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/1285

Alerts

visit_count is highly skewed (γ1 = 34.82511272)Skewed
shop_id has unique valuesUnique

Reproduction

Analysis started2023-12-11 20:09:41.342782
Analysis finished2023-12-11 20:09:42.298405
Duration0.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

shop_type
Categorical

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
3753 
일반음식점
3162 
휴게음식점
724 
미용업
389 
실내 체육시설
 
252
Other values (31)
1720 

Length

Max length10
Median length8
Mean length3.9075
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row일반음식점
2nd row금융‧보험‧사무
3rd row기타
4th row기타
5th row일반음식점

Common Values

ValueCountFrequency (%)
기타 3753
37.5%
일반음식점 3162
31.6%
휴게음식점 724
 
7.2%
미용업 389
 
3.9%
실내 체육시설 252
 
2.5%
공공기관 206
 
2.1%
병‧의원 200
 
2.0%
숙박업(일반,생활) 187
 
1.9%
종교시설 133
 
1.3%
금융‧보험‧사무 114
 
1.1%
Other values (26) 880
 
8.8%

Length

2023-12-12T05:09:42.404846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 3753
36.2%
일반음식점 3162
30.5%
휴게음식점 724
 
7.0%
미용업 389
 
3.8%
실내 252
 
2.4%
체육시설 252
 
2.4%
공공기관 206
 
2.0%
병‧의원 200
 
1.9%
숙박업(일반,생활 187
 
1.8%
종교시설 133
 
1.3%
Other values (28) 1104
 
10.7%

shop_id
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35551.689
Minimum623
Maximum71447
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:09:42.565135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum623
5-th percentile2991.85
Q117864.5
median35351.5
Q352382.25
95-th percentile68897.05
Maximum71447
Range70824
Interquartile range (IQR)34517.75

Descriptive statistics

Standard deviation21257.661
Coefficient of variation (CV)0.59793675
Kurtosis-1.211799
Mean35551.689
Median Absolute Deviation (MAD)17259
Skewness0.057465523
Sum3.5551689 × 108
Variance4.5188815 × 108
MonotonicityNot monotonic
2023-12-12T05:09:42.744231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27574 1
 
< 0.1%
52353 1
 
< 0.1%
8905 1
 
< 0.1%
42530 1
 
< 0.1%
53545 1
 
< 0.1%
16588 1
 
< 0.1%
16492 1
 
< 0.1%
70340 1
 
< 0.1%
52284 1
 
< 0.1%
32992 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
623 1
< 0.1%
624 1
< 0.1%
628 1
< 0.1%
636 1
< 0.1%
637 1
< 0.1%
638 1
< 0.1%
647 1
< 0.1%
652 1
< 0.1%
660 1
< 0.1%
661 1
< 0.1%
ValueCountFrequency (%)
71447 1
< 0.1%
71434 1
< 0.1%
71414 1
< 0.1%
71401 1
< 0.1%
71399 1
< 0.1%
71340 1
< 0.1%
71330 1
< 0.1%
71318 1
< 0.1%
71314 1
< 0.1%
71298 1
< 0.1%

visit_count
Real number (ℝ)

SKEWED 

Distinct1331
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270.2512
Minimum1
Maximum79765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:09:42.941518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median42
Q3198
95-th percentile1084
Maximum79765
Range79764
Interquartile range (IQR)188

Descriptive statistics

Standard deviation1266.7238
Coefficient of variation (CV)4.6872089
Kurtosis1850.7852
Mean270.2512
Median Absolute Deviation (MAD)39
Skewness34.825113
Sum2702512
Variance1604589.2
MonotonicityNot monotonic
2023-12-12T05:09:43.096206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 547
 
5.5%
2 411
 
4.1%
3 307
 
3.1%
4 250
 
2.5%
5 235
 
2.4%
6 228
 
2.3%
7 191
 
1.9%
9 156
 
1.6%
11 142
 
1.4%
8 140
 
1.4%
Other values (1321) 7393
73.9%
ValueCountFrequency (%)
1 547
5.5%
2 411
4.1%
3 307
3.1%
4 250
2.5%
5 235
2.4%
6 228
2.3%
7 191
 
1.9%
8 140
 
1.4%
9 156
 
1.6%
10 135
 
1.4%
ValueCountFrequency (%)
79765 1
< 0.1%
50447 1
< 0.1%
28099 1
< 0.1%
23901 1
< 0.1%
18421 1
< 0.1%
18229 1
< 0.1%
14276 1
< 0.1%
13996 1
< 0.1%
12967 1
< 0.1%
11892 1
< 0.1%

Interactions

2023-12-12T05:09:41.867938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:09:41.576935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:09:42.006793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:09:41.695668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:09:43.210327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
shop_typeshop_idvisit_count
shop_type1.0000.4920.429
shop_id0.4921.0000.045
visit_count0.4290.0451.000
2023-12-12T05:09:43.311913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
shop_idvisit_countshop_type
shop_id1.000-0.0760.194
visit_count-0.0761.0000.187
shop_type0.1940.1871.000

Missing values

2023-12-12T05:09:42.174988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:09:42.256740image/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
26057일반음식점2757415
20834금융‧보험‧사무4773733
26148기타6206915
35414기타30522
26242일반음식점3026915
11032일반음식점3169157
3160기타27555708
11453일반음식점45503146
15482유흥주점영업4815174
21765기타6496428
shop_typeshop_idvisit_count
26611기타2012614
33020기타334124
6916기타14028318
31236미용업656636
26375비영리법인4799014
10220일반음식점29769179
31043일반음식점44186
32772실내 체육시설517234
36457기타618301
20766미용업6876633