{"id":1097161,"date":"2025-01-08T15:09:03","date_gmt":"2025-01-08T07:09:03","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1097161.html"},"modified":"2025-01-08T15:09:06","modified_gmt":"2025-01-08T07:09:06","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e5%ae%8c%e6%88%90%e5%b7%a5%e8%b5%84%e8%a1%a8%e8%ae%a1%e7%ae%97-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1097161.html","title":{"rendered":"\u5982\u4f55\u7528python\u5b8c\u6210\u5de5\u8d44\u8868\u8ba1\u7b97"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u5b8c\u6210\u5de5\u8d44\u8868\u8ba1\u7b97\"<\/p>\n

\u4f7f\u7528Python\u5b8c\u6210\u5de5\u8d44\u8868\u8ba1\u7b97\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u5305\u62ec\u4f7f\u7528\u5185\u7f6e\u6570\u636e\u7c7b\u578b\u548c\u6a21\u5757\u3001Pandas\u5e93\u3001\u4ee5\u53ca\u5176\u4ed6\u7b2c\u4e09\u65b9\u5e93\u3002\u5173\u952e\u6b65\u9aa4\u5305\u62ec\u8bfb\u53d6\u6570\u636e\u3001\u5904\u7406\u6570\u636e\u3001\u8ba1\u7b97\u5de5\u8d44\u603b\u989d\u3001\u7a0e\u6536\u548c\u5176\u4ed6\u6263\u6b3e\u3001\u8f93\u51fa\u7ed3\u679c\u7b49\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5176\u4e2d\u4e00\u79cd\u65b9\u6cd5\uff0c\u5373\u4f7f\u7528Pandas\u5e93\u6765\u5b8c\u6210\u5de5\u8d44\u8868\u8ba1\u7b97\u3002<\/strong><\/p>\n<\/p>\n

Pandas\u5e93\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5de5\u5177\uff0c\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5904\u7406\u65b9\u6cd5\uff0c\u975e\u5e38\u9002\u5408\u5904\u7406\u5de5\u8d44\u8868\u7b49\u7ed3\u6784\u5316\u6570\u636e\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u548cPandas\u5e93\u5b8c\u6210\u5de5\u8d44\u8868\u7684\u8ba1\u7b97\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h3>\n<\/p>\n

\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u3002Pandas\u5e93\u662f\u5904\u7406\u6570\u636e\u7684\u4e3b\u8981\u5de5\u5177\uff0cNumPy\u5e93\u53ef\u4ee5\u7528\u6765\u8fdb\u884c\u4e00\u4e9b\u6570\u503c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u8bfb\u53d6\u6570\u636e<\/h3>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u7684\u5de5\u8d44\u8868\u6570\u636e\u5b58\u50a8\u5728\u4e00\u4e2aCSV\u6587\u4ef6\u4e2d\uff0c\u6587\u4ef6\u683c\u5f0f\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
\u5458\u5de5ID,\u59d3\u540d,\u57fa\u672c\u5de5\u8d44,\u5956\u91d1,\u7a0e\u524d\u6263\u6b3e<\/p>\n
1,\u5f20\u4e09,5000,1000,500<\/p>\n
2,\u674e\u56db,6000,1200,600<\/p>\n
3,\u738b\u4e94,5500,1100,550<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u7684read_csv<\/code>\u51fd\u6570\u8bfb\u53d6\u6570\u636e\uff1a<\/p>\n<\/p>\n
df = pd.read_csv('salary_data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u8ba1\u7b97\u7a0e\u540e\u5de5\u8d44<\/h3>\n<\/p>\n
