パンダが配列を返すためにウィンドウを回転

Question

サンプルコードです。

df = pd.DataFrame(np.random.randn(10, 2), columns=list('AB')) 
df['C'] = df.B.rolling(window=3) 

出力：

  A   B          C 
0 -0.108897 1.877987 Rolling [window=3,center=False,axis=0] 
1 -1.276055 -0.424382 Rolling [window=3,center=False,axis=0] 
2 1.578561 -1.094649 Rolling [window=3,center=False,axis=0] 
3 -0.443294 1.683261 Rolling [window=3,center=False,axis=0] 
4 0.674124 0.281077 Rolling [window=3,center=False,axis=0] 
5 0.587773 0.697557 Rolling [window=3,center=False,axis=0] 
6 -0.258038 -1.230902 Rolling [window=3,center=False,axis=0] 
7 -0.443269 0.647107 Rolling [window=3,center=False,axis=0] 
8 0.347187 0.753585 Rolling [window=3,center=False,axis=0] 
9 -0.369179 0.975155 Rolling [window=3,center=False,axis=0] 

私は 'C' の列が[0.1231、-1.132、0.8766]のような配列になりたいです。 ローリングを適用しようとしましたが無駄でした。

予想される出力：

 A   B     C 
0 -0.108897 1.877987 [] 
1 -1.276055 -0.424382 [] 
2 1.578561 -1.094649 [-1.094649, -0.424382, 1.877987] 
3 -0.443294 1.683261 [1.683261, -1.094649, -0.424382] 
4 0.674124 0.281077 [0.281077, 1.683261, -1.094649] 
5 0.587773 0.697557 [0.697557, 0.281077, 1.683261] 
6 -0.258038 -1.230902 [-1.230902, 0.697557, 0.281077] 
7 -0.443269 0.647107 [0.647107, -1.230902, 0.697557] 
8 0.347187 0.753585 [0.753585, 0.647107, -1.230902] 
9 -0.369179 0.975155 [0.975155, 0.753585, 0.647107] 

Answer 1

あなたが使用することができnp.stride_tricks：

import numpy as np 
as_strided = np.lib.stride_tricks.as_strided 

df 

      A   B 
0 -0.272824 -1.606357 
1 -0.350643 0.000510 
2 0.247222 1.627117 
3 -1.601180 0.550903 
4 0.803039 -1.231291 
5 -0.536713 -0.313384 
6 -0.840931 -0.675352 
7 -0.930186 -0.189356 
8 0.151349 0.522533 
9 -0.046146 0.507406 

win = 3 # window size 

# https://stackoverflow.com/a/47483615/4909087 
v = as_strided(df.B, (len(df) - (win - 1), win), (df.B.values.strides * 2)) 

v 
array([[ -1.60635669e+00, 5.10129842e-04, 1.62711678e+00], 
     [ 5.10129842e-04, 1.62711678e+00, 5.50902812e-01], 
     [ 1.62711678e+00, 5.50902812e-01, -1.23129111e+00], 
     [ 5.50902812e-01, -1.23129111e+00, -3.13383794e-01], 
     [ -1.23129111e+00, -3.13383794e-01, -6.75352179e-01], 
     [ -3.13383794e-01, -6.75352179e-01, -1.89356194e-01], 
     [ -6.75352179e-01, -1.89356194e-01, 5.22532550e-01], 
     [ -1.89356194e-01, 5.22532550e-01, 5.07405549e-01]]) 

df['C'] = pd.Series(v.tolist(), index=df.index[win - 1:]) 
df 

      A   B             C 
0 -0.272824 -1.606357            NaN 
1 -0.350643 0.000510            NaN 
2 0.247222 1.627117 [-1.606356691642917, 0.0005101298424200881, 1.... 
3 -1.601180 0.550903 [0.0005101298424200881, 1.6271167809032248, 0.... 
4 0.803039 -1.231291 [1.6271167809032248, 0.5509028122535129, -1.23... 
5 -0.536713 -0.313384 [0.5509028122535129, -1.2312911105674484, -0.3... 
6 -0.840931 -0.675352 [-1.2312911105674484, -0.3133837943758246, -0.... 
7 -0.930186 -0.189356 [-0.3133837943758246, -0.6753521794378446, -0.... 
8 0.151349 0.522533 [-0.6753521794378446, -0.18935619377656243, 0.... 
9 -0.046146 0.507406 [-0.18935619377656243, 0.52253255045267, 0.507... 

Answer 2

おそらくも

def get_list(x,m) : return list(zip(*(x[i:] for i in range(m)))) 

# get_list(df['B'],3) would return 

[(-1.606357, 0.0005099999999999999, 1.627117), 
(0.0005099999999999999, 1.627117, 0.5509029999999999), 
(1.627117, 0.5509029999999999, -1.231291), 
(0.5509029999999999, -1.231291, -0.313384), 
(-1.231291, -0.313384, -0.6753520000000001), 
(-0.313384, -0.6753520000000001, -0.189356), 
(-0.6753520000000001, -0.189356, 0.522533), 
(-0.189356, 0.522533, 0.507406)] 

df['C'] = pd.Series(get_list(df['B'],3), index=df.index[3 - 1:]) 
# Little help form @coldspeed 

print(df) 

      A   B             C 
0 -0.272824 -1.606357            NaN 
1 -0.350643 0.000510            NaN 
2 0.247222 1.627117  (-1.606357, 0.0005099999999999999, 1.627117) 
3 -1.601180 0.550903 (0.0005099999999999999, 1.627117, 0.5509029999... 
4 0.803039 -1.231291   (1.627117, 0.5509029999999999, -1.231291) 
5 -0.536713 -0.313384   (0.5509029999999999, -1.231291, -0.313384) 
6 -0.840931 -0.675352  (-1.231291, -0.313384, -0.6753520000000001) 
7 -0.930186 -0.189356  (-0.313384, -0.6753520000000001, -0.189356) 
8 0.151349 0.522533   (-0.6753520000000001, -0.189356, 0.522533) 
9 -0.046146 0.507406     (-0.189356, 0.522533, 0.507406)