{"id":1134401,"date":"2025-01-08T21:15:20","date_gmt":"2025-01-08T13:15:20","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1134401.html"},"modified":"2025-01-08T21:15:22","modified_gmt":"2025-01-08T13:15:22","slug":"python-%e4%b8%ad%e5%a6%82%e4%bd%95%e6%88%aa%e5%8f%96%e6%95%b0%e6%8d%ae%e5%9c%a8%e4%b8%80%e5%ae%9a%e5%8c%ba%e9%97%b4","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1134401.html","title":{"rendered":"python \u4e2d\u5982\u4f55\u622a\u53d6\u6570\u636e\u5728\u4e00\u5b9a\u533a\u95f4"},"content":{"rendered":"

\"python<\/p>\n

\u5728Python\u4e2d\u622a\u53d6\u6570\u636e\u5728\u4e00\u5b9a\u533a\u95f4\u7684\u65b9\u5f0f\u6709\u591a\u79cd\uff0c\u4e3b\u8981\u5305\u62ec\uff1a\u4f7f\u7528\u5207\u7247\u64cd\u4f5c\u3001\u5229\u7528\u5217\u8868\u63a8\u5bfc\u5f0f\u3001\u4f7f\u7528Pandas\u5e93\u4e2d\u7684\u65b9\u6cd5\u7b49\u3002<\/strong> \u5207\u7247\u64cd\u4f5c\u662f\u6700\u5e38\u89c1\u4e14\u9ad8\u6548\u7684\u65b9\u6cd5\u4e4b\u4e00\uff0c\u9002\u7528\u4e8e\u5904\u7406\u5217\u8868\u3001\u5b57\u7b26\u4e32\u7b49\u5e8f\u5217\u7c7b\u578b\u7684\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u63cf\u8ff0\uff1a<\/p>\n<\/p>\n

Python\u7684\u5207\u7247\u64cd\u4f5c\u975e\u5e38\u5f3a\u5927\uff0c\u5141\u8bb8\u4f60\u901a\u8fc7\u6307\u5b9a\u8d77\u59cb\u548c\u7ed3\u675f\u7d22\u5f15\u6765\u622a\u53d6\u6570\u636e\u3002\u5047\u8bbe\u6709\u4e00\u4e2a\u5217\u8868 data<\/code>\uff0c\u4f60\u53ef\u4ee5\u901a\u8fc7 data[start:end]<\/code> \u6765\u622a\u53d6\u4ece\u7d22\u5f15 start<\/code> \u5230 end-1<\/code> \u7684\u6570\u636e\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5207\u7247\u64cd\u4f5c<\/h3>\n<\/p>\n

\u5207\u7247\u64cd\u4f5c\u662fPython\u4e2d\u5904\u7406\u5e8f\u5217\u6570\u636e\u7684\u57fa\u672c\u65b9\u6cd5\uff0c\u9002\u7528\u4e8e\u5217\u8868\u3001\u5b57\u7b26\u4e32\u3001\u5143\u7ec4\u7b49\u5e8f\u5217\u7c7b\u578b\u3002\u901a\u8fc7\u6307\u5b9a\u8d77\u59cb\u548c\u7ed3\u675f\u7d22\u5f15\uff0c\u4f60\u53ef\u4ee5\u7075\u6d3b\u5730\u622a\u53d6\u4f60\u9700\u8981\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n

1.1 \u5217\u8868\u5207\u7247<\/h4>\n<\/p>\n

\u5217\u8868\u5207\u7247\u5141\u8bb8\u4f60\u901a\u8fc7\u7d22\u5f15\u8303\u56f4\u6765\u83b7\u53d6\u5b50\u5217\u8868\u3002\u5047\u8bbe\u6709\u4e00\u4e2a\u5217\u8868 data<\/code>\uff1a<\/p>\n<\/p>\n

