{"id":1174428,"date":"2025-01-15T17:17:49","date_gmt":"2025-01-15T09:17:49","guid":{"rendered":""},"modified":"2025-01-15T17:17:52","modified_gmt":"2025-01-15T09:17:52","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e8%bf%9b%e8%a1%8c%e8%b4%a2%e5%8a%a1%e5%af%b9%e5%b8%90","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1174428.html","title":{"rendered":"\u5982\u4f55\u7528python\u8fdb\u884c\u8d22\u52a1\u5bf9\u5e10"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u8fdb\u884c\u8d22\u52a1\u5bf9\u5e10\"<\/p>\n

\u8981\u7528Python\u8fdb\u884c\u8d22\u52a1\u5bf9\u5e10\uff0c\u4f60\u9700\u8981\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u3001\u4f7f\u7528Numpy\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u3001\u4f7f\u7528Matplotlib\u6216Seaborn\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3001\u4ee5\u53ca\u4f7f\u7528SQLAlchemy\u8fdb\u884c\u6570\u636e\u5e93\u4ea4\u4e92\u3002<\/strong><\/p>\n<\/p>\n

\u9996\u5148\uff0cPandas\u5e93\u662f\u5904\u7406\u548c\u5206\u6790\u6570\u636e\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u901a\u8fc7DataFrame\u7ed3\u6784\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6\u3001\u5904\u7406\u548c\u6e05\u7406\u8d22\u52a1\u6570\u636e\u3002<\/strong>\u4f60\u53ef\u4ee5\u4ece\u591a\u4e2a\u6570\u636e\u6e90\uff08\u5982CSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\uff09\u8bfb\u53d6\u6570\u636e\uff0c\u5e76\u8fdb\u884c\u6570\u636e\u6e05\u7406\u548c\u683c\u5f0f\u5316\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u8be6\u7ec6\u7684\u4f8b\u5b50\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528Pandas\u8fdb\u884c\u8d22\u52a1\u5bf9\u5e10\u3002<\/p>\n<\/p>\n

\u4e00\u3001Pandas\u8bfb\u53d6\u548c\u5904\u7406\u6570\u636e<\/h3>\n<\/p>\n

Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u5b83\u80fd\u591f\u4ece\u5404\u79cd\u6587\u4ef6\u683c\u5f0f\u4e2d\u8bfb\u53d6\u6570\u636e\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u4e3aDataFrame\u5bf9\u8c61\u3002\u901a\u8fc7Pandas\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u5730\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3001\u8f6c\u6362\u3001\u5408\u5e76\u548c\u5206\u7ec4\u64cd\u4f5c\u3002<\/p>\n<\/p>\n

1\u3001\u8bfb\u53d6\u6570\u636e<\/h4>\n<\/p>\n

\u4f60\u53ef\u4ee5\u4f7f\u7528Pandas\u7684read_csv<\/code>\u3001read_excel<\/code>\u7b49\u65b9\u6cd5\u4eceCSV\u6587\u4ef6\u3001Excel\u6587\u4ef6\u7b49\u8bfb\u53d6\u6570\u636e\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e24\u4e2aCSV\u6587\u4ef6\uff0c\u4e00\u4e2a\u662f\u516c\u53f8\u7684\u8d26\u5355\u6570\u636e\uff0c\u53e6\u4e00\u4e2a\u662f\u94f6\u884c\u5bf9\u8d26\u5355\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u8bfb\u53d6\u516c\u53f8\u7684\u8d26\u5355\u6570\u636e<\/strong><\/h2>\n
company_data = pd.read_csv('company_data.csv')<\/p>\n
\u8bfb\u53d6\u94f6\u884c\u5bf9\u8d26\u5355<\/strong><\/h2>\n
bank_data = pd.read_csv('bank_data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u6e05\u6d17\u548c\u683c\u5f0f\u5316<\/h4>\n<\/p>\n
