SARIMAX customisation in python
Unfortunately, there is not much that can be done. Estimation via the SARIMAX model requires a lot of matrix operations, and if you have e.g. seasonal_order=(1, 0, 0, 365), then you're going to have matrices at least sized 365 x 365, and if you increase to seasonal_order=(1, 0, 0, 365) then you have at least 730 x 730. Even for moderate seasonal periods with long lag lengths, the operations are just going to take some time.
One option that was recently introduced (it is not yet in a released version, so you would have to install the latest version from Github) that can improve things somewhat is to fit the model using options low_memory=True and cov_type='none'. Two things to keep in mind:

There is no getting around the matrix operations that have to be performed, so this will not be a massive speedup. The best case may be on the order of a 10x speedup.
This will limit the things you can do post-estimation. You can still do out-of-sample forecasting, but not in-sample dynamic prediction, and you won't see standard errors for estimated parameters.
i have one question 
why SARIMAX takes lot of time for execution ??
length of data is only 2200 , if i take period (24 or 168 or 365) it takes so much time 
while , for monthly data(2200 entries ) period is 12 , it does not take much time !!
is there any way to solve this problem??