![\"python\u5982\u4f55\u7edf\u8ba1\u8f93\u51fa\u7684\u7ed3\u679c\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25130539\/1e0e3f41-b9f7-4d3f-9545-ec8f2806e12f.webp\")
<\/p>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u7edf\u8ba1\u8f93\u51fa\u7ed3\u679c\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u3001\u5e93\u51fd\u6570\u4ee5\u53ca\u81ea\u5b9a\u4e49\u51fd\u6570\u7b49\u3002\u4ee5\u4e0b\u662f\u51e0\u79cd\u5e38\u7528\u7684\u65b9\u6cd5\uff1a\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u8fdb\u884c\u7edf\u8ba1\u3001\u5229\u7528NumPy\u548cPandas\u5e93\u8fdb\u884c\u7edf\u8ba1\u3001\u4f7f\u7528Counter\u548cdefaultdict\u8fdb\u884c\u7edf\u8ba1\u3001\u7f16\u5199\u81ea\u5b9a\u4e49\u7edf\u8ba1\u51fd\u6570\u3002<\/strong><\/p>\n<\/p>\n \u5728Python\u4e2d\uff0c\u7edf\u8ba1\u8f93\u51fa\u7684\u7ed3\u679c\u662f\u4e00\u4e2a\u5e38\u89c1\u7684\u4efb\u52a1\uff0c\u65e0\u8bba\u662f\u7edf\u8ba1\u7b80\u5355\u7684\u6570\u636e\u5982\u5e73\u5747\u503c\u3001\u6700\u5927\u503c\uff0c\u8fd8\u662f\u590d\u6742\u7684\u6570\u636e\u5982\u9891\u7387\u5206\u5e03\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u4e00\u79cd\u65b9\u6cd5\u2014\u2014\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u8fdb\u884c\u7edf\u8ba1\u3002<\/p>\n<\/p>\n Python\u5185\u7f6e\u4e86\u4e00\u4e9b\u975e\u5e38\u5f3a\u5927\u7684\u51fd\u6570\u548c\u65b9\u6cd5\uff0c\u53ef\u4ee5\u7528\u4e8e\u57fa\u672c\u7684\u7edf\u8ba1\u5206\u6790\u3002\u4ee5\u4e0b\u662f\u51e0\u4e2a\u5e38\u7528\u7684\u5185\u7f6e\u51fd\u6570\uff1a<\/p>\n<\/p>\n \u4f8b\u5982\uff0c\u5bf9\u4e8e\u4e00\u4e2a\u5305\u542b\u6570\u5b57\u7684\u5217\u8868\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e9b\u5185\u7f6e\u51fd\u6570\u6765\u8fdb\u884c\u57fa\u672c\u7684\u7edf\u8ba1\u5206\u6790\uff1a<\/p>\n<\/p>\n total_sum = sum(data)<\/p>\n count = len(data)<\/p>\n maximum = max(data)<\/p>\n minimum = min(data)<\/p>\n sorted_data = sorted(data)<\/p>\n print("\u603b\u548c:", total_sum)<\/p>\n print("\u5143\u7d20\u6570\u91cf:", count)<\/p>\n print("\u6700\u5927\u503c:", maximum)<\/p>\n print("\u6700\u5c0f\u503c:", minimum)<\/p>\n print("\u6392\u5e8f\u540e\u7684\u6570\u636e:", sorted_data)<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u83b7\u5f97\u6570\u636e\u7684\u603b\u548c\u3001\u6570\u91cf\u3001\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u4ee5\u53ca\u6392\u5e8f\u540e\u7684\u6570\u636e\u3002\u8fd9\u4e9b\u57fa\u672c\u7684\u7edf\u8ba1\u4fe1\u606f\u5728\u5f88\u591a\u60c5\u51b5\u4e0b\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n Python\u5185\u7f6e\u4e86\u4e00\u7cfb\u5217\u51fd\u6570\uff0c\u53ef\u4ee5\u7528\u6765\u5feb\u901f\u7edf\u8ba1\u548c\u5206\u6790\u6570\u636e\u3002\u8fd9\u4e9b\u51fd\u6570\u7b80\u5355\u6613\u7528\uff0c\u9002\u5408\u5904\u7406\u8f83\u5c0f\u89c4\u6a21\u7684\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n total_sum = sum(data)<\/p>\n count = len(data)<\/p>\n average = total_sum \/ count<\/p>\n print("\u603b\u548c:", total_sum)<\/p>\n print("\u5143\u7d20\u6570\u91cf:", count)<\/p>\n print("\u5e73\u5747\u503c:", average)<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528 maximum = max(data)<\/p>\n minimum = min(data)<\/p>\n print("\u6700\u5927\u503c:", maximum)<\/p>\n print("\u6700\u5c0f\u503c:", minimum)<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u627e\u5230\u6570\u636e\u4e2d\u7684\u6700\u5927\u503c\u548c\u6700\u5c0f\u503c\u3002<\/p>\n<\/p>\n sorted_data = sorted(data)<\/p>\n print("\u6392\u5e8f\u540e\u7684\u6570\u636e:", sorted_data)<\/p>\n middle_index = len(sorted_data) \/\/ 2<\/p>\n if len(sorted_data) % 2 == 0:<\/p>\n median = (sorted_data[middle_index - 1] + sorted_data[middle_index]) \/ 2<\/p>\n else:<\/p>\n median = sorted_data[middle_index]<\/p>\n print("\u4e2d\u4f4d\u6570:", median)<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528 \u5bf9\u4e8e\u66f4\u590d\u6742\u7684\u6570\u636e\u5206\u6790\u4efb\u52a1\uff0cPython\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u5982NumPy\u548cPandas\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u548c\u65b9\u6cd5\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u8fdb\u884c\u6570\u636e\u7edf\u8ba1\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u7edf\u8ba1\u51fd\u6570\uff0c\u5982 data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])<\/p>\n average = np.mean(data)<\/p>\n median = np.median(data)<\/p>\n std_dev = np.std(data)<\/p>\n print("\u5e73\u5747\u503c:", average)<\/p>\n print("\u4e2d\u4f4d\u6570:", median)<\/p>\n print("\u6807\u51c6\u5dee:", std_dev)<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u8ba1\u7b97\u6570\u636e\u7684\u5e73\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u548c\u6807\u51c6\u5dee\u3002NumPy\u7684\u8ba1\u7b97\u901f\u5ea6\u975e\u5e38\u5feb\uff0c\u9002\u5408\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u3002<\/p>\n<\/p>\n Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u7279\u522b\u9002\u5408\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\u3002Pandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u64cd\u4f5c\u548c\u7edf\u8ba1\u51fd\u6570\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n data = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])<\/p>\n average = data.mean()<\/p>\n median = data.median()<\/p>\n std_dev = data.std()<\/p>\n print("\u5e73\u5747\u503c:", average)<\/p>\n print("\u4e2d\u4f4d\u6570:", median)<\/p>\n print("\u6807\u51c6\u5dee:", std_dev)<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528Pandas\u7684 \u5728\u5904\u7406\u9891\u7387\u5206\u5e03\u7b49\u4efb\u52a1\u65f6\uff0cPython\u7684 data = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']<\/p>\n counter = Counter(data)<\/p>\n print("\u9891\u7387\u5206\u5e03:", counter)<\/p>\n print("\u6700\u5e38\u89c1\u7684\u5143\u7d20:", counter.most_common(1))<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528 data = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']<\/p>\n frequency = defaultdict(int)<\/p>\n for item in data:<\/p>\n frequency[item] += 1<\/p>\n print("\u9891\u7387\u5206\u5e03:", frequency)<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u53ef\u80fd\u9700\u8981\u7f16\u5199\u81ea\u5b9a\u4e49\u7684\u7edf\u8ba1\u51fd\u6570\u6765\u6ee1\u8db3\u7279\u5b9a\u7684\u9700\u6c42\u3002\u4ee5\u4e0b\u662f\u51e0\u4e2a\u5e38\u89c1\u7684\u81ea\u5b9a\u4e49\u7edf\u8ba1\u51fd\u6570\u7684\u4f8b\u5b50\u3002<\/p>\n<\/p>\n \u4f17\u6570\u662f\u6570\u636e\u96c6\u4e2d\u51fa\u73b0\u9891\u7387\u6700\u9ad8\u7684\u503c\u3002\u6211\u4eec\u53ef\u4ee5\u7f16\u5199\u4e00\u4e2a\u51fd\u6570\u6765\u8ba1\u7b97\u6570\u636e\u7684\u4f17\u6570\u3002<\/p>\n<\/p>\n frequency = Counter(data)<\/p>\n mode = frequency.most_common(1)[0][0]<\/p>\n return mode<\/p>\n data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10]<\/p>\n mode = calculate_mode(data)<\/p>\n print("\u4f17\u6570:", mode)<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528 \u65b9\u5dee\u662f\u6570\u636e\u96c6\u4e2d\u5404\u4e2a\u6570\u636e\u4e0e\u5e73\u5747\u503c\u4e4b\u95f4\u7684\u5dee\u7684\u5e73\u65b9\u7684\u5e73\u5747\u503c\u3002\u6211\u4eec\u53ef\u4ee5\u7f16\u5199\u4e00\u4e2a\u51fd\u6570\u6765\u8ba1\u7b97\u6570\u636e\u7684\u65b9\u5dee\u3002<\/p>\n<\/p>\n