有幸参加阿里云天池寒假训练营活动,目前活动已经圆满结束,收获颇丰,仅在此做一些学习笔进记行巩固和继续学习
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学习赛事地址:https://tianchi.aliyun.com/competition/entrance/531837/introduction
所有候选人信息
该文件为每个候选人提供一份记录,并显示候选人的信息、总收入、从授权委员会收到的转账、付款总额、给授权委员会的转账、库存现金总额、贷款和债务以及其他财务汇总信息。
数据字段描述详细:https://www.fec.gov/campaign-finance-data/all-candidates-file-description/
关键字段说明
数据来源:https://www.fec.gov/files/bulk-downloads/2020/weball20.zip
候选人委员会链接信息
该文件显示候选人的身份证号码、候选人的选举年份、联邦选举委员会选举年份、委员会识别号、委员会类型、委员会名称和链接标识号。
信息描述详细:https://www.fec.gov/campaign-finance-data/candidate-committee-linkage-file-description/
关键字段说明
数据来源:https://www.fec.gov/files/bulk-downloads/2020/ccl20.zip
个人捐款档案信息
【注意】由于文件较大,本数据集只包含2020.7.22-2020.8.20的相关数据,如果需要更全数据可以通过数据来源中的地址下载。
该文件包含有关收到捐款的委员会、披露捐款的报告、提供捐款的个人、捐款日期、金额和有关捐款的其他信息。
信息描述详细:https://www.fec.gov/campaign-finance-data/contributions-individuals-file-description/
关键字段说明
数据来源:https://www.fec.gov/files/bulk-downloads/2020/indiv20.zip
# 安装词云处理包wordcloud
!pip install wordcloud --user
Looking in indexes: https://mirrors.aliyun.com/pypi/simple
Requirement already satisfied: wordcloud in /data/nas/workspace/envs/python3.6/site-packages (1.8.1)
Requirement already satisfied: numpy>=1.6.1 in /opt/conda/lib/python3.6/site-packages (from wordcloud) (1.19.4)
Requirement already satisfied: pillow in /opt/conda/lib/python3.6/site-packages (from wordcloud) (8.0.1)
Requirement already satisfied: matplotlib in /opt/conda/lib/python3.6/site-packages (from wordcloud) (3.3.3)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /opt/conda/lib/python3.6/site-packages (from matplotlib->wordcloud) (2.4.7)
Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib->wordcloud) (2.8.1)
Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib->wordcloud) (1.2.0)
Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.6/site-packages (from matplotlib->wordcloud) (0.10.0)
Requirement already satisfied: six in /opt/conda/lib/python3.6/site-packages (from cycler>=0.10->matplotlib->wordcloud) (1.15.0)
[33mWARNING: You are using pip version 20.3.3; however, version 21.0 is available.
You should consider upgrading via the '/opt/conda/bin/python -m pip install --upgrade pip' command.[0m
'''
WARNING: You are using pip version 20.3.3; however, version 21.0 is available.
You should consider upgrading via the '/opt/conda/bin/python -m pip install --upgrade pip' command.
