{"id":1080522,"date":"2025-01-08T12:30:56","date_gmt":"2025-01-08T04:30:56","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1080522.html"},"modified":"2025-01-08T12:30:58","modified_gmt":"2025-01-08T04:30:58","slug":"%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8python%e6%9d%a5%e6%b1%82%e5%b9%b3%e5%9d%87%e6%95%b0-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1080522.html","title":{"rendered":"\u5982\u4f55\u4f7f\u7528python\u6765\u6c42\u5e73\u5747\u6570"},"content":{"rendered":"

\"\u5982\u4f55\u4f7f\u7528python\u6765\u6c42\u5e73\u5747\u6570\"<\/p>\n

\u4f7f\u7528Python\u6765\u6c42\u5e73\u5747\u6570\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff1a\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u3001\u4f7f\u7528Numpy\u5e93\u3001\u7f16\u5199\u81ea\u5b9a\u4e49\u51fd\u6570\u7b49\u3002\u5185\u7f6e\u51fd\u6570\u65b9\u4fbf\u5feb\u6377\u3001Numpy\u5e93\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u503c\u8ba1\u7b97\u3001\u81ea\u5b9a\u4e49\u51fd\u6570\u5219\u53ef\u4ee5\u7075\u6d3b\u5904\u7406\u590d\u6742\u9700\u6c42\u3002\u63a5\u4e0b\u6765\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u4f7f\u7528Python\u6c42\u5e73\u5747\u6570\u7684\u51e0\u79cd\u65b9\u6cd5\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u6c42\u5e73\u5747\u6570<\/p>\n

Python\u5185\u7f6e\u7684\u51fd\u6570\u53ef\u4ee5\u65b9\u4fbf\u5feb\u6377\u5730\u6c42\u5f97\u5217\u8868\u6216\u5143\u7ec4\u7684\u5e73\u5747\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n

data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n
average = sum(data) \/ len(data)<\/p>\n
print(f"The average is {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u65b9\u6cd5\u975e\u5e38\u7b80\u5355\uff0c\u53ea\u9700\u4f7f\u7528 sum()<\/code> \u51fd\u6570\u6c42\u548c\uff0c\u518d\u4f7f\u7528 len()<\/code> \u51fd\u6570\u8ba1\u7b97\u6570\u91cf\uff0c\u6700\u540e\u5c06\u603b\u548c\u9664\u4ee5\u6570\u91cf\u5373\u53ef\u5f97\u5230\u5e73\u5747\u6570\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u7b80\u5355\u7684\u5217\u8868\u6216\u5143\u7ec4\uff0c\u4e14\u4e0d\u9700\u8981\u989d\u5916\u5b89\u88c5\u7b2c\u4e09\u65b9\u5e93\uff0c\u975e\u5e38\u9002\u5408\u521d\u5b66\u8005\u3002<\/strong><\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Numpy\u5e93\u6c42\u5e73\u5747\u6570<\/p>\n
Numpy\u5e93\u662fPython\u4e2d\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5904\u7406\u591a\u7ef4\u6570\u7ec4\u548c\u77e9\u9635\u8fd0\u7b97\u3002\u4f7f\u7528Numpy\u5e93\u6c42\u5e73\u5747\u6570\u66f4\u52a0\u9ad8\u6548\uff0c\u7279\u522b\u662f\u5f53\u6570\u636e\u91cf\u8f83\u5927\u65f6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])<\/p>\n
average = np.mean(data)<\/p>\n
print(f"The average is {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
Numpy\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u503c\u8ba1\u7b97\u51fd\u6570\uff0c\u5176\u4e2d np.mean()<\/code> \u51fd\u6570\u53ef\u4ee5\u76f4\u63a5\u8ba1\u7b97\u6570\u7ec4\u7684\u5e73\u5747\u6570\u3002\u4f7f\u7528Numpy\u5e93\u4e0d\u4ec5\u53ef\u4ee5\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\uff0c\u8fd8\u53ef\u4ee5\u65b9\u4fbf\u5730\u5904\u7406\u591a\u7ef4\u6570\u7ec4\u548c\u77e9\u9635\u8fd0\u7b97\u3002<\/strong><\/p>\n<\/p>\n