average = sum(data) \/ len(data)<\/p>\n variance = sum((x - average) 2 for x in data) \/ len(data)<\/p>\n return variance<\/p>\n data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n variance = calculate_variance(data)<\/p>\n print("\u65b9\u5dee:", variance)<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u8ba1\u7b97\u6570\u636e\u7684\u5e73\u5747\u503c\uff0c\u7136\u540e\u8ba1\u7b97\u6bcf\u4e2a\u6570\u636e\u4e0e\u5e73\u5747\u503c\u4e4b\u95f4\u7684\u5dee\u7684\u5e73\u65b9\uff0c\u6700\u540e\u6c42\u8fd9\u4e9b\u5e73\u65b9\u7684\u5e73\u5747\u503c\u3002<\/p>\n<\/p>\n \u6807\u51c6\u5dee\u662f\u65b9\u5dee\u7684\u5e73\u65b9\u6839\u3002\u6211\u4eec\u53ef\u4ee5\u57fa\u4e8e\u65b9\u5dee\u7684\u8ba1\u7b97\u51fd\u6570\uff0c\u7f16\u5199\u4e00\u4e2a\u51fd\u6570\u6765\u8ba1\u7b97\u6570\u636e\u7684\u6807\u51c6\u5dee\u3002<\/p>\n<\/p>\n variance = calculate_variance(data)<\/p>\n standard_deviation = variance 0.5<\/p>\n return standard_deviation<\/p>\n data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n standard_deviation = calculate_standard_deviation(data)<\/p>\n print("\u6807\u51c6\u5dee:", standard_deviation)<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u8c03\u7528\u8ba1\u7b97\u65b9\u5dee\u7684\u51fd\u6570\uff0c\u7136\u540e\u8ba1\u7b97\u65b9\u5dee\u7684\u5e73\u65b9\u6839\uff0c\u5373\u5f97\u5230\u6807\u51c6\u5dee\u3002<\/p>\n<\/p>\n \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u7ecf\u5e38\u9700\u8981\u5bf9\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u3002\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u662f\u6309\u65f6\u95f4\u987a\u5e8f\u6392\u5217\u7684\u6570\u636e\uff0c\u901a\u5e38\u7528\u4e8e\u91d1\u878d\u3001\u6c14\u8c61\u7b49\u9886\u57df\u7684\u5206\u6790\u3002<\/p>\n<\/p>\n Pandas\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u65f6\u95f4\u5e8f\u5217\u5904\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u7edf\u8ba1\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n date_range = pd.date_range(start='2023-01-01', periods=10, freq='D')<\/p>\n data = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], index=date_range)<\/p>\n average = data.mean()<\/p>\n median = data.median()<\/p>\n std_dev = data.std()<\/p>\n print("\u5e73\u5747\u503c:", average)<\/p>\n print("\u4e2d\u4f4d\u6570:", median)<\/p>\n print("\u6807\u51c6\u5dee:", std_dev)<\/p>\n rolling_average = data.rolling(window=3).mean()<\/p>\n print("\u6eda\u52a8\u5e73\u5747\u503c:\\n", rolling_average)<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u7136\u540e\u4f7f\u7528Pandas\u7684\u7edf\u8ba1\u51fd\u6570\u8fdb\u884c\u57fa\u672c\u7684\u7edf\u8ba1\u5206\u6790\u3002\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528 \u5728\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u4e2d\uff0c\u6570\u636e\u7684\u53ef\u89c6\u5316\u975e\u5e38\u91cd\u8981\u3002Matplotlib\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u7684\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n date_range = pd.date_range(start='2023-01-01', periods=10, freq='D')<\/p>\n data = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], index=date_range)<\/p>\n plt.figure(figsize=(10, 6))<\/p>\n plt.plot(data, label='\u539f\u59cb\u6570\u636e')<\/p>\n plt.plot(data.rolling(window=3).mean(), label='\u6eda\u52a8\u5e73\u5747\u503c', linestyle='--')<\/p>\n plt.xlabel('\u65e5\u671f')<\/p>\n plt.ylabel('\u503c')<\/p>\n