Overview

Dataset statistics

Number of variables14
Number of observations100
Missing cells39
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.9 KiB
Average record size in memory122.3 B

Variable types

Numeric3
Text1
Categorical6
DateTime4

Dataset

Description한국주택금융공사 주택연금부 연대보증인 관리 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터) 입니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15072972/fileData.do

Alerts

순번 has constant value ""Constant
채무관계자코드 has constant value ""Constant
연보인입보구분코드 has constant value ""Constant
등록자사번 is highly overall correlated with 책임종료사번High correlation
책임종료사번 is highly overall correlated with 등록자사번High correlation
조변순번 is highly imbalanced (52.9%)Imbalance
입조자격구분코드 is highly imbalanced (79.7%)Imbalance
책임종료일자 has 13 (13.0%) missing valuesMissing
책임종료사번 has 13 (13.0%) missing valuesMissing
책임종료일시 has 13 (13.0%) missing valuesMissing
등록일시 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:25:15.398596
Analysis finished2023-12-12 20:25:18.071217
Duration2.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고객번호
Real number (ℝ)

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0293721 × 108
Minimum8708524
Maximum1.2837462 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T05:25:18.153521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8708524
5-th percentile63967751
Q195467582
median1.0966341 × 108
Q31.169142 × 108
95-th percentile1.2721259 × 108
Maximum1.2837462 × 108
Range1.1966609 × 108
Interquartile range (IQR)21446620

Descriptive statistics

Standard deviation23907116
Coefficient of variation (CV)0.23224952
Kurtosis6.4281716
Mean1.0293721 × 108
Median Absolute Deviation (MAD)10540829
Skewness-2.3380504
Sum1.0293721 × 1010
Variance5.7155022 × 1014
MonotonicityNot monotonic
2023-12-13T05:25:18.318685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127212589 3
 
3.0%
112586343 2
 
2.0%
94138851 2
 
2.0%
125248597 1
 
1.0%
108312602 1
 
1.0%
86026010 1
 
1.0%
114284287 1
 
1.0%
89786975 1
 
1.0%
92917249 1
 
1.0%
18509548 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
8708524 1
1.0%
10774973 1
1.0%
16718690 1
1.0%
18509548 1
1.0%
33129576 1
1.0%
65590813 1
1.0%
71383328 1
1.0%
79132458 1
1.0%
84760543 1
1.0%
84787665 1
1.0%
ValueCountFrequency (%)
128374617 1
 
1.0%
127643420 1
 
1.0%
127419816 1
 
1.0%
127212589 3
3.0%
125630024 1
 
1.0%
125248597 1
 
1.0%
125159521 1
 
1.0%
124352002 1
 
1.0%
122957836 1
 
1.0%
122412630 1
 
1.0%

보증번호
Text

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-13T05:25:18.576816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1400
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)93.0%

Sample

1st rowRQAD2018000791
2nd rowRQAD2016001197
3rd rowRTMA2012000192
4th rowRTHA2019000355
5th rowRTAD2017000454
ValueCountFrequency (%)
rtbb2019000098 3
 
3.0%
rtba2016000780 2
 
2.0%
rqad2013000573 2
 
2.0%
rtho2012000333 1
 
1.0%
rqad2016000801 1
 
1.0%
rtac2017000111 1
 
1.0%
rtac2012000496 1
 
1.0%
rtaa2013000216 1
 
1.0%
rtha2013000016 1
 
1.0%
rqad2016000014 1
 
1.0%
Other values (86) 86
86.0%
2023-12-13T05:25:19.001340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 438
31.3%
1 140
 
10.0%
2 137
 
9.8%
R 100
 
7.1%
A 94
 
6.7%
T 82
 
5.9%
8 47
 
3.4%
3 44
 
3.1%
4 42
 
3.0%
5 40
 
2.9%
Other values (12) 236
16.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
71.4%
Uppercase Letter 400
 
28.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 100
25.0%
A 94
23.5%
T 82
20.5%
D 29
 
