pandas 在 DataFrame 对象上使用滚动应用
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Using rolling_apply on a DataFrame object
提问by nitin
I am trying to calculate Volume Weighted Average Price on a rolling basis.
我正在尝试以滚动方式计算成交量加权平均价格。
To do this, I have a function vwap that does this for me, like so:
为此，我有一个函数 vwap 为我执行此操作，如下所示：
def vwap(bars):
return ((bars.Close*bars.Volume).sum()/bars.Volume.sum()).round(2)
When I try to use this function with rolling_apply, as shown, I get an error:
当我尝试将此函数与 rolling_apply 一起使用时，如图所示，出现错误：
import pandas.io.data as web
bars = web.DataReader('AAPL','yahoo')
print pandas.rolling_apply(bars,30,vwap)
AttributeError: 'numpy.ndarray' object has no attribute 'Close'
The error makes sense to me because the rolling_apply requires not DataSeries or a ndarray as an input and not a dataFrame.. the way I am doing it.
这个错误对我来说很有意义，因为 rolling_apply 不需要 DataSeries 或 ndarray 作为输入，而不是 dataFrame ......我这样做的方式。
Is there a way to use rolling_apply to a DataFrame to solve my problem?
有没有办法将rolling_apply 用于DataFrame 来解决我的问题？
采纳答案by Jeff
This is not directly enabled, but you can do it like this
这不是直接启用的，但你可以这样做
In [29]: bars
Out[29]:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 942 entries, 2010-01-04 00:00:00 to 2013-09-30 00:00:00
Data columns (total 6 columns):
Open 942 non-null values
High 942 non-null values
Low 942 non-null values
Close 942 non-null values
Volume 942 non-null values
Adj Close 942 non-null values
dtypes: float64(5), int64(1)
window=30
In [30]: concat([ (Series(vwap(bars.iloc[i:i+window]),
index=[bars.index[i+window]])) for i in xrange(len(df)-window) ])
Out[30]:
2010-02-17 203.21
2010-02-18 202.95
2010-02-19 202.64
2010-02-22 202.41
2010-02-23 202.19
2010-02-24 201.85
2010-02-25 201.65
2010-02-26 201.50
2010-03-01 201.31
2010-03-02 201.35
2010-03-03 201.42
2010-03-04 201.09
2010-03-05 200.95
2010-03-08 201.50
2010-03-09 202.02
...
2013-09-10 485.94
2013-09-11 487.38
2013-09-12 486.77
2013-09-13 487.23
2013-09-16 487.20
2013-09-17 486.09
2013-09-18 485.52
2013-09-19 485.30
2013-09-20 485.37
2013-09-23 484.87
2013-09-24 485.81
2013-09-25 486.41
2013-09-26 486.07
2013-09-27 485.30
2013-09-30 484.74
Length: 912
回答by mathtick
A cleaned up version for reference, hopefully got the indexing correct:
供参考的清理版本，希望索引正确：
def myrolling_apply(df, N, f, nn=1):
ii = [int(x) for x in arange(0, df.shape[0] - N + 1, nn)]
out = [f(df.iloc[i:(i + N)]) for i in ii]
out = pandas.Series(out)
out.index = df.index[N-1::nn]
return(out)
回答by citynorman
Modified @mathtick's answer to include na_fill. Also note that your function fneeds to return a single value, this can't return a dataframe with multiple columns.
修改了@mathtick 的答案以包含na_fill. 另请注意，您的函数f需要返回单个值，这不能返回具有多列的数据框。
def rolling_apply_df(dfg, N, f, nn=1, na_fill=True):
ii = [int(x) for x in np.arange(0, dfg.shape[0] - N + 1, nn)]
out = [f(dfg.iloc[i:(i + N)]) for i in ii]
if(na_fill):
out = pd.Series(np.concatenate([np.repeat(np.nan, N-1),np.array(out)]))
out.index = dfg.index[::nn]
else:
out = pd.Series(out)
out.index = dfg.index[N-1::nn]
return(out)
