在数据框上滚动一个函数 [英] Rolling a function on a data frame
问题描述
我有以下数据框架 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
我想在固定窗口（6这里）添加一个新列， d ，我在哪里应用滚动功能，我不知何故行（或日期），修复值 c 。这个滚动函数中的一个循环应该是（伪）：
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
在这个循环之后，我想在 d 中获取所有这六个计算的行，并运行一个函数调用，而这又返回一个值应该存储在另一列中， e 说：
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
然后将该过程迭代到下一个窗口（再6长）喜欢：
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
希望这个很清楚，
谢谢，
N
Thanks, N
推荐答案
您可以使用 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)
产生
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
code> args 和 kwargs 参数添加到 rolling_apply
由于在我上面的例子中， df 是一个全局变量，它不是真的必要的
将其传递给 foo 作为论据。您可以从 def 行中删除
foo df ，并省略在。 rolling_apply 的调用中，args =（df，）
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.
然而，在 df 可能没有在 foo 可访问的范围内定义的时候。在这种情况下，有一个简单的解决方法 - 关闭：
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))
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