diff --git a/examples/pylab_examples/spectrum_demo.py b/examples/pylab_examples/spectrum_demo.py
index 3ad242d2c7ae..44e1b31a42d9 100644
--- a/examples/pylab_examples/spectrum_demo.py
+++ b/examples/pylab_examples/spectrum_demo.py
@@ -1,32 +1,52 @@
+"""
+========================
+Spectrum Representations
+========================
+
+The plots show different spectrum representations of a sine signal with
+additive noise. A (frequency) spectrum of a discrete-time signal is calculated
+by utilizing the fast Fourier transform (FFT).
+"""
 import matplotlib.pyplot as plt
 import numpy as np
 
 
 np.random.seed(0)
 
-dt = 0.01
-Fs = 1/dt
+dt = 0.01  # sampling interval
+Fs = 1/dt  # sampling frequency
 t = np.arange(0, 10, dt)
+
+# generate noise:
 nse = np.random.randn(len(t))
 r = np.exp(-t/0.05)
-
 cnse = np.convolve(nse, r)*dt
 cnse = cnse[:len(t)]
-s = 0.1*np.sin(2*np.pi*t) + cnse
 
-plt.subplot(3, 2, 1)
-plt.plot(t, s)
+s = 0.1*np.sin(4*np.pi*t) + cnse  # the signal
+
+fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(7, 7))
+
+# plot time signal:
+axes[0, 0].set_title("Signal")
+axes[0, 0].plot(t, s, color='C0')
+axes[0, 0].set_xlabel("Time")
+axes[0, 0].set_ylabel("Amplitude")
+
+# plot different spectrum types:
+axes[1, 0].set_title("Magnitude Spectrum")
+axes[1, 0].magnitude_spectrum(s, Fs=Fs, color='C1')
 
-plt.subplot(3, 2, 3)
-plt.magnitude_spectrum(s, Fs=Fs)
+axes[1, 1].set_title("Log. Magnitude Spectrum")
+axes[1, 1].magnitude_spectrum(s, Fs=Fs, scale='dB', color='C1')
 
-plt.subplot(3, 2, 4)
-plt.magnitude_spectrum(s, Fs=Fs, scale='dB')
+axes[2, 0].set_title("Phase Spectrum ")
+axes[2, 0].phase_spectrum(s, Fs=Fs, color='C2')
 
-plt.subplot(3, 2, 5)
-plt.angle_spectrum(s, Fs=Fs)
+axes[2, 1].set_title("Angle Spectrum")
+axes[2, 1].angle_spectrum(s, Fs=Fs, color='C2')
 
-plt.subplot(3, 2, 6)
-plt.phase_spectrum(s, Fs=Fs)
+axes[0, 1].remove()  # don't display empty ax
 
+fig.tight_layout()
 plt.show()