title: np.asarray() gives me one column array where data was multi column
body: <pre><code>print(X_train_bow.shape) #Output: (897, 2794)
  print(type(X_train_bow)) #Output: &lt;class 'scipy.sparse.csr.csr_matrix'&gt;
  
  x_train_groups = [X_train_bow[i::5] for i in range(5)]
  
  print(x_train_groups[0].shape) #Output: (299, 2794)
  print(type(X_train_bow[0])) #Output: &lt;class 'scipy.sparse.csr.csr_matrix'&gt;
  
  K = 2
  train_data = []
  test_data = []
  
  for j in range(0, 5):
      if(j != K):
          train_data.extend(x_train_groups[j]) 
  test_data.extend(x_train_groups[K])   
  
  print(np.asarray(train_data).shape) #Output: (598,)
  print(np.asarray(test_data).shape) #Output: (299,)
  </code></pre>
  
  <p>I'm trying k-fold cross-validation. 
  So I have created a method that merges train and test data.
  But the problem is that as when I called np.asarray, it returns different shape array that original data shape.
  You can see the code. I have also printed output for help.</p>