I am using statsmodels to create some regression outputs: 

```
import statsmodels.api as sm
import statsmodels.formula.api as smf
from statsmodels.iolib.summary2 import summary_col
import numpy as np 
import pandas as pd 

x1 = pd.Series(np.random.randn(2000))
x2 = pd.Series(np.random.randn(2000))
aa_milne_arr = ['a', 'b', 'c', 'd', "e", "f", "g", "h", "i"]
dummy = pd.Series(np.random.choice(aa_milne_arr, 2000,))
depen = pd.Series(np.random.randn(2000))
df = pd.DataFrame({"y": depen, "x1": x1, "x2": x2, "dummy": dummy})
df['const'] = 1
df['xsqr'] = df['x1']**2  
mod = smf.ols('y ~ x1 + x2 + dummy', data=df)
mod2 = smf.ols('y ~ x1 + x2 + xsqr + dummy', data=df)
res = mod.fit()
res2 = mod2.fit()

print (summary_col([res,res2],stars=True,float_format='%0.3f',
                  model_names=['one\n(0)','two\n(1)'],
                  info_dict={'N':lambda x: "{0:d}".format(int(x.nobs)),
                             'R2':lambda x: "{:.2f}".format(x.rsquared)}))

```

It works all fine, but I have a big dataset with many dummies (way more than in the example). Therefore I would like to exclude the dummies from the summary output (not from the regression itself). It that somehow possible?