2012联邦选举委员会数据库中赞助人和赞助模式的分析统计

2023-11-04 03:20
文章标签 分析 统计 模式 数据库 2012 选举 委员会 赞助 联邦 赞助人

本文主要是介绍2012联邦选举委员会数据库中赞助人和赞助模式的分析统计，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

本例为《利用Python进行数据分析》中第九章最后一节实例，数据来源Github
https://github.com/wesm/pydata-book/tree/2nd-edition/datasets/fec

#2012联邦选举委员会数据库中赞助人和赞助模式的分析统计
import pandas as pd
import numpy as np
from pandas import Series,DataFrame
from numpy.random import randn,rand
import matplotlib.pyplot as plt

fec=pd.read_csv('P00000001-ALL.csv')
fec.head()

D:\application_setup\Anaconda\lib\site-packages\IPython\core\interactiveshell.py:3063: DtypeWarning: Columns (6) have mixed types.Specify dtype option on import or set low_memory=False.interactivity=interactivity, compiler=compiler, result=result)
cmte_idcand_idcand_nmcontbr_nmcontbr_citycontbr_stcontbr_zipcontbr_employercontbr_occupationcontb_receipt_amtcontb_receipt_dtreceipt_descmemo_cdmemo_textform_tpfile_num
0C00410118P20002978Bachmann, MichelleHARVEY, WILLIAMMOBILEAL3.6601e+08RETIREDRETIRED250.020-JUN-11NaNNaNNaNSA17A736166
1C00410118P20002978Bachmann, MichelleHARVEY, WILLIAMMOBILEAL3.6601e+08RETIREDRETIRED50.023-JUN-11NaNNaNNaNSA17A736166
2C00410118P20002978Bachmann, MichelleSMITH, LANIERLANETTAL3.68633e+08INFORMATION REQUESTEDINFORMATION REQUESTED250.005-JUL-11NaNNaNNaNSA17A749073
3C00410118P20002978Bachmann, MichelleBLEVINS, DARONDAPIGGOTTAR7.24548e+08NONERETIRED250.001-AUG-11NaNNaNNaNSA17A749073
4C00410118P20002978Bachmann, MichelleWARDENBURG, HAROLDHOT SPRINGS NATIONAR7.19016e+08NONERETIRED300.020-JUN-11NaNNaNNaNSA17A736166
fec.columns

Index(['cmte_id', 'cand_id', 'cand_nm', 'contbr_nm', 'contbr_city','contbr_st', 'contbr_zip', 'contbr_employer', 'contbr_occupation','contb_receipt_amt', 'contb_receipt_dt', 'receipt_desc', 'memo_cd','memo_text', 'form_tp', 'file_num'],dtype='object')

fec.iloc[123456]

cmte_id                             C00431445
cand_id                             P80003338
cand_nm                         Obama, Barack
contbr_nm                         ELLMAN, IRA
contbr_city                             TEMPE
contbr_st                                  AZ
contbr_zip                          852816719
contbr_employer      ARIZONA STATE UNIVERSITY
contbr_occupation                   PROFESSOR
contb_receipt_amt                          50
contb_receipt_dt                    01-DEC-11
receipt_desc                              NaN
memo_cd                                   NaN
memo_text                                 NaN
form_tp                                 SA17A
file_num                               772372
Name: 123456, dtype: object

unique_cands=fec.cand_nm.unique()

unique_cands

array(['Bachmann, Michelle', 'Romney, Mitt', 'Obama, Barack',"Roemer, Charles E. 'Buddy' III", 'Pawlenty, Timothy','Johnson, Gary Earl', 'Paul, Ron', 'Santorum, Rick','Cain, Herman', 'Gingrich, Newt', 'McCotter, Thaddeus G','Huntsman, Jon', 'Perry, Rick'], dtype=object)

unique_cands[2]

'Obama, Barack'

