{"id":979652,"date":"2024-12-27T06:48:15","date_gmt":"2024-12-26T22:48:15","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/979652.html"},"modified":"2024-12-27T06:48:18","modified_gmt":"2024-12-26T22:48:18","slug":"python%e5%a6%82%e4%bd%95%e5%ae%9e%e7%8e%b0%e5%8a%a0%e6%9d%83%e6%b1%82%e5%92%8c","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/979652.html","title":{"rendered":"python\u5982\u4f55\u5b9e\u73b0\u52a0\u6743\u6c42\u548c"},"content":{"rendered":"

\"python\u5982\u4f55\u5b9e\u73b0\u52a0\u6743\u6c42\u548c\"<\/p>\n

\u5728Python\u4e2d\uff0c\u5b9e\u73b0\u52a0\u6743\u6c42\u548c\u7684\u5e38\u7528\u65b9\u6cd5\u662f\u4f7f\u7528\u5217\u8868\u548c\u5faa\u73af\u3001NumPy\u5e93\u6216pandas\u5e93\u3002NumPy\u5e93\u56e0\u5176\u9ad8\u6548\u7684\u6570\u7ec4\u8fd0\u7b97\u80fd\u529b\uff0c\u662f\u52a0\u6743\u6c42\u548c\u6700\u5e38\u7528\u7684\u5de5\u5177\u4e4b\u4e00\u3002<\/strong><\/p>\n<\/p>\n

\u52a0\u6743\u6c42\u548c\u7684\u6838\u5fc3\u601d\u60f3\u662f\u5c06\u6bcf\u4e2a\u5143\u7d20\u4e58\u4ee5\u5176\u5bf9\u5e94\u7684\u6743\u91cd\uff0c\u7136\u540e\u5c06\u6240\u6709\u4e58\u79ef\u76f8\u52a0\uff0c\u4ece\u800c\u5f97\u5230\u6700\u7ec8\u7684\u52a0\u6743\u548c\u3002\u5177\u4f53\u5b9e\u73b0\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684\u57fa\u672c\u5faa\u73af\u6765\u904d\u5386\u6570\u636e\u548c\u6743\u91cd\uff0c\u9010\u4e00\u8ba1\u7b97\u4e58\u79ef\u5e76\u7d2f\u52a0\uff0c\u4e5f\u53ef\u4ee5\u501f\u52a9NumPy\u5e93\u8fdb\u884c\u5411\u91cf\u5316\u8fd0\u7b97\uff0c\u4ece\u800c\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u548c\u4ee3\u7801\u7b80\u6d01\u6027<\/strong>\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u51e0\u79cd\u65b9\u6cd5\u6765\u5b9e\u73b0\u52a0\u6743\u6c42\u548c\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u57fa\u672c\u5faa\u73af\u5b9e\u73b0\u52a0\u6743\u6c42\u548c<\/p>\n<\/p>\n

\u5728\u6ca1\u6709\u7b2c\u4e09\u65b9\u5e93\u7684\u60c5\u51b5\u4e0b\uff0c\u53ef\u4ee5\u901a\u8fc7Python\u7684\u57fa\u672c\u5faa\u73af\u5b9e\u73b0\u52a0\u6743\u6c42\u548c\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u5408\u521d\u5b66\u8005\u6216\u4e0d\u9700\u8981\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u4f7f\u7528\u3002<\/p>\n<\/p>\n

def weighted_sum(values, weights):<\/p>\n
    if len(values) != len(weights):<\/p>\n
        rAI<\/a>se ValueError("Values and weights must have the same length.")<\/p>\n
    weighted_sum = 0<\/p>\n
    for v, w in zip(values, weights):<\/p>\n
        weighted_sum += v * w<\/p>\n
    return weighted_sum<\/p>\n
values = [10, 20, 30]<\/p>\n
weights = [0.1, 0.3, 0.6]<\/p>\n
result = weighted_sum(values, weights)<\/p>\n