\u5728\u8bfb\u53d6\u6570\u636e\u4e4b\u540e\uff0c\u4f60\u53ef\u80fd\u9700\u8981\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u548c\u683c\u5f0f\u5316\u3002\u4f8b\u5982\uff0c\u5904\u7406\u7f3a\u5931\u503c\u3001\u5220\u9664\u91cd\u590d\u6570\u636e\u3001\u683c\u5f0f\u5316\u65e5\u671f\u7b49\u3002<\/p>\n<\/p>\n
# \u5220\u9664\u7f3a\u5931\u503c<\/p>\n
company_data.dropna(inplace=True)<\/p>\n
bank_data.dropna(inplace=True)<\/p>\n
\u5220\u9664\u91cd\u590d\u6570\u636e<\/strong><\/h2>\n
company_data.drop_duplicates(inplace=True)<\/p>\n
bank_data.drop_duplicates(inplace=True)<\/p>\n
\u683c\u5f0f\u5316\u65e5\u671f<\/strong><\/h2>\n
company_data['date'] = pd.to_datetime(company_data['date'])<\/p>\n
bank_data['date'] = pd.to_datetime(bank_data['date'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u6570\u636e\u5bf9\u6bd4\u548c\u5bf9\u8d26<\/h3>\n<\/p>\n
\u5728\u6e05\u6d17\u548c\u683c\u5f0f\u5316\u6570\u636e\u4e4b\u540e\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528Pandas\u7684\u5408\u5e76\u548c\u5206\u7ec4\u529f\u80fd\u8fdb\u884c\u6570\u636e\u5bf9\u6bd4\u548c\u5bf9\u8d26\u3002<\/p>\n<\/p>\n
1\u3001\u5408\u5e76\u6570\u636e<\/h4>\n<\/p>\n
\u4f60\u53ef\u4ee5\u4f7f\u7528Pandas\u7684merge<\/code>\u51fd\u6570\u5c06\u516c\u53f8\u7684\u8d26\u5355\u6570\u636e\u548c\u94f6\u884c\u5bf9\u8d26\u5355\u8fdb\u884c\u5408\u5e76\uff0c\u6839\u636e\u5171\u540c\u7684\u5217\uff08\u5982\u65e5\u671f\u548c\u91d1\u989d\uff09\u8fdb\u884c\u5bf9\u6bd4\u3002<\/p>\n<\/p>\n
# \u5408\u5e76\u6570\u636e\uff0c\u6839\u636e\u65e5\u671f\u548c\u91d1\u989d\u8fdb\u884c\u5bf9\u6bd4<\/p>\n
merged_data = pd.merge(company_data, bank_data, on=['date', 'amount'], how='outer', indicator=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6807\u8bb0\u5bf9\u8d26\u7ed3\u679c<\/h4>\n<\/p>\n
\u5408\u5e76\u540e\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528indicator<\/code>\u53c2\u6570\u6765\u6807\u8bb0\u6bcf\u6761\u8bb0\u5f55\u7684\u5bf9\u8d26\u7ed3\u679c\u3002\u4f8b\u5982\uff0c\u6807\u8bb0\u4e3a\u201cboth\u201d\u7684\u8bb0\u5f55\u8868\u793a\u5728\u516c\u53f8\u8d26\u5355\u548c\u94f6\u884c\u5bf9\u8d26\u5355\u4e2d\u90fd\u6709\u5339\u914d\uff0c\u6807\u8bb0\u4e3a\u201cleft_only\u201d\u7684\u8bb0\u5f55\u8868\u793a\u53ea\u5728\u516c\u53f8\u8d26\u5355\u4e2d\u6709\uff0c\u6807\u8bb0\u4e3a\u201cright_only\u201d\u7684\u8bb0\u5f55\u8868\u793a\u53ea\u5728\u94f6\u884c\u5bf9\u8d26\u5355\u4e2d\u6709\u3002<\/p>\n<\/p>\n
# \u6807\u8bb0\u5bf9\u8d26\u7ed3\u679c<\/p>\n
merged_data['reconciliation_status'] = merged_data['_merge'].map({<\/p>\n
    'both': 'matched',<\/p>\n
    'left_only': 'company_only',<\/p>\n
    'right_only': 'bank_only'<\/p>\n
})<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
\u6570\u636e\u53ef\u89c6\u5316\u53ef\u4ee5\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u5bf9\u8d26\u7ed3\u679c\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528Matplotlib\u6216Seaborn\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5Matplotlib\u548cSeaborn<\/h4>\n<\/p>\n
