There was a post a few years ago on this but only a quick fix was included. Specifying a Constant in Statsmodels Linear Regression?
Quick fix was to run first and subtract interception equation And then run again. Tedious if you are running over and over again.
I would think you could pass a parameter telling it to set the intercept as zero. Also open to using other stats packages besides statsmodels.
It depends which api you use. If you are using statsmodels.api then you need to explicitly add the constant to your model by adding a column of 1s to exog. If you don't then there is no intercept.
import pandas as pd import statsmodels.formula.api as smf import statsmodels.api as sm df = pd.DataFrame({'x': range(0,10)}).assign(y=lambda x: x+8) # Fit y = B*x, no intercept res1 = sm.OLS(endog=df.y, exog=df.x).fit() ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ x 2.2632 0.269 8.421 0.000 1.655 2.871 ============================================================================== # fit y = B*x + C, by adding a column of ones res2 = sm.OLS(endog=df.y, exog=df[['x']].assign(intercept=1)).fit() ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ x 1.0000 8.64e-16 1.16e+15 0.000 1.000 1.000 intercept 8.0000 4.61e-15 1.73e+15 0.000 8.000 8.000 ============================================================================== If instead you are using the smf api, you can add -1 to the Patsy formula, which will tell it to remove the constant, otherwise the Intercept is included.
res3 = smf.ols('y ~ x -1', data=df).fit() ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ x 2.2632 0.269 8.421 0.000 1.655 2.871 ============================================================================== # Default includes the constant res4 = smf.ols('y ~ x', data=df).fit() ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ Intercept 8.0000 2.72e-15 2.94e+15 0.000 8.000 8.000 x 1.0000 5.09e-16 1.96e+15 0.000 1.000 1.000 ============================================================================== 