{"id":1073727,"date":"2025-01-08T11:28:16","date_gmt":"2025-01-08T03:28:16","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1073727.html"},"modified":"2025-01-08T11:28:18","modified_gmt":"2025-01-08T03:28:18","slug":"python%e4%b8%8b%e5%a6%82%e4%bd%95%e5%af%b9%e6%95%b0%e6%8d%ae%e8%bf%9b%e8%a1%8c%e5%8e%bb%e8%ba%81-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1073727.html","title":{"rendered":"python\u4e0b\u5982\u4f55\u5bf9\u6570\u636e\u8fdb\u884c\u53bb\u8e81"},"content":{"rendered":"

\"python\u4e0b\u5982\u4f55\u5bf9\u6570\u636e\u8fdb\u884c\u53bb\u8e81\"<\/p>\n

\u5728Python\u4e2d\u5bf9\u6570\u636e\u8fdb\u884c\u53bb\u566a\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528\u6ee4\u6ce2\u5668\u3001\u5e94\u7528\u79fb\u52a8\u5e73\u5747\u6cd5\u3001\u5229\u7528\u5c0f\u6ce2\u53d8\u6362\u3001\u91c7\u7528\u81ea\u9002\u5e94\u6ee4\u6ce2\u5668\u3001\u4f7f\u7528\u5085\u91cc\u53f6\u53d8\u6362\u3002<\/strong> \u5176\u4e2d\uff0c\u6ee4\u6ce2\u5668<\/strong>\u662f\u4e00\u79cd\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u5b83\u53ef\u4ee5\u901a\u8fc7\u9009\u62e9\u6027\u5730\u6d88\u9664\u7279\u5b9a\u9891\u7387\u7684\u566a\u58f0\u6765\u51c0\u5316\u6570\u636e\u3002<\/p>\n<\/p>\n

\u6ee4\u6ce2\u5668\u53ef\u4ee5\u5206\u4e3a\u4f4e\u901a\u6ee4\u6ce2\u5668\u3001\u9ad8\u901a\u6ee4\u6ce2\u5668\u3001\u5e26\u901a\u6ee4\u6ce2\u5668\u548c\u5e26\u963b\u6ee4\u6ce2\u5668\u7b49\u3002\u4f4e\u901a\u6ee4\u6ce2\u5668\u5141\u8bb8\u4f4e\u9891\u4fe1\u53f7\u901a\u8fc7\u5e76\u963b\u6b62\u9ad8\u9891\u566a\u58f0\uff0c\u4f7f\u4fe1\u53f7\u66f4\u52a0\u5e73\u6ed1\u3002\u9ad8\u901a\u6ee4\u6ce2\u5668\u5219\u76f8\u53cd\uff0c\u7528\u4e8e\u53bb\u9664\u4f4e\u9891\u566a\u58f0\u3002\u5e26\u901a\u6ee4\u6ce2\u5668\u5141\u8bb8\u7279\u5b9a\u9891\u7387\u8303\u56f4\u5185\u7684\u4fe1\u53f7\u901a\u8fc7\uff0c\u800c\u5e26\u963b\u6ee4\u6ce2\u5668\u5219\u7528\u4e8e\u963b\u6b62\u7279\u5b9a\u9891\u7387\u8303\u56f4\u5185\u7684\u4fe1\u53f7\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u6ee4\u6ce2\u5668\u53bb\u566a<\/h3>\n<\/p>\n

\u6ee4\u6ce2\u5668\u662f\u4fe1\u53f7\u5904\u7406\u4e2d\u7684\u91cd\u8981\u5de5\u5177\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u53bb\u9664\u566a\u58f0\u3002\u5728Python\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528SciPy\u5e93\u4e2d\u7684\u6ee4\u6ce2\u529f\u80fd\u6765\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002<\/p>\n<\/p>\n

1\u3001\u4f4e\u901a\u6ee4\u6ce2\u5668<\/h4>\n<\/p>\n

\u4f4e\u901a\u6ee4\u6ce2\u5668\u7528\u4e8e\u5141\u8bb8\u4f4e\u9891\u4fe1\u53f7\u901a\u8fc7\uff0c\u540c\u65f6\u963b\u6b62\u9ad8\u9891\u566a\u58f0\u3002\u5b83\u5728\u53bb\u9664\u9ad8\u9891\u566a\u58f0\u7684\u540c\u65f6\u4fdd\u7559\u4e86\u4fe1\u53f7\u7684\u4e3b\u8981\u7279\u5f81\u3002<\/p>\n<\/p>\n