data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8981\u622a\u53d6\u4ece\u7d22\u5f152\u5230\u7d22\u5f155\u7684\u6570\u636e\uff0c\u53ef\u4ee5\u8fd9\u6837\u505a\uff1a<\/p>\n<\/p>\n
sub_data = data[2:6]<\/p>\n
print(sub_data)  # \u8f93\u51fa: [3, 4, 5, 6]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u91cc\u7684 data[2:6]<\/code> \u8868\u793a\u4ece\u7d22\u5f152\u5f00\u59cb\uff08\u5305\u542b\uff09\u5230\u7d22\u5f156\u7ed3\u675f\uff08\u4e0d\u5305\u542b\uff09\u7684\u6240\u6709\u5143\u7d20\u3002<\/p>\n<\/p>\n
1.2 \u5b57\u7b26\u4e32\u5207\u7247<\/h4>\n<\/p>\n
\u5b57\u7b26\u4e32\u5207\u7247\u4e0e\u5217\u8868\u5207\u7247\u7c7b\u4f3c\u3002\u5047\u8bbe\u6709\u4e00\u4e2a\u5b57\u7b26\u4e32 text<\/code>\uff1a<\/p>\n<\/p>\n
text = "Hello, World!"<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8981\u622a\u53d6\u4ece\u7d22\u5f157\u5230\u7d22\u5f1511\u7684\u5b50\u5b57\u7b26\u4e32\uff0c\u53ef\u4ee5\u8fd9\u6837\u505a\uff1a<\/p>\n<\/p>\n
sub_text = text[7:12]<\/p>\n
print(sub_text)  # \u8f93\u51fa: World<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3 \u5143\u7ec4\u5207\u7247<\/h4>\n<\/p>\n
\u5143\u7ec4\u5207\u7247\u4e5f\u4e0e\u5217\u8868\u7c7b\u4f3c\u3002\u5047\u8bbe\u6709\u4e00\u4e2a\u5143\u7ec4 numbers<\/code>\uff1a<\/p>\n<\/p>\n
numbers = (10, 20, 30, 40, 50, 60, 70, 80, 90, 100)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8981\u622a\u53d6\u4ece\u7d22\u5f153\u5230\u7d22\u5f157\u7684\u5b50\u5143\u7ec4\uff0c\u53ef\u4ee5\u8fd9\u6837\u505a\uff1a<\/p>\n<\/p>\n
sub_numbers = numbers[3:8]<\/p>\n
print(sub_numbers)  # \u8f93\u51fa: (40, 50, 60, 70, 80)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u5217\u8868\u63a8\u5bfc\u5f0f<\/h3>\n<\/p>\n
\u5217\u8868\u63a8\u5bfc\u5f0f\u63d0\u4f9b\u4e86\u4e00\u79cd\u7b80\u6d01\u7684\u65b9\u6cd5\u6765\u521b\u5efa\u65b0\u5217\u8868\uff0c\u5c24\u5176\u9002\u5408\u5728\u6ee1\u8db3\u7279\u5b9a\u6761\u4ef6\u7684\u6570\u636e\u8303\u56f4\u5185\u8fdb\u884c\u7b5b\u9009\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
2.1 \u8fc7\u6ee4\u6570\u636e<\/h4>\n<\/p>\n
\u5047\u8bbe\u6709\u4e00\u4e2a\u5305\u542b\u968f\u673a\u6570\u7684\u5217\u8868 data<\/code>\uff0c\u4f60\u60f3\u8981\u622a\u53d6\u6240\u6709\u5728\u4e00\u5b9a\u8303\u56f4\u5185\u7684\u6570\uff0c\u4f8b\u5982\u57285\u523015\u4e4b\u95f4\uff1a<\/p>\n<\/p>\n
data = [1, 5, 8, 12, 15, 20, 25, 30]<\/p>\n
filtered_data = [x for x in data if 5 <= x <= 15]<\/p>\n
print(filtered_data)  # \u8f93\u51fa: [5, 8, 12, 15]<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2 \u590d\u6742\u6761\u4ef6<\/h4>\n<\/p>\n
\u5217\u8868\u63a8\u5bfc\u5f0f\u8fd8\u53ef\u4ee5\u7528\u4e8e\u66f4\u590d\u6742\u7684\u6761\u4ef6\u7b5b\u9009\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u4f60\u6709\u4e00\u4e2a\u5305\u542b\u5b57\u5178\u7684\u5217\u8868\uff0c\u4f60\u60f3\u8981\u622a\u53d6\u67d0\u4e2a\u7279\u5b9a\u952e\u503c\u5728\u4e00\u5b9a\u8303\u56f4\u5185\u7684\u5b57\u5178\uff1a<\/p>\n<\/p>\n
data = [<\/p>\n
    {'name': 'Alice', 'age': 25},<\/p>\n
    {'name': 'Bob', 'age': 30},<\/p>\n
    {'name': 'Charlie', 'age': 35},<\/p>\n
    {'name': 'David', 'age': 40}<\/p>\n
]<\/p>\n
filtered_data = [person for person in data if 30 <= person['age'] <= 35]<\/p>\n
