{"id":1144791,"date":"2025-01-08T23:02:45","date_gmt":"2025-01-08T15:02:45","guid":{"rendered":""},"modified":"2025-01-08T23:02:49","modified_gmt":"2025-01-08T15:02:49","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e5%b0%86%e5%a4%9a%e5%88%97%e6%94%be%e4%b8%ba%e5%90%8c%e4%b8%80%e8%a1%8c","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1144791.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u5c06\u591a\u5217\u653e\u4e3a\u540c\u4e00\u884c"},"content":{"rendered":"

\"python\u4e2d\u5982\u4f55\u5c06\u591a\u5217\u653e\u4e3a\u540c\u4e00\u884c\"<\/p>\n

\u5728Python\u4e2d\uff0c\u6709\u51e0\u79cd\u65b9\u6cd5\u53ef\u4ee5\u5c06\u591a\u5217\u6570\u636e\u5408\u5e76\u5230\u540c\u4e00\u884c\u3002\u8fd9\u4e9b\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Pandas\u3001NumPy\u548c\u5176\u4ed6Python\u5185\u7f6e\u51fd\u6570\u3002<\/strong> \u5176\u4e2d\uff0cPandas\u662f\u6700\u5e38\u7528\u548c\u6700\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u3002\u4e0b\u9762\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Pandas\u6765\u5b9e\u73b0\u8fd9\u4e00\u64cd\u4f5c\uff0c\u5e76\u4e14\u7b80\u8981\u8ba8\u8bba\u5176\u4ed6\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Pandas<\/h3>\n<\/p>\n

Pandas\u662fPython\u4e2d\u975e\u5e38\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5de5\u5177\uff0c\u7279\u522b\u9002\u5408\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\u3002\u901a\u8fc7Pandas\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u591a\u5217\u6570\u636e\u5408\u5e76\u5230\u540c\u4e00\u884c\u3002<\/p>\n<\/p>\n

1.1 \u4f7f\u7528 concat<\/code> \u51fd\u6570<\/h4>\n<\/p>\n

Pandas\u4e2d\u7684 concat<\/code> \u51fd\u6570\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u5c06\u591a\u4e2aDataFrame\u6216Series\u5bf9\u8c61\u8fdb\u884c\u5408\u5e76\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u521b\u5efa\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
df1 = pd.DataFrame({<\/p>\n
    'A': [1, 2, 3],<\/p>\n
    'B': [4, 5, 6]<\/p>\n
})<\/p>\n
df2 = pd.DataFrame({<\/p>\n
    'C': [7, 8, 9],<\/p>\n
    'D': [10, 11, 12]<\/p>\n
})<\/p>\n
\u5408\u5e76\u4e24\u5217\u5230\u540c\u4e00\u884c<\/strong><\/h2>\n
result = pd.concat([df1, df2], axis=1)<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2 \u4f7f\u7528 merge<\/code> \u51fd\u6570<\/h4>\n<\/p>\n
merge<\/code> \u51fd\u6570\u66f4\u9002\u7528\u4e8e\u57fa\u4e8e\u67d0\u4e2a\u952e\uff08key\uff09\u8fdb\u884c\u5408\u5e76\u64cd\u4f5c\uff0c\u5f53\u6570\u636e\u96c6\u6709\u5171\u540c\u7684\u5217\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528 merge<\/code> \u51fd\u6570\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u793a\u4f8b\u6570\u636e<\/p>\n
df1 = pd.DataFrame({<\/p>\n
    'key': ['A', 'B', 'C'],<\/p>\n
    'value1': [1, 2, 3]<\/p>\n
})<\/p>\n
df2 = pd.DataFrame({<\/p>\n
    'key': ['A', 'B', 'C'],<\/p>\n
    'value2': [4, 5, 6]<\/p>\n
})<\/p>\n
\u5408\u5e76\u4e24\u5217\u5230\u540c\u4e00\u884c<\/strong><\/h2>\n
result = pd.merge(df1, df2, on='key')<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3 \u4f7f\u7528 join<\/code> \u51fd\u6570<\/h4>\n<\/p>\n
