{"id":1134411,"date":"2025-01-08T21:15:24","date_gmt":"2025-01-08T13:15:24","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1134411.html"},"modified":"2025-01-08T21:15:27","modified_gmt":"2025-01-08T13:15:27","slug":"python%e5%a6%82%e4%bd%95%e6%8c%89%e5%88%97%e5%90%8d%e6%8f%90%e5%8f%96%e6%9f%90%e4%b8%80%e5%88%97%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1134411.html","title":{"rendered":"python\u5982\u4f55\u6309\u5217\u540d\u63d0\u53d6\u67d0\u4e00\u5217\u6570\u636e"},"content":{"rendered":"

\"python\u5982\u4f55\u6309\u5217\u540d\u63d0\u53d6\u67d0\u4e00\u5217\u6570\u636e\"<\/p>\n

\u5728Python\u4e2d\uff0c\u6309\u5217\u540d\u63d0\u53d6\u67d0\u4e00\u5217\u6570\u636e\u7684\u65b9\u6cd5\u4e3b\u8981\u6709\u4f7f\u7528Pandas\u5e93\u3001Numpy\u5e93\u548ccsv\u6a21\u5757\u7b49\u3002<\/strong> \u8fd9\u4e9b\u65b9\u6cd5\u5404\u6709\u4f18\u52bf\uff0c\u4f46\u6700\u5e38\u7528\u548c\u6700\u65b9\u4fbf\u7684\u65b9\u5f0f\u662f\u4f7f\u7528Pandas\u5e93\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u529f\u80fd\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Pandas\u5e93\u6309\u5217\u540d\u63d0\u53d6\u6570\u636e\uff0c\u5e76\u63a2\u8ba8\u5176\u4ed6\u65b9\u6cd5\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001Pandas\u5e93\u7684\u4f7f\u7528<\/h3>\n<\/p>\n

Pandas\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u5904\u7406\u5e93\u4e4b\u4e00\uff0c\u5c24\u5176\u9002\u5408\u5904\u7406\u8868\u683c\u6570\u636e\u3002\u4f7f\u7528Pandas\u5e93\u6309\u5217\u540d\u63d0\u53d6\u6570\u636e\u975e\u5e38\u7b80\u5355\uff0c\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Pandas\u5e93\u6765\u5b9e\u73b0\u8fd9\u4e00\u529f\u80fd\u3002<\/p>\n<\/p>\n

