Pandas temporal cumulative sum by group

问题

I have a data frame where 1 or more events are recorded for each id. For each event the id, a metric x and a date are recorded. Something like this:

import pandas as pd
import datetime as dt
import numpy as np
x = range(0, 6)
id = ['a', 'a', 'b', 'a', 'b', 'b']
dates = [dt.datetime(2012, 5, 2),dt.datetime(2012, 4, 2),dt.datetime(2012, 6, 2),
         dt.datetime(2012, 7, 30),dt.datetime(2012, 4, 1),dt.datetime(2012, 5, 9)]

df =pd.DataFrame(np.column_stack((id,x,dates)), columns = ['id', 'x', 'dates'])

I'd like to be able to set a lookback period (i.e. 70 days) and calculate, for each row in the dataset, a cumulative sum of x for any preceding event for that id and within the desired lookback (excluding x for the row the calculation is being performed for). Should end up looking like:

  id  x                dates    want
0  a  0  2012-05-02 00:00:00    1
1  a  1  2012-04-02 00:00:00    0
2  b  2  2012-06-02 00:00:00    9
3  a  3  2012-07-30 00:00:00    0
4  b  4  2012-04-01 00:00:00    0
5  b  5  2012-05-09 00:00:00    4

回答1:

Well, one approach is the following: (1) do a groupby/apply with 'id' as grouping variable. (2) Within the apply, resample the group to a daily time series. (3) Then just using rolling_sum (and shift so you don't include the current rows 'x' value) to compute the sum of your 70 day lookback periods. (4) Reduce the group back to only the original observations:

In [12]: df = df.sort(['id','dates'])
In [13]: df
Out[13]: 
  id  x      dates
1  a  1 2012-04-02
0  a  0 2012-05-02
3  a  3 2012-07-30
4  b  4 2012-04-01
5  b  5 2012-05-09
2  b  2 2012-06-02

You are going to need your data sorted by ['id','dates']. Now we can do the groupby/apply:

In [15]: def past70(g):
             g = g.set_index('dates').resample('D','last')
             g['want'] = pd.rolling_sum(g['x'],70,0).shift(1)
             return g[g.x.notnull()]            

In [16]: df = df.groupby('id').apply(past70).drop('id',axis=1)
In [17]: df
Out[17]: 
               x  want
id dates              
a  2012-04-02  1   NaN
   2012-05-02  0     1
   2012-07-30  3     0
b  2012-04-01  4   NaN
   2012-05-09  5     4
   2012-06-02  2     9

If you don't want the NaNs then just do:

In [28]: df.fillna(0)
Out[28]: 
               x  want
id dates              
a  2012-04-02  1     0
   2012-05-02  0     1
   2012-07-30  3     0
b  2012-04-01  4     0
   2012-05-09  5     4
   2012-06-02  2     9

Edit: If you want to make the lookback window a parameter do something like the following:

def past_window(g,win=70):
    g = g.set_index('dates').resample('D','last')
    g['want'] = pd.rolling_sum(g['x'],win,0).shift(1)
    return g[g.x.notnull()]            

df = df.groupby('id').apply(past_window,win=10)
print df.fillna(0)

来源：https://stackoverflow.com/questions/23836114/pandas-temporal-cumulative-sum-by-group

标签

python

pandas

group-by

time-series