개요
데이터 집합을 분류하고 각 그룹별로 집계나 변형 같은 어떤 함수를 적용 하는 건 데이터 분석 과정에서 무척 중요한 일이다.
데이터를 불러오고 취합해서 하나의 데이터 집합을 준비하고 나면 그룹 통계를 구하거나 가능하면 피벗 테이블을 구해서 보고서를 만들거나 시각화하게 된다.
Pandas는 데이터 집합을 자연스럽게 나누고 요약할 수 있는 groupby라는 유연한 방법을 제공한다.
GroupBy 메카닉
분리 - 적용 - 결합
객체에 들어 있는 데이터를 하나 이상의 색인 기준으로 분리한다.
함수를 각 그룹에 적용시켜 새로운 값을 얻어 낸다.
함수를 적용한 결과를 하나의 객체로 결합한다.
from tokenize import group
import pandas as pd
import numpy as np
df = pd.DataFrame({'key1':['a','a','b','b','a'],
'key2':['one','two','one','two','one'],
'data1':np.random.randn(5),
'data2':np.random.randn(5)})
print(df)
print()
grouped1 = df.groupby('key1')
print(grouped1)
print()
for name,gd in grouped1:
print(name)
print(gd, end='\n\n')
print()
print(grouped1[['data1','data2']].mean())
print()
print(grouped1[['data2']].mean())
print()
print(list(grouped1))
ldata = list(grouped1)
print(ldata[1])
print(ldata[1][1])
print()
print(dict(ldata)['b'])
print()
grouped2 = df.groupby(['key1','key2'])
for gd in grouped2:
print(gd, end='\n\n')
print()
print(grouped2[['data1','data2']].sum())
key1 key2 data1 data2
0 a one 1.930759 0.353781
1 a two -0.511214 1.521039
2 b one 0.550386 0.577365
3 b two 0.611680 -0.079296
4 a one -0.423424 1.144649
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x00000165C11CCFA0>
a
key1 key2 data1 data2
0 a one 1.930759 0.353781
1 a two -0.511214 1.521039
4 a one -0.423424 1.144649
b
key1 key2 data1 data2
2 b one 0.550386 0.577365
3 b two 0.611680 -0.079296
data1 data2
key1
a 0.332040 1.006489
b 0.581033 0.249035
data2
key1
a 1.006489
b 0.249035
[('a', key1 key2 data1 data2
0 a one 1.930759 0.353781
1 a two -0.511214 1.521039
4 a one -0.423424 1.144649), ('b', key1 key2 data1 data2
2 b one 0.550386 0.577365
3 b two 0.611680 -0.079296)]
('b', key1 key2 data1 data2
2 b one 0.550386 0.577365
3 b two 0.611680 -0.079296)
key1 key2 data1 data2
2 b one 0.550386 0.577365
3 b two 0.611680 -0.079296
key1 key2 data1 data2
2 b one 0.550386 0.577365
3 b two 0.611680 -0.079296
(('a', 'one'), key1 key2 data1 data2
0 a one 1.930759 0.353781
4 a one -0.423424 1.144649)
(('a', 'two'), key1 key2 data1 data2
1 a two -0.511214 1.521039)
(('b', 'one'), key1 key2 data1 data2
2 b one 0.550386 0.577365)
(('b', 'two'), key1 key2 data1 data2
3 b two 0.61168 -0.079296)
data1 data2
key1 key2
a one 1.507335 1.498430
two -0.511214 1.521039
b one 0.550386 0.577365
two 0.611680 -0.079296
그룹 간 순회
GroupBy객체는 이터레이션을 지원하는데, 그룹 이름과 그에 따른 데이터 묶음을 튜플로 반환한다.
