python----/北川/数据整理.ipynb

In [1]:

import pandas as pd

In [72]:

df = pd.read_excel('./在校生综合信息查询.xlsx')

In [73]:

df.shape

Out [73]:

(2982, 42)

In [81]:

tem = df['学号'].sort_values(key=lambda s: s.fillna('').astype(str).str.len())

In [82]:

tem = tem.astype(str).tolist()

In [83]:

tem

Out [83]:

['202205270',
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 '250802300',
 '250802400',
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 '250802600',
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 '250801300',
 '250801400',
 '250801500',
 '250801600',
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 ...]

In [84]:

lens = {}
records = {}
for item in tem:
    lens[f'长度为{len(item)}'] = lens.get(f'长度为{len(item)}', 0) + 1
    records[f'长度为{len(item)}'] = records.get(f'长度为{len(item)}', []) + [item]

In [85]:

lens

Out [85]:

{'长度为9': 2974, '长度为10': 5, '长度为13': 3}

In [45]:

records['长度为10']

Out [45]:

['2023030432', '2024040613', '2024040128', '2023010303', '2024040152']

In [77]:

#将学号不足9位的学号后面补0，超过9位的截取前9位
df['学号'] = df['学号'].apply(lambda x: f'{str(x):0<9}')

In [78]:

df['学号'].value_counts().head(20)

Out [78]:

学号
202415017    2
202322076    2
202302030    2
202314030    2
252604500    2
202415001    2
202301020    2
202415019    2
202304010    2
202301030    2
202306040    2
250803100    1
250803600    1
250803300    1
250803400    1
250803500    1
250803200    1
250803900    1
250803700    1
250803800    1
Name: count, dtype: int64

In [87]:

df.to_excel('数据整理.xlsx', index=False)

In [88]:

df1 = pd.read_excel('数据整理.xlsx')
df1['学号'].value_counts().head(20)

Out [88]:

学号
202205270    1
250803300    1
250802400    1
250802500    1
250802600    1
250802700    1
250802800    1
250802900    1
250803000    1
250803100    1
250803200    1
250803400    1
250900500    1
250803500    1
250803600    1
250803700    1
250803800    1
250803900    1
250804000    1
250900100    1
Name: count, dtype: int64

In [ ]:

#取出df1中学生和学号组合成字典
student = df1[['姓名', '学号']].set_index('姓名').T.to_dict('list')

C:\Users\Administrator\AppData\Local\Temp\ipykernel_34060\2571666087.py:2: UserWarning: DataFrame columns are not unique, some columns will be omitted.
  student = df1[['姓名', '学号']].set_index('姓名').T.to_dict()

In [93]:

df1['姓名'].value_counts()

Out [93]:

姓名
王子轩     4
洛绒丁真    4
王俊杰     4
李鑫      3
杨杰      3
       ..
未煜祺     1
魏颖煊     1
薛永宏     1
于伟杰     1
董芷妍     1
Name: count, Length: 2892, dtype: int64

In [106]:

df2 = pd.read_excel('学生信息.xls',sheet_name='Sheet0')

In [107]:

df2

Out [107]:

	学号	姓名	性别	年级	班级	电话	身份证件号	专业代码	专业名称
0	202304009	韩震	男	2023	2328数字媒体2班	15883775701	510726200805055219	710204	数字媒体技术应用
1	202304023	胥倩文	女	2023	2328数字媒体2班	17378613606	510704200802282127	710204	数字媒体技术应用
2	202328001	四郎	男	2023	2328数字媒体2班	15983736073	513332200405011637	710204	数字媒体技术应用
3	202328005	东红丹巴达吉	男	2023	2328数字媒体2班	15708362028	513327200706166519	710204	数字媒体技术应用
4	202328006	尼谢交	男	2023	2328数字媒体2班	18828867303	513333200805241614	710204	数字媒体技术应用
...	...	...	...	...	...	...	...	...	...
2977	2530028	杨马浩	男	2025	2530智维升学	19114073889	510726201003181410	660201	智能设备运行与维护
2978	2530029	杨逸晨	男	2025	2530智维升学	18781190071	510724201007141616	660201	智能设备运行与维护
2979	2530030	张文博	男	2025	2530智维升学	13698138546	510724201005172056	660201	智能设备运行与维护
2980	2530031	张玺毓	男	2025	2530智维升学	18982542062	50022820100822337X	660201	智能设备运行与维护
2981	2530032	张宇鹏	男	2025	2530智维升学	15328205746	510724201007140816	660201	智能设备运行与维护

2982 rows × 9 columns

In [111]:

df2.columns

Out [111]:

Index(['学号', '姓名', '性别', '年级', '班级', '电话', '身份证件号', '专业代码', '专业名称'], dtype='object')

In [118]:

df3 = df2[['专业名称','专业代码']].drop_duplicates()

In [119]:

major_map = dict(zip(df3['专业名称'], df3['专业代码']))

In [120]:

major_map

Out [120]:

{'数字媒体技术应用': 710204,
 '运动训练': 770303,
 '智慧健康养老服务': 590302,
 '旅游服务与管理': 740101,
 '民族音乐与舞蹈': 750203,
 '会计事务': 120100,
 '建筑工程施工': 640301,
 '电子信息技术': 510101,
 '新能源汽车运用与维修': 700209,
 '智能设备运行与维护': 660201}

In [121]:

df1.columns

Out [121]:

