我最喜欢的解决方案是Creating lowpass filter in SciPy - understanding methods and units，我将其更改为带通示例：

# !/usr/bin/env python3
# -*- coding: utf-8 -*-
""" 
bandpass example, from
https://stackoverflow.com/questions/25191620/creating-lowpass-filter-in-scipy-understanding-methods-and-units
pl, 22.05.2022
"""
import numpy as np
from scipy.signal import butter, lfilter, freqz
import matplotlib.pyplot as plt
def butter_bandpass(lowcut, highcut, fs, order=5):
    b, a = butter(order, [lowcut, highcut], fs=fs, btype='band')
    return b, a
def butter_bandpass_filter(data, lowcut, highcut, fs, order=6):
    b, a = butter_bandpass(lowcut, highcut, fs, order=order)
    y = lfilter(b, a, data)
    return y
# Filter requirements.
order = 6
fs = 60.0       # sample rate, Hz
# cutoff = 3.667  # desired cutoff frequency of the filter, Hz
cutoffLo = 2.0
cutoffHi = 4.0
# Demonstrate the use of the filter.
# First make some data to be filtered.
T = 5.0         # seconds
n = int(T * fs) # total number of samples
t = np.linspace(0, T, n, endpoint=False)
# "Noisy" data.  We want to recover the 1.2 Hz signal from this.
fl=1.2      # Hz
f0=3.0      # Hz
fh=7.0      # Hz
al=2.5
a0=1.0
ah=3.5
w=2*np.pi   # Omega
data = f0 * np.sin(w*f0*t) \
     + fl * np.sin(w*fl*t) \
     + fh * np.sin(w*fh*t) \
     + 8.0
# Get the filter coefficients so we can check its frequency response.
b, a = butter_bandpass(cutoffLo, cutoffHi, fs, order)
# Plot the frequency response.
w, h = freqz(b, a, fs=fs, worN=8000)
plt.subplot(2, 1, 1)
plt.plot(w, np.abs(h), 'b')
plt.plot(cutoffLo, 0.5*np.sqrt(2), 'ko')
plt.plot(cutoffHi, 0.5*np.sqrt(2), 'ko')
plt.axvline(cutoffLo, color='k')
plt.axvline(cutoffHi, color='k')
plt.axvline(fl, color='r',linestyle=":")
plt.axvline(f0, color='r',linestyle="-.")
plt.axvline(fh, color='r',linestyle=":")
plt.xlim(0, 2.0*cutoffHi)
plt.title("Bandpass Filter Frequency Response")
plt.xlabel('Frequency [Hz]')
plt.grid()
# Filter the data, and plot both the original and filtered signals.
y = butter_bandpass_filter(data, cutoffLo, cutoffHi, fs, order)
plt.subplot(2, 1, 2)
plt.plot(t, data, 'b-', label='data')
plt.plot(t, y, 'g-', linewidth=2, label='filtered data')
plt.xlabel('Time [sec]')
plt.grid()
plt.legend()
plt.subplots_adjust(hspace=0.35)
plt.show()