import numpy as np
import pandas as pd
np.random.seed(12345)
frame = pd.DataFrame(np.random.randn(5,5),
index=['joe','steve','wes','jim','travis'],
columns=list('abcde'))
print(frame)
print()
mapping = {'a':'red','b':'red','c':'blue','d':'blue','e':'red'}
grouped1= frame.T.groupby(mapping)
print()
print(grouped1.sum())
print()
labeling = ['one','one','one','two','two']
grouped2 = frame.groupby(labeling)
print(grouped2.mean())
print()
grouped3 = frame.groupby(len)
print(grouped3.sum())
grouped3 = frame.groupby(lambda x: x[-1])
print(grouped3.sum())
print()
print(frame.groupby([len, labeling]).min())
a b c d e
joe -0.204708 0.478943 -0.519439 -0.555730 1.965781
steve 1.393406 0.092908 0.281746 0.769023 1.246435
wes 1.007189 -1.296221 0.274992 0.228913 1.352917
jim 0.886429 -2.001637 -0.371843 1.669025 -0.438570
travis -0.539741 0.476985 3.248944 -1.021228 -0.577087
joe steve wes jim travis
blue -1.075169 1.050769 0.503905 1.297183 2.227716
red 2.240016 2.732748 1.063885 -1.553778 -0.639844
a b c d e
one 0.731963 -0.241457 0.012433 0.147402 1.521711
two 0.173344 -0.762326 1.438551 0.323899 -0.507829
a b c d e
3 1.688911 -2.818915 -0.616290 1.342208 2.880128
5 1.393406 0.092908 0.281746 0.769023 1.246435
6 -0.539741 0.476985 3.248944 -1.021228 -0.577087
a b c d e
e 1.188698 0.571851 -0.237693 0.213292 3.212215
m 0.886429 -2.001637 -0.371843 1.669025 -0.438570
s 0.467448 -0.819236 3.523936 -0.792315 0.775830
a b c d e
3 one -0.204708 -1.296221 -0.519439 -0.555730 1.352917
two 0.886429 -2.001637 -0.371843 1.669025 -0.438570
5 one 1.393406 0.092908 0.281746 0.769023 1.246435
6 two -0.539741 0.476985 3.248944 -1.021228 -0.577087칼럼 선택
DataFrame 에서 만든 GroupBy 객체를 칼럼 이름이나 칼럼 이름이 담긴 배열로 색인하면 수집을 위해 해당 칼럼을 선택 하게 된다.
import numpy as np
import pandas as pd
tips = pd.read_csv('tips.csv')
print(tips.head(10))
tips.info()
print()
tips['tip_pct'] = tips['tip'] / tips['total_bill']
print(tips.head())
print()
grouped = tips.groupby(['sex','smoker'])
grouped_pct = grouped['tip_pct']
print(grouped_pct.mean())
print()
print(grouped_pct.agg('mean'))
print()
print(grouped_pct.agg(['mean','std'])) # function 여러가지 이용가능
print()
print(grouped_pct.agg([('평균값','mean'),'std'])) # 튜플을 사용하여 이름을 바꿀 수 있음
print(grouped.agg({'tip':'max','size':'sum'}))
print()
print(grouped.agg({'tip_pct':['min','max'],
'size':['sum','mean','std']}))
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4
5 25.29 4.71 Male No Sun Dinner 4
6 8.77 2.00 Male No Sun Dinner 2
7 26.88 3.12 Male No Sun Dinner 4
8 15.04 1.96 Male No Sun Dinner 2
9 14.78 3.23 Male No Sun Dinner 2
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 244 entries, 0 to 243
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 total_bill 244 non-null float64
