{"id":1126825,"date":"2025-01-08T20:04:42","date_gmt":"2025-01-08T12:04:42","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1126825.html"},"modified":"2025-01-08T20:04:44","modified_gmt":"2025-01-08T12:04:44","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%ad%9b%e5%87%ba%e4%b8%a4%e5%88%97%e7%9b%b8%e7%ad%89%e7%9a%84%e6%96%87%e6%9c%ac","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1126825.html","title":{"rendered":"\u5982\u4f55\u7528python\u7b5b\u51fa\u4e24\u5217\u76f8\u7b49\u7684\u6587\u672c"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u7b5b\u51fa\u4e24\u5217\u76f8\u7b49\u7684\u6587\u672c\"<\/p>\n

\u5982\u4f55\u7528Python\u7b5b\u51fa\u4e24\u5217\u76f8\u7b49\u7684\u6587\u672c<\/strong><\/p>\n<\/p>\n

\u5728\u5904\u7406\u6570\u636e\u65f6\uff0c\u7279\u522b\u662f\u5f53\u6211\u4eec\u9700\u8981\u6bd4\u8f83\u4e24\u5217\u6587\u672c\u6570\u636e\u5e76\u7b5b\u9009\u51fa\u5176\u4e2d\u76f8\u7b49\u7684\u90e8\u5206\u65f6\uff0cPython\u63d0\u4f9b\u4e86\u591a\u79cd\u9ad8\u6548\u7684\u65b9\u6cd5\u3002Pandas\u5e93\u3001\u6761\u4ef6\u7d22\u5f15\u3001\u5217\u8868\u63a8\u5bfc\u5f0f<\/strong>\u662f\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\u7684\u6709\u6548\u5de5\u5177\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u6765\u7b5b\u9009\u51fa\u4e24\u5217\u76f8\u7b49\u7684\u6587\u672c\uff0c\u5e76\u63a2\u8ba8\u5176\u4e2d\u7684\u7ec6\u8282\u548c\u6700\u4f73\u5b9e\u8df5\u3002\u6211\u4eec\u5c06\u91cd\u70b9\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Pandas\u5e93<\/strong>\u6765\u5b9e\u73b0\u8fd9\u4e00\u4efb\u52a1\uff0c\u56e0\u4e3a\u5b83\u662f\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u7684\u5f3a\u5927\u5de5\u5177\u3002<\/p>\n<\/p>\n

\u4e00\u3001Pandas\u5e93<\/h3>\n<\/p>\n

Pandas\u662fPython\u4e2d\u6700\u53d7\u6b22\u8fce\u7684\u6570\u636e\u5904\u7406\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u8f7b\u677e\u5730\u52a0\u8f7d\u3001\u64cd\u4f5c\u548c\u5206\u6790\u6570\u636e\u3002<\/p>\n<\/p>\n

1.1 \u5b89\u88c5Pandas<\/h4>\n<\/p>\n

\u5982\u679c\u4f60\u8fd8\u6ca1\u6709\u5b89\u88c5Pandas\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2 \u5bfc\u5165\u6570\u636e<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165Pandas\u5e93\u5e76\u52a0\u8f7d\u6570\u636e\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2aCSV\u6587\u4ef6\uff0c\u5176\u4e2d\u5305\u542b\u4e24\u5217\u6587\u672c\u6570\u636e\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
df = pd.read_csv('data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3 \u6bd4\u8f83\u4e24\u5217\u6587\u672c<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u7684\u5185\u7f6e\u529f\u80fd\u6765\u6bd4\u8f83\u4e24\u5217\u6587\u672c\uff0c\u5e76\u7b5b\u9009\u51fa\u76f8\u7b49\u7684\u90e8\u5206\uff1a<\/p>\n<\/p>\n
# \u5047\u8bbe\u6211\u4eec\u7684\u6570\u636e\u6846\u5305\u542b 'column1' \u548c 'column2'<\/p>\n
equal_rows = df[df['column1'] == df['column2']]<\/p>\n
print(equal_rows)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e2a\u7b80\u5355\u7684\u4ee3\u7801\u6bb5\u5c06\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684\u6570\u636e\u6846\uff0c\u5176\u4e2d\u5305\u542b\u4e24\u5217\u6587\u672c\u76f8\u7b49\u7684\u884c\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u6761\u4ef6\u7d22\u5f15<\/h3>\n<\/p>\n
