{"id":966979,"date":"2024-12-27T04:52:43","date_gmt":"2024-12-26T20:52:43","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/966979.html"},"modified":"2024-12-27T04:52:45","modified_gmt":"2024-12-26T20:52:45","slug":"python%e5%a6%82%e4%bd%95txt%e8%bd%accsv","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/966979.html","title":{"rendered":"Python\u5982\u4f55TXT\u8f6ccsv"},"content":{"rendered":"

\"Python\u5982\u4f55TXT\u8f6ccsv\"<\/p>\n

\u5728Python\u4e2d\uff0cTXT\u6587\u4ef6\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u8f6c\u6362\u4e3aCSV\u6587\u4ef6\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Pandas\u5e93\u3001CSV\u6a21\u5757\u4ee5\u53caNumpy\u5e93\u3002Pandas\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\u3001CSV\u6a21\u5757\u652f\u6301\u7b80\u5355\u7684\u6587\u672c\u6587\u4ef6\u64cd\u4f5c\u3001Numpy\u5e93\u5219\u9002\u5408\u5904\u7406\u6570\u503c\u6570\u636e\u3002<\/strong><\/p>\n<\/p>\n

\u4f7f\u7528Pandas\u5e93\u662f\u6700\u4e3a\u63a8\u8350\u7684\u65b9\u6cd5\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\uff0c\u5e76\u4e14\u80fd\u591f\u81ea\u52a8\u5904\u7406\u6570\u636e\u4e2d\u7684\u7f3a\u5931\u503c\u548c\u4e0d\u540c\u7684\u6570\u636e\u7c7b\u578b\u3002\u5177\u4f53\u64cd\u4f5c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Pandas\u5e93<\/h3>\n<\/p>\n

Pandas\u662f\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u80fd\u591f\u8f7b\u677e\u5730\u8bfb\u5199CSV\u6587\u4ef6\u3002\u4f7f\u7528Pandas\u5c06TXT\u8f6c\u6362\u4e3aCSV\u975e\u5e38\u7b80\u5355\uff0c\u4ee5\u4e0b\u662f\u5177\u4f53\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