#竞选人所属政党
parties={'Bachmann, Michelle':'Republican','Romney, Mitt':'Republican','Gingrich, Newt':'Republican','Huntsman, Jon':'Republican','Johnson, Gary Earl':'Republican','McCotter, Thaddeus G':'Republican','Obama, Barack':'Democrat','Paul, Ron':'Republican','Pawlenty, Timothy':'Republican','Perry, Rick':'Republican',"Roemer, Charles E. 'Buddy' III":'Republican','Romney, Mitt':'Republican','Santorum, Rick':'Republican'}

#这里出现了一个问题，就是我辛辛苦苦把竞选人匹配党派敲出来以后，一检查，发现#这里出现了一个问题，
#parties字典长度跟unique_cands的长度不匹配，这不是坑爹么，应该是少敲了一个
len(parties)

12

len(unique_cands)

13

cands=[name for name in parties]#取parties字典的key值，以列表推导式形式输出

cands=np.array(cands)
cands

array(['Bachmann, Michelle', 'Romney, Mitt', 'Gingrich, Newt','Huntsman, Jon', 'Johnson, Gary Earl', 'McCotter, Thaddeus G','Obama, Barack', 'Paul, Ron', 'Pawlenty, Timothy', 'Perry, Rick',"Roemer, Charles E. 'Buddy' III", 'Santorum, Rick'], dtype='<U30')

#np.in1d()为测试一个数组在另一个数组中的成员资格，
np.in1d(unique_cands,cands)

array([ True,  True,  True,  True,  True,  True,  True,  True, False,True,  True,  True,  True])

#np.setdiff1d()集合差函数
np.setdiff1d(unique_cands,unique_cands[np.in1d(unique_cands,cands)])

array(['Cain, Herman'], dtype=object)

#把漏掉的加到parties字典上
parties['Cain, Herman']='Republican'

parties

{'Bachmann, Michelle': 'Republican','Romney, Mitt': 'Republican','Gingrich, Newt': 'Republican','Huntsman, Jon': 'Republican','Johnson, Gary Earl': 'Republican','McCotter, Thaddeus G': 'Republican','Obama, Barack': 'Democrat','Paul, Ron': 'Republican','Pawlenty, Timothy': 'Republican','Perry, Rick': 'Republican',"Roemer, Charles E. 'Buddy' III": 'Republican','Santorum, Rick': 'Republican','Cain, Herman': 'Republican'}

#在原数据集上新增党派关系列
fec['party']=fec.cand_nm.map(parties)

fec.head()
cmte_idcand_idcand_nmcontbr_nmcontbr_citycontbr_stcontbr_zipcontbr_employercontbr_occupationcontb_receipt_amtcontb_receipt_dtreceipt_descmemo_cdmemo_textform_tpfile_numparty
0C00410118P20002978Bachmann, MichelleHARVEY, WILLIAMMOBILEAL3.6601e+08RETIREDRETIRED250.020-JUN-11NaNNaNNaNSA17A736166Republican
1C00410118P20002978Bachmann, MichelleHARVEY, WILLIAMMOBILEAL3.6601e+08RETIREDRETIRED50.023-JUN-11NaNNaNNaNSA17A736166Republican
2C00410118P20002978Bachmann, MichelleSMITH, LANIERLANETTAL3.68633e+08INFORMATION REQUESTEDINFORMATION REQUESTED250.005-JUL-11NaNNaNNaNSA17A749073Republican
3C00410118P20002978Bachmann, MichelleBLEVINS, DARONDAPIGGOTTAR7.24548e+08NONERETIRED250.001-AUG-11NaNNaNNaNSA17A749073Republican
4C00410118P20002978Bachmann, MichelleWARDENBURG, HAROLDHOT SPRINGS NATIONAR7.19016e+08NONERETIRED300.020-JUN-11NaNNaNNaNSA17A736166Republican
#该数据赞助也包括退款，及存在负数出资额
(fec.contb_receipt_amt>0).value_counts()

True     991475
False     10256
Name: contb_receipt_amt, dtype: int64

fec.cand_nm.isin(['Obama, Barack','Romney, Mitt'])