print(f"The weighted sum is: {result}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u6b64\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u68c0\u67e5\u8f93\u5165\u5217\u8868\u7684\u957f\u5ea6\u662f\u5426\u4e00\u81f4\uff0c\u7136\u540e\u901a\u8fc7zip<\/code>\u51fd\u6570\u5c06\u503c\u548c\u6743\u91cd\u914d\u5bf9\uff0c\u5faa\u73af\u904d\u5386\u6bcf\u5bf9\u5143\u7d20\uff0c\u8ba1\u7b97\u4e58\u79ef\u5e76\u7d2f\u52a0\u5230weighted_sum<\/code>\u4e2d\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528NumPy\u5b9e\u73b0\u52a0\u6743\u6c42\u548c<\/p>\n<\/p>\n
NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\u529f\u80fd\u3002\u4f7f\u7528NumPy\u8fdb\u884c\u52a0\u6743\u6c42\u548c\u53ef\u4ee5\u5927\u5927\u7b80\u5316\u4ee3\u7801\uff0c\u5e76\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
def weighted_sum_numpy(values, weights):<\/p>\n
    values_array = np.array(values)<\/p>\n
    weights_array = np.array(weights)<\/p>\n
    if values_array.shape != weights_array.shape:<\/p>\n
        raise ValueError("Values and weights must have the same shape.")<\/p>\n
    return np.sum(values_array * weights_array)<\/p>\n
values = [10, 20, 30]<\/p>\n
weights = [0.1, 0.3, 0.6]<\/p>\n
result = weighted_sum_numpy(values, weights)<\/p>\n
print(f"The weighted sum using NumPy is: {result}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4f7f\u7528NumPy\uff0c\u6211\u4eec\u9996\u5148\u5c06\u5217\u8868\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4\uff0c\u7136\u540e\u76f4\u63a5\u5bf9\u4e24\u4e2a\u6570\u7ec4\u8fdb\u884c\u5143\u7d20\u7ea7\u4e58\u6cd5\u8fd0\u7b97\uff0c\u6700\u540e\u4f7f\u7528np.sum<\/code>\u51fd\u6570\u8ba1\u7b97\u603b\u548c\u3002\u8fd9\u6837\u4e0d\u4ec5\u63d0\u9ad8\u4e86\u4ee3\u7801\u7684\u53ef\u8bfb\u6027\uff0c\u8fd8\u589e\u5f3a\u4e86\u8ba1\u7b97\u6548\u7387\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528pandas\u5b9e\u73b0\u52a0\u6743\u6c42\u548c<\/p>\n<\/p>\n
pandas\u662f\u4e00\u4e2a\u7528\u4e8e\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u7684\u5f3a\u5927\u5e93\uff0c\u5c24\u5176\u5728\u6570\u636e\u6846\u683c\u5f0f\u4e0b\uff0c\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u52a0\u6743\u6c42\u548c\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
def weighted_sum_pandas(values, weights):<\/p>\n
    df = pd.DataFrame({'Values': values, 'Weights': weights})<\/p>\n
    df['Weighted'] = df['Values'] * df['Weights']<\/p>\n
    return df['Weighted'].sum()<\/p>\n
values = [10, 20, 30]<\/p>\n
weights = [0.1, 0.3, 0.6]<\/p>\n
result = weighted_sum_pandas(values, weights)<\/p>\n
print(f"The weighted sum using pandas is: {result}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u503c\u548c\u6743\u91cd\u7684DataFrame\uff0c\u7136\u540e\u901a\u8fc7\u65b0\u589e\u4e00\u5217\u6765\u5b58\u50a8\u6bcf\u4e2a\u503c\u4e0e\u5176\u5bf9\u5e94\u6743\u91cd\u7684\u4e58\u79ef\uff0c\u6700\u540e\u5bf9\u8be5\u5217\u6c42\u548c\u5f97\u5230\u52a0\u6743\u548c\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5e94\u7528\u573a\u666f\u5206\u6790<\/p>\n<\/p>\n
    \n
  1. \n