7.2%
B 25
 
6.2%
H 23
 
5.8%
Q 20
 
5.0%
C 15
 
3.8%
O 6
 
1.5%
M 4
 
1.0%
Other values (2) 2
 
0.5%
Decimal Number
ValueCountFrequency (%)
0 438
43.8%
1 140
 
14.0%
2 137
 
13.7%
8 47
 
4.7%
3 44
 
4.4%
4 42
 
4.2%
5 40
 
4.0%
7 40
 
4.0%
6 39
 
3.9%
9 33
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
71.4%
Latin 400
 
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 100
25.0%
A 94
23.5%
T 82
20.5%
D 29
 
7.2%
B 25
 
6.2%
H 23
 
5.8%
Q 20
 
5.0%
C 15
 
3.8%
O 6
 
1.5%
M 4
 
1.0%
Other values (2) 2
 
0.5%
Common
ValueCountFrequency (%)
0 438
43.8%
1 140
 
14.0%
2 137
 
13.7%
8 47
 
4.7%
3 44
 
4.4%
4 42
 
4.2%
5 40
 
4.0%
7 40
 
4.0%
6 39
 
3.9%
9 33
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 438
31.3%
1 140
 
10.0%
2 137
 
9.8%
R 100
 
7.1%
A 94
 
6.7%
T 82
 
5.9%
8 47
 
3.4%
3 44
 
3.1%
4 42
 
3.0%
5 40
 
2.9%
Other values (12) 236
16.9%

조변순번
Categorical

IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
78 
-1
12 
2
 
7
-2
 
2
-3
 
1

Length

Max length2
Median length1
Mean length1.15
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 78
78.0%
-1 12
 
12.0%
2 7
 
7.0%
-2 2
 
2.0%
-3 1
 
1.0%

Length

2023-12-13T05:25:19.147638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:25:19.259408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 90
90.0%
2 9
 
9.0%
3 1
 
1.0%

순번
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 100
100.0%

Length

2023-12-13T05:25:19.382970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:25:19.505671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
100.0%

채무관계자코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
4
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 100
100.0%

Length

2023-12-13T05:25:19.615360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:25:19.717688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 100
100.0%

연보인입보구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 100
100.0%

Length

2023-12-13T05:25:19.820489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:25:19.938236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
100.0%

연보인해지구분코드
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2
72 
99
14 
<NA>
13 
1
 
1

Length

Max length4
Median length1
Mean length1.53
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2
2nd row2
3rd row<NA>
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 72
72.0%
99 14
 
14.0%
<NA> 13
 
13.0%
1 1
 
1.0%

Length

2023-12-13T05:25:20.056563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:25:20.207386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 72
72.0%
99 14
 
14.0%
na 13
 
13.0%
1 1
 
1.0%

책임개시일자
Date

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2008-11-17 00:00:00
Maximum2019-12-10 00:00:00
2023-12-13T05:25:20.391218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:20.601669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

책임종료일자
Date

MISSING 

Distinct77
Distinct (%)88.5%
Missing13
Missing (%)13.0%
Memory size932.0 B
Minimum2012-12-24 00:00:00
Maximum2019-12-06 00:00:00
2023-12-13T05:25:20.777278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:20.948480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

입조자격구분코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
95 
99
 
4
2
 
1

Length

Max length2
Median length1
Mean length1.04
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 95
95.0%
99 4
 
4.0%
2 1
 
1.0%

Length

2023-12-13T05:25:21.096684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:25:21.204802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 95
95.0%
99 4
 
4.0%
2 1
 
1.0%

등록자사번
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1681.95
Minimum1174
Maximum1932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T05:25:21.307682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1174
5-th percentile1371
Q11590.25
median1656
Q31823.75
95-th percentile1917.65
Maximum1932
Range758
Interquartile range (IQR)233.5

Descriptive statistics

Standard deviation167.5621
Coefficient of variation (CV)0.099623712
Kurtosis0.44519776
Mean1681.95
Median Absolute Deviation (MAD)119
Skewness-0.6671534
Sum168195
Variance28077.058
MonotonicityNot monotonic
2023-12-13T05:25:21.466438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1650 7
 