import numpy as np<\/p>\n
from scipy.signal import butter, filtfilt<\/p>\n
\u751f\u6210\u4e00\u4e2a\u793a\u4f8b\u4fe1\u53f7<\/strong><\/h2>\n
t = np.linspace(0, 1, 500, False)  # 1\u79d2\u949f\u5185\u7684\u91c7\u6837<\/p>\n
signal = np.sin(2 * np.pi * 7 * t) + np.random.normal(0, 0.5, t.shape)<\/p>\n
\u8bbe\u8ba1\u4e00\u4e2a\u4f4e\u901a\u6ee4\u6ce2\u5668<\/strong><\/h2>\n
def butter_lowpass(cutoff, fs, order=5):<\/p>\n
    nyq = 0.5 * fs<\/p>\n
    normal_cutoff = cutoff \/ nyq<\/p>\n
    b, a = butter(order, normal_cutoff, btype='low', analog=False)<\/p>\n
    return b, a<\/p>\n
def lowpass_filter(data, cutoff, fs, order=5):<\/p>\n
    b, a = butter_lowpass(cutoff, fs, order=order)<\/p>\n
    y = filtfilt(b, a, data)<\/p>\n
    return y<\/p>\n
\u5e94\u7528\u4f4e\u901a\u6ee4\u6ce2\u5668<\/strong><\/h2>\n
cutoff = 3.5  # \u622a\u6b62\u9891\u7387<\/p>\n
fs = 500  # \u91c7\u6837\u9891\u7387<\/p>\n
filtered_signal = lowpass_filter(signal, cutoff, fs)<\/p>\n
\u7ed8\u5236\u539f\u59cb\u4fe1\u53f7\u548c\u6ee4\u6ce2\u540e\u7684\u4fe1\u53f7<\/strong><\/h2>\n
import matplotlib.pyplot as plt<\/p>\n
plt.plot(t, signal, label='Noisy signal')<\/p>\n
plt.plot(t, filtered_signal, label='Filtered signal', linewidth=2)<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u9ad8\u901a\u6ee4\u6ce2\u5668<\/h4>\n<\/p>\n
\u9ad8\u901a\u6ee4\u6ce2\u5668\u7528\u4e8e\u53bb\u9664\u4f4e\u9891\u566a\u58f0\uff0c\u4fdd\u7559\u9ad8\u9891\u4fe1\u53f7\u3002\u5b83\u9002\u7528\u4e8e\u90a3\u4e9b\u9ad8\u9891\u6210\u5206\u5360\u4e3b\u8981\u5730\u4f4d\u7684\u4fe1\u53f7\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
from scipy.signal import butter, filtfilt<\/p>\n
\u8bbe\u8ba1\u4e00\u4e2a\u9ad8\u901a\u6ee4\u6ce2\u5668<\/strong><\/h2>\n
def butter_highpass(cutoff, fs, order=5):<\/p>\n
    nyq = 0.5 * fs<\/p>\n
    normal_cutoff = cutoff \/ nyq<\/p>\n
    b, a = butter(order, normal_cutoff, btype='high', analog=False)<\/p>\n
    return b, a<\/p>\n
def highpass_filter(data, cutoff, fs, order=5):<\/p>\n
    b, a = butter_highpass(cutoff, fs, order=order)<\/p>\n
    y = filtfilt(b, a, data)<\/p>\n
    return y<\/p>\n
\u5e94\u7528\u9ad8\u901a\u6ee4\u6ce2\u5668<\/strong><\/h2>\n
cutoff = 3.5  # \u622a\u6b62\u9891\u7387<\/p>\n
fs = 500  # \u91c7\u6837\u9891\u7387<\/p>\n
filtered_signal = highpass_filter(signal, cutoff, fs)<\/p>\n
\u7ed8\u5236\u539f\u59cb\u4fe1\u53f7\u548c\u6ee4\u6ce2\u540e\u7684\u4fe1\u53f7<\/strong><\/h2>\n
plt.plot(t, signal, label='Noisy signal')<\/p>\n
plt.plot(t, filtered_signal, label='Filtered signal', linewidth=2)<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u79fb\u52a8\u5e73\u5747\u6cd5\u53bb\u566a<\/h3>\n<\/p>\n