1 tip 244 non-null float64
2 sex 244 non-null object
3 smoker 244 non-null object
4 day 244 non-null object
5 time 244 non-null object
6 size 244 non-null int64
dtypes: float64(2), int64(1), object(4)
memory usage: 13.5+ KB
total_bill tip sex smoker day time size tip_pct
0 16.99 1.01 Female No Sun Dinner 2 0.059447
1 10.34 1.66 Male No Sun Dinner 3 0.160542
2 21.01 3.50 Male No Sun Dinner 3 0.166587
3 23.68 3.31 Male No Sun Dinner 2 0.139780
4 24.59 3.61 Female No Sun Dinner 4 0.146808
sex smoker
Female No 0.156921
Yes 0.182150
Male No 0.160669
Yes 0.152771
Name: tip_pct, dtype: float64
sex smoker
Female No 0.156921
Yes 0.182150
Male No 0.160669
Yes 0.152771
Name: tip_pct, dtype: float64
mean std
sex smoker
Female No 0.156921 0.036421
Yes 0.182150 0.071595
Male No 0.160669 0.041849
Yes 0.152771 0.090588
평균값 std
sex smoker
Female No 0.156921 0.036421
Yes 0.182150 0.071595
Male No 0.160669 0.041849
Yes 0.152771 0.090588
tip size
sex smoker
Female No 5.2 140
Yes 6.5 74
Male No 9.0 263
Yes 10.0 150
tip_pct size
min max sum mean std
sex smoker
Female No 0.056797 0.252672 140 2.592593 1.073146
Yes 0.056433 0.416667 74 2.242424 0.613917
Male No 0.071804 0.291990 263 2.711340 0.989094
Yes 0.035638 0.710345 150 2.500000 0.892530
데이터 묶기
함수로 묶기
pandas는 파이썬 함수를 사용해서 그룹을 매핑할 수 있는 기능을 제공한다.
색인 단계로 묶기
계층적으로 색인된 데이터 묶음은 축 색인의 단계 중 하나를 사용해서 편리하게 모을 수 있는 기능을 제공한다.
데이터 수집
Pandas는 데이터 수집(데이터 묶음에 대한 준비된 통계)을 위한 최적화 된 메서드를 제공한다.
count : 그룹 내에 NA 값이 아닌 값의 수를 반환한다.
sum : NA 값이 아닌 값들의 합을 구한다.
mean : NA 값이 아닌 값들의 평균 값을 구한다.
median : NA 값이 아닌 값들의 산술 중간 값을 구한다.
std,var : 편향되지 않은(n-1을 분모로 하는) 표준편차와 분산
min,max : NA 값이 아닌 값 중 최소 값과 최대 값
prod : NA 값이 아닌 값의 곱
first,last : NA 값이 아닌 값들 중 첫 번째 값과 마지막 값
칼럼에 여러 가지 함수 적용하기
그룹별 연산과 변형
그룹 연산
pandas.apply() : 분리 - 적용 - 병합
import numpy as np
import pandas as pd
tips = pd.read_csv('tips.csv')
tips['tip_pct'] = tips['tip'] / tips['total_bill']
def g_func(gdata, n=5, column='tip_pct'):
return gdata.sort_values(by=column, ascending=False)[:n]
print(tips.groupby('smoker').apply(g_func))
print()
print(tips.groupby(['smoker','day']).apply(g_func, n=3, column='total_bill'))
total_bill tip sex smoker day time size tip_pct
smoker
No 232 11.61 3.39 Male No Sat Dinner 2 0.291990
149 7.51 2.00 Male No Thur Lunch 2 0.266312
51 10.29 2.60 Female No Sun Dinner 2 0.252672
185 20.69 5.00 Male No Sun Dinner 5 0.241663
88 24.71 5.85 Male No Thur Lunch 2 0.236746
Yes 172 7.25 5.15 Male Yes Sun Dinner 2 0.710345
178 9.60 4.00 Female Yes Sun Dinner 2 0.416667
67 3.07 1.00 Female Yes Sat Dinner 1 0.325733
183 23.17 6.50 Male Yes Sun Dinner 4 0.280535
109 14.31 4.00 Female Yes Sat Dinner 2 0.279525
total_bill tip sex smoker day time size tip_pct
smoker day
