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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《JavaNoClassDefFoundError运行时错误分析解决》在Java开发中,NoClassDefFoundError是一种常见的运行时错误,它通常表明Java虚拟机在尝试加载一个类时未能... 目录前言一、问题分析二、报错原因三、解决思路检查类路径配置检查依赖库检查类文件调试类加载器问题四、常见
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MySQL数据库约束深入详解

MySQL数据库约束深入详解

《MySQL数据库约束深入详解》：本文主要介绍MySQL数据库约束,在MySQL数据库中,约束是用来限制进入表中的数据类型的一种技术,通过使用约束,可以确保数据的准确性、完整性和可靠性,需要的朋友... 目录一、数据库约束的概念二、约束类型三、NOT NULL 非空约束四、DEFAULT 默认值约束五、UN
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Pandas中统计汇总可视化函数plot()的使用

Pandas中统计汇总可视化函数plot()的使用

《Pandas中统计汇总可视化函数plot()的使用》Pandas提供了许多强大的数据处理和分析功能,其中plot()函数就是其可视化功能的一个重要组成部分,本文主要介绍了Pandas中统计汇总可视化... 目录一、plot()函数简介二、plot()函数的基本用法三、plot()函数的参数详解四、使用pl
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Python中的Walrus运算符分析示例详解

Python中的Walrus运算符分析示例详解

《Python中的Walrus运算符分析示例详解》Python中的Walrus运算符（:=）是Python3.8引入的一个新特性,允许在表达式中同时赋值和返回值,它的核心作用是减少重复计算,提升代码简... 目录1. 在循环中避免重复计算2. 在条件判断中同时赋值变量3. 在列表推导式或字典推导式中简化逻辑
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Pandas统计每行数据中的空值的方法示例

Pandas统计每行数据中的空值的方法示例

《Pandas统计每行数据中的空值的方法示例》处理缺失数据（NaN值）是一个非常常见的问题,本文主要介绍了Pandas统计每行数据中的空值的方法示例,具有一定的参考价值,感兴趣的可以了解一下... 目录什么是空值？为什么要统计空值？准备工作创建示例数据统计每行空值数量进一步分析www.chinasem.cn处
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Nginx location匹配模式与规则详解

Nginx location匹配模式与规则详解

《Nginxlocation匹配模式与规则详解》：本文主要介绍Nginxlocation匹配模式与规则,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、环境二、匹配模式1. 精准模式2. 前缀模式（不继续匹配正则）3. 前缀模式（继续匹配正则）4. 正则模式（大
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Java程序进程起来了但是不打印日志的原因分析

Java程序进程起来了但是不打印日志的原因分析

《Java程序进程起来了但是不打印日志的原因分析》：本文主要介绍Java程序进程起来了但是不打印日志的原因分析,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Java程序进程起来了但是不打印日志的原因1、日志配置问题2、日志文件权限问题3、日志文件路径问题4、程序
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Java字符串操作技巧之语法、示例与应用场景分析

Java字符串操作技巧之语法、示例与应用场景分析

《Java字符串操作技巧之语法、示例与应用场景分析》在Java算法题和日常开发中,字符串处理是必备的核心技能,本文全面梳理Java中字符串的常用操作语法,结合代码示例、应用场景和避坑指南,可快速掌握字... 目录引言1. 基础操作1.1 创建字符串1.2 获取长度1.3 访问字符2. 字符串处理2.1 子字
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数据库面试必备之MySQL中的乐观锁与悲观锁

数据库面试必备之MySQL中的乐观锁与悲观锁

《数据库面试必备之MySQL中的乐观锁与悲观锁》：本文主要介绍数据库面试必备之MySQL中乐观锁与悲观锁的相关资料,乐观锁适用于读多写少的场景,通过版本号检查避免冲突,而悲观锁适用于写多读少且对数... 目录一、引言二、乐观锁（一）原理（二）应用场景（三）示例代码三、悲观锁（一）原理（二）应用场景（三）示例
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