sklearn MinMaxScaler - ValueError：预期的二维数组

Question

我想在分析之前使用MinMaxScalerfrom来缩放测试和训练数据。sklearn

我一直在学习教程 ( https://mc.ai/an-introduction-on-time-series-forecasting-with-simple-neura-networks-lstm/ )，但我收到一条错误消息ValueError: Expected 2D array, got 1D array instead。

我尝试查看Print predict ValueError: Expected 2D array, got 1D array instead，但如果我尝试train = train.reshape(-1, 1)或test = test.reshape(-1, 1)因为它们是系列，我会收到一条错误消息（错误消息AttributeError: 'Series' object has no attribute 'reshape'）

我该如何最好地解决这个问题？

# Import libraries 

import pandas as pd 

from sklearn.preprocessing import MinMaxScaler 

# Create MWE dataset 

data = [['1981-11-03', 510], ['1982-11-03', 540], ['1983-11-03', 480],

   ['1984-11-03', 490], ['1985-11-03', 492], ['1986-11-03', 380],

   ['1987-11-03', 440], ['1988-11-03', 640], ['1989-11-03', 560], 

   ['1990-11-03', 660], ['1991-11-03', 610], ['1992-11-03', 480]] 

df = pd.DataFrame(data, columns = ['Date', 'Tickets']) 

# Set 'Date' to datetime data type 

df['Date'] = pd.to_datetime(df['Date'])

# Set 'Date to index   

df = df.set_index(['Date'], drop=True)

# Split dataset into train and test  

split_date = pd.Timestamp('1989-11-03')

df =  df['Tickets']

train = df.loc[:split_date]

test = df.loc[split_date:]

# Scale train and test data 

scaler = MinMaxScaler(feature_range=(-1, 1))

train_sc = scaler.fit_transform(train)

test_sc = scaler.transform(test)

X_train = train_sc[:-1]

y_train = train_sc[1:]

X_test = test_sc[:-1]

y_test = test_sc[1:]

# ERROR MESSAGE 

  ValueError: Expected 2D array, got 1D array instead:

  array=[510. 540. 480. 490. 492. 380. 440. 640. 560.].

  Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.