\u7a0e\u540e\u5de5\u8d44\u7684\u8ba1\u7b97\u516c\u5f0f\u4e3a\uff1a\u7a0e\u540e\u5de5\u8d44 = \u57fa\u672c\u5de5\u8d44 + \u5956\u91d1 – \u7a0e\u524d\u6263\u6b3e – \u6240\u5f97\u7a0e<\/strong><\/p>\n<\/p>\n
\u5176\u4e2d\uff0c\u6240\u5f97\u7a0e\u53ef\u4ee5\u6839\u636e\u4e0d\u540c\u7684\u7a0e\u7387\u8fdb\u884c\u8ba1\u7b97\u3002\u5047\u8bbe\u6240\u5f97\u7a0e\u7684\u8ba1\u7b97\u65b9\u6cd5\u4e3a\uff1a\u6240\u5f97\u7a0e = (\u57fa\u672c\u5de5\u8d44 + \u5956\u91d1 – \u7a0e\u524d\u6263\u6b3e) * \u7a0e\u7387<\/strong>\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u8ba1\u7b97\u6bcf\u4e2a\u5458\u5de5\u7684\u7a0e\u540e\u5de5\u8d44\uff1a<\/p>\n<\/p>\n
\u7a0e\u7387 = 0.2  # \u5047\u8bbe\u7a0e\u7387\u4e3a20%<\/p>\n
df['\u5e94\u7a0e\u6536\u5165'] = df['\u57fa\u672c\u5de5\u8d44'] + df['\u5956\u91d1'] - df['\u7a0e\u524d\u6263\u6b3e']<\/p>\n
df['\u6240\u5f97\u7a0e'] = df['\u5e94\u7a0e\u6536\u5165'] * \u7a0e\u7387<\/p>\n
df['\u7a0e\u540e\u5de5\u8d44'] = df['\u5e94\u7a0e\u6536\u5165'] - df['\u6240\u5f97\u7a0e']<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u8f93\u51fa\u7ed3\u679c<\/h3>\n<\/p>\n
\u8ba1\u7b97\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u7ed3\u679c\u8f93\u51fa\u5230\u4e00\u4e2a\u65b0\u7684CSV\u6587\u4ef6\u4e2d\uff0c\u4ee5\u4fbf\u8fdb\u4e00\u6b65\u5904\u7406\u6216\u5b58\u6863\uff1a<\/p>\n<\/p>\n
df.to_csv('salary_data_with_tax.csv', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u5b8c\u6574\u4ee3\u7801\u793a\u4f8b<\/h3>\n<\/p>\n
import pandas as pd<\/p>\n
import numpy as np<\/p>\n
\u5bfc\u5165\u6570\u636e<\/strong><\/h2>\n
df = pd.read_csv('salary_data.csv')<\/p>\n
\u8bbe\u7f6e\u7a0e\u7387<\/strong><\/h2>\n
\u7a0e\u7387 = 0.2<\/p>\n
\u8ba1\u7b97\u5e94\u7a0e\u6536\u5165\u3001\u6240\u5f97\u7a0e\u548c\u7a0e\u540e\u5de5\u8d44<\/strong><\/h2>\n
df['\u5e94\u7a0e\u6536\u5165'] = df['\u57fa\u672c\u5de5\u8d44'] + df['\u5956\u91d1'] - df['\u7a0e\u524d\u6263\u6b3e']<\/p>\n
df['\u6240\u5f97\u7a0e'] = df['\u5e94\u7a0e\u6536\u5165'] * \u7a0e\u7387<\/p>\n
df['\u7a0e\u540e\u5de5\u8d44'] = df['\u5e94\u7a0e\u6536\u5165'] - df['\u6240\u5f97\u7a0e']<\/p>\n
\u8f93\u51fa\u7ed3\u679c<\/strong><\/h2>\n
df.to_csv('salary_data_with_tax.csv', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u6269\u5c55\u529f\u80fd<\/h3>\n<\/p>\n
\u4ee5\u4e0a\u4ee3\u7801\u5c55\u793a\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u5de5\u8d44\u8868\u8ba1\u7b97\u65b9\u6cd5\uff0c\u5b9e\u9645\u4e0a\uff0c\u6211\u4eec\u53ef\u4ee5\u8fdb\u4e00\u6b65\u6269\u5c55\u529f\u80fd\uff0c\u4ee5\u6ee1\u8db3\u66f4\u591a\u7684\u9700\u6c42\u3002<\/p>\n<\/p>\n
1\u3001\u8003\u8651\u4e0d\u540c\u7684\u7a0e\u7387<\/h4>\n<\/p>\n
\u4e0d\u540c\u7684\u5458\u5de5\u53ef\u80fd\u9002\u7528\u4e0d\u540c\u7684\u7a0e\u7387\uff0c\u6211\u4eec\u53ef\u4ee5\u5728\u5de5\u8d44\u8868\u4e2d\u6dfb\u52a0\u4e00\u4e2a\u201c\u7a0e\u7387\u201d\u5217\uff0c\u5e76\u6839\u636e\u6bcf\u4e2a\u5458\u5de5\u7684\u7a0e\u7387\u8ba1\u7b97\u6240\u5f97\u7a0e\uff1a<\/p>\n<\/p>\n