0          False
1          False
2          False
3          False
4          False...  
1001726    False
1001727    False
1001728    False
1001729    False
1001730    False
Name: cand_nm, Length: 1001731, dtype: bool

#筛选出只包含指定两位竞选人的赞助信息
fec_mrbo=fec[fec.cand_nm.isin(['Obama, Barack','Romney, Mitt'])]

fec_mrbo.head()
cmte_idcand_idcand_nmcontbr_nmcontbr_citycontbr_stcontbr_zipcontbr_employercontbr_occupationcontb_receipt_amtcontb_receipt_dtreceipt_descmemo_cdmemo_textform_tpfile_numparty
411C00431171P80003353Romney, MittELDERBAUM, WILLIAMDPOAA3.4023e+08US GOVERNMENTFOREIGN SERVICE OFFICER25.001-FEB-12NaNNaNNaNSA17A780124Republican
412C00431171P80003353Romney, MittELDERBAUM, WILLIAMDPOAA3.4023e+08US GOVERNMENTFOREIGN SERVICE OFFICER110.001-FEB-12NaNNaNNaNSA17A780124Republican
413C00431171P80003353Romney, MittCARLSEN, RICHARDAPOAE9.128e+07DEFENSE INTELLIGENCE AGENCYINTELLIGENCE ANALYST250.013-APR-12NaNNaNNaNSA17A785689Republican
414C00431171P80003353Romney, MittDELUCA, PIERREAPOAE9.128e+07CISCOENGINEER30.021-AUG-11NaNNaNNaNSA17A760261Republican
415C00431171P80003353Romney, MittSARGENT, MICHAELAPOAE9.01201e+07RAYTHEON TECHNICAL SERVICES CORPCOMPUTER SYSTEMS ENGINEER100.007-MAR-12NaNNaNNaNSA17A780128Republican
#根据职业和雇主统计赞助信息
occs=fec['contbr_occupation'].unique()

occs

array(['RETIRED', 'INFORMATION REQUESTED', 'RN', ...,'SAFETY SALES MANAGER', 'PRESIDENT & GENERAL MANAGER','LONGWALL MAINTENANCE FOREMAN'], dtype=object)

len(fec['contbr_occupation'].unique())

45074

#许多职业都涉及相同的基本工作类型，或者同一样东西有多种变体。清理一些这类的数据
occ_mapping={'INFORMATION REQUESTED':'NOT PROVIDED','INFORMATION REQUESTED PER BEST EFFORTS':'NOT PROVIDED','INFORMATION REQUESTED(BEST EFFORTS)':'NOT PROVIDED','C.E.O.':'CEO'
}

fec.groupby('contbr_occupation')['party'].count()
contbr_occupationMIXED-MEDIA ARTIST / STORYTELLER    1AREA VICE PRESIDENT                   1RESEARCH ASSOCIATE                    1TEACHER                               1THERAPIST                             3..
ZOOKEEPER                              1
ZOOLOGIST                              3
ZOOLOGY EDUCATION                      1
\NONE\                                 1
~                                      1
Name: party, Length: 45073, dtype: int64


#如果没有提供相关映射，返回x
f=lambda x: occ_mapping.get(x,x)
fec.contbr_occupation=fec.contbr_occupation.map(f)
#对雇主信息进行同样的处理
emp_mapping={'INFORMATION REQUESTED PER BEST EFFORTS':'NOT PROVIDED','INFORMATION REQUESTED':'NOT PROVIDED','SELF':'SELF-EMPLOYED','SELF EMPLOYED':'SELF-EMPLOYED'}
#如果没有提供相关的映射，则返回x
f=lambda x:emp_mapping.get(x,x)
fec.contbr_employer=fec.contbr_employer.map(f)

#根据党派和职业对数据进行整合，滤除总出资额不足200万美金的数据
by_occupation=fec.pivot_table(values='contb_receipt_amt',index='contbr_occupation',columns='party',aggfunc='sum')

by_occupation
partyDemocratRepublican
contbr_occupation
MIXED-MEDIA ARTIST / STORYTELLER100.0NaN
AREA VICE PRESIDENT250.0NaN
RESEARCH ASSOCIATE100.0NaN
TEACHER500.0NaN
THERAPIST3900.0NaN
.........
ZOOKEEPER35.0NaN
ZOOLOGIST400.0NaN
ZOOLOGY EDUCATION25.0NaN
\NONE\NaN250.0
~NaN75.0

45071 rows × 2 columns

#过滤掉总出资额不足200万美金的数据
over_2mm=by_occupation[by_occupation.sum(1)>2000000]

over_2mm
partyDemocratRepublican
contbr_occupation
ATTORNEY11126932.977.343540e+06
CEO2071474.793.932686e+06
CONSULTANT2459812.712.472815e+06
ENGINEER950425.551.780102e+06
EXECUTIVE1355161.053.918557e+06
HOMEMAKER4243394.301.324141e+07
INVESTOR884133.002.320349e+06
LAWYER3159391.873.785903e+05
MANAGER762693.221.404878e+06
NOT PROVIDED4849801.961.975207e+07
OWNER998867.362.279621e+06
PHYSICIAN3732387.443.491657e+06
PRESIDENT1878009.954.469834e+06
PROFESSOR2163571.082.944627e+05
REAL ESTATE528752.091.581747e+06
RETIRED25270507.232.290594e+07
SELF-EMPLOYED667393.401.591758e+06
#用直方图来展现更为清晰
over_2mm.plot(kind='barh')

<matplotlib.axes._subplots.AxesSubplot at 0x1700e31efc8>

在这里插入图片描述

#统计对Romney, Mitt和Obama, Barack总出资额最高的职业和企业家

fec_mrbo
cmte_idcand_idcand_nmcontbr_nmcontbr_citycontbr_stcontbr_zipcontbr_employercontbr_occupationcontb_receipt_amtcontb_receipt_dtreceipt_descmemo_cdmemo_textform_tpfile_numparty
411C00431171P80003353Romney, MittELDERBAUM, WILLIAMDPOAA3.4023e+08US GOVERNMENTFOREIGN SERVICE OFFICER25.001-FEB-12NaNNaNNaNSA17A780124Republican
412C00431171P80003353Romney, MittELDERBAUM, WILLIAMDPOAA3.4023e+08US GOVERNMENTFOREIGN SERVICE OFFICER110.001-FEB-12NaNNaNNaNSA17A780124Republican
413C00431171P80003353Romney, MittCARLSEN, RICHARDAPOAE9.128e+07DEFENSE INTELLIGENCE AGENCYINTELLIGENCE ANALYST250.013-APR-12NaNNaNNaNSA17A785689Republican
414C00431171P80003353Romney, MittDELUCA, PIERREAPOAE9.128e+07CISCOENGINEER30.021-AUG-11NaNNaNNaNSA17A760261Republican
415C00431171P80003353Romney, MittSARGENT, MICHAELAPOAE9.01201e+07RAYTHEON TECHNICAL SERVICES CORPCOMPUTER SYSTEMS ENGINEER100.007-MAR-12NaNNaNNaNSA17A780128Republican
......................................................
701381C00431445P80003338Obama, BarackMOUNTS, ROBERTAPOZZ962043077HQ USFK (FKDC-SA)GS-15 INTERNATIONAL RELATIONS OFFICER25.026-FEB-12NaNNaNNaNSA17A787803Democrat
701382C00431445P80003338Obama, BarackTAITANO, TYRONEHAGATNAZZ969323373NOT EMPLOYEDRETIRED250.020-JAN-12NaNNaNNaNSA17A775668Democrat
701383C00431445P80003338Obama, BarackTUCKER, PAMELAAPOZZ963190030DODEAEDUCATOR3.020-JAN-12NaNNaNNaNSA17A775668Democrat
701384C00431445P80003338Obama, BarackMOUNTS, ROBERTAPOZZ962043077HQ USFK (FKDC-SA)GS-15 INTERNATIONAL RELATIONS OFFICER25.026-APR-12NaNNaNNaNSA17A785239Democrat
701385C00431445P80003338Obama, BarackNEAL, AMBERAPOZZ091022065THE DEPARTMENT OF DEFENSE EDUCATION ACTEACHER135.004-SEP-11NaNX* OBAMA VICTORY FUND 2012SA18756218Democrat

