回答編集履歴
2
結局追記
test
CHANGED
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@@ -81,3 +81,69 @@
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81
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print((B.T).shape) # ②(492, 2)だが正解は？
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```
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84
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結局は
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---
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91
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92
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かなり推測（エスパー）入っていますが、以下が求めるコードになるかと思われます。
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```Python
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import pandas as pd
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import numpy as np
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100
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102
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dat = np.arange(164*14).reshape(164,-1)
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104
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105
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L5 = ['W-A0', 'W-B0', 'W-F0', 'W-C1', 'W-E1', 'W-A2', 'W-B2', 'W-D2', 'W-F2', 'W-C3', 'W-E3',
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106
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'W-A4', 'W-B4', 'W-F4']
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108
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109
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110
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111
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Ds1 = pd.DataFrame(dat,columns=L5)
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112
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Ds2 = pd.DataFrame(dat,columns=L5)
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115
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print(Ds1.shape,Ds2.shape) # 質問文よりどちらも(164, 14) (164, 14)
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116
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118
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dL = list(range(100, 2100, 200)) # [100,300,500,...]
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120
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#B = np.zeros((0, Ds1.values.shape[0])) # 不要
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122
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for i in range(0,2):
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# Df1とDf2の'W=??'列 同士（＋dL）を足し合わせる shape(164,xx) xx=3,2
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128
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col1 = Ds1.columns.str.contains(r'(?=.*W)(?=.*%s)' % i)
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col2 = Ds2.columns.str.contains(r'(?=.*W)(?=.*%s)' % i)
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A1 = dL[i] + Ds2.loc[:, col2].values + Ds1.loc[:, col1].values
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if i == 0:
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B = A1.copy()
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else:
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B = np.c_[B, A1] # 列方向に足す！
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print(B.shape) # 欲しいshapeは(164, 5)
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```
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1
コード修正
test
CHANGED
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@@ -5,6 +5,8 @@
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参考：[Python: Resize an existing array and fill with zeros](https://stackoverflow.com/questions/9251635/python-resize-an-existing-array-and-fill-with-zeros)
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なお、２点ほど疑問点をコメント中に記載しましたので回答願います。
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```Python
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dat = np.arange(4
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dat = np.arange(164*14).reshape(164,-1)
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L5 = ['W-A0', 'W-B0', 'W-F0', 'W-C1', 'W-E1', 'W-A2', 'W-B2', 'W-D2', 'W-F2', 'W-C3', 'W-E3',
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@@ -25,6 +27,8 @@
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Ds1 = pd.DataFrame(dat,columns=L5)
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Ds2 = pd.DataFrame(dat,columns=L5)
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print(Ds1.shape,Ds2.shape) # 質問文よりどちらも(164, 14) (164, 14)
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@@ -42,7 +46,7 @@
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A1 = dL[i] + Ds2.loc[:, col2].values + Ds1.loc[:, col1].values
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# copy()が必要
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@@ -50,28 +54,30 @@
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# https://stackoverflow.com/questions/23253144/numpy-the-array-doesnt-have-its-own-data
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A
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A1 = A1.reshape(1, -1).copy()
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print(A1.shape) # ①i=0で(1, 492)、i=1で(1, 328)だが正しいか？
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if i == 0:
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B = A
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B = A1.copy()
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else: # 列数を大きい方に合わせる
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if B.shape[1] < A
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if B.shape[1] < A1.shape[1]:
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B.resize((B.shape[0],A
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B.resize((B.shape[0],A1.shape[1]))
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elif A
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elif A1.shape[1] < B.shape[1]:
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A
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A1.resize((1,B.shape[1]))
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B = np.r_[B, A
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B = np.r_[B, A1]
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print(B.T)
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81
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print((B.T).shape) # ②(492, 2)だが正解は？
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76
82
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77
83
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```
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