| Name | hzexcel JSON |
| Version |
0.0.13
JSON |
| download |
| home_page | |
| Summary | 提供常用django、Python后端的excel导入导出功能 |
| upload_time | 2023-08-09 11:11:46 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.7 |
| license | |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
## **1概述**
# 1.1可根据配置、参数提供校验和导入导出excel功能
# 1.2贡献者：深圳市汉卓软件
# 2.功能详细说明(API列表)：
# (1)export_excel(data, data_type{可选},file_name{可选} , rules{可选}, freeze{可选})
**导出excel** 。 返回参数：成功时：返回excel 文件；失败时状态码为非200，返回错误信息。
# (2)import_excel(file, check_dict{可选},type_dict{可选})
**导入excel并根据规则校验**。返回参数：成功时：返回{"status":200,"msg":"校验成功！","data":[{"Name":"Alice","Age":25,"City":"New York"},{"Name":"Bob","Age":32,"City":"Los Angeles"},{"Name":"Charlie","Age":22,"City":"London"}]}；失败时，返回的状态码为非200，错误信息例子如下："缺少列名： 'City';<br>"
### 2.应用说明：
#### (1)export_excel参数说明
data:必选数据，有两种不同的数据结构可选，一种是每一行是一条数据，如：{'测试':['Alice','Bob','Charlie'],'Name':['Alice','Bob','Charlie'],'Age':[25,32,22],'City':['New York','Los Angeles','London']}
另一种是类似 DataFrame 的数据格式，比如：data:{"data":[['Alice',25,'New York'],['Bob',32,'Los Angeles'],['Charlie',22,'London']],"columns":['Name','Age','City']}
data_type: 可选参数，默认为1。可选值为 1 或 2。当值为 1 时，表示每一行是一条数据；当值为 2 时，表示使用 DataFrame 类型的数据格式。
file_name: 可选参数，文件名称，比如 '默认名称.xlsx'。
rules: 可选参数，导出规则，包括表头设置和数据验证下拉列表等。
freeze：可选参数，表示从第几行第几列开始冻结，比如值为”A2 ”其中2表示第从第2行（行索引从1开始）， A 表示第一列。
#### (2)import_excel参数说明
file :必选参数，二进制excel文件，一般从前端传来的 self.request.FILES['file'] 中获取
check_dict:可选参数，校验的对象 ，其中header表示必须必须有那些列，content表示那些列 必填、而且指定类型，
type_dict:可选参数，
判断非表头的数据类型，可传入配置设置可校验类型，不传时默认值如下{
"str": str,
"int": int,
"float": float,
"bool": bool,
"datetime": pd.to_datetime,
}
## 3.应用实例
### （1）export_excel例子：
from hzexcel import export_excel
rules = {
"head": {"Font": {"bold": True, "color": "FF0000", "size": 14}},
"validation": [
{
"column_name": "Name",
"type": "list",
"formula1": '"$ 价格,A 可获得性,P 包装,P 性能,E 易用性,A 保证,L 生命周期成本,S 社会影响力"',
"showDropDown": False,
}
],
}
data = {
'测试': ['Alice', 'Bob', 'Charlie'],
'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 32, 22],
'City': ['New York', 'Los Angeles', 'London']
}
return export_excel(data, 2, '默认名称.xlsx', rules, "A2")
## （2）export_excel例子：
from hzexcel import import_excel
self.request = request
file = self.request.FILES['file']
check_dict = {
"header": ['Name', 'Age', 'City'],
"content": {'Name': 'str', 'City': 'str'}
}
rs0 = import_excel(file, check_dict)
rs = json.loads(rs0)
if rs['status']:
# 通过验证
# 将文件中的数据转为数据 ，批量插入到数据库
if rs['status'] == 200:
return Response(rs, status=status.HTTP_200_OK)