700975 rows × 17 columns

grouped=fec_mrbo.groupby('cand_nm')

def get_top_amounts(group,key,n=5):totals=group.groupby(key)['contb_receipt_amt'].sum()return(totals.sort_values(ascending=False)[:n])
grouped.apply(get_top_amounts,'contbr_occupation',n=7)

cand_nm        contbr_occupation
Obama, Barack  RETIRED              25270507.23ATTORNEY             11126932.97NOT PROVIDED          4849801.96HOMEMAKER             4243394.30PHYSICIAN             3732387.44LAWYER                3159391.87CONSULTANT            2459812.71
Romney, Mitt   RETIRED              11266949.23NOT PROVIDED         11173374.84HOMEMAKER             8037250.86ATTORNEY              5302578.82PRESIDENT             2403439.77CEO                   2247242.03EXECUTIVE             2230653.79
Name: contb_receipt_amt, dtype: float64

grouped.apply(get_top_amounts,'contbr_employer',n=10)

cand_nm        contbr_employer   
Obama, Barack  RETIRED               22665902.20SELF-EMPLOYED         18584277.16NOT EMPLOYED           8584118.70NOT PROVIDED           5036178.37HOMEMAKER              2599987.04STUDENT                 318831.45VOLUNTEER               257104.00MICROSOFT               215585.36SIDLEY AUSTIN LLP       168254.00REFUSED                 149516.07
Romney, Mitt   NOT PROVIDED          11827237.12RETIRED               11264701.35HOMEMAKER              8037000.86SELF-EMPLOYED          7265136.53STUDENT                 488642.82CREDIT SUISSE           265650.00MORGAN STANLEY          262266.00GOLDMAN SACH & CO.      233250.00BARCLAYS CAPITAL        162750.00H.I.G. CAPITAL          139500.00
Name: contb_receipt_amt, dtype: float64

#对出资额分组
bins=np.array([0,1,10,100,1000,10000,100000,1000000,10000000])

labels=pd.cut(fec_mrbo.contb_receipt_amt,bins)

labels

411         (10, 100]
412       (100, 1000]
413       (100, 1000]
414         (10, 100]
415         (10, 100]...     
701381      (10, 100]
701382    (100, 1000]
701383        (1, 10]
701384      (10, 100]
701385    (100, 1000]
Name: contb_receipt_amt, Length: 700975, dtype: category
Categories (8, interval[int64]): [(0, 1] < (1, 10] < (10, 100] < (100, 1000] < (1000, 10000] < (10000, 100000] < (100000, 1000000] < (1000000, 10000000]]

grouped=fec_mrbo.groupby(['cand_nm',labels])

grouped.size()

cand_nm        contb_receipt_amt  
Obama, Barack  (0, 1]                    493(1, 10]                 40070(10, 100]              372280(100, 1000]            153991(1000, 10000]           22284(10000, 100000]             2(100000, 1000000]           3(1000000, 10000000]         4
Romney, Mitt   (0, 1]                     77(1, 10]                  3681(10, 100]               31853(100, 1000]             43357(1000, 10000]           26186(10000, 100000]             1(100000, 1000000]           0(1000000, 10000000]         0
dtype: int64

grouped.size().unstack(0)
cand_nmObama, BarackRomney, Mitt
contb_receipt_amt
(0, 1]49377
(1, 10]400703681
(10, 100]37228031853
(100, 1000]15399143357
(1000, 10000]2228426186
(10000, 100000]21
(100000, 1000000]30
(1000000, 10000000]40
bucket_sums=grouped.contb_receipt_amt.sum()#对出资额求和