\u6761\u4ef6\u7d22\u5f15\u662fPython\u4e2d\u53e6\u4e00\u79cd\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5de5\u5177\u3002\u901a\u8fc7\u4f7f\u7528\u6761\u4ef6\u7d22\u5f15\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u52a0\u7075\u6d3b\u5730\u7b5b\u9009\u6570\u636e\u3002<\/p>\n<\/p>\n
2.1 \u4f7f\u7528\u6761\u4ef6\u7d22\u5f15<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b\u6570\u636e\uff1a<\/p>\n<\/p>\n
data = [<\/p>\n
    {'column1': 'apple', 'column2': 'apple'},<\/p>\n
    {'column1': 'banana', 'column2': 'orange'},<\/p>\n
    {'column1': 'cherry', 'column2': 'cherry'}<\/p>\n
]<\/p>\n
df = pd.DataFrame(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u6761\u4ef6\u7d22\u5f15\u6765\u7b5b\u9009\u51fa\u76f8\u7b49\u7684\u6587\u672c\uff1a<\/p>\n<\/p>\n
equal_rows = df[df['column1'] == df['column2']]<\/p>\n
print(equal_rows)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u5217\u8868\u63a8\u5bfc\u5f0f<\/h3>\n<\/p>\n
\u5217\u8868\u63a8\u5bfc\u5f0f\u662fPython\u4e2d\u4e00\u79cd\u7b80\u6d01\u9ad8\u6548\u7684\u521b\u5efa\u5217\u8868\u7684\u65b9\u6cd5\u3002\u6211\u4eec\u4e5f\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f\u6765\u6bd4\u8f83\u4e24\u5217\u6587\u672c\u3002<\/p>\n<\/p>\n
3.1 \u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b\u6570\u636e\uff1a<\/p>\n<\/p>\n
column1 = ['apple', 'banana', 'cherry']<\/p>\n
column2 = ['apple', 'orange', 'cherry']<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f\u6765\u7b5b\u9009\u51fa\u76f8\u7b49\u7684\u6587\u672c\uff1a<\/p>\n<\/p>\n
equal_texts = [(c1, c2) for c1, c2 in zip(column1, column2) if c1 == c2]<\/p>\n
print(equal_texts)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e2a\u4ee3\u7801\u6bb5\u5c06\u8fd4\u56de\u4e00\u4e2a\u5305\u542b\u76f8\u7b49\u6587\u672c\u7684\u5217\u8868\u3002<\/p>\n<\/p>\n
\u56db\u3001\u8be6\u7ec6\u8ba8\u8bbaPandas\u5e93\u7684\u4f7f\u7528<\/h3>\n<\/p>\n
\u5728\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u4e2d\uff0cPandas\u5e93\u7684\u4f7f\u7528\u662f\u975e\u5e38\u5e7f\u6cdb\u548c\u6df1\u5165\u7684\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u8ba8\u8bba\u5982\u4f55\u5229\u7528Pandas\u5e93\u6765\u5904\u7406\u66f4\u590d\u6742\u7684\u6587\u672c\u6bd4\u8f83\u548c\u7b5b\u9009\u4efb\u52a1\u3002<\/p>\n<\/p>\n
4.1 \u52a0\u8f7d\u66f4\u590d\u6742\u7684\u6570\u636e<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u66f4\u590d\u6742\u7684CSV\u6587\u4ef6\uff0c\u5176\u4e2d\u5305\u542b\u591a\u5217\u6570\u636e\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
df = pd.read_csv('complex_data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2 \u6bd4\u8f83\u591a\u5217\u6587\u672c<\/h4>\n<\/p>\n
\u9664\u4e86\u6bd4\u8f83\u4e24\u5217\u6587\u672c\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u6bd4\u8f83\u591a\u5217\u6587\u672c\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e09\u5217\u6587\u672c\u6570\u636e\uff0c\u6211\u4eec\u9700\u8981\u627e\u51fa\u4e09\u5217\u6587\u672c\u4e2d\u76f8\u7b49\u7684\u884c\uff1a<\/p>\n<\/p>\n
equal_rows = df[(df['column1'] == df['column2']) & (df['column2'] == df['column3'])]<\/p>\n
