如何在1D阵列上使用SimpleImputer？

Question

我的因变量列中有一个具有多个NaN值的数据集。我已将该集拆分为因变量和自变量，目前正尝试将因变量列中的所有NaN值替换为0。但是，我在使用SimpleImputer进行此操作时遇到了一个错误

这是我的密码：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

"""
----- Read Dataset and Split into Dependent and Independent Variables: -----
"""
dataset = pd.read_csv('Salary_Data.csv')
x = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values

print("\nIndependent Variables: \n%s" % x)
print("\nDependent Variables: \n%s" % y)


"""
----- Fill in Missing Values: -----
"""
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values = np.nan, strategy = 'constant', fill_value = 0)
y = imputer.fit(y)

print("\nDependent Variables After Missing Values Adjusted: \n%s" % y)

下面是我得到的错误：

Expected 2D array, got 1D array instead:
array=[270000. 200000. 250000.     nan 425000.     nan     nan 252000. 231000.
     nan 260000. 250000.     nan 218000.     nan 200000. 300000.     nan

Answer 1

您可以使用pandas.DataFrame.fillna函数在所有位置直接填充0

y.fillna(0, inplace=True)