiveBrokersPriceHandler, self).__init__()
        self.conn = ibConnection(
            clientId=IB.data_handler_id.value, port=IB.port.value
        )
        self.conn.register(self.__tick_price_handler, message.tickPrice)
        if not self.conn.connect():
            raise ValueError(
                "Odin was unable to connect to the Trader Workstation."
            )

        # Set the target field to download data from.
        today = dt.datetime.today()
        open_t, close_t = dt.time(9, 30), dt.time(16)
        cur_t = today.time()
        # If today is a weekday and the timing is correct, then we use the most
        # recently observed price. Otherwise we use the close price.
        if today.weekday() < 5 and cur_t >= open_t and cur_t <= close_t:
            self.field = TickType.LAST
        else:
            self.field = TickType.CLOSE

        # Initialize a pandas panel to store the price data.
        self.bar = pd.Panel(items=[PriceFields.current_price.value]) 

def test_panel_fails(self):
        x = Panel(randn(3, 4, 5))
        y = Series(randn(10))
        with pytest.raises(NotImplementedError):
            self.eval('x + y',
                      local_dict={'x': x, 'y': y}) 

def get_data_google(symbols=None, start=None, end=None, retry_count=3,
                    pause=0.001, adjust_price=False, ret_index=False,
                    chunksize=25, name=None):
    """
    Returns DataFrame/Panel of historical stock prices from symbols, over date
    range, start to end. To avoid being penalized by Google Finance servers,
    pauses between downloading 'chunks' of symbols can be specified.

    Parameters
    ----------
    symbols : string, array-like object (list, tuple, Series), or DataFrame
        Single stock symbol (ticker), array-like object of symbols or
        DataFrame with index containing stock symbols.
    start : string, (defaults to '1/1/2010')
        Starting date, timestamp. Parses many different kind of date
        representations (e.g., 'JAN-01-2010', '1/1/10', 'Jan, 1, 1980')
    end : string, (defaults to today)
        Ending date, timestamp. Same format as starting date.
    retry_count : int, default 3
        Number of times to retry query request.
    pause : int, default 0
        Time, in seconds, to pause between consecutive queries of chunks. If
        single value given for symbol, represents the pause between retries.
    chunksize : int, default 25
        Number of symbols to download consecutively before intiating pause.

    Returns
    -------
    hist_data : DataFrame (str) or Panel (array-like object, DataFrame)
    """
    return _get_data_from(symbols, start, end, retry_count, pause,
                          adjust_price, ret_index, chunksize, 'google', name) 

def panel_fillna(panel, type="bfill"):
    """
    fill nan along the 3rd axis
    :param panel: the panel to be filled
    :param type: bfill or ffill
    """
    frames = {}
    for item in panel.items:
        if type == "both":
            frames[item] = panel.loc[item].fillna(axis=1, method="bfill").\
                fillna(axis=1, method="ffill")
        else:
            frames[item] = panel.loc[item].fillna(axis=1, method=type)
    return pd.Panel(frames) 

def panel_to_excel(self):
        """
        Use pandas.Panel.to_excel to save a file out.xls.
        """
        file_name = os.path.join(self.data_dir, "out.xls")
        panel = self.get_panel()
        panel.to_excel(file_name)
        os.remove(file_name) 

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

    with catch_warnings(record=True):
        simplefilter("ignore", FutureWarning)
        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).mean()

        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').mean())
        tm.assert_panel_equal(result, expected)

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

def _align_panel(self, indexer, df):
        is_frame = self.obj.ndim == 2
        is_panel = self.obj.ndim >= 3
        raise NotImplementedError("cannot set using an indexer with a Panel "
                                  "yet!") 

def panel_to_hdf(self):
        """
        Use pandas.Panel.to_hdf to save a file out.hdf.
        """
        file_name = os.path.join(self.data_dir, "out.hdf")
        panel = self.get_panel()
        panel.to_hdf(file_name, key="Sample", mode="w")
        os.remove(file_name)