06wk-2: 막대그래프, 심슨의 역설 (1)

plotnine

Author

최규빈

Published

October 11, 2023

1. 강의영상

2. Imports

import pandas as pd
import numpy as np
from plotnine import *

3. 비교를 위한 시각화

A. `geom_col()`

- 예시1: 기본적인 막대그래프

df = pd.DataFrame({'x':[0,1],'y':[40,60]})
df

	x	y
0	0	40
1	1	60

fig = ggplot(df)
col = geom_col(aes(x='x',y='y'))
fig + col

- 예시2: \(x\)축이 범주인 경우

df = pd.DataFrame({'sex':['male','female'],'score':[40,60]})
df

	sex	score
0	male	40
1	female	60

fig = ggplot(df)
col = geom_col(aes(x='sex',y='score'))
fig + col

- 예시3: 예시2에서 색깔로 구분하고 싶은 경우

df = pd.DataFrame({'sex':['male','female'],'score':[40,60]})
df

	sex	score
0	male	40
1	female	60

fig = ggplot(df)
col = geom_col(aes(x='sex',y='score',fill='sex'))
fig + col

- 예시4: 예시3에서 scale_fill_manual()을 이용하여 색상변경 하기

df = pd.DataFrame({'sex':['male','female'],'score':[40,60]})
df

	sex	score
0	male	40
1	female	60

fig = ggplot(df)
col = geom_col(aes(x='sex',y='score',fill='sex'))
fig + col + scale_fill_manual(['red','blue'])

B. `facet_wrap()`

- 예시1: facet_wrap()을 이용한 면분할 – 반별로 면분할

df = pd.DataFrame({'sex':['male','female','male','female'],'score':[40,60,50,20],'class':['A','A','B','B']})
df

	sex	score	class
0	male	40	A
1	female	60	A
2	male	50	B
3	female	20	B

fig = ggplot(df)
col = geom_col(aes(x='sex',y='score',fill='sex')) 
fig + col + facet_wrap('class')

- 예시2: facet_wrap()을 이용한 면분할 – 성별로 면분할

df = pd.DataFrame({'sex':['male','female','male','female'],'score':[40,60,50,20],'class':['A','A','B','B']})
df

	sex	score	class
0	male	40	A
1	female	60	A
2	male	50	B
3	female	20	B

fig = ggplot(df)
col = geom_col(aes(x='class',y='score',fill='sex')) 
fig + col + facet_wrap('sex')

4. 심슨의 역설

- 버클리대학교의 입학데이터

https://github.com/guebin/DV2023/blob/main/posts/ds.pdf

- 주장: 버클리대학에 gender bias가 존재한다.

1973년 가을학기의 입학통계에 따르면 지원하는 남성이 여성보다 훨씬 많이 합격했고, 그 차이가 너무 커서 우연의 일치라 보기 어렵다.

df=pd.read_csv("https://raw.githubusercontent.com/guebin/DV2022/master/posts/Simpson.csv",index_col=0,header=[0,1])\
.stack().stack().reset_index()\
.rename({'level_0':'department','level_1':'result','level_2':'gender',0:'count'},axis=1)
df

	department	result	gender	count
0	A	fail	male	314
1	A	fail	female	19
2	A	pass	male	511
3	A	pass	female	89
4	B	fail	male	208
5	B	fail	female	7
6	B	pass	male	352
7	B	pass	female	18
8	C	fail	male	204
9	C	fail	female	391
10	C	pass	male	121
11	C	pass	female	202
12	D	fail	male	279
13	D	fail	female	244
14	D	pass	male	138
15	D	pass	female	131
16	E	fail	male	137
17	E	fail	female	299
18	E	pass	male	54
19	E	pass	female	94
20	F	fail	male	149
21	F	fail	female	103
22	F	pass	male	224
23	F	pass	female	238

A. 시각화1: 전체합격률 시각화 – pandas 초보

- 여성지원자의 합격률

df.query('gender == "female" and result =="pass"')['count'].sum() / df.query('gender == "female"')['count'].sum()

0.420708446866485

- 남성지원자의 합격률

df.query('gender == "male" and result =="pass"')['count'].sum() / df.query('gender == "male"')['count'].sum()

0.5202526941657376

- 시각화

tidydata = pd.DataFrame({'male':[0.5202526941657376],'female':[0.420708446866485]})
tidydata

	male	female
0	0.520253	0.420708

이렇게 데이터 프레임을 만들면 망해요

tidydata = pd.DataFrame({'sex':['male','female'],'rate':[0.5202526941657376,0.420708446866485]})
tidydata

	sex	rate
0	male	0.520253
1	female	0.420708

fig = ggplot(tidydata)
col = geom_col(aes(x='sex',y='rate',fill='sex'))
fig + col

B. 시각화1: 전체합격률 시각화 – pandas 고수

df.pivot_table(index='gender', columns='result', values='count', aggfunc=sum)

/tmp/ipykernel_3693597/1414298521.py:1: FutureWarning: The provided callable <built-in function sum> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.

result	fail	pass
gender
female	1063	772
male	1291	1400

df.pivot_table(index='gender', columns='result', values='count', aggfunc=sum)\
.assign(rate = lambda _df: _df['pass'] / (_df['fail'] + _df['pass']))\
.reset_index()

/tmp/ipykernel_3693597/3036569198.py:1: FutureWarning: The provided callable <built-in function sum> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.

result	gender	fail	pass	rate
0	female	1063	772	0.420708
1	male	1291	1400	0.520253

tidydata = df.pivot_table(index='gender', columns='result', values='count', aggfunc=sum)\
.assign(rate = lambda _df: _df['pass'] / (_df['fail'] + _df['pass']))\
.reset_index()

fig = ggplot(tidydata) 
col = geom_col(aes(x='gender',y='rate',fill='gender'))
fig + col

/tmp/ipykernel_3693597/1840989269.py:1: FutureWarning: The provided callable <built-in function sum> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.

5. HW

적당한 데이터프레임을 선언하고 아래와 같은 barplot을 그려라.