Lesson 17: 핸드폰점유율, 팁, 인사자료 분석 (깨끗한 데이터)

Author

최규빈

Published

July 26, 2023

강의영상

import

import numpy as np 
import pandas as pd

data: 핸드폰점유율

df = pd.read_csv('https://raw.githubusercontent.com/kalilurrahman/datasets/main/mobilephonemktshare2020.csv')
df

	Date	Samsung	Apple	Huawei	Xiaomi	Oppo	Mobicel	Motorola	LG	Others	Realme	Google	Nokia	Lenovo	OnePlus	Sony	Asus
0	2019-10	31.49	22.09	10.02	7.79	4.10	3.15	2.41	2.40	9.51	0.54	2.35	0.95	0.96	0.70	0.84	0.74
1	2019-11	31.36	22.90	10.18	8.16	4.42	3.41	2.40	2.40	9.10	0.78	0.66	0.97	0.97	0.73	0.83	0.75
2	2019-12	31.37	24.79	9.95	7.73	4.23	3.19	2.50	2.54	8.13	0.84	0.75	0.90	0.87	0.74	0.77	0.70
3	2020-01	31.29	24.76	10.61	8.10	4.25	3.02	2.42	2.40	7.55	0.88	0.69	0.88	0.86	0.79	0.80	0.69
4	2020-02	30.91	25.89	10.98	7.80	4.31	2.89	2.36	2.34	7.06	0.89	0.70	0.81	0.77	0.78	0.80	0.69
5	2020-03	30.80	27.03	10.70	7.70	4.30	2.87	2.35	2.28	6.63	0.93	0.73	0.72	0.74	0.78	0.76	0.66
6	2020-04	30.41	28.79	10.28	7.60	4.20	2.75	2.51	2.28	5.84	0.90	0.75	0.69	0.71	0.80	0.76	0.70
7	2020-05	30.18	26.72	10.39	8.36	4.70	3.12	2.46	2.19	6.31	1.04	0.70	0.73	0.77	0.81	0.78	0.76
8	2020-06	31.06	25.26	10.69	8.55	4.65	3.18	2.57	2.11	6.39	1.04	0.68	0.74	0.75	0.77	0.78	0.75
9	2020-07	30.95	24.82	10.75	8.94	4.69	3.46	2.45	2.03	6.41	1.13	0.65	0.76	0.74	0.76	0.75	0.72
10	2020-08	31.04	25.15	10.73	8.90	4.69	3.38	2.39	1.96	6.31	1.18	0.63	0.74	0.72	0.75	0.73	0.70
11	2020-09	30.57	24.98	10.58	9.49	4.94	3.50	2.27	1.88	6.12	1.45	0.63	0.74	0.67	0.81	0.69	0.67
12	2020-10	30.25	26.53	10.44	9.67	4.83	2.54	2.21	1.79	6.04	1.55	0.63	0.69	0.65	0.85	0.67	0.64

matplotlib: 2개의 y를 겹쳐그리기

- 예시1

df.plot.bar(x='Date',y=['Samsung','Apple'])

<Axes: xlabel='Date'>

- 예시2: width 옵션으로 폭조정

df.plot.bar(x='Date',y=['Samsung','Apple'],width=0.8)

<Axes: xlabel='Date'>

matplotlib: 2개의 y를 겹쳐그리기 + x,y 플립

- 예시1: barh를 이용하여 플립

df.plot.barh(x='Date',y=['Samsung','Apple'],width=0.8)

<Axes: ylabel='Date'>

plotly: 모든y를 stacked bar로 나타내기

- 예시1

df.melt(id_vars='Date').plot.bar(backend='plotly',x='Date',y='value',color='variable')

plotly: 3개의 y를 겹쳐그리기

- 예시1

df.melt(id_vars='Date')\
.query(' variable=="Samsung" or variable=="Apple" or variable=="Huawei"')

	Date	variable	value
0	2019-10	Samsung	31.49
1	2019-11	Samsung	31.36
2	2019-12	Samsung	31.37
3	2020-01	Samsung	31.29
4	2020-02	Samsung	30.91
5	2020-03	Samsung	30.80
6	2020-04	Samsung	30.41
7	2020-05	Samsung	30.18
8	2020-06	Samsung	31.06
9	2020-07	Samsung	30.95
10	2020-08	Samsung	31.04
11	2020-09	Samsung	30.57
12	2020-10	Samsung	30.25
13	2019-10	Apple	22.09
14	2019-11	Apple	22.90
15	2019-12	Apple	24.79
16	2020-01	Apple	24.76
17	2020-02	Apple	25.89
18	2020-03	Apple	27.03
19	2020-04	Apple	28.79
20	2020-05	Apple	26.72
21	2020-06	Apple	25.26
22	2020-07	Apple	24.82
23	2020-08	Apple	25.15
24	2020-09	Apple	24.98
25	2020-10	Apple	26.53
26	2019-10	Huawei	10.02
27	2019-11	Huawei	10.18
28	2019-12	Huawei	9.95
29	2020-01	Huawei	10.61
30	2020-02	Huawei	10.98
31	2020-03	Huawei	10.70
32	2020-04	Huawei	10.28
33	2020-05	Huawei	10.39
34	2020-06	Huawei	10.69
35	2020-07	Huawei	10.75
36	2020-08	Huawei	10.73
37	2020-09	Huawei	10.58
38	2020-10	Huawei	10.44

df.melt(id_vars='Date')\
.query(' variable=="Samsung" or variable=="Apple" or variable=="Huawei"')\
.plot.bar(backend='plotly',x='Date',y='value',color='variable',barmode='group')

