Overview

Dataset statistics

Number of variables3
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory332.0 KiB
Average record size in memory34.0 B

Variable types

Categorical1
Numeric2

Dataset

Descriptionㅇ ‘20.12.21. 안심코드앱 출시 이후 안심코드를 설치한 매장(비식별) 및 업종별 월 방문객 추이 데이터 ㅇ 데이터 기간 : '20.12.26.~'22. 2.18. ㅇ 데이터 분류 - shop-type : 업종/ shop-id : 제주안심코드를 신청한 사업장 순번대로 부여된 id/ visit_count : 방문자 수 ㅇ 각 파일은 ‘20.12.26.(토)부터 28일(4주)째 금요일까지의 데이터임.(각 파일별 연속) * 토요일~28일째 금요일
Author제주데이터허브
URLhttps://www.jejudatahub.net/data/view/data/1286

Alerts

visit_count is highly skewed (γ1 = 43.54743693)Skewed
shop_id has unique valuesUnique

Reproduction

Analysis started2023-12-11 20:09:50.070658
Analysis finished2023-12-11 20:09:51.034456
Duration0.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

shop_type
Categorical

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
3754 
일반음식점
3145 
휴게음식점
673 
미용업
 
351
실내 체육시설
 
269
Other values (32)
1808 

Length

Max length10
Median length8
Mean length3.9241
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row노래연습장
2nd row일반음식점
3rd row일반음식점
4th row기타
5th row기타

Common Values

ValueCountFrequency (%)
기타 3754
37.5%
일반음식점 3145
31.4%
휴게음식점 673
 
6.7%
미용업 351
 
3.5%
실내 체육시설 269
 
2.7%
공공기관 220
 
2.2%
병‧의원 215
 
2.1%
숙박업(일반,생활) 199
 
2.0%
종교시설 143
 
1.4%
유흥주점영업 130
 
1.3%
Other values (27) 901
 
9.0%

Length

2023-12-12T05:09:51.145882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 3754
36.1%
일반음식점 3145
30.3%
휴게음식점 673
 
6.5%
미용업 351
 
3.4%
실내 269
 
2.6%
체육시설 269
 
2.6%
공공기관 220
 
2.1%
병‧의원 215
 
2.1%
숙박업(일반,생활 199
 
1.9%
종교시설 143
 
1.4%
Other values (29) 1157
 
11.1%

shop_id
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35536.401
Minimum205
Maximum71589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:09:51.343381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum205
5-th percentile3131.65
Q118171
median34281
Q352230
95-th percentile69028.3
Maximum71589
Range71384
Interquartile range (IQR)34059

Descriptive statistics

Standard deviation21194.796
Coefficient of variation (CV)0.59642495
Kurtosis-1.2028261
Mean35536.401
Median Absolute Deviation (MAD)17052
Skewness0.077537194
Sum3.5536401 × 108
Variance4.4921939 × 108
MonotonicityNot monotonic
2023-12-12T05:09:51.510564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67841 1
 
< 0.1%
25473 1
 
< 0.1%
44096 1
 
< 0.1%
10345 1
 
< 0.1%
37698 1
 
< 0.1%
21767 1
 
< 0.1%
48955 1
 
< 0.1%
38402 1
 
< 0.1%
50096 1
 
< 0.1%
7157 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
205 1
< 0.1%
623 1
< 0.1%
626 1
< 0.1%
631 1
< 0.1%
637 1
< 0.1%
655 1
< 0.1%
656 1
< 0.1%
662 1
< 0.1%
664 1
< 0.1%
668 1
< 0.1%
ValueCountFrequency (%)
71589 1
< 0.1%
71570 1
< 0.1%
71553 1
< 0.1%
71545 1
< 0.1%
71542 1
< 0.1%
71532 1
< 0.1%
71531 1
< 0.1%
71513 1
< 0.1%
71508 1
< 0.1%
71497 1
< 0.1%

visit_count
Real number (ℝ)

SKEWED 

Distinct1272
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean257.4747
Minimum1
Maximum99232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:09:51.730541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median40
Q3193.25
95-th percentile992
Maximum99232
Range99231
Interquartile range (IQR)184.25

Descriptive statistics

Standard deviation1643.1489
Coefficient of variation (CV)6.3817879
Kurtosis2260.7332
Mean257.4747
Median Absolute Deviation (MAD)37
Skewness43.547437
Sum2574747
Variance2699938.4
MonotonicityNot monotonic
2023-12-12T05:09:51.928350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 572
 
5.7%
2 406
 
4.1%
3 324
 
3.2%
4 288
 
2.9%
5 236
 
2.4%
6 225
 
2.2%
8 170
 
1.7%
7 170
 
1.7%
9 166
 
1.7%
10 141
 
1.4%
Other values (1262) 7302
73.0%
ValueCountFrequency (%)
1 572
5.7%
2 406
4.1%
3 324
3.2%
4 288
2.9%
5 236
2.4%
6 225
 
2.2%
7 170
 
1.7%
8 170
 
1.7%
9 166
 
1.7%
10 141
 
1.4%
ValueCountFrequency (%)
99232 1
< 0.1%
78918 1
< 0.1%
72596 1
< 0.1%
41894 1
< 0.1%
19216 1
< 0.1%
15113 1
< 0.1%
11655 1
< 0.1%
10154 1
< 0.1%
8394 1
< 0.1%
8285 1
< 0.1%

Interactions

2023-12-12T05:09:50.639856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:09:50.367696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:09:50.766959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:09:50.515890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:09:52.048128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
shop_typeshop_idvisit_count
shop_type1.0000.5090.428
shop_id0.5091.0000.046
visit_count0.4280.0461.000
2023-12-12T05:09:52.171735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
shop_idvisit_countshop_type
shop_id1.000-0.0770.203
visit_count-0.0771.0000.216
shop_type0.2030.2161.000

Missing values

2023-12-12T05:09:50.892756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:09:50.991018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

shop_typeshop_idvisit_count
123노래연습장678416065
20714일반음식점5253831
3835일반음식점10971571
26431기타6825413
30836기타642236
6312일반음식점68783338
7140일반음식점70860289
30342기타159867
20398휴게게시판3188233
12801일반음식점30389111
shop_typeshop_idvisit_count
10232공공기관703170
22551기타6889324
15333유흥주점영업4443971
19435기타1281838
16022기타2055464
14026일반음식점3961689
15410일반음식점6264270
31040일반음식점301656
34505농어촌민박529443
16873일반음식점6224255