蟒蛇熊猫动态范围之间的时间差

Question

我是一个新的python/pandas用户。我试图在时间序列数据帧的动态范围（基于值的差异）之间获得时间增量（以秒为单位）。我的样本数据帧是：

time       price 
2013-04-26 09:30:03-04:00  101 
2013-04-26 09:30:04-04:00  101 
2013-04-26 09:30:05-04:00  102 
2013-04-26 09:30:06-04:00  105 
2013-04-26 09:30:07-04:00  104 
2013-04-26 09:30:08-04:00  105 
2013-04-26 09:30:09-04:00  106 
2013-04-26 09:30:10-04:00  104 
2013-04-26 09:30:11-04:00  110 
2013-04-26 09:30:12-04:00  109 
2013-04-26 09:30:13-04:00  111 
2013-04-26 09:30:14-04:00  108 
2013-04-26 09:30:15-04:00  106 
2013-04-26 09:30:16-04:00  107 
2013-04-26 09:30:17-04:00  107 
2013-04-26 09:30:18-04:00  108 
2013-04-26 09:30:19-04:00  109 
2013-04-26 09:30:20-04:00  109 
2013-04-26 09:30:21-04:00  110 

我想一旦达到在价格上的差异来获得4.价格差异之间的时间差，这个价格点成为下一个计算等的“起点” 。 期望的结果是类似的信息（时间差以秒为单位）：

time      price time delta 
2013-04-26 09:30:03-04:00 101 
2013-04-26 09:30:04-04:00 101 
2013-04-26 09:30:05-04:00 102 
2013-04-26 09:30:06-04:00 105  3 
2013-04-26 09:30:07-04:00 104 
2013-04-26 09:30:08-04:00 105 
2013-04-26 09:30:09-04:00 106 
2013-04-26 09:30:10-04:00 104 
2013-04-26 09:30:11-04:00 110  5 
2013-04-26 09:30:12-04:00 109 
2013-04-26 09:30:13-04:00 111 
2013-04-26 09:30:14-04:00 108 
2013-04-26 09:30:15-04:00 106  4 
2013-04-26 09:30:16-04:00 107 
2013-04-26 09:30:17-04:00 107 
2013-04-26 09:30:18-04:00 108 
2013-04-26 09:30:19-04:00 109 
2013-04-26 09:30:20-04:00 109 
2013-04-26 09:30:21-04:00 110  6 

Answer 1

不知道如何伟大，这是明智的性能。

import numpy as np 
import pandas as pd 

gen = df.price.iteritems() 

def get_deltas(gen): 
    time, value = next(gen) 
    deltas = [np.nan] # initial value 
    for line in gen: 
     if np.abs(line[1] - value) >= 4: 
      deltas.append(np.abs(line[0] - time)) 
      time, value = line 
     else: 
      deltas.append(np.nan)  
    return deltas 

deltas = get_deltas(df.price.iteritems()) 
df['deltas'] = deltas 

In [58]: df 
Out[58]: 
        price deltas 
time        
2013-04-26 13:30:03 101  NaN 
2013-04-26 13:30:04 101  NaN 
2013-04-26 13:30:05 102  NaN 
2013-04-26 13:30:06 105 0:00:03 
2013-04-26 13:30:07 104  NaN 
2013-04-26 13:30:08 105  NaN 
2013-04-26 13:30:09 106  NaN 
2013-04-26 13:30:10 104  NaN 
2013-04-26 13:30:11 110 0:00:05 
2013-04-26 13:30:12 109  NaN 
2013-04-26 13:30:13 111  NaN 
2013-04-26 13:30:14 108  NaN 
2013-04-26 13:30:15 106 0:00:04 
2013-04-26 13:30:16 107  NaN 
2013-04-26 13:30:17 107  NaN 
2013-04-26 13:30:18 108  NaN 
2013-04-26 13:30:19 109  NaN 
2013-04-26 13:30:20 109  NaN 
2013-04-26 13:30:21 110 0:00:06