Index(['身份证件号', '姓名', '性别', '出生日期', '民族', '籍贯', '户口性质', '学生类别', '入学年月', '学校名称',
       '专业', '专业方向', '学号', '户口所在地', '户口所在地区县以下详细地址', '身份证件类型', '学籍号', '婚姻状况',
       '政治面貌', '学生来源', '港澳台侨外', '国籍', '家庭现地址', '入学方式', '学制', '年级', '班级',
       '家庭邮政编码', '联招合作类型', '联招合作学校机构代码', '学生联系电话', '出生地', '电子信箱', '英文姓名',
       '姓名拼音', '省', '市', '县', '学习形式', '专业简称', '在校生状态', '健康状况'],
      dtype='object')

In [123]:

df2.columns

Out [123]:

Index(['学号', '姓名', '性别', '年级', '班级', '电话', '身份证件号', '专业代码', '专业名称'], dtype='object')

In [126]:

df5 = df1[['学号', '姓名', '性别', '年级', '班级', '学生联系电话', '身份证件号', '专业']]

In [127]:

df5.rename(columns={'专业':'专业名称','学生联系电话':'电话'},inplace=True)

C:\Users\Administrator\AppData\Local\Temp\ipykernel_34060\2414071842.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df5.rename(columns={'专业':'专业名称','学生联系电话':'电话'},inplace=True)

In [129]:

major_map

Out [129]:

{'数字媒体技术应用': 710204,
 '运动训练': 770303,
 '智慧健康养老服务': 590302,
 '旅游服务与管理': 740101,
 '民族音乐与舞蹈': 750203,
 '会计事务': 120100,
 '建筑工程施工': 640301,
 '电子信息技术': 510101,
 '新能源汽车运用与维修': 700209,
 '智能设备运行与维护': 660201}

In [130]:

df5['专业代码'] = df5['专业名称'].map(major_map)

C:\Users\Administrator\AppData\Local\Temp\ipykernel_34060\3695747400.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df5['专业代码'] = df5['专业名称'].map(major_map)

In [137]:

df5 = df5.astype('str')

In [138]:

df5.to_excel("学生信息_v1.xlsx",index=False)

In [149]:

grademap = {}
def fun(x):
    grademap[x.name] = list(x['班级'].value_counts().to_dict().keys())
df5.groupby('年级').apply(fun)

Out [149]:

C:\Users\Administrator\AppData\Local\Temp\ipykernel_34060\2087057881.py:4: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  df5.groupby('年级').apply(fun)

In [150]:

grademap

Out [150]:

{'2023': ['2322数字媒体',
  '2317汽修2班',
  '2309旅游升学',
  '2312电商升学',
  '2316汽修1班',
  '2321建筑升学',
  '2306融通1班',
  '2319会计升学',
  '2310融通3班',
  '2314智维升学',
  '2315融通5班',
  '2302五年制会计',
  '2311电商军事',
  '2304五年制媒体',
  '2318会计融通',
  '2301五年制电子',
  '2325体育',
  '2308旅游融通',
  '2303五年制建筑',
  '2323舞蹈',
  '2326会计2班',
  '2305五年制汽修',
  '2320建筑融通',
  '2328数字媒体2班',
  '2327汽修3班',
  '2324音乐'],
 '2024': ['2422汽修2班',
  '2420融通7班',
  '2411融通3班',
  '2409会计升学',
  '2418数媒1班',
  '2421汽修1班',
  '2414旅游升学',
  '2427智维升学',
  '2406融通1班',
  '2408融通2班',
  '2407电商升学',
  '2402五年制会计',
  '2417融通6班',
  '2424运动训练',
  '2410建筑升学',
  '2423汽修3班',
  '2419数媒2班',
  '2413融通5班',
  '2425智慧服务',
  '2426融通8班',
  '2415舞蹈',
  '2404五年制数媒',
  '2405五年制汽修',
  '2403五年制建筑',
  '2401五年制电子',
  '2416音乐',
  '2412融通4班',
  '2418数字媒体1班'],
 '2025': ['2506电商升学',
  '2524汽修2班',
  '2525汽修3班',
  '2523汽修1班',
  '2514旅游升学',
  '2521融通9班',
  '2522融通10班',
  '2519数字媒体2班',
  '2518数字媒体1班',
  '2528智慧服务',
  '2511融通4班',
  '2517融通8班',
  '2512融通5班',
  '2516融通7班',
  '2526汽修4班',
  '2505融通1班',
  '2508会计升学',
  '2529融通11班',
  '2530智维升学',
  '2527运动训练',
  '2513融通6班',
  '2503五年制数字媒体',
  '2515音乐舞蹈',
  '2520数字媒体3班',
  '2501五年制会计',
  '2507融通2班',
  '2531一年制',
  '2510建筑升学',
  '2502五年制建筑',
  '2504五年制汽修',
  '2509融通3班']}

In [153]:

from requests import get,post
import pandas as pd
import time
teamId = 106
ip = "http://192.168.0.244:8101"
param = {
    "username": "tangchao",
    "password": "123456a",
    "client_id": "client",
    "grant_type": "password",
    "client_secret": "123456",
}
data = get(url=f"{ip}/oauth/token",params=param).json()
token = data["access_token"]
header = {
    "content-type": "application/json",
    "authorization": f"Bearer {token}",
}

In [157]:

for item in grademap['2023']:
    post(f'{ip}/uc/class/add',headers=header,json={"gradeId":327,"teamId":teamId,"className":item,"enabled":True})

In [ ]:

post(f'{ip}/uc/class/add',headers=header,json={"gradeId":322,"teamId":106,"className":"测试班","enabled":true})