enumerate<\/code> \u51fd\u6570<\/h3>\n<\/p>\nenumerate<\/code> \u51fd\u6570\u53ef\u4ee5\u5728\u904d\u5386\u5217\u8868\u7684\u540c\u65f6\u83b7\u53d6\u5143\u7d20\u7684\u7d22\u5f15\uff0c\u53ef\u4ee5\u7ed3\u5408\u4e24\u4e2a\u5217\u8868\u7684\u957f\u5ea6\u8fdb\u884c\u53cc\u91cd\u904d\u5386\u8f93\u51fa\u3002<\/p>\n<\/p>\n# \u793a\u4f8b\u6570\u636e<\/p>\ncolumn1 = [1, 2, 3, 4, 5]<\/p>\n
column2 = ['a', 'b', 'c', 'd', 'e']<\/p>\n
\u4f7f\u7528 enumerate \u51fd\u6570\u904d\u5386\u6570\u636e<\/strong><\/h2>\nfor i, (col1, col2) in enumerate(zip(column1, column2)):<\/p>\n
print col1, col2<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528\u683c\u5f0f\u5316\u5b57\u7b26\u4e32<\/h3>\n<\/p>\n
\u683c\u5f0f\u5316\u5b57\u7b26\u4e32\u53ef\u4ee5\u4f7f\u8f93\u51fa\u66f4\u52a0\u7f8e\u89c2\u6574\u9f50\u3002Python2 \u4e2d\u53ef\u4ee5\u4f7f\u7528 %<\/code> \u8fdb\u884c\u5b57\u7b26\u4e32\u683c\u5f0f\u5316\u3002<\/p>\n<\/p>\n# \u793a\u4f8b\u6570\u636e<\/p>\ncolumn1 = [1, 2, 3, 4, 5]<\/p>\n
column2 = ['a', 'b', 'c', 'd', 'e']<\/p>\n
\u4f7f\u7528\u683c\u5f0f\u5316\u5b57\u7b26\u4e32\u8f93\u51fa\u6570\u636e<\/strong><\/h2>\nfor col1, col2 in zip(column1, column2):<\/p>\n
print "%-10s %-10s" % (col1, col2)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528 pandas<\/code> \u5e93<\/h3>\n<\/p>\n\u5982\u679c\u6570\u636e\u91cf\u8f83\u5927\u4e14\u9700\u8981\u5904\u7406\u548c\u8f93\u51fa\u4e24\u5217\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528 pandas<\/code> \u5e93\u3002pandas<\/code> \u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5904\u7406\u548c\u8f93\u51fa\u6570\u636e\u3002<\/p>\n<\/p>\nimport pandas as pd<\/p>\n\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\ndata = {<\/p>\n
'Column1': [1, 2, 3, 4, 5],<\/p>\n
'Column2': ['a', 'b', 'c', 'd', 'e']<\/p>\n
}<\/p>\n
\u521b\u5efa DataFrame<\/strong><\/h2>\ndf = pd.DataFrame(data)<\/p>\n
\u8f93\u51fa DataFrame<\/strong><\/h2>\nprint df<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528 itertools<\/code> \u5e93<\/h3>\n<\/p>\nitertools<\/code> \u5e93\u63d0\u4f9b\u4e86\u591a\u4e2a\u8fed\u4ee3\u5668\u751f\u6210\u51fd\u6570\uff0c\u53ef\u4ee5\u7528\u4e8e\u751f\u6210\u590d\u6742\u7684\u8fed\u4ee3\u5668\u3002\u53ef\u4ee5\u4f7f\u7528 itertools.izip<\/code> \u6765\u914d\u5bf9\u4e24\u4e2a\u5217\u8868\u7684\u6570\u636e\u3002<\/p>\n<\/p>\nimport itertools<\/p>\n\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\ncolumn1 = [1, 2, 3, 4, 5]<\/p>\n
column2 = ['a', 'b', 'c', 'd', 'e']<\/p>\n
\u4f7f\u7528 itertools.izip \u914d\u5bf9\u6570\u636e<\/strong><\/h2>\npaired_columns = itertools.izip(column1, column2)<\/p>\n
\u8f93\u51fa\u914d\u5bf9\u540e\u7684\u6570\u636e<\/strong><\/h2>\nfor col1, col2 in paired_columns:<\/p>\n
print col1, col2<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u4f7f\u7528 csv<\/code> \u5e93<\/h3>\n<\/p>\n\u5982\u679c\u9700\u8981\u5c06\u4e24\u5217\u6570\u636e\u8f93\u51fa\u5230 CSV \u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528 csv<\/code> \u5e93\u3002<\/p>\n<\/p>\nimport csv<\/p>\n\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\ncolumn1 = [1, 2, 3, 4, 5]<\/p>\n
column2 = ['a', 'b', 'c', 'd', 'e']<\/p>\n