df['\u6240\u5f97\u7a0e'] = df['\u5e94\u7a0e\u6536\u5165'] * df['\u7a0e\u7387']<\/p>\n
df['\u7a0e\u540e\u5de5\u8d44'] = df['\u5e94\u7a0e\u6536\u5165'] - df['\u6240\u5f97\u7a0e']<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6dfb\u52a0\u5176\u4ed6\u6263\u6b3e\u9879<\/h4>\n<\/p>\n
\u9664\u4e86\u7a0e\u524d\u6263\u6b3e\u5916\uff0c\u8fd8\u53ef\u80fd\u6709\u5176\u4ed6\u6263\u6b3e\u9879\uff0c\u5982\u793e\u4fdd\u3001\u516c\u79ef\u91d1\u7b49\u3002\u6211\u4eec\u53ef\u4ee5\u5728\u6570\u636e\u4e2d\u6dfb\u52a0\u76f8\u5e94\u7684\u5217\uff0c\u5e76\u5728\u8ba1\u7b97\u7a0e\u540e\u5de5\u8d44\u65f6\u4e00\u5e76\u8003\u8651\uff1a<\/p>\n<\/p>\n
df['\u5e94\u7a0e\u6536\u5165'] = df['\u57fa\u672c\u5de5\u8d44'] + df['\u5956\u91d1'] - df['\u7a0e\u524d\u6263\u6b3e'] - df['\u793e\u4fdd'] - df['\u516c\u79ef\u91d1']<\/p>\n
df['\u6240\u5f97\u7a0e'] = df['\u5e94\u7a0e\u6536\u5165'] * df['\u7a0e\u7387']<\/p>\n
df['\u7a0e\u540e\u5de5\u8d44'] = df['\u5e94\u7a0e\u6536\u5165'] - df['\u6240\u5f97\u7a0e']<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u751f\u6210\u62a5\u8868<\/h4>\n<\/p>\n
\u9664\u4e86\u8ba1\u7b97\u7a0e\u540e\u5de5\u8d44\u5916\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u751f\u6210\u6708\u5ea6\u6216\u5e74\u5ea6\u7684\u5de5\u8d44\u62a5\u8868\uff0c\u7edf\u8ba1\u6bcf\u4e2a\u5458\u5de5\u7684\u603b\u5de5\u8d44\u3001\u603b\u6263\u6b3e\u7b49\u4fe1\u606f\uff1a<\/p>\n<\/p>\n
monthly_report = df.groupby('\u5458\u5de5ID').agg({<\/p>\n
    '\u57fa\u672c\u5de5\u8d44': 'sum',<\/p>\n
    '\u5956\u91d1': 'sum',<\/p>\n
    '\u7a0e\u524d\u6263\u6b3e': 'sum',<\/p>\n
    '\u6240\u5f97\u7a0e': 'sum',<\/p>\n
    '\u7a0e\u540e\u5de5\u8d44': 'sum'<\/p>\n
})<\/p>\n
monthly_report.to_csv('monthly_salary_report.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u7ed3\u8bba<\/h3>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u548cPandas\u5e93\u9ad8\u6548\u5730\u5b8c\u6210\u5de5\u8d44\u8868\u7684\u8ba1\u7b97\u548c\u5904\u7406\u3002\u5173\u952e\u6b65\u9aa4\u5305\u62ec\u5bfc\u5165\u6570\u636e\u3001\u8ba1\u7b97\u5e94\u7a0e\u6536\u5165\u548c\u6240\u5f97\u7a0e\u3001\u8ba1\u7b97\u7a0e\u540e\u5de5\u8d44\u3001\u8f93\u51fa\u7ed3\u679c\u7b49\u3002<\/strong>\u6839\u636e\u5b9e\u9645\u9700\u6c42\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u6269\u5c55\u529f\u80fd\uff0c\u8003\u8651\u4e0d\u540c\u7684\u7a0e\u7387\u3001\u6dfb\u52a0\u5176\u4ed6\u6263\u6b3e\u9879\u3001\u751f\u6210\u62a5\u8868\u7b49\u3002Python\u548cPandas\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u80fd\u529b\uff0c\u4f7f\u5f97\u5de5\u8d44\u8868\u7684\u8ba1\u7b97\u53d8\u5f97\u7b80\u5355\u9ad8\u6548\u3002<\/strong><\/p>\n<\/p>\n
