Any Python Library Produces Publication Style Regression Tables

Question

I've been using Python for regression analysis. After getting the regression results, I need to summarize all the results into one single table and convert them to LaTex (for publication). Is there any package that does this in Python? Something like estout in Stata that gives the following table:

Answer 1

Well, there is summary_col in statsmodels; it doesn't have all the bells and whistles of estout, but it does have the basic functionality you are looking for (including export to LaTeX):

import statsmodels.api as sm
from statsmodels.iolib.summary2 import summary_col

p['const'] = 1
reg0 = sm.OLS(p['p0'],p[['const','exmkt','smb','hml']]).fit()
reg1 = sm.OLS(p['p2'],p[['const','exmkt','smb','hml']]).fit()
reg2 = sm.OLS(p['p4'],p[['const','exmkt','smb','hml']]).fit()

print summary_col([reg0,reg1,reg2],stars=True,float_format='%0.2f')

===============================
         p0       p2      p4   
-------------------------------
const -1.03*** -0.01   0.62*** 
      (0.11)   (0.04)  (0.07)  
exmkt 1.28***  0.97*** 0.98*** 
       (0.02)   (0.01)  (0.01)  
smb   0.37***  0.28*** -0.14***
      (0.03)   (0.01)  (0.02)  
hml   0.77***  0.46*** 0.69*** 
      (0.04)   (0.01)  (0.02)  
===============================
Standard errors in parentheses.
* p<.1, ** p<.05, ***p<.01

Or here is a version where I add R-Squared and the number of observations:

print summary_col([reg0,reg1,reg2],stars=True,float_format='%0.2f',
                  info_dict={'N':lambda x: "{0:d}".format(int(x.nobs)),
                             'R2':lambda x: "{:.2f}".format(x.rsquared)})

===============================
         p0       p2      p4   
-------------------------------
const -1.03*** -0.01   0.62*** 
      (0.11)   (0.04)  (0.07)  
exmkt 1.28***  0.97*** 0.98*** 
      (0.02)   (0.01)  (0.01)  
smb   0.37***  0.28*** -0.14***
      (0.03)   (0.01)  (0.02)  
hml   0.77***  0.46*** 0.69*** 
      (0.04)   (0.01)  (0.02)  
R2    0.86     0.95    0.88    
N     1044     1044    1044    
===============================
Standard errors in parentheses.
* p<.1, ** p<.05, ***p<.01

Another example, this time showing the use of the model_names option and regressions where the independent variables vary:

reg3 = sm.OLS(p['p4'],p[['const','exmkt']]).fit()
reg4 = sm.OLS(p['p4'],p[['const','exmkt','smb','hml']]).fit()
reg5 = sm.OLS(p['p4'],p[['const','exmkt','smb','hml','umd']]).fit()

print summary_col([reg3,reg4,reg5],stars=True,float_format='%0.2f',
                  model_names=['p4\n(0)','p4\n(1)','p4\n(2)'],
                  info_dict={'N':lambda x: "{0:d}".format(int(x.nobs)),
                             'R2':lambda x: "{:.2f}".format(x.rsquared)})

==============================
         p4      p4       p4  
        (0)     (1)      (2)  
------------------------------
const 0.66*** 0.62***  0.15***
      (0.10)  (0.07)   (0.04) 
exmkt 1.10*** 0.98***  1.08***
      (0.02)  (0.01)   (0.01) 
hml           0.69***  0.72***
              (0.02)   (0.01) 
smb           -0.14*** 0.07***
              (0.02)   (0.01) 
umd                    0.46***
                       (0.01) 
R2    0.78    0.88     0.96   
N     1044    1044     1044   
==============================
Standard errors in
parentheses.
* p<.1, ** p<.05, ***p<.01

To export to LaTeX use the as_latex method.

I could be wrong but I don't think an option for t-stats instead of standard errors (like in your example) is implemented.

Answer 2

One alternative is Stargazer. To get started quickly, refer to the set of demo tables that Stargazer can produce.

Related posts include: post1 and post2.

Answer 3

In addition to @Karl D. 's great answer with Statsmodels as_latex method, you can also check out the pystout package.

!pip install pystout

import pandas as pd
from sklearn.datasets import load_iris
import statsmodels.api as sm 
from pystout import pystout

data = load_iris()
df = pd.DataFrame(data = data.data, columns = data.feature_names)
df.columns = ['s_len', 's_w', 'p_len', 'p_w']

y = df['p_w']

X = df[['s_len', 's_w', 'p_len']]
m1 = sm.OLS(y, X).fit()

X = df[['s_len', 's_w']]
m2 =  sm.OLS(y, X).fit()

X = df[['s_len']]
m3 =  sm.OLS(y, X).fit()

pystout(models=[m1, m2, m3],
        file='test_table.tex',
        addnotes=['Note above','Note below'],
        digits=2,
        endog_names=['petal width', 'petal width', 'petal width'],
        varlabels={'const':'Constant',
                   'displacement':'Disp','mpg':'MPG'},
        mgroups={'First Group':[1,2],'Second Group':3},
        modstat={'nobs':'Obs','rsquared_adj':'Adj. R\sym{2}','fvalue':'F-stat'}
        )

Don't spend hours like me trying to print out pystout, the LateX output is directly written on the .tex document you pass for file.

When compiled, the output looks like this: