Overview

Dataset statistics

Number of variables8
Number of observations500
Missing cells62
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.8 KiB
Average record size in memory69.3 B

Variable types

Numeric5
Categorical2
Text1

Dataset

Description샘플 데이터
Author신한카드
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=51

Alerts

기준연월(TS_YM) is highly overall correlated with 일별(TS_YMD)High correlation
일별(TS_YMD) is highly overall correlated with 기준연월(TS_YM)High correlation
고객주소광역시(SIDO) is highly imbalanced (51.5%)Imbalance
고객주소시군구(SGG) has 62 (12.4%) missing valuesMissing

Reproduction

Analysis started2023-12-10 14:54:38.894122
Analysis finished2023-12-10 14:54:43.549014
Duration4.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

가맹점집계구코드(TOT_REG_CD)
Real number (ℝ)

Distinct457
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.11447 × 1012
Minimum1.101053 × 1012
Maximum1.125074 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:43.667383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.101053 × 1012
5-th percentile1.101072 × 1012
Q11.1070593 × 1012
median1.116061 × 1012
Q31.122054 × 1012
95-th percentile1.1240582 × 1012
Maximum1.125074 × 1012
Range2.402102 × 1010
Interquartile range (IQR)1.4994762 × 1010

Descriptive statistics

Standard deviation7.7981597 × 109
Coefficient of variation (CV)0.0069971912
Kurtosis-1.3424173
Mean1.11447 × 1012
Median Absolute Deviation (MAD)6.9915 × 109
Skewness-0.34337685
Sum5.57235 × 1014
Variance6.0811295 × 1019
MonotonicityNot monotonic
2023-12-10T23:54:43.898786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1123079010001 3
 
0.6%
1122066010001 3
 
0.6%
1104066020001 3
 
0.6%
1123064050002 2
 
0.4%
1101061030004 2
 
0.4%
1115065030002 2
 
0.4%
1111053020101 2
 
0.4%
1119074020001 2
 
0.4%
1101064020004 2
 
0.4%
1104066020008 2
 
0.4%
Other values (447) 477
95.4%
ValueCountFrequency (%)
1101053010004 1
0.2%
1101053020001 1
0.2%
1101053020002 2
0.4%
1101054010002 1
0.2%
1101055030002 1
0.2%
1101057010001 1
0.2%
1101060020001 1
0.2%
1101061020001 1
0.2%
1101061020002 1
0.2%
1101061030001 1
0.2%
ValueCountFrequency (%)
1125074030009 1
0.2%
1125072020501 1
0.2%
1125067020005 1
0.2%
1125066010008 2
0.4%
1125066010006 1
0.2%
1125066010004 1
0.2%
1125056030001 1
0.2%
1125055020007 1
0.2%
1125053020001 1
0.2%
1125052010001 1
0.2%

내국인업종코드(SB_UPJONG_CD)
Categorical

Distinct50
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
SB016
92 
SB001
64 
SB008
60 
SB006
26 
SB013
26 
Other values (45)
232 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique11 ?
Unique (%)2.2%

Sample

1st rowSB001
2nd rowSB016
3rd rowSB020
4th rowSB021
5th rowSB005

Common Values

ValueCountFrequency (%)
SB016 92
18.4%
SB001 64
12.8%
SB008 60
 
12.0%
SB006 26
 
5.2%
SB013 26
 
5.2%
SB005 22
 
4.4%
SB020 20
 
4.0%
SB054 16
 
3.2%
SB007 12
 
2.4%
SB051 11
 
2.2%
Other values (40) 151
30.2%

Length

2023-12-10T23:54:44.103394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sb016 92
18.4%
sb001 64
12.8%
sb008 60
 
12.0%
sb006 26
 
5.2%
sb013 26
 
5.2%
sb005 22
 
4.4%
sb020 20
 
4.0%
sb054 16
 
3.2%
sb007 12
 
2.4%
sb051 11
 
2.2%
Other values (40) 151
30.2%

기준연월(TS_YM)
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201880.74
Minimum201701
Maximum202107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:44.299793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201701
5-th percentile201704
Q1201802
median201903
Q3202005
95-th percentile202103
Maximum202107
Range406
Interquartile range (IQR)203

Descriptive statistics

Standard deviation128.87896
Coefficient of variation (CV)0.00063839156
Kurtosis-1.1430881
Mean201880.74
Median Absolute Deviation (MAD)101
Skewness0.15473907
Sum1.0094037 × 108
Variance16609.787
MonotonicityNot monotonic
2023-12-10T23:54:44.501343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201811 15
 
3.0%
201904 15
 
3.0%
202011 14
 
2.8%
201802 14
 
2.8%
202010 14
 
2.8%
201912 13
 
2.6%
202002 13
 
2.6%
201709 13
 
2.6%
201705 12
 
2.4%
201903 12
 
2.4%
Other values (45) 365
73.0%
ValueCountFrequency (%)
201701 5
 
1.0%
201702 7
1.4%
201703 12
2.4%
201704 10
2.0%
201705 12
2.4%
201706 9
1.8%
201707 11
2.2%
201708 5
 
1.0%
201709 13
2.6%
201710 6
1.2%
ValueCountFrequency (%)
202107 5
 
1.0%
202106 11
2.2%
202105 4
 
0.8%
202104 3
 
0.6%
202103 11
2.2%
202102 8
1.6%
202101 8
1.6%
202012 11
2.2%
202011 14
2.8%
202010 14
2.8%

일별(TS_YMD)
Real number (ℝ)

HIGH CORRELATION 

Distinct430
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20188090
Minimum20170110
Maximum20210726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:44.710792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170110
5-th percentile20170404
Q120180213
median20190304
Q320200509
95-th percentile20210317
Maximum20210726
Range40616
Interquartile range (IQR)20296

Descriptive statistics

Standard deviation12887.695
Coefficient of variation (CV)0.00063838113
Kurtosis-1.1432014
Mean20188090
Median Absolute Deviation (MAD)10102.5
Skewness0.15472674
Sum1.0094045 × 1010
Variance1.6609269 × 108
MonotonicityNot monotonic
2023-12-10T23:54:44.902313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20181009 4
 
0.8%
20170327 4
 
0.8%
20180303 3
 
0.6%
20200630 3
 
0.6%
20190417 3
 
0.6%
20190109 3
 
0.6%
20181106 3
 
0.6%
20180206 3
 
0.6%
20181116 3
 
0.6%
20171229 3
 
0.6%
Other values (420) 468
93.6%
ValueCountFrequency (%)
20170110 1
0.2%
20170111 1
0.2%
20170112 1
0.2%
20170124 1
0.2%
20170129 1
0.2%
20170204 2
0.4%
20170209 1
0.2%
20170210 1
0.2%
20170213 1
0.2%
20170216 1
0.2%
ValueCountFrequency (%)
20210726 2
0.4%
20210719 1
0.2%
20210710 1
0.2%
20210701 1
0.2%
20210627 1
0.2%
20210625 1
0.2%
20210624 1
0.2%
20210621 1
0.2%
20210619 1
0.2%
20210614 1
0.2%

고객주소광역시(SIDO)
Categorical

IMBALANCE 

Distinct18
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
서울
283 
경기
135 
충북
 
10
인천
 
9
부산
 
8
Other values (13)
55 

Length

Max length5
Median length2
Mean length2.05
Min length2

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row서울
2nd row서울
3rd row서울
4th row서울
5th row경기

Common Values

ValueCountFrequency (%)
서울 283
56.6%
경기 135
27.0%
충북 10
 
2.0%
인천 9
 
1.8%
부산 8
 
1.6%
충남 8
 
1.6%
서울특별시 7
 
1.4%
강원 7
 
1.4%
광주 6
 
1.2%
대전 5
 
1.0%
Other values (8) 22
 
4.4%

Length

2023-12-10T23:54:45.079707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 283
56.6%
경기 135
27.0%
충북 10
 
2.0%
인천 9
 
1.8%
부산 8
 
1.6%
충남 8
 
1.6%
서울특별시 7
 
1.4%
강원 7
 
1.4%
광주 6
 
1.2%
경북 5
 
1.0%
Other values (8) 22
 
4.4%

고객주소시군구(SGG)
Text

MISSING 

Distinct53
Distinct (%)12.1%
Missing62
Missing (%)12.4%
Memory size4.0 KiB
2023-12-10T23:54:45.351538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0799087
Min length2

Characters and Unicode

Total characters1349
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)1.4%

Sample

1st row성동구
2nd row김포시
3rd row서초구
4th row양주시
5th row서대문구
ValueCountFrequency (%)
송파구 19
 
4.3%
성북구 18
 
4.1%
중랑구 17
 
3.9%
서초구 17
 
3.9%
강서구 16
 
3.7%
강동구 16
 
3.7%
고양시 16
 
3.7%
동작구 14
 
3.2%
광진구 14
 
3.2%
은평구 14
 
3.2%
Other values (43) 277
63.2%
2023-12-10T23:54:45.824733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 316
23.4%
시 145
 
10.7%
강 53
 
3.9%
동 52
 
3.9%
양 44
 
3.3%
서 43
 
3.2%
성 41
 
3.0%
남 41
 
3.0%
천 32
 
2.4%
광 31
 
2.3%
Other values (49) 551
40.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1349
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 316
23.4%
시 145
 
10.7%
강 53
 
3.9%
동 52
 
3.9%
양 44
 
3.3%
서 43
 
3.2%
성 41
 
3.0%
남 41
 
3.0%
천 32
 
2.4%
광 31
 
2.3%
Other values (49) 551
40.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1349
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 316
23.4%
시 145
 
10.7%
강 53
 
3.9%
동 52
 
3.9%
양 44
 
3.3%
서 43
 
3.2%
성 41
 
3.0%
남 41
 
3.0%
천 32
 
2.4%
광 31
 
2.3%
Other values (49) 551
40.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1349
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 316
23.4%
시 145
 
10.7%
강 53
 
3.9%
동 52
 
3.9%
양 44
 
3.3%
서 43
 
3.2%
성 41
 
3.0%
남 41
 
3.0%
천 32
 
2.4%
광 31
 
2.3%
Other values (49) 551
40.8%

카드이용금액계(AMT_CORR)
Real number (ℝ)

Distinct316
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean317946.32
Minimum1509
Maximum8299500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:45.999498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1509
5-th percentile10034.85
Q130557.25
median81611.75
Q3226350
95-th percentile1280738.6
Maximum8299500
Range8297991
Interquartile range (IQR)195792.75

Descriptive statistics

Standard deviation788676.56
Coefficient of variation (CV)2.4805337
Kurtosis47.38413
Mean317946.32
Median Absolute Deviation (MAD)62598.35
Skewness6.1412781
Sum1.5897316 × 108
Variance6.2201072 × 1011
MonotonicityNot monotonic
2023-12-10T23:54:46.210564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20120.0 15
 
3.0%
45270.0 13
 
2.6%
22635.0 12
 
2.4%
25150.0 8
 
1.6%
40240.0 7
 
1.4%
12575.0 7
 
1.4%
10060.0 7
 
1.4%
50300.0 6
 
1.2%
9054.0 6
 
1.2%
52815.0 6
 
1.2%
Other values (306) 413
82.6%
ValueCountFrequency (%)
1509.0 1
 
0.2%
2012.0 2
 
0.4%
2515.0 2
 
0.4%
3521.0 1
 
0.2%
5030.0 3
0.6%
5533.0 1
 
0.2%
6036.0 1
 
0.2%
7042.0 1
 
0.2%
7545.0 5
1.0%
7997.7 1
 
0.2%
ValueCountFrequency (%)
8299500.0 1
0.2%
7407328.9 1
0.2%
6761778.7 1
0.2%
4677900.0 1
0.2%
4588059.17 1
0.2%
4527000.0 1
0.2%
3513304.1 1
0.2%
2914532.9 1
0.2%
2503833.4 1
0.2%
2487335.0 1
0.2%

카드이용건수(USECT_CORR)
Real number (ℝ)

Distinct27
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.97528
Minimum5.03
Maximum432.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:46.423921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.03
5-th percentile5.03
Q15.03
median5.03
Q310.06
95-th percentile45.27
Maximum432.58
Range427.55
Interquartile range (IQR)5.03

Descriptive statistics

Standard deviation42.384438
Coefficient of variation (CV)2.6531264
Kurtosis63.416035
Mean15.97528
Median Absolute Deviation (MAD)0
Skewness7.5696866
Sum7987.64
Variance1796.4406
MonotonicityNot monotonic
2023-12-10T23:54:46.566288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5.03 306
61.2%
10.06 84
 
16.8%
15.09 31
 
6.2%
20.12 24
 
4.8%
25.15 14
 
2.8%
30.18 6
 
1.2%
45.27 5
 
1.0%
35.21 4
 
0.8%
40.24 3
 
0.6%
50.3 3
 
0.6%
Other values (17) 20
 
4.0%
ValueCountFrequency (%)
5.03 306
61.2%
10.06 84
 
16.8%
15.09 31
 
6.2%
20.12 24
 
4.8%
25.15 14
 
2.8%
30.18 6
 
1.2%
35.21 4
 
0.8%
40.24 3
 
0.6%
45.27 5
 
1.0%
50.3 3
 
0.6%
ValueCountFrequency (%)
432.58 1
0.2%
402.4 1
0.2%
397.37 1
0.2%
382.28 1
0.2%
347.07 1
0.2%
186.11 1
0.2%
176.05 1
0.2%
125.75 1
0.2%
120.72 1
0.2%
110.66 1
0.2%