\u4e09\u3001\u81ea\u5b9a\u4e49\u51fd\u6570\u6c42\u5e73\u5747\u6570<\/p>\n
\u6709\u65f6\u6211\u4eec\u9700\u8981\u6839\u636e\u5177\u4f53\u9700\u6c42\u7f16\u5199\u81ea\u5b9a\u4e49\u51fd\u6570\u6765\u6c42\u5e73\u5747\u6570\uff0c\u4f8b\u5982\u9700\u8981\u5904\u7406\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u7b49\u60c5\u51b5\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
def calculate_average(data):<\/p>\n
    if not data:<\/p>\n
        return 0<\/p>\n
    total = 0<\/p>\n
    count = 0<\/p>\n
    for value in data:<\/p>\n
        if value is not None:  # \u5904\u7406\u7f3a\u5931\u503c<\/p>\n
            total += value<\/p>\n
            count += 1<\/p>\n
    return total \/ count if count != 0 else 0<\/p>\n
data = [1, 2, 3, None, 4, 5, 6, 7, 8, 9, 10]<\/p>\n
average = calculate_average(data)<\/p>\n
print(f"The average is {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u81ea\u5b9a\u4e49\u51fd\u6570\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u7075\u6d3b\u5904\u7406\u6570\u636e\uff0c\u4f8b\u5982\u5904\u7406\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u7b49\u3002\u901a\u8fc7\u7f16\u5199\u81ea\u5b9a\u4e49\u51fd\u6570\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u6ee1\u8db3\u7279\u5b9a\u9700\u6c42\uff0c\u63d0\u9ad8\u4ee3\u7801\u7684\u53ef\u8bfb\u6027\u548c\u53ef\u7ef4\u62a4\u6027\u3002<\/strong><\/p>\n<\/p>\n
\u56db\u3001\u4f7f\u7528Pandas\u5e93\u6c42\u5e73\u5747\u6570<\/p>\n
Pandas\u5e93\u662fPython\u4e2d\u5e38\u7528\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u7279\u522b\u9002\u7528\u4e8e\u5904\u7406\u8868\u683c\u6570\u636e\u3002\u4f7f\u7528Pandas\u5e93\u6c42\u5e73\u5747\u6570\u975e\u5e38\u65b9\u4fbf\uff0c\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])<\/p>\n
average = data.mean()<\/p>\n
print(f"The average is {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
Pandas\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5904\u7406\u51fd\u6570\uff0c\u5176\u4e2d mean()<\/code> \u51fd\u6570\u53ef\u4ee5\u76f4\u63a5\u8ba1\u7b97Series\u6216DataFrame\u7684\u5e73\u5747\u6570\u3002\u4f7f\u7528Pandas\u5e93\u4e0d\u4ec5\u53ef\u4ee5\u65b9\u4fbf\u5730\u5904\u7406\u8868\u683c\u6570\u636e\uff0c\u8fd8\u53ef\u4ee5\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3001\u8f6c\u6362\u7b49\u64cd\u4f5c\uff0c\u662f\u6570\u636e\u5206\u6790\u7684\u5f3a\u5927\u5de5\u5177\u3002<\/strong><\/p>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528\u7edf\u8ba1\u6a21\u5757\u6c42\u5e73\u5747\u6570<\/p>\n
Python\u5185\u7f6e\u7684\u7edf\u8ba1\u6a21\u5757\u63d0\u4f9b\u4e86\u4e00\u4e9b\u5e38\u7528\u7684\u7edf\u8ba1\u51fd\u6570\uff0c\u5305\u62ec\u8ba1\u7b97\u5e73\u5747\u6570\u7684 statistics.mean()<\/code> \u51fd\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import statistics<\/p>\n
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n
average = statistics.mean(data)<\/p>\n