'''
#/opt/conda/bin/python -m pip install--upgrade pip
"\nWARNING: You are using pip version 20.3.3; however, version 21.0 is available.\nYou should consider upgrading via the '/opt/conda/bin/python -m pip install --upgrade pip' command.\n"
下载本案例数据集2020_US_President_political_contributions
,
进行数据处理前,我们需要知道我们最终想要的数据是什么样的,因为我们是想分析候选人与捐赠人之间的关系,所以我们想要一张数据表中有捐赠人与候选人一一对应的关系,所以需要将目前的三张数据表进行一一关联,汇总到需要的数据。
CAND_ID
关联两个表由于候选人和委员会的联系表中无候选人姓名,只有候选人ID(CAND_ID
),所以需要通过CAND_ID
从候选人表中获取到候选人姓名,最终得到候选人与委员会联系表ccl
。
#导入相关的处理包
import pandas as pd
#读取候选人信息,由于原始数据没有表头,需要添加表头
candidates=pd.read_csv("weball20.txt",sep='|',names=['CAND_ID','CAND_NAME','CAND_ICI','PTY_CD','CAND_PTY_AFFILIATION','TTL_RECEIPTS',
'TRANS_FROM_AUTH','TTL_DISB','TRANS_TO_AUTH','COH_BOP','COH_COP','CAND_CONTRIB',
'CAND_LOANS','OTHER_LOANS','CAND_LOAN_REPAY','OTHER_LOAN_REPAY','DEBTS_OWED_BY',
'TTL_INDIV_CONTRIB','CAND_OFFICE_ST','CAND_OFFICE_DISTRICT','SPEC_ELECTION','PRIM_ELECTION','RUN_ELECTION'
,'GEN_ELECTION','GEN_ELECTION_PRECENT','OTHER_POL_CMTE_CONTRIB','POL_PTY_CONTRIB',
'CVG_END_DT','INDIV_REFUNDS','CMTE_REFUNDS'])
#读取候选人和委员会的联系信息
ccl=pd.read_csv("ccl.txt",sep='|',names=['CAND_ID','CAND_ELECTION_YR','FEC_ELECTION_YR','CMTE_ID','CMTE_TP','CMTE_DSGN','LINKAGE_ID'])
# 关联两个表数据
ccl = pd.merge(ccl,candidates)
# 提取出所需要的列
ccl = pd.DataFrame(ccl, columns=[ 'CMTE_ID','CAND_ID', 'CAND_NAME','CAND_PTY_AFFILIATION'])
数据字段说明:
# 查看目前ccl数据前10行
ccl.head(10)
CMTE_ID | CAND_ID | CAND_NAME | CAND_PTY_AFFILIATION | |
---|---|---|---|---|
0 | C00697789 | H0AL01055 | CARL, JERRY LEE, JR | REP |
1 | C00701557 | H0AL01063 | LAMBERT, DOUGLAS WESTLEY III | REP |
2 | C00701409 | H0AL01071 | PRINGLE, CHRISTOPHER PAUL | REP |
3 | C00703066 | H0AL01089 | HIGHTOWER, BILL | REP |
4 | C00708867 | H0AL01097 | AVERHART, JAMES | DEM |
5 | C00710947 | H0AL01105 | GARDNER, KIANI A | DEM |
6 | C00722512 | H0AL01121 | CASTORANI, JOHN | REP |
7 | C00725069 | H0AL01139 | COLLINS, FREDERICK G. RICK' | DEM |
8 | C00462143 | H0AL02087 | ROBY, MARTHA | REP |
9 | C00493783 | H0AL02087 | ROBY, MARTHA | REP |
CMTE_ID
关联两个表通过CMTE_ID
将目前处理好的候选人和委员会关系表与人捐款档案表进行关联,得到候选人与捐赠人一一对应联系表cil
。
# 读取个人捐赠数据,由于原始数据没有表头,需要添加表头
# 提示:读取本文件大概需要5-10s
itcont = pd.read_csv('itcont_2020_20200722_20200820.txt', sep='|',names=['CMTE_ID','AMNDT_IND','RPT_TP','TRANSACTION_PGI',
'IMAGE_NUM','TRANSACTION_TP','ENTITY_TP','NAME','CITY',
'STATE','ZIP_CODE','EMPLOYER','OCCUPATION','TRANSACTION_DT',
'TRANSACTION_AMT','OTHER_ID','TRAN_ID','FILE_NUM','MEMO_CD',
'MEMO_TEXT','SUB_ID'])
/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (10,15,16,18) have mixed types.Specify dtype option on import or set low_memory=False.