    \u91d1\u878d\u6570\u636e\u5206\u6790<\/strong>\uff1a\u5728\u6295\u8d44\u7ec4\u5408\u7ba1\u7406\u4e2d\uff0c\u52a0\u6743\u6c42\u548c\u7528\u4e8e\u8ba1\u7b97\u7ec4\u5408\u7684\u603b\u6536\u76ca\u6216\u98ce\u9669\uff0c\u6743\u91cd\u4ee3\u8868\u6bcf\u79cd\u8d44\u4ea7\u5728\u7ec4\u5408\u4e2d\u7684\u5360\u6bd4\u3002<\/p>\n<\/p>\n<\/li>\n
  2. \n
    \u6559\u80b2\u8bc4\u5206\u7cfb\u7edf<\/strong>\uff1a\u5728\u4e00\u4e9b\u6559\u80b2\u7cfb\u7edf\u4e2d\uff0c\u8bfe\u7a0b\u6210\u7ee9\u7684\u6700\u7ec8\u5f97\u5206\u53ef\u80fd\u662f\u5404\u9879\u8bc4\u5206\u6309\u6743\u91cd\u52a0\u6743\u6c42\u548c\u7684\u7ed3\u679c\uff0c\u6743\u91cd\u8868\u793a\u5404\u9879\u8bc4\u5206\u7684\u91cd\u8981\u6027\u3002<\/p>\n<\/p>\n<\/li>\n
  3. \n
    \u7edf\u8ba1\u4e0e\u6570\u636e\u5206\u6790<\/strong>\uff1a\u5728\u7edf\u8ba1\u5206\u6790\u4e2d\uff0c\u52a0\u6743\u6c42\u548c\u7528\u4e8e\u5904\u7406\u52a0\u6743\u5e73\u5747\u6570\u6216\u6307\u6570\u7b49\u573a\u666f\uff0c\u6743\u91cd\u4ee3\u8868\u5404\u6570\u636e\u70b9\u7684\u91cd\u8981\u6027\u3002<\/p>\n<\/p>\n<\/li>\n
  4. \n
    \u5de5\u7a0b\u4e0e\u7269\u7406\u8ba1\u7b97<\/strong>\uff1a\u5728\u67d0\u4e9b\u5de5\u7a0b\u5e94\u7528\u4e2d\uff0c\u6d4b\u91cf\u6570\u636e\u53ef\u80fd\u9700\u8981\u6839\u636e\u8bbe\u5907\u7684\u53ef\u9760\u6027\u6216\u5b9e\u9a8c\u6761\u4ef6\u8fdb\u884c\u52a0\u6743\uff0c\u4ee5\u5f97\u5230\u66f4\u51c6\u786e\u7684\u7ed3\u679c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e94\u3001\u4f18\u5316\u4e0e\u6ce8\u610f\u4e8b\u9879<\/p>\n<\/p>\n
      \n
    1. \n
      \u6570\u636e\u4e00\u81f4\u6027\u68c0\u67e5<\/strong>\uff1a\u5728\u8fdb\u884c\u52a0\u6743\u6c42\u548c\u4e4b\u524d\uff0c\u786e\u4fdd\u503c\u5217\u8868\u548c\u6743\u91cd\u5217\u8868\u957f\u5ea6\u4e00\u81f4\uff0c\u5426\u5219\u4f1a\u5bfc\u81f4\u8ba1\u7b97\u9519\u8bef\u3002<\/p>\n<\/p>\n<\/li>\n
    2. \n
      \u4f7f\u7528\u5411\u91cf\u5316\u64cd\u4f5c<\/strong>\uff1a\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\uff0c\u4f18\u5148\u8003\u8651\u4f7f\u7528NumPy\u7684\u5411\u91cf\u5316\u64cd\u4f5c\uff0c\u8fd9\u80fd\u591f\u663e\u8457\u63d0\u9ad8\u8ba1\u7b97\u901f\u5ea6\u548c\u6548\u7387\u3002<\/p>\n<\/p>\n<\/li>\n
    3. \n
      \u5f02\u5e38\u5904\u7406<\/strong>\uff1a\u5728\u51fd\u6570\u4e2d\u6dfb\u52a0\u5f02\u5e38\u5904\u7406\u673a\u5236\uff0c\u4f8b\u5982\u5f53\u8f93\u5165\u5217\u8868\u957f\u5ea6\u4e0d\u4e00\u81f4\u65f6\u629b\u51fa\u5f02\u5e38\uff0c\u4ee5\u63d0\u9ad8\u4ee3\u7801\u7684\u5065\u58ee\u6027\u3002<\/p>\n<\/p>\n<\/li>\n
    4. \n
      \u6570\u636e\u7cbe\u5ea6\u95ee\u9898<\/strong>\uff1a\u5728\u6d89\u53ca\u5c0f\u6570\u8fd0\u7b97\u65f6\uff0c\u6ce8\u610f\u6570\u636e\u7cbe\u5ea6\u95ee\u9898\uff0c\u5fc5\u8981\u65f6\u53ef\u4ee5\u4f7f\u7528decimal<\/code>\u6a21\u5757\u63d0\u9ad8\u7cbe\u5ea6\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
      \u516d\u3001\u603b\u7ed3<\/p>\n<\/p>\n