print(equal_rows)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.3 \u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
\u5728\u5b9e\u9645\u6570\u636e\u5904\u7406\u4e2d\uff0c\u6211\u4eec\u53ef\u80fd\u4f1a\u9047\u5230\u7f3a\u5931\u503c\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u7684\u5185\u7f6e\u51fd\u6570\u6765\u5904\u7406\u7f3a\u5931\u503c\uff0c\u7136\u540e\u8fdb\u884c\u6bd4\u8f83\uff1a<\/p>\n<\/p>\n
# \u586b\u5145\u7f3a\u5931\u503c<\/p>\n
df.fillna('', inplace=True)<\/p>\n
\u6bd4\u8f83\u4e24\u5217\u6587\u672c<\/strong><\/h2>\n
equal_rows = df[df['column1'] == df['column2']]<\/p>\n
print(equal_rows)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u4f18\u5316\u548c\u63d0\u5347\u6027\u80fd<\/h3>\n<\/p>\n
\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\uff0c\u6027\u80fd\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u8003\u8651\u56e0\u7d20\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u6765\u4f18\u5316\u548c\u63d0\u5347\u6027\u80fd\u3002<\/p>\n<\/p>\n
5.1 \u4f7f\u7528NumPy\u52a0\u901f\u8ba1\u7b97<\/h4>\n<\/p>\n
NumPy\u662f\u53e6\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u5b83\u53ef\u4ee5\u52a0\u901f\u6570\u7ec4\u548c\u77e9\u9635\u8fd0\u7b97\u3002\u6211\u4eec\u53ef\u4ee5\u7ed3\u5408Pandas\u548cNumPy\u6765\u63d0\u5347\u6027\u80fd\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
df = pd.read_csv('data.csv')<\/p>\n
\u4f7f\u7528NumPy\u8fdb\u884c\u6bd4\u8f83<\/strong><\/h2>\n
equal_rows = df[np.where(df['column1'] == df['column2'], True, False)]<\/p>\n
print(equal_rows)<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.2 \u5e76\u884c\u5904\u7406<\/h4>\n<\/p>\n
\u5bf9\u4e8e\u975e\u5e38\u5927\u7684\u6570\u636e\u96c6\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5e76\u884c\u5904\u7406\u6765\u63d0\u5347\u6027\u80fd\u3002Python\u4e2d\u7684\u591a\u7ebf\u7a0b\u548c\u591a\u8fdb\u7a0b\u5e93\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5b9e\u73b0\u5e76\u884c\u5904\u7406\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
from multiprocessing import Pool<\/p>\n
def compare_rows(row):<\/p>\n
    return row['column1'] == row['column2']<\/p>\n
\u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
df = pd.read_csv('data.csv')<\/p>\n
\u4f7f\u7528\u591a\u8fdb\u7a0b\u8fdb\u884c\u6bd4\u8f83<\/strong><\/h2>\n
with Pool(processes=4) as pool:<\/p>\n
    result = pool.map(compare_rows, [row for _, row in df.iterrows()])<\/p>\n
equal_rows = df[result]<\/p>\n
print(equal_rows)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u5b9e\u9645\u6848\u4f8b\u5206\u6790<\/h3>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u5982\u4f55\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\u4f7f\u7528Python\u7b5b\u9009\u4e24\u5217\u76f8\u7b49\u7684\u6587\u672c\uff0c\u6211\u4eec\u5c06\u63a2\u8ba8\u4e00\u4e2a\u5177\u4f53\u7684\u6848\u4f8b\u3002<\/p>\n<\/p>\n
6.1 \u6848\u4f8b\u80cc\u666f<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u662f\u4e00\u5bb6\u7535\u5b50\u5546\u52a1\u516c\u53f8\uff0c\u6211\u4eec\u7684\u6570\u636e\u5305\u542b\u8ba2\u5355\u4fe1\u606f\u548c\u5ba2\u6237\u53cd\u9988\uff0c\u6211\u4eec\u9700\u8981\u7b5b\u9009\u51fa\u90a3\u4e9b\u5ba2\u6237\u53cd\u9988\u4e0e\u8ba2\u5355\u5907\u6ce8\u76f8\u540c\u7684\u8bb0\u5f55\uff0c\u4ee5\u4fbf\u8fdb\u4e00\u6b65\u5206\u6790\u3002<\/p>\n<\/p>\n
