将具有多个参数的函数应用于滚动 DataFrame Pandas

Question

I have a Pandas DataFrame similar to the below. Please not this does not need to be in this format, it can be put back into pivoted fashion and have each variable be a column if that's easier.

      date      variable  value
0   2014-01-31    item1  0.070898
1   2014-02-28    item1  0.064725
2   2014-03-31    item1  0.101292
3   2014-04-30    item1  0.041898
4   2014-05-31    item1  0.094894
5   2014-06-30    item1  0.110287
6   2014-07-31    item1  0.081844
7   2014-08-31    item1  0.043819
8   2014-09-30    item1  0.009196
9   2014-10-31    item1  0.076482
10  2014-11-30    item1  0.055906
11  2014-12-31    item1  0.042028
12  2015-01-31    item1  0.115469
13  2015-02-28    item1  0.130457
14  2015-03-31    item1  0.054314
15  2015-04-30    item1  0.141286
16  2015-05-31    item1  0.109387
17  2015-06-30    item1  0.066154
18  2015-07-31    item1  0.039390
19  2015-08-31    item1  0.071276
20  2015-09-30    item1  0.088784
21  2015-10-31    item1  0.054234
22  2015-11-30    item1  0.111854
23  2015-12-31    item1  0.005053
24  2016-01-31    item1  0.145953
25  2016-02-29    item1  0.138051
26  2016-03-31    item1  0.030395
27  2016-04-30    item1  0.055843
28  2016-05-31    item1  0.037960
29  2016-06-30    item1  0.147318
..         ...      ...       ...
120 2015-09-30      ref  0.043185
121 2015-10-31      ref  0.046849
122 2015-11-30      ref  0.008139
123 2015-12-31      ref  0.011222
124 2016-01-31      ref  0.026408
125 2016-02-29      ref  0.040404
126 2016-03-31      ref  0.039585
127 2016-04-30      ref  0.028782
128 2016-05-31      ref  0.002799
129 2016-06-30      ref  0.040413
130 2016-07-31      ref  0.004451
131 2016-08-31      ref  0.001946
132 2016-09-30      ref  0.029548
133 2016-10-31      ref  0.029416
134 2016-11-30      ref  0.024893
135 2016-12-31      ref  0.037656
136 2017-01-31      ref  0.045642
137 2017-02-28      ref  0.046690
138 2017-03-31      ref  0.019193
139 2017-04-30      ref  0.016362
140 2017-05-31      ref  0.025512
141 2017-06-30      ref  0.021625
142 2017-07-31      ref  0.026472
143 2017-08-31      ref  0.016531
144 2017-09-30      ref  0.047447
145 2017-10-31      ref  0.032180
146 2017-11-30      ref  0.048635
147 2017-12-31      ref  0.040450
148 2018-01-31      ref  0.047735
149 2018-02-28      ref  0.013727          

For each item in variable I would like to apply a rolling 12 month calculation that accepts as arguments the rolling 12 months of value for that item, and the rolling 12 months of values where variable equals ref . The function is of the form:

def func(series1,series2):
   do something with series
   return single_value_from_doiong_something

My issue is how to pass two different series into this function to return my single value. Does anyone know how to do this?

Answer 1

Regarding the function you mention, after you restructure your original dataframe with a join() , this may be closer to what you are after:

combined = df[df['variable']!='ref'].set_index('date').join(df[df['variable']=='ref'].set_index('date'), lsuffix='', rsuffix='_ref').drop('variable_ref', axis=1)

def func(series, ref_series):

    #As an example
    return series.mean()/ref_series.mean()

combined.groupby('variable').rolling(12).apply(lambda x: func(x, combined.loc[x.index]['value_ref']), raw=False).drop('value_ref', axis=1)

This example yields the following ( NaN are present due to the gaps in your sample data):

                        value
variable date                
item1    2014-01-31       NaN
         2014-02-28       NaN
         2014-03-31       NaN
         2014-04-30       NaN
         2014-05-31       NaN
         2014-06-30       NaN
         2014-07-31       NaN
         2014-08-31       NaN
         2014-09-30       NaN
         2014-10-31       NaN
         2014-11-30       NaN
         2014-12-31       NaN
         2015-01-31       NaN
         2015-02-28       NaN
         2015-03-31       NaN
         2015-04-30       NaN
         2015-05-31       NaN
         2015-06-30       NaN
         2015-07-31       NaN
         2015-08-31       NaN
         2015-09-30  1.912186
         2015-10-31  1.793184
         2015-11-30  2.609254
         2015-12-31  3.009455
         2016-01-31  3.123833
         2016-02-29  2.910599
         2016-03-31  2.708132
         2016-04-30  2.497878
         2016-05-31  2.561760
         2016-06-30  2.681712