{"id":1139496,"date":"2025-01-08T22:10:47","date_gmt":"2025-01-08T14:10:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1139496.html"},"modified":"2025-01-08T22:10:50","modified_gmt":"2025-01-08T14:10:50","slug":"python%e5%a6%82%e4%bd%95%e5%b0%86%e4%b8%80%e5%88%97%e6%95%b0%e6%8d%ae%e5%88%86%e6%88%90%e5%a4%9a%e5%88%97","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1139496.html","title":{"rendered":"python\u5982\u4f55\u5c06\u4e00\u5217\u6570\u636e\u5206\u6210\u591a\u5217"},"content":{"rendered":"

\"python\u5982\u4f55\u5c06\u4e00\u5217\u6570\u636e\u5206\u6210\u591a\u5217\"<\/p>\n

Python \u5c06\u4e00\u5217\u6570\u636e\u5206\u6210\u591a\u5217\u7684\u5e38\u7528\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528 pandas<\/code> \u5e93\u4e2d\u7684 str.split()<\/code> \u65b9\u6cd5\u3001\u4f7f\u7528 numpy<\/code> \u5e93\u4e2d\u7684 reshape()<\/code> \u65b9\u6cd5\u3001\u4f7f\u7528 itertools<\/code> \u6a21\u5757\u7684 zip_longest()<\/code> \u65b9\u6cd5<\/strong>\u3002\u5176\u4e2d\uff0c\u6700\u5e38\u89c1\u7684\u65b9\u6cd5\u662f\u4f7f\u7528 pandas<\/code> \u5e93\uff0c\u56e0\u4e3a\u5b83\u5177\u6709\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\uff0c\u5e76\u4e14 str.split()<\/code> \u65b9\u6cd5\u53ef\u4ee5\u8f7b\u677e\u5904\u7406\u6570\u636e\u7684\u5206\u5217\u64cd\u4f5c\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528 pandas<\/code> \u5e93\u5c06\u4e00\u5217\u6570\u636e\u5206\u6210\u591a\u5217\u3002<\/p>\n<\/p>\n

\u4e00\u3001Pandas \u5e93\u7b80\u4ecb<\/h3>\n<\/p>\n

Pandas \u662f Python \u8bed\u8a00\u4e2d\u6700\u6d41\u884c\u7684\u6570\u636e\u64cd\u4f5c\u5e93\u4e4b\u4e00\uff0c\u4e13\u4e3a\u6570\u636e\u5206\u6790\u800c\u8bbe\u8ba1\u3002\u5b83\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\uff0c\u4f7f\u5f97\u6570\u636e\u5904\u7406\u53d8\u5f97\u975e\u5e38\u7b80\u5355\u3002Pandas \u5e93\u7684\u6838\u5fc3\u6570\u636e\u7ed3\u6784\u662f DataFrame<\/code>\uff0c\u5b83\u53ef\u4ee5\u770b\u4f5c\u662f\u4e00\u4e2a\u4e8c\u7ef4\u7684\u8868\u683c\uff0c\u7c7b\u4f3c\u4e8e\u7535\u5b50\u8868\u683c\u6216 SQL \u8868\u3002<\/p>\n<\/p>\n

1\u3001\u5b89\u88c5 Pandas \u5e93<\/h4>\n<\/p>\n

\u5982\u679c\u8fd8\u6ca1\u6709\u5b89\u88c5 Pandas \u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5bfc\u5165 Pandas \u5e93<\/h4>\n<\/p>\n
\u5728\u5f00\u59cb\u4f7f\u7528 Pandas \u4e4b\u524d\uff0c\u9700\u8981\u5148\u5bfc\u5165\u5e93\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528 str.split()<\/code> \u65b9\u6cd5<\/h3>\n<\/p>\n
str.split()<\/code> \u65b9\u6cd5\u662f Pandas \u5e93\u4e2d\u7528\u4e8e\u5b57\u7b26\u4e32\u64cd\u4f5c\u7684\u4e00\u4e2a\u65b9\u6cd5\uff0c\u5b83\u53ef\u4ee5\u6309\u7167\u6307\u5b9a\u7684\u5206\u9694\u7b26\u5c06\u5b57\u7b26\u4e32\u5206\u5272\u6210\u591a\u4e2a\u90e8\u5206\uff0c\u5e76\u8fd4\u56de\u4e00\u4e2a\u5305\u542b\u8fd9\u4e9b\u90e8\u5206\u7684\u65b0 DataFrame\u3002<\/p>\n<\/p>\n