6.2 \u5b9e\u73b0\u6b65\u9aa4<\/h4>\n<\/p>\n
    \n
  1. \u52a0\u8f7d\u6570\u636e<\/strong>\uff1a<\/li>\n<\/ol>\n
    import pandas as pd<\/p>\n
    \u52a0\u8f7d\u8ba2\u5355\u6570\u636e\u548c\u5ba2\u6237\u53cd\u9988\u6570\u636e<\/strong><\/h2>\n
    orders = pd.read_csv('orders.csv')<\/p>\n
    feedback = pd.read_csv('feedback.csv')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u5408\u5e76\u6570\u636e<\/strong>\uff1a<\/li>\n<\/ol>\n
      # \u5047\u8bbe\u8ba2\u5355\u6570\u636e\u548c\u5ba2\u6237\u53cd\u9988\u6570\u636e\u6709\u4e00\u4e2a\u5171\u540c\u7684\u5b57\u6bb5 'order_id'<\/p>\n
      merged_data = pd.merge(orders, feedback, on='order_id')<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u6bd4\u8f83\u8ba2\u5355\u5907\u6ce8\u548c\u5ba2\u6237\u53cd\u9988<\/strong>\uff1a<\/li>\n<\/ol>\n
        # \u6bd4\u8f83\u8ba2\u5355\u5907\u6ce8\u548c\u5ba2\u6237\u53cd\u9988<\/p>\n
        equal_feedback = merged_data[merged_data['order_note'] == merged_data['customer_feedback']]<\/p>\n
        print(equal_feedback)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u5206\u6790\u7ed3\u679c<\/strong>\uff1a<\/li>\n<\/ol>\n
          # \u5206\u6790\u7ed3\u679c<\/p>\n
          analysis = equal_feedback.groupby('product_id').size()<\/p>\n
          print(analysis)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e03\u3001\u7ed3\u8bba<\/h3>\n<\/p>\n
          \u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528Python\u7b5b\u51fa\u4e24\u5217\u76f8\u7b49\u7684\u6587\u672c\uff0c\u91cd\u70b9\u63a2\u8ba8\u4e86Pandas\u5e93\u3001\u6761\u4ef6\u7d22\u5f15\u3001\u5217\u8868\u63a8\u5bfc\u5f0f<\/strong>\u7b49\u65b9\u6cd5\u3002\u6211\u4eec\u8fd8\u8ba8\u8bba\u4e86\u5982\u4f55\u5904\u7406\u66f4\u590d\u6742\u7684\u6570\u636e\u548c\u4f18\u5316\u6027\u80fd\u3002\u6700\u540e\uff0c\u901a\u8fc7\u4e00\u4e2a\u5b9e\u9645\u6848\u4f8b\u5206\u6790\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u5982\u4f55\u5728\u771f\u5b9e\u9879\u76ee\u4e2d\u5e94\u7528\u8fd9\u4e9b\u6280\u672f\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u638c\u63e1Python\u5728\u6570\u636e\u5904\u7406\u4e2d\u7684\u5f3a\u5927\u529f\u80fd\u3002<\/p>\n<\/p>\n
          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
           \u5982\u4f55\u4f7f\u7528Python\u6bd4\u8f83\u4e24\u4e2a\u6587\u672c\u5217\u7684\u5185\u5bb9\uff1f<\/strong>
          \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u6bd4\u8f83\u4e24\u4e2a\u6587\u672c\u5217\u7684\u5185\u5bb9\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u5c06\u6570\u636e\u52a0\u8f7d\u5230DataFrame\u4e2d\uff0c\u7136\u540e\u901a\u8fc7\u5e03\u5c14\u7d22\u5f15\u6765\u7b5b\u9009\u51fa\u4e24\u5217\u76f8\u7b49\u7684\u884c\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
          import pandas as pd\n\n# \u521b\u5efa\u793a\u4f8b\u6570\u636e\ndata = {'col1': ['apple', 'banana', 'orange'], 'col2': ['apple', 'grape', 'orange']}\ndf = pd.DataFrame(data)\n\n# \u7b5b\u9009\u51fa\u4e24\u5217\u76f8\u7b49\u7684\u884c\nequal_rows = df[df['col1'] == df['col2']]\nprint(equal_rows)\n<\/code><\/pre>\n
