pandas分组聚合案例

美国2012年总统候选人政治献金数据分析

导入包

import numpy as np

import pandas as pd

from pandas import Series,DataFrame

方便操作，将月份和参选人以及所在政党进行定义

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}

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'

 }

df = pd.read_csv('./data/usa_election.txt')

df.head()

C:\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:2728: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.

  interactivity=interactivity, compiler=compiler, result=result)

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {

    vertical-align: top;

}

.dataframe thead th {

    text-align: right;

}

	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
0	C00410118	P20002978	Bachmann, Michelle	HARVEY, WILLIAM	MOBILE	AL	3.6601e+08	RETIRED	RETIRED	250.0	20-JUN-11	NaN	NaN	NaN	SA17A	736166
1	C00410118	P20002978	Bachmann, Michelle	HARVEY, WILLIAM	MOBILE	AL	3.6601e+08	RETIRED	RETIRED	50.0	23-JUN-11	NaN	NaN	NaN	SA17A	736166
2	C00410118	P20002978	Bachmann, Michelle	SMITH, LANIER	LANETT	AL	3.68633e+08	INFORMATION REQUESTED	INFORMATION REQUESTED	250.0	05-JUL-11	NaN	NaN	NaN	SA17A	749073
3	C00410118	P20002978	Bachmann, Michelle	BLEVINS, DARONDA	PIGGOTT	AR	7.24548e+08	NONE	RETIRED	250.0	01-AUG-11	NaN	NaN	NaN	SA17A	749073
4	C00410118	P20002978	Bachmann, Michelle	WARDENBURG, HAROLD	HOT SPRINGS NATION	AR	7.19016e+08	NONE	RETIRED	300.0	20-JUN-11	NaN	NaN	NaN	SA17A	736166

# 新建一列各个候选人所在党派party

df['party'] = df['cand_nm'].map(parties)

df.head()

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {

    vertical-align: top;

}

.dataframe thead th {

    text-align: right;

}

	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	party
0	C00410118	P20002978	Bachmann, Michelle	HARVEY, WILLIAM	MOBILE	AL	3.6601e+08	RETIRED	RETIRED	250.0	20-JUN-11	NaN	NaN	NaN	SA17A	736166	Republican
1	C00410118	P20002978	Bachmann, Michelle	HARVEY, WILLIAM	MOBILE	AL	3.6601e+08	RETIRED	RETIRED	50.0	23-JUN-11	NaN	NaN	NaN	SA17A	736166	Republican
2	C00410118	P20002978	Bachmann, Michelle	SMITH, LANIER	LANETT	AL	3.68633e+08	INFORMATION REQUESTED	INFORMATION REQUESTED	250.0	05-JUL-11	NaN	NaN	NaN	SA17A	749073	Republican
3	C00410118	P20002978	Bachmann, Michelle	BLEVINS, DARONDA	PIGGOTT	AR	7.24548e+08	NONE	RETIRED	250.0	01-AUG-11	NaN	NaN	NaN	SA17A	749073	Republican
4	C00410118	P20002978	Bachmann, Michelle	WARDENBURG, HAROLD	HOT SPRINGS NATION	AR	7.19016e+08	NONE	RETIRED	300.0	20-JUN-11	NaN	NaN	NaN	SA17A	736166	Republican

# party这一列中有哪些元素

df['party'].unique()

array(['Republican', 'Democrat', 'Reform', 'Libertarian'], dtype=object)

# 统计party列中各个元素出现次数

df['party'].value_counts()

Democrat       292400

Republican     237575

Reform           5364

Libertarian       702

Name: party, dtype: int64

# 查看各个党派收到的政治献金总数contb_receipt_amt

df.groupby(by='party')['contb_receipt_amt'].sum()

party

Democrat       8.105758e+07

Libertarian    4.132769e+05

Reform         3.390338e+05

Republican     1.192255e+08

Name: contb_receipt_amt, dtype: float64

# 查看每天各个党派收到的政治献金总数contb_receipt_amt

df.groupby(by=['contb_receipt_dt','party'])['contb_receipt_amt'].sum()

contb_receipt_dt  party

01-APR-11         Reform              50.00

                  Republican       12635.00

01-AUG-11         Democrat        175281.00

                  Libertarian       1000.00

                  Reform            1847.00

                  Republican      234598.46

01-DEC-11         Democrat        651532.82

                  Libertarian        725.00

                  Reform             875.00

                  Republican      486405.96

01-FEB-11         Republican         250.00

01-JAN-11         Republican        8600.00

01-JAN-12         Democrat         58098.80

                  Reform             515.00

                  Republican       75704.72

01-JUL-11         Democrat        165961.00

                  Libertarian       2000.00

                  Reform             100.00

                  Republican      115848.72

01-JUN-11         Democrat        145459.00

                  Libertarian        500.00

                  Reform              50.00

                  Republican      433109.20

01-MAR-11         Republican        1000.00

01-MAY-11         Democrat         82644.00

                  Reform             480.00

                  Republican       28663.87

01-NOV-11         Democrat        122529.87

                  Libertarian       3000.00

                  Reform            1792.00

                                    ...

30-OCT-11         Reform            3910.00

                  Republican       43913.16

30-SEP-11         Democrat       3373517.24

                  Libertarian        550.00

                  Reform            2050.00

                  Republican     4886331.76

31-AUG-11         Democrat        374387.44

                  Libertarian      10750.00

                  Reform             450.00

                  Republican     1017735.02

31-DEC-11         Democrat       3553072.57

                  Reform             695.00

                  Republican     1094376.72

31-JAN-11         Republican        6000.00

31-JAN-12         Democrat       1418410.31

                  Reform             150.00

                  Republican      869890.41

31-JUL-11         Democrat         20305.00

                  Reform             966.00

                  Republican       12781.02

31-MAR-11         Reform             200.00

                  Republican       62475.00

31-MAY-11         Democrat        351705.66

                  Libertarian        250.00

                  Reform             100.00

                  Republican      301339.80

31-OCT-11         Democrat        204996.87

                  Libertarian       4250.00

                  Reform            3105.00

                  Republican      734601.83

Name: contb_receipt_amt, Length: 1183, dtype: float64

# 将表中日期格式转换为'yyyy-mm-dd'  day-m-y

def transformDate(d):

    day,month,year = d.split('-')

    month = months[month]

    return '20'+year+'-'+str(month)+'-'+day

df['contb_receipt_dt'] = df['contb_receipt_dt'].apply(transformDate)

df.head()

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {

    vertical-align: top;

}

.dataframe thead th {

    text-align: right;

}

	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	party
0	C00410118	P20002978	Bachmann, Michelle	HARVEY, WILLIAM	MOBILE	AL	3.6601e+08	RETIRED	RETIRED	250.0	2011-6-20	NaN	NaN	NaN	SA17A	736166	Republican
1	C00410118	P20002978	Bachmann, Michelle	HARVEY, WILLIAM	MOBILE	AL	3.6601e+08	RETIRED	RETIRED	50.0	2011-6-23	NaN	NaN	NaN	SA17A	736166	Republican
2	C00410118	P20002978	Bachmann, Michelle	SMITH, LANIER	LANETT	AL	3.68633e+08	INFORMATION REQUESTED	INFORMATION REQUESTED	250.0	2011-7-05	NaN	NaN	NaN	SA17A	749073	Republican
3	C00410118	P20002978	Bachmann, Michelle	BLEVINS, DARONDA	PIGGOTT	AR	7.24548e+08	NONE	RETIRED	250.0	2011-8-01	NaN	NaN	NaN	SA17A	749073	Republican
4	C00410118	P20002978	Bachmann, Michelle	WARDENBURG, HAROLD	HOT SPRINGS NATION	AR	7.19016e+08	NONE	RETIRED	300.0	2011-6-20	NaN	NaN	NaN	SA17A	736166	Republican

# 查看老兵(捐献者职业)主要支持谁  ：查看老兵们捐赠给谁的钱最多

# 1.将老兵对应的行数据取出

df['contbr_occupation'] == 'DISABLED VETERAN'

old_bing = df.loc[df['contbr_occupation'] == 'DISABLED VETERAN']

# 2.根据候选人分组

old_bing.groupby(by='cand_nm')['contb_receipt_amt'].sum()

cand_nm

Cain, Herman       300.00

Obama, Barack     4205.00

Paul, Ron         2425.49

Santorum, Rick     250.00

Name: contb_receipt_amt, dtype: float64

df['contb_receipt_amt'].max()

1944042.43

#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")

df.query('contb_receipt_amt == 1944042.43')

.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {

    vertical-align: top;

}

.dataframe thead th {

    text-align: right;