plt.title('\u65f6\u95f4\u5e8f\u5217\u6570\u636e')<\/p>\n plt.legend()<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528Matplotlib\u7ed8\u5236\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u6298\u7ebf\u56fe\uff0c\u5e76\u6dfb\u52a0\u6eda\u52a8\u5e73\u5747\u503c\u7684\u66f2\u7ebf\u3002\u901a\u8fc7\u8fd9\u79cd\u53ef\u89c6\u5316\u65b9\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u76f4\u89c2\u5730\u89c2\u5bdf\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002<\/p>\n<\/p>\n \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u7ecf\u5e38\u9700\u8981\u5bf9\u5206\u7ec4\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u3002\u5206\u7ec4\u6570\u636e\u662f\u6839\u636e\u67d0\u4e9b\u7279\u5f81\u5c06\u6570\u636e\u5206\u6210\u4e0d\u540c\u7684\u7ec4\uff0c\u7136\u540e\u5bf9\u6bcf\u7ec4\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n<\/p>\n Pandas\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u5206\u7ec4\u529f\u80fd\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5bf9\u5206\u7ec4\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n<\/p>\n data = pd.DataFrame({<\/p>\n '\u7c7b\u522b': ['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B'],<\/p>\n '\u503c': [1, 2, 3, 4, 5, 6, 7, 8]<\/p>\n })<\/p>\n grouped = data.groupby('\u7c7b\u522b').agg({<\/p>\n '\u503c': ['sum', 'mean', 'max', 'min']<\/p>\n })<\/p>\n print(grouped)<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u6846\uff0c\u7136\u540e\u4f7f\u7528 \u5728\u5904\u7406\u6570\u636e\u5e93\u4e2d\u7684\u5206\u7ec4\u6570\u636e\u65f6\uff0cSQLAlchemy\u662f\u4e00\u4e2a\u975e\u5e38\u6709\u7528\u7684\u5de5\u5177\u3002SQLAlchemy\u662f\u4e00\u4e2aPython SQL\u5de5\u5177\u5305\u548c\u5bf9\u8c61\u5173\u7cfb\u6620\u5c04\uff08ORM\uff09\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u5e93\u64cd\u4f5c\u3002<\/p>\n<\/p>\n from sqlalchemy.ext.declarative import declarative_base<\/p>\n from sqlalchemy.orm import sessionmaker<\/p>\n engine = create_engine('sqlite:\/\/\/:memory:')<\/p>\n Base = declarative_base()<\/p>\n class Data(Base):<\/p>\n __tablename__ = 'data'<\/p>\n id = Column(Integer, primary_key=True)<\/p>\n category = Column(String)<\/p>\n value = Column(Integer)<\/p>\n Base.metadata.create_all(engine)<\/p>\n Session = sessionmaker(bind=engine)<\/p>\n session = Session()<\/p>\n session.add_all([<\/p>\n Data(category='A', value=1),<\/p>\n Data(category='B', value=2),<\/p>\n Data(category='A', value=3),<\/p>\n Data(category='B', value=4),<\/p>\n Data(category='A', value=5),<\/p>\n Data(category='B', value=6),<\/p>\n Data(category='A', value=7),<\/p>\n Data(category='B', value=8),<\/p>\n ])<\/p>\n session.commit()<\/p>\n results = session.query(<\/p>\n Data.category,<\/p>\n func.sum(Data.value).label('sum'),<\/p>\n func.avg(Data.value).label('average'),<\/p>\n func.max(Data.value).label('max'),<\/p>\n func.min(Data.value).label('min')<\/p>\n ).group_by(Data.category).all()<\/p>\n for result in results:<\/p>\n print(f"\u7c7b\u522b: {result.category}, \u603b\u548c: {result.sum}, \u5e73\u5747\u503c: {result.average}, \u6700\u5927\u503c: {result.max}, \u6700\u5c0f\u503c: {result.min}")<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528SQLAlchemy\u521b\u5efa\u4e00\u4e2a\u5185\u5b58\u6570\u636e\u5e93\uff0c\u5e76\u5b9a\u4e49\u6570\u636e\u8868\u548c\u63d2\u5165\u6570\u636e\u3002\u7136\u540e\uff0c\u6211\u4eec\u4f7f\u7528SQLAlchemy\u7684\u67e5\u8be2\u529f\u80fd\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u7edf\u8ba1\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u5bf9\u6570\u636e\u5e93\u4e2d\u7684\u5206\u7ec4\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n<\/p>\n \u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\u9886\u57df\uff0c\u6587\u672c\u6570\u636e\u7684\u7edf\u8ba1\u5206\u6790\u662f\u4e00\u4e2a\u5e38\u89c1\u7684\u4efb\u52a1\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u7684\u591a\u79cd\u5de5\u5177\u548c\u5e93\u6765\u7edf\u8ba1\u548c\u5206\u6790\u6587\u672c\u6570\u636e\u3002<\/p>\n<\/p>\n import re<\/p>\n text = "Python is great. Python is dynamic. Python is easy to learn."<\/p>\n words = re.findall(r'\\w+', text.lower())<\/p>\n counter = Counter(words)<\/p>\n print("\u8bcd\u9891\u5206\u5e03:", counter)<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u5bf9\u6587\u672c\u8fdb\u884c\u5206\u8bcd\uff0c\u7136\u540e\u4f7f\u7528 \u81ea\u7136\u8bed\u8a00\u5de5\u5177\u5305\uff08NLTK\uff09\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6587\u672c\u5904\u7406\u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u5de5\u5177\u548c\u65b9\u6cd5\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6587\u672c\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n<\/p>\n from nltk.probability import FreqDist<\/p>\n nltk.download('punkt')<\/p>\n text = "Python is great. Python is dynamic. Python is easy to learn."<\/p>\n words = nltk.word_tokenize(text.lower())<\/p>\n fdist = FreqDist(words)<\/p>\n print("\u8bcd\u9891\u5206\u5e03:", fdist)<\/p>\n print("\u6700\u5e38\u89c1\u7684\u8bcd:", fdist.most_common(3))<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528NLTK\u7684\u5206\u8bcd\u5de5\u5177\u5bf9\u6587\u672c\u8fdb\u884c\u5206\u8bcd\uff0c\u7136\u540e\u4f7f\u7528 \u5728\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\uff0c\u7edf\u8ba1\u56fe\u50cf\u6570\u636e\u662f\u4e00\u4e2a\u5e38\u89c1\u7684\u4efb\u52a1\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u7684\u591a\u79cd\u5de5\u5177\u548c\u5e93\u6765\u7edf\u8ba1\u548c\u5206\u6790\u56fe\u50cf\u6570\u636e\u3002<\/p>\n<\/p>\n \u5982\u4f55\u5728Python\u4e2d\u7edf\u8ba1\u8f93\u51fa\u7ed3\u679c\u7684\u6570\u91cf\uff1f<\/strong> Python\u4e2d\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u5e2e\u52a9\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\uff1f<\/strong> \u5982\u4f55\u5c06\u7edf\u8ba1\u7ed3\u679c\u53ef\u89c6\u5316\uff1f<\/strong> \u5982\u4f55\u5904\u7406\u7f3a\u5931\u7684\u6570\u636e\u4ee5\u4fbf\u8fdb\u884c\u7edf\u8ba1\uff1f<\/strong>\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u8fdb\u884c\u7edf\u8ba1<\/h3>\n<\/p>\n
\n
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n\u4e00\u3001\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u8fdb\u884c\u7edf\u8ba1<\/h3>\n<\/p>\n
1\u3001\u4f7f\u7528sum()\u548clen()\u51fd\u6570<\/h4>\n<\/p>\n
sum()<\/code>\u51fd\u6570\u7528\u4e8e\u8ba1\u7b97\u6570\u636e\u7684\u603b\u548c\uff0clen()<\/code>\u51fd\u6570\u7528\u4e8e\u7edf\u8ba1\u6570\u636e\u5143\u7d20\u7684\u6570\u91cf\u3002\u8fd9\u4e24\u4e2a\u51fd\u6570\u7ed3\u5408\u4f7f\u7528\uff0c\u53ef\u4ee5\u8ba1\u7b97\u6570\u636e\u7684\u5e73\u5747\u503c\u3002<\/p>\n<\/p>\ndata = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\nsum()<\/code>\u51fd\u6570\u8ba1\u7b97\u6570\u636e\u7684\u603b\u548c\uff0c\u7136\u540e\u4f7f\u7528len()<\/code>\u51fd\u6570\u7edf\u8ba1\u6570\u636e\u7684\u6570\u91cf\uff0c\u6700\u540e\u901a\u8fc7\u4e24\u8005\u7684\u6bd4\u503c\u8ba1\u7b97\u5e73\u5747\u503c\u3002<\/p>\n<\/p>\n2\u3001\u4f7f\u7528max()\u548cmin()\u51fd\u6570<\/h4>\n<\/p>\n
