Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory849.6 KiB
Average record size in memory87.0 B

Variable types

Numeric7
Categorical2

Dataset

Description부산광역시상수도사업본부_수용가정보시스템_고지집계정보_고지전수집계_20210601
Author부산광역시 상수도사업본부
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15083673

Alerts

상하수도구분 has constant value ""Constant
연번 is highly overall correlated with 고지년월High correlation
구경 is highly overall correlated with 전수High correlation
전수 is highly overall correlated with 구경High correlation
고지년월 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:49:15.732562
Analysis finished2023-12-10 16:49:25.372259
Duration9.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10774.55
Minimum2
Maximum21528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:49:25.455283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1084.9
Q15429.75
median10744.5
Q316104
95-th percentile20481.1
Maximum21528
Range21526
Interquartile range (IQR)10674.25

Descriptive statistics

Standard deviation6209.4057
Coefficient of variation (CV)0.57630303
Kurtosis-1.1946968
Mean10774.55
Median Absolute Deviation (MAD)5338
Skewness0.00018996161
Sum1.077455 × 108
Variance38556720
MonotonicityNot monotonic
2023-12-11T01:49:25.595341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19661 1
 
< 0.1%
7111 1
 
< 0.1%
17437 1
 
< 0.1%
2720 1
 
< 0.1%
19294 1
 
< 0.1%
2 1
 
< 0.1%
4410 1
 
< 0.1%
17962 1
 
< 0.1%
7285 1
 
< 0.1%
20609 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
4 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
20 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
26 1
< 0.1%
ValueCountFrequency (%)
21528 1
< 0.1%
21527 1
< 0.1%
21526 1
< 0.1%
21523 1
< 0.1%
21520 1
< 0.1%
21519 1
< 0.1%
21518 1
< 0.1%
21516 1
< 0.1%
21515 1
< 0.1%
21512 1
< 0.1%

고지년월
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-04
2021 
2021-02
2006 
2021-03
1996 
2021-05
1994 
2021-01
1983 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-05
2nd row2021-01
3rd row2021-05
4th row2021-05
5th row2021-03

Common Values

ValueCountFrequency (%)
2021-04 2021
20.2%
2021-02 2006
20.1%
2021-03 1996
20.0%
2021-05 1994
19.9%
2021-01 1983
19.8%

Length

2023-12-11T01:49:25.723183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:49:25.822545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-04 2021
20.2%
2021-02 2006
20.1%
2021-03 1996
20.0%
2021-05 1994
19.9%
2021-01 1983
19.8%

사업소코드
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.3814
Minimum244
Maximum312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:49:25.932564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum244
5-th percentile244
Q1301
median304
Q3307
95-th percentile311
Maximum312
Range68
Interquartile range (IQR)6

Descriptive statistics

Standard deviation24.838543
Coefficient of variation (CV)0.084662979
Kurtosis0.19338752
Mean293.3814
Median Absolute Deviation (MAD)3
Skewness-1.4577056
Sum2933814
Variance616.95323
MonotonicityNot monotonic
2023-12-11T01:49:26.044346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
244 2001
20.0%
306 1369
13.7%
307 1215
12.2%
304 1204
12.0%
301 861
8.6%
309 840
8.4%
308 822
8.2%
303 574
 
5.7%
302 544
 
5.4%
311 312
 
3.1%
ValueCountFrequency (%)
244 2001
20.0%
301 861
8.6%
302 544
 
5.4%
303 574
 
5.7%
304 1204
12.0%
306 1369
13.7%
307 1215
12.2%
308 822
8.2%
309 840
8.4%
311 312
 
3.1%
ValueCountFrequency (%)
312 258
 
2.6%
311 312
 
3.1%
309 840
8.4%
308 822
8.2%
307 1215
12.2%
306 1369
13.7%
304 1204
12.0%
303 574
5.7%
302 544
 
5.4%
301 861
8.6%

구코드
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean329.777
Minimum110
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:49:26.158185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile140
Q1230
median320
Q3410
95-th percentile530
Maximum710
Range600
Interquartile range (IQR)180

Descriptive statistics

Standard deviation132.31746
Coefficient of variation (CV)0.40123315
Kurtosis0.021774249
Mean329.777
Median Absolute Deviation (MAD)90
Skewness0.51438875
Sum3297770
Variance17507.911
MonotonicityNot monotonic
2023-12-11T01:49:26.283814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
230 1204
12.0%
290 866
 
