Pandas按列表累积和分组- lstm准备

import pandas as pd

# 创建数据表
data = {'姓名': ['张三', '李四', '王五', '张三', '李四', '王五'],
        '科目': ['语文', '语文', '语文', '数学', '数学', '数学'],
        '成绩': [80, 90, 85, 70, 75, 95]}
df = pd.DataFrame(data)

# 按科目分组并计算每个分组的累积和
result = df.groupby('科目')['成绩'].cumsum()

print(result)

0     80
1    170
2    255
3     70
4    145
5    240
Name: 成绩, dtype: int64

import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# 准备数据
data = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
df = pd.DataFrame(data, columns=['数值'])

# 归一化数据
scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(df)

# 切分序列数据
window_size = 3
X = []
y = []
for i in range(len(scaled_data) - window_size):
    X.append(scaled_data[i:i+window_size])
    y.append(scaled_data[i+window_size])
X = np.array(X)
y = np.array(y)

# 构建LSTM模型
model = Sequential()
model.add(LSTM(64, activation='relu', input_shape=(window_size, 1)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')

# 训练模型
model.fit(X, y, epochs=100, batch_size=1)

# 预测结果
prediction = model.predict(X)