sklearn MinMaxScaler - ValueError: Expected 2D array, got 1D array instead - data as series objects答案

【问题标题】：sklearn MinMaxScaler - ValueError: Expected 2D array, got 1D array instead - data as series objectssklearn MinMaxScaler - ValueError: Expected 2D array, got 1D array instead - data as series objects
【发布时间】：2020-11-22 01:20:51
【问题描述】：

我想在分析之前使用sklearn 中的MinMaxScaler 来扩展测试和训练数据。

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

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

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.

【问题讨论】：

标签： python machine-learning scikit-learn data-science

【解决方案1】：

线

df =  df['Tickets']

将您的数据转换为 pd.Series。

如果你想获得一个数据框，你可以使用

df =  df[['Tickets']]

这应该可以解决您的问题；数据帧可以直接输入到 scaler fit 函数中，无需重新整形。

【讨论】：