<p>试着把你的代码改成这个</p>
<pre><code>import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Import Dataset
dataset = pd.read_csv('Data2.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 5].values
df_X = pd.DataFrame(X)
df_y = pd.DataFrame(y)
# Replace Missing Values
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
imputer = imputer.fit(X[:, 3:5 ])
X[:, 3:5] = imputer.transform(X[:, 3:5])
# Encoding Categorical Data "Name"
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_x = LabelEncoder()
X[:, 0] = labelencoder_x.fit_transform(X[:, 0])
# Transform into a Matrix
onehotencoder1 = OneHotEncoder(categorical_features = [0])
res_0 = onehotencoder1.fit_transform(X[:, 0].reshape(-1, 1)) # <=== Change
X[:, 0] = res_0.ravel()
# Encoding Categorical Data "University"
from sklearn.preprocessing import LabelEncoder
labelencoder_x1 = LabelEncoder()
X[:, 1] = labelencoder_x1.fit_transform(X[:, 1])
</code></pre>
<p>如果您在<code>labelencoder_x1.fit_transform(X[:, 1])</code>处遇到错误,请将其设为<code>labelencoder_x1.fit_transform(X[:, 1].reshape(-1, 1))</code></p>