148 lines
7.3 KiB
Python
148 lines
7.3 KiB
Python
from datetime import datetime, timedelta
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from django.core.management.base import BaseCommand
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from pandas._libs.tslibs.timestamps import Timestamp
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import pandas as pd
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from resumes.models import ResumeBasic
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import re
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import traceback
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class Command(BaseCommand):
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help = "导入会计类简历（支持 --keyword 和 --source 参数）"
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def add_arguments(self, parser):
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parser.add_argument('--file', required=True, help='Excel 文件路径')
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parser.add_argument('--keyword', default='', help='crawl_keywords 值')
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parser.add_argument('--source', type=int, default=1, help='source_id 值')
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def handle(self, *args, **options):
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filepath = options['file']
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default_keyword = options['keyword']
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default_source = options['source']
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df = pd.read_excel(filepath)
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rename_map = {
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'姓名': 'name', '性别': 'gender', '年龄': 'age', '求职区域': 'job_location', '生日': 'birthday',
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'学校': 'school', '期望职务': 'expected_position',
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'手机': 'phone', '婚姻': 'marital_status', '身高': 'height', '体重': 'weight', '电话': 'phone',
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'学历': 'education', '毕业学校': 'school', '工作经验': 'work_years',
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'现居住地': 'current_location', '工作地点': 'job_location', '到岗时间': 'available_time',
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'最后活跃时间': 'update_time', '最高学历': 'education', '婚姻状态': 'marital_status',
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'民族': 'ethnicity', '工作职能': 'job_function', '意向岗位': 'intended_position',
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'从事行业': 'industry', '期望薪资': 'expected_salary', '求职类型': 'job_property',
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'现居地': 'current_location',
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'求职状态': 'job_status', '工作1经历': 'work_1_experience', '工作1时间': 'work_1_time',
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'工作1内容': 'work_1_description',
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'工作2经历': 'work_2_experience', '工作2时间': 'work_2_time', '工作2内容': 'work_2_description',
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'工作3经历': 'work_3_experience', '工作3时间': 'work_3_time', '工作3内容': 'work_3_description',
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'工作4经历': 'work_4_experience', '工作4时间': 'work_4_time', '工作4内容': 'work_4_description',
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}
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df.rename(columns={k: v for k, v in rename_map.items() if k in df.columns}, inplace=True)
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df['source_id'] = default_source
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df['crawl_keywords'] = default_keyword
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def val(v, field=None):
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if v is None or pd.isna(v):
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if field == 'update_time':
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return datetime(2019, 12, 12)
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return None
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if field == 'update_time':
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if isinstance(v, Timestamp):
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return v.to_pydatetime()
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if isinstance(v, str):
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try:
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return pd.to_datetime(v).to_pydatetime()
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except Exception:
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return datetime(2019, 12, 12)
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if isinstance(v, datetime):
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return v
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return datetime(2019, 12, 12)
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if isinstance(v, Timestamp):
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return v.to_pydatetime()
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return v
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success_count = 0
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fail_count = 0
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errors = []
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for i, row in df.iterrows():
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try:
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resume_id = val(row.get('resume_id'))
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# 自动从年龄字段中提取整数（如 "38岁" → 38）
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raw_age = row.get('age')
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try:
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extracted_age = int(re.search(r'\d+', str(raw_age)).group()) if raw_age else None
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except:
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extracted_age = None
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defaults = {
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'name': val(row.get('name')),
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'gender': val(row.get('gender')),
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'age': extracted_age,
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'phone': val(row.get('phone')),
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'marital_status': val(row.get('marital_status')),
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'height': val(row.get('height')),
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'weight': val(row.get('weight')),
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'education': val(row.get('education')),
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'school': val(row.get('school')),
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'work_years': val(row.get('work_years')),
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'current_location': val(row.get('current_location')),
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'job_location': val(row.get('job_location')),
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'available_time': val(row.get('available_time')),
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'update_time': val(row['update_time'], field='update_time'),
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'ethnicity': val(row.get('ethnicity')),
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'job_function': val(row.get('job_function')),
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'intended_position': val(row.get('intended_position')),
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'industry': val(row.get('industry')),
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'expected_salary': val(row.get('expected_salary')),
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'job_property': val(row.get('job_property')),
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'job_status': val(row.get('job_status')),
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'source_id': val(row.get('source_id')),
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'crawl_keywords': val(row.get('crawl_keywords')),
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'birthday': val(row.get('birthday')),
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'expected_position': val(row.get('expected_position')),
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'work_1_experience': val(row.get('work_1_experience')),
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'work_1_time': val(row.get('work_1_time')),
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'work_1_description': val(row.get('work_1_description')),
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'work_2_experience': val(row.get('work_2_experience')),
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'work_2_time': val(row.get('work_2_time')),
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'work_2_description': val(row.get('work_2_description')),
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'work_3_experience': val(row.get('work_3_experience')),
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'work_3_time': val(row.get('work_3_time')),
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'work_3_description': val(row.get('work_3_description')),
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'work_4_experience': val(row.get('work_4_experience')),
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'work_4_time': val(row.get('work_4_time')),
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'work_4_description': val(row.get('work_4_description')),
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}
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# 安全方式：get_or_create + 逐字段 set
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obj, _ = ResumeBasic.objects.get_or_create(resume_id=resume_id)
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for k, v in defaults.items():
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try:
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setattr(obj, k, v)
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except Exception as field_error:
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print(f"[字段设置错误] {k} = {v!r} ({type(v)}) → {field_error}")
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raise
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obj.save()
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success_count += 1
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except Exception as e:
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fail_count += 1
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errors.append((i + 2, str(e)))
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print(f"\n❌ 第 {i + 2} 行出错：{e}")
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print(f"resume_id: {repr(resume_id)} ({type(resume_id).__name__})")
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for k, v in defaults.items():
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print(f"{k:<20} | {repr(v):<30} | {type(v).__name__}")
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traceback.print_exc()
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self.stdout.write(self.style.SUCCESS(f"导入完成！总数：{len(df)}，成功：{success_count}，失败：{fail_count}"))
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if errors:
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self.stdout.write(self.style.WARNING("失败记录如下："))
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for line_no, msg in errors:
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self.stdout.write(f" 第 {line_no} 行出错：{msg}")
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