          \u8fd9\u6837\u53ef\u4ee5\u8f7b\u677e\u5730\u627e\u51fa\u4e24\u5217\u4e2d\u76f8\u540c\u7684\u6587\u672c\u3002<\/p>\n
          \u5982\u4f55\u5904\u7406\u6587\u672c\u5217\u4e2d\u53ef\u80fd\u7684\u7a7a\u503c\u6216\u7f3a\u5931\u503c\uff1f<\/strong>
          \u5728\u6bd4\u8f83\u6587\u672c\u5217\u65f6\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u7a7a\u503c\u6216\u7f3a\u5931\u503c\u7684\u60c5\u51b5\u3002\u4f7f\u7528Pandas\u7684dropna()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u5148\u79fb\u9664\u5305\u542b\u7a7a\u503c\u7684\u884c\uff0c\u6216\u8005\u4f7f\u7528fillna()<\/code>\u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u503c\u3002\u793a\u4f8b\uff1a<\/p>\n
          # \u5904\u7406\u7f3a\u5931\u503c\ndf['col1'].fillna('', inplace=True)\ndf['col2'].fillna('', inplace=True)\n\n# \u7b5b\u9009\u76f8\u7b49\u7684\u884c\nequal_rows = df[df['col1'] == df['col2']]\nprint(equal_rows)\n<\/code><\/pre>\n
          \u8fd9\u79cd\u65b9\u5f0f\u53ef\u4ee5\u786e\u4fdd\u6bd4\u8f83\u8fc7\u7a0b\u4e0d\u53d7\u7f3a\u5931\u503c\u7684\u5f71\u54cd\u3002<\/p>\n
          \u662f\u5426\u53ef\u4ee5\u4f7f\u7528\u5176\u4ed6\u5e93\u6765\u8fdb\u884c\u6587\u672c\u5217\u7684\u6bd4\u8f83\uff1f<\/strong>
          \u9664\u4e86Pandas\uff0cPython\u8fd8\u6709\u5176\u4ed6\u5e93\u53ef\u4ee5\u8fdb\u884c\u6587\u672c\u6bd4\u8f83\uff0c\u4f8b\u5982NumPy\u548cOpenpyxl\u3002NumPy\u53ef\u4ee5\u7528\u4e8e\u5904\u7406\u5927\u578b\u6570\u7ec4\u548c\u77e9\u9635\uff0c\u800cOpenpyxl\u5219\u9002\u5408\u5904\u7406Excel\u6587\u4ef6\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528NumPy\u8fdb\u884c\u7b80\u5355\u6bd4\u8f83\u7684\u793a\u4f8b\uff1a<\/p>\n
          import numpy as np\n\n# \u521b\u5efa\u793a\u4f8b\u6570\u636e\ncol1 = np.array(['apple', 'banana', 'orange'])\ncol2 = np.array(['apple', 'grape', 'orange'])\n\n# \u6bd4\u8f83\u4e24\u4e2a\u6570\u7ec4\nequal_elements = col1[col1 == col2]\nprint(equal_elements)\n<\/code><\/pre>\n
          \u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u9700\u8981\u5904\u7406\u6570\u503c\u8fd0\u7b97\u7684\u573a\u666f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5982\u4f55\u7528Python\u7b5b\u51fa\u4e24\u5217\u76f8\u7b49\u7684\u6587\u672c \u5728\u5904\u7406\u6570\u636e\u65f6\uff0c\u7279\u522b\u662f\u5f53\u6211\u4eec\u9700\u8981\u6bd4\u8f83\u4e24\u5217\u6587\u672c\u6570\u636e\u5e76\u7b5b\u9009\u51fa\u5176\u4e2d\u76f8\u7b49\u7684\u90e8\u5206\u65f6\uff0c […]","protected":false},"author":3,"featured_media":1126835,"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\/1126825"}],"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=1126825"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1126825\/revisions"}],"predecessor-version":[{"id":1126836,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1126825\/revisions\/1126836"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1126835"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1126825"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1126825"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1126825"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}