1. \u5b89\u88c5Pandas\u5e93<\/h4>\n<\/p>\n

\u5728\u4f7f\u7528Pandas\u4e4b\u524d\uff0c\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86\u8be5\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\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. \u8bfb\u53d6TXT\u6587\u4ef6<\/h4>\n<\/p>\n
\u4f7f\u7528Pandas\u7684read_csv<\/code>\u65b9\u6cd5\u8bfb\u53d6TXT\u6587\u4ef6\u3002\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0c\u5982\u679cTXT\u6587\u4ef6\u662f\u4ee5\u7279\u5b9a\u5206\u9694\u7b26\uff08\u5982\u5236\u8868\u7b26\u3001\u9017\u53f7\u7b49\uff09\u5206\u9694\u7684\uff0c\u9700\u8981\u5728\u8bfb\u53d6\u65f6\u6307\u5b9a\u5206\u9694\u7b26\u3002\u4f8b\u5982\uff0c\u8bfb\u53d6\u4ee5\u5236\u8868\u7b26\u5206\u9694\u7684TXT\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
df = pd.read_csv('data.txt', delimiter='\\t')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u5c06DataFrame\u5199\u5165CSV\u6587\u4ef6<\/h4>\n<\/p>\n
\u8bfb\u53d6TXT\u6587\u4ef6\u540e\uff0c\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528Pandas\u7684to_csv<\/code>\u65b9\u6cd5\u5c06DataFrame\u5199\u5165CSV\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
df.to_csv('data.csv', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
index=False<\/code>\u53c2\u6570\u7528\u4e8e\u907f\u514d\u5c06\u7d22\u5f15\u5217\u5199\u5165CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528CSV\u6a21\u5757<\/h3>\n<\/p>\n
Python\u5185\u7f6e\u7684CSV\u6a21\u5757\u4e5f\u53ef\u4ee5\u7528\u4e8e\u5c06TXT\u6587\u4ef6\u8f6c\u6362\u4e3aCSV\u6587\u4ef6\u3002\u9002\u5408\u4e8e\u7b80\u5355\u7684\u6587\u672c\u6587\u4ef6\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
1. \u8bfb\u53d6TXT\u6587\u4ef6<\/h4>\n<\/p>\n
\u9996\u5148\u6253\u5f00TXT\u6587\u4ef6\u8fdb\u884c\u8bfb\u53d6\uff1a<\/p>\n<\/p>\n
import csv<\/p>\n
with open('data.txt', 'r') as txt_file:<\/p>\n
    txt_reader = txt_file.readlines()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u5199\u5165CSV\u6587\u4ef6<\/h4>\n<\/p>\n
\u4f7f\u7528CSV\u6a21\u5757\u7684writer<\/code>\u5bf9\u8c61\uff0c\u5c06\u8bfb\u53d6\u7684\u5185\u5bb9\u5199\u5165CSV\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
with open('data.csv', 'w', newline='') as csv_file:<\/p>\n
    csv_writer = csv.writer(csv_file)<\/p>\n
    for line in txt_reader:<\/p>\n
        csv_writer.writerow(line.split())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Numpy\u5e93<\/h3>\n<\/p>\n
Numpy\u5e93\u9002\u5408\u5904\u7406\u6570\u503c\u578b\u6570\u636e\uff0c\u5c24\u5176\u662f\u5728\u6570\u636e\u9700\u8981\u8fdb\u884c\u8fdb\u4e00\u6b65\u6570\u5b66\u8ba1\u7b97\u65f6\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5Numpy\u5e93<\/h4>\n<\/p>\n
\u5982\u679c\u6ca1\u6709\u5b89\u88c5Numpy\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6TXT\u6587\u4ef6<\/h4>\n<\/p>\n
\u4f7f\u7528Numpy\u7684loadtxt<\/code>\u65b9\u6cd5\u8bfb\u53d6TXT\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
data = np.loadtxt('data.txt', delimiter='\\t')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u5199\u5165CSV\u6587\u4ef6<\/h4>\n<\/p>\n
\u4f7f\u7528Numpy\u7684savetxt<\/code>\u65b9\u6cd5\u5c06\u6570\u636e\u5199\u5165CSV\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
np.savetxt('data.csv', data, delimiter=',', fmt='%s')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5904\u7406TXT\u6587\u4ef6\u4e2d\u7684\u7279\u6b8a\u60c5\u51b5<\/h3>\n<\/p>\n
\u5728\u8f6c\u6362\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u80fd\u4f1a\u9047\u5230TXT\u6587\u4ef6\u4e2d\u7684\u4e00\u4e9b\u7279\u6b8a\u60c5\u51b5\uff0c\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
1. \u542b\u6709\u6807\u9898\u884c<\/h4>\n<\/p>\n
\u5982\u679cTXT\u6587\u4ef6\u4e2d\u5305\u542b\u6807\u9898\u884c\uff0c\u9700\u8981\u5728\u8bfb\u53d6\u65f6\u8fdb\u884c\u5904\u7406\u3002\u4f8b\u5982\uff0c\u5728Pandas\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7header<\/code>\u53c2\u6570\u6307\u5b9a\u6807\u9898\u884c\u7684\u4f4d\u7f6e\uff1a<\/p>\n<\/p>\n
df = pd.read_csv('data.txt', delimiter='\\t', header=0)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u6570\u636e\u7f3a\u5931<\/h4>\n<\/p>\n
\u5728\u8bfb\u53d6\u6570\u636e\u65f6\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u6570\u636e\u7f3a\u5931\u7684\u60c5\u51b5\u3002Pandas\u80fd\u591f\u81ea\u52a8\u8bc6\u522b\u7f3a\u5931\u503c\u5e76\u5904\u7406\u3002\u5982\u679c\u9700\u8981\u81ea\u5b9a\u4e49\u7f3a\u5931\u503c\u5904\u7406\u65b9\u5f0f\uff0c\u53ef\u4ee5\u4f7f\u7528na_values<\/code>\u53c2\u6570\uff1a<\/p>\n<\/p>\n
df = pd.read_csv('data.txt', delimiter='\\t', na_values='NaN')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u4e0d\u540c\u7684\u6570\u636e\u7c7b\u578b<\/h4>\n<\/p>\n
