Lesson 20: 핸드폰점유율, 팁, 인사자료 분석 (깨끗한 데이터)

Author

최규빈

Published

February 1, 2023

ref

https://guebin.github.io/DV2022/posts/2022-11-14-11wk-1.html#data2-핸드폰점유율

import

import numpy as np 
import pandas as pd 
#!pip install pandas_datareader 
from pandas_datareader import data as pdr

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'])
<AxesSubplot:xlabel='Date'>

- 예시2: width 옵션으로 폭조정

df.plot.bar(x='Date',y=['Samsung','Apple'],width=0.8)
<AxesSubplot:xlabel='Date'>

matplotlib: 2개의 y를 겹쳐그리기 + x,y 플립

- 예시1: barh를 이용하여 플립

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