Python DataFrame.plot方法代碼示例

# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import plot [as 別名]
    def plot(self):
        """
            Plots 2 graphs. One for N-period moving average, lower and upper bands.
            One for P/N and position.
        """

        columns = {"Upper Bands": self.upper_bands,
                   "Lower Bands": self.lower_bands,
                   "Moving Means": self.moving_means,
                   "Opening Prices": self.prices}
        df = DataFrame(columns, index=self.dates)
        df.plot()

        fig = plt.figure(num=None, figsize=(18, 10), dpi=80, facecolor='w', edgecolor='k')
        fig.add_subplot(121)
        trans_dates = [tran.date for tran in self.transactions]
        # we negate the value here to show profit/loss
        trans = Series([-tran.value() for tran in self.transactions], index=trans_dates)
        position = Series([tran.units for tran in self.transactions], index=trans_dates)

        position.cumsum().plot(label="Position")
        plt.xlabel("Date")
        plt.ylabel("Position")
        plt.title("Position over Time")
        plt.legend(loc="best")

        fig.add_subplot(122)
        trans.cumsum().plot(label="P/L")
        plt.xlabel("Date")
        plt.ylabel("Profit/Loss")
        plt.title("Profit and Loss over Time")
        plt.legend(loc="best")

        plt.show()

# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import plot [as 別名]
def plot_barplot(
    matrix, columns, fpath=None, stacked=True, mpl_params=None, axes=None, color=None, figsize=(70, 10), **kwargs
):
    if mpl_params is None:
        mpl_params = {}

    df = DataFrame(matrix, columns=columns)
    axes = df.plot(kind="bar", stacked=stacked, figsize=figsize, axes=axes, color=color, **kwargs)
    for function_name, params in mpl_params.items():
        function = getattr(axes, function_name)
        function(*params["args"], **params["kwargs"])
    if fpath is not None:
        figure = axes.get_figure()
        figure.savefig(fpath)
    return axes

# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import plot [as 別名]
#    pp.savefig()
#
#    plt.clf()
#    plt.cla()
#    plt.scatter(dailyrets[:,marketsymbolpos],dailyrets[:,sym],c='blue') # $SPX v XOM
#    plt.ylabel(symbols[sym])
#    plt.xlabel(symbols[marketsymbolpos])
#    pp.savefig()
    sym_todo = len(close.columns) - sym - 1
    print str(sym_todo) + " to do!"


plt.clf()
plt.cla()

#take out stocks with nana > 10%
#for sym in symbols:
#    if sharperatios['NaNs'][sym] > 0.1:
#        sharperatios.

sharperatios = sharperatios.sort_index(by = 'sr', ascending = False)
sharperatios = sharperatios[0:10]

plt.figure()
sharperatios.plot()


pp.savefig()

pp.close()

# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import plot [as 別名]
            axes = axarr.reshape(nrows, ncols).squeeze()
    else:
        # returned axis array will be always 2-d, even if nrows=ncols=1
        axes = axarr.reshape(nrows, ncols)

    return fig, axes

if __name__ == '__main__':
    # import pandas.rpy.common as com
    # sales = com.load_data('sanfrancisco.home.sales', package='nutshell')
    # top10 = sales['zip'].value_counts()[:10].index
    # sales2 = sales[sales.zip.isin(top10)]
    # _ = scatter_plot(sales2, 'squarefeet', 'price', by='zip')

    # plt.show()

    import matplotlib.pyplot as plt

    import pandas.tools.plotting as plots
    import pandas.core.frame as fr
    reload(plots)
    reload(fr)
    from pandas.core.frame import DataFrame

    data = DataFrame([[3, 6, -5], [4, 8, 2], [4, 9, -6],
                      [4, 9, -3], [2, 5, -1]],
                     columns=['A', 'B', 'C'])
    data.plot(kind='barh', stacked=True)

    plt.show()

# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import plot [as 別名]
            return fig, axarr[0]
        else:
            return fig, axarr.reshape(nrows, ncols).squeeze()
    else:
        # returned axis array will be always 2-d, even if nrows=ncols=1
        return fig, axarr.reshape(nrows, ncols)


if __name__ == "__main__":
    # import pandas.rpy.common as com
    # sales = com.load_data('sanfrancisco.home.sales', package='nutshell')
    # top10 = sales['zip'].value_counts()[:10].index
    # sales2 = sales[sales.zip.isin(top10)]
    # _ = scatter_plot(sales2, 'squarefeet', 'price', by='zip')

    # plt.show()

    import matplotlib.pyplot as plt

    import pandas.tools.plotting as plots
    import pandas.core.frame as fr

    reload(plots)
    reload(fr)
    from pandas.core.frame import DataFrame

    data = DataFrame([[3, 6, -5], [4, 8, 2], [4, 9, -6], [4, 9, -3], [2, 5, -1]], columns=["A", "B", "C"])
    data.plot(kind="barh", stacked=True)

    plt.show()

# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import plot [as 別名]
def main():
    # stations = cehq_station.read_grdc_stations(st_id_list=["2903430", "2909150", "2912600", "4208025"])

    selected_station_ids = [
        "05LM006",
        "05BN012",
        "05AK001",
        "05QB003",
        "06EA002"
    ]

    stations = cehq_station.load_from_hydat_db(natural=None, province=None, selected_ids=selected_station_ids, skip_data_checks=True)

    stations_mh = cehq_station.get_manitoba_hydro_stations()

    # copy metadata from the corresponding hydat stations
    for s in stations:
        assert isinstance(s, Station)
        for s_mh in stations_mh:
            assert isinstance(s_mh, Station)


            if s == s_mh:
                s_mh.copy_metadata(s)
                break



    stations = [s for s in stations_mh if s.id in selected_station_ids and s.longitude is not None]

    stations_to_mp = None

    import matplotlib.pyplot as plt

    # labels = ["CanESM", "MPI"]
    # paths = ["/skynet3_rech1/huziy/offline_stfl/canesm/discharge_1958_01_01_00_00.nc",
    # "/skynet3_rech1/huziy/offline_stfl/mpi/discharge_1958_01_01_00_00.nc"]
    #
    # colors = ["r", "b"]

    # labels = ["ERA", ]
    # colors = ["r", ]
    # paths = ["/skynet3_rech1/huziy/arctic_routing/era40/discharge_1958_01_01_00_00.nc"]


    labels = ["Model", ]
    colors = ["r", ]
    paths = [
        "/RESCUE/skynet3_rech1/huziy/water_route_mh_bc_011deg_wc/discharge_1980_01_01_12_00.nc"
    ]

    infocell_path = "/RESCUE/skynet3_rech1/huziy/water_route_mh_bc_011deg_wc/infocell.nc"

    start_year = 1980
    end_year = 2014




    stations_filtered = []
    for s in stations:
        # Also filter out stations with small accumulation areas
        # if s.drainage_km2 is not None and s.drainage_km2 < 100:
        #     continue

        # Filter stations with data out of the required time frame
        year_list = s.get_list_of_complete_years()

        print("Complete years for {}: {}".format(s.id, year_list))

        stations_filtered.append(s)

    stations = stations_filtered


    print("Retained {} stations.".format(len(stations)))

    sim_to_time = {}

    monthly_dates = [datetime(2001, m, 15) for m in range(1, 13)]
    fmt = FuncFormatter(lambda x, pos: num2date(x).strftime("%b")[0])
    locator = MonthLocator(bymonthday=15)

    fig = plt.figure()

    axes = []
    row_indices = []
    col_indices = []

    ncols = 1
    shiftrow = 0 if len(stations) % ncols == 0 else 1
    nrows = len(stations) // ncols + shiftrow
    shared_ax = None
    gs = gridspec.GridSpec(ncols=ncols, nrows=nrows)

    for i, s in enumerate(stations):
        row = i // ncols
        col = i % ncols

        row_indices.append(row)
#.........這裏部分代碼省略.........