\u79fb\u52a8\u5e73\u5747\u6cd5\u662f\u4e00\u79cd\u7b80\u5355\u800c\u6709\u6548\u7684\u53bb\u566a\u65b9\u6cd5\uff0c\u901a\u8fc7\u5bf9\u6570\u636e\u8fdb\u884c\u5c40\u90e8\u5e73\u5747\u6765\u5e73\u6ed1\u4fe1\u53f7\u3002\u79fb\u52a8\u5e73\u5747\u6cd5\u7684\u57fa\u672c\u601d\u60f3\u662f\u7528\u4e00\u7ec4\u6570\u636e\u70b9\u7684\u5e73\u5747\u503c\u6765\u4ee3\u66ff\u8be5\u7ec4\u6570\u636e\u70b9\u7684\u4e2d\u5fc3\u503c\uff0c\u4ece\u800c\u51cf\u5c11\u968f\u673a\u566a\u58f0\u7684\u5f71\u54cd\u3002<\/p>\n<\/p>\n
1\u3001\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u6cd5<\/h4>\n<\/p>\n
\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u6cd5\u662f\u4e00\u79cd\u6700\u57fa\u672c\u7684\u79fb\u52a8\u5e73\u5747\u65b9\u6cd5\uff0c\u5b83\u901a\u8fc7\u5bf9\u4e00\u6bb5\u6570\u636e\u53d6\u5e73\u5747\u503c\u6765\u5e73\u6ed1\u6570\u636e\u3002\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u4e2d\u7684rolling\u65b9\u6cd5\u6765\u5b9e\u73b0\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u6cd5\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u751f\u6210\u4e00\u4e2a\u793a\u4f8b\u4fe1\u53f7<\/strong><\/h2>\n
signal = pd.Series(signal)<\/p>\n
\u5e94\u7528\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u6cd5<\/strong><\/h2>\n
window_size = 5  # \u7a97\u53e3\u5927\u5c0f<\/p>\n
moving_average = signal.rolling(window=window_size).mean()<\/p>\n
\u7ed8\u5236\u539f\u59cb\u4fe1\u53f7\u548c\u79fb\u52a8\u5e73\u5747\u540e\u7684\u4fe1\u53f7<\/strong><\/h2>\n
plt.plot(t, signal, label='Noisy signal')<\/p>\n
plt.plot(t, moving_average, label='Moving Average', linewidth=2)<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u52a0\u6743\u79fb\u52a8\u5e73\u5747\u6cd5<\/h4>\n<\/p>\n
\u52a0\u6743\u79fb\u52a8\u5e73\u5747\u6cd5\u5728\u8ba1\u7b97\u5e73\u5747\u503c\u65f6\uff0c\u5bf9\u4e0d\u540c\u7684\u6570\u636e\u70b9\u8d4b\u4e88\u4e0d\u540c\u7684\u6743\u91cd\uff0c\u901a\u5e38\u662f\u6700\u8fd1\u7684\u6570\u636e\u70b9\u6743\u91cd\u8f83\u9ad8\uff0c\u8f83\u8fdc\u7684\u6570\u636e\u70b9\u6743\u91cd\u8f83\u4f4e\u3002\u8fd9\u79cd\u65b9\u6cd5\u80fd\u591f\u66f4\u597d\u5730\u4fdd\u7559\u4fe1\u53f7\u7684\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
# \u5e94\u7528\u52a0\u6743\u79fb\u52a8\u5e73\u5747\u6cd5<\/p>\n
weights = np.arange(1, window_size + 1)<\/p>\n
weighted_moving_average = signal.rolling(window=window_size).apply(lambda x: np.dot(x, weights) \/ weights.sum(), raw=True)<\/p>\n
\u7ed8\u5236\u539f\u59cb\u4fe1\u53f7\u548c\u52a0\u6743\u79fb\u52a8\u5e73\u5747\u540e\u7684\u4fe1\u53f7<\/strong><\/h2>\n
plt.plot(t, signal, label='Noisy signal')<\/p>\n
plt.plot(t, weighted_moving_average, label='Weighted Moving Average', linewidth=2)<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528\u5c0f\u6ce2\u53d8\u6362\u53bb\u566a<\/h3>\n<\/p>\n
