python代码实现将数据集划分为九份训练集和一份测试集并且一次完成之后将从训练集中选取一个未被选作测试集的和测试集交换

可以使用sklearn库中的train_test_split函数来将数据集划分为训练集和测试集。代码如下：

from sklearn.model_selection import train_test_split

# X为特征矩阵，y为标签
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)

# 将训练集划分为九份训练集和一份测试集
X_train, X_test_swap, y_train, y_test_swap = train_test_split(X_train, y_train, test_size=0.1)

# 从训练集中选取一个未被选作测试集的和测试集交换
X_test, X_train_swap, y_test, y_train_swap = train_test_split(X_train_swap, y_train_swap, test_size=0.1)

# 最终的训练集、测试集和交换后的训练集、测试集
print('Train set:', X_train.shape, y_train.shape)
print('Test set:', X_test.shape, y_test.shape)
print('Train set after swap:', X_train_swap.shape, y_train_swap.shape)
print('Test set after swap:', X_test_swap.shape, y_test_swap.shape)

注意，这里的test_size参数表示测试集所占比例，可以根据实际情况进行调整。