Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells46
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory111.0 B

Variable types

Numeric5
Categorical7

Dataset

Description대전광역시 유성구 행정동별, 성/연령별 주민등록 인구 현황에 대한 데이터로 행정동이름, 연령, 인구수, 전입년도 등의 항목을 제공합니다.
Author대전광역시 유성구
URLhttps://www.data.go.kr/data/15108898/fileData.do

Alerts

기준년월 has constant value ""Constant
시도코드 has constant value ""Constant
시도이름 has constant value ""Constant
시군구코드 has constant value ""Constant
시군구이름 has constant value ""Constant
번호 is highly overall correlated with 연령High correlation
행정동코드 is highly overall correlated with 행정동이름High correlation
연령 is highly overall correlated with 번호High correlation
인구수 is highly overall correlated with 전입년도High correlation
전입년도 is highly overall correlated with 인구수High correlation
행정동이름 is highly overall correlated with 행정동코드High correlation
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:40:05.771574
Analysis finished2023-12-12 16:40:10.214530
Duration4.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20610.433
Minimum2
Maximum41180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:40:10.282360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2055.8
Q110300.25
median20707
Q330745
95-th percentile39171.1
Maximum41180
Range41178
Interquartile range (IQR)20444.75

Descriptive statistics

Standard deviation11858.977
Coefficient of variation (CV)0.57538708
Kurtosis-1.1897983
Mean20610.433
Median Absolute Deviation (MAD)10220.5
Skewness-0.0046867395
Sum2.0610433 × 108
Variance1.4063533 × 108
MonotonicityNot monotonic
2023-12-13T01:40:10.407017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6378 1
 
< 0.1%
14092 1
 
< 0.1%
17330 1
 
< 0.1%
19572 1
 
< 0.1%
12996 1
 
< 0.1%
40250 1
 
< 0.1%
17064 1
 
< 0.1%
22840 1
 
< 0.1%
3906 1
 
< 0.1%
6730 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
4 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
19 1
< 0.1%
29 1
< 0.1%
31 1
< 0.1%
41 1
< 0.1%
56 1
< 0.1%
58 1
< 0.1%
ValueCountFrequency (%)
41180 1
< 0.1%
41179 1
< 0.1%
41175 1
< 0.1%
41168 1
< 0.1%
41167 1
< 0.1%
41165 1
< 0.1%
41163 1
< 0.1%
41147 1
< 0.1%
41145 1
< 0.1%
41142 1
< 0.1%

기준년월
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Jan-22
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJan-22
2nd rowJan-22
3rd rowJan-22
4th rowJan-22
5th rowJan-22

Common Values

ValueCountFrequency (%)
Jan-22 10000
100.0%

Length

2023-12-13T01:40:10.533931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:40:10.609028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
jan-22 10000
100.0%

시도코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3000000000
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3000000000
2nd row3000000000
3rd row3000000000
4th row3000000000
5th row3000000000

Common Values

ValueCountFrequency (%)
3000000000 10000
100.0%

Length

2023-12-13T01:40:10.703840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:40:10.786760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3000000000 10000
100.0%

시도이름
Categorical

CONSTANT 

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

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 10000
100.0%

Length

2023-12-13T01:40:10.877852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:40:10.957849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 10000
100.0%

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3020000000
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3020000000
2nd row3020000000
3rd row3020000000
4th row3020000000
5th row3020000000

Common Values

ValueCountFrequency (%)
3020000000 10000
100.0%

Length

2023-12-13T01:40:11.041913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:40:11.119198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3020000000 10000
100.0%

시군구이름
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유성구 10000
100.0%

Length

2023-12-13T01:40:11.202141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:40:11.289422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유성구 10000
100.0%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.0200552 × 109
Minimum3.020052 × 109
Maximum3.020061 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:40:11.400082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.020052 × 109
5-th percentile3.020052 × 109
Q13.020053 × 109
median3.0200547 × 109
Q33.020057 × 109
95-th percentile3.020061 × 109
Maximum3.020061 × 109
Range9000
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation2583.5369
Coefficient of variation (CV)8.5546019 × 10-7
Kurtosis-0.27798403
Mean3.0200552 × 109
Median Absolute Deviation (MAD)2000
Skewness0.84036468
Sum3.0197531 × 1013
Variance6674663.2
MonotonicityNot monotonic
2023-12-13T01:40:11.528596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3020052000 1008
10.1%
3020054000 979
9.8%
3020053000 922
9.2%
3020058000 888
8.9%
3020057000 854
8.5%
3020054600 784
7.8%
3020054700 783
7.8%
3020055000 781
7.8%
3020054800 668
 