print(filtered_data)  # \u8f93\u51fa: [{'name': 'Bob', 'age': 30}, {'name': 'Charlie', 'age': 35}]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001Pandas\u5e93<\/h3>\n<\/p>\n
Pandas\u662f\u5904\u7406\u6570\u636e\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u7279\u522b\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5206\u6790\u3002\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u65b9\u6cd5\u6765\u622a\u53d6\u548c\u8fc7\u6ee4\u6570\u636e\u3002<\/p>\n<\/p>\n
3.1 \u4f7f\u7528Pandas\u622a\u53d6\u884c\u6570\u636e<\/h4>\n<\/p>\n
\u5047\u8bbe\u6709\u4e00\u4e2aPandas DataFrame df<\/code>\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528 iloc<\/code> \u65b9\u6cd5\u6309\u884c\u7d22\u5f15\u622a\u53d6\u6570\u636e\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {<\/p>\n
    'name': ['Alice', 'Bob', 'Charlie', 'David'],<\/p>\n
    'age': [25, 30, 35, 40]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u622a\u53d6\u7b2c1\u5230\u7b2c3\u884c\uff08\u7d22\u5f15\u4ece0\u5f00\u59cb\uff09<\/strong><\/h2>\n
sub_df = df.iloc[1:4]<\/p>\n
print(sub_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
      name  age<\/p>\n
1      Bob   30<\/p>\n
2  Charlie   35<\/p>\n
3    David   40<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u4f7f\u7528Pandas\u7b5b\u9009\u6570\u636e<\/h4>\n<\/p>\n
Pandas\u8fd8\u5141\u8bb8\u4f60\u6839\u636e\u6761\u4ef6\u7b5b\u9009\u6570\u636e\u3002\u4f8b\u5982\uff0c\u4f60\u60f3\u8981\u83b7\u53d6\u6240\u6709\u5e74\u9f84\u572830\u523035\u5c81\u4e4b\u95f4\u7684\u4eba\uff1a<\/p>\n<\/p>\n
filtered_df = df[(df['age'] >= 30) & (df['age'] <= 35)]<\/p>\n
print(filtered_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
      name  age<\/p>\n
1      Bob   30<\/p>\n
2  Charlie   35<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001Numpy\u5e93<\/h3>\n<\/p>\n
Numpy\u662f\u5904\u7406\u5927\u89c4\u6a21\u6570\u503c\u6570\u636e\u7684\u9ad8\u6548\u5de5\u5177\uff0c\u7279\u522b\u9002\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u3002\u5b83\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u622a\u53d6\u548c\u8fc7\u6ee4\u6570\u7ec4\u6570\u636e\u3002<\/p>\n<\/p>\n
4.1 \u4f7f\u7528Numpy\u622a\u53d6\u6570\u7ec4<\/h4>\n<\/p>\n
\u5047\u8bbe\u6709\u4e00\u4e2aNumpy\u6570\u7ec4 arr<\/code>\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528\u5207\u7247\u64cd\u4f5c\u6765\u622a\u53d6\u6570\u636e\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])<\/p>\n
sub_arr = arr[2:6]<\/p>\n
print(sub_arr)  # \u8f93\u51fa: [3 4 5 6]<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2 \u4f7f\u7528Numpy\u7b5b\u9009\u6570\u636e<\/h4>\n<\/p>\n
Numpy\u8fd8\u5141\u8bb8\u4f60\u6839\u636e\u6761\u4ef6\u7b5b\u9009\u6570\u636e\u3002\u4f8b\u5982\uff0c\u4f60\u60f3\u8981\u83b7\u53d6\u6240\u6709\u57285\u523015\u4e4b\u95f4\u7684\u5143\u7d20\uff1a<\/p>\n<\/p>\n
arr = np.array([1, 5, 8, 12, 15, 20, 25, 30])<\/p>\n
filtered_arr = arr[(arr >= 5) & (arr <= 15)]<\/p>\n