1.1\u3001\u5b89\u88c5\u548c\u5bfc\u5165Pandas\u5e93<\/h4>\n<\/p>\n

\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Pandas\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
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165Pandas\u5e93\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2\u3001\u8bfb\u53d6\u6570\u636e<\/h4>\n<\/p>\n
Pandas\u652f\u6301\u591a\u79cd\u6570\u636e\u683c\u5f0f\u7684\u8bfb\u53d6\uff0c\u5982CSV\u3001Excel\u7b49\u3002\u4ee5\u4e0b\u662f\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u8bfb\u53d6CSV\u6587\u4ef6<\/p>\n
df = pd.read_csv('data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3\u3001\u6309\u5217\u540d\u63d0\u53d6\u6570\u636e<\/h4>\n<\/p>\n
\u4e00\u65e6\u6570\u636e\u88ab\u8bfb\u53d6\u5230DataFrame\u4e2d\uff0c\u5c31\u53ef\u4ee5\u8f7b\u677e\u5730\u6309\u5217\u540d\u63d0\u53d6\u67d0\u4e00\u5217\u6570\u636e\u3002\u4f8b\u5982\uff0c\u5982\u679c\u6211\u4eec\u6709\u4e00\u4e2a\u540d\u4e3a'data.csv'\u7684\u6587\u4ef6\uff0c\u5176\u4e2d\u5305\u542b\u5982\u4e0b\u6570\u636e\uff1a<\/p>\n<\/p>\n
Name, Age, Gender<\/p>\n
Alice, 24, Female<\/p>\n
Bob, 30, Male<\/p>\n
Charlie, 22, Male<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u6309\u5217\u540d\u63d0\u53d6'Age'\u5217\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n
# \u63d0\u53d6'Age'\u5217\u7684\u6570\u636e<\/p>\n
age_column = df['Age']<\/p>\n
print(age_column)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5c06\u662f\uff1a<\/p>\n<\/p>\n
0    24<\/p>\n
1    30<\/p>\n
2    22<\/p>\n
Name: Age, dtype: int64<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.4\u3001\u8fdb\u4e00\u6b65\u64cd\u4f5c<\/h4>\n<\/p>\n
\u63d0\u53d6\u7684\u5217\u6570\u636e\u53ef\u4ee5\u8fdb\u4e00\u6b65\u8fdb\u884c\u64cd\u4f5c\uff0c\u4f8b\u5982\u8ba1\u7b97\u5e73\u5747\u503c\u3001\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u7b49\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97'Age'\u5217\u7684\u5e73\u5747\u503c<\/p>\n
mean_age = df['Age'].mean()<\/p>\n
print(f"Mean Age: {mean_age}")<\/p>\n
\u8ba1\u7b97'Age'\u5217\u7684\u6700\u5927\u503c\u548c\u6700\u5c0f\u503c<\/strong><\/h2>\n
max_age = df['Age'].max()<\/p>\n
min_age = df['Age'].min()<\/p>\n
print(f"Max Age: {max_age}, Min Age: {min_age}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001Numpy\u5e93\u7684\u4f7f\u7528<\/h3>\n<\/p>\n
\u867d\u7136Pandas\u5e93\u66f4\u9002\u5408\u5904\u7406\u8868\u683c\u6570\u636e\uff0c\u4f46\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\u4e5f\u53ef\u4ee5\u4f7f\u7528Numpy\u5e93\u6765\u6309\u5217\u540d\u63d0\u53d6\u6570\u636e\u3002Numpy\u4e3b\u8981\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\uff0c\u4ee5\u4e0b\u662f\u4f7f\u7528Numpy\u5e93\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
2.1\u3001\u5b89\u88c5\u548c\u5bfc\u5165Numpy\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
\u5bfc\u5165Numpy\u5e93\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2\u3001\u8bfb\u53d6\u6570\u636e<\/h4>\n<\/p>\n
Numpy\u8bfb\u53d6\u6570\u636e\u7684\u65b9\u5f0f\u4e0ePandas\u6709\u6240\u4e0d\u540c\uff0c\u901a\u5e38\u9700\u8981\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u6570\u7ec4\u3002\u4f8b\u5982\uff0c\u8bfb\u53d6CSV\u6587\u4ef6\u5e76\u8f6c\u6362\u4e3aNumpy\u6570\u7ec4\uff1a<\/p>\n<\/p>\n
# \u8bfb\u53d6CSV\u6587\u4ef6\u5e76\u8f6c\u6362\u4e3aNumpy\u6570\u7ec4<\/p>\n
data = np.genfromtxt('data.csv', delimiter=',', dtype=None, encoding=None, names=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.3\u3001\u6309\u5217\u540d\u63d0\u53d6\u6570\u636e<\/h4>\n<\/p>\n
Numpy\u6570\u7ec4\u652f\u6301\u6309\u5217\u540d\u8bbf\u95ee\u6570\u636e\uff0c\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
# \u63d0\u53d6'Age'\u5217\u7684\u6570\u636e<\/p>\n
age_column = data['Age']<\/p>\n
print(age_column)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5c06\u662f\uff1a<\/p>\n<\/p>\n
[24 30 22]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001csv\u6a21\u5757\u7684\u4f7f\u7528<\/h3>\n<\/p>\n
\u9664\u4e86Pandas\u548cNumpy\uff0cPython\u5185\u7f6e\u7684csv\u6a21\u5757\u4e5f\u53ef\u4ee5\u7528\u6765\u8bfb\u53d6CSV\u6587\u4ef6\u5e76\u6309\u5217\u540d\u63d0\u53d6\u6570\u636e\u3002\u867d\u7136csv\u6a21\u5757\u64cd\u4f5c\u76f8\u5bf9\u7e41\u7410\uff0c\u4f46\u5728\u67d0\u4e9b\u7b80\u5355\u573a\u666f\u4e0b\u4e5f\u975e\u5e38\u5b9e\u7528\u3002<\/p>\n<\/p>\n
3.1\u3001\u5bfc\u5165csv\u6a21\u5757<\/h4>\n<\/p>\n
csv\u6a21\u5757\u662fPython\u7684\u6807\u51c6\u5e93\uff0c\u4e0d\u9700\u8981\u989d\u5916\u5b89\u88c5\u3002\u53ef\u4ee5\u76f4\u63a5\u5bfc\u5165\uff1a<\/p>\n<\/p>\n
