如何使循環跨數據框python執行迴歸

Question

我目前正在從一個數據框（稱爲df）跨不同的因變量運行迴歸。我想知道如何在48迴歸中做出迴應。這個函數的非環狀的版本如下：

agric_ff = ols(formula = 'agric ~ prem + smb + hml', data=df).fit() 
agric_ff_df = pd.DataFrame({'params': agric_ff.params})  
agric_ff_df.columns = ['agric'] 

food_ff = ols(formula = 'food ~ prem + smb + hml', data=df).fit() 
food_ff_df = pd.DataFrame({'params': food_ff.params})  
food_ff_df.columns = ['food'] 

soda_ff = ols(formula = 'soda ~ prem + smb + hml', data=df).fit() 
soda_ff_df = pd.DataFrame({'params': soda_ff.params})  
soda_ff_df.columns = ['soda'] 

beer_ff = ols(formula = 'beer ~ prem + smb + hml', data=df).fit() 
beer_ff_df = pd.DataFrame({'beer': beer_ff.params})  
beer_ff_df.columns = ['beer'] 

smoke_ff = ols(formula = 'smoke ~ prem + smb + hml', data=df).fit() 
smoke_ff_df = pd.DataFrame({'smoke': smoke_ff.params})  
smoke_ff_df.columns = ['smoke'] 

toys_ff = ols(formula = 'toys ~ prem + smb + hml', data=df).fit() 
toys_ff_df = pd.DataFrame({'toys': toys_ff.params})  
toys_ff_df.columns = ['toys'] 

fun_ff = ols(formula = 'fun~ prem + smb + hml', data=df).fit() 
fun_ff_df = pd.DataFrame({'fun': fun_ff.params})  
fun_ff_df.columns = ['fun'] 

books_ff = ols(formula = 'books ~ prem + smb + hml', data=df).fit() 
books_ff_df = pd.DataFrame({'books': fun_ff.params})  
books_ff_df.columns = ['books'] 

使用公式符號一種方法你的幫助是非常讚賞

Answer 1

這裏：

import statsmodels.regression.linear_model as sm 
import pandas as pd 
from sklearn import datasets # load a dummy dataset 

# build a model using 4 columns, regressed on 4 others 
boston = pd.DataFrame(boston.data, columns = boston.feature_names) 
boston.head() 


     CRIM  ZN   INDUS CHAS NOX RM  AGE  DIS  RAD TAX   PTRATIO B  LSTAT 
0  0.00632 18.0  2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0  15.3  396.90 4.98 
1  0.02731 0.0  7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0  17.8  396.90 9.14 
2  0.02729 0.0  7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0  17.8  392.83 4.03 
3  0.03237 0.0  2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0  18.7  394.63 2.94 
4  0.06905 0.0  2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0  18.7  396.90 5.33 

list_of_responses = ["LSTAT","RM","RAD","B"] 

# list of models 
models = [] 

for resp in list_of_responses: 
    formula = resp + " ~ CRIM + ZN + INDUS + NOX" 
    models.append(sm.OLS.from_formula(formula, data = boston).fit()) 

# each element is your model. For example, you can access its params 
models[0].params