python - 从 pandas Python 和 Numpy 将 3D 数组转换为 2d 数组

Question

我使用 pandas 和 numpy 从 csv 创建了数组。这是我将 2D csv 转换为 3D 数组的代码:

>>> import pandas as pd
>>> import numpy as npp
>>> df = pd.read_csv("test.csv")
>>> df_mat = df.values
>>> seq_len = 3
>>> data=[]
>>> for index in range(len(df_mat) - seq_len):
...     data.append(df_mat[index: index + seq_len])
...
>>> data = np.array(data)
>>> data.shape
(4, 3, 9)

使用的csv是:

input1,input2,input3,input4,input5,input6,input7,input8,output
1,2,3,4,5,6,7,8,1
2,3,4,5,6,7,8,9,0
3,4,5,6,7,8,9,10,-1
4,5,6,7,8,9,10,11,-1
5,6,7,8,9,10,11,12,1
6,7,8,9,10,11,12,13,0
7,8,9,10,11,12,13,14,1

现在我想将 3D 数组恢复为 2D 数组格式。
请让我知道我该怎么做。没有得到任何线索。

Answer 1

沿着每个 block 对第0行进行切片，直到最后一个 block 并与最后一个 block 堆叠 -

np.vstack((data[np.arange(data.shape[0]-1),0],data[-1]))

给定样本数据的输出 -

In [24]: np.vstack((data[np.arange(data.shape[0]-1),0],data[-1]))
Out[24]: 
array([[ 1,  2,  3,  4,  5,  6,  7,  8,  1],
       [ 2,  3,  4,  5,  6,  7,  8,  9,  0],
       [ 3,  4,  5,  6,  7,  8,  9, 10, -1],
       [ 4,  5,  6,  7,  8,  9, 10, 11, -1],
       [ 5,  6,  7,  8,  9, 10, 11, 12,  1],
       [ 6,  7,  8,  9, 10, 11, 12, 13,  0],
       [ 7,  8,  9, 10, 11, 12, 13, 14,  1]], dtype=int64)

或在所有 block 中切片第 0 行并堆叠，最后一个 block 跳过第一行 -

In [28]: np.vstack((data[np.arange(data.shape[0]),0],data[-1,1:]))
Out[28]: 
array([[ 1,  2,  3,  4,  5,  6,  7,  8,  1],
       [ 2,  3,  4,  5,  6,  7,  8,  9,  0],
       [ 3,  4,  5,  6,  7,  8,  9, 10, -1],
       [ 4,  5,  6,  7,  8,  9, 10, 11, -1],
       [ 5,  6,  7,  8,  9, 10, 11, 12,  1],
       [ 6,  7,  8,  9, 10, 11, 12, 13,  0],
       [ 7,  8,  9, 10, 11, 12, 13, 14,  1]], dtype=int64)