print(f"The average is {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7edf\u8ba1\u6a21\u5757\u63d0\u4f9b\u4e86\u4e00\u4e9b\u5e38\u7528\u7684\u7edf\u8ba1\u51fd\u6570\uff0c\u4f7f\u7528\u8fd9\u4e9b\u51fd\u6570\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u3002statistics.mean()<\/code> \u51fd\u6570\u53ef\u4ee5\u76f4\u63a5\u8ba1\u7b97\u5217\u8868\u6216\u5143\u7ec4\u7684\u5e73\u5747\u6570\uff0c\u975e\u5e38\u9002\u5408\u8fdb\u884c\u7b80\u5355\u7684\u7edf\u8ba1\u5206\u6790\u3002<\/strong><\/p>\n<\/p>\n
\u516d\u3001\u5904\u7406\u591a\u7ef4\u6570\u7ec4\u7684\u5e73\u5747\u6570<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u80fd\u9700\u8981\u8ba1\u7b97\u591a\u7ef4\u6570\u7ec4\u7684\u5e73\u5747\u6570\uff0c\u4f8b\u5982\u4e8c\u7ef4\u6570\u7ec4\uff08\u77e9\u9635\uff09\u7684\u5e73\u5747\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528Numpy\u5e93\u8ba1\u7b97\u591a\u7ef4\u6570\u7ec4\u5e73\u5747\u6570\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
average = np.mean(data)<\/p>\n
print(f"The overall average is {average}")<\/p>\n
row_average = np.mean(data, axis=1)<\/p>\n
print(f"The row averages are {row_average}")<\/p>\n
column_average = np.mean(data, axis=0)<\/p>\n
print(f"The column averages are {column_average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528Numpy\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u8ba1\u7b97\u591a\u7ef4\u6570\u7ec4\u7684\u5e73\u5747\u6570\uff0c\u5e76\u53ef\u4ee5\u6307\u5b9a\u8ba1\u7b97\u7684\u7ef4\u5ea6\uff08\u884c\u6216\u5217\uff09\u3002\u8fd9\u79cd\u65b9\u6cd5\u7279\u522b\u9002\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u548c\u5de5\u7a0b\u5e94\u7528\u4e2d\u5bf9\u77e9\u9635\u7684\u64cd\u4f5c\u3002<\/strong><\/p>\n<\/p>\n
\u4e03\u3001\u5904\u7406\u5927\u6570\u636e\u96c6\u7684\u5e73\u5747\u6570<\/p>\n
\u5f53\u6570\u636e\u91cf\u8f83\u5927\u65f6\uff0c\u8ba1\u7b97\u5e73\u5747\u6570\u53ef\u80fd\u4f1a\u5360\u7528\u5927\u91cf\u5185\u5b58\u548c\u8ba1\u7b97\u8d44\u6e90\u3002\u6b64\u65f6\u53ef\u4ee5\u4f7f\u7528\u751f\u6210\u5668\u6216\u5206\u5757\u5904\u7406\u7684\u65b9\u6cd5\u6765\u8ba1\u7b97\u5e73\u5747\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
def calculate_average_large_dataset(file_path):<\/p>\n
    total = 0<\/p>\n
    count = 0<\/p>\n
    with open(file_path, 'r') as file:<\/p>\n
        for line in file:<\/p>\n
            value = float(line.strip())<\/p>\n
            total += value<\/p>\n
            count += 1<\/p>\n
    return total \/ count if count != 0 else 0<\/p>\n
file_path = 'large_dataset.txt'<\/p>\n
average = calculate_average_large_dataset(file_path)<\/p>\n
print(f"The average is {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5bf9\u4e8e\u5927\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u4f7f\u7528\u751f\u6210\u5668\u6216\u5206\u5757\u5904\u7406\u7684\u65b9\u6cd5\u6765\u9010\u6b65\u8ba1\u7b97\u5e73\u5747\u6570\uff0c\u4ee5\u51cf\u5c11\u5185\u5b58\u5360\u7528\u3002\u901a\u8fc7\u9010\u884c\u8bfb\u53d6\u6570\u636e\u5e76\u7d2f\u52a0\u6c42\u548c\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u5904\u7406\u5927\u6570\u636e\u96c6\u7684\u5e73\u5747\u6570\u8ba1\u7b97\u3002<\/strong><\/p>\n<\/p>\n