No Fri 94 22.75 3.25 Female No Fri Dinner 2 0.142857
91 22.49 3.50 Male No Fri Dinner 2 0.155625
223 15.98 3.00 Female No Fri Lunch 3 0.187735
Sat 212 48.33 9.00 Male No Sat Dinner 4 0.186220
59 48.27 6.73 Male No Sat Dinner 4 0.139424
23 39.42 7.58 Male No Sat Dinner 4 0.192288
Sun 156 48.17 5.00 Male No Sun Dinner 6 0.103799
112 38.07 4.00 Male No Sun Dinner 3 0.105070
11 35.26 5.00 Female No Sun Dinner 4 0.141804
Thur 142 41.19 5.00 Male No Thur Lunch 5 0.121389
85 34.83 5.17 Female No Thur Lunch 4 0.148435
141 34.30 6.70 Male No Thur Lunch 6 0.195335
Yes Fri 95 40.17 4.73 Male Yes Fri Dinner 4 0.117750
90 28.97 3.00 Male Yes Fri Dinner 2 0.103555
96 27.28 4.00 Male Yes Fri Dinner 2 0.146628
Sat 170 50.81 10.00 Male Yes Sat Dinner 3 0.196812
102 44.30 2.50 Female Yes Sat Dinner 3 0.056433
207 38.73 3.00 Male Yes Sat Dinner 4 0.077459
Sun 182 45.35 3.50 Male Yes Sun Dinner 3 0.077178
184 40.55 3.00 Male Yes Sun Dinner 2 0.073983
180 34.65 3.68 Male Yes Sun Dinner 4 0.106205
Thur 197 43.11 5.00 Female Yes Thur Lunch 4 0.115982
83 32.68 5.00 Male Yes Thur Lunch 2 0.152999
192 28.44 2.56 Male Yes Thur Lunch 2 0.090014
그룹에 국한된 값으로 누락된 값 채우기
import pandas as pd
import numpy as np
states = ['ohio','new york','vermont','florida',
'oregon','nevada','california','idaho']
group_key = ['East'] * 4 + ['West'] * 4
data = pd.Series(np.random.randn(8),index=states)
# print(data)
data[['vermont','nevada','idaho']] = np.nan
print(data)
print(data.groupby(group_key).mean())
print()
fill_mean = lambda g : g.fillna(g.mean())
print(data.groupby(group_key).apply(fill_mean))
print()
fill_values = {'East':0.5,'West':-1}
fill_func = lambda g : g.fillna(fill_values[g.name])
print(data.groupby(group_key).apply(fill_func))
ohio 0.449226
new york -0.584617
vermont NaN
florida -0.823783
oregon -0.718627
nevada NaN
california 0.526202
idaho NaN
dtype: float64
East -0.319725
West -0.096212
dtype: float64
East ohio 0.449226
new york -0.584617
vermont -0.319725
florida -0.823783
West oregon -0.718627
nevada -0.096212
california 0.526202
idaho -0.096212
dtype: float64
East ohio 0.449226
new york -0.584617
vermont 0.500000
florida -0.823783
West oregon -0.718627
nevada -1.000000
california 0.526202
idaho -1.000000
dtype: float64
랜덤 표본과 순열
가중 평균과 상관 관계
피벗 테이블과 교차일람표
피벗 테이블은 스프레드시트 프로그램과 다른 데이터 분석 소프트웨어에서 흔히 볼 수 있는 데이터 요약화 도구이다.
피벗 테이블은 하나 이상의 키로 수집해서 로우, 칼럼에 나열해서 데이터를 정렬한다,.
DataFrame.pivot_table()
values : 집계하려는 칼럼 이름 혹은 이름의 리스트, 기본 값으로 모든 숫자 칼럼을 집계한다.
rows : 만들어지는 피벗 테이블의 로우의 그룹으로 묶을 칼럼이나 그룹 키
cols : 만들어지는 피벗 테이블의 칼럼의 그룹으로 묶을 칼럼이나 그룹 키
aggfunc : 집계 함수나 함수 리스트, 기본 값은 'mean'이고 groupby 안에서 유효한 어떤 함수라도 가능하다.
fill_value : 누락된 값을 대체하기 위한 값
margins : 부분합이나 총계를 담기 위한 로우/칼럼을 추가할지의 여부
교차일람표는 그룹 빈도를 게산하기 위한 피벗 테이블의 특수한 경우이다.
