优化这段代码 x_train_YS, y_train_YS = data.iloc[0:418+20*i,list(range(0))+list(range(8,95))],data.iloc[0:418+20*i,95] x_test_YS, y_test_YS = data.iloc[418+20*i:438+20*i,list(range(0))+list(range(8,95))],data.iloc[418+20*i:438+20*i,95] x_train_YS_1,y_train_YS_1 = x_train_YS.iloc[1:418+20*i,1:87].values,y_train_YS.iloc[1:418+20*i,87].values x_test_YS_1,y_test_YS_1 = x_test_YS.iloc[418+20*i:438+20*i,1:87].values,y_test_YS.iloc[418+20*i:438+20*i,87].values

可以对这段代码进行如下优化：

start = 0
end = 418 + 20 * i
cols = list(range(0)) + list(range(8, 95))
x_train_YS = data.iloc[start:end, cols]
y_train_YS = data.iloc[start:end, 95]

start = 418 + 20 * i
end = 438 + 20 * i
x_test_YS = data.iloc[start:end, cols]
y_test_YS = data.iloc[start:end, 95]

cols_1 = list(range(1, 87))
x_train_YS_1 = x_train_YS.iloc[1:end-1, cols_1].values
y_train_YS_1 = y_train_YS.iloc[1:end-1].values
x_test_YS_1 = x_test_YS.iloc[:, cols_1].values
y_test_YS_1 = y_test_YS.iloc[:, -1].values

这样，我们可以避免重复代码，减少重复计算，提高代码的可读性和可维护性。同时，将计算结果存储在变量中，可以提高代码的执行效率。

相关问题

优化这段代码 x_train_YS, y_train_YS = data.iloc[0:418+20*i,cols],data.iloc[0:418+20*i,95] x_test_YS, y_test_YS = data.iloc[418+20*i:438+20*i,cols],data.iloc[418+20*i:438+20*i,95] print(x_train_YS) x_train_YS_1,y_train_YS_1 = x_train_YS.values[0:418+20*i,1:85],y_train_YS.values[0:418+20*i,85]

可以将代码进行优化，避免多次索引和切片，可以使用数据切分函数train_test_split()来代替手动切分数据。

具体实现如下：

1. 首先需要导入train_test_split函数：from sklearn.model_selection import train_test_split

2. 对数据进行切分：

X = data.iloc[:, cols]
y = data.iloc[:, 95]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, random_state=42)

其中，test_size表示测试数据占比，random_state为随机种子，保证每次运行结果一致。

3. 对训练数据进行切分：

X_train_YS = X_train.iloc[:418+20*i, 1:85]
y_train_YS = y_train.iloc[:418+20*i]

4. 对测试数据进行切分：

X_test_YS = X_test.iloc[418+20*i:438+20*i, 1:85]
y_test_YS = y_test.iloc[418+20*i:438+20*i]

这样就可以得到和原代码相同的训练数据和测试数据了。

优化这段代码for i in range(14): x_train_YS, y_train_YS = data.iloc[0:418+20*i,list(range(0))+list(range(8,95))],data.iloc[0:418+20*i,95] x_test_YS, y_test_YS = data.iloc[418+20*i:438+20*i,list(range(0))+list(range(8,95))],data.iloc[418+20*i:438+20*i,95] x_train_YS_1,y_train_YS_1 = x_train_YS.iloc[1:418+20*i,1:87].values,y_train_YS.iloc[1:418+20*i,87].values x_test_YS_1,y_test_YS_1 = x_test_YS.iloc[418+20*i:438+20*i,1:87].values,y_test_YS.iloc[418+20*i:438+20*i,87].values

可以对这段代码进行如下优化：

start = 0
end = 418
cols = list(range(0)) + list(range(8, 95))
cols_1 = list(range(1, 87))
train_data = []
test_data = []

for i in range(14):
    x_train_YS, y_train_YS = data.iloc[start:end, cols], data.iloc[start:end, 95]
    x_test_YS, y_test_YS = data.iloc[start+418:end+438, cols], data.iloc[start+418:end+438, 95]
    x_train_YS_1, y_train_YS_1 = x_train_YS.iloc[1:end-1, cols_1].values, y_train_YS.iloc[1:end-1].values
    x_test_YS_1, y_test_YS_1 = x_test_YS.iloc[:, cols_1].values, y_test_YS.iloc[:, -1].values
    train_data.append((x_train_YS_1, y_train_YS_1))
    test_data.append((x_test_YS_1, y_test_YS_1))
    start = end
    end += 20 * (i + 2)

这样，我们可以将数据处理部分的代码放入循环中，避免了重复代码，同时将结果存储在列表中，便于后续处理。