\u516b\u3001\u5904\u7406\u5e26\u6743\u91cd\u7684\u5e73\u5747\u6570<\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u8ba1\u7b97\u5e73\u5747\u6570\u65f6\u9700\u8981\u8003\u8651\u6743\u91cd\uff0c\u5373\u52a0\u6743\u5e73\u5747\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
def calculate_weighted_average(data, weights):<\/p>\n
    total = 0<\/p>\n
    total_weights = 0<\/p>\n
    for value, weight in zip(data, weights):<\/p>\n
        total += value * weight<\/p>\n
        total_weights += weight<\/p>\n
    return total \/ total_weights if total_weights != 0 else 0<\/p>\n
data = [1, 2, 3, 4, 5]<\/p>\n
weights = [0.1, 0.2, 0.3, 0.2, 0.2]<\/p>\n
average = calculate_weighted_average(data, weights)<\/p>\n
print(f"The weighted average is {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u52a0\u6743\u5e73\u5747\u6570\u8003\u8651\u4e86\u6bcf\u4e2a\u6570\u636e\u70b9\u7684\u6743\u91cd\uff0c\u53ef\u4ee5\u66f4\u51c6\u786e\u5730\u53cd\u6620\u6570\u636e\u7684\u91cd\u8981\u6027\u3002\u901a\u8fc7\u7f16\u5199\u81ea\u5b9a\u4e49\u51fd\u6570\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8ba1\u7b97\u52a0\u6743\u5e73\u5747\u6570\uff0c\u6ee1\u8db3\u7279\u5b9a\u9700\u6c42\u3002<\/strong><\/p>\n<\/p>\n
\u4e5d\u3001\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u5e73\u5747\u6570<\/p>\n
\u5728\u91d1\u878d\u548c\u7ecf\u6d4e\u7b49\u9886\u57df\uff0c\u7ecf\u5e38\u9700\u8981\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u5e76\u8ba1\u7b97\u5176\u5e73\u5747\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528Pandas\u5e93\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/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
print(f"The average is {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528Pandas\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u5e76\u8ba1\u7b97\u5176\u5e73\u5747\u6570\u3002Pandas\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u65f6\u95f4\u5e8f\u5217\u5904\u7406\u51fd\u6570\uff0c\u53ef\u4ee5\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u3001\u6570\u636e\u6e05\u6d17\u3001\u8f6c\u6362\u7b49\u64cd\u4f5c\u3002<\/strong><\/p>\n<\/p>\n
\u5341\u3001\u5904\u7406\u5206\u7ec4\u6570\u636e\u7684\u5e73\u5747\u6570<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u53ef\u80fd\u9700\u8981\u8ba1\u7b97\u5206\u7ec4\u6570\u636e\u7684\u5e73\u5747\u6570\u3002\u4f8b\u5982\uff0c\u8ba1\u7b97\u4e0d\u540c\u7c7b\u522b\u7684\u5e73\u5747\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528Pandas\u5e93\u5904\u7406\u5206\u7ec4\u6570\u636e\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = pd.DataFrame({<\/p>\n
    'category': ['A', 'A', 'B', 'B', 'C', 'C'],<\/p>\n
    'value': [1, 2, 3, 4, 5, 6]<\/p>\n
})<\/p>\n
grouped_average = data.groupby('category').mean()<\/p>\n
print(f"The grouped averages are:\\n{grouped_average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528Pandas\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u5904\u7406\u5206\u7ec4\u6570\u636e\uff0c\u5e76\u8ba1\u7b97\u5404\u7ec4\u7684\u5e73\u5747\u6570\u3002Pandas\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5206\u7ec4\u64cd\u4f5c\u51fd\u6570\uff0c\u53ef\u4ee5\u8fdb\u884c\u5206\u7ec4\u7edf\u8ba1\u3001\u805a\u5408\u7b49\u64cd\u4f5c\uff0c\u662f\u6570\u636e\u5206\u6790\u7684\u5f3a\u5927\u5de5\u5177\u3002<\/strong><\/p>\n<\/p>\n
