如何连接下面两个矩阵？

Question

你好我有下面的矩阵叫做tfidf2，这个矩阵的形状是 （111591985）它有11159行和1985列，我想将一个新的矩阵连接到这个矩阵中，名为datesNumpy的矩阵具有（11159，12）的形状，它们具有相同的行数，因此可以连接它，新矩阵tfidf3的形状应该是（111591997）

import numpy as np
tfidf2 = tdf.transform(list_cluster)
print("Shape tfidf2",tfidf2.shape)
listAux=[]
for l in listMonth:
        listAux.append([int(y) for y in l])
datesNumpy=np.array([np.array(xi) for xi in listAux])
print("Shape datesNumpy",datesNumpy.shape)

我试过了：

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不管我得到了什么，我都很感激能帮助我克服这种情况：

Shape tfidf2 (11159, 1985)
Shape datesNumpy (11159, 12)
Traceback (most recent call last):
  File "Main.py", line 235, in <module>
    tfidf3=np.stack((tfidf2, datesNumpy), axis=-1)
  File "/usr/local/lib/python3.5/dist-packages/numpy/core/shape_base.py", line 339, in stack
    raise ValueError('all input arrays must have the same shape')
ValueError: all input arrays must have the same shape

从这里得到反馈后，我尝试：

tfidf3=np.concatenate([tfidf2, datesNumpy], axis=1)

但我得到了：

Traceback (most recent call last):
  File "Main.py", line 235, in <module>
    tfidf3=np.concatenate([tfidf2, datesNumpy], axis=1)
ValueError: zero-dimensional arrays cannot be concatenated

Answer 1

numpy.stack(arrays, axis=0)

Join a sequence of arrays along a new axis.

The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension.

Parameters:

arrays : sequence of array_like Each array must have the same shape.

axis : int, optional The axis in the result array along which the input arrays are stacked.

Returns:

stacked : ndarray The stacked array has one more dimension than the input arrays.

根据文件必须具有相同的形状。在

您必须是concatenate

示例：

tfidf2 = np.zeros((11159, 1985))
datesNumpy = np.ones((11159, 12))

tfidf3=np.concatenate([tfidf2, datesNumpy], axis=1)
print(tfidf3.shape)

输出：

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