Pandasの要素をラベルリストに対応させて抽出

x_df
         0        1        2        3        4        5        6        7  
0 -7608.00 -7608.00 -7608.00 -7608.00 -7608.00 -7608.00 -7608.00 -7608.00   
1 -7502.61 -7502.61 -7502.61 -7502.61 -7502.61 -7502.61 -7502.61 -7502.61   
2 -7264.54 -7264.54 -7264.54 -7264.54 -7264.54 -7264.54 -7264.54 -7264.54   
3 -7322.23 -7322.23 -7322.23 -7322.23 -7322.23 -7322.23 -7322.23 -7322.23   
4 -7606.57 -7606.57 -7606.57 -7606.57 -7606.57 -7606.57 -7606.57 -7606.57   
5 -7552.79 -7552.79 -7552.79 -7552.79 -7552.79 -7552.79 -7552.79 -7552.79   
6 -7313.62 -7313.62 -7313.62 -7313.62 -7313.62 -7313.62 -7313.62 -7313.62   
7 -7288.97 -7288.97 -7288.97 -7288.97 -7288.97 -7288.97 -7288.97 -7288.97   
8 -7544.70 -7544.70 -7544.70 -7544.70 -7544.70 -7544.70 -7544.70 -7544.70

list0 = [2, 2, 0, 1, 2, 1, 1, 0]

以上のようなデータフレームからlist0のラベルに対応させて列を抽出したいと考えています。

例）０に対応する列は２と７の列なので、

　　　2      　 7  
0 -7608.00 -7608.00   
1 -7502.61 -7502.61   
2 -7264.54 -7264.54   
3 -7322.23 -7322.23   
4 -7606.57 -7606.57   
5 -7552.79 -7552.79   
6 -7313.62 -7313.62   
7 -7288.97 -7288.97   
8 -7544.70 -7544.70

以上のように列を抽出したいと考えています。

Python
1list0 = [2, 2, 0, 1, 2, 1, 1, 0]
2
3for i in range(10):
4    x = gov_df[0].ix[1:10,0]
5    x = np.array(x)
6    x_df[i] = x
7
8print(x_df)
9
10#以下だと行の抽出になってしまいます
11x_df['label'] = list0
12
13a = x_df[x_df01['label'] == 0] 
14a = x_df[x_df01['label'] == 1] 
15c = x_df[x_df01['label'] == 2]

よろしくお願いいたします

行動規範の内容に同意します

回答2件

こうゆうことですか？

x_df.loc[:,pd.Series(list0) == 0]

以下サンプル

Python
1import pandas as pd
2
3x_df = pd.DataFrame(
4  [[-7608.00,-7608.00,-7608.00,-7608.00,-7608.00,-7608.00,-7608.00,-7608.00],
5   [-7502.61,-7502.61,-7502.61,-7502.61,-7502.61,-7502.61,-7502.61,-7502.61],
6   [-7264.54,-7264.54,-7264.54,-7264.54,-7264.54,-7264.54,-7264.54,-7264.54],
7   [-7322.23,-7322.23,-7322.23,-7322.23,-7322.23,-7322.23,-7322.23,-7322.23],
8   [-7606.57,-7606.57,-7606.57,-7606.57,-7606.57,-7606.57,-7606.57,-7606.57],
9   [-7552.79,-7552.79,-7552.79,-7552.79,-7552.79,-7552.79,-7552.79,-7552.79],
10   [-7313.62,-7313.62,-7313.62,-7313.62,-7313.62,-7313.62,-7313.62,-7313.62],
11   [-7288.97,-7288.97,-7288.97,-7288.97,-7288.97,-7288.97,-7288.97,-7288.97],
12   [-7544.70,-7544.70,-7544.70,-7544.70,-7544.70,-7544.70,-7544.70,-7544.70]])
13list0 = [2, 2, 0, 1, 2, 1, 1, 0]
14
15
16print(x_df.loc[:,pd.Series(list0) == 0])
17#         2        7
18#0 -7608.00 -7608.00
19#1 -7502.61 -7502.61
20#2 -7264.54 -7264.54
21#3 -7322.23 -7322.23
22#4 -7606.57 -7606.57
23#5 -7552.79 -7552.79
24#6 -7313.62 -7313.62
25#7 -7288.97 -7288.97
26#8 -7544.70 -7544.70

投稿2018/05/16 04:20

magichan

総合スコア15898

ベストアンサー

numpy使ったらできましたが、pandas的に最善手かどうかはよくわかりません。もっとうまい書き方があるかも。

python
1import numpy as np
2import pandas as pd
3
4df = pd.DataFrame([
5[-7608.00, -7608.00, -7608.00, -7608.00, -7608.00, -7608.00, -7608.00, -7608.00],   
6[-7502.61, -7502.61, -7502.61, -7502.61, -7502.61, -7502.61, -7502.61, -7502.61],   
7[-7264.54, -7264.54, -7264.54, -7264.54, -7264.54, -7264.54, -7264.54, -7264.54],   
8[-7322.23, -7322.23, -7322.23, -7322.23, -7322.23, -7322.23, -7322.23, -7322.23],   
9[-7606.57, -7606.57, -7606.57, -7606.57, -7606.57, -7606.57, -7606.57, -7606.57],   
10[-7552.79, -7552.79, -7552.79, -7552.79, -7552.79, -7552.79, -7552.79, -7552.79],   
11[-7313.62, -7313.62, -7313.62, -7313.62, -7313.62, -7313.62, -7313.62, -7313.62],  
12[-7288.97, -7288.97, -7288.97, -7288.97, -7288.97, -7288.97, -7288.97, -7288.97],   
13[-7544.70, -7544.70, -7544.70, -7544.70, -7544.70, -7544.70, -7544.70, -7544.70]]) 
14
15print(df)
16list0 = np.array([2, 2, 0, 1, 2, 1, 1, 0])
17index = np.arange(len(list0))
18print(df[index[list0 == 0]])
19
20""" =>
21         0        1        2        3        4        5        6        7
220 -7608.00 -7608.00 -7608.00 -7608.00 -7608.00 -7608.00 -7608.00 -7608.00
231 -7502.61 -7502.61 -7502.61 -7502.61 -7502.61 -7502.61 -7502.61 -7502.61
242 -7264.54 -7264.54 -7264.54 -7264.54 -7264.54 -7264.54 -7264.54 -7264.54
253 -7322.23 -7322.23 -7322.23 -7322.23 -7322.23 -7322.23 -7322.23 -7322.23
264 -7606.57 -7606.57 -7606.57 -7606.57 -7606.57 -7606.57 -7606.57 -7606.57
275 -7552.79 -7552.79 -7552.79 -7552.79 -7552.79 -7552.79 -7552.79 -7552.79
286 -7313.62 -7313.62 -7313.62 -7313.62 -7313.62 -7313.62 -7313.62 -7313.62
297 -7288.97 -7288.97 -7288.97 -7288.97 -7288.97 -7288.97 -7288.97 -7288.97
308 -7544.70 -7544.70 -7544.70 -7544.70 -7544.70 -7544.70 -7544.70 -7544.70
31         2        7
320 -7608.00 -7608.00
331 -7502.61 -7502.61
342 -7264.54 -7264.54
353 -7322.23 -7322.23
364 -7606.57 -7606.57
375 -7552.79 -7552.79
386 -7313.62 -7313.62
397 -7288.97 -7288.97
408 -7544.70 -7544.70
41"""
42