Python 熊猫-以不等间隔绘制事件
我有一个表示事件日志的datetime对象列表:Python 熊猫-以不等间隔绘制事件,python,matplotlib,pandas,ipython-notebook,Python,Matplotlib,Pandas,Ipython Notebook,我有一个表示事件日志的datetime对象列表: [datetime.datetime(2014, 12, 16, 0, 18, 12), datetime.datetime(2014, 12, 16, 0, 18, 27), datetime.datetime(2014, 12, 16, 0, 18, 27), datetime.datetime(2014, 12, 16, 0, 19, 9), datetime.datetime(2014, 12, 16, 0, 19, 3
[datetime.datetime(2014, 12, 16, 0, 18, 12),
datetime.datetime(2014, 12, 16, 0, 18, 27),
datetime.datetime(2014, 12, 16, 0, 18, 27),
datetime.datetime(2014, 12, 16, 0, 19, 9),
datetime.datetime(2014, 12, 16, 0, 19, 39),
datetime.datetime(2014, 12, 16, 0, 19, 49),
datetime.datetime(2014, 12, 16, 0, 20, 2),
datetime.datetime(2014, 12, 16, 0, 20, 19),
datetime.datetime(2014, 12, 16, 0, 20, 47),
...
datetime.datetime(2014, 12, 16, 6, 23, 43),
datetime.datetime(2014, 12, 16, 6, 25, 45)]
如何创建每秒事件数的绘图?例如,价值应为:
- 1表示日期时间。日期时间(2014、12、16、0、18、12)
- 0表示datetime.datetime(2014,12,16,0,18,13)-datetime.datetime(2014,12,16,0,18,26)
- 2表示日期时间。日期时间(2014、12、16、0、18、27)
- 等等
pd.Series([1 for _ in xrange(len(events_list))], index=events_list).plot()
这是:
df = pd.DataFrame({'ts': t, 'value': 1} for t in events_list)
df.pivot_table(index='ts', columns='value', aggfunc=len, fill_value=0).plot()
显然我得到了错误的结果:
我可以要求您指导我完成这项工作吗?您可能希望使用“value\u counts”来计算特定时间事件实例的数量,然后对数据帧重新采样以填充na,如下所示
import pandas as pd
import datetime
events = [datetime.datetime(2014, 12, 16, 0, 18, 12),
datetime.datetime(2014, 12, 16, 0, 18, 27),
datetime.datetime(2014, 12, 16, 0, 18, 27),
datetime.datetime(2014, 12, 16, 0, 19, 9),
datetime.datetime(2014, 12, 16, 0, 19, 39),
datetime.datetime(2014, 12, 16, 0, 19, 49),
datetime.datetime(2014, 12, 16, 0, 20, 2),
datetime.datetime(2014, 12, 16, 0, 20, 19),
datetime.datetime(2014, 12, 16, 0, 20, 47),
datetime.datetime(2014, 12, 16, 6, 23, 43),
datetime.datetime(2014, 12, 16, 6, 25, 45)]
df = pd.DataFrame ({'ts' : events})
df2 = df.ts.value_counts()
df2 = df2.resample('s').fillna(0)
print (df2.head(30))
这应该是让步的,
2014-12-16 00:18:12 1
2014-12-16 00:18:13 0
2014-12-16 00:18:14 0
2014-12-16 00:18:15 0
2014-12-16 00:18:16 0
2014-12-16 00:18:17 0
2014-12-16 00:18:18 0
2014-12-16 00:18:19 0
2014-12-16 00:18:20 0
2014-12-16 00:18:21 0
2014-12-16 00:18:22 0
2014-12-16 00:18:23 0
2014-12-16 00:18:24 0
2014-12-16 00:18:25 0
2014-12-16 00:18:26 0
2014-12-16 00:18:27 2
2014-12-16 00:18:28 0
2014-12-16 00:18:29 0
2014-12-16 00:18:30 0
2014-12-16 00:18:31 0
2014-12-16 00:18:32 0
2014-12-16 00:18:33 0
2014-12-16 00:18:34 0
2014-12-16 00:18:35 0
2014-12-16 00:18:36 0
2014-12-16 00:18:37 0
2014-12-16 00:18:38 0
2014-12-16 00:18:39 0
2014-12-16 00:18:40 0
2014-12-16 00:18:41 0
Freq: S, dtype: float64