\u5728\u8bfb\u53d6\u6570\u636e\u65f6\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u4e0d\u540c\u7684\u6570\u636e\u7c7b\u578b\u3002Pandas\u80fd\u591f\u81ea\u52a8\u8bc6\u522b\u6570\u636e\u7c7b\u578b\uff0c\u4f46\u5982\u679c\u9700\u8981\u81ea\u5b9a\u4e49\uff0c\u53ef\u4ee5\u4f7f\u7528dtype<\/code>\u53c2\u6570\uff1a<\/p>\n<\/p>\n
df = pd.read_csv('data.txt', delimiter='\\t', dtype={'column1': 'int32', 'column2': 'float64'})<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728Python\u4e2d\uff0cTXT\u6587\u4ef6\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u8f6c\u6362\u4e3aCSV\u6587\u4ef6\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u9700\u6c42\u548c\u6570\u636e\u7279\u70b9\u3002Pandas\u5e93\u662f\u6700\u4e3a\u63a8\u8350\u7684\u9009\u62e9\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\uff0c\u80fd\u591f\u81ea\u52a8\u5904\u7406\u6570\u636e\u4e2d\u7684\u7f3a\u5931\u503c\u548c\u4e0d\u540c\u7684\u6570\u636e\u7c7b\u578b\u3002\u6b64\u5916\uff0cPython\u5185\u7f6e\u7684CSV\u6a21\u5757\u548cNumpy\u5e93\u4e5f\u63d0\u4f9b\u4e86\u7b80\u5355\u6709\u6548\u7684\u89e3\u51b3\u65b9\u6848\u3002\u6839\u636e\u5b9e\u9645\u60c5\u51b5\uff0c\u53ef\u4ee5\u9009\u62e9\u6700\u9002\u5408\u7684\u65b9\u6cd5\u8fdb\u884c\u8f6c\u6362\uff0c\u4ece\u800c\u63d0\u9ad8\u5de5\u4f5c\u6548\u7387\u548c\u6570\u636e\u5904\u7406\u80fd\u529b\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5c06TXT\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u683c\u5f0f\u5316\u4e3aCSV\uff1f<\/strong>
\u5728\u8fdb\u884cTXT\u5230CSV\u7684\u8f6c\u6362\u65f6\uff0c\u9996\u5148\u9700\u8981\u786e\u4fdd\u6570\u636e\u7684\u683c\u5f0f\u4e00\u81f4\u3002\u4e00\u822c\u800c\u8a00\uff0cTXT\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u53ef\u80fd\u4f7f\u7528\u7279\u5b9a\u7684\u5206\u9694\u7b26\uff08\u5982\u7a7a\u683c\u3001\u5236\u8868\u7b26\u6216\u9017\u53f7\uff09\u8fdb\u884c\u5206\u9694\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684pandas<\/code>\u5e93\uff0c\u901a\u8fc7read_csv()<\/code>\u51fd\u6570\u8bfb\u53d6TXT\u6587\u4ef6\uff0c\u5e76\u6307\u5b9a\u5206\u9694\u7b26\u3002\u8f6c\u6362\u540e\uff0c\u518d\u4f7f\u7528to_csv()<\/code>\u51fd\u6570\u5c06\u5176\u4fdd\u5b58\u4e3aCSV\u683c\u5f0f\u3002<\/p>\n
\u5728\u8f6c\u6362\u8fc7\u7a0b\u4e2d\uff0c\u5982\u4f55\u5904\u7406\u7f3a\u5931\u7684\u6570\u636e\uff1f<\/strong>
\u5728TXT\u6587\u4ef6\u4e2d\uff0c\u7f3a\u5931\u7684\u6570\u636e\u53ef\u80fd\u4ee5\u7a7a\u767d\u6216\u7279\u5b9a\u5b57\u7b26\u8868\u793a\u3002\u4f7f\u7528pandas<\/code>\u65f6\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7fillna()<\/code>\u51fd\u6570\u6765\u586b\u8865\u7f3a\u5931\u503c\uff0c\u6216\u8005\u9009\u62e9\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u3002\u6839\u636e\u6570\u636e\u7684\u6027\u8d28\uff0c\u60a8\u53ef\u4ee5\u9009\u62e9\u9002\u5408\u7684\u5904\u7406\u65b9\u5f0f\uff0c\u4ee5\u786e\u4fdd\u6700\u7ec8\u7684CSV\u6587\u4ef6\u8d28\u91cf\u3002<\/p>\n
\u5bf9\u4e8e\u5927\u578bTXT\u6587\u4ef6\uff0c\u5982\u4f55\u4f18\u5316\u8f6c\u6362\u8fc7\u7a0b\u4ee5\u63d0\u9ad8\u6548\u7387\uff1f<\/strong>
\u5904\u7406\u5927\u578bTXT\u6587\u4ef6\u65f6\uff0c\u53ef\u4ee5\u8003\u8651\u5206\u5757\u8bfb\u53d6\u6570\u636e\u3002pandas<\/code>\u63d0\u4f9b\u4e86chunksize<\/code>\u53c2\u6570\uff0c\u53ef\u4ee5\u5728\u8bfb\u53d6\u65f6\u5c06\u6587\u4ef6\u5206\u4e3a\u591a\u4e2a\u5c0f\u5757\u8fdb\u884c\u5904\u7406\u3002\u8fd9\u6837\u4e0d\u4ec5\u53ef\u4ee5\u51cf\u5c11\u5185\u5b58\u7684\u4f7f\u7528\uff0c\u8fd8\u80fd\u63d0\u5347\u8f6c\u6362\u6548\u7387\u3002\u8f6c\u6362\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5c06\u6bcf\u4e2a\u5757\u7684\u6570\u636e\u5206\u522b\u5199\u5165CSV\u6587\u4ef6\uff0c\u907f\u514d\u4e00\u6b21\u6027\u52a0\u8f7d\u6574\u4e2a\u6587\u4ef6\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0cTXT\u6587\u4ef6\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u8f6c\u6362\u4e3aCSV\u6587\u4ef6\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Pandas\u5e93\u3001CSV\u6a21\u5757\u4ee5\u53ca […]","protected":false},"author":3,"featured_media":966987,"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\/966979"}],"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=966979"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/966979\/revisions"}],"predecessor-version":[{"id":966988,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/966979\/revisions\/966988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/966987"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=966979"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=966979"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=966979"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}