I have the following data frame C.
>>> C
a b c
2011-01-01 0 0 NaN
2011-01-02 41 12 NaN
2011-01-03 82 24 NaN
2011-01-04 123 36 NaN
2011-01-05 164 48 NaN
2011-01-06 205 60 2
2011-01-07 246 72 4
2011-01-08 287 84 6
2011-01-09 328 96 8
2011-01-10 369 108 10
I would like to add a new column, d, where I apply a rolling function, on a fixed window (6 here), where I somehow, for each row (or date), fix the value c. One loop in this rolling function should be (pseudo):
a b c d
2011-01-01 0 0 NaN a + b*2 (a,b from this row, '2' is from 'c' on 2011-01-06)
2011-01-02 41 12 NaN a + b*2 (a,b from this row, '2' is still from 2011-01-06)
2011-01-03 82 24 NaN a + b*2
2011-01-04 123 36 NaN a + b*2
2011-01-05 164 48 NaN a + b*2
2011-01-06 205 60 2 a + b*2
2011-01-07 246 72 4
2011-01-08 287 84 6
2011-01-09 328 96 8
2011-01-10 369 108 10
After this "loop" I want to take all of these 6 calculated rows in d and run a function call, which in turn will return one value, that should be stored in another column, e say:
a b c d e
2011-01-01 0 0 NaN a + b*2 ---| NaN
2011-01-02 41 12 NaN a + b*2 | NaN
2011-01-03 82 24 NaN a + b*2 | These values NaN
2011-01-04 123 36 NaN a + b*2 | are input to NaN
2011-01-05 164 48 NaN a + b*2 | function NaN
2011-01-06 205 60 2 a + b*2 ---| yielding X
2011-01-07 246 72 4 value X in
2011-01-08 287 84 6 column 'e'
2011-01-09 328 96 8
2011-01-10 369 108 10
This procedure would then be iterated onto the next window (again 6 long) like:
a b c d e
2011-01-01 0 0 NaN
2011-01-02 41 12 NaN a + b*4 (a,b from this row, '4' is from 'c' now from 2011-01-07)
2011-01-03 82 24 NaN a + b*4 (a,b from this row, '4' is still from 2011-01-07)
2011-01-04 123 36 NaN a + b*4
2011-01-05 164 48 NaN a + b*4
2011-01-06 205 60 2 a + b*4 X
2011-01-07 246 72 4 a + b*4
2011-01-08 287 84 6
2011-01-09 328 96 8
2011-01-10 369 108 10
a b c d e
2011-01-01 0 0 NaN NaN
2011-01-02 41 12 NaN a + b*4 ---| NaN
2011-01-03 82 24 NaN a + b*4 | These values NaN
2011-01-04 123 36 NaN a + b*4 | are input to NaN
2011-01-05 164 48 NaN a + b*4 | function NaN
2011-01-06 205 60 2 a + b*4 | yielding X
2011-01-07 246 72 4 a + b*4 ---| value Y in Y
2011-01-08 287 84 6 column 'e'
2011-01-09 328 96 8
2011-01-10 369 108 10
Hopefully this is clear enough,
Thanks, N
You could use pd.rolling_apply:
import numpy as np
import pandas as pd
df = pd.read_table('data', sep='\s+')
def foo(x, df):
window = df.iloc[x]
# print(window)
c = df.ix[int(x[-1]), 'c']
dvals = window['a'] + window['b']*c
return bar(dvals)
def bar(dvals):
# print(dvals)
return dvals.mean()
df['e'] = pd.rolling_apply(np.arange(len(df)), 6, foo, args=(df,))
print(df)
yields
a b c e
2011-01-01 0 0 NaN NaN
2011-01-02 41 12 NaN NaN
2011-01-03 82 24 NaN NaN
2011-01-04 123 36 NaN NaN
2011-01-05 164 48 NaN NaN
2011-01-06 205 60 2 162.5
2011-01-07 246 72 4 311.5
2011-01-08 287 84 6 508.5
2011-01-09 328 96 8 753.5
2011-01-10 369 108 10 1046.5
The args and kwargs parameters were added to rolling_apply in Pandas version 0.14.0.
Since in my example above df is a global variable, it is not really necessary
to pass it to foo as an argument. You could simply remove df from the def
foo line and also omit the args=(df,) in the call to rolling_apply.
However, there are times when df might not be defined in a scope accessible by foo. In that case, there is a simple workaround -- make a closure:
def foo(df):
def inner_foo(x):
window = df.iloc[x]
# print(window)
c = df.ix[int(x[-1]), 'c']
dvals = window['a'] + window['b']*c
return bar(dvals)
return inner_foo
df['e'] = pd.rolling_apply(np.arange(len(df)), 6, foo(df))
来源：https://stackoverflow.com/questions/28190383/rolling-a-function-on-a-data-frame