在 Python 中标准化变量

Question

我有以下代码：

var_list = ['a', 'b', 'c', 'd', 'd', 'e', 'f', 'g', 'h', 'i']
    
y_var = 'lp'
for x_var in var_list:
    formula = y_var + ' ~ ' + x_var
    results = smf.ols(formula, data=df).fit()

我想标准化列表中的变量并重新配置我的循环以改用标准化变量。

我自己没有任何代码。我搜索了一下，发现以下代码https://medium.com/@rrfd/standardize-or-normalize-examples-in-python-e3f174b65dfc 进行了转换：

from sklearn import preprocessing
import numpy as np

# Get dataset
df = pd.read_csv("https://storage.googleapis.com/mledudatasets/california_housing_train.csv", sep=",")# Normalize total_bedrooms column

# Create the Scaler object
scaler = preprocessing.StandardScaler()

# Fit your data on the scaler object
scaled_df = scaler.fit_transform(df)
scaled_df = pd.DataFrame(scaled_df, columns=names)

Answer 1

这个呢？ （警告：代码未经测试）

from sklearn import preprocessing
import numpy as np

# function that returns the scaled dataframe
def scale_df(df):

    # Get column names first
    names = df.columns

    # Create the Scaler object
    scaler = preprocessing.StandardScaler()

    # Fit your data on the scaler object
    scaled_df = scaler.fit_transform(df)

    return pd.DataFrame(scaled_df, columns=names)

# suppose you have two df's that need to be scalled
df_1 = pd.read_csv("https://storage.googleapis.com/mledudatasets/california_housing_train.csv", sep=",")
df_2 = pd.read_csv("https://storage.googleapis.com/mledudatasets/california_housing_train.csv", sep=",")

var_list = ['a', 'b', 'c', 'd', 'd', 'e', 'f', 'g', 'h', 'i']
results = []

# loop dataframes
for df in [df_1, df_2]:
    scaled_df = scale_df(df)
    
    # loop variables
    y_var = 'lp'
    for x_var in var_list:
        formula = y_var + ' ~ ' + x_var
        results.append(smf.ols(formula, data=scaled_df).fit())

结果将附加到列表results。