interactivity=interactivity, compiler=compiler, result=result)
# 将候选人与委员会关系表ccl和个人捐赠数据表itcont合并,通过 CMTE_ID
c_itcont = pd.merge(ccl,itcont)
# 提取需要的数据列
c_itcont = pd.DataFrame(c_itcont, columns=[ 'CAND_NAME','NAME', 'STATE','EMPLOYER','OCCUPATION',
'TRANSACTION_AMT', 'TRANSACTION_DT','CAND_PTY_AFFILIATION'])
数据说明
# 查看目前数据前10行
c_itcont.head(10)
CAND_NAME | NAME | STATE | EMPLOYER | OCCUPATION | TRANSACTION_AMT | TRANSACTION_DT | CAND_PTY_AFFILIATION | |
---|---|---|---|---|---|---|---|---|
0 | MORGAN, JOSEPH DAVID | MARTIN, WILLIAM II | AZ | RETIRED | RETIRED | 100 | 7242020 | REP |
1 | MORGAN, JOSEPH DAVID | RODRIGUEZ, GERARDO | AZ | VA HOSPITAL | LAB TECH | 40 | 7242020 | REP |
2 | MORGAN, JOSEPH DAVID | RODRIGUEZ, GERARDO | AZ | VA HOSPITAL | LAB TECH | 40 | 7312020 | REP |
3 | WOOD, DANIEL | HOPKINS, RICHARD | AZ | POWERS-LEAVITT | INSURANCE AGENT | 300 | 8102020 | REP |
4 | WOOD, DANIEL | PENDLETON, DIANE | AZ | UNEMPLOYED | NaN | 500 | 8072020 | REP |
5 | WOOD, DANIEL | PREVATT, WILLIAM | AZ | SELF-EMPLOYED | DVM | 500 | 7312020 | REP |
6 | WOOD, DANIEL | HARDING, DOUG | AZ | MICROSURE | OPERATIONS MANAGER | 2800 | 8102020 | REP |
7 | WOOD, DANIEL | HARDING, MARI | AZ | NaN | NaN | 1400 | 8152020 | REP |
8 | WOOD, DANIEL | HEDGER, CYNTHIA | TX | NaN | NaN | 200 | 7312020 | REP |
9 | HUANG, PEGGY | HUANG - PERSONAL FUNDS, PEGGY | CA | OFFICE OF THE ATTORNEY GENERAL | DEPUTY ATTORNEY GENERAL | 2600 | 7252020 | REP |
进过数据处理部分,我们获得了可用的数据集,现在我们可以利用调用shape
属性查看数据的规模,调用info
函数查看数据信息,调用describe
函数查看数据分布。
# 查看数据规模 多少行 多少列
c_itcont.shape
(756205, 8)
# 查看整体数据信息,包括每个字段的名称、非空数量、字段的数据类型
c_itcont.info()
Int64Index: 756205 entries, 0 to 756204
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 CAND_NAME 756205 non-null object
1 NAME 756205 non-null object
2 STATE 756160 non-null object
3 EMPLOYER 737413 non-null object
4 OCCUPATION 741294 non-null object
5 TRANSACTION_AMT 756205 non-null int64
6 TRANSACTION_DT 756205 non-null int64
7 CAND_PTY_AFFILIATION 756205 non-null object
dtypes: int64(2), object(6)
memory usage: 51.9+ MB
通过上面的探索我们知道目前数据集的一些基本情况,目前数据总共有756205行,8列,总占用内存51.9+MB,STATE
、EMPLOYER
、OCCUPATION
有缺失值,另外日期列目前为int64类型,需要进行转换为str类型。
#空值处理,统一填充 NOT PROVIDED
c_itcont['STATE'].fillna('NOT PROVIDED',inplace=True)
c_itcont['EMPLOYER'].fillna('NOT PROVIDED',inplace=True)
c_itcont['OCCUPATION'].fillna('NOT PROVIDED',inplace=True)
# 对日期TRANSACTION_DT列进行处理
c_itcont['TRANSACTION_DT'] = c_itcont['TRANSACTION_DT'] .astype(str)
# 将日期格式改为年月日 7242020
c_itcont['TRANSACTION_DT'] = [i[3:7]+i[0]+i[1:3] for i in c_itcont['TRANSACTION_DT'] ]
# 再次查看数据信息
c_itcont.info()
Int64Index: 756205 entries, 0 to 756204
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 CAND_NAME 756205 non-null object
1 NAME 756205 non-null object
2 STATE 756205 non-null object
3 EMPLOYER 756205 non-null object
4 OCCUPATION 756205 non-null object
5 TRANSACTION_AMT 756205 non-null int64
6 TRANSACTION_DT 756205 non-null object
7 CAND_PTY_AFFILIATION 756205 non-null object
dtypes: int64(1), object(7)
memory usage: 51.9+ MB
# 查看数据前3行
c_itcont.head(3)
CAND_NAME | NAME | STATE | EMPLOYER | OCCUPATION | TRANSACTION_AMT | TRANSACTION_DT | CAND_PTY_AFFILIATION | |
---|---|---|---|---|---|---|---|---|
0 | MORGAN, JOSEPH DAVID | MARTIN, WILLIAM II | AZ | RETIRED | RETIRED | 100 | 2020724 | REP |
1 | MORGAN, JOSEPH DAVID | RODRIGUEZ, GERARDO | AZ | VA HOSPITAL | LAB TECH | 40 | 2020724 | REP |
2 | MORGAN, JOSEPH DAVID | RODRIGUEZ, GERARDO | AZ | VA HOSPITAL | LAB TECH | 40 | 2020731 | REP |
# 查看数据表中数据类型的列的数据分布情况
c_itcont.describe()
TRANSACTION_AMT | |
---|---|
count | 7.562050e+05 |
mean | 1.504307e+02 |
std | 2.320452e+03 |
min | -5.600000e+03 |
25% | 2.000000e+01 |
50% | 3.500000e+01 |
75% | 1.000000e+02 |
max | 1.500000e+06 |
# 查看单列的数据发布情况
c_itcont['CAND_NAME'].describe()
count 756205
unique 312
top BIDEN, JOSEPH R JR
freq 507816
Name: CAND_NAME, dtype: object
# 计算每个党派的所获得的捐款总额,然后排序,取前十位
c_itcont.groupby("CAND_PTY_AFFILIATION").sum().sort_values("TRANSACTION_AMT",ascending=False).head(10)
TRANSACTION_AMT | |
---|---|
CAND_PTY_AFFILIATION | |
DEM | 75961730 |
REP | 37170653 |
IND | 328802 |
LIB | 169202 |
DFL | 76825 |
GRE | 18607 |
NON | 11256 |
UNK | 10195 |
CON | 4117 |
BDY | 3250 |
# 计算每个总统候选人所获得的捐款总额,然后排序,取前十位
c_itcont.groupby("CAND_NAME").sum().sort_values("TRANSACTION_AMT",ascending=False).head(10)
TRANSACTION_AMT | |
---|---|
CAND_NAME | |
BIDEN, JOSEPH R JR | 68111142 |
TRUMP, DONALD J. | 16594982 |
SULLIVAN, DAN | 9912465 |
JACOBS, CHRISTOPHER L. | 6939209 |
BLOOMBERG, MICHAEL R. | 3451916 |
MARKEY, EDWARD J. SEN. | 606832 |
SHAHEEN, JEANNE | 505446 |
KENNEDY, JOSEPH P III | 467738 |
CORNYN, JOHN SEN | 345959 |
FIGLESTHALER, WILLIAM MATTHEW MD | 258221 |
获得捐赠最多的党派有DEM(民主党)
、REP(共和党)
,分别对应BIDEN, JOSEPH R JR(拜登)
和TRUMP, DONALD J.(特朗普)
,从我们目前分析的2020.7.22-2020.8.20这一个月的数据来看,在选民的捐赠数据中拜登代表的民主党完胜特朗普代表的共和党,由于完整数据量过大,所以没有对所有数据进行汇总分析,因此也不能确定11月大选公布结果就一定是拜登当选
# 查看不同职业的人捐款的总额,然后排序,取前十位
c_itcont.groupby('OCCUPATION').sum().sort_values("TRANSACTION_AMT",ascending=False).head(10)
TRANSACTION_AMT | |
---|---|
OCCUPATION | |
NOT EMPLOYED | 24436214 |
RETIRED | 18669950 |
NOT PROVIDED | 5089355 |
ATTORNEY | 4443569 |
FOUNDER | 3519109 |
PHYSICIAN | 3295595 |
CONSULTANT | 1647033 |
LAWYER | 1565976 |
PROFESSOR | 1481260 |
EXECUTIVE | 1467865 |
# 查看每个职业捐款人的数量
c_itcont['OCCUPATION'].value_counts().head(10)
NOT EMPLOYED 224109
RETIRED 151834
ATTORNEY 19666
NOT PROVIDED 14912
PHYSICIAN 14033
CONSULTANT 8333
PROFESSOR 8022
TEACHER 8013
ENGINEER 7922
SALES 6435
Name: OCCUPATION, dtype: int64
从捐款人的职业这个角度分析,我们会发现NOT EMPLOYED(自由职业)
的总捐赠额是最多,通过查看每个职业捐赠的人数来看,我们就会发现是因为NOT EMPLOYED(自由职业)
人数多的原因,另外退休人员捐款人数也特别多,所以捐款总数对应的也多,其他比如像:律师、创始人、医生、顾问、教授、主管这些高薪人才虽然捐款总人数少,但是捐款总金额也占据了很大比例。
# 每个州获捐款的总额,然后排序,取前五位
c_itcont.groupby('STATE').sum().sort_values("TRANSACTION_AMT",ascending=False).head(5)
TRANSACTION_AMT | |
---|---|
STATE | |
CA | 19999115 |
NY | 11468537 |
FL | 8128789 |
TX | 8101871 |
MA | 5187957 |
# 查看每个州捐款人的数量
c_itcont['STATE'].value_counts().head(5)
CA 127895
TX 54457
FL 54343
NY 49453
MA 29314
Name: STATE, dtype: int64
最后查看每个州的捐款总金额,我们会发现CA(加利福利亚)
、NY(纽约)
、FL(弗罗里达)
这几个州的捐款是最多的,在捐款人数上也是在Top端,另一方面也凸显出这些州的经济水平发达。
大家也可以通过数据查看下上面列举的高端职业在各州的分布情况,进行进一步的分析探索。
首先导入相关Python库
# 导入matplotlib中的pyplot
import matplotlib.pyplot as plt
# 为了使matplotlib图形能够内联显示
%matplotlib inline
# 导入词云库
from wordcloud import WordCloud,ImageColorGenerator
# 各州总捐款数可视化
st_amt = c_itcont.groupby('STATE').sum().sort_values("TRANSACTION_AMT",ascending=False)[:10]
st_amt=pd.DataFrame(st_amt, columns=['TRANSACTION_AMT'])
st_amt.plot(kind='bar')
# 各州捐款总人数可视化,取前10个州的数据
st_amt = c_itcont.groupby('STATE').size().sort_values(ascending=False).head(10)
st_amt.plot(kind='bar')
# 从所有数据中取出支持拜登的数据
biden = c_itcont[c_itcont['CAND_NAME']=='BIDEN, JOSEPH R JR']
# 统计各州对拜登的捐款总数
biden_state = biden.groupby('STATE').sum().sort_values("TRANSACTION_AMT", ascending=False).head(10)
# 饼图可视化各州捐款数据占比
biden_state.plot.pie(figsize=(10, 10),autopct='%0.2f%%',subplots=True)
array([], dtype=object)
通过数据分析中获得捐赠总额前三的候选人统计中可以看出拜登在2020.7.22-2020.8.20这期间获得捐赠的总额是最多的,所以我们以拜登为原模型,制作词云图。