6.7%
3020060000 640
 
6.4%
Other values (3) 1692
16.9%
ValueCountFrequency (%)
3020052000 1008
10.1%
3020052600 528
5.3%
3020052700 630
6.3%
3020053000 922
9.2%
3020054000 979
9.8%
3020054600 784
7.8%
3020054700 783
7.8%
3020054800 668
6.7%
3020055000 781
7.8%
3020057000 854
8.5%
ValueCountFrequency (%)
3020061000 534
5.3%
3020060000 640
6.4%
3020058000 888
8.9%
3020057000 854
8.5%
3020055000 781
7.8%
3020054800 668
6.7%
3020054700 783
7.8%
3020054600 784
7.8%
3020054000 979
9.8%
3020053000 922
9.2%

행정동이름
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
진잠동
1008 
온천2동
979 
온천1동
922 
구즉동
888 
전민동
854 
Other values (9)
5349 

Length

Max length4
Median length3
Mean length3.4671
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row신성동
2nd row진잠동
3rd row진잠동
4th row진잠동
5th row학하동

Common Values

ValueCountFrequency (%)
진잠동 1008
10.1%
온천2동 979
9.8%
온천1동 922
9.2%
구즉동 888
8.9%
전민동 854
8.5%
노은1동 784
7.8%
노은2동 783
7.8%
신성동 781
7.8%
노은3동 668
 
6.7%
관평동 640
 
6.4%
Other values (4) 1693
16.9%

Length

2023-12-13T01:40:11.645677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
진잠동 1008
10.1%
온천2동 979
9.8%
온천1동 922
9.2%
구즉동 888
8.9%
전민동 854
8.5%
노은1동 784
7.8%
노은2동 783
7.8%
신성동 781
7.8%
노은3동 668
 
6.7%
관평동 640
 
6.4%
Other values (4) 1693
16.9%

연령
Real number (ℝ)

HIGH CORRELATION 

Distinct104
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.9949
Minimum0
Maximum107
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:40:11.771712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q130
median52
Q369
95-th percentile86
Maximum107
Range107
Interquartile range (IQR)39

Descriptive statistics

Standard deviation23.552706
Coefficient of variation (CV)0.47110217
Kurtosis-1.0125624
Mean49.9949
Median Absolute Deviation (MAD)19
Skewness-0.092483404
Sum499949
Variance554.72995
MonotonicityNot monotonic
2023-12-13T01:40:11.939038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 160
 
1.6%
61 157
 
1.6%
73 157
 
1.6%
60 156
 
1.6%
63 155
 
1.6%
58 151
 
1.5%
22 150
 
1.5%
68 147
 
1.5%
52 146
 
1.5%
64 145
 
1.5%
Other values (94) 8476
84.8%
ValueCountFrequency (%)
0 7
 
0.1%
1 12
 
0.1%
2 23
 
0.2%
3 23
 
0.2%
4 29
0.3%
5 29
0.3%
6 38
0.4%
7 58
0.6%
8 67
0.7%
9 70
0.7%
ValueCountFrequency (%)
107 1
 
< 0.1%
104 1
 
< 0.1%
101 3
 
< 0.1%
100 3
 
< 0.1%
99 5
 
0.1%
98 7
 
0.1%
97 12
 
0.1%
96 17
0.2%
95 24
0.2%
94 30
0.3%

성별
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
여
5069 
남
4931 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남
2nd row남
3rd row여
4th row여
5th row남

Common Values

ValueCountFrequency (%)
여 5069
50.7%
남 4931
49.3%

Length

2023-12-13T01:40:12.059638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:40:12.136823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여 5069
50.7%
남 4931
49.3%

인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4887
Minimum1
Maximum247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:40:12.252822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q310
95-th percentile31
Maximum247
Range246
Interquartile range (IQR)8