Interactions

2023-12-10T23:54:42.598409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:39.456569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:40.130140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:40.820713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:41.959867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:42.731995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:39.592493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:40.268933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:40.974194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:42.093192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:42.849992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:39.741944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:40.410794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:41.147060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:42.231754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:42.969877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:39.883075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:40.599015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:41.331958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:42.371624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:43.112696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:39.999711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:40.707787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:41.495819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:42.482846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:54:46.674626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점집계구코드(TOT_REG_CD)내국인업종코드(SB_UPJONG_CD)기준연월(TS_YM)일별(TS_YMD)고객주소광역시(SIDO)고객주소시군구(SGG)카드이용금액계(AMT_CORR)카드이용건수(USECT_CORR)
가맹점집계구코드(TOT_REG_CD)1.0000.2280.0400.0760.1050.0000.1700.000
내국인업종코드(SB_UPJONG_CD)0.2281.0000.1550.1630.0000.1900.0000.590
기준연월(TS_YM)0.0400.1551.0001.0000.1270.3830.1640.000
일별(TS_YMD)0.0760.1631.0001.0000.1710.3480.1730.000
고객주소광역시(SIDO)0.1050.0000.1270.1711.0000.4970.0000.000
고객주소시군구(SGG)0.0000.1900.3830.3480.4971.0000.5570.000
카드이용금액계(AMT_CORR)0.1700.0000.1640.1730.0000.5571.0000.000
카드이용건수(USECT_CORR)0.0000.5900.0000.0000.0000.0000.0001.000
2023-12-10T23:54:46.857435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고객주소광역시(SIDO)내국인업종코드(SB_UPJONG_CD)
고객주소광역시(SIDO)1.0000.000
내국인업종코드(SB_UPJONG_CD)0.0001.000
2023-12-10T23:54:47.000850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점집계구코드(TOT_REG_CD)기준연월(TS_YM)일별(TS_YMD)카드이용금액계(AMT_CORR)카드이용건수(USECT_CORR)내국인업종코드(SB_UPJONG_CD)고객주소광역시(SIDO)
가맹점집계구코드(TOT_REG_CD)1.0000.0140.0120.010-0.0490.0720.037
기준연월(TS_YM)0.0141.0001.000-0.041-0.0440.0500.053
일별(TS_YMD)0.0121.0001.000-0.042-0.0440.0630.068
카드이용금액계(AMT_CORR)0.010-0.041-0.0421.0000.0440.0000.000
카드이용건수(USECT_CORR)-0.049-0.044-0.0440.0441.0000.2580.000
내국인업종코드(SB_UPJONG_CD)0.0720.0500.0630.0000.2581.0000.000
고객주소광역시(SIDO)0.0370.0530.0680.0000.0000.0001.000

Missing values

2023-12-10T23:54:43.304558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:54:43.483696image/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

가맹점집계구코드(TOT_REG_CD)내국인업종코드(SB_UPJONG_CD)기준연월(TS_YM)일별(TS_YMD)고객주소광역시(SIDO)고객주소시군구(SGG)카드이용금액계(AMT_CORR)카드이용건수(USECT_CORR)
01124058010012SB00120190820190828서울성동구140840.010.06
11123064040004SB01620190420190407서울김포시122732.05.03
21124080020103SB02020171220171229서울<NA>25150.085.51
31124056020010SB02120210120210107서울<NA>17605.05.03
41122059010001SB00520210620210607경기서초구1674990.0125.75
51125053020001SB00620170720170714충북양주시40240.020.12
61108082020002SB02020210120210129서울서대문구128768.05.03
71118057010001SB00120180720180730경기강동구25150.025.15
81110055010005SB05620171220171223경기<NA>284698.025.15
91121080010005SB06020190120190121서울중랑구241440.05.03
가맹점집계구코드(TOT_REG_CD)내국인업종코드(SB_UPJONG_CD)기준연월(TS_YM)일별(TS_YMD)고객주소광역시(SIDO)고객주소시군구(SGG)카드이용금액계(AMT_CORR)카드이용건수(USECT_CORR)
4901120054010001SB00620170920170906경기광명시1083462.025.15
4911123079020004SB01320180220180215경기광진구1031150.05.03
4921119062010003SB06220181220181213경기구로구135810.05.03
4931124066010201SB04520170920170904서울강남구826429.05.03
4941117054030002SB00120181120181108충북고양시226350.05.03
4951107057010011SB02520181120181105경기<NA>30180.025.15
4961118059010002SB01620210620210614광주금천구10060.05.03
4971125052010001SB00820201020201013서울금천구7997.75.03
4981108071040102SB00520200820200805부산마포구22635.05.03
4991103071050002SB01320180820180813경기도종로구80480.05.03