python 划分数据集为训练集和测试集的方法

from sklearn.cross_validation import train_test_split
#x为数据集的feature熟悉，y为label.
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3)

from sklearn.cross_validation import train_test_split
#dat为数据集,含有feature和label.
train, test = train_test_split(dat, test_size = 0.3)

#X:含label的数据集：分割成训练集和测试集
#test_size:测试集占整个数据集的比例
def trainTestSplit(X,test_size=0.3):
 X_num=X.shape[0]
 train_index=range(X_num)
 test_index=[]
 test_num=int(X_num*test_size)
 for i in range(test_num):
  randomIndex=int(np.random.uniform(0,len(train_index)))
  test_index.append(train_index[randomIndex])
  del train_index[randomIndex]
 #train,test的index是抽取的数据集X的序号
 train=X.ix[train_index] 
 test=X.ix[test_index]
 return train,test