\u5982\u679c\u4f60\u8fd8\u6ca1\u6709\u5b89\u88c5\u8fd9\u4e9b\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install matplotlib seaborn<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7ed8\u5236\u56fe\u8868<\/h4>\n<\/p>\n
\u4f60\u53ef\u4ee5\u4f7f\u7528Matplotlib\u6216Seaborn\u7ed8\u5236\u5404\u79cd\u56fe\u8868\uff0c\u5982\u67f1\u72b6\u56fe\u3001\u997c\u56fe\u3001\u6298\u7ebf\u56fe\u7b49\uff0c\u6765\u5c55\u793a\u5bf9\u8d26\u7ed3\u679c\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
\u7ed8\u5236\u5bf9\u8d26\u7ed3\u679c\u7684\u67f1\u72b6\u56fe<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))<\/p>\n
sns.countplot(data=merged_data, x='reconciliation_status')<\/p>\n
plt.title('Reconciliation Status')<\/p>\n
plt.xlabel('Status')<\/p>\n
plt.ylabel('Count')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6570\u636e\u5e93\u4ea4\u4e92<\/h3>\n<\/p>\n
\u5982\u679c\u4f60\u7684\u8d22\u52a1\u6570\u636e\u5b58\u50a8\u5728\u6570\u636e\u5e93\u4e2d\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528SQLAlchemy\u5e93\u4e0e\u6570\u636e\u5e93\u8fdb\u884c\u4ea4\u4e92\u3002SQLAlchemy\u63d0\u4f9b\u4e86\u4e00\u4e2a\u9ad8\u6548\u7684\u6570\u636e\u5e93\u8bbf\u95ee\u63a5\u53e3\uff0c\u4f7f\u4f60\u80fd\u591f\u8f7b\u677e\u5730\u8bfb\u53d6\u548c\u5199\u5165\u6570\u636e\u5e93\u4e2d\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5SQLAlchemy<\/h4>\n<\/p>\n
\u5982\u679c\u4f60\u8fd8\u6ca1\u6709\u5b89\u88c5SQLAlchemy\uff0c\u53ef\u4ee5\u4f7f\u7528pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install sqlalchemy<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8fde\u63a5\u6570\u636e\u5e93<\/h4>\n<\/p>\n
\u4f60\u53ef\u4ee5\u4f7f\u7528SQLAlchemy\u7684create_engine<\/code>\u51fd\u6570\u8fde\u63a5\u5230\u6570\u636e\u5e93\uff0c\u5e76\u4f7f\u7528Pandas\u7684read_sql<\/code>\u51fd\u6570\u8bfb\u53d6\u6570\u636e\u3002<\/p>\n<\/p>\n
from sqlalchemy import create_engine<\/p>\n
\u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce<\/strong><\/h2>\n
engine = create_engine('mysql+pymysql:\/\/username:password@host:port\/database')<\/p>\n
\u8bfb\u53d6\u516c\u53f8\u7684\u8d26\u5355\u6570\u636e<\/strong><\/h2>\n
company_data = pd.read_sql('SELECT * FROM company_data', engine)<\/p>\n
\u8bfb\u53d6\u94f6\u884c\u5bf9\u8d26\u5355<\/strong><\/h2>\n
bank_data = pd.read_sql('SELECT * FROM bank_data', engine)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u81ea\u52a8\u5316\u5bf9\u8d26\u6d41\u7a0b<\/h3>\n<\/p>\n
\u4f60\u53ef\u4ee5\u5c06\u4e0a\u8ff0\u6b65\u9aa4\u5c01\u88c5\u5230\u4e00\u4e2a\u51fd\u6570\u4e2d\uff0c\u81ea\u52a8\u5316\u5bf9\u8d26\u6d41\u7a0b\u3002\u8fd9\u6837\u4f60\u53ef\u4ee5\u5b9a\u671f\u8fd0\u884c\u8fd9\u4e2a\u51fd\u6570\uff0c\u81ea\u52a8\u8fdb\u884c\u8d22\u52a1\u5bf9\u8d26\u3002<\/p>\n<\/p>\n
def reconcile_data(company_data_path, bank_data_path):<\/p>\n
    # \u8bfb\u53d6\u6570\u636e<\/p>\n
    company_data = pd.read_csv(company_data_path)<\/p>\n
    bank_data = pd.read_csv(bank_data_path)<\/p>\n
    # \u6570\u636e\u6e05\u6d17\u548c\u683c\u5f0f\u5316<\/p>\n