\u5c0f\u6ce2\u53d8\u6362\u662f\u4e00\u79cd\u9002\u7528\u4e8e\u975e\u5e73\u7a33\u4fe1\u53f7\u7684\u53bb\u566a\u65b9\u6cd5\uff0c\u5b83\u53ef\u4ee5\u901a\u8fc7\u5206\u89e3\u4fe1\u53f7\u6765\u63d0\u53d6\u4e0d\u540c\u5c3a\u5ea6\u7684\u4fe1\u606f\u3002\u5728Python\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528PyWavelets\u5e93\u6765\u5b9e\u73b0\u5c0f\u6ce2\u53d8\u6362\u53bb\u566a\u3002<\/p>\n<\/p>\n
import pywt<\/p>\n
\u751f\u6210\u4e00\u4e2a\u793a\u4f8b\u4fe1\u53f7<\/strong><\/h2>\n
signal = np.sin(2 * np.pi * 7 * t) + np.random.normal(0, 0.5, t.shape)<\/p>\n
\u5e94\u7528\u5c0f\u6ce2\u53d8\u6362\u53bb\u566a<\/strong><\/h2>\n
def wavelet_denoise(data, wavelet='db1', level=1):<\/p>\n
    coeffs = pywt.wavedec(data, wavelet, level=level)<\/p>\n
    sigma = np.median(np.abs(coeffs[-1])) \/ 0.6745<\/p>\n
    uthresh = sigma * np.sqrt(2 * np.log(len(data)))<\/p>\n
    coeffs = list(map(lambda x: pywt.threshold(x, uthresh, mode='soft'), coeffs))<\/p>\n
    return pywt.waverec(coeffs, wavelet)<\/p>\n
\u53bb\u566a<\/strong><\/h2>\n
denoised_signal = wavelet_denoise(signal, wavelet='db1', level=2)<\/p>\n
\u7ed8\u5236\u539f\u59cb\u4fe1\u53f7\u548c\u53bb\u566a\u540e\u7684\u4fe1\u53f7<\/strong><\/h2>\n
plt.plot(t, signal, label='Noisy signal')<\/p>\n
plt.plot(t, denoised_signal, label='Denoised signal', linewidth=2)<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u81ea\u9002\u5e94\u6ee4\u6ce2\u5668\u53bb\u566a<\/h3>\n<\/p>\n
\u81ea\u9002\u5e94\u6ee4\u6ce2\u5668\u662f\u4e00\u79cd\u6839\u636e\u8f93\u5165\u4fe1\u53f7\u7684\u53d8\u5316\u81ea\u52a8\u8c03\u6574\u6ee4\u6ce2\u53c2\u6570\u7684\u6ee4\u6ce2\u5668\uff0c\u5e38\u7528\u4e8e\u5904\u7406\u975e\u5e73\u7a33\u4fe1\u53f7\u3002\u5728Python\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528NLMS\u7b97\u6cd5\u6765\u5b9e\u73b0\u81ea\u9002\u5e94\u6ee4\u6ce2\u5668\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u4e00\u4e2a\u793a\u4f8b\u4fe1\u53f7<\/strong><\/h2>\n
signal = np.sin(2 * np.pi * 7 * t) + np.random.normal(0, 0.5, t.shape)<\/p>\n
\u5e94\u7528NLMS\u7b97\u6cd5<\/strong><\/h2>\n
def nlms_filter(x, d, mu=0.01, M=32):<\/p>\n
    N = len(x)<\/p>\n
    w = np.zeros(M)<\/p>\n
    y = np.zeros(N)<\/p>\n
    e = np.zeros(N)<\/p>\n
    for n in range(M, N):<\/p>\n
        x_n = x[n-M:n][::-1]<\/p>\n
        y[n] = np.dot(w, x_n)<\/p>\n
        e[n] = d[n] - y[n]<\/p>\n
        w = w + mu * e[n] * x_n \/ (np.dot(x_n, x_n) + 1e-5)<\/p>\n
    return y, e<\/p>\n
\u53bb\u566a<\/strong><\/h2>\n
filtered_signal, error = nlms_filter(signal, signal)<\/p>\n
\u7ed8\u5236\u539f\u59cb\u4fe1\u53f7\u548c\u53bb\u566a\u540e\u7684\u4fe1\u53f7<\/strong><\/h2>\n
plt.plot(t, signal, label='Noisy signal')<\/p>\n
