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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《C++统计函数执行时间的最佳实践》在软件开发过程中,性能分析是优化程序的重要环节,了解函数的执行时间分布对于识别性能瓶颈至关重要,本文将分享一个C++函数执行时间统计工具,希望对大家有所帮助... 目录前言工具特性核心设计1. 数据结构设计2. 单例模式管理器3. RAII自动计时使用方法基本用法高级用法
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如何通过try-catch判断数据库唯一键字段是否重复

如何通过try-catch判断数据库唯一键字段是否重复

《如何通过try-catch判断数据库唯一键字段是否重复》在MyBatis+MySQL中,通过try-catch捕获唯一约束异常可避免重复数据查询,优点是减少数据库交互、提升并发安全,缺点是异常处理开... 目录1、原理2、怎么理解“异常走的是数据库错误路径，开销比普通逻辑分支稍高”？1. 普通逻辑分支 v
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Python与MySQL实现数据库实时同步的详细步骤

Python与MySQL实现数据库实时同步的详细步骤

《Python与MySQL实现数据库实时同步的详细步骤》在日常开发中,数据同步是一项常见的需求,本篇文章将使用Python和MySQL来实现数据库实时同步,我们将围绕数据变更捕获、数据处理和数据写入这... 目录前言摘要概述：数据同步方案1. 基本思路2. mysql Binlog 简介实现步骤与代码示例1
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使用shardingsphere实现mysql数据库分片方式

使用shardingsphere实现mysql数据库分片方式

《使用shardingsphere实现mysql数据库分片方式》本文介绍如何使用ShardingSphere-JDBC在SpringBoot中实现MySQL水平分库,涵盖分片策略、路由算法及零侵入配置... 目录一、ShardingSphere 简介1.1 对比1.2 核心概念1.3 Sharding-Sp
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Go语言连接MySQL数据库执行基本的增删改查

Go语言连接MySQL数据库执行基本的增删改查

《Go语言连接MySQL数据库执行基本的增删改查》在后端开发中,MySQL是最常用的关系型数据库之一,本文主要为大家详细介绍了如何使用Go连接MySQL数据库并执行基本的增删改查吧... 目录Go语言连接mysql数据库准备工作安装 MySQL 驱动代码实现运行结果注意事项Go语言执行基本的增删改查准备工作
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IDEA与MyEclipse代码量统计方式

IDEA与MyEclipse代码量统计方式

《IDEA与MyEclipse代码量统计方式》文章介绍在项目中不安装第三方工具统计代码行数的方法,分别说明MyEclipse通过正则搜索（排除空行和注释）及IDEA使用Statistic插件或调整搜索... 目录项目场景MyEclipse代码量统计IDEA代码量统计总结项目场景在项目中，有时候我们需要统计
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C#和Unity中的中介者模式使用方式

C#和Unity中的中介者模式使用方式

《C#和Unity中的中介者模式使用方式》中介者模式通过中介者封装对象交互,降低耦合度,集中控制逻辑,适用于复杂系统组件交互场景,C#中可用事件、委托或MediatR实现,提升可维护性与灵活性... 目录C#中的中介者模式详解一、中介者模式的基本概念1. 定义2. 组成要素3. 模式结构二、中介者模式的特点
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MySQL 数据库表操作完全指南:创建、读取、更新与删除实战

MySQL 数据库表操作完全指南:创建、读取、更新与删除实战

《MySQL数据库表操作完全指南:创建、读取、更新与删除实战》本文系统讲解MySQL表的增删查改（CURD）操作,涵盖创建、更新、查询、删除及插入查询结果,也是贯穿各类项目开发全流程的基础数据交互原... 目录mysql系列前言一、Create(创建)并插入数据1.1 单行数据 + 全列插入1.2 多行数据
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MySQL 数据库表与查询操作实战案例

MySQL 数据库表与查询操作实战案例

《MySQL数据库表与查询操作实战案例》本文将通过实际案例,详细介绍MySQL中数据库表的设计、数据插入以及常用的查询操作,帮助初学者快速上手,感兴趣的朋友跟随小编一起看看吧... 目录mysql 数据库表操作与查询实战案例项目一：产品相关数据库设计与创建一、数据库及表结构设计二、数据库与表的创建项目二：员
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