Python 随时间变化的绘图总和

提供了使用matplotlib将日期时间列表（见下文）绘制为随时间累积计数的好方法：

[
    datetime.datetime(2015, 12, 22),
    datetime.datetime(2015, 12, 23),
    datetime.datetime(2015, 12, 23), # note duplicate entry (graph increases by 2)
    datetime.datetime(2015, 12, 24),
    datetime.datetime(2015, 12, 25),
    ...
]

然而，我有一个新的数据集，其中每个条目都有一个关联的值（见下文）。如何将其绘制为累积？或者我只需要迭代数据并将其累积成x，y绘图对吗

[
    (datetime.datetime(2015, 12, 22), 6), # graph increases by 6
    (datetime.datetime(2015, 12, 23), 5),
    (datetime.datetime(2015, 12, 23), 4), # graph increases by 9
    (datetime.datetime(2015, 12, 24), 12),
    (datetime.datetime(2015, 12, 25), 14),
]

---

您只需拆分

x

和

y

轴，然后使用或累积y值。下面是一个例子：

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import datetime
import numpy as np

r = [(datetime.datetime(2015, 12, 22), 6), (datetime.datetime(2015, 12, 23), 5), (datetime.datetime(2015, 12, 23), 4), (datetime.datetime(2015, 12, 24), 12), (datetime.datetime(2015, 12, 25), 14)]

x, v = zip(*[(d[0], d[1]) for d in r])  # same as #x , v = [d[0] for d in r], [d[1] for d in r]
v = np.array(v).cumsum()  # cumulative sum of y values

# now plot the results
fig, ax = plt.subplots(1)
ax.plot(x, v, '-o')
fig.autofmt_xdate()
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
ax.xaxis.set_major_locator(mdates.DayLocator())
plt.show()