join<\/code> \u51fd\u6570\u7c7b\u4f3c\u4e8eSQL\u4e2d\u7684JOIN\u64cd\u4f5c\uff0c\u53ef\u4ee5\u975e\u5e38\u65b9\u4fbf\u5730\u5408\u5e76\u4e24\u4e2aDataFrame\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u793a\u4f8b\u6570\u636e<\/p>\n
df1 = pd.DataFrame({<\/p>\n
    'value1': [1, 2, 3]<\/p>\n
}, index=['A', 'B', 'C'])<\/p>\n
df2 = pd.DataFrame({<\/p>\n
    'value2': [4, 5, 6]<\/p>\n
}, index=['A', 'B', 'C'])<\/p>\n
\u5408\u5e76\u4e24\u5217\u5230\u540c\u4e00\u884c<\/strong><\/h2>\n
result = df1.join(df2)<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528NumPy<\/h3>\n<\/p>\n
NumPy\u662fPython\u4e2d\u53e6\u4e00\u4e2a\u5f3a\u5927\u7684\u5e93\uff0c\u4e3b\u8981\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\u3002\u867d\u7136NumPy\u4e0d\u5982Pandas\u7075\u6d3b\uff0c\u4f46\u5b83\u4e5f\u53ef\u4ee5\u7528\u6765\u5408\u5e76\u591a\u5217\u6570\u636e\u3002<\/p>\n<\/p>\n
2.1 \u4f7f\u7528 hstack<\/code> \u51fd\u6570<\/h4>\n<\/p>\n
hstack<\/code> \u51fd\u6570\u53ef\u4ee5\u5c06\u591a\u4e2a\u6570\u7ec4\u6c34\u5e73\u5806\u53e0\u8d77\u6765\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
arr1 = np.array([[1, 2, 3], [4, 5, 6]])<\/p>\n
arr2 = np.array([[7, 8, 9], [10, 11, 12]])<\/p>\n
\u5408\u5e76\u591a\u5217\u6570\u636e<\/strong><\/h2>\n
result = np.hstack((arr1, arr2))<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2 \u4f7f\u7528 column_stack<\/code> \u51fd\u6570<\/h4>\n<\/p>\n
column_stack<\/code> \u51fd\u6570\u7c7b\u4f3c\u4e8e hstack<\/code>\uff0c\u4f46\u66f4\u9002\u5408\u5c06\u4e00\u7ef4\u6570\u7ec4\u5408\u5e76\u6210\u4e8c\u7ef4\u6570\u7ec4\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u793a\u4f8b\u6570\u636e<\/p>\n
arr1 = np.array([1, 2, 3])<\/p>\n
arr2 = np.array([4, 5, 6])<\/p>\n
\u5408\u5e76\u591a\u5217\u6570\u636e<\/strong><\/h2>\n
result = np.column_stack((arr1, arr2))<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Python\u5185\u7f6e\u51fd\u6570<\/h3>\n<\/p>\n
\u5982\u679c\u6570\u636e\u91cf\u4e0d\u5927\uff0c\u6216\u8005\u4e0d\u9700\u8981\u4f7f\u7528Pandas\u548cNumPy\u7b49\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u5185\u7f6e\u51fd\u6570\u6765\u5b9e\u73b0\u591a\u5217\u5408\u5e76\u3002<\/p>\n<\/p>\n
3.1 \u4f7f\u7528 zip<\/code> \u51fd\u6570<\/h4>\n<\/p>\n
zip<\/code> \u51fd\u6570\u53ef\u4ee5\u5c06\u591a\u4e2a\u53ef\u8fed\u4ee3\u5bf9\u8c61\uff08\u5982\u5217\u8868\u3001\u5143\u7ec4\u7b49\uff09\u6253\u5305\u6210\u4e00\u4e2a\u53ef\u8fed\u4ee3\u5bf9\u8c61\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u793a\u4f8b\u6570\u636e<\/p>\n
list1 = [1, 2, 3]<\/p>\n
list2 = [4, 5, 6]<\/p>\n
\u5408\u5e76\u591a\u5217\u6570\u636e<\/strong><\/h2>\n
result = list(zip(list1, list2))<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f<\/h4>\n<\/p>\n
\u5217\u8868\u63a8\u5bfc\u5f0f\u662f\u4e00\u79cd\u7b80\u6d01\u4e14\u9ad8\u6548\u7684\u65b9\u5f0f\u6765\u521b\u5efa\u5217\u8868\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u793a\u4f8b\u6570\u636e<\/p>\n
list1 = [1, 2, 3]<\/p>\n
list2 = [4, 5, 6]<\/p>\n
\u5408\u5e76\u591a\u5217\u6570\u636e<\/strong><\/h2>\n