8.7%
380 840
 
8.4%
350 822
 
8.2%
410 702
 
7.0%
260 663
 
6.6%
470 636
 
6.4%
320 613
 
6.1%
530 602
 
6.0%
200 574
 
5.7%
Other values (6) 2478
24.8%
ValueCountFrequency (%)
110 347
 
3.5%
140 544
5.4%
170 514
5.1%
200 574
5.7%
230 1204
12.0%
260 663
6.6%
290 866
8.7%
320 613
6.1%
350 822
8.2%
380 840
8.4%
ValueCountFrequency (%)
710 258
 
2.6%
530 602
6.0%
500 503
5.0%
470 636
6.4%
440 312
 
3.1%
410 702
7.0%
380 840
8.4%
350 822
8.2%
320 613
6.1%
290 866
8.7%

동코드
Real number (ℝ)

Distinct63
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean602.2559
Minimum250
Maximum800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:49:26.404011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile510
Q1550
median591
Q3660
95-th percentile760
Maximum800
Range550
Interquartile range (IQR)110

Descriptive statistics

Standard deviation90.070927
Coefficient of variation (CV)0.14955591
Kurtosis2.8998375
Mean602.2559
Median Absolute Deviation (MAD)51
Skewness-0.70502328
Sum6022559
Variance8112.7719
MonotonicityNot monotonic
2023-12-11T01:49:26.538803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
530 533
 
5.3%
560 486
 
4.9%
510 486
 
4.9%
520 464
 
4.6%
590 425
 
4.2%
550 405
 
4.0%
620 403
 
4.0%
580 392
 
3.9%
610 388
 
3.9%
660 387
 
3.9%
Other values (53) 5631
56.3%
ValueCountFrequency (%)
250 61
 
0.6%
253 55
 
0.5%
256 45
 
0.4%
310 51
 
0.5%
320 2
 
< 0.1%
330 44
 
0.4%
510 486
4.9%
520 464
4.6%
521 53
 
0.5%
525 38
 
0.4%
ValueCountFrequency (%)
800 58
 
0.6%
790 83
0.8%
780 88
0.9%
770 105
1.1%
762 55
 
0.5%
761 49
 
0.5%
760 123
1.2%
750 155
1.6%
740 155
1.6%
730 156
1.6%

상하수도구분
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
S
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 10000
100.0%

Length

2023-12-11T01:49:26.679672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:49:26.777159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 10000
100.0%

상수도업종
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.787
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:49:26.866617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q33
95-th percentile6
Maximum13
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8399917
Coefficient of variation (CV)0.66020514
Kurtosis1.300264
Mean2.787
Median Absolute Deviation (MAD)2
Skewness1.0475106
Sum27870
Variance3.3855696
MonotonicityNot monotonic
2023-12-11T01:49:26.970871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 3942
39.4%
3 3709
37.1%
6 1254
 
12.5%
4 887
 
8.9%
8 109
 
1.1%
7 60
 
0.6%
12 16
 
0.2%
11 13
 
0.1%
9 7
 
0.1%
13 3
 
< 0.1%
ValueCountFrequency (%)
1 3942
39.4%
3 3709
37.1%
4 887
 
8.9%
6 1254
 
12.5%
7 60
 
0.6%
8 109
 
1.1%
9 7
 
0.1%
11 13
 
0.1%
12 16
 
0.2%
13 3
 
< 0.1%
ValueCountFrequency (%)
13 3
 
< 0.1%
12 16
 
0.2%
11 13
 
0.1%
9 7
 
0.1%
8 109
 
1.1%
7 60
 
0.6%
6 1254
 
12.5%
4 887
 
8.9%
3 3709
37.1%
1 3942
39.4%

구경
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.8953
Minimum13
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:49:27.075035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15
Q120
median32
Q350
95-th percentile150
Maximum300
Range287
Interquartile range (IQR)30

Descriptive statistics

Standard deviation48.628327
Coefficient of variation (CV)0.95545811
Kurtosis6.7997646
Mean50.8953
Median Absolute Deviation (MAD)17
Skewness2.4459718
Sum508953
Variance2364.7142
MonotonicityNot monotonic
2023-12-11T01:49:27.183029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
15 1534
15.3%
40 1470
14.7%
25 1451
14.5%
20 1352
13.5%
50 1217
12.2%
80 809
8.1%
32 708
7.1%
100 649
6.5%
150 435
 
4.3%
200 207
 
2.1%
Other values (3) 168
 
1.7%
ValueCountFrequency (%)
13 4
 
< 0.1%
15 1534
15.3%
20 1352
13.5%
25 1451
14.5%
32 708
7.1%
40 1470
14.7%
50 1217
12.2%
80 809
8.1%
100 649
6.5%
150 435
 
