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/1281

Alerts

visit_count is highly skewed (γ1 = 36.54883601)Skewed
shop_id has unique valuesUnique

Reproduction

Analysis started2023-12-11 20:02:02.406172
Analysis finished2023-12-11 20:02:03.033904
Duration0.63 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
3744 
일반음식점
3142 
휴게음식점
670 
미용업
 
339
실내 체육시설
 
286
Other values (32)
1819 

Length

Max length10
Median length8
Mean length3.9362
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row종교시설
2nd row기타
3rd row기타
4th row기타
5th row기타

Common Values

ValueCountFrequency (%)
기타 3744
37.4%
일반음식점 3142
31.4%
휴게음식점 670
 
6.7%
미용업 339
 
3.4%
실내 체육시설 286
 
2.9%
숙박업(일반,생활) 211
 
2.1%
병‧의원 210
 
2.1%
공공기관 197
 
2.0%
종교시설 168
 
1.7%
유흥주점영업 133
 
1.3%
Other values (27) 900
 
9.0%

Length

2023-12-12T05:02:03.100118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 3744
36.0%
일반음식점 3142
30.2%
휴게음식점 670
 
6.4%
미용업 339
 
3.3%
실내 286
 
2.8%
체육시설 286
 
2.8%
숙박업(일반,생활 211
 
2.0%
병‧의원 210
 
2.0%
공공기관 197
 
1.9%
종교시설 168
 
1.6%
Other values (29) 1146
 
11.0%

shop_id
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35555.762
Minimum203
Maximum70599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:02:03.213485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum203
5-th percentile3339.95
Q118507
median34339
Q352142.5
95-th percentile68196.1
Maximum70599
Range70396
Interquartile range (IQR)33635.5

Descriptive statistics

Standard deviation20756.552
Coefficient of variation (CV)0.58377464
Kurtosis-1.1907353
Mean35555.762
Median Absolute Deviation (MAD)16681
Skewness0.050927476
Sum3.5555762 × 108
Variance4.3083446 × 108
MonotonicityNot monotonic
2023-12-12T05:02:03.330920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24947 1
 
< 0.1%
1147 1
 
< 0.1%
13765 1
 
< 0.1%
63389 1
 
< 0.1%
65315 1
 
< 0.1%
64993 1
 
< 0.1%
41231 1
 
< 0.1%
34044 1
 
< 0.1%
50674 1
 
< 0.1%
49124 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
203 1
< 0.1%
205 1
< 0.1%
623 1
< 0.1%
625 1
< 0.1%
630 1
< 0.1%
648 1
< 0.1%
654 1
< 0.1%
655 1
< 0.1%
664 1
< 0.1%
666 1
< 0.1%
ValueCountFrequency (%)
70599 1
< 0.1%
70584 1
< 0.1%
70575 1
< 0.1%
70574 1
< 0.1%
70572 1
< 0.1%
70570 1
< 0.1%
70566 1
< 0.1%
70553 1
< 0.1%
70549 1
< 0.1%
70533 1
< 0.1%

visit_count
Real number (ℝ)

SKEWED 

Distinct1343
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.3261
Minimum1
Maximum110055
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:02:03.456344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median43
Q3213
95-th percentile1097.2
Maximum110055
Range110054
Interquartile range (IQR)203

Descriptive statistics

Standard deviation2069.5876
Coefficient of variation (CV)6.778286
Kurtosis1592.1921
Mean305.3261
Median Absolute Deviation (MAD)40
Skewness36.548836
Sum3053261
Variance4283193
MonotonicityNot monotonic
2023-12-12T05:02:03.620363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 518
 
5.2%
2 380
 
3.8%
3 296
 
3.0%
4 292
 
2.9%
5 232
 
2.3%
6 204
 
2.0%
7 176
 
1.8%
10 161
 
1.6%
8 155
 
1.6%
9 145
 
1.5%
Other values (1333) 7441
74.4%
ValueCountFrequency (%)
1 518
5.2%
2 380
3.8%
3 296
3.0%
4 292
2.9%
5 232
2.3%
6 204
 
2.0%
7 176
 
1.8%
8 155
 
1.6%
9 145
 
1.5%
10 161
 
1.6%
ValueCountFrequency (%)
110055 1
< 0.1%
88685 1
< 0.1%
85141 1
< 0.1%
67246 1
< 0.1%
54666 1
< 0.1%
48005 1
< 0.1%
18256 1
< 0.1%
17366 1
< 0.1%
16587 1
< 0.1%
15459 1
< 0.1%

Interactions

2023-12-12T05:02:02.754536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:02:02.571878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:02:02.841110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:02:02.667781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:02:03.701998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
shop_typeshop_idvisit_count
shop_type1.0000.5130.502
shop_id0.5131.0000.035
visit_count0.5020.0351.000
2023-12-12T05:02:03.792217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
shop_idvisit_countshop_type
shop_id1.000-0.1020.206
visit_count-0.1021.0000.232
shop_type0.2060.2321.000

Missing values

2023-12-12T05:02:02.940243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:02:03.002125image/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
21535종교시설2494734
21842기타524832
33223기타672225
28409기타2407612
36045기타524632
23490기타6873126
34313숙박업(일반,생활)315913
15017일반음식점6406190
19476기타6901445
18773기타4996450
shop_typeshop_idvisit_count
374종교시설21663280
26431기타6467317
33123미용업628305
20182일반음식점3220541
35748기타107282
4313일반음식점37965575
27835기타2091513
19095종교시설2481548
37280기타611901
28460일반음식점1381412