Apply Low pass Butterworth filter in Python
You should not be using the analog filter - use a digital filter instead. You want the filter to be defined in Z-domain, not S-domain. Also, you should define the time vector with known sampling frequency to avoid any confusion.

The design of the digital filter requires cut-off frequency to be normalized by fs/2.

Here is a working example:

import numpy as np
import matplotlib.pyplot as plt
from scipy import signal

fs = 1000  # Sampling frequency
# Generate the time vector properly
t = np.arange(1000) / fs
signala = np.sin(2*np.pi*100*t) # with frequency of 100
plt.plot(t, signala, label='a')

signalb = np.sin(2*np.pi*20*t) # frequency 20
plt.plot(t, signalb, label='b')

signalc = signala + signalb
plt.plot(t, signalc, label='c')

fc = 30  # Cut-off frequency of the filter
w = fc / (fs / 2) # Normalize the frequency
b, a = signal.butter(5, w, 'low')
output = signal.filtfilt(b, a, signalc)
plt.plot(t, output, label='filtered')
plt.legend()
plt.show()