Using categorical variables in statsmodels OLS class

Question

I want to use statsmodels OLS class to create a multiple regression model. Consider the following dataset:

import statsmodels.api as sm
import pand         


        

Answer 1

I also had this problem as well and have lots of columns needed to be treated as categorical, and this makes it quite annoying to deal with dummify. And converting to string doesn't work for me.

For anyone looking for a solution without onehot-encoding the data, The R interface provides a nice way of doing this:

import statsmodels.formula.api as smf
import pandas as pd
import numpy as np

dict = {'industry': ['mining', 'transportation', 'hospitality', 'finance', 'entertainment'],
  'debt_ratio':np.random.randn(5), 'cash_flow':np.random.randn(5) + 90} 

df = pd.DataFrame.from_dict(dict)

x = df[['debt_ratio', 'industry']]
y = df['cash_flow']

# NB. unlike sm.OLS, there is "intercept" term is included here
smf.ols(formula="cash_flow ~ debt_ratio + C(industry)", data=df).fit()

Reference: https://www.statsmodels.org/stable/example_formulas.html#categorical-variables