\u5341\u4e00\u3001\u5904\u7406\u5e26\u7f3a\u5931\u503c\u7684\u6570\u636e\u7684\u5e73\u5747\u6570<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6570\u636e\u4e2d\u53ef\u80fd\u5b58\u5728\u7f3a\u5931\u503c\uff0c\u9700\u8981\u5728\u8ba1\u7b97\u5e73\u5747\u6570\u65f6\u8fdb\u884c\u5904\u7406\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528Pandas\u5e93\u5904\u7406\u5e26\u7f3a\u5931\u503c\u7684\u6570\u636e\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = pd.Series([1, 2, None, 4, 5, None, 7, 8, 9, 10])<\/p>\n
average = data.mean(skipna=True)  # skipna=True\u8868\u793a\u5ffd\u7565\u7f3a\u5931\u503c<\/p>\n
print(f"The average is {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u5904\u7406\u5e26\u7f3a\u5931\u503c\u7684\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u7684 mean()<\/code> \u51fd\u6570\uff0c\u5e76\u8bbe\u7f6e skipna=True<\/code> \u53c2\u6570\u6765\u5ffd\u7565\u7f3a\u5931\u503c\u3002\u8fd9\u6837\u53ef\u4ee5\u6709\u6548\u5730\u8ba1\u7b97\u5e26\u7f3a\u5931\u503c\u6570\u636e\u7684\u5e73\u5747\u6570\u3002<\/strong><\/p>\n<\/p>\n
\u5341\u4e8c\u3001\u5904\u7406\u5f02\u5e38\u503c\u7684\u6570\u636e\u7684\u5e73\u5747\u6570<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6570\u636e\u4e2d\u53ef\u80fd\u5b58\u5728\u5f02\u5e38\u503c\uff0c\u9700\u8981\u5728\u8ba1\u7b97\u5e73\u5747\u6570\u65f6\u8fdb\u884c\u5904\u7406\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
def calculate_average_without_outliers(data, threshold=1.5):<\/p>\n
    if not data:<\/p>\n
        return 0<\/p>\n
    q1 = np.percentile(data, 25)<\/p>\n
    q3 = np.percentile(data, 75)<\/p>\n
    iqr = q3 - q1<\/p>\n
    lower_bound = q1 - threshold * iqr<\/p>\n
    upper_bound = q3 + threshold * iqr<\/p>\n
    filtered_data = [x for x in data if lower_bound <= x <= upper_bound]<\/p>\n
    return np.mean(filtered_data) if filtered_data else 0<\/p>\n
data = [1, 2, 3, 100, 4, 5, 6, 7, 8, 9, 10]<\/p>\n
average = calculate_average_without_outliers(data)<\/p>\n
print(f"The average without outliers is {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u5904\u7406\u5f02\u5e38\u503c\u7684\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528\u56db\u5206\u4f4d\u8ddd\uff08IQR\uff09\u65b9\u6cd5\u6765\u8bc6\u522b\u548c\u5254\u9664\u5f02\u5e38\u503c\u3002\u901a\u8fc7\u7f16\u5199\u81ea\u5b9a\u4e49\u51fd\u6570\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u8ba1\u7b97\u53bb\u9664\u5f02\u5e38\u503c\u540e\u7684\u5e73\u5747\u6570\u3002<\/strong><\/p>\n<\/p>\n
\u5341\u4e09\u3001\u5904\u7406\u5b9e\u65f6\u6570\u636e\u6d41\u7684\u5e73\u5747\u6570<\/p>\n
\u5728\u5b9e\u65f6\u6570\u636e\u5904\u7406\u573a\u666f\u4e2d\uff0c\u9700\u8981\u8ba1\u7b97\u5b9e\u65f6\u6570\u636e\u6d41\u7684\u5e73\u5747\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
def calculate_running_average(new_value, current_average, count):<\/p>\n