plotly: 3개의 y를 겹쳐그리기 + text

- 예시1

df.melt(id_vars='Date')\
.query(' variable=="Samsung" or variable=="Apple" or variable=="Huawei"')\
.plot.bar(backend='plotly',x='value',y='Date',color='variable',barmode='group',text='value',height=1200)

plotly: 면분할로 subplot그리기 (facet_col)

df.melt(id_vars='Date').query(' variable=="Samsung" or variable=="Apple"')\
.plot.bar(backend='plotly',x='Date',y='value',color='variable',barmode='group',facet_col='variable')

plotly: 면분할로 subplot그리기 (facet_row)

df.melt(id_vars='Date').query(' variable=="Samsung" or variable=="Apple"')\
.plot.bar(backend='plotly',x='Date',y='value',color='variable',barmode='group',facet_row='variable')

boxplot

data: 팁

import plotly.express as px 
df = px.data.tips() 
df

	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
...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

244 rows × 7 columns

plotly: 팁의 박스플랏

- y=‘tip’

df.plot.box(backend='plotly',y='tip',width=500,height=500)

plotly: 시간에 따른 팁의 박스플랏

- y='tip', x='time'

df.plot.box(backend='plotly',x='time',y='tip',width=500,height=500)

저녁에 좀 더 잘주는것 같음

plotly: 시간과 성별에 따른 팁의 박스플랏

- 예시1: y='tip', x='time', color='sex'

df.plot.box(backend='plotly',x='time',y='tip',color='sex',width=500,height=500)

- 예시2: y='tip', x='time', color='sex', points='all'

df.plot.box(backend='plotly',x='time',y='tip',color='sex',points='all',width=500,height=500)

plotly: 시간,성별,요일에 따른 팁의 박스플랏

- 예시1: y='tip', x='time', color='sex', facet_col='day'

df.plot.box(backend='plotly', facet_row='day',x='time',y='tip',color='sex',points='all',height=1000)

- 예시2: y='tip', color='sex', facet_col='time', facet_row='day'

df.plot.box(backend='plotly',facet_col='time', facet_row='day',y='tip',color='sex',points='all',height=1000)

plotly: 시간,성별,요일,흡연에 따른 팁의 박스플랏

df.plot.box(backend='plotly',facet_col='time', facet_row='day',x='smoker',y='tip',color='sex',points='all',height=1000)

histogram

data: 인사자료

df = pd.read_csv('https://raw.githubusercontent.com/guebin/DV2022/master/posts/HRDataset_v14.csv')
df

	Employee_Name	EmpID	MarriedID	MaritalStatusID	GenderID	EmpStatusID	DeptID	PerfScoreID	FromDiversityJobFairID	Salary	...	ManagerName	ManagerID	RecruitmentSource	PerformanceScore	EngagementSurvey	EmpSatisfaction	SpecialProjectsCount	LastPerformanceReview_Date	DaysLateLast30	Absences
0	Adinolfi, Wilson K	10026	0	0	1	1	5	4	0	62506	...	Michael Albert	22.0	LinkedIn	Exceeds	4.60	5	0	1/17/2019	0	1
1	Ait Sidi, Karthikeyan	10084	1	1	1	5	3	3	0	104437	...	Simon Roup	4.0	Indeed	Fully Meets	4.96	3	6	2/24/2016	0	17
2	Akinkuolie, Sarah	10196	1	1	0	5	5	3	0	64955	...	Kissy Sullivan	20.0	LinkedIn	Fully Meets	3.02	3	0	5/15/2012	0	3
3	Alagbe,Trina	10088	1	1	0	1	5	3	0	64991	...	Elijiah Gray	16.0	Indeed	Fully Meets	4.84	5	0	1/3/2019	0	15
4	Anderson, Carol	10069	0	2	0	5	5	3	0	50825	...	Webster Butler	39.0	Google Search	Fully Meets	5.00	4	0	2/1/2016	0	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
306	Woodson, Jason	10135	0	0	1	1	5	3	0	65893	...	Kissy Sullivan	20.0	LinkedIn	Fully Meets	4.07	4	0	2/28/2019	0	13
307	Ybarra, Catherine	10301	0	0	0	5	5	1	0	48513	...	Brannon Miller	12.0	Google Search	PIP	3.20	2	0	9/2/2015	5	4
308	Zamora, Jennifer	10010	0	0	0	1	3	4	0	220450	...	Janet King	2.0	Employee Referral	Exceeds	4.60	5	6	2/21/2019	0	16
309	Zhou, Julia	10043	0	0	0	1	3	3	0	89292	...	Simon Roup	4.0	Employee Referral	Fully Meets	5.00	3	5	2/1/2019	0	11
310	Zima, Colleen	10271	0	4	0	1	5	3	0	45046	...	David Stanley	14.0	LinkedIn	Fully Meets	4.50	5	0	1/30/2019	0	2

311 rows × 36 columns

인종별 급여비교 (단순 groupby)

df.groupby('RaceDesc').agg({'Salary':[np.mean,"count"]})

	Salary
	mean	count
RaceDesc
American Indian or Alaska Native	65806.000000	3
Asian	68521.206897	29
Black or African American	74431.025000	80
Hispanic	83667.000000	1
Two or more races	59998.181818	11
White	67287.545455	187

급여의 시각화

- 예시1

df.query('RaceDesc == "Black or African American" or RaceDesc == "White"')\
.plot.hist(backend='plotly',x='Salary',color='RaceDesc',facet_col='RaceDesc')

- 예시2

df.query('RaceDesc == "Black or African American" or RaceDesc == "White"')\
.plot.hist(backend='plotly',x='Salary',color='RaceDesc',facet_col='RaceDesc',histnorm='probability')