滑动窗口迭代器使用大 pandas 滚动 [英] Sliding window iterator using rolling in pandas

问题描述

如果是单行，我可以得到如下的迭代器：

If it's single row, I can get the iterator as following

import pandas as pd
import numpy as np

a = np.zeros((100,40))
X = pd.DataFrame(a)

for index, row in X.iterrows():
    print index
    print row

现在我希望每个迭代器都会返回子集X [0：9，...]，X [5:14，...]，X [10:19，...]等如何通过滚动（pandas.DataFrame.rolling）实现这一点？

Now I want each iterator will return a subset X[0:9, :], X[5:14, :], X[10:19, :] etc. How do I achieve this with rolling (pandas.DataFrame.rolling)?

推荐答案

我将尝试以下数据框。

import pandas as pd
import numpy as np
from string import uppercase

def generic_portfolio_df(start, end, freq, num_port, num_sec, seed=314):
    np.random.seed(seed)
    portfolios = pd.Index(['Portfolio {}'.format(i) for i in uppercase[:num_port]],
                          name='Portfolio')
    securities = ['s{:02d}'.format(i) for i in range(num_sec)]
    dates = pd.date_range(start, end, freq=freq)
    return pd.DataFrame(np.random.rand(len(dates) * num_sec, num_port),
                        index=pd.MultiIndex.from_product([dates, securities],
                                                         names=['Date', 'Id']),
                        columns=portfolios
                       ).groupby(level=0).apply(lambda x: x / x.sum())    


df = generic_portfolio_df('2014-12-31', '2015-05-30', 'BM', 3, 5)

df.head(10)

我现在将介绍一个函数来滚动多行，并连接到一个数据框中，在列索引中添加一个顶层，指示卷中的位置。

I'll now introduce a function to roll a number of rows and concatenate into a single dataframe where I'll add a top level to the column index that indicates the location in the roll.

def rolled(df, n):
    k = range(df.columns.nlevels)
    _k = [i - len(k) for i in k]
    myroll = pd.concat([df.shift(i).stack(level=k) for i in range(n)],
                       axis=1, keys=range(n)).unstack(level=_k)
    return [(i, row.unstack(0)) for i, row in myroll.iterrows()]

虽然它隐藏在函数中， myroll 像这样

Though its hidden in the function, myroll would look like this

现在我们可以使用它像一个迭代器。

Now we can use it just like an iterator.

for i, roll in rolled(df.head(5), 3):
    print roll
    print

                    0   1   2
Portfolio                    
Portfolio A  0.326164 NaN NaN
Portfolio B  0.201597 NaN NaN
Portfolio C  0.085340 NaN NaN

                    0         1   2
Portfolio                          
Portfolio A  0.278614  0.326164 NaN
Portfolio B  0.314448  0.201597 NaN
Portfolio C  0.266392  0.085340 NaN

                    0         1         2
Portfolio                                
Portfolio A  0.258958  0.278614  0.326164
Portfolio B  0.089224  0.314448  0.201597
Portfolio C  0.293570  0.266392  0.085340

                    0         1         2
Portfolio                                
Portfolio A  0.092760  0.258958  0.278614
Portfolio B  0.262511  0.089224  0.314448
Portfolio C  0.084208  0.293570  0.266392

                    0         1         2
Portfolio                                
Portfolio A  0.043503  0.092760  0.258958
Portfolio B  0.132221  0.262511  0.089224
Portfolio C  0.270490  0.084208  0.293570

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