result = [(x, y) for x, y in zip(list1, list2)]<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u7efc\u5408\u5e94\u7528<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u80fd\u4f1a\u9700\u8981\u7efc\u5408\u4f7f\u7528\u591a\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u590d\u6742\u7684\u6570\u636e\u96c6\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7efc\u5408\u5e94\u7528\u7684\u793a\u4f8b\u3002<\/p>\n<\/p>\n
4.1 \u8bfb\u53d6CSV\u6587\u4ef6\u5e76\u5408\u5e76\u591a\u5217<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e24\u4e2aCSV\u6587\u4ef6\uff0c\u5206\u522b\u5305\u542b\u4e0d\u540c\u7684\u5217\u6570\u636e\uff0c\u6211\u4eec\u5e0c\u671b\u5c06\u5b83\u4eec\u5408\u5e76\u5230\u540c\u4e00\u884c\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
df1 = pd.read_csv('file1.csv')<\/p>\n
df2 = pd.read_csv('file2.csv')<\/p>\n
\u5408\u5e76\u591a\u5217\u6570\u636e<\/strong><\/h2>\n
result = pd.concat([df1, df2], axis=1)<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2 \u6570\u636e\u6e05\u6d17\u548c\u9884\u5904\u7406<\/h4>\n<\/p>\n
\u5728\u5408\u5e76\u6570\u636e\u4e4b\u524d\uff0c\u53ef\u80fd\u9700\u8981\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u548c\u9884\u5904\u7406\u3002<\/p>\n<\/p>\n
# \u8bfb\u53d6CSV\u6587\u4ef6<\/p>\n
df1 = pd.read_csv('file1.csv')<\/p>\n
df2 = pd.read_csv('file2.csv')<\/p>\n
\u6570\u636e\u6e05\u6d17<\/strong><\/h2>\n
df1 = df1.dropna()  # \u5220\u9664\u7f3a\u5931\u503c<\/p>\n
df2 = df2.drop_duplicates()  # \u5220\u9664\u91cd\u590d\u503c<\/p>\n
\u5408\u5e76\u591a\u5217\u6570\u636e<\/strong><\/h2>\n
result = pd.concat([df1, df2], axis=1)<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u591a\u5217\u6570\u636e\u5408\u5e76\u5230\u540c\u4e00\u884c\u3002\u4f7f\u7528Pandas\u662f\u6700\u5e38\u89c1\u548c\u6700\u65b9\u4fbf\u7684\u65b9\u6cd5\uff0c\u7279\u522b\u662f\u5728\u5904\u7406\u5927\u6570\u636e\u96c6\u65f6\u3002<\/strong> NumPy\u867d\u7136\u4e0d\u5982Pandas\u7075\u6d3b\uff0c\u4f46\u5728\u6570\u503c\u8ba1\u7b97\u65b9\u9762\u8868\u73b0\u4f18\u5f02\u3002Python\u5185\u7f6e\u51fd\u6570\u5219\u9002\u7528\u4e8e\u7b80\u5355\u7684\u6570\u636e\u5408\u5e76\u64cd\u4f5c\u3002\u6839\u636e\u5b9e\u9645\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5927\u5927\u63d0\u9ad8\u6570\u636e\u5904\u7406\u7684\u6548\u7387\u548c\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
\u5e0c\u671b\u901a\u8fc7\u672c\u6587\uff0c\u60a8\u5bf9\u5982\u4f55\u5728Python\u4e2d\u5c06\u591a\u5217\u6570\u636e\u5408\u5e76\u5230\u540c\u4e00\u884c\u6709\u4e86\u66f4\u6df1\u5165\u7684\u4e86\u89e3\u3002\u65e0\u8bba\u662f\u4f7f\u7528Pandas\u3001NumPy\u8fd8\u662fPython\u5185\u7f6e\u51fd\u6570\uff0c\u90fd\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u60c5\u51b5\u7075\u6d3b\u8fd0\u7528\uff0c\u89e3\u51b3\u6570\u636e\u5904\u7406\u4e2d\u7684\u5404\u79cd\u95ee\u9898\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5728Python\u4e2d\uff0c\u5982\u4f55\u5c06DataFrame\u7684\u591a\u5217\u5408\u5e76\u4e3a\u4e00\u5217\uff1f<\/strong>
\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u4e2d\u7684apply<\/code>\u51fd\u6570\u7ed3\u5408join<\/code>\u65b9\u6cd5\u6765\u5c06\u591a\u4e2a\u5217\u7684\u6570\u636e\u5408\u5e76\u4e3a\u4e00\u4e2a\u65b0\u7684\u5217\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n