max()<\/code>\u51fd\u6570\u7528\u4e8e\u627e\u5230\u6570\u636e\u4e2d\u7684\u6700\u5927\u503c\uff0cmin()<\/code>\u51fd\u6570\u7528\u4e8e\u627e\u5230\u6570\u636e\u4e2d\u7684\u6700\u5c0f\u503c\u3002<\/p>\n<\/p>\ndata = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n3\u3001\u4f7f\u7528sorted()\u51fd\u6570<\/h4>\n<\/p>\n
sorted()<\/code>\u51fd\u6570\u7528\u4e8e\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u5e8f\uff0c\u5e76\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684\u5217\u8868\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7sorted()<\/code>\u51fd\u6570\u6765\u83b7\u53d6\u6570\u636e\u7684\u4e2d\u4f4d\u6570\u3002<\/p>\n<\/p>\ndata = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n\u8ba1\u7b97\u4e2d\u4f4d\u6570<\/strong><\/h2>\n
sorted()<\/code>\u51fd\u6570\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u5e8f\uff0c\u7136\u540e\u901a\u8fc7\u7d22\u5f15\u8ba1\u7b97\u4e2d\u4f4d\u6570\u3002\u5982\u679c\u6570\u636e\u5143\u7d20\u7684\u6570\u91cf\u662f\u5076\u6570\uff0c\u4e2d\u4f4d\u6570\u662f\u4e2d\u95f4\u4e24\u4e2a\u6570\u7684\u5e73\u5747\u503c\uff1b\u5982\u679c\u6570\u636e\u5143\u7d20\u7684\u6570\u91cf\u662f\u5947\u6570\uff0c\u4e2d\u4f4d\u6570\u662f\u4e2d\u95f4\u7684\u6570\u3002<\/p>\n<\/p>\n\u4e8c\u3001\u5229\u7528NumPy\u548cPandas\u5e93\u8fdb\u884c\u7edf\u8ba1<\/h3>\n<\/p>\n
1\u3001\u4f7f\u7528NumPy\u8fdb\u884c\u7edf\u8ba1<\/h4>\n<\/p>\n
mean()<\/code>\u3001median()<\/code>\u3001std()<\/code>\u7b49\u3002<\/p>\n<\/p>\nimport numpy as np<\/p>\n2\u3001\u4f7f\u7528Pandas\u8fdb\u884c\u7edf\u8ba1<\/h4>\n<\/p>\n
import pandas as pd<\/p>\nSeries<\/code>\u5bf9\u8c61\u6765\u5b58\u50a8\u6570\u636e\uff0c\u5e76\u4f7f\u7528mean()<\/code>\u3001median()<\/code>\u548cstd()<\/code>\u65b9\u6cd5\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u3002Pandas\u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u5176\u4ed6\u6709\u7528\u7684\u51fd\u6570\uff0c\u5982sum()<\/code>\u3001max()<\/code>\u3001min()<\/code>\u7b49\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u7edf\u8ba1\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n\u4e09\u3001\u4f7f\u7528Counter\u548cdefaultdict\u8fdb\u884c\u7edf\u8ba1<\/h3>\n<\/p>\n
collections<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86\u975e\u5e38\u6709\u7528\u7684\u5de5\u5177\uff0c\u5982Counter<\/code>\u548cdefaultdict<\/code>\u3002\u8fd9\u4e9b\u5de5\u5177\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u7edf\u8ba1\u548c\u9891\u7387\u5206\u6790\u3002<\/p>\n<\/p>\n1\u3001\u4f7f\u7528Counter\u8fdb\u884c\u7edf\u8ba1<\/h4>\n<\/p>\n
Counter<\/code>\u662f\u4e00\u4e2a\u4e13\u95e8\u7528\u4e8e\u8ba1\u6570\u7684\u5de5\u5177\uff0c\u53ef\u4ee5\u8f7b\u677e\u7edf\u8ba1\u6570\u636e\u5143\u7d20\u7684\u9891\u7387\u5206\u5e03\u3002<\/p>\n<\/p>\nfrom collections import Counter<\/p>\nCounter<\/code>\u7edf\u8ba1\u6570\u636e\u5143\u7d20\u7684\u9891\u7387\u5206\u5e03\uff0c\u5e76\u4f7f\u7528most_common()<\/code>\u65b9\u6cd5\u627e\u5230\u6700\u5e38\u89c1\u7684\u5143\u7d20\u3002<\/p>\n<\/p>\n2\u3001\u4f7f\u7528defaultdict\u8fdb\u884c\u7edf\u8ba1<\/h4>\n<\/p>\n
defaultdict<\/code>\u662f\u4e00\u4e2a\u5e26\u6709\u9ed8\u8ba4\u503c\u7684\u5b57\u5178\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u7edf\u8ba1\u3002<\/p>\n<\/p>\nfrom collections import defaultdict<\/p>\ndefaultdict<\/code>\u7edf\u8ba1\u6570\u636e\u5143\u7d20\u7684\u9891\u7387\u5206\u5e03\u3002defaultdict<\/code>\u7684\u4f7f\u7528\u975e\u5e38\u7075\u6d3b\uff0c\u53ef\u4ee5\u6839\u636e\u9700\u8981\u6307\u5b9a\u9ed8\u8ba4\u503c\u7684\u7c7b\u578b\u3002<\/p>\n<\/p>\n\u56db\u3001\u7f16\u5199\u81ea\u5b9a\u4e49\u7edf\u8ba1\u51fd\u6570<\/h3>\n<\/p>\n