\u8f93\u51fa\u5230 CSV \u6587\u4ef6<\/strong><\/h2>\nwith open('output.csv', 'w') as csvfile:<\/p>\n
writer = csv.writer(csvfile)<\/p>\n
writer.writerow(['Column1', 'Column2'])<\/p>\n
for col1, col2 in zip(column1, column2):<\/p>\n
writer.writerow([col1, col2])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u7ed3\u5408\u5176\u4ed6\u5e93\u8fdb\u884c\u66f4\u590d\u6742\u7684\u8f93\u51fa<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\uff0c\u53ef\u80fd\u9700\u8981\u7ed3\u5408\u5176\u4ed6\u5e93\u8fdb\u884c\u66f4\u590d\u6742\u7684\u8f93\u51fa\u3002\u4f8b\u5982\uff0c\u7ed3\u5408 numpy<\/code> \u8fdb\u884c\u6570\u503c\u8ba1\u7b97\uff0c\u7ed3\u5408 matplotlib<\/code> \u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\nimport numpy as np<\/p>\nimport matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\ncolumn1 = np.array([1, 2, 3, 4, 5])<\/p>\n
column2 = np.array([10, 20, 30, 40, 50])<\/p>\n
\u6253\u5370\u4e24\u5217\u6570\u636e<\/strong><\/h2>\nfor col1, col2 in zip(column1, column2):<\/p>\n
print col1, col2<\/p>\n
\u7ed8\u5236\u6570\u636e\u56fe\u8868<\/strong><\/h2>\nplt.scatter(column1, column2)<\/p>\n
plt.xlabel('Column 1')<\/p>\n
plt.ylabel('Column 2')<\/p>\n
plt.title('Scatter plot of Column 1 vs Column 2')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516b\u3001\u5904\u7406\u548c\u8f93\u51fa\u5927\u89c4\u6a21\u6570\u636e<\/h3>\n<\/p>\n
\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\uff0c\u53ef\u80fd\u9700\u8981\u4f18\u5316\u4ee3\u7801\u4ee5\u63d0\u9ad8\u6548\u7387\u3002\u53ef\u4ee5\u4f7f\u7528\u751f\u6210\u5668\u548c\u5185\u5b58\u6620\u5c04\u6587\u4ef6\u6765\u5904\u7406\u548c\u8f93\u51fa\u4e24\u5217\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u751f\u6210\u5927\u89c4\u6a21\u6570\u636e<\/p>\ncolumn1 = (i for i in range(1, 1000001))<\/p>\n
column2 = (chr(65 + i % 26) for i in range(1000000))<\/p>\n
\u4f7f\u7528\u751f\u6210\u5668\u8f93\u51fa\u6570\u636e<\/strong><\/h2>\nfor col1, col2 in zip(column1, column2):<\/p>\n
print col1, col2<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e5d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728Python2\u4e2d\uff0c\u8f93\u51fa\u4e24\u5217\u6570\u636e\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528 zip<\/code> \u51fd\u6570\u3001enumerate<\/code> \u51fd\u6570\u3001\u683c\u5f0f\u5316\u5b57\u7b26\u4e32\u3001pandas<\/code> \u5e93\u3001itertools<\/code> \u5e93\u3001csv<\/code> \u5e93\u7b49\u3002\u5bf9\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\uff0c\u53ef\u4ee5\u4f7f\u7528\u751f\u6210\u5668\u548c\u5185\u5b58\u6620\u5c04\u6587\u4ef6\u6765\u63d0\u9ad8\u6548\u7387\u3002\u7ed3\u5408\u5176\u4ed6\u5e93\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u53ef\u89c6\u5316\uff0c\u53ef\u4ee5\u4f7f\u6570\u636e\u5904\u7406\u8fc7\u7a0b\u66f4\u52a0\u9ad8\u6548\u548c\u76f4\u89c2\u3002\u5e0c\u671b\u672c\u6587\u6240\u63d0\u4f9b\u7684\u65b9\u6cd5\u548c\u793a\u4f8b\u4ee3\u7801\u5bf9\u60a8\u5728Python2\u4e2d\u8f93\u51fa\u4e24\u5217\u6570\u636e\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n \u5982\u4f55\u5728Python 2\u4e2d\u6253\u5370\u4e24\u5217\u6570\u636e\uff1f<\/strong>
