在 dataframe 上使用序列设置数组元素的工作和解决值错误

Question

这个问题已经在几个地方讨论过，包括这里、这里和这里。 然而，作为经典，我正在努力实现对我的代码提出的想法。

我收到错误消息：

• ValueError：使用序列设置数组元素。

这意味着如果我们尝试将某些内容写入数组的单个位置（数组单元格，矩阵条目）并且该内容不是标量值，则会发生使用序列设置数组元素的错误。

我试图发送给我的预测 model 的数据类型是：

(14, 34)
<class 'pandas.core.frame.DataFrame'>

在我“切片”我的数据之后，这就是我得到的：

这是我的代码：

print("-----------------")
result = result.reset_index(drop=True) # Reindex a dataframe with duplicate index values
print(result)
print("-----------------")
print(result.shape)
print(type(result))


##############################################
####### Block for the prediction model #######
##############################################

# Which fiels do I have the features and which I have the prediciton field?    
#Slicing the data
array = result.values
numcols = len(array[0]) # total number of column
lastcolumn=numcols-1
X = array[:,0:lastcolumn] # recieves the data of the channels (all columns besides last)
Y = array[:,lastcolumn] # recives the data of the class (last column)

#print(X)
print(Y)

# prepare configuration for cross validation test harness
seed = 7

# prepare models
models = []
models.append(('Linear Discriminant Analysis', LinearDiscriminantAnalysis()))

# evaluate each model in turn
results = []
names = []
scoring = 'accuracy'

for name, model in models:
    # k=10: The value for k is fixed to 10, a value that has been found through experimentation to generally result in a model skill estimate with low bias a modest variance.
    kfold = model_selection.KFold(n_splits=10, random_state=seed) 
    cv_results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
    results.append(cv_results)
    names.append(name)
    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())        
    print(msg)