# 首先下载图片模型,这里提供的是已经处理好的图片,有兴趣的选手可以自己写代码进行图片处理
# 处理结果:需要将人图像和背景颜色分离,并纯色填充,词云才会只显示在人图像区域
# 拜登原图:https://img.alicdn.com/tfs/TB1pUcwmZVl614jSZKPXXaGjpXa-689-390.jpg
# 拜登处理后图片:https://img.alicdn.com/tfs/TB10Jx4pBBh1e4jSZFhXXcC9VXa-689-390.jpg
# 特朗普原图:https://img.alicdn.com/tfs/TB1D0l4pBBh1e4jSZFhXXcC9VXa-298-169.jpg
# 特朗普处理后图片:https://img.alicdn.com/tfs/TB1BoowmZVl614jSZKPXXaGjpXa-298-169.jpg
# 这里我们先下载处理后的图片
!wget https://img.alicdn.com/tfs/TB10Jx4pBBh1e4jSZFhXXcC9VXa-689-390.jpg
--2021-01-31 11:39:47-- https://img.alicdn.com/tfs/TB10Jx4pBBh1e4jSZFhXXcC9VXa-689-390.jpg
Resolving img.alicdn.com (img.alicdn.com)... 180.97.251.252, 180.97.251.251
Connecting to img.alicdn.com (img.alicdn.com)|180.97.251.252|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 4236 (4.1K) [image/jpeg]
Saving to: ‘TB10Jx4pBBh1e4jSZFhXXcC9VXa-689-390.jpg’
100%[======================================>] 4,236 --.-K/s in 0s
2021-01-31 11:39:47 (56.4 MB/s) - ‘TB10Jx4pBBh1e4jSZFhXXcC9VXa-689-390.jpg’ saved [4236/4236]
# 由于下载图片文件名过长,我们对文件名进行重命名
import os
os.rename('TB10Jx4pBBh1e4jSZFhXXcC9VXa-689-390.jpg', 'biden.jpg')
# 在4.2 热门候选人拜登在各州的获得的捐赠占比 中我们已经取出了所有支持拜登的人的数据,存在变量:biden中
# 将所有捐赠者姓名连接成一个字符串
data = ' '.join(biden["NAME"].tolist())
# 读取图片文件
bg = plt.imread("biden.jpg")
# 生成
wc = WordCloud(# FFFAE3
background_color="white", # 设置背景为白色,默认为黑色
width=890, # 设置图片的宽度
height=600, # 设置图片的高度
mask=bg, # 画布
margin=10, # 设置图片的边缘
max_font_size=100, # 显示的最大的字体大小
random_state=20, # 为每个单词返回一个PIL颜色
).generate_from_text(data)
# 图片背景
bg_color = ImageColorGenerator(bg)
# 开始画图
plt.imshow(wc.recolor(color_func=bg_color))
# 为云图去掉坐标轴
plt.axis("off")
# 画云图,显示
# 保存云图
wc.to_file("biden_wordcloud.png")
# 按州总捐款热力地图
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
# 各州总捐款数可视化
st_amt = c_itcont.groupby('STATE').sum().sort_values("TRANSACTION_AMT",ascending=False)[:10]
st_amt=pd.DataFrame(st_amt, columns=['TRANSACTION_AMT'])
st_amt.head(10)
TRANSACTION_AMT | |
---|---|
STATE | |
CA | 19999115 |
NY | 11468537 |
FL | 8128789 |
TX | 8101871 |
MA | 5187957 |
WA | 4455361 |
IL | 3788997 |
VA | 3659134 |
PA | 3501967 |
MD | 2923730 |
sns.heatmap(st_amt,cmap='Reds')
#cmap颜色
# 收到捐赠额最多的两位候选人的总捐赠额变化趋势
# 将候选人与委员会关系表ccl和个人捐赠数据表itcont合并,通过 CMTE_ID
c_itcont2 = pd.merge(ccl,itcont)
# 提取需要的数据列
c_itcont2 = pd.DataFrame(c_itcont2, columns=[ 'CAND_NAME','TRANSACTION_AMT', 'TRANSACTION_DT'])
#c_itcont2.head(10)