import pandas as pd
tips = pd.read_csv('tips.csv')
tips['tip_pct'] = tips['tip'] / tips['total_bill']
print(tips.pivot_table(['tip_pct','size'],
index=['sex','smoker'],
aggfunc='mean'))
print()
print(tips.pivot_table(['tip_pct','size'],
index=['sex','smoker'],
aggfunc=['mean','std']))
print()
print(tips.pivot_table(['tip_pct','size'],
columns=['sex'],
index='day',
aggfunc='mean'))
print() size tip_pct
sex smoker
Female No 2.592593 0.156921
Yes 2.242424 0.182150
Male No 2.711340 0.160669
Yes 2.500000 0.152771
mean std
size tip_pct size tip_pct
sex smoker
Female No 2.592593 0.156921 1.073146 0.036421
Yes 2.242424 0.182150 0.613917 0.071595
Male No 2.711340 0.160669 0.989094 0.041849
Yes 2.500000 0.152771 0.892530 0.090588
size tip_pct
sex Female Male Female Male
day
Fri 2.111111 2.100000 0.199388 0.143385
Sat 2.250000 2.644068 0.156470 0.151577
Sun 2.944444 2.810345 0.181569 0.162344
Thur 2.468750 2.433333 0.157525 0.165276
문제
import pandas as pd
import numpy as np
fec = pd.read_csv('P00000001-ALL.csv')
fec.info()
print()
print(fec.iloc[7])
print()
unique_cands = fec.cand_nm.unique()
print(unique_cands)
print()
parties = {'Bachmann, Michelle':'Republican',
'Cain, Herman':'Republican',
'Gingrich, Newt':'Republican',
'Huntsman, Jon':'Republican',
'Johnson, 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'}
occ_mapping = {
'INFORMATION REQUESTED PER BEST EFFORTS':'NOT PROVIDED',
'INFORMATION REQUESTED':'NOT PROVIDED',
'INFORMATION REQUESTED (BEST EFFORTS)':'NOT PROVIDED',
'C.E.O':'CEO',
'C.E.O.':'CEO'
}
fec['party']= fec.cand_nm.map(parties)
print(fec.head())
print()
print(fec['party'].value_counts())
print()
fec = fec[fec.contb_receipt_amt > 0]
fec.info()
print()
print(fec.contbr_occupation.value_counts()[:10])
print()
cf = lambda x : occ_mapping.get(x, x)
fec.contbr_occupation = fec.contbr_occupation.map(cf)
print(fec.contbr_occupation.value_counts()[:10])
print()
by_occupation = fec.pivot_table('contb_receipt_amt',
index='contbr_occupation',
columns='party',
aggfunc='sum')
print(by_occupation)
print()
over_2mm = by_occupation[by_occupation.sum(axis=1) > 2000000]
print(over_2mm)
print()
# import matplotlib.pyplot as plt
# over_2mm.plot(kind='barh')
# plt.show()
fec_mrbo = fec[fec.cand_nm.isin(['Obama, Barack','Romney, Mitt'])]
fec_mrbo.info()
def get_top_amounts(group, key, n=5):
totals = group.groupby(key)['contb_receipt_amt'].sum()
return totals.sort_values(ascending=False)[:n]
grouped = fec_mrbo.groupby('cand_nm')
print(grouped.apply(get_top_amounts, 'contbr_occupation', n=7, include_groups=False))
print()
bins = np.array([0, 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000])
labels = pd.cut(fec_mrbo.contb_receipt_amt , bins)
print(labels)
print()
grouped = fec_mrbo.groupby(['cand_nm',labels],observed=False)
bucket_sums = grouped.contb_receipt_amt.sum().unstack(level=0)
print(bucket_sums)
print()
normed_sums = bucket_sums.div(bucket_sums.sum(axis=1),axis=0) * 100
print(normed_sums)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1001731 entries, 0 to 1001730
