机器学习-数据科学库 13 政治献金数据案例

数据准备

参考：https://wwa.lanzoul.com/io4Lo00kvkob

字段含义：

• cand_nm ：候选人姓名
• contbr_nm ： 捐赠人姓名
• contbr_st ：捐赠人所在州
• contbr_employer ： 捐赠人所在公司
• contbr_occupation ： 捐赠人职业
• contb_receipt_amt ：捐赠数额（美元）
• contb_receipt_dt ： 捐款的日期

import pandas as pd

# 加载数据，查看数据的基本信息
df = pd.read_csv('./usa_election.txt', on_bad_lines='skip')

# 截取指定列，其他数据舍弃
df = df[['cand_nm', 'contbr_nm', 'contbr_st', 'contbr_employer', 'contbr_occupation', 'contb_receipt_amt',
         'contb_receipt_dt']]
print(df.head())
print(df.info())

              cand_nm           contbr_nm  ... contb_receipt_amt contb_receipt_dt
0  Bachmann, Michelle     HARVEY, WILLIAM  ...             250.0        20-JUN-11
1  Bachmann, Michelle     HARVEY, WILLIAM  ...              50.0        23-JUN-11
2  Bachmann, Michelle       SMITH, LANIER  ...             250.0        05-JUL-11
3  Bachmann, Michelle    BLEVINS, DARONDA  ...             250.0        01-AUG-11
4  Bachmann, Michelle  WARDENBURG, HAROLD  ...             300.0        20-JUN-11
[5 rows x 7 columns]

RangeIndex: 536041 entries, 0 to 536040
Data columns (total 7 columns):
 #   Column             Non-Null Count   Dtype  
---  ------             --------------   -----  
 0   cand_nm            536041 non-null  object 
 1   contbr_nm          536041 non-null  object 
 2   contbr_st          536040 non-null  object 
 3   contbr_employer    525088 non-null  object 
 4   contbr_occupation  530520 non-null  object 
 5   contb_receipt_amt  536041 non-null  float64
 6   contb_receipt_dt   536041 non-null  object 
dtypes: float64(1), object(6)
memory usage: 28.6+ MB
None

空值处理

# 使用 NOT PROVIDE 对空值进行填充
df.fillna(value='NOT PROVIDE', inplace=True)

# 重新查看列是否有空值
print(df.info())

# 异常值处理。将捐款金额<=0的数据删除
df = df.loc[~(df['contb_receipt_amt'] <= 0)]

# 等效于
# df.drop(labels=df.loc[df['contb_receipt_amt'] <= 0].index, axis=0, inplace=True)
print(df.info())

输出：

RangeIndex: 536041 entries, 0 to 536040
Data columns (total 7 columns):
 #   Column             Non-Null Count   Dtype  
---  ------             --------------   -----  
 0   cand_nm            536041 non-null  object 
 1   contbr_nm          536041 non-null  object 
 2   contbr_st          536041 non-null  object 
 3   contbr_employer    536041 non-null  object 
 4   contbr_occupation  536041 non-null  object 
 5   contb_receipt_amt  536041 non-null  float64
 6   contb_receipt_dt   536041 non-null  object 
dtypes: float64(1), object(6)
memory usage: 28.6+ MB
None

Int64Index: 530314 entries, 0 to 536040
Data columns (total 7 columns):
 #   Column             Non-Null Count   Dtype  
---  ------             --------------   -----  
 0   cand_nm            530314 non-null  object 
 1   contbr_nm          530314 non-null  object 
 2   contbr_st          530314 non-null  object 
 3   contbr_employer    530314 non-null  object 
 4   contbr_occupation  530314 non-null  object 
 5   contb_receipt_amt  530314 non-null  float64
 6   contb_receipt_dt   530314 non-null  object 
dtypes: float64(1), object(6)
memory usage: 32.4+ MB
None