import pandas as pd\n\n# \u521b\u5efa\u793a\u4f8bDataFrame\ndf = pd.DataFrame({\n    'A': ['Hello', 'Good'],\n    'B': ['World', 'Morning'],\n    'C': ['!', '?']\n})\n\n# \u5408\u5e76\u591a\u5217\u4e3a\u4e00\u5217\ndf['Merged'] = df[['A', 'B', 'C']].apply(lambda x: ' '.join(x), axis=1)\nprint(df)\n<\/code><\/pre>\n
\u8fd9\u6837\u5c31\u80fd\u5c06A\u3001B\u3001C\u4e09\u5217\u5408\u5e76\u4e3a\u4e00\u4e2a\u65b0\u7684'Merged'\u5217\u3002<\/p>\n
\u5982\u4f55\u4f7f\u7528Pandas\u4e2d\u7684melt<\/code>\u51fd\u6570\u8fdb\u884c\u5217\u7684\u8f6c\u6362\uff1f<\/strong>
melt<\/code>\u51fd\u6570\u53ef\u4ee5\u5c06\u591a\u4e2a\u5217\u8f6c\u6362\u4e3a\u884c\u683c\u5f0f\uff0c\u9002\u5408\u5904\u7406\u5bbd\u683c\u5f0f\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n
import pandas as pd\n\n# \u521b\u5efa\u793a\u4f8bDataFrame\ndf = pd.DataFrame({\n    'ID': [1, 2],\n    'A': ['Hello', 'Good'],\n    'B': ['World', 'Morning']\n})\n\n# \u4f7f\u7528melt\u51fd\u6570\nmelted_df = pd.melt(df, id_vars=['ID'], value_vars=['A', 'B'])\nprint(melted_df)\n<\/code><\/pre>\n
\u8fd9\u4e2a\u65b9\u6cd5\u4f1a\u5c06A\u548cB\u5217\u7684\u5185\u5bb9\u8f6c\u6362\u4e3a\u884c\uff0c\u9002\u5408\u9700\u8981\u8fdb\u884c\u6570\u636e\u900f\u89c6\u7684\u60c5\u51b5\u3002<\/p>\n
\u5728Python\u4e2d\uff0c\u6709\u54ea\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u5b9e\u73b0\u5217\u7684\u62fc\u63a5\uff1f<\/strong>
\u9664\u4e86\u4f7f\u7528apply<\/code>\u548cjoin<\/code>\uff0c\u8fd8\u6709\u5176\u4ed6\u65b9\u6cd5\u53ef\u4ee5\u5b9e\u73b0\u5217\u7684\u62fc\u63a5\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528agg<\/code>\u65b9\u6cd5\uff1a<\/p>\n
import pandas as pd\n\n# \u521b\u5efa\u793a\u4f8bDataFrame\ndf = pd.DataFrame({\n    'A': ['Hello', 'Good'],\n    'B': ['World', 'Morning']\n})\n\n# \u4f7f\u7528agg\u65b9\u6cd5\u62fc\u63a5\u5217\ndf['Combined'] = df[['A', 'B']].agg(' '.join, axis=1)\nprint(df)\n<\/code><\/pre>\n
\u8fd9\u79cd\u65b9\u5f0f\u4e5f\u80fd\u5feb\u901f\u5c06\u591a\u5217\u62fc\u63a5\u4e3a\u4e00\u5217\uff0c\u9002\u5408\u9700\u8981\u5bf9\u5217\u8fdb\u884c\u64cd\u4f5c\u7684\u60c5\u51b5\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u6709\u51e0\u79cd\u65b9\u6cd5\u53ef\u4ee5\u5c06\u591a\u5217\u6570\u636e\u5408\u5e76\u5230\u540c\u4e00\u884c\u3002\u8fd9\u4e9b\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Pandas\u3001NumPy\u548c\u5176\u4ed6Pyth […]","protected":false},"author":3,"featured_media":1144804,"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\/1144791"}],"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=1144791"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1144791\/revisions"}],"predecessor-version":[{"id":1144805,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1144791\/revisions\/1144805"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1144804"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1144791"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1144791"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1144791"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}