    company_data.dropna(inplace=True)<\/p>\n
    bank_data.dropna(inplace=True)<\/p>\n
    company_data.drop_duplicates(inplace=True)<\/p>\n
    bank_data.drop_duplicates(inplace=True)<\/p>\n
    company_data['date'] = pd.to_datetime(company_data['date'])<\/p>\n
    bank_data['date'] = pd.to_datetime(bank_data['date'])<\/p>\n
    # \u5408\u5e76\u6570\u636e<\/p>\n
    merged_data = pd.merge(company_data, bank_data, on=['date', 'amount'], how='outer', indicator=True)<\/p>\n
    # \u6807\u8bb0\u5bf9\u8d26\u7ed3\u679c<\/p>\n
    merged_data['reconciliation_status'] = merged_data['_merge'].map({<\/p>\n
        'both': 'matched',<\/p>\n
        'left_only': 'company_only',<\/p>\n
        'right_only': 'bank_only'<\/p>\n
    })<\/p>\n
    # \u7ed8\u5236\u5bf9\u8d26\u7ed3\u679c\u7684\u67f1\u72b6\u56fe<\/p>\n
    plt.figure(figsize=(10, 6))<\/p>\n
    sns.countplot(data=merged_data, x='reconciliation_status')<\/p>\n
    plt.title('Reconciliation Status')<\/p>\n
    plt.xlabel('Status')<\/p>\n
    plt.ylabel('Count')<\/p>\n
    plt.show()<\/p>\n
    return merged_data<\/p>\n
\u8c03\u7528\u51fd\u6570\u8fdb\u884c\u5bf9\u8d26<\/strong><\/h2>\n
reconciliation_result = reconcile_data('company_data.csv', 'bank_data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4f7f\u7528Pandas\u3001Numpy\u3001Matplotlib\u548cSQLAlchemy\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u5730\u8fdb\u884c\u8d22\u52a1\u5bf9\u8d26\u3002Pandas\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u529f\u80fd\uff0cNumpy\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u503c\u8ba1\u7b97\uff0cMatplotlib\u548cSeaborn\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u53ef\u89c6\u5316\u529f\u80fd\uff0cSQLAlchemy\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u5e93\u8bbf\u95ee\u63a5\u53e3\u3002\u901a\u8fc7\u7ed3\u5408\u8fd9\u4e9b\u5e93\uff0c\u4f60\u53ef\u4ee5\u5b9e\u73b0\u81ea\u52a8\u5316\u7684\u8d22\u52a1\u5bf9\u8d26\u6d41\u7a0b\uff0c\u63d0\u9ad8\u5bf9\u8d26\u6548\u7387\u548c\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5728\u4f7f\u7528Python\u8fdb\u884c\u8d22\u52a1\u5bf9\u5e10\u65f6\uff0c\u54ea\u4e9b\u5e93\u662f\u6700\u5e38\u7528\u7684\uff1f<\/strong>
Python\u6709\u591a\u4e2a\u5e93\u53ef\u4ee5\u5e2e\u52a9\u8fdb\u884c\u8d22\u52a1\u5bf9\u5e10\u3002\u5176\u4e2d\uff0cPandas\u662f\u6700\u5e38\u7528\u7684\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u80fd\u529b\uff0c\u9002\u5408\u5904\u7406\u8868\u683c\u6570\u636e\u3002\u6b64\u5916\uff0cNumPy\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\uff0cMatplotlib\u548cSeaborn\u53ef\u4ee5\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u65b9\u4fbf\u7528\u6237\u7406\u89e3\u5bf9\u5e10\u7ed3\u679c\u3002\u7ed3\u5408\u8fd9\u4e9b\u5e93\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3001\u5904\u7406\u548c\u5206\u6790\u3002<\/p>\n
\u5982\u4f55\u51c6\u5907\u6570\u636e\u4ee5\u4fbf\u7528Python\u8fdb\u884c\u8d22\u52a1\u5bf9\u5e10\uff1f<\/strong>
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