7.0%
1656 7
 
7.0%
1569 5
 
5.0%
1623 5
 
5.0%
1707 5
 
5.0%
1773 4
 
4.0%
1932 4
 
4.0%
1582 3
 
3.0%
1844 3
 
3.0%
1253 3
 
3.0%
Other values (34) 54
54.0%
ValueCountFrequency (%)
1174 1
 
1.0%
1253 3
3.0%
1371 2
2.0%
1406 2
2.0%
1410 1
 
1.0%
1475 1
 
1.0%
1500 1
 
1.0%
1530 1
 
1.0%
1532 3
3.0%
1535 1
 
1.0%
ValueCountFrequency (%)
1932 4
4.0%
1930 1
 
1.0%
1917 3
3.0%
1914 1
 
1.0%
1880 2
2.0%
1875 1
 
1.0%
1866 3
3.0%
1859 2
2.0%
1851 2
2.0%
1846 1
 
1.0%

등록일시
Date

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2018-11-06 10:25:00
Maximum2020-01-02 10:57:00
2023-12-13T05:25:21.621795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:21.759872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

책임종료사번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)47.1%
Missing13
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean1688.8046
Minimum1253
Maximum1932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T05:25:21.884436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1253
5-th percentile1406
Q11595.5
median1656
Q31825.5
95-th percentile1926.1
Maximum1932
Range679
Interquartile range (IQR)230

Descriptive statistics

Standard deviation158.4824
Coefficient of variation (CV)0.093842946
Kurtosis0.03372582
Mean1688.8046
Median Absolute Deviation (MAD)117
Skewness-0.45484147
Sum146926
Variance25116.671
MonotonicityNot monotonic
2023-12-13T05:25:22.010781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1650 7
 
7.0%
1656 7
 
7.0%
1707 5
 
5.0%
1623 4
 
4.0%
1932 4
 
4.0%
1917 3
 
3.0%
1532 3
 
3.0%
1866 3
 
3.0%
1773 3
 
3.0%
1721 3
 
3.0%
Other values (31) 45
45.0%
(Missing) 13
 
13.0%
ValueCountFrequency (%)
1253 2
2.0%
1371 2
2.0%
1406 2
2.0%
1410 1
 
1.0%
1475 1
 
1.0%
1500 1
 
1.0%
1530 1
 
1.0%
1532 3
3.0%
1569 3
3.0%
1571 1
 
1.0%
ValueCountFrequency (%)
1932 4
4.0%
1930 1
 
1.0%
1917 3
3.0%
1914 1
 
1.0%
1880 2
2.0%
1866 3
3.0%
1859 1
 
1.0%
1851 2
2.0%
1846 1
 
1.0%
1844 2
2.0%

책임종료일시
Date

MISSING 

Distinct87
Distinct (%)100.0%
Missing13
Missing (%)13.0%
Memory size932.0 B
Minimum2018-11-06 10:25:00
Maximum2020-01-02 10:57:00
2023-12-13T05:25:22.156308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:22.290178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-13T05:25:16.821550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:16.216304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:16.509400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:16.930476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:16.308551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:16.598467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:17.040526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:16.409334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:16.712954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:25:22.389890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고객번호보증번호조변순번연보인해지구분코드책임개시일자책임종료일자입조자격구분코드등록자사번등록일시책임종료사번책임종료일시
고객번호1.0001.0000.0000.2031.0000.8250.0000.1591.0000.0001.000
보증번호1.0001.0000.0001.0001.0001.0001.0000.9941.0000.9561.000
조변순번0.0000.0001.0000.2980.0000.7730.0000.0001.0000.3241.000
연보인해지구분코드0.2031.0000.2981.0000.0000.9840.0000.4891.0000.4481.000
책임개시일자1.0001.0000.0000.0001.0000.9971.0000.9891.0000.9271.000
책임종료일자0.8251.0000.7730.9840.9971.0000.0000.9291.0000.9441.000
입조자격구분코드0.0001.0000.0000.0001.0000.0001.0000.4271.0000.1641.000
등록자사번0.1590.9940.0000.4890.9890.9290.4271.0001.0000.9441.000
등록일시1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
책임종료사번0.0000.9560.3240.4480.9270.9440.1640.9441.0001.0001.000
책임종료일시1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-13T05:25:22.540367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조변순번입조자격구분코드연보인해지구분코드
조변순번1.0000.0000.232
입조자격구분코드0.0001.0000.000
연보인해지구분코드0.2320.0001.000
2023-12-13T05:25:22.634683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고객번호등록자사번책임종료사번조변순번연보인해지구분코드입조자격구분코드
고객번호1.0000.1200.1370.0000.1160.000
등록자사번0.1201.0001.0000.0000.2360.175
책임종료사번0.1371.0001.0000.2030.1920.123
조변순번0.0000.0000.2031.0000.2320.000
연보인해지구분코드0.1160.2360.1920.2321.0000.000
입조자격구분코드0.0000.1750.1230.0000.0001.000