bucket_sums

cand_nm        contb_receipt_amt  
Obama, Barack  (0, 1]                      318.24(1, 10]                  337267.62(10, 100]              20288981.41(100, 1000]            54798531.46(1000, 10000]          51753705.67(10000, 100000]           59100.00(100000, 1000000]       1490683.08(1000000, 10000000]     7148839.76
Romney, Mitt   (0, 1]                       77.00(1, 10]                   29819.66(10, 100]               1987783.76(100, 1000]            22363381.69(1000, 10000]          63942145.42(10000, 100000]           12700.00(100000, 1000000]              NaN(1000000, 10000000]            NaN
Name: contb_receipt_amt, dtype: float64

bucket_sums=bucket_sums.unstack(0)

bucket_sums
cand_nmObama, BarackRomney, Mitt
contb_receipt_amt
(0, 1]318.2477.00
(1, 10]337267.6229819.66
(10, 100]20288981.411987783.76
(100, 1000]54798531.4622363381.69
(1000, 10000]51753705.6763942145.42
(10000, 100000]59100.0012700.00
(100000, 1000000]1490683.08NaN
(1000000, 10000000]7148839.76NaN
normed_sums=bucket_sums.div(bucket_sums.sum(axis=1),axis=0)#在面元内进行规格化

normed_sums
cand_nmObama, BarackRomney, Mitt
contb_receipt_amt
(0, 1]0.8051820.194818
(1, 10]0.9187670.081233
(10, 100]0.9107690.089231
(100, 1000]0.7101760.289824
(1000, 10000]0.4473260.552674
(10000, 100000]0.8231200.176880
(100000, 1000000]1.000000NaN
(1000000, 10000000]1.000000NaN
normed_sums[:-2].plot(kind='barh',stacked=True)

<matplotlib.axes._subplots.AxesSubplot at 0x1700ecfd988>

在这里插入图片描述

#根据州统计赞助信息
grouped=fec_mrbo.groupby(['cand_nm','contbr_st'])

totals=grouped.contb_receipt_amt.sum().unstack(0).fillna(0)

totals=totals[totals.sum(1)>100000]

totals[:10]
cand_nmObama, BarackRomney, Mitt
contbr_st
AK275353.1586204.24
AL537835.48504882.08
AR324802.28105351.50
AZ1484241.841850484.23
CA23370680.8410908232.46
CO2104551.381477810.82
CT2037216.663377421.85
DC4317865.85999740.50
DE325394.1481404.00
FL7138932.528008067.40
percent=totals.div(totals.sum(1),axis=0)

percent
cand_nmObama, BarackRomney, Mitt
contbr_st
AK0.7615750.238425
AL0.5158020.484198
AR0.7550840.244916
AZ0.4450870.554913
CA0.6817800.318220
CO0.5874760.412524
CT0.3762420.623758
DC0.8119940.188006
DE0.7998910.200109
FL0.4713100.528690
GA0.5874900.412510
HI0.8763340.123666
IA0.7413760.258624
ID0.2004240.799576
IL0.8276490.172351
IN0.6225490.377451
KS0.5751670.424833
KY0.5165020.483498
LA0.3578430.642157
MA0.5912560.408744
MD0.7506310.249369
ME0.9105300.089470
MI0.5227050.477295
MN0.8552630.144737
MO0.4951270.504873
MS0.3745930.625407
MT0.6530630.346937
NC0.6558790.344121
NE0.5874310.412569
NH0.5862260.413774
NJ0.4906870.509313
NM0.8441330.155867
NV0.5403620.459638
NY0.5953930.404607
OH0.4894120.510588
OK0.4135490.586451
OR0.6966950.303305
PA0.6618560.338144
PR0.8897720.110228
RI0.7078510.292149
SC0.6105310.389469
SD0.3459480.654052
TN0.4248300.575170
TX0.5185050.481495
UT0.1229480.877052
VA0.5554020.444598
VT0.9461760.053824
WA0.7656340.234366
WI0.8053430.194657
WV0.5738910.426109
WY0.4408230.559177
XX0.0000001.000000