#c_itcont2.shape
#c_itcont2.info()
# 对日期TRANSACTION_DT列进行处理
c_itcont2['TRANSACTION_DT'] = c_itcont2['TRANSACTION_DT'] .astype(str)
# 将日期格式改为年月日 7242020
c_itcont2['TRANSACTION_DT'] = [i[3:7]+i[0]+i[1:3] for i in c_itcont2['TRANSACTION_DT'] ]
#c_itcont2.info()
c_itcont2.head(5)
CAND_NAME | TRANSACTION_AMT | TRANSACTION_DT | |
---|---|---|---|
0 | MORGAN, JOSEPH DAVID | 100 | 2020724 |
1 | MORGAN, JOSEPH DAVID | 40 | 2020724 |
2 | MORGAN, JOSEPH DAVID | 40 | 2020731 |
3 | WOOD, DANIEL | 300 | 2020810 |
4 | WOOD, DANIEL | 500 | 2020807 |
#获取拜登每天获得的捐款数
biden=c_itcont2[c_itcont2['CAND_NAME']=='BIDEN, JOSEPH R JR']
#biden.head(10)
biden=biden.groupby('TRANSACTION_DT').sum().sort_values("TRANSACTION_AMT", ascending=False)
biden=biden.sort_values('TRANSACTION_DT', ascending=True)
biden=biden.rename(columns={'TRANSACTION_AMT':'BIDEN_TRANSACTION_AMT'})
biden.head(10)
BIDEN_TRANSACTION_AMT | |
---|---|
TRANSACTION_DT | |
2020722 | 888622 |
2020723 | 963605 |
2020724 | 1172065 |
2020725 | 919555 |
2020726 | 1108782 |
2020727 | 1097421 |
2020728 | 1475198 |
2020729 | 1275603 |
2020730 | 1063531 |
2020731 | 2341590 |
#获取特朗普每天获得的捐款数
trump=c_itcont2[c_itcont2['CAND_NAME']=='TRUMP, DONALD J.']
#trump.head(10)
trump=trump.groupby('TRANSACTION_DT').sum().sort_values('TRANSACTION_AMT', ascending=False)
trump=trump.sort_values('TRANSACTION_DT', ascending=True)
trump=trump.rename(columns={'TRANSACTION_AMT':'TRUMP_TRANSACTION_AMT'})
#trump.columns
trump.head(10)
#TRUMP, DONALD J.
TRUMP_TRANSACTION_AMT | |
---|---|
TRANSACTION_DT | |
2020722 | 330603 |
2020723 | 224317 |
2020724 | 273289 |
2020725 | 207652 |
2020726 | 493181 |
2020727 | 515034 |
2020728 | 455095 |
2020729 | 716851 |
2020730 | 1130337 |
2020731 | 1992496 |
#将得到的拜登和特朗普的每天获得的捐款数进行整合
c_itcont4 = pd.concat([biden,trump],axis=1)
c_itcont4.head(10)
BIDEN_TRANSACTION_AMT | TRUMP_TRANSACTION_AMT | |
---|---|---|
TRANSACTION_DT | ||
2020722 | 888622 | 330603 |
2020723 | 963605 | 224317 |
2020724 | 1172065 | 273289 |
2020725 | 919555 | 207652 |
2020726 | 1108782 | 493181 |
2020727 | 1097421 | 515034 |
2020728 | 1475198 | 455095 |
2020729 | 1275603 | 716851 |
2020730 | 1063531 | 1130337 |
2020731 | 2341590 | 1992496 |
c_itcont4.plot(y={'TRUMP_TRANSACTION_AMT','BIDEN_TRANSACTION_AMT'},grid=True, rot=45)
1.赛前准备:
2.数据处理:
3.数据分析:
shape
属性查看数据的规模,调用info
函数查看数据信息,调用describe
函数查看数据分布等等。4.数据可视化: