[本次資料分析所用到的資料集連結]
(http://github.com/wesm/pydata-book)

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
fec = pd.read_csv('datasets/fec/P00000001-ALL.csv') #載入資料
print(fec.info())
print(fec.iloc[123456])
unique_cands = fec.cand_nm.unique() #使用unique獲得所有不同的政治候選人名單
parties = {'Bachmann, Michelle':'Republican',
           'Cain, Herman':'Republican',
           'Gingrich, Newt':'Republican',
           'Huntsman, Jon':'Republican',
           'John, 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'}  #表示政黨背景的方式之一是使用相應的字典
print(fec.cand_nm[123456:123461].map(parties)) #從候選人姓名中計算出政黨的陣列
fec['party'] = fec.cand_nm.map(parties) #將它作為一列加入
print(fec['party'].value_counts())
print((fec.contb_receipt_amt > 0).value_counts()) #資料準備的要點，這些資料既包括捐款也包括退款
fec = fec[fec.contb_receipt_amt > 0] #將分析範圍限制在正向貢獻中
fec_mrbo = fec[fec.cand_nm.isin(['Obama, Barack','Romney, Mitt'])] #由於Barack Obama和Mitt Romney是主要的兩位候選人，所以準備一個僅對他們的競選有貢獻的子集
print(fec.contbr_occupation.value_counts()[:10]) #獲得按職業的捐獻總數
occ_mapping = {
    'INFORMATION REQUESTED PER BEST EFFORTS':'NOT PROVIDED',
    'INFORMATION REQUESTED':'NOT PROVIDED',
    'INFORMATION REQUESTED (BEST EFFORTS)':'NOT PROVIDED',
    'C.E.O':'CEO'
}
f = lambda x:occ_mapping.get(x,x) #如果沒有對映，則返回x
fec.contbr_occupation = fec.contbr_occupation.map(f)
emp_mapping = {
    'INFORMATION REQUESTED PER BEST EFFORTS':'NOT PROVIDED',
    'INFORMATION REQUESTED':'NOT PROVIDED',
    'SELF':'SELF-EMPLOYED',
    'SELF EMPLOYED':'SELF-EMPLOYED',
}
f = lambda x:emp_mapping.get(x,x) #如果沒有對映，則返回x
fec.contbr_employer = fec.contbr_employer.map(f)
by_occupation = fec.pivot_table('contb_receipt_amt',
                                index = 'contbr_occupation',
                                columns = 'party',aggfunc = 'sum') #按照黨派和職業聚合資料
over_2mm = by_occupation[by_occupation.sum(1) > 2000000] #過濾出至少捐贈200萬美元的子集
over_2mm.plot(kind = 'barh') #以條形圖的方式進行資料視覺化,按黨派劃分各職業捐贈總量
plt.show()
def get_top_amounts(group,key,n = 5):
    totals = group.groupby(key)['contb_receipt_amt'].sum()
    return totals.nlargest(n)
grouped = fec_mrbo.groupby('cand_nm')
grouped.apply(get_top_amounts,'contbr_occupation',n = 7) #按照職業進行聚合
grouped.apply(get_top_amounts,'contbr_employer',n = 10) #按照僱主進行聚合
bins = np.array([0,1,10,100,1000,10000,
                 100000,1000000,10000000])
labels = pd.cut(fec_mrbo.contb_receipt_amt, bins) #使用cut函式將貢獻者的數量按貢獻大小離散化分桶
print(labels)
grouped = fec_mrbo.groupby(['cand_nm',labels]) #將Obama和Romney的資料按名稱和分類標籤進行分組，以獲得捐贈規模的直方圖
print(grouped.size().unstack(0))
bucket_sums = grouped.contb_receipt_amt.sum().unstack(0)
normed_sums = bucket_sums.div(bucket_sums.sum(axis = 1),axis = 0) #對捐款數額進行歸一化
print(normed_sums)
normed_sums[:-2].plot(kind = 'barh') #畫出不同捐贈規模的候選人收到的捐贈總額的百分比，這裡排除了最大的兩個箱體，因為這些箱體不是由個人捐贈的
plt.show()
grouped = fec_mrbo.groupby(['cand_nm','contbr_st']) #按照候選人和州進行聚合
totals = grouped.contb_receipt_amt.sum().unstack(0).fillna(0)
totals = totals[totals.sum(1) > 100000]
print(totals[:10])
percent = totals.div(totals.sum(1),axis = 0) #將每一行除以捐款總額，就可以得到每個候選人按州的捐贈總額的相對百分比
print(percent[:10])