我应该如何标准化四列进行聚类?两列包含 (0, 1, 2) 等值,而另外两列包含“1”等普通值。我尝试使用 StandardScaler,但遇到错误。我应该考虑哪些替代方案或调整?
处理包含不同类型数据(例如数值和分类值)的列时,标准化方法可能会有所不同。以下是处理不同类型数据的一些一般准则:
标准定标器:
from sklearn.preprocessing import StandardScaler
import pandas as pd
# Assuming df is your DataFrame
numerical_columns = ['numerical_col1', 'numerical_col2']
scaler = StandardScaler()
df[numerical_columns] = scaler.fit_transform(df[numerical_columns])
标签编码:
LabelEncoder
可以用于此目的。from sklearn.preprocessing import LabelEncoder
# Assuming df is your DataFrame
categorical_columns = ['cat_col1', 'cat_col2']
label_encoder = LabelEncoder()
df[categorical_columns] = df[categorical_columns].apply(label_encoder.fit_transform)
单独标准化:
# Assuming df is your DataFrame
numerical_columns = ['numerical_col1', 'numerical_col2']
categorical_columns = ['cat_col1', 'cat_col2']
# Normalize numerical columns
scaler = StandardScaler()
df[numerical_columns] = scaler.fit_transform(df[numerical_columns])
# Normalize categorical columns
label_encoder = LabelEncoder()
df[categorical_columns] = df[categorical_columns].apply(label_encoder.fit_transform)
自定义标准化:
def custom_numerical_normalization(data):
# Custom normalization logic for numerical data
# ...
def custom_categorical_normalization(data):
# Custom normalization logic for categorical data
# ...
# Apply custom normalization functions
df['numerical_col'] = custom_numerical_normalization(df['numerical_col'])
df['categorical_col'] = custom_categorical_normalization(df['categorical_col'])
选择最适合您的数据特征和聚类算法要求的方法。始终确保所选的标准化方法与每列中数据的性质一致。