\u516b\u3001\u9644\u52a0\u529f\u80fd\u793a\u4f8b<\/h3>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u5c55\u793aPython\u5728\u5de5\u8d44\u8868\u8ba1\u7b97\u4e2d\u7684\u5f3a\u5927\u529f\u80fd\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u6dfb\u52a0\u4e00\u4e9b\u9644\u52a0\u529f\u80fd\uff0c\u5982\u6570\u636e\u53ef\u89c6\u5316\u3001\u5f02\u5e38\u6570\u636e\u5904\u7406\u7b49\u3002<\/p>\n<\/p>\n
1\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Matplotlib\u6216Seaborn\u5e93\u5bf9\u5de5\u8d44\u6570\u636e\u8fdb\u884c\u53ef\u89c6\u5316\uff0c\u5e2e\u52a9\u6211\u4eec\u66f4\u76f4\u89c2\u5730\u4e86\u89e3\u6570\u636e\u5206\u5e03\u548c\u8d8b\u52bf\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
\u5de5\u8d44\u5206\u5e03\u76f4\u65b9\u56fe<\/strong><\/h2>\n
sns.histplot(df['\u7a0e\u540e\u5de5\u8d44'], kde=True)<\/p>\n
plt.title('\u7a0e\u540e\u5de5\u8d44\u5206\u5e03')<\/p>\n
plt.xlabel('\u7a0e\u540e\u5de5\u8d44')<\/p>\n
plt.ylabel('\u9891\u6570')<\/p>\n
plt.show()<\/p>\n
\u5de5\u8d44\u8d8b\u52bf\u6298\u7ebf\u56fe<\/strong><\/h2>\n
df['\u65e5\u671f'] = pd.to_datetime(df['\u65e5\u671f'])<\/p>\n
df.set_index('\u65e5\u671f', inplace=True)<\/p>\n
df.groupby('\u65e5\u671f')['\u7a0e\u540e\u5de5\u8d44'].sum().plot()<\/p>\n
plt.title('\u7a0e\u540e\u5de5\u8d44\u8d8b\u52bf')<\/p>\n
plt.xlabel('\u65e5\u671f')<\/p>\n
plt.ylabel('\u7a0e\u540e\u5de5\u8d44\u603b\u989d')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5f02\u5e38\u6570\u636e\u5904\u7406<\/h4>\n<\/p>\n
\u5728\u5b9e\u9645\u6570\u636e\u4e2d\uff0c\u53ef\u80fd\u5b58\u5728\u4e00\u4e9b\u5f02\u5e38\u6570\u636e\uff0c\u5982\u7f3a\u5931\u503c\u3001\u9519\u8bef\u6570\u636e\u7b49\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u63d0\u4f9b\u7684\u65b9\u6cd5\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u548c\u5904\u7406\uff1a<\/p>\n<\/p>\n
# \u586b\u8865\u7f3a\u5931\u503c<\/p>\n
df.fillna(0, inplace=True)<\/p>\n
\u5904\u7406\u9519\u8bef\u6570\u636e<\/strong><\/h2>\n
df = df[df['\u57fa\u672c\u5de5\u8d44'] >= 0]<\/p>\n
df = df[df['\u5956\u91d1'] >= 0]<\/p>\n
df = df[df['\u7a0e\u524d\u6263\u6b3e'] >= 0]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4ee5\u4e0a\u5c31\u662f\u5982\u4f55\u4f7f\u7528Python\u548cPandas\u5e93\u5b8c\u6210\u5de5\u8d44\u8868\u8ba1\u7b97\u7684\u8be6\u7ec6\u6b65\u9aa4\u548c\u65b9\u6cd5\u3002\u901a\u8fc7\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u9ad8\u6548\u5730\u5904\u7406\u548c\u5206\u6790\u5de5\u8d44\u6570\u636e\uff0c\u5e76\u6839\u636e\u5b9e\u9645\u9700\u6c42\u8fdb\u884c\u6269\u5c55\u548c\u4f18\u5316\u3002Python\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u80fd\u529b\uff0c\u4f7f\u5f97\u5de5\u8d44\u8868\u7684\u8ba1\u7b97\u53d8\u5f97\u7b80\u5355\u9ad8\u6548\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u7528Python\u5feb\u901f\u751f\u6210\u5de5\u8d44\u8868\uff1f<\/strong>
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