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《SpringBoot实现数据库读写分离的3种方法小结》为了提高系统的读写性能和可用性,读写分离是一种经典的数据库架构模式,在SpringBoot应用中,有多种方式可以实现数据库读写分离,本文将介绍三... 目录一、数据库读写分离概述二、方案一：基于AbstractRoutingDataSource实现动态
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Linux系统配置NAT网络模式的详细步骤(附图文)

Linux系统配置NAT网络模式的详细步骤(附图文)

《Linux系统配置NAT网络模式的详细步骤(附图文)》本文详细指导如何在VMware环境下配置NAT网络模式,包括设置主机和虚拟机的IP地址、网关,以及针对Linux和Windows系统的具体步骤,... 目录一、配置NAT网络模式二、设置虚拟机交换机网关2.1 打开虚拟机2.2 管理员授权2.3 设置子
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C# WinForms存储过程操作数据库的实例讲解

C# WinForms存储过程操作数据库的实例讲解

《C#WinForms存储过程操作数据库的实例讲解》：本文主要介绍C#WinForms存储过程操作数据库的实例,具有很好的参考价值,希望对大家有所帮助,如有错误或未考虑完全的地方,望不吝赐教... 目录一、存储过程基础二、C# 调用流程1. 数据库连接配置2. 执行存储过程（增删改）3. 查询数据三、事务处
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Spring事务中@Transactional注解不生效的原因分析与解决

Spring事务中@Transactional注解不生效的原因分析与解决

《Spring事务中@Transactional注解不生效的原因分析与解决》在Spring框架中,@Transactional注解是管理数据库事务的核心方式,本文将深入分析事务自调用的底层原理,解释为... 目录1. 引言2. 事务自调用问题重现2.1 示例代码2.2 问题现象3. 为什么事务自调用会失效3
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mysql数据库重置表主键id的实现

mysql数据库重置表主键id的实现

《mysql数据库重置表主键id的实现》在我们的开发过程中,难免在做测试的时候会生成一些杂乱无章的SQL主键数据,本文主要介绍了mysql数据库重置表主键id的实现,具有一定的参考价值,感兴趣的可以了... 目录关键语法演示案例在我们的开发过程中，难免在做测试的时候会生成一些杂乱无章的SQL主键数据，当我们
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找不到Anaconda prompt终端的原因分析及解决方案

找不到Anaconda prompt终端的原因分析及解决方案

《找不到Anacondaprompt终端的原因分析及解决方案》因为anaconda还没有初始化,在安装anaconda的过程中,有一行是否要添加anaconda到菜单目录中,由于没有勾选,导致没有菜... 目录问题原因问http://www.chinasem.cn题解决安装了 Anaconda 却找不到 An
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Spring定时任务只执行一次的原因分析与解决方案

Spring定时任务只执行一次的原因分析与解决方案

《Spring定时任务只执行一次的原因分析与解决方案》在使用Spring的@Scheduled定时任务时,你是否遇到过任务只执行一次,后续不再触发的情况？这种情况可能由多种原因导致,如未启用调度、线程... 目录1. 问题背景2. Spring定时任务的基本用法3. 为什么定时任务只执行一次？3.1 未启用
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SpringBoot如何通过Map实现策略模式

SpringBoot如何通过Map实现策略模式

《SpringBoot如何通过Map实现策略模式》策略模式是一种行为设计模式,它允许在运行时选择算法的行为,在Spring框架中,我们可以利用@Resource注解和Map集合来优雅地实现策略模式,这... 目录前言底层机制解析Spring的集合类型自动装配@Resource注解的行为实现原理使用直接使用M
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Spring Boot 整合 MyBatis 连接数据库及常见问题

Spring Boot 整合 MyBatis 连接数据库及常见问题

《SpringBoot整合MyBatis连接数据库及常见问题》MyBatis是一个优秀的持久层框架,支持定制化SQL、存储过程以及高级映射,下面详细介绍如何在SpringBoot项目中整合My... 目录一、基本配置1. 添加依赖2. 配置数据库连接二、项目结构三、核心组件实现（示例）1. 实体类2. Ma
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