使用 Pandas (StatsModels) 在多元回归上创建 for 循环

Question

我正在对 50 个州进行多元回归，以确定基于几个变量的每个州的预期寿命。 目前我的数据集只过滤到缅因州，我想知道是否有办法创建一个 For 循环来遍历整个状态列并为每个状态执行回归。 这比创建 50 个过滤器更有效。 任何帮助都会很棒！

import pandas
import pandas as pd
import openpyxl
import statsmodels.formula.api as smf
import statsmodels.formula.api as ols

df = pd.read_excel(C:/Users/File1.xlsx, sheet_name = 'States')

dfME = df[(df[State] == "Maine")]

pd.set_option('display.max_columns', None)

dfME.head()

model = smf.ols(Life Expectancy ~ Race + Age + Weight + C(Pets), data = dfME) 
modelfit = model.fit()
modelfit.summary

Answer 1

###### Assuming rest of your code is ok I am sharing a strategy for the loop and storing model outputs:
pd.set_option('display.max_columns', None)
state_modelfit_summary = {}
states = df['State'].unique() # As you only need to loop once for each state
for st in states:
    dfME = df[(df['State'] == st)]     
    model = smf.ols(Life Expectancy ~ Race + Age + Weight + C(Pets), data = dfME) 
    modelfit = model.fit()
    # Store output in a dictionary with state name as key
    state_modelfit_summary[st] = modelfit.summary