4.3%
ValueCountFrequency (%)
300 60
 
0.6%
250 104
 
1.0%
200 207
 
2.1%
150 435
 
4.3%
100 649
6.5%
80 809
8.1%
50 1217
12.2%
40 1470
14.7%
32 708
7.1%
25 1451
14.5%

전수
Real number (ℝ)

HIGH CORRELATION 

Distinct747
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.652
Minimum1
Maximum4561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:49:27.721135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q322
95-th percentile609.05
Maximum4561
Range4560
Interquartile range (IQR)20

Descriptive statistics

Standard deviation280.4149
Coefficient of variation (CV)3.2361041
Kurtosis33.803846
Mean86.652
Median Absolute Deviation (MAD)4
Skewness5.1551795
Sum866520
Variance78632.513
MonotonicityNot monotonic
2023-12-11T01:49:27.972692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2436
24.4%
2 1216
 
12.2%
3 762
 
7.6%
4 510
 
5.1%
5 413
 
4.1%
6 371
 
3.7%
8 236
 
2.4%
7 227
 
2.3%
9 214
 
2.1%
10 156
 
1.6%
Other values (737) 3459
34.6%
ValueCountFrequency (%)
1 2436
24.4%
2 1216
12.2%
3 762
 
7.6%
4 510
 
5.1%
5 413
 
4.1%
6 371
 
3.7%
7 227
 
2.3%
8 236
 
2.4%
9 214
 
2.1%
10 156
 
1.6%
ValueCountFrequency (%)
4561 1
< 0.1%
2900 1
< 0.1%
2898 1
< 0.1%
2897 1
< 0.1%
2733 1
< 0.1%
2725 2
< 0.1%
2600 1
< 0.1%
2594 1
< 0.1%
2586 1
< 0.1%
2583 1
< 0.1%

Interactions

2023-12-11T01:49:24.308800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:18.033281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:19.220306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:20.278398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:21.613213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:22.628027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:23.425694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:24.410924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:18.190354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:19.370528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:20.731794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:21.761440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:22.736525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:23.533748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:24.532641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:18.345196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:19.531411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:20.925799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:21.905463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:22.854719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:23.650095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:24.663125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:18.511643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:19.693577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:21.062021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:22.069185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:22.977170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:23.803391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:24.781771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:18.713275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:19.873547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:21.210031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:22.222356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:23.104556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:23.946289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:24.885172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:18.881073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:20.022293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:21.340931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:22.355299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:23.199171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:24.069012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:24.999481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:19.071645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:20.167184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:21.476197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:22.489194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:23.307509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:24.190582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:49:28.113345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번고지년월사업소코드구코드동코드상수도업종구경전수
연번1.0001.0000.9990.5270.1900.0660.0500.016
고지년월1.0001.0000.0000.0000.0000.0000.0000.000
사업소코드0.9990.0001.0001.0000.2920.1390.1160.059
구코드0.5270.0001.0001.0000.8460.1740.1050.135
동코드0.1900.0000.2920.8461.0000.1070.0430.061
상수도업종0.0660.0000.1390.1740.1071.0000.3890.205
구경0.0500.0000.1160.1050.0430.3891.0000.195
전수0.0160.0000.0590.1350.0610.2050.1951.000
2023-12-11T01:49:28.284917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사업소코드구코드동코드상수도업종구경전수고지년월
연번1.0000.2030.085-0.040-0.0030.0190.0010.999
사업소코드0.2031.0000.374-0.2600.0220.0390.0410.000
구코드0.0850.3741.0000.0960.0160.0160.0430.000
동코드-0.040-0.2600.0961.000-0.0130.012-0.0390.000
상수도업종-0.0030.0220.016-0.0131.000-0.221-0.2390.000
구경0.0190.0390.0160.012-0.2211.000-0.5610.000
전수0.0010.0410.043-0.039-0.239-0.5611.0000.000
고지년월0.9990.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-11T01:49:25.171067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:49:25.312305image/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

연번고지년월사업소코드구코드동코드상하수도구분상수도업종구경전수
19660196612021-05306290620S33001
370037012021-01309380510S32072
19917199182021-05306500760S3802
20382203832021-05307530620S120111
11588115892021-03307320560S3325
16853168542021-04309380590S12035
18577185782021-05302140590S4401
16042160432021-04307530600S1321
752275232021-02307530650S1322
21119211202021-05309380572S88027
연번고지년월사업소코드구코드동코드상하수도구분상수도업종구경전수
354435452021-01308350570S6252
17680176812021-05244410600S315583
747074712021-02307530620S12541
926692672021-03244470630S12012
19647196482021-05306290620S1802
12736127372021-03311440540S11508
257125722021-01306290710S31002
12137121382021-03308350570S12512
11137111382021-03306290690S315444
11447114482021-03307320521S1325