import csv<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2\u3001\u8bfb\u53d6\u6570\u636e<\/h4>\n<\/p>\n
\u4f7f\u7528csv\u6a21\u5757\u8bfb\u53d6CSV\u6587\u4ef6\u5e76\u6309\u5217\u540d\u63d0\u53d6\u6570\u636e\u7684\u793a\u4f8b\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
# \u8bfb\u53d6CSV\u6587\u4ef6<\/p>\n
with open('data.csv', mode='r') as file:<\/p>\n
    csv_reader = csv.DictReader(file)<\/p>\n
    age_column = [row['Age'] for row in csv_reader]<\/p>\n
    print(age_column)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5c06\u662f\uff1a<\/p>\n<\/p>\n
['24', '30', '22']<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u6309\u5217\u540d\u63d0\u53d6\u67d0\u4e00\u5217\u6570\u636e\u7684\u65b9\u6cd5\u4e3b\u8981\u6709\u4f7f\u7528Pandas\u5e93\u3001Numpy\u5e93\u548ccsv\u6a21\u5757\u3002Pandas\u5e93\u662f\u6700\u5e38\u7528\u548c\u6700\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u9002\u5408\u5904\u7406\u590d\u6742\u7684\u8868\u683c\u6570\u636e\uff0c\u652f\u6301\u591a\u79cd\u6570\u636e\u683c\u5f0f\u7684\u8bfb\u53d6\u548c\u64cd\u4f5c\u3002Numpy\u5e93\u4e3b\u8981\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\uff0c\u53ef\u4ee5\u5728\u7279\u5b9a\u573a\u666f\u4e0b\u4f7f\u7528\u3002csv\u6a21\u5757\u662fPython\u7684\u6807\u51c6\u5e93\uff0c\u9002\u5408\u5904\u7406\u7b80\u5355\u7684CSV\u6587\u4ef6\u3002<\/strong><\/p>\n<\/p>\n
\u65e0\u8bba\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\uff0c\u90fd\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u8fdb\u884c\u7075\u6d3b\u9009\u62e9\u3002\u638c\u63e1\u8fd9\u4e9b\u65b9\u6cd5\u80fd\u591f\u5927\u5927\u63d0\u9ad8\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u7684\u6548\u7387\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u6839\u636e\u5217\u540d\u63d0\u53d6DataFrame\u4e2d\u7684\u7279\u5b9a\u5217\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u5904\u7406\u6570\u636e\u3002\u901a\u8fc7DataFrame\u5bf9\u8c61\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u63d0\u53d6\u7279\u5b9a\u5217\u7684\u6570\u636e\uff0c\u53ea\u9700\u4f7f\u7528\u5217\u540d\u4f5c\u4e3a\u7d22\u5f15\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u4f60\u7684DataFrame\u540d\u4e3adf<\/code>\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528df['\u5217\u540d']<\/code>\u6765\u63d0\u53d6\u5bf9\u5e94\u7684\u5217\u6570\u636e\u3002<\/p>\n
\u4f7f\u7528Pandas\u63d0\u53d6\u5217\u65f6\u662f\u5426\u9700\u8981\u6ce8\u610f\u6570\u636e\u7c7b\u578b\uff1f<\/strong>
\u786e\u5b9e\uff0c\u63d0\u53d6\u5217\u65f6\u9700\u8981\u5173\u6ce8\u6570\u636e\u7c7b\u578b\u3002\u67d0\u4e9b\u64cd\u4f5c\u6216\u5206\u6790\u53ef\u80fd\u5bf9\u6570\u636e\u7c7b\u578b\u654f\u611f\uff0c\u4f8b\u5982\u8fdb\u884c\u6570\u5b66\u8fd0\u7b97\u65f6\u9700\u8981\u786e\u4fdd\u6570\u636e\u7c7b\u578b\u4e3a\u6570\u503c\u578b\u3002\u5982\u679c\u63d0\u53d6\u7684\u5217\u5305\u542b\u5b57\u7b26\u4e32\u6216\u5176\u4ed6\u7c7b\u578b\u7684\u6570\u636e\uff0c\u53ef\u80fd\u9700\u8981\u5148\u8fdb\u884c\u8f6c\u6362\uff0c\u53ef\u4ee5\u4f7f\u7528astype()<\/code>\u65b9\u6cd5\u8fdb\u884c\u7c7b\u578b\u8f6c\u6362\u3002<\/p>\n
\u5728\u63d0\u53d6\u5217\u65f6\uff0c\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\uff1f<\/strong>
\u5728\u63d0\u53d6\u5217\u4e4b\u540e\uff0c\u5904\u7406\u7f3a\u5931\u503c\u662f\u5f88\u91cd\u8981\u7684\u3002Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u6570\u636e\uff0c\u5982\u4f7f\u7528dropna()<\/code>\u65b9\u6cd5\u5220\u9664\u7f3a\u5931\u503c\uff0c\u6216\u4f7f\u7528fillna()<\/code>\u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u503c\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u63d0\u9ad8\u540e\u7eed\u6570\u636e\u5206\u6790\u7684\u51c6\u786e\u6027\u548c\u6709\u6548\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u6309\u5217\u540d\u63d0\u53d6\u67d0\u4e00\u5217\u6570\u636e\u7684\u65b9\u6cd5\u4e3b\u8981\u6709\u4f7f\u7528Pandas\u5e93\u3001Numpy\u5e93\u548ccsv\u6a21\u5757\u7b49\u3002 \u8fd9\u4e9b\u65b9\u6cd5\u5404 […]","protected":false},"author":3,"featured_media":1134420,"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\/1134411"}],"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=1134411"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1134411\/revisions"}],"predecessor-version":[{"id":1134423,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1134411\/revisions\/1134423"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1134420"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1134411"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1134411"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1134411"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}