    return (current_average * count + new_value) \/ (count + 1)<\/p>\n
current_average = 0<\/p>\n
count = 0<\/p>\n
data_stream = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n
for value in data_stream:<\/p>\n
    count += 1<\/p>\n
    current_average = calculate_running_average(value, current_average, count)<\/p>\n
    print(f"New value: {value}, Running average: {current_average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u5904\u7406\u5b9e\u65f6\u6570\u636e\u6d41\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528\u8fd0\u884c\u5e73\u5747\u6570\u7684\u65b9\u6cd5\u3002\u901a\u8fc7\u9010\u6b65\u66f4\u65b0\u5e73\u5747\u6570\uff0c\u53ef\u4ee5\u5b9e\u65f6\u8ba1\u7b97\u6570\u636e\u6d41\u7684\u5e73\u5747\u6570\uff0c\u9002\u7528\u4e8e\u5b9e\u65f6\u76d1\u63a7\u548c\u5206\u6790\u3002<\/strong><\/p>\n<\/p>\n
\u5341\u56db\u3001\u5904\u7406\u5206\u4f4d\u6570\u7684\u5e73\u5747\u6570<\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u9700\u8981\u8ba1\u7b97\u5206\u4f4d\u6570\u7684\u5e73\u5747\u6570\uff0c\u4f8b\u5982\u4e2d\u4f4d\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n
median = np.median(data)<\/p>\n
print(f"The median is {median}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5206\u4f4d\u6570\uff08\u4f8b\u5982\u4e2d\u4f4d\u6570\uff09\u662f\u6570\u636e\u96c6\u4e2d\u95f4\u4f4d\u7f6e\u7684\u503c\uff0c\u53ef\u4ee5\u53cd\u6620\u6570\u636e\u7684\u96c6\u4e2d\u8d8b\u52bf\u3002\u4f7f\u7528Numpy\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u8ba1\u7b97\u5206\u4f4d\u6570\uff0c\u9002\u7528\u4e8e\u7edf\u8ba1\u5206\u6790\u3002<\/strong><\/p>\n<\/p>\n
\u5341\u4e94\u3001\u603b\u7ed3<\/p>\n
\u7efc\u4e0a\u6240\u8ff0\uff0c\u4f7f\u7528Python\u6c42\u5e73\u5747\u6570\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002\u5185\u7f6e\u51fd\u6570\u9002\u7528\u4e8e\u7b80\u5355\u7684\u5217\u8868\u6216\u5143\u7ec4\uff0cNumpy\u5e93\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u503c\u8ba1\u7b97\uff0c\u81ea\u5b9a\u4e49\u51fd\u6570\u53ef\u4ee5\u7075\u6d3b\u5904\u7406\u590d\u6742\u9700\u6c42\uff0cPandas\u5e93\u9002\u7528\u4e8e\u6570\u636e\u5206\u6790\uff0c\u7edf\u8ba1\u6a21\u5757\u63d0\u4f9b\u4e86\u5e38\u7528\u7684\u7edf\u8ba1\u51fd\u6570\uff0c\u751f\u6210\u5668\u548c\u5206\u5757\u5904\u7406\u9002\u7528\u4e8e\u5927\u6570\u636e\u96c6\uff0c\u52a0\u6743\u5e73\u5747\u6570\u3001\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3001\u5206\u7ec4\u6570\u636e\u3001\u5e26\u7f3a\u5931\u503c\u6570\u636e\u3001\u5f02\u5e38\u503c\u6570\u636e\u3001\u5b9e\u65f6\u6570\u636e\u6d41\u548c\u5206\u4f4d\u6570\u7684\u5e73\u5747\u6570\u7b49\u7279\u5b9a\u9700\u6c42\u53ef\u4ee5\u901a\u8fc7\u76f8\u5e94\u7684\u65b9\u6cd5\u5904\u7406\u3002\u901a\u8fc7\u638c\u63e1\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u5206\u6790\uff0c\u63d0\u9ad8\u5de5\u4f5c\u6548\u7387\u548c\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8ba1\u7b97\u4e00\u7ec4\u6570\u7684\u5e73\u5747\u6570\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u5229\u7528\u5185\u7f6e\u7684sum()<\/code>\u51fd\u6570\u548clen()<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u4e00\u7ec4\u6570\u7684\u5e73\u5747\u6570\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\u5c06\u6570\u636e\u5b58\u50a8\u5728\u4e00\u4e2a\u5217\u8868\u4e2d\uff0c\u4f7f\u7528sum()<\/code>\u8ba1\u7b97\u603b\u548c\uff0c\u7136\u540e\u7528\u603b\u548c\u9664\u4ee5\u5217\u8868\u7684\u957f\u5ea6\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