plt.plot(t, filtered_signal, label='Filtered signal', linewidth=2)<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528\u5085\u91cc\u53f6\u53d8\u6362\u53bb\u566a<\/h3>\n<\/p>\n
\u5085\u91cc\u53f6\u53d8\u6362\u7528\u4e8e\u5c06\u4fe1\u53f7\u4ece\u65f6\u57df\u8f6c\u6362\u5230\u9891\u57df\uff0c\u901a\u8fc7\u5206\u6790\u4fe1\u53f7\u7684\u9891\u7387\u6210\u5206\u6765\u53bb\u9664\u566a\u58f0\u3002\u5728Python\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u4e2d\u7684FFT\u51fd\u6570\u6765\u5b9e\u73b0\u5085\u91cc\u53f6\u53d8\u6362\u53bb\u566a\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u4e00\u4e2a\u793a\u4f8b\u4fe1\u53f7<\/strong><\/h2>\n
signal = np.sin(2 * np.pi * 7 * t) + np.random.normal(0, 0.5, t.shape)<\/p>\n
\u5e94\u7528\u5085\u91cc\u53f6\u53d8\u6362\u53bb\u566a<\/strong><\/h2>\n
def fft_denoise(data, threshold=50):<\/p>\n
    fft_coeffs = np.fft.fft(data)<\/p>\n
    fft_coeffs[np.abs(fft_coeffs) < threshold] = 0<\/p>\n
    return np.fft.ifft(fft_coeffs)<\/p>\n
\u53bb\u566a<\/strong><\/h2>\n
denoised_signal = fft_denoise(signal)<\/p>\n
\u7ed8\u5236\u539f\u59cb\u4fe1\u53f7\u548c\u53bb\u566a\u540e\u7684\u4fe1\u53f7<\/strong><\/h2>\n
plt.plot(t, signal, label='Noisy signal')<\/p>\n
plt.plot(t, denoised_signal, label='Denoised signal', linewidth=2)<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4e2d\u4f7f\u7528\u591a\u79cd\u6280\u672f\u5bf9\u6570\u636e\u8fdb\u884c\u53bb\u566a\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u4fe1\u53f7\u7684\u7279\u6027\u548c\u5177\u4f53\u7684\u5e94\u7528\u573a\u666f\u3002\u5e0c\u671b\u8fd9\u4e9b\u65b9\u6cd5\u80fd\u591f\u5e2e\u52a9\u60a8\u6709\u6548\u5730\u53bb\u9664\u6570\u636e\u4e2d\u7684\u566a\u58f0\uff0c\u63d0\u9ad8\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5728Python\u4e2d\uff0c\u54ea\u4e9b\u5e38\u7528\u5e93\u53ef\u4ee5\u5e2e\u52a9\u6211\u8fdb\u884c\u6570\u636e\u53bb\u566a\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u6709\u591a\u4e2a\u5e93\u53ef\u4ee5\u5e2e\u52a9\u5b9e\u73b0\u6570\u636e\u53bb\u566a\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecNumPy\u548cPandas\uff0c\u524d\u8005\u64c5\u957f\u5904\u7406\u6570\u7ec4\u548c\u6570\u5b66\u8fd0\u7b97\uff0c\u540e\u8005\u5219\u975e\u5e38\u9002\u5408\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u3002\u9664\u6b64\u4e4b\u5916\uff0cScikit-learn\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\uff0c\u53ef\u4ee5\u7528\u4e8e\u53bb\u566a\uff0c\u5982K\u5747\u503c\u805a\u7c7b\u548c\u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09\u3002\u6b64\u5916\uff0cOpenCV\u548cSciPy\u4e5f\u63d0\u4f9b\u4e86\u4e00\u4e9b\u56fe\u50cf\u548c\u4fe1\u53f7\u5904\u7406\u529f\u80fd\uff0c\u6709\u52a9\u4e8e\u53bb\u9664\u566a\u58f0\u3002<\/p>\n
\u5982\u4f55\u9009\u62e9\u5408\u9002\u7684\u53bb\u566a\u65b9\u6cd5\uff1f<\/strong>
