Python Pandas计算事件发生之间的时间值

时间:2016-07-14 09:57:47

标签: python pandas timedelta

我有一个Pandas(0.14.1)数据框,其中包含datetimeevent列,如下所示:

import pandas as pd
from datetime import datetime
from datetime import timedelta
def perdelta(start, end, delta):
   curr = start
   while curr < end:
     yield curr;  
     curr += delta;
events = [np.nan] * 20; events[5]=20; events[12]=3; events[15]=10;
n = len(events)
signal = [i/10.0 for i in range(n)] + np.random.randn(n)
df = pd.DataFrame( { 'level1': signal,
                     'event' : events, 
                     'datetime': [r for r in perdelta(datetime.now(),  datetime.now() + timedelta(minutes=10) , timedelta(seconds=30))]}, 
                      index=range(n))

df.head(7)
                    datetime  event    level1
0 2016-07-14 10:44:47.035000    NaN  0.158594
1 2016-07-14 10:45:17.035000    NaN  0.282749
2 2016-07-14 10:45:47.035000    NaN  0.448012
3 2016-07-14 10:46:17.035000    NaN  0.590702
4 2016-07-14 10:46:47.035000    NaN -0.346073
5 2016-07-14 10:47:17.035000     20  0.072986
6 2016-07-14 10:47:47.035000    NaN  1.493900

我想要包含一个t_since_last_event列,用于计算自上次事件发生以来每个时间步的deltatime。生成的df应如下所示:

df
                     datetime  event    level1  t_since_last_event
0  2016-07-14 10:44:47.035000    NaN  0.158594                  0
1  2016-07-14 10:45:17.035000    NaN  0.282749                 30
2  2016-07-14 10:45:47.035000    NaN  0.448012                 60
3  2016-07-14 10:46:17.035000    NaN  0.590702                 90
4  2016-07-14 10:46:47.035000    NaN -0.346073                120
5  2016-07-14 10:47:17.035000     20  0.072986                  0
6  2016-07-14 10:47:47.035000    NaN  1.493900                 30
7  2016-07-14 10:48:17.035000    NaN -0.143081                 60
8  2016-07-14 10:48:47.035000    NaN  0.173715                 90
9  2016-07-14 10:49:17.035000    NaN  1.232040                120
10 2016-07-14 10:49:47.035000    NaN  3.497438                150
11 2016-07-14 10:50:17.035000    NaN  0.956582                180
12 2016-07-14 10:50:47.035000      3  2.976383                  0
13 2016-07-14 10:51:17.035000    NaN  0.599698                 30
14 2016-07-14 10:51:47.035000    NaN  2.538005                 60
15 2016-07-14 10:52:17.035000     10  1.362104                  0
16 2016-07-14 10:52:47.035000    NaN  2.224680                 30
17 2016-07-14 10:53:17.035000    NaN  3.221037                 60
18 2016-07-14 10:53:47.035000    NaN  1.869479                 90
19 2016-07-14 10:54:17.035000    NaN  1.447430                120

在熊猫中有这种巧妙的方法吗?它涉及水平分组(通过事件发生)和垂直计数,所以解决方案对我来说不是太明显。我在下面发布了我的传统解决方案。

2 个答案:

答案 0 :(得分:2)

这里的矢量化应该很简单:

  • 添加另一个可容纳上次活动时间的列
  • 如果event不是NaN,则
  • 在此列中设置事件时间,否则为NaN
  • 使用方法ffill
  • 填充NaN值
  • datetime列中扣除。

这应该适用于pandas 0.14.1:

mask = df['event'].notnull()
df['last_event_time'] = np.NaN
df.loc[mask, 'last_event_time'] = df.loc[mask, 'datetime']
df['last_event_time'] = df['last_event_time'].fillna(method='ffill')
df['t_since_last_event'] = df['datetime'] - df['last_event_time']

您可能还想在一开始就将event中的第一个元素设置为零;或者,mask[0] = True

答案 1 :(得分:1)

这是我令人兴奋的解决方案。我怀疑应该有更快的Panads解决方案。纵向和横向依赖性的存在使得使用apply()groupby()等更难处理。

last_trade_time = df.iloc[0]['datetime']
t=[np.nan] * len(df)
for i, row in df.iterrows():
    if np.isnan(row['event']):
        t[i] = row['datetime'] - last_trade_time
    else:
        t[i] = 0
        last_trade_time = row['datetime']
df['t_since_last_event'] = t