1\u3001\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b DataFrame<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u4e00\u5217\u6570\u636e\u7684 DataFrame\uff1a<\/p>\n<\/p>\n
data = {'col': ['A,B,C', 'D,E,F', 'G,H,I']}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n<\/p>\n
     col<\/p>\n
0  A,B,C<\/p>\n
1  D,E,F<\/p>\n
2  G,H,I<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528 str.split()<\/code> \u65b9\u6cd5\u5206\u5217<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u4f7f\u7528 str.split()<\/code> \u65b9\u6cd5\u5c06\u5b57\u7b26\u4e32\u6309\u7167\u9017\u53f7\u5206\u9694\uff0c\u5e76\u5c06\u7ed3\u679c\u5b58\u50a8\u5230\u65b0\u7684\u5217\u4e2d\uff1a<\/p>\n<\/p>\n
df[['col1', 'col2', 'col3']] = df['col'].str.split(',', expand=True)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n<\/p>\n
     col col1 col2 col3<\/p>\n
0  A,B,C    A    B    C<\/p>\n
1  D,E,F    D    E    F<\/p>\n
2  G,H,I    G    H    I<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5982\u4e0a\u6240\u793a\uff0c\u901a\u8fc7\u4f7f\u7528 str.split()<\/code> \u65b9\u6cd5\uff0c\u6210\u529f\u5730\u5c06\u4e00\u5217\u6570\u636e\u5206\u6210\u4e86\u4e09\u5217\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528 numpy<\/code> \u5e93\u7684 reshape()<\/code> \u65b9\u6cd5<\/h3>\n<\/p>\n
\u9664\u4e86\u4f7f\u7528 Pandas \u5e93\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528 numpy<\/code> \u5e93\u7684 reshape()<\/code> \u65b9\u6cd5\u5c06\u4e00\u5217\u6570\u636e\u5206\u6210\u591a\u5217\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5\u548c\u5bfc\u5165 Numpy \u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u5b89\u88c5 Numpy \u5e93\uff1a<\/p>\n<\/p>\n
pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\uff0c\u5bfc\u5165\u5e93\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u7ec4<\/h4>\n<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u6570\u636e\u7684 Numpy \u6570\u7ec4\uff1a<\/p>\n<\/p>\n
data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4f7f\u7528 reshape()<\/code> \u65b9\u6cd5\u5206\u5217<\/h4>\n<\/p>\n
\u4f7f\u7528 reshape()<\/code> \u65b9\u6cd5\u5c06\u4e00\u7ef4\u6570\u7ec4\u91cd\u65b0\u5f62\u72b6\u4e3a\u4e8c\u7ef4\u6570\u7ec4\uff1a<\/p>\n<\/p>\n
reshaped_data = data.reshape(-1, 3)<\/p>\n
print(reshaped_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n<\/p>\n
[[1 2 3]<\/p>\n
 [4 5 6]<\/p>\n
 [7 8 9]]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7 reshape()<\/code> \u65b9\u6cd5\uff0c\u5c06\u4e00\u5217\u6570\u636e\u6210\u529f\u5730\u5206\u6210\u4e86\u4e09\u5217\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4f7f\u7528 itertools<\/code> \u6a21\u5757\u7684 zip_longest()<\/code> \u65b9\u6cd5<\/h3>\n<\/p>\n
itertools<\/code> \u6a21\u5757\u63d0\u4f9b\u4e86\u4e00\u7ec4\u7528\u4e8e\u9ad8\u6548\u5faa\u73af\u548c\u8fed\u4ee3\u7684\u5de5\u5177\uff0c\u5176\u4e2d zip_longest()<\/code> \u65b9\u6cd5\u53ef\u4ee5\u7528\u4e8e\u5c06\u4e00\u5217\u6570\u636e\u5206\u6210\u591a\u5217\u3002<\/p>\n<\/p>\n