Data columns (total 16 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 cmte_id 1001731 non-null object
1 cand_id 1001731 non-null object
2 cand_nm 1001731 non-null object
3 contbr_nm 1001731 non-null object
4 contbr_city 1001712 non-null object
5 contbr_st 1001727 non-null object
6 contbr_zip 1001620 non-null object
7 contbr_employer 988002 non-null object
8 contbr_occupation 993301 non-null object
9 contb_receipt_amt 1001731 non-null float64
10 contb_receipt_dt 1001731 non-null object
11 receipt_desc 14166 non-null object
12 memo_cd 92482 non-null object
13 memo_text 97770 non-null object
14 form_tp 1001731 non-null object
15 file_num 1001731 non-null int64
dtypes: float64(1), int64(1), object(14)
memory usage: 122.3+ MB
cmte_id C00410118
cand_id P20002978
cand_nm Bachmann, Michelle
contbr_nm BLEVINS, DARONDA
contbr_city PIGGOTT
contbr_st AR
contbr_zip 724548253.0
contbr_employer NONE
contbr_occupation RETIRED
contb_receipt_amt 250.0
contb_receipt_dt 05-JUL-11
receipt_desc NaN
memo_cd NaN
memo_text NaN
form_tp SA17A
file_num 749073
Name: 7, dtype: object
['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']
cmte_id cand_id cand_nm ... form_tp file_num party
0 C00410118 P20002978 Bachmann, Michelle ... SA17A 736166 Republican
1 C00410118 P20002978 Bachmann, Michelle ... SA17A 736166 Republican
2 C00410118 P20002978 Bachmann, Michelle ... SA17A 749073 Republican
3 C00410118 P20002978 Bachmann, Michelle ... SA17A 749073 Republican
4 C00410118 P20002978 Bachmann, Michelle ... SA17A 736166 Republican
[5 rows x 17 columns]
party
Democrat 593746
Republican 407985
Name: count, dtype: int64
<class 'pandas.core.frame.DataFrame'>
Index: 991475 entries, 0 to 1001730
Data columns (total 17 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 cmte_id 991475 non-null object
1 cand_id 991475 non-null object
2 cand_nm 991475 non-null object
3 contbr_nm 991475 non-null object
4 contbr_city 991457 non-null object
5 contbr_st 991471 non-null object
6 contbr_zip 991373 non-null object
7 contbr_employer 983833 non-null object
8 contbr_occupation 989133 non-null object
9 contb_receipt_amt 991475 non-null float64
10 contb_receipt_dt 991475 non-null object
11 receipt_desc 5219 non-null object
12 memo_cd 89461 non-null object
13 memo_text 94274 non-null object
14 form_tp 991475 non-null object
15 file_num 991475 non-null int64
16 party 991475 non-null object
dtypes: float64(1), int64(1), object(15)
memory usage: 136.2+ MB
contbr_occupation
RETIRED 233990
INFORMATION REQUESTED 35107
ATTORNEY 34286
HOMEMAKER 29931
PHYSICIAN 23432
INFORMATION REQUESTED PER BEST EFFORTS 21138
ENGINEER 14334
TEACHER 13990
CONSULTANT 13273
PROFESSOR 12555
Name: count, dtype: int64
contbr_occupation
RETIRED 233990
NOT PROVIDED 57151
ATTORNEY 34286
HOMEMAKER 29931
PHYSICIAN 23432
ENGINEER 14334
TEACHER 13990
CONSULTANT 13273
PROFESSOR 12555
NOT EMPLOYED 9828
Name: count, dtype: int64
party Democrat Republican
contbr_occupation
MIXED-MEDIA ARTIST / STORYTELLER 100.0 NaN
AREA VICE PRESIDENT 250.0 NaN
RESEARCH ASSOCIATE 100.0 NaN
TEACHER 500.0 NaN
THERAPIST 3900.0 NaN
... ... ...
