#US 大选的数据分析
import numpy as npimport pandas as pdfrom pandas import Series,DataFramemonths = { 'JAN' : 1, 'FEB' : 2, 'MAR' : 3, 'APR' : 4, 'MAY' : 5, 'JUN' : 6, 'JUL' : 7, 'AUG' : 8, 'SEP' : 9, 'OCT': 10, 'NOV': 11, 'DEC' : 12}of_interest = ['Obama, Barack', 'Romney, Mitt', 'Santorum, Rick', 'Paul, Ron', 'Gingrich, Newt']parties = { 'Bachmann, Michelle': 'Republican', 'Romney, Mitt': 'Republican', 'Obama, Barack': 'Democrat', "Roemer, Charles E. 'Buddy' III": 'Reform', 'Pawlenty, Timothy': 'Republican', 'Johnson, Gary Earl': 'Libertarian', 'Paul, Ron': 'Republican', 'Santorum, Rick': 'Republican', 'Cain, Herman': 'Republican', 'Gingrich, Newt': 'Republican', 'McCotter, Thaddeus G': 'Republican', 'Huntsman, Jon': 'Republican', 'Perry, Rick': 'Republican' }data = pd.read_csv('data/usa_election.txt') #读文件data.head() #看前5行data.shape #文件样式data.dtypes #数据类型1 # 使用map函数+字典，新建一列各个候选人所在党派partydata['party']=data['cand_nm'].map(parties) #parties {}data.head(3) #看前3行数据2 #使用np.unique()函数查看colums：party这一列中有哪些元素 有哪些党派参加竞选data['party'].unique()3 # 使用value_counts()函数，统计party列中各个元素出现次数 各党派出现的次数data['party'].value_counts()4 # 各个党派收到政治献金总数data.groupby('party')['contb_receipt_amt'].sum() #分组 聚合5 #查看具体每天各个党派收到的政治献金总数contb_receipt_amt 使用groupby([多个分组参数])data.groupby(['contb_receipt_dt','party'])['contb_receipt_amt'].sum() 6 # 20-JUN-11 转时间格式def transform_date(date): day,month,year = date.split('-') month = months[month] #dict['key'] return '20'+year + '-' + str(month) + '-' + daydata['contb_receipt_dt'] = data['contb_receipt_dt'].map(transform_date) #applydata.head() 7 #查看是否转化成功 data['contb_eceipt_dt'] 8 #查看老兵最支持谁? Series索引 现将老兵行数据取出来 data['contbr_occupation'] == 'DISABLED VETERAN' #返回布尔值
old_bing = data.loc[data['contbr_occupation'] == 'DISABLED VETERAN']old_bingr3 = old_bing.groupby('cand_nm',axis=0)['contb_receipt_amt'].sum() #竞选者分组r39 #找出各个候选人的捐赠者中，捐赠金额最大的人的职业以及捐献额 通过query("查询条件来查找捐献人职业")?
data.query("contb_receipt_amt == %s"%(data['contb_receipt_amt'].max()))