1\u3001\u5bfc\u5165 itertools<\/code> \u6a21\u5757<\/h4>\n<\/p>\n
import itertools<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u5217\u8868<\/h4>\n<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u6570\u636e\u7684\u5217\u8868\uff1a<\/p>\n<\/p>\n
data = [1, 2, 3, 4, 5, 6, 7, 8, 9]<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4f7f\u7528 zip_longest()<\/code> \u65b9\u6cd5\u5206\u5217<\/h4>\n<\/p>\n
\u4f7f\u7528 zip_longest()<\/code> \u65b9\u6cd5\u5c06\u5217\u8868\u5206\u6210\u591a\u4e2a\u90e8\u5206\uff0c\u5e76\u5c06\u7ed3\u679c\u8f6c\u6362\u4e3a DataFrame\uff1a<\/p>\n<\/p>\n
chunk_size = 3<\/p>\n
chunks = list(itertools.zip_longest(*[iter(data)]*chunk_size, fillvalue=None))<\/p>\n
df = pd.DataFrame(chunks)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n<\/p>\n
   0  1  2<\/p>\n
0  1  2  3<\/p>\n
1  4  5  6<\/p>\n
2  7  8  9<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7 zip_longest()<\/code> \u65b9\u6cd5\uff0c\u5c06\u4e00\u5217\u6570\u636e\u6210\u529f\u5730\u5206\u6210\u4e86\u591a\u5217\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528 Python \u5c06\u4e00\u5217\u6570\u636e\u5206\u6210\u591a\u5217\u7684\u51e0\u79cd\u5e38\u7528\u65b9\u6cd5\u3002Pandas \u5e93\u7684 str.split()<\/code> \u65b9\u6cd5<\/strong>\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u56e0\u4e3a\u5b83\u7b80\u5355\u6613\u7528\u4e14\u529f\u80fd\u5f3a\u5927\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u4ecb\u7ecd\u4e86\u4f7f\u7528 Numpy \u5e93\u7684 reshape()<\/code> \u65b9\u6cd5<\/strong>\u548c itertools<\/code> \u6a21\u5757\u7684 zip_longest()<\/code> \u65b9\u6cd5<\/strong>\u3002\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\uff0c\u5177\u4f53\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u9700\u6c42\u8fdb\u884c\u51b3\u5b9a\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u638c\u63e1\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u80fd\u591f\u6709\u6548\u5730\u5904\u7406\u548c\u8f6c\u6362\u6570\u636e\uff0c\u63d0\u9ad8\u6570\u636e\u5206\u6790\u7684\u6548\u7387\u548c\u51c6\u786e\u6027\u3002\u5982\u679c\u5728\u5b9e\u9645\u64cd\u4f5c\u4e2d\u9047\u5230\u95ee\u9898\uff0c\u53ef\u4ee5\u53c2\u8003\u5b98\u65b9\u6587\u6863\u6216\u76f8\u5173\u6559\u7a0b\uff0c\u4ee5\u83b7\u5f97\u66f4\u591a\u7684\u5e2e\u52a9\u548c\u6307\u5bfc\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5c06\u4e00\u5217\u6570\u636e\u8f6c\u6362\u4e3a\u591a\u5217\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u8f7b\u677e\u5b9e\u73b0\u5c06\u4e00\u5217\u6570\u636e\u5206\u6210\u591a\u5217\u7684\u64cd\u4f5c\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86Pandas\u5e93\u3002\u63a5\u4e0b\u6765\uff0c\u53ef\u4ee5\u4f7f\u7528str.split()<\/code>\u65b9\u6cd5\uff0c\u5c06\u76ee\u6807\u5217\u7684\u6570\u636e\u62c6\u5206\u4e3a\u591a\u4e2a\u5217\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
import pandas as pd\n\n# \u521b\u5efa\u793a\u4f8b\u6570\u636e\u6846\ndata = {'column1': ['a,b,c', 'd,e,f', 'g,h,i']}\ndf = pd.DataFrame(data)\n\n# \u5c06\u4e00\u5217\u6570\u636e\u5206\u6210\u591a\u5217\nnew_df = df['column1'].str.split(',', expand=True)\n\nprint(new_df)\n<\/code><\/pre>\n
\u4e0a\u8ff0\u4ee3\u7801\u5c06column1<\/code>\u5217\u4e2d\u7684\u5b57\u7b26\u4e32\u6309\u9017\u53f7\u5206\u5272\uff0c\u5e76\u5c06\u7ed3\u679c\u5b58\u50a8\u5728\u65b0\u7684\u6570\u636e\u6846\u4e2d\u3002<\/p>\n
\u5728\u6570\u636e\u5206\u5217\u65f6\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\uff1f<\/strong>
\u5728\u5206\u5217\u64cd\u4f5c\u4e2d\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u67d0\u4e9b\u884c\u7f3a\u5931\u6570\u636e\u7684\u60c5\u51b5\u3002\u4f7f\u7528str.split()<\/code>\u65f6\uff0c\u7f3a\u5931\u503c\u4f1a\u81ea\u52a8\u586b\u5145\u4e3aNaN\uff08Not a Number\uff09\u3002\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6efillna()<\/code>\u65b9\u6cd5\u6765\u5904\u7406\u8fd9\u4e9b\u7f3a\u5931\u503c\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u5c06NaN\u66ff\u6362\u4e3a\u7279\u5b9a\u7684\u503c\uff0c\u6216\u8005\u9009\u62e9\u5220\u9664\u542b\u6709NaN\u7684\u884c\u3002<\/p>\n
new_df = df['column1'].str.split(',', expand=True).fillna('missing')\n<\/code><\/pre>\n
\u5982\u4f55\u5728\u5206\u5217\u540e\u91cd\u547d\u540d\u65b0\u5217\uff1f<\/strong>
\u5c06\u6570\u636e\u5206\u5217\u540e\uff0c\u751f\u6210\u7684\u65b0\u5217\u4f1a\u81ea\u52a8\u547d\u540d\u4e3a\u6570\u5b57\u7d22\u5f15\uff08\u59820, 1, 2\u7b49\uff09\u3002\u5982\u679c\u5e0c\u671b\u5bf9\u8fd9\u4e9b\u65b0\u5217\u8fdb\u884c\u66f4\u76f4\u89c2\u7684\u547d\u540d\uff0c\u53ef\u4ee5\u4f7f\u7528rename()<\/code>\u65b9\u6cd5\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u91cd\u547d\u540d\u65b0\u5217\u7684\u793a\u4f8b\uff1a<\/p>\n
new_df.columns = ['first', 'second', 'third']\n<\/code><\/pre>\n
\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u7684\u6570\u636e\u5185\u5bb9\uff0c\u4e3a\u65b0\u751f\u6210\u7684\u5217\u8d4b\u4e88\u66f4\u5177\u63cf\u8ff0\u6027\u7684\u540d\u79f0\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python \u5c06\u4e00\u5217\u6570\u636e\u5206\u6210\u591a\u5217\u7684\u5e38\u7528\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528 pandas \u5e93\u4e2d\u7684 str.split() \u65b9\u6cd5\u3001\u4f7f\u7528 […]","protected":false},"author":3,"featured_media":1139505,"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\/1139496"}],"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=1139496"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1139496\/revisions"}],"predecessor-version":[{"id":1139508,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1139496\/revisions\/1139508"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1139505"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1139496"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1139496"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1139496"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}