ZOOKEEPER 35.0 NaN
ZOOLOGIST 400.0 NaN
ZOOLOGY EDUCATION 25.0 NaN
\NONE\ NaN 250.0
~ NaN 75.0
[45063 rows x 2 columns]
party Democrat Republican
contbr_occupation
ATTORNEY 11141982.97 7477194.43
CEO 2075974.79 4233741.52
CONSULTANT 2459912.71 2544725.45
ENGINEER 951525.55 1818373.70
EXECUTIVE 1355161.05 4138850.09
HOMEMAKER 4248875.80 13634275.78
INVESTOR 884133.00 2431768.92
LAWYER 3160478.87 391224.32
MANAGER 762883.22 1444532.37
NOT PROVIDED 4866973.96 20565473.01
OWNER 1001567.36 2408286.92
PHYSICIAN 3735124.94 3594320.24
PRESIDENT 1878509.95 4720923.76
PROFESSOR 2165071.08 296702.73
REAL ESTATE 528902.09 1625902.25
RETIRED 25305116.38 23561244.49
SELF-EMPLOYED 672393.40 1640252.54
<class 'pandas.core.frame.DataFrame'>
Index: 694282 entries, 411 to 701385
Data columns (total 17 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 cmte_id 694282 non-null object
1 cand_id 694282 non-null object
2 cand_nm 694282 non-null object
3 contbr_nm 694282 non-null object
4 contbr_city 694275 non-null object
5 contbr_st 694278 non-null object
6 contbr_zip 694234 non-null object
7 contbr_employer 693607 non-null object
8 contbr_occupation 693524 non-null object
9 contb_receipt_amt 694282 non-null float64
10 contb_receipt_dt 694282 non-null object
11 receipt_desc 2345 non-null object
12 memo_cd 87387 non-null object
13 memo_text 90672 non-null object
14 form_tp 694282 non-null object
15 file_num 694282 non-null int64
16 party 694282 non-null object
dtypes: float64(1), int64(1), object(15)
memory usage: 95.3+ MB
cand_nm contbr_occupation
Obama, Barack RETIRED 25305116.38
ATTORNEY 11141982.97
NOT PROVIDED 4866973.96
HOMEMAKER 4248875.80
PHYSICIAN 3735124.94
LAWYER 3160478.87
CONSULTANT 2459912.71
Romney, Mitt RETIRED 11508473.59
NOT PROVIDED 11396894.84
HOMEMAKER 8147446.22
ATTORNEY 5364718.82
PRESIDENT 2491244.89
CEO 2343547.03
EXECUTIVE 2300947.03
Name: contb_receipt_amt, dtype: float64
411 (10, 100]
412 (100, 1000]
413 (100, 1000]
414 (10, 100]
415 (10, 100]
...
701381 (10, 100]
701382 (100, 1000]
701383 (1, 10]
701384 (10, 100]
701385 (100, 1000]
Name: contb_receipt_amt, Length: 694282, dtype: category
Categories (8, interval[int64, right]): [(0, 1] < (1, 10] < (10, 100] < (100, 1000] <
(1000, 10000] < (10000, 100000] < (100000, 1000000] <
(1000000, 10000000]]
cand_nm Obama, Barack Romney, Mitt
contb_receipt_amt
(0, 1] 318.24 77.00
(1, 10] 337267.62 29819.66
(10, 100] 20288981.41 1987783.76
(100, 1000] 54798531.46 22363381.69
(1000, 10000] 51753705.67 63942145.42
(10000, 100000] 59100.00 12700.00
(100000, 1000000] 1490683.08 0.00
(1000000, 10000000] 7148839.76 0.00
cand_nm Obama, Barack Romney, Mitt
contb_receipt_amt
(0, 1] 80.518166 19.481834
(1, 10] 91.876684 8.123316
(10, 100] 91.076874 8.923126
(100, 1000] 71.017590 28.982410
(1000, 10000] 44.732551 55.267449
(10000, 100000] 82.311978 17.688022
(100000, 1000000] 100.000000 0.000000
(1000000, 10000000] 100.000000 0.000000
+AI to AI+