\u9009\u62e9\u5408\u9002\u7684\u53bb\u566a\u65b9\u6cd5\u901a\u5e38\u53d6\u51b3\u4e8e\u6570\u636e\u7684\u7c7b\u578b\u548c\u566a\u58f0\u7684\u7279\u5f81\u3002\u4f8b\u5982\uff0c\u5bf9\u4e8e\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u79fb\u52a8\u5e73\u5747\u6216\u6307\u6570\u52a0\u6743\u79fb\u52a8\u5e73\u5747\u6cd5\u3002\u800c\u5bf9\u4e8e\u56fe\u50cf\u6570\u636e\uff0c\u5e38\u89c1\u7684\u53bb\u566a\u6280\u672f\u5305\u62ec\u9ad8\u65af\u6ee4\u6ce2\u3001\u4e2d\u503c\u6ee4\u6ce2\u548c\u53cc\u8fb9\u6ee4\u6ce2\u3002\u4e86\u89e3\u6570\u636e\u7684\u566a\u58f0\u6a21\u5f0f\u4ee5\u53ca\u5b9e\u9a8c\u4e0d\u540c\u7684\u65b9\u6cd5\uff0c\u80fd\u591f\u5e2e\u52a9\u60a8\u627e\u5230\u6700\u4f73\u7684\u53bb\u566a\u7b56\u7565\u3002<\/p>\n
\u5728\u8fdb\u884c\u6570\u636e\u53bb\u566a\u65f6\uff0c\u6709\u54ea\u4e9b\u5e38\u89c1\u7684\u9677\u9631\u9700\u8981\u907f\u514d\uff1f<\/strong>
\u8fdb\u884c\u6570\u636e\u53bb\u566a\u65f6\uff0c\u5e38\u89c1\u7684\u9677\u9631\u5305\u62ec\u8fc7\u5ea6\u53bb\u566a\uff0c\u8fd9\u53ef\u80fd\u5bfc\u81f4\u91cd\u8981\u4fe1\u606f\u7684\u635f\u5931\u3002\u9009\u62e9\u4e0d\u5408\u9002\u7684\u53c2\u6570\u8bbe\u7f6e\u53ef\u80fd\u4f1a\u4f7f\u53bb\u566a\u6548\u679c\u9002\u5f97\u5176\u53cd\uff0c\u56e0\u6b64\u5728\u8c03\u6574\u53c2\u6570\u65f6\u8981\u8c28\u614e\u3002\u6b64\u5916\uff0c\u8fc7\u5ea6\u4f9d\u8d56\u5355\u4e00\u65b9\u6cd5\u800c\u4e0d\u8fdb\u884c\u5bf9\u6bd4\u5206\u6790\uff0c\u53ef\u80fd\u4f1a\u9650\u5236\u60a8\u627e\u5230\u6700\u4f73\u89e3\u51b3\u65b9\u6848\u7684\u53ef\u80fd\u6027\u3002\u59cb\u7ec8\u5efa\u8bae\u5728\u53bb\u566a\u524d\u5bf9\u6570\u636e\u8fdb\u884c\u53ef\u89c6\u5316\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u7406\u89e3\u566a\u58f0\u7684\u5f71\u54cd\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u5bf9\u6570\u636e\u8fdb\u884c\u53bb\u566a\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528\u6ee4\u6ce2\u5668\u3001\u5e94\u7528\u79fb\u52a8\u5e73\u5747\u6cd5\u3001\u5229\u7528\u5c0f\u6ce2\u53d8\u6362\u3001\u91c7\u7528\u81ea\u9002\u5e94\u6ee4\u6ce2\u5668\u3001\u4f7f\u7528\u5085\u91cc […]","protected":false},"author":3,"featured_media":1073733,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1073727"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1073727"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1073727\/revisions"}],"predecessor-version":[{"id":1073738,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1073727\/revisions\/1073738"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1073733"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1073727"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1073727"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1073727"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}