python - How to get the regression intercept using Statsmodels.api -

i trying calculate regression output using python library unabl;e intercept value when use library:

import statsmodels.api sm 

it prints regression analysis except intercept.

but when use:

from pandas.stats.api import ols 

my code pandas:

regression = ols(y= sorted_data3['net_realization_rate'],x = sorted_data3[['cohort_2','cohort_3']]) print regression   

i the intercept warning librabry deprecated in future trying use statsmodels.

the warning while using pandas.stats.api:

warning (from warnings module): file "c:\python27\lib\idlelib\run.py", line 325 exec code in self.locals futurewarning: pandas.stats.ols module deprecated , removed in future version. refer external packages statsmodels, see examples here: http://statsmodels.sourceforge.net/stable/regression.html

my code statsmodels:

import pandas pd import numpy np pandas.stats.api import ols import statsmodels.api sm  data1 = pd.read_csv('c:\shank\regression.csv')  #importing csv print data1 

running cleaning code

sm_model = sm.ols(sorted_data3['net_realization_rate'],sorted_data3[['cohort_2','cohort_3']]) results = sm_model.fit() print '\n' print results.summary() 

i tried statsmodels.formula.api: as:

sm_model = sm.ols(formula ="net_realization_rate ~ cohort_2 + cohort_3", data = sorted_data3) results = sm_model.fit() print '\n' print result.params print '\n' print results.summary() 

but error:

typeerror: init() takes @ least 2 arguments (1 given)

final output: 1st pandas 2nd stats.... want intercept vaule 1 pandas stats also:

so, statsmodels has add_constant method need use explicitly add intercept values. imho, better r alternative intercept added default.

in case, need this:

import statsmodels.api sm endog = sorted_data3['net_realization_rate'] exog = sm.add_constant(sorted_data3[['cohort_2','cohort_3']])  # fit , summarize ols model mod = sm.ols(endog, exog) results = mod.fit() print results.summary() 

note can add constant before array, or after passing true (default) or false prepend kwag in sm.add_constant

---

or, not recommended, can use numpy explicitly add constant column so:

exog = np.concatenate((np.repeat(1, len(sorted_data3))[:, none],                         sorted_data3[['cohort_2','cohort_3']].values),                        axis = 1)