\u5728Python 2\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u683c\u5f0f\u5316\u5b57\u7b26\u4e32\u6216\u7b80\u5355\u7684print\u8bed\u53e5\u8f93\u51fa\u4e24\u5217\u6570\u636e\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u5b9e\u73b0\uff1a <\/p>\ndata1 = ["Alice", "Bob", "Charlie"]\ndata2 = [25, 30, 35]\n\nfor name, age in zip(data1, data2):\n print "{:<10} {}".format(name, age)\n<\/code><\/pre>\n\u6b64\u4ee3\u7801\u5c06\u8f93\u51fa\u4e24\u5217\uff0c\u7b2c\u4e00\u5217\u4e3a\u59d3\u540d\uff0c\u7b2c\u4e8c\u5217\u4e3a\u5e74\u9f84\uff0c\u4e14\u59d3\u540d\u5217\u5de6\u5bf9\u9f50\u3002<\/p>\n
\u5982\u4f55\u4f7f\u7528pandas\u5e93\u5728Python 2\u4e2d\u8f93\u51fa\u4e24\u5217\u6570\u636e\uff1f<\/strong>
\u5982\u679c\u4f60\u5728\u6570\u636e\u5904\u7406\u65f6\u4f7f\u7528pandas\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u8f93\u51fa\u4e24\u5217\u6570\u636e\u3002\u5b89\u88c5pandas\u540e\uff0c\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u793a\u4f8b\uff1a <\/p>\nimport pandas as pd\n\ndata = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}\ndf = pd.DataFrame(data)\n\nprint df[['Name', 'Age']]\n<\/code><\/pre>\n\u8fd9\u5c06\u4ee5\u8868\u683c\u5f62\u5f0f\u8f93\u51fa\u4e24\u5217\u6570\u636e\uff0c\u4fbf\u4e8e\u9605\u8bfb\u548c\u5206\u6790\u3002<\/p>\n
\u5728Python 2\u4e2d\uff0c\u5982\u4f55\u5c06\u4e24\u5217\u6570\u636e\u5199\u5165\u6587\u4ef6\uff1f<\/strong>
\u53ef\u4ee5\u901a\u8fc7\u6587\u4ef6\u64cd\u4f5c\u5c06\u4e24\u5217\u6570\u636e\u5199\u5165\u6587\u672c\u6587\u4ef6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a <\/p>\ndata1 = ["Alice", "Bob", "Charlie"]\ndata2 = [25, 30, 35]\n\nwith open('output.txt', 'w') as f:\n for name, age in zip(data1, data2):\n f.write("{:<10} {}\\n".format(name, age))\n<\/code><\/pre>\n\u8fd9\u6bb5\u4ee3\u7801\u5c06\u4e24\u5217\u6570\u636e\u5199\u5165\u540d\u4e3aoutput.txt<\/code>\u7684\u6587\u4ef6\u4e2d\uff0c\u6bcf\u884c\u5305\u542b\u4e00\u4e2a\u59d3\u540d\u548c\u5bf9\u5e94\u7684\u5e74\u9f84\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python2\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u8f93\u51fa\u4e24\u5217\u6570\u636e\uff0c\u4f8b\u5982\u4f7f\u7528print\u8bed\u53e5\u3001\u5faa\u73af\u3001\u683c\u5f0f\u5316\u5b57\u7b26\u4e32\u7b49\u3002\u5176\u4e2d\u4e00\u79cd\u5e38\u89c1\u65b9 […]","protected":false},"author":3,"featured_media":1043455,"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\/1043418"}],"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=1043418"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1043418\/revisions"}],"predecessor-version":[{"id":1043457,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1043418\/revisions\/1043457"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1043455"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1043418"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1043418"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1043418"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"python2\u5982\u4f55\u8f93\u51fa\u4e24\u5217\u6570\u636e\"](\"https:\/\/cdn-docs.pingcode.com\/wp-content\/uploads\/2024\/12\/7e085543-8538-48c1-8ff6-3afd45f41646.webp?x-oss-process=image\/auto-orient,1\/format,webp\")
<\/p>\n