print(filtered_arr)  # \u8f93\u51fa: [ 5  8 12 15]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728Python\u4e2d\u622a\u53d6\u6570\u636e\u5728\u4e00\u5b9a\u533a\u95f4\u5185\u7684\u65b9\u6cd5\u591a\u79cd\u591a\u6837\uff0c\u5177\u4f53\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\u53d6\u51b3\u4e8e\u4f60\u7684\u6570\u636e\u7c7b\u578b\u548c\u5177\u4f53\u9700\u6c42\u3002\u5207\u7247\u64cd\u4f5c<\/strong> \u662f\u6700\u57fa\u7840\u4e14\u9ad8\u6548\u7684\u65b9\u6cd5\uff0c\u9002\u7528\u4e8e\u5904\u7406\u5e8f\u5217\u7c7b\u578b\u7684\u6570\u636e\u3002\u5217\u8868\u63a8\u5bfc\u5f0f<\/strong> \u63d0\u4f9b\u4e86\u4e00\u79cd\u7b80\u6d01\u7684\u65b9\u5f0f\u6765\u7b5b\u9009\u6570\u636e\uff0c\u7279\u522b\u9002\u5408\u5904\u7406\u7b80\u5355\u6761\u4ef6\u7684\u8fc7\u6ee4\u3002Pandas\u5e93<\/strong> \u662f\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u7684\u5229\u5668\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u65b9\u6cd5\u6765\u622a\u53d6\u548c\u8fc7\u6ee4\u6570\u636e\u3002Numpy\u5e93<\/strong> \u5219\u662f\u79d1\u5b66\u8ba1\u7b97\u548c\u5904\u7406\u6570\u503c\u6570\u636e\u7684\u9996\u9009\u5de5\u5177\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u638c\u63e1\u8fd9\u4e9b\u6280\u672f\uff0c\u4f60\u5c06\u80fd\u591f\u5728Python\u4e2d\u7075\u6d3b\u9ad8\u6548\u5730\u5904\u7406\u5404\u79cd\u6570\u636e\u622a\u53d6\u548c\u7b5b\u9009\u4efb\u52a1\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728 Python \u4e2d\u622a\u53d6\u7279\u5b9a\u8303\u56f4\u5185\u7684\u6570\u636e\uff1f<\/strong>
\u5728 Python \u4e2d\uff0c\u4f7f\u7528\u5207\u7247\u64cd\u4f5c\u53ef\u4ee5\u8f7b\u677e\u622a\u53d6\u6570\u636e\u3002\u5bf9\u4e8e\u5217\u8868\u3001\u5143\u7ec4\u548c\u5b57\u7b26\u4e32\u7b49\u5e8f\u5217\u7c7b\u578b\uff0c\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u8d77\u59cb\u548c\u7ed3\u675f\u7d22\u5f15\u6765\u83b7\u53d6\u6240\u9700\u7684\u5b50\u96c6\u3002\u4f8b\u5982\uff0c\u5bf9\u4e8e\u5217\u8868 data = [1, 2, 3, 4, 5]<\/code>\uff0c\u82e5\u5e0c\u671b\u622a\u53d6\u7d22\u5f15\u4ece1\u52303\u7684\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528 data[1:4]<\/code>\uff0c\u8fd4\u56de\u7684\u7ed3\u679c\u5c06\u662f [2, 3, 4]<\/code>\u3002\u6b64\u5916\uff0cNumPy \u5e93\u4e5f\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\uff0c\u9002\u7528\u4e8e\u591a\u7ef4\u6570\u7ec4\u548c\u5927\u6570\u636e\u96c6\u3002<\/p>\n
\u5982\u4f55\u4f7f\u7528 Pandas \u622a\u53d6 DataFrame \u4e2d\u7684\u6570\u636e\uff1f<\/strong>
Pandas \u662f\u5904\u7406\u6570\u636e\u5206\u6790\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u65b9\u6cd5\u6765\u622a\u53d6 DataFrame \u4e2d\u7684\u6570\u636e\u3002\u5982\u679c\u6709\u4e00\u4e2a DataFrame df<\/code>\uff0c\u53ef\u4ee5\u4f7f\u7528\u6761\u4ef6\u7b5b\u9009\u6765\u83b7\u53d6\u7279\u5b9a\u8303\u56f4\u7684\u6570\u636e\u3002\u4f8b\u5982\uff0cdf[(df['column_name'] >= lower_bound) & (df['column_name'] <= upper_bound)]<\/code> \u53ef\u4ee5\u8fd4\u56de\u5728\u67d0\u4e00\u5217\u4e2d\u6ee1\u8db3\u6761\u4ef6\u7684\u6240\u6709\u884c\u6570\u636e\u3002\u8fd9\u79cd\u65b9\u5f0f\u975e\u5e38\u9002\u5408\u4e8e\u6570\u636e\u6e05\u6d17\u548c\u5206\u6790\u4efb\u52a1\u3002<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u4e3a\u4ec0\u4e48\u8981\u622a\u53d6\u7279\u5b9a\u8303\u56f4\u7684\u6570\u636e\uff1f<\/strong>
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