How to obtain small bins after FFT in python?
Even if you use another transform, that will not make more data.
If you have a sampling of 1kHz and 2s of samples, then your precision is 0.5Hz. You can interpolate this with chirpz (or just use sinc(), that's the shape of your data between the samples of your comb), but the data you have on your current point is the data that determines what you have in the lobes (between 0Hz and 0.5Hz).
If you want a real precision of 0.1Hz, you need 10s of data.
I'm using scipy.signal.fft.rfft() to calculate power spectral density of a signal. The sampling rate is 1000Hz and the signal contains 2000 points. So frequency bin is (1000/2)/(2000/2)=0.5Hz. But I need to analyze the signal in [0-0.1]Hz. 
I saw several answers recommending chirp-Z transform, but I didn't find any toolbox of it written in Python. 
So how can I complete this small-bin analysis in Python? Or can I just filter this signal to [0-0.1]Hz using like Butterworth filter?
Thanks a lot!