}

	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	party
176127	C00431445	P80003338	Obama, Barack	OBAMA VICTORY FUND 2012 - UNITEMIZED	CHICAGO	IL	60680	NaN	NaN	1944042.43	2011-12-31	NaN	X	*	SA18	763233	Democrat

pandas分组聚合案例的更多相关文章

Pandas 分组聚合
# 导入相关库 import numpy as np import pandas as pd 创建数据 index = pd.Index(data=["Tom", "Bo ...
Python Pandas分组聚合
Pycharm 鼠标移动到函数上,CTRL+Q可以快速查看文档,CTR+P可以看基本的参数. apply(),applymap()和map() apply()和applymap()是DataFrame ...
Pandas 分组聚合：分组、分组对象操作
1.概述 1.1 group语法 df.groupby(self, by=None, axis=0, level=None, as_index: bool=True, sort: bool=True, ...
DataAnalysis-Pandas分组聚合
title: Pandas分组聚合 tags: 数据分析 python categories: DataAnalysis toc: true date: 2020-02-10 16:28:49 Des ...
pandas分组和聚合
Pandas分组与聚合分组 (groupby) 对数据集进行分组,然后对每组进行统计分析 SQL能够对数据进行过滤,分组聚合 pandas能利用groupby进行更加复杂的分组运算分组运算过程:s ...
Pandas分组运算（groupby）修炼
Pandas分组运算(groupby)修炼 Pandas的groupby()功能很强大,用好了可以方便的解决很多问题,在数据处理以及日常工作中经常能施展拳脚. 今天,我们一起来领略下groupby() ...
34.分组聚合操作—bucket
主要知识点: 学习聚合知识一.准备数据 1.家电卖场案例背景建立index 以一个家电卖场中的电视销售数据为背景,来对各种品牌,各种颜色的电视的销量和销售额,进行各种各样角度的分析 ...
白日梦的Elasticsearch实战笔记，ES账号免费借用、32个查询案例、15个聚合案例、7个查询优化技巧。
目录一.导读二.福利:账号借用三._search api 搜索api 3.1.什么是query string search? 3.2.什么是query dsl? 3.3.干货!32个查询案例! ...
白日梦的Elasticsearch实战笔记，32个查询案例、15个聚合案例、7个查询优化技巧。
目录一.导读三._search api 搜索api 3.1.什么是query string search? 3.2.什么是query dsl? 3.3.干货!32个查询案例! 四.聚合分析 4.1 ...

随机推荐

ASE Alpha Sprint - backend scrum 9
本次scrum于2019.11.14再sky garden进行,持续15分钟. 参与人: Xin Kang, Zhikai Chen, Jia Ning, Hao Wang 请假: Lihao Ran ...
脚本_获取本机 MAC 地址
#!bin/bash#作者:liusingbon#功能:获取本机 MAC 地址ip a s |awk 'BEGIN{print "本机 MAC 地址信息如下:"}/^[0-9]/{ ...
Jenkins构建触发器（定时构建项目）
如上图所示,Jenkins通常通过点击“立即构建”来进行手动构建项目,其实也可以使用配置中的 Poll SCM和Build periodically来进行定时自动构建项目: 在“配置”——>“构 ...
css--图片整合（精灵图）
图片整合(精灵图) 精灵图的优点: 减少图片的字节减少了网页的http请求,从而大大的提高了页面的性能解决了网页设计师在图片命名上的困扰,只需对一张集合的图片上命名就可以了,不需要对每一个小元素进 ...
容器（collection）初步
容器(集合)的分类: 泛型(generic):本质是数据类型的参数化(提前告诉编译器,在调用泛型时必须传入实际类型) 例:E即为在主函数中定义的传入的实际类型 class MyCollection&l ...
Python---基础---dict和set2
2019-05-21 写一个程序来管理用户登陆系统的用户信息:登陆名字和密码,登陆用户账号建立后,已存在用户可以用登陆名字和密码重返系统,新用户不能用别人的用户名建立用户账号 ------------ ...
【leetcode】1052. Grumpy Bookstore Owner
题目如下: Today, the bookstore owner has a store open for customers.length minutes. Every minute, some ...
Dubbo学习-8-dubbo启动时检查
启动时检查的作用如下: (1)Dubbo 缺省会在启动时检查依赖的服务是否可用,不可用时会抛出异常,阻止 Spring 初始化完成,以便上线时,能及早发现问题,默认 check="true& ...
阿里云祝顺民（江鹤）：云原生SDWAN加速企业上云引领未来智能网络
第二届中国SD-WAN峰会于11月16日在北京盛大开幕,阿里云以黄金赞助商之名隆重参与.作为全球前三,亚太第一的云计算厂商,阿里云一直引领云网技术的演进及应用落地.过去一年,阿里云发布以云为中心的云原 ...
（3.1）狄泰软件学院C++课程学习剖析二
深度剖析C++第二部分 1.通过对象名能够访问public成员变量.每个对象的成员变量都是专属的,成员变量不能够在对象之间共享. 2.需求:统计在程序运行期间某个类的对象数目,保证程序的安全性(不能使 ...

pandas分组聚合案例

美国2012年总统候选人政治献金数据分析

pandas分组聚合案例的更多相关文章

随机推荐

热门专题