在这种情况下,只需使用条形图来绘制内容,并告诉matplotlib轴是日期.

为了获得时间,您可以利用matplotlib的内部日期格式是浮点数的事实,其中每个整数对应于当天的0:00.因此,为了获得时间,我们可以做的时间=日期％1.

举个例子(其中90％是生成和操作日期.绘图只是对bar的一次调用.)：

import datetime as dt
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl

def main():
    start,stop = dt.datetime(2012,3,1),dt.datetime(2012,4,1)

    fig,ax = plt.subplots()
    for color in ['blue','red','green']:
        starts,stops = generate_data(start,stop)
        plot_durations(starts,stops,ax,facecolor=color,alpha=0.5)
    plt.show()

def plot_durations(starts,ax=None,**kwargs):
    if ax is None:
        ax = plt.gca()
    # Make the default alignment center,unless specified otherwise
    kwargs['align'] = kwargs.get('align','center')

    # Convert things to matplotlib's internal date format...
    starts,stops = mpl.dates.date2num(starts),mpl.dates.date2num(stops)

    # Break things into start days and start times 
    start_times = starts % 1
    start_days = starts - start_times
    durations = stops - starts
    start_times += int(starts[0]) # So that we have a valid date...

    # Plot the bars
    artist = ax.bar(start_days,durations,bottom=start_times,**kwargs)

    # Tell matplotlib to treat the axes as dates...
    ax.xaxis_date()
    ax.yaxis_date()
    ax.figure.autofmt_xdate()
    return artist

def generate_data(start,stop):
    """Generate some random data..."""
    # Make a series of events 1 day apart
    starts = mpl.dates.drange(start,stop,dt.timedelta(days=1))

    # Vary the datetimes so that they occur at random times
    # Remember,1.0 is equivalent to 1 day in this case...
    starts += np.random.random(starts.size)

    # Make some random stopping times...
    stops = starts + 0.2 * np.random.random(starts.size)

    # Convert back to datetime objects...
    return mpl.dates.num2date(starts),mpl.dates.num2date(stops)

if __name__ == '__main__':
    main()

在旁注中,对于从一天开始到下一天结束的事件,这将使y轴延伸到第二天.如果您愿意,可以通过其他方式处理它,但我认为这是最简单的选择.