Missing values

2023-12-13T05:25:17.203050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:25:17.827238image/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.
2023-12-13T05:25:18.001601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

고객번호보증번호조변순번순번채무관계자코드연보인입보구분코드연보인해지구분코드책임개시일자책임종료일자입조자격구분코드등록자사번등록일시책임종료사번책임종료일시
0125159521RQAD2018000791114122018-11-272019-11-06116032020-01-02 10:5716032020-01-02 10:57
1114077601RQAD2016001197114122017-01-042019-07-25116032019-12-24 11:2916032019-12-24 11:29
290296784RTMA20120001922141<NA>2019-12-10<NA>118442019-12-23 11:12<NA><NA>
3127419816RTHA2019000355114122019-03-282019-10-16115692019-12-16 17:4915692019-12-16 17:49
4116290415RTAD2017000454114122017-06-132019-11-01116562019-12-16 16:2116562019-12-16 16:21
510774973RTHB20160007362141992016-11-302019-12-05117732019-12-16 11:4517732019-12-16 11:45
690041366RQAD20190006471141992019-09-062019-12-06114062019-12-06 10:1114062019-12-06 10:12
7128374617RTHA2019000469-1141<NA>2019-05-17<NA>111742019-12-04 10:05<NA><NA>
8115838894RTHA2017000358114122017-05-122019-11-29115692019-11-29 16:3615692019-11-29 16:36
9115836359RTHB20170003341141<NA>2019-11-11<NA>217732019-11-19 14:38<NA><NA>
고객번호보증번호조변순번순번채무관계자코드연보인입보구분코드연보인해지구분코드책임개시일자책임종료일자입조자격구분코드등록자사번등록일시책임종료사번책임종료일시
9094051019RTHA2013000284114122013-07-312018-08-17118442018-11-30 9:3118442018-11-30 9:35
91110534614RTBA2016000442114122016-06-102018-11-10118292018-11-27 16:4118292018-11-27 16:41
92113352806RQAD2016001049114122016-11-212018-10-13116232018-11-27 9:0316232018-11-27 9:03
9398121408RTHB2014000256114122014-07-242018-10-11113712018-11-26 11:0213712018-11-26 11:02
94101508112RTAC2015000172114122015-04-132018-06-26112532018-11-21 8:0412532018-11-21 8:04
9588066418RTAC2012000199-114122012-07-182018-08-24112532018-11-16 11:0612532018-11-16 11:06
96122366032RTAA2018000322114122018-06-072018-10-25115302018-11-16 9:3815302018-11-16 9:38
97108890001RQAD2015000800114122016-01-052018-10-08116232018-11-15 18:2416232018-11-15 18:24
9899909328RTAC2014000547114122014-12-022018-05-02115932018-11-07 16:1515932018-11-07 16:15
9986109843RQAD2012000298114122012-03-212018-05-31114752018-11-06 10:2514752018-11-06 10:25