numbers = [10, 20, 30, 40, 50]\naverage = sum(numbers) \/ len(numbers)\nprint("\u5e73\u5747\u6570\u4e3a:", average)\n<\/code><\/pre>\n
\u5728Python\u4e2d\u5904\u7406\u7f3a\u5931\u503c\u65f6\uff0c\u5982\u4f55\u8ba1\u7b97\u5e73\u5747\u6570\uff1f<\/strong>
\u5728\u5904\u7406\u6570\u636e\u65f6\uff0c\u7f3a\u5931\u503c\u53ef\u80fd\u5f71\u54cd\u5e73\u5747\u6570\u7684\u8ba1\u7b97\u3002\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u6765\u5904\u7406\u7f3a\u5931\u503c\uff0c\u786e\u4fdd\u51c6\u786e\u8ba1\u7b97\u5e73\u5747\u6570\u3002\u53ef\u4ee5\u9009\u62e9\u5728\u8ba1\u7b97\u5e73\u5747\u6570\u524d\u5220\u9664\u7f3a\u5931\u503c\u6216\u7528\u5176\u4ed6\u503c\u586b\u5145\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
import pandas as pd\n\ndata = [10, 20, None, 40, 50]\nseries = pd.Series(data)\naverage = series.mean()  # \u81ea\u52a8\u5ffd\u7565\u7f3a\u5931\u503c\nprint("\u5904\u7406\u7f3a\u5931\u503c\u540e\u7684\u5e73\u5747\u6570\u4e3a:", average)\n<\/code><\/pre>\n
\u6709\u4ec0\u4e48Python\u5e93\u53ef\u4ee5\u7b80\u5316\u5e73\u5747\u6570\u7684\u8ba1\u7b97\uff1f<\/strong>
Python\u4e2d\u6709\u591a\u4e2a\u5e93\u53ef\u4ee5\u7b80\u5316\u7edf\u8ba1\u8ba1\u7b97\uff0c\u4f8b\u5982numpy<\/code>\u548cpandas<\/code>\u3002\u4f7f\u7528numpy<\/code>\u5e93\u7684mean()<\/code>\u51fd\u6570\u53ef\u4ee5\u5feb\u901f\u8ba1\u7b97\u5e73\u5747\u6570\uff0c\u800cpandas<\/code>\u5e93\u5219\u9002\u5408\u5904\u7406\u66f4\u590d\u6742\u7684\u6570\u636e\u7ed3\u6784\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
import numpy as np\n\nnumbers = [10, 20, 30, 40, 50]\naverage = np.mean(numbers)\nprint("\u4f7f\u7528numpy\u8ba1\u7b97\u7684\u5e73\u5747\u6570\u4e3a:", average)\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u6765\u6c42\u5e73\u5747\u6570\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff1a\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u3001\u4f7f\u7528Numpy\u5e93\u3001\u7f16\u5199\u81ea\u5b9a\u4e49\u51fd\u6570\u7b49\u3002\u5185\u7f6e\u51fd\u6570\u65b9\u4fbf\u5feb\u6377\u3001N […]","protected":false},"author":3,"featured_media":1080530,"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\/1080522"}],"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=1080522"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1080522\/revisions"}],"predecessor-version":[{"id":1080531,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1080522\/revisions\/1080531"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1080530"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1080522"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1080522"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1080522"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}