1\u3001\u8ba1\u7b97\u4f17\u6570\u7684\u51fd\u6570<\/h4>\n<\/p>\n
def calculate_mode(data):<\/p>\nCounter<\/code>\u7edf\u8ba1\u6570\u636e\u5143\u7d20\u7684\u9891\u7387\u5206\u5e03\uff0c\u7136\u540e\u901a\u8fc7most_common()<\/code>\u65b9\u6cd5\u627e\u5230\u4f17\u6570\u3002<\/p>\n<\/p>\n2\u3001\u8ba1\u7b97\u65b9\u5dee\u7684\u51fd\u6570<\/h4>\n<\/p>\n
def calculate_variance(data):<\/p>\n3\u3001\u8ba1\u7b97\u6807\u51c6\u5dee\u7684\u51fd\u6570<\/h4>\n<\/p>\n
def calculate_standard_deviation(data):<\/p>\n\u4e94\u3001\u7edf\u8ba1\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/h3>\n<\/p>\n
1\u3001\u4f7f\u7528Pandas\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n\u521b\u5efa\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/strong><\/h2>\n
\u7edf\u8ba1\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u57fa\u672c\u4fe1\u606f<\/strong><\/h2>\n
\u8ba1\u7b97\u6eda\u52a8\u5e73\u5747\u503c<\/strong><\/h2>\n
rolling()<\/code>\u65b9\u6cd5\u8ba1\u7b97\u6eda\u52a8\u5e73\u5747\u503c\uff0c\u8fd9\u5728\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u4e2d\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n2\u3001\u4f7f\u7528Matplotlib\u53ef\u89c6\u5316\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n\u521b\u5efa\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/strong><\/h2>\n
\u7ed8\u5236\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/strong><\/h2>\n
\u516d\u3001\u7edf\u8ba1\u5206\u7ec4\u6570\u636e<\/h3>\n<\/p>\n
1\u3001\u4f7f\u7528Pandas\u8fdb\u884c\u5206\u7ec4\u7edf\u8ba1<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n\u521b\u5efa\u6570\u636e\u6846<\/strong><\/h2>\n
\u5206\u7ec4\u7edf\u8ba1<\/strong><\/h2>\n
groupby()<\/code>\u65b9\u6cd5\u6839\u636e\u7c7b\u522b\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u4f7f\u7528agg()<\/code>\u65b9\u6cd5\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u5bf9\u5206\u7ec4\u6570\u636e\u8fdb\u884c\u591a\u79cd\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n<\/p>\n2\u3001\u4f7f\u7528SQLAlchemy\u8fdb\u884c\u5206\u7ec4\u7edf\u8ba1<\/h4>\n<\/p>\n
from sqlalchemy import create_engine, Column, Integer, String, func<\/p>\n\u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce<\/strong><\/h2>\n
\u5b9a\u4e49\u6570\u636e\u8868<\/strong><\/h2>\n
\u521b\u5efa\u6570\u636e\u8868<\/strong><\/h2>\n
\u521b\u5efa\u4f1a\u8bdd<\/strong><\/h2>\n
\u63d2\u5165\u6570\u636e<\/strong><\/h2>\n
\u5206\u7ec4\u7edf\u8ba1<\/strong><\/h2>\n
\u4e03\u3001\u7edf\u8ba1\u6587\u672c\u6570\u636e<\/h3>\n<\/p>\n
1\u3001\u4f7f\u7528collections.Counter\u7edf\u8ba1\u8bcd\u9891<\/h4>\n<\/p>\n
collections.Counter<\/code>\u662f\u4e00\u4e2a\u975e\u5e38\u6709\u7528\u7684\u5de5\u5177\uff0c\u53ef\u4ee5\u7528\u6765\u7edf\u8ba1\u6587\u672c\u4e2d\u7684\u8bcd\u9891\u3002<\/p>\n<\/p>\nfrom collections import Counter<\/p>\n\u5206\u8bcd<\/strong><\/h2>\n
\u7edf\u8ba1\u8bcd\u9891<\/strong><\/h2>\n
Counter<\/code>\u7edf\u8ba1\u8bcd\u9891\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u83b7\u5f97\u6587\u672c\u4e2d\u7684\u8bcd\u9891\u5206\u5e03\u3002<\/p>\n<\/p>\n2\u3001\u4f7f\u7528NLTK\u8fdb\u884c\u6587\u672c\u7edf\u8ba1\u5206\u6790<\/h4>\n<\/p>\n
import nltk<\/p>\n\u5206\u8bcd<\/strong><\/h2>\n
\u7edf\u8ba1\u8bcd\u9891<\/strong><\/h2>\n
\u627e\u5230\u6700\u5e38\u89c1\u7684\u8bcd<\/strong><\/h2>\n
