python - 已知半衰期时如何为药物的不规则时间序列填充/缺失值

Question

我有一个数据框 (df)，其中 A 列是在时间戳给出的时间点给药的药物单位。鉴于药物的半衰期(180 分钟)，我想用药物浓度填充缺失值 (NaN)。我正在努力处理 pandas 中的代码。非常感谢帮助和洞察力。提前致谢

df
                       A     
Timestamp                                                      
1991-04-21 09:09:00   9.0        
1991-04-21 3:00:00   NaN       
1991-04-21 9:00:00   NaN       
1991-04-22 07:35:00  10.0      
1991-04-22 13:40:00   NaN        
1991-04-22 16:56:00   NaN    

鉴于药物的半衰期为 180 分钟。我想将 na(values) 作为耗时和药物半衰期的函数来填充

有点像

Timestamp             A     

1991-04-21 09:00:00   9.0  
1991-04-21 3:00:00   ~2.25   
1991-04-21 9:00:00   ~0.55   
1991-04-22 07:35:00  10.0  
1991-04-22 13:40:00   ~2.5   
1991-04-22 16:56:00   ~0.75  

Answer 1

您的时间戳未排序，我假设这是一个错字。我在下面修复了它。

import pandas as pd
import numpy as np
from StringIO import StringIO

text = """TimeStamp                    A     
1991-04-21 09:09:00   9.0        
1991-04-21 13:00:00   NaN       
1991-04-21 19:00:00   NaN       
1991-04-22 07:35:00  10.0      
1991-04-22 13:40:00   NaN        
1991-04-22 16:56:00   NaN  """

df = pd.read_csv(StringIO(text), sep='\s{2,}', engine='python', parse_dates=[0])

这是神奇的代码。

# half-life of 180 minutes is 10,800 seconds
# we need to calculate lamda (intentionally mis-spelled)
lamda = 10800 / np.log(2)

# returns time difference for each element
# relative to first element
def time_diff(x):
    return x - x.iloc[0]

# create partition of non-nulls with subsequent nulls
partition = df.A.notnull().cumsum()

# calculate time differences in seconds for each
# element relative to most recent non-null observation
# use .dt accessor and method .total_seconds()
tdiffs = df.TimeStamp.groupby(partition).apply(time_diff).dt.total_seconds()

# apply exponential decay
decay = np.exp(-tdiffs / lamda)

# finally, forward fill the observations and multiply by decay
decay * df.A.ffill()

0     9.000000
1     3.697606
2     0.924402
3    10.000000
4     2.452325
5     1.152895
dtype: float64