数据分析

# 不同候选人党派对应表
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'
}

# 查看共有多少个不同的候选人,返回所有值
num1 = df['cand_nm'].unique()
print(num1)
print("*" * 100)

# 查看候选人的个数，返回所有值的个数
num2 = df['cand_nm'].nunique()
print(num2)
print("*" * 100)

# 利用映射为每个候选人添加党派信息
df['party'] = df['cand_nm'].map(parties)
print(df.head())
print("*" * 100)

# 查看party这一列中有哪些不同的元素
print(df['party'].unique())
print("*" * 100)

# 统计party列中各个元素出现次数
print(df['party'].value_counts())
print("*" * 100)

# 查看各个党派收到的政治献金总数contb_receipt_amt
df_sum = df.groupby(by='party')['contb_receipt_amt'].sum()
print(df_sum)
print("*" * 100)

# 查看具体每天各个党派收到的政治献金总数contb_receipt_amt
df_sum2 = df.groupby(by=['contb_receipt_dt', 'party'])['contb_receipt_amt'].sum()
print(df_sum2)
print("*" * 100)

# 将表中日期格式转换为'yyyy-mm-dd'
months = {"JAN": 1, "FEB": 2, "MAR": 3, "APR": 4, "MAY": 5, "JUN": 6,
          "JUL": 7, "AUG": 8, "SEP": 9, "OCT": 10, "NOV": 11, "DEC": 12}


def transform_date(d):
    day, month, year = d.split("-")
    month = months[month]
    return '20' + year + '-' + str(month) + '-' + day


df['contb_receipt_dt'] = df['contb_receipt_dt'].map(transform_date)
print(df.head())
print("*" * 100)

# 查看老兵(捐献者职业)DISABLED VETERAN主要支持谁
disabled_veteran_df = df.loc[df['contbr_occupation'] == 'DISABLED VETERAN']
disabled_veteran_support = disabled_veteran_df.groupby(by='cand_nm')['contb_receipt_amt'].sum()
print(disabled_veteran_support)
print("*" * 100)

输出：

['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']
****************************************************************************************************
13
****************************************************************************************************
              cand_nm           contbr_nm  ... contb_receipt_dt       party
0  Bachmann, Michelle     HARVEY, WILLIAM  ...        20-JUN-11  Republican
1  Bachmann, Michelle     HARVEY, WILLIAM  ...        23-JUN-11  Republican
2  Bachmann, Michelle       SMITH, LANIER  ...        05-JUL-11  Republican
3  Bachmann, Michelle    BLEVINS, DARONDA  ...        01-AUG-11  Republican
4  Bachmann, Michelle  WARDENBURG, HAROLD  ...        20-JUN-11  Republican

[5 rows x 8 columns]
****************************************************************************************************
['Republican' 'Democrat' 'Reform' 'Libertarian']
****************************************************************************************************
Democrat       289999
Republican     234300
Reform           5313
Libertarian       702
Name: party, dtype: int64
****************************************************************************************************
party
Democrat       8.259441e+07
Libertarian    4.132769e+05
Reform         3.429658e+05
Republican     1.251181e+08
Name: contb_receipt_amt, dtype: float64
****************************************************************************************************
contb_receipt_dt  party      
01-APR-11         Reform             50.00
                  Republican      12635.00
01-AUG-11         Democrat       182198.00
                  Libertarian      1000.00
                  Reform           1847.00
                                   ...    
31-MAY-11         Republican     313839.80
31-OCT-11         Democrat       216971.87
                  Libertarian      4250.00
                  Reform           3205.00
                  Republican     751542.36
Name: contb_receipt_amt, Length: 1183, dtype: float64
****************************************************************************************************
              cand_nm           contbr_nm  ... contb_receipt_dt       party
0  Bachmann, Michelle     HARVEY, WILLIAM  ...        2011-6-20  Republican
1  Bachmann, Michelle     HARVEY, WILLIAM  ...        2011-6-23  Republican
2  Bachmann, Michelle       SMITH, LANIER  ...        2011-7-05  Republican
3  Bachmann, Michelle    BLEVINS, DARONDA  ...        2011-8-01  Republican
4  Bachmann, Michelle  WARDENBURG, HAROLD  ...        2011-6-20  Republican

[5 rows x 8 columns]
****************************************************************************************************
cand_nm
Cain, Herman       300.00
Obama, Barack     4205.00
Paul, Ron         2425.49
Santorum, Rick     250.00
Name: contb_receipt_amt, dtype: float64
****************************************************************************************************