{"id":1130803,"date":"2025-01-08T20:43:20","date_gmt":"2025-01-08T12:43:20","guid":{"rendered":""},"modified":"2025-01-08T20:43:22","modified_gmt":"2025-01-08T12:43:22","slug":"%e5%a6%82%e4%bd%95%e6%8f%90%e5%8f%96%e6%95%b0%e6%8d%ae%e6%a1%86%e7%9a%84%e6%9f%90%e4%b8%80%e5%88%97python","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1130803.html","title":{"rendered":"\u5982\u4f55\u63d0\u53d6\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217python"},"content":{"rendered":"

\"\u5982\u4f55\u63d0\u53d6\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217python\"<\/p>\n

\u5728Python\u4e2d\u63d0\u53d6\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u5305\u62ec\u4f7f\u7528\u65b9\u62ec\u53f7\u3001\u70b9\u7b26\u53f7\u4ee5\u53caloc\u548ciloc\u65b9\u6cd5\u7b49\u3002<\/strong><\/p>\n<\/p>\n

1\u3001\u65b9\u62ec\u53f7<\/strong>\uff1a\u901a\u8fc7\u4f7f\u7528\u65b9\u62ec\u53f7\u5e76\u63d0\u4f9b\u5217\u540d\uff0c\u53ef\u4ee5\u8f7b\u677e\u63d0\u53d6\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\u3002<\/p>\n<\/p>\n

2\u3001\u70b9\u7b26\u53f7<\/strong>\uff1a\u901a\u8fc7\u70b9\u7b26\u53f7\u76f4\u63a5\u8bbf\u95ee\u5217\u540d\uff0c\u53ef\u4ee5\u63d0\u53d6\u67d0\u4e00\u5217\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n

3\u3001loc\u65b9\u6cd5<\/strong>\uff1a\u901a\u8fc7\u4f7f\u7528loc\u65b9\u6cd5\u53ef\u4ee5\u6309\u6807\u7b7e\u9009\u62e9\u5217\u3002<\/p>\n<\/p>\n

4\u3001iloc\u65b9\u6cd5<\/strong>\uff1a\u901a\u8fc7iloc\u65b9\u6cd5\u53ef\u4ee5\u6309\u4f4d\u7f6e\u9009\u62e9\u5217\u3002<\/p>\n<\/p>\n

\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u63cf\u8ff0\u7b2c\u4e00\u79cd\u65b9\u6cd5\uff0c\u5373\u901a\u8fc7\u65b9\u62ec\u53f7\u6765\u63d0\u53d6\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u65b9\u62ec\u53f7\u65b9\u6cd5<\/h3>\n<\/p>\n

\u65b9\u62ec\u53f7\u65b9\u6cd5\u662f\u6700\u76f4\u89c2\u7684\u4e00\u79cd\u65b9\u6cd5\uff0c\u901a\u8fc7\u65b9\u62ec\u53f7\u5e76\u63d0\u4f9b\u5217\u540d\uff0c\u53ef\u4ee5\u8f7b\u677e\u63d0\u53d6\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\u3002\u6b64\u65b9\u6cd5\u4e0d\u4ec5\u7b80\u5355\u6613\u61c2\uff0c\u800c\u4e14\u5f88\u7075\u6d3b\uff0c\u9002\u7528\u4e8e\u5927\u591a\u6570\u60c5\u51b5\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u6709\u4e00\u4e2a\u540d\u4e3adf<\/code>\u7684\u6570\u636e\u6846\uff0c\u6211\u4eec\u5e0c\u671b\u63d0\u53d6\u5176\u4e2d\u7684column_name<\/code>\u5217\uff0c\u53ea\u9700\u4f7f\u7528df['column_name']<\/code>\u5373\u53ef\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u521b\u5efa\u793a\u4f8b\u6570\u636e\u6846<\/strong><\/h2>\n
data = {'name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
        'age': [25, 30, 35],<\/p>\n
        'city': ['New York', 'Los Angeles', 'Chicago']}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u63d0\u53d6\u5217<\/strong><\/h2>\n
age_column = df['age']<\/p>\n
print(age_column)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u70b9\u7b26\u53f7\u65b9\u6cd5<\/h3>\n<\/p>\n
\u70b9\u7b26\u53f7\u65b9\u6cd5\u4e5f\u662f\u4e00\u79cd\u5e38\u89c1\u7684\u63d0\u53d6\u5217\u7684\u65b9\u6cd5\u3002\u5b83\u901a\u8fc7\u70b9\u7b26\u53f7\u76f4\u63a5\u8bbf\u95ee\u5217\u540d\uff0c\u9002\u5408\u5217\u540d\u6ca1\u6709\u7279\u6b8a\u5b57\u7b26\u7684\u60c5\u51b5\u3002\u4f7f\u7528\u70b9\u7b26\u53f7\u8bbf\u95ee\u5217\u540d\u7684\u65b9\u5f0f\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
# \u63d0\u53d6\u5217<\/p>\n
age_column = df.age<\/p>\n
print(age_column)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u867d\u7136\u70b9\u7b26\u53f7\u65b9\u6cd5\u7b80\u6d01\u6613\u7528\uff0c\u4f46\u4e0d\u63a8\u8350\u5728\u5217\u540d\u5305\u542b\u7a7a\u683c\u6216\u7279\u6b8a\u5b57\u7b26\u7684\u60c5\u51b5\u4e0b\u4f7f\u7528\u3002<\/p>\n<\/p>\n
\u4e09\u3001loc\u65b9\u6cd5<\/h3>\n<\/p>\n
loc\u65b9\u6cd5\u662fPandas\u4e2d\u4e00\u79cd\u975e\u5e38\u5f3a\u5927\u7684\u9009\u62e9\u6570\u636e\u7684\u65b9\u6cd5\u3002\u5b83\u4e0d\u4ec5\u53ef\u4ee5\u6309\u6807\u7b7e\u9009\u62e9\u884c\uff0c\u8fd8\u53ef\u4ee5\u6309\u6807\u7b7e\u9009\u62e9\u5217\u3002\u4f7f\u7528loc\u65b9\u6cd5\u63d0\u53d6\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\u7684\u65b9\u5f0f\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
# \u63d0\u53d6\u5217<\/p>\n
age_column = df.loc[:, 'age']<\/p>\n
print(age_column)<\/p>\n
<\/code><\/pre>\n<\/p>\n
loc\u65b9\u6cd5\u7684\u5f3a\u5927\u4e4b\u5904\u5728\u4e8e\u5b83\u53ef\u4ee5\u540c\u65f6\u9009\u62e9\u884c\u548c\u5217\uff0c\u56e0\u6b64\u9002\u5408\u590d\u6742\u7684\u6570\u636e\u9009\u62e9\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
\u56db\u3001iloc\u65b9\u6cd5<\/h3>\n<\/p>\n
iloc\u65b9\u6cd5\u901a\u8fc7\u4f4d\u7f6e\u7d22\u5f15\u6765\u9009\u62e9\u6570\u636e\u3002\u5bf9\u4e8e\u63d0\u53d6\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\uff0ciloc\u65b9\u6cd5\u7684\u4f7f\u7528\u65b9\u5f0f\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
# \u63d0\u53d6\u5217<\/p>\n
age_column = df.iloc[:, 1]  # \u5047\u8bbeage\u5217\u662f\u6570\u636e\u6846\u7684\u7b2c\u4e8c\u5217<\/p>\n
print(age_column)<\/p>\n
<\/code><\/pre>\n<\/p>\n
iloc\u65b9\u6cd5\u5728\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u65f6\u975e\u5e38\u6709\u7528\uff0c\u56e0\u4e3a\u5b83\u901a\u8fc7\u4f4d\u7f6e\u7d22\u5f15\u8fdb\u884c\u9009\u62e9\uff0c\u901f\u5ea6\u8f83\u5feb\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5b9e\u4f8b\u5206\u6790\u4e0e\u5e94\u7528\u573a\u666f<\/h3>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u8fd9\u4e9b\u65b9\u6cd5\u7684\u5b9e\u9645\u5e94\u7528\uff0c\u6211\u4eec\u5c06\u901a\u8fc7\u51e0\u4e2a\u5177\u4f53\u7684\u5b9e\u4f8b\u6765\u5206\u6790\u4e0d\u540c\u65b9\u6cd5\u5728\u4e0d\u540c\u573a\u666f\u4e2d\u7684\u4f18\u7f3a\u70b9\u3002<\/p>\n<\/p>\n
1\u3001\u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\uff0c\u5904\u7406\u7f3a\u5931\u503c\u662f\u975e\u5e38\u91cd\u8981\u7684\u4e00\u73af\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u7f3a\u5931\u503c\u7684\u6570\u636e\u6846\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
data_with_nan = {'name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
                 'age': [25, np.nan, 35],<\/p>\n
                 'city': ['New York', 'Los Angeles', 'Chicago']}<\/p>\n
df_with_nan = pd.DataFrame(data_with_nan)<\/p>\n
\u63d0\u53d6\u5305\u542b\u7f3a\u5931\u503c\u7684\u5217<\/strong><\/h2>\n
age_column_with_nan = df_with_nan['age']<\/p>\n
print(age_column_with_nan)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u53ef\u4ee5\u4f7f\u7528fillna<\/code>\u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u503c\uff0c\u6216\u8005\u4f7f\u7528dropna<\/code>\u65b9\u6cd5\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\uff1a<\/p>\n<\/p>\n
# \u586b\u5145\u7f3a\u5931\u503c<\/p>\n
filled_age_column = age_column_with_nan.fillna(age_column_with_nan.mean())<\/p>\n
print(filled_age_column)<\/p>\n
\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c<\/strong><\/h2>\n
cleaned_df = df_with_nan.dropna(subset=['age'])<\/p>\n
print(cleaned_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u591a\u5217\u9009\u62e9<\/h4>\n<\/p>\n
\u6709\u65f6\u6211\u4eec\u9700\u8981\u4e00\u6b21\u6027\u63d0\u53d6\u591a\u5217\u6570\u636e\u3002\u4f7f\u7528loc\u65b9\u6cd5\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u5b9e\u73b0\u8fd9\u4e00\u70b9\uff1a<\/p>\n<\/p>\n
# \u63d0\u53d6\u591a\u5217<\/p>\n
selected_columns = df.loc[:, ['name', 'age']]<\/p>\n
print(selected_columns)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u590d\u6742\u6761\u4ef6\u9009\u62e9<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u7ecf\u5e38\u9700\u8981\u6839\u636e\u590d\u6742\u6761\u4ef6\u9009\u62e9\u6570\u636e\u3002loc\u65b9\u6cd5\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6761\u4ef6\u9009\u62e9\u80fd\u529b\uff1a<\/p>\n<\/p>\n
# \u9009\u62e9\u5e74\u9f84\u5927\u4e8e30\u7684\u884c<\/p>\n
selected_rows = df.loc[df['age'] > 30, :]<\/p>\n
print(selected_rows)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3\u4e0e\u6700\u4f73\u5b9e\u8df5<\/h3>\n<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u8fc7\u7a0b\u4e2d\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u63d0\u53d6\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u4e86\u89e3\u4e86\u65b9\u62ec\u53f7\u65b9\u6cd5\u3001\u70b9\u7b26\u53f7\u65b9\u6cd5\u3001loc\u65b9\u6cd5\u548ciloc\u65b9\u6cd5\u7684\u4f7f\u7528\u65b9\u5f0f\u548c\u4f18\u7f3a\u70b9\u3002<\/p>\n<\/p>\n
1\u3001\u65b9\u62ec\u53f7\u65b9\u6cd5<\/strong>\uff1a\u9002\u7528\u4e8e\u5927\u591a\u6570\u60c5\u51b5\uff0c\u7b80\u5355\u76f4\u89c2\u3002<\/p>\n<\/p>\n
2\u3001\u70b9\u7b26\u53f7\u65b9\u6cd5<\/strong>\uff1a\u7b80\u6d01\u6613\u7528\uff0c\u4f46\u4e0d\u9002\u5408\u5217\u540d\u5305\u542b\u7279\u6b8a\u5b57\u7b26\u7684\u60c5\u51b5\u3002<\/p>\n<\/p>\n
3\u3001loc\u65b9\u6cd5<\/strong>\uff1a\u5f3a\u5927\u7075\u6d3b\uff0c\u9002\u5408\u590d\u6742\u6761\u4ef6\u9009\u62e9\u3002<\/p>\n<\/p>\n
4\u3001iloc\u65b9\u6cd5<\/strong>\uff1a\u901a\u8fc7\u4f4d\u7f6e\u7d22\u5f15\u9009\u62e9\uff0c\u901f\u5ea6\u8f83\u5feb\uff0c\u9002\u5408\u5927\u578b\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u5e94\u6839\u636e\u5177\u4f53\u60c5\u51b5\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002\u540c\u65f6\uff0c\u7ed3\u5408\u6570\u636e\u5904\u7406\u9700\u6c42\uff0c\u53ef\u4ee5\u7075\u6d3b\u8fd0\u7528\u7f3a\u5931\u503c\u5904\u7406\u3001\u591a\u5217\u9009\u62e9\u548c\u590d\u6742\u6761\u4ef6\u9009\u62e9\u7b49\u6280\u672f\uff0c\u63d0\u9ad8\u6570\u636e\u5206\u6790\u7684\u6548\u7387\u548c\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u672c\u6587\u7684\u5b66\u4e60\uff0c\u76f8\u4fe1\u60a8\u5df2\u7ecf\u638c\u63e1\u4e86\u5982\u4f55\u5728Python\u4e2d\u63d0\u53d6\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\uff0c\u5e76\u80fd\u5728\u5b9e\u9645\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\u7075\u6d3b\u8fd0\u7528\u8fd9\u4e9b\u65b9\u6cd5\u3002\u5e0c\u671b\u8fd9\u4e9b\u5185\u5bb9\u5bf9\u60a8\u7684\u5de5\u4f5c\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u63d0\u53d6\u6570\u636e\u6846\u7684\u7279\u5b9a\u5217\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u5904\u7406\u6570\u636e\u6846\u3002\u8981\u63d0\u53d6\u7279\u5b9a\u5217\uff0c\u53ea\u9700\u4f7f\u7528\u6570\u636e\u6846\u7684\u5217\u540d\u3002\u6bd4\u5982\uff0c\u5982\u679c\u6570\u636e\u6846\u540d\u4e3adf<\/code>\uff0c\u8981\u63d0\u53d6\u540d\u4e3acolumn_name<\/code>\u7684\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528df['column_name']<\/code>\u3002\u8fd9\u79cd\u65b9\u6cd5\u4f1a\u8fd4\u56de\u4e00\u4e2a\u5305\u542b\u8be5\u5217\u6240\u6709\u6570\u636e\u7684Series\u5bf9\u8c61\u3002<\/p>\n
\u63d0\u53d6\u6570\u636e\u6846\u591a\u5217\u65f6\u5e94\u8be5\u5982\u4f55\u64cd\u4f5c\uff1f<\/strong>
\u5982\u679c\u9700\u8981\u63d0\u53d6\u591a\u4e2a\u5217\uff0c\u53ef\u4ee5\u5c06\u5217\u540d\u653e\u5728\u4e00\u4e2a\u5217\u8868\u4e2d\u3002\u4f8b\u5982\uff0c\u4f7f\u7528df[['column1', 'column2']]<\/code>\u53ef\u4ee5\u540c\u65f6\u63d0\u53d6column1<\/code>\u548ccolumn2<\/code>\u7684\u6240\u6709\u6570\u636e\u3002\u8fd9\u5c06\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684\u6570\u636e\u6846\uff0c\u4ec5\u5305\u542b\u6240\u9009\u7684\u5217\u3002<\/p>\n
\u5982\u4f55\u5904\u7406\u63d0\u53d6\u5217\u65f6\u9047\u5230\u7684\u7f3a\u5931\u503c\uff1f<\/strong>
\u5728\u63d0\u53d6\u5217\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u7684dropna()<\/code>\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u503c\u3002\u5982\u679c\u53ea\u5e0c\u671b\u63d0\u53d6\u975e\u7f3a\u5931\u503c\u7684\u6570\u636e\uff0c\u53ef\u4ee5\u5728\u63d0\u53d6\u5217\u540e\u8c03\u7528df['column_name'].dropna()<\/code>\u3002\u8fd9\u6837\u53ef\u4ee5\u786e\u4fdd\u5f97\u5230\u7684\u6570\u636e\u662f\u5b8c\u6574\u7684\uff0c\u65b9\u4fbf\u540e\u7eed\u5206\u6790\u548c\u5904\u7406\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u63d0\u53d6\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u5305\u62ec\u4f7f\u7528\u65b9\u62ec\u53f7\u3001\u70b9\u7b26\u53f7\u4ee5\u53caloc\u548ciloc\u65b9\u6cd5\u7b49\u3002 1 […]","protected":false},"author":3,"featured_media":1130808,"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\/1130803"}],"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=1130803"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1130803\/revisions"}],"predecessor-version":[{"id":1130809,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1130803\/revisions\/1130809"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1130808"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1130803"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1130803"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1130803"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}