else:
return Response(f"{rs['msg']}", status=status.HTTP_400_BAD_REQUEST)
else:
return Response(f"校验数据时出错", status=status.HTTP_400_BAD_REQUEST)
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"description": "## **1\u6982\u8ff0**\n\n# 1.1\u53ef\u6839\u636e\u914d\u7f6e\u3001\u53c2\u6570\u63d0\u4f9b\u6821\u9a8c\u548c\u5bfc\u5165\u5bfc\u51faexcel\u529f\u80fd\n\n# 1.2\u8d21\u732e\u8005\uff1a\u6df1\u5733\u5e02\u6c49\u5353\u8f6f\u4ef6\n\n# 2.\u529f\u80fd\u8be6\u7ec6\u8bf4\u660e(API\u5217\u8868)\uff1a\n# (1)export_excel(data, data_type{\u53ef\u9009},file_name{\u53ef\u9009} , rules{\u53ef\u9009}, freeze{\u53ef\u9009})\n**\u5bfc\u51faexcel** \u3002\t \u8fd4\u56de\u53c2\u6570\uff1a\u6210\u529f\u65f6\uff1a\u8fd4\u56deexcel \u6587\u4ef6\uff1b\u5931\u8d25\u65f6\u72b6\u6001\u7801\u4e3a\u975e200\uff0c\u8fd4\u56de\u9519\u8bef\u4fe1\u606f\u3002\n\n# (2)import_excel(file, check_dict{\u53ef\u9009},type_dict{\u53ef\u9009})\n**\u5bfc\u5165excel\u5e76\u6839\u636e\u89c4\u5219\u6821\u9a8c**\u3002\u8fd4\u56de\u53c2\u6570\uff1a\u6210\u529f\u65f6\uff1a\u8fd4\u56de{\"status\":200,\"msg\":\"\u6821\u9a8c\u6210\u529f\uff01\",\"data\":[{\"Name\":\"Alice\",\"Age\":25,\"City\":\"New York\"},{\"Name\":\"Bob\",\"Age\":32,\"City\":\"Los Angeles\"},{\"Name\":\"Charlie\",\"Age\":22,\"City\":\"London\"}]}\uff1b\u5931\u8d25\u65f6\uff0c\u8fd4\u56de\u7684\u72b6\u6001\u7801\u4e3a\u975e200\uff0c\u9519\u8bef\u4fe1\u606f\u4f8b\u5b50\u5982\u4e0b\uff1a\"\u7f3a\u5c11\u5217\u540d\uff1a 'City';<br>\"\n\n\n### 2.\u5e94\u7528\u8bf4\u660e\uff1a\n#### (1)export_excel\u53c2\u6570\u8bf4\u660e\ndata:\u5fc5\u9009\u6570\u636e\uff0c\u6709\u4e24\u79cd\u4e0d\u540c\u7684\u6570\u636e\u7ed3\u6784\u53ef\u9009\uff0c\u4e00\u79cd\u662f\u6bcf\u4e00\u884c\u662f\u4e00\u6761\u6570\u636e\uff0c\u5982\uff1a{'\u6d4b\u8bd5':['Alice','Bob','Charlie'],'Name':['Alice','Bob','Charlie'],'Age':[25,32,22],'City':['New York','Los Angeles','London']}\n\u53e6\u4e00\u79cd\u662f\u7c7b\u4f3c DataFrame \u7684\u6570\u636e\u683c\u5f0f\uff0c\u6bd4\u5982\uff1adata:{\"data\":[['Alice',25,'New York'],['Bob',32,'Los Angeles'],['Charlie',22,'London']],\"columns\":['Name','Age','City']}\n\ndata_type: \u53ef\u9009\u53c2\u6570\uff0c\u9ed8\u8ba4\u4e3a1\u3002\u53ef\u9009\u503c\u4e3a 1 \u6216 2\u3002\u5f53\u503c\u4e3a 1 \u65f6\uff0c\u8868\u793a\u6bcf\u4e00\u884c\u662f\u4e00\u6761\u6570\u636e\uff1b\u5f53\u503c\u4e3a 2 \u65f6\uff0c\u8868\u793a\u4f7f\u7528 DataFrame \u7c7b\u578b\u7684\u6570\u636e\u683c\u5f0f\u3002\nfile_name: \u53ef\u9009\u53c2\u6570\uff0c\u6587\u4ef6\u540d\u79f0\uff0c\u6bd4\u5982 '\u9ed8\u8ba4\u540d\u79f0.xlsx'\u3002\n\nrules: \u53ef\u9009\u53c2\u6570\uff0c\u5bfc\u51fa\u89c4\u5219\uff0c\u5305\u62ec\u8868\u5934\u8bbe\u7f6e\u548c\u6570\u636e\u9a8c\u8bc1\u4e0b\u62c9\u5217\u8868\u7b49\u3002\n\nfreeze\uff1a\u53ef\u9009\u53c2\u6570\uff0c\u8868\u793a\u4ece\u7b2c\u51e0\u884c\u7b2c\u51e0\u5217\u5f00\u59cb\u51bb\u7ed3\uff0c\u6bd4\u5982\u503c\u4e3a\u201dA2 \u201d\u5176\u4e2d2\u8868\u793a\u7b2c\u4ece\u7b2c2\u884c\uff08\u884c\u7d22\u5f15\u4ece1\u5f00\u59cb\uff09\uff0c A \u8868\u793a\u7b2c\u4e00\u5217\u3002\n\n#### (2)import_excel\u53c2\u6570\u8bf4\u660e\n\nfile :\u5fc5\u9009\u53c2\u6570\uff0c\u4e8c\u8fdb\u5236excel\u6587\u4ef6\uff0c\u4e00\u822c\u4ece\u524d\u7aef\u4f20\u6765\u7684 self.request.FILES['file'] \u4e2d\u83b7\u53d6\ncheck_dict:\u53ef\u9009\u53c2\u6570\uff0c\u6821\u9a8c\u7684\u5bf9\u8c61 \uff0c\u5176\u4e2dheader\u8868\u793a\u5fc5\u987b\u5fc5\u987b\u6709\u90a3\u4e9b\u5217\uff0ccontent\u8868\u793a\u90a3\u4e9b\u5217 \u5fc5\u586b\u3001\u800c\u4e14\u6307\u5b9a\u7c7b\u578b\uff0c\n\ntype_dict:\u53ef\u9009\u53c2\u6570\uff0c\n\u5224\u65ad\u975e\u8868\u5934\u7684\u6570\u636e\u7c7b\u578b\uff0c\u53ef\u4f20\u5165\u914d\u7f6e\u8bbe\u7f6e\u53ef\u6821\u9a8c\u7c7b\u578b\uff0c\u4e0d\u4f20\u65f6\u9ed8\u8ba4\u503c\u5982\u4e0b{\n \"str\": str,\n \"int\": int,\n \"float\": float,\n \"bool\": bool,\n \"datetime\": pd.to_datetime,\n }\n\n## 3.\u5e94\u7528\u5b9e\u4f8b\n### \uff081\uff09export_excel\u4f8b\u5b50\uff1a\nfrom hzexcel import export_excel\nrules = {\n \"head\": {\"Font\": {\"bold\": True, \"color\": \"FF0000\", \"size\": 14}},\n \"validation\": [\n {\n \"column_name\": \"Name\",\n \"type\": \"list\",\n \"formula1\": '\"$ \u4ef7\u683c,A \u53ef\u83b7\u5f97\u6027,P \u5305\u88c5,P \u6027\u80fd,E \u6613\u7528\u6027,A \u4fdd\u8bc1,L \u751f\u547d\u5468\u671f\u6210\u672c,S \u793e\u4f1a\u5f71\u54cd\u529b\"',\n \"showDropDown\": False,\n }\n ],\n}\ndata = {\n '\u6d4b\u8bd5': ['Alice', 'Bob', 'Charlie'],\n 'Name': ['Alice', 'Bob', 'Charlie'],\n 'Age': [25, 32, 22],\n 'City': ['New York', 'Los Angeles', 'London']\n}\nreturn export_excel(data, 2, '\u9ed8\u8ba4\u540d\u79f0.xlsx', rules, \"A2\")\n\n\n## \uff082\uff09export_excel\u4f8b\u5b50\uff1a\nfrom hzexcel import import_excel\nself.request = request\nfile = self.request.FILES['file']\ncheck_dict = {\n \"header\": ['Name', 'Age', 'City'],\n \"content\": {'Name': 'str', 'City': 'str'}\n}\n\nrs0 = import_excel(file, check_dict)\nrs = json.loads(rs0)\nif rs['status']:\n # 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