      \u52a0\u6743\u6c42\u548c\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u57fa\u7840\u64cd\u4f5c\uff0c\u867d\u7136\u5b9e\u73b0\u8d77\u6765\u76f8\u5bf9\u7b80\u5355\uff0c\u4f46\u5728\u4e0d\u540c\u573a\u666f\u4e0b\u6709\u4e0d\u540c\u7684\u5b9e\u73b0\u65b9\u5f0f\u3002\u901a\u8fc7\u57fa\u672c\u5faa\u73af\u3001NumPy\u6216pandas\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002\u5728\u5927\u591a\u6570\u60c5\u51b5\u4e0b\uff0cNumPy\u548cpandas\u56e0\u5176\u9ad8\u6548\u7684\u8ba1\u7b97\u80fd\u529b\u548c\u7b80\u6d01\u7684\u8bed\u6cd5\uff0c\u662f\u9996\u9009\u7684\u5de5\u5177\u3002\u65e0\u8bba\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\uff0c\u90fd\u9700\u8981\u6ce8\u610f\u6570\u636e\u7684\u4e00\u81f4\u6027\u548c\u51c6\u786e\u6027\uff0c\u4ee5\u786e\u4fdd\u8ba1\u7b97\u7ed3\u679c\u7684\u6b63\u786e\u6027\u3002<\/p>\n<\/p>\n
      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
       \u5982\u4f55\u5728Python\u4e2d\u8fdb\u884c\u52a0\u6743\u6c42\u548c\u7684\u64cd\u4f5c\uff1f<\/strong>
      \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u548cnumpy\u5e93\u6765\u5b9e\u73b0\u52a0\u6743\u6c42\u548c\u3002\u60a8\u53ef\u4ee5\u5c06\u6570\u636e\u548c\u76f8\u5e94\u7684\u6743\u91cd\u653e\u5165\u4e24\u4e2a\u5217\u8868\u4e2d\uff0c\u7136\u540e\u4f7f\u7528numpy\u7684dot\u51fd\u6570\u8fdb\u884c\u8ba1\u7b97\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
      import numpy as np\n\ndata = [10, 20, 30]  # \u6570\u636e\nweights = [0.2, 0.3, 0.5]  # \u6743\u91cd\nweighted_sum = np.dot(data, weights)  # \u52a0\u6743\u6c42\u548c\nprint(weighted_sum)  # \u8f93\u51fa\u7ed3\u679c\n<\/code><\/pre>\n
      \u8fd9\u4e2a\u65b9\u6cd5\u975e\u5e38\u7b80\u6d01\u4e14\u9ad8\u6548\uff0c\u9002\u5408\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u3002<\/p>\n
      Python\u4e2d\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u5e2e\u52a9\u5b9e\u73b0\u52a0\u6743\u6c42\u548c\uff1f<\/strong>
      \u9664\u4e86numpy\uff0cpandas\u4e5f\u662f\u4e00\u4e2a\u975e\u5e38\u6d41\u884c\u7684\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u8fdb\u884c\u52a0\u6743\u6c42\u548c\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528DataFrame\u6765\u5b58\u50a8\u6570\u636e\u548c\u6743\u91cd\uff0c\u5e76\u5229\u7528apply\u65b9\u6cd5\u8fdb\u884c\u8ba1\u7b97\u3002\u5982\u4e0b\u6240\u793a\uff1a  <\/p>\n
      import pandas as pd\n\ndf = pd.DataFrame({\n    'data': [10, 20, 30],\n    'weights': [0.2, 0.3, 0.5]\n})\n\nweighted_sum = (df['data'] * df['weights']).sum()  # \u52a0\u6743\u6c42\u548c\nprint(weighted_sum)  # \u8f93\u51fa\u7ed3\u679c\n<\/code><\/pre>\n
      \u8fd9\u79cd\u65b9\u5f0f\u63d0\u4f9b\u4e86\u66f4\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\uff0c\u9002\u5408\u8fdb\u884c\u590d\u6742\u7684\u6570\u636e\u5206\u6790\u3002<\/p>\n
      \u5728\u8fdb\u884c\u52a0\u6743\u6c42\u548c\u65f6\uff0c\u5982\u4f55\u786e\u4fdd\u6743\u91cd\u7684\u5408\u7406\u6027\uff1f<\/strong>
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