如何使用多列参数调用pandas.rolling.apply？

Question

我有一个数据集:

    Open     High      Low    Close        
0  132.960  133.340  132.940  133.105
1  133.110  133.255  132.710  132.755
2  132.755  132.985  132.640  132.735 
3  132.730  132.790  132.575  132.685
4  132.685  132.785  132.625  132.755

我尝试对所有行使用rolling.apply函数,如下所示:

df['new_col']= df[['Open']].rolling(2).apply(AccumulativeSwingIndex(df['High'],df['Low'],df['Close']))

• 显示错误

要么

df['new_col']=  df[['Open', 'High', 'Low', 'Close']].rolling(2).apply(AccumulativeSwingIndex)

• 仅传递"打开"列中的参数

有谁能够帮我？

Answer 1

请参阅/sf/answers/2624424561/

定义我们自己的 roll

from numpy.lib.stride_tricks import as_strided as stride
import pandas as pd

def roll(df, w, **kwargs):
    v = df.values
    d0, d1 = v.shape
    s0, s1 = v.strides

    a = stride(v, (d0 - (w - 1), w, d1), (s0, s0, s1))

    rolled_df = pd.concat({
        row: pd.DataFrame(values, columns=df.columns)
        for row, values in zip(df.index, a)
    })

    return rolled_df.groupby(level=0, **kwargs)

roll(df, 2).mean()

       Open      High       Low    Close
0  133.0350  133.2975  132.8250  132.930
1  132.9325  133.1200  132.6750  132.745
2  132.7425  132.8875  132.6075  132.710
3  132.7075  132.7875  132.6000  132.720

你应该能够:

df.pipe(roll, w=2).mean()

运用 w

def roll(df, w, **kwargs):
    roll_array = np.dstack([df.values[i:i+w, :] for i in range(len(df.index) - w + 1)]).T
    panel = pd.Panel(roll_array, 
                     items=df.index[w-1:],
                     major_axis=df.columns,
                     minor_axis=pd.Index(range(w), name='roll'))
    return panel.to_frame().unstack().T.groupby(level=0, **kwargs)

roll(df, 2).apply(your_function)