FreqDist<\/code>\u7edf\u8ba1\u8bcd\u9891\u3002\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528most_common()<\/code>\u65b9\u6cd5\u627e\u5230\u6700\u5e38\u89c1\u7684\u8bcd\u3002<\/p>\n<\/p>\n\u516b\u3001\u7edf\u8ba1\u56fe\u50cf\u6570\u636e<\/h3>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u7684len()<\/code>\u51fd\u6570\u6765\u7edf\u8ba1\u8f93\u51fa\u7ed3\u679c\u7684\u6570\u91cf\u3002\u4f8b\u5982\uff0c\u5982\u679c\u4f60\u6709\u4e00\u4e2a\u5217\u8868\u6216\u5b57\u7b26\u4e32\uff0c\u53ef\u4ee5\u7b80\u5355\u5730\u4f7f\u7528len(my_list)<\/code>\u6216len(my_string)<\/code>\u6765\u83b7\u53d6\u5143\u7d20\u6216\u5b57\u7b26\u7684\u6570\u91cf\u3002\u8fd9\u79cd\u65b9\u6cd5\u975e\u5e38\u76f4\u89c2\u4e14\u6613\u4e8e\u5b9e\u73b0\u3002<\/p>\n
Python\u6709\u8bb8\u591a\u5f3a\u5927\u7684\u5e93\u53ef\u4ee5\u7528\u4e8e\u7edf\u8ba1\u5206\u6790\uff0c\u6bd4\u5982pandas<\/code>\u3001numpy<\/code>\u548cscipy<\/code>\u3002pandas<\/code>\u63d0\u4f9b\u4e86\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u5904\u7406\u6570\u636e\u96c6\uff1bnumpy<\/code>\u5219\u4e13\u6ce8\u4e8e\u6570\u503c\u8ba1\u7b97\uff0c\u652f\u6301\u591a\u7ef4\u6570\u7ec4\u548c\u77e9\u9635\u64cd\u4f5c\uff1b\u800cscipy<\/code>\u5219\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u7edf\u8ba1\u51fd\u6570\u548c\u7b97\u6cd5\uff0c\u53ef\u4ee5\u8fdb\u884c\u590d\u6742\u7684\u6570\u636e\u5206\u6790\u3002<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u7edf\u8ba1\u7ed3\u679c\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib<\/code>\u6216seaborn<\/code>\u7b49\u53ef\u89c6\u5316\u5e93\u3002\u8fd9\u4e9b\u5e93\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u5c06\u6570\u636e\u4ee5\u56fe\u8868\u7684\u5f62\u5f0f\u5448\u73b0\uff0c\u4ece\u800c\u66f4\u76f4\u89c2\u5730\u5206\u6790\u6570\u636e\u7684\u5206\u5e03\u548c\u8d8b\u52bf\u3002\u4f8b\u5982\uff0c\u901a\u8fc7plt.plot()<\/code>\u53ef\u4ee5\u7ed8\u5236\u7ebf\u56fe\uff0c\u4f7f\u7528sns.barplot()<\/code>\u53ef\u4ee5\u751f\u6210\u6761\u5f62\u56fe\uff0c\u8fd9\u4e9b\u56fe\u8868\u80fd\u591f\u6709\u6548\u5730\u5c55\u793a\u7edf\u8ba1\u7ed3\u679c\u3002<\/p>\n
\u5728\u8fdb\u884c\u7edf\u8ba1\u4e4b\u524d\uff0c\u5904\u7406\u7f3a\u5931\u6570\u636e\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u4e2d\u7684dropna()<\/code>\u65b9\u6cd5\u6765\u5220\u9664\u7f3a\u5931\u503c\uff0c\u6216\u8005\u4f7f\u7528fillna()<\/code>\u65b9\u6cd5\u586b\u8865\u8fd9\u4e9b\u7f3a\u5931\u503c\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u7528\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u6216\u7279\u5b9a\u503c\u66ff\u6362\u7f3a\u5931\u6570\u636e\uff0c\u8fd9\u6837\u53ef\u4ee5\u786e\u4fdd\u7edf\u8ba1\u7ed3\u679c\u7684\u51c6\u786e\u6027\u548c\u53ef\u9760\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u7edf\u8ba1\u8f93\u51fa\u7ed3\u679c\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u3001\u5e93\u51fd\u6570\u4ee5\u53ca\u81ea\u5b9a\u4e49\u51fd\u6570\u7b49\u3002\u4ee5\u4e0b\u662f\u51e0\u79cd\u5e38\u7528\u7684\u65b9\u6cd5\uff1a\u4f7f […]","protected":false},"author":3,"featured_media":1182619,"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\/1182611"}],"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=1182611"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1182611\/revisions"}],"predecessor-version":[{"id":1182622,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1182611\/revisions\/1182622"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1182619"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1182611"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1182611"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1182611"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}