Regression summary output: Order of categories

Question

This question is about the way the result of a GLM is printed, that is, the order in which the coefficients are printed. By "order" I'm not referring to any statistical meaning of this term.

The following code determines a linear model:

from pandas import *
import statsmodels.api as sm
import patsy as patsy

df = read_csv("http://vincentarelbundock.github.io/Rdatasets/csv/ggplot2/diamonds.csv")

y, X = patsy.dmatrices( 'price ~  cut', data = df )

sm.GLM( y, X, family= sm.families.Gaussian()  ).fit().summary()

... And produces the output below, in which the categories are ordered:

(Fair), Good, Ideal, Premium, Very Good

====================================================================================
                       coef    std err          z      P>|z|      [95.0% Conf. Int.]
------------------------------------------------------------------------------------
Intercept         4358.7578     98.788     44.122      0.000      4165.137  4552.379
cut[T.Good]       -429.8933    113.849     -3.776      0.000      -653.034  -206.753
cut[T.Ideal]      -901.2158    102.412     -8.800      0.000     -1101.939  -700.493
cut[T.Premium]     225.4999    104.395      2.160      0.031        20.889   430.111
cut[T.Very Good]  -376.9979    105.164     -3.585      0.000      -583.116  -170.880
====================================================================================

What I'm trying to do:

I would like them to be ordered like:

(Fair), Good, Very Good, Premium, Ideal

What I'm trying to do would look like this in R:

df = read.table( file = "http://vincentarelbundock.github.io/Rdatasets/csv/ggplot2/diamonds.csv",
            sep = ",",  header = TRUE)


df$cut = factor( df$cut, levels = c("Fair", "Good", "Very Good", "Premium", "Ideal"))

glm( price ~ cut, data = df, family = gaussian  )

Notice the ordering in the output follows the factor ordering:

(Fair), Good, Very Good, Premium, Ideal

Call:  glm(formula = price ~ cut, family = gaussian, data = df)

Coefficients:
 (Intercept)       cutGood  cutVery Good    cutPremium      cutIdeal  
      4358.8        -429.9        -377.0         225.5        -901.2

How do I do this in Python?

Answer 1

This is a known issue. I'm sure a PR would be welcome. Maybe continue the conversation here?

https://github.com/pydata/patsy/issues/27