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/1283

Alerts

visit_count is highly skewed (γ1 = 41.07911766)Skewed
shop_id has unique valuesUnique

Reproduction

Analysis started2023-12-11 19:40:29.040956
Analysis finished2023-12-11 19:40:30.204070
Duration1.16 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

shop_type
Categorical

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
3701 
일반음식점
3128 
휴게음식점
708 
미용업
373 
실내 체육시설
 
264
Other values (29)
1826 

Length

Max length10
Median length8
Mean length3.9259
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 3701
37.0%
일반음식점 3128
31.3%
휴게음식점 708
 
7.1%
미용업 373
 
3.7%
실내 체육시설 264
 
2.6%
공공기관 238
 
2.4%
병‧의원 210
 
2.1%
숙박업(일반,생활) 196
 
2.0%
종교시설 147
 
1.5%
유흥주점영업 129
 
1.3%
Other values (24) 906
 
9.1%

Length

2023-12-12T04:40:30.380354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 3701
35.7%
일반음식점 3128
30.2%
휴게음식점 708
 
6.8%
미용업 373
 
3.6%
실내 264
 
2.5%
체육시설 264
 
2.5%
공공기관 238
 
2.3%
병‧의원 210
 
2.0%
숙박업(일반,생활 196
 
1.9%
종교시설 147
 
1.4%
Other values (26) 1142
 
11.0%

shop_id
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35489.159
Minimum634
Maximum70980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:40:30.663427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum634
5-th percentile3258.75
Q118562.75
median35720.5
Q351688.25
95-th percentile68269.25
Maximum70980
Range70346
Interquartile range (IQR)33125.5

Descriptive statistics

Standard deviation20715.463
Coefficient of variation (CV)0.58371242
Kurtosis-1.1776332
Mean35489.159
Median Absolute Deviation (MAD)16569.5
Skewness0.040668221
Sum3.5489159 × 108
Variance4.2913041 × 108
MonotonicityNot monotonic
2023-12-12T04:40:30.967607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19341 1
 
< 0.1%
4032 1
 
< 0.1%
63505 1
 
< 0.1%
50104 1
 
< 0.1%
4452 1
 
< 0.1%
41046 1
 
< 0.1%
62051 1
 
< 0.1%
69702 1
 
< 0.1%
46474 1
 
< 0.1%
64180 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
634 1
< 0.1%
639 1
< 0.1%
640 1
< 0.1%
648 1
< 0.1%
652 1
< 0.1%
656 1
< 0.1%
663 1
< 0.1%
668 1
< 0.1%
675 1
< 0.1%
676 1
< 0.1%
ValueCountFrequency (%)
70980 1
< 0.1%
70949 1
< 0.1%
70946 1
< 0.1%
70908 1
< 0.1%
70905 1
< 0.1%
70902 1
< 0.1%
70889 1
< 0.1%
70879 1
< 0.1%
70876 1
< 0.1%
70862 1
< 0.1%

visit_count
Real number (ℝ)

SKEWED 

Distinct1366
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean276.6957
Minimum1
Maximum83488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:40:31.214633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median44
Q3220
95-th percentile1139
Maximum83488
Range83487
Interquartile range (IQR)210

Descriptive statistics

Standard deviation1304.6777
Coefficient of variation (CV)4.7152076
Kurtosis2340.7984
Mean276.6957
Median Absolute Deviation (MAD)41
Skewness41.079118
Sum2766957
Variance1702183.8
MonotonicityNot monotonic
2023-12-12T04:40:31.900869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 526
 
5.3%
2 389
 
3.9%
3 291
 
2.9%
4 284
 
2.8%
5 265
 
2.6%
6 201
 
2.0%
7 181
 
1.8%
8 179
 
1.8%
9 155
 
1.6%
10 150
 
1.5%
Other values (1356) 7379
73.8%
ValueCountFrequency (%)
1 526
5.3%
2 389
3.9%
3 291
2.9%
4 284
2.8%
5 265
2.6%
6 201
 
2.0%
7 181
 
1.8%
8 179
 
1.8%
9 155
 
1.6%
10 150
 
1.5%
ValueCountFrequency (%)
83488 1
< 0.1%
66593 1
< 0.1%
21760 1
< 0.1%
17498 1
< 0.1%
16047 1
< 0.1%
13144 1
< 0.1%
12847 1
< 0.1%
12229 1
< 0.1%
11912 1
< 0.1%
11732 1
< 0.1%

Interactions

2023-12-12T04:40:29.666907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:40:29.343050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:40:29.838977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:40:29.511845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:40:32.067647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
shop_typeshop_idvisit_count
shop_type1.0000.5170.418
shop_id0.5171.0000.080
visit_count0.4180.0801.000
2023-12-12T04:40:32.229703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
shop_idvisit_countshop_type
shop_id1.000-0.0740.209
visit_count-0.0741.0000.210
shop_type0.2090.2101.000

Missing values

2023-12-12T04:40:30.002902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:40:30.133489image/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
23649기타1934124
6387휴게음식점4382388
24192기타2354222
1863일반음식점442291166
25996기타4202317
32693기타239975
5917일반음식점1924421
32553일반음식점308495
36501기타269922
8798일반음식점53841251
shop_typeshop_idvisit_count
29917금융‧보험‧사무665679
18927기타2730748
14950일반음식점3164290
8056일반음식점28586286
16735기타1302368
7782일반음식점26647301
13189실내 체육시설49683121
33192기타242814
9177일반음식점7930236
16245일반음식점6547973