python操作resultym.csv数据表（有Date（YYYY/MM）、TotalPrice两列数据），数据表第一行为表头信息，数据表中前27行都有数据，以此为基础，python调用resultym.csv表进行操作：循环调用以resultym.csv为数据集构建的pytorch lstm预测模型（模型实现过程：读取csv表，然后将TotalPrice归一化，接着按照0.8划分训练集和测试集，lookback=4，然后将划分好的数据转为PyTorch张量，之后定义超参数和算法模型、优化器，最后训练模型），该模型能够根据Date值来预测TotalPrice值，然后将第一次预测出的y_test_pred赋值给B26、将第二次预测出的值赋给B27、将第三次预测出的值赋给B28，一直循环直到求出B50的数值。每预测出一个值就在表的最后一行插入一组数据，插入的数据为：Date插入的值按照前面的年月往下延(即按照2023/03、2023/04、2023/05········2025/01的顺序)，TotalPrice插入的值定义为2222222.5。直到求出第50行的数值，脚本停止运行。

以下是大致的代码实现思路，需要根据具体数据集和模型进行调整：

import pandas as pd
import numpy as np
import torch
import torch.nn as nn
from sklearn.preprocessing import MinMaxScaler

# 读取数据集
data = pd.read_csv("resultym.csv")
data['Date'] = pd.to_datetime(data['Date'], format='%Y/%m')
data.set_index('Date', inplace=True)

# 数据归一化
scaler = MinMaxScaler(feature_range=(0, 1))
data_scaled = scaler.fit_transform(data)

# 划分训练集和测试集
train_size = int(len(data_scaled) * 0.8)
test_size = len(data_scaled) - train_size
train_data = data_scaled[0:train_size, :]
test_data = data_scaled[train_size:len(data_scaled), :]

# 将数据转为 PyTorch 张量
def create_dataset(dataset, look_back=4):
    X, y = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), :]
        X.append(a)
        y.append(dataset[i + look_back, 1])
    return torch.tensor(X), torch.tensor(y)

look_back = 4
X_train, y_train = create_dataset(train_data, look_back)
X_test, y_test = create_dataset(test_data, look_back)

# 定义超参数和模型
input_dim = X_train.shape[-1]
hidden_dim = 32
num_layers = 1
output_dim = 1
num_epochs = 100
learning_rate = 0.01

class LSTM(nn.Module):
    def __init__(self, input_dim, hidden_dim, num_layers, output_dim):
        super(LSTM, self).__init__()
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)
        
    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()
        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
        out = self.fc(out[:, -1, :])
        return out

model = LSTM(input_dim, hidden_dim, num_layers, output_dim)

# 定义优化器和损失函数
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    outputs = model(X_train.float())
    optimizer.zero_grad()
    loss = criterion(outputs.squeeze(), y_train.float())
    loss.backward()
    optimizer.step()
    if epoch % 10 == 0:
        print("Epoch: %d, loss: %1.5f" % (epoch, loss.item()))

# 预测测试集数据
model.eval()
X_test = X_test.float()
y_test_pred = model(X_test).squeeze().detach().numpy()
y_test_pred = scaler.inverse_transform(np.concatenate((np.zeros((look_back, 1)), y_test_pred.reshape(-1, 1))))

# 将预测结果插入数据表
for i in range(len(y_test_pred)):
    date = pd.to_datetime(data.index[train_size+i+look_back], format='%Y-%m-%d')
    data.loc[date] = [2222222.5] * len(data.columns)
    data.iloc[-1, 1] = y_test_pred[i, 0]
    if i == 23:
        data.to_csv("resultym_predicted.csv", index=True) # 保存预测结果
    if i == 46:
        break

注意，由于我没有完整的数据集和模型，以上代码可能还需要进行一些修改和调整。