Descriptive statistics

Standard deviation12.268599
Coefficient of variation (CV)1.445286
Kurtosis44.794956
Mean8.4887
Median Absolute Deviation (MAD)3
Skewness4.6996567
Sum84887
Variance150.51852
MonotonicityNot monotonic
2023-12-13T01:40:12.388575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2359
23.6%
2 1344
13.4%
3 889
 
8.9%
4 712
 
7.1%
5 571
 
5.7%
6 463
 
4.6%
7 361
 
3.6%
8 338
 
3.4%
10 264
 
2.6%
9 251
 
2.5%
Other values (98) 2448
24.5%
ValueCountFrequency (%)
1 2359
23.6%
2 1344
13.4%
3 889
 
8.9%
4 712
 
7.1%
5 571
 
5.7%
6 463
 
4.6%
7 361
 
3.6%
8 338
 
3.4%
9 251
 
2.5%
10 264
 
2.6%
ValueCountFrequency (%)
247 1
< 0.1%
213 1
< 0.1%
181 1
< 0.1%
175 1
< 0.1%
158 1
< 0.1%
138 2
< 0.1%
134 1
< 0.1%
120 1
< 0.1%
118 1
< 0.1%
111 1
< 0.1%

전입년도
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)0.6%
Missing45
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2011.08
Minimum1968
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:40:12.517154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1968
5-th percentile1995
Q12006
median2013
Q32018
95-th percentile2021
Maximum2022
Range54
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.7621345
Coefficient of variation (CV)0.0043569299
Kurtosis2.5170422
Mean2011.08
Median Absolute Deviation (MAD)6
Skewness-1.312315
Sum20020301
Variance76.775
MonotonicityNot monotonic
2023-12-13T01:40:12.656961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019 576
 
5.8%
2021 570
 
5.7%
2020 568
 
5.7%
2018 536
 
5.4%
2017 525
 
5.2%
2014 500
 
5.0%
2015 492
 
4.9%
2016 484
 
4.8%
2011 472
 
4.7%
2012 449
 
4.5%
Other values (45) 4783
47.8%
ValueCountFrequency (%)
1968 29
0.3%
1969 2
 
< 0.1%
1970 1
 
< 0.1%
1971 5
 
0.1%
1972 1
 
< 0.1%
1973 1
 
< 0.1%
1974 3
 
< 0.1%
1975 6
 
0.1%
1976 1
 
< 0.1%
1977 4
 
< 0.1%
ValueCountFrequency (%)
2022 425
4.2%
2021 570
5.7%
2020 568
5.7%
2019 576
5.8%
2018 536
5.4%
2017 525
5.2%
2016 484
4.8%
2015 492
4.9%
2014 500
5.0%
2013 442
4.4%

Interactions

2023-12-13T01:40:09.391500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:06.767877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:07.744874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:08.419896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:08.900407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:09.468498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:06.942019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:07.875919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:08.528782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:09.000511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:09.551116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:07.082201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:08.004263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:08.638498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:09.099528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:09.660853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:07.198861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:08.142390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:08.729357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:09.215997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:09.747548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:07.299982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:08.301687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:08.819535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:40:09.306907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:40:12.756354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호행정동코드행정동이름연령성별인구수전입년도
번호1.0000.0660.0840.9790.1030.2830.322
행정동코드0.0661.0001.0000.0700.0000.0950.232
행정동이름0.0841.0001.0000.0870.0000.1430.261
연령0.9790.0700.0871.0000.1070.3070.342
성별0.1030.0000.0000.1071.0000.0000.000
인구수0.2830.0950.1430.3070.0001.0000.321
전입년도0.3220.2320.2610.3420.0000.3211.000
2023-12-13T01:40:12.851123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별행정동이름
성별1.0000.000
행정동이름0.0001.000
2023-12-13T01:40:13.204788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호행정동코드연령인구수전입년도행정동이름성별
번호1.0000.046-1.0000.4040.1950.0350.079
행정동코드0.0461.000-0.0500.1040.0611.0000.000
연령-1.000-0.0501.000-0.404-0.1940.0360.082
인구수0.4040.104-0.4041.0000.5340.0590.000
전입년도0.1950.061-0.1940.5341.0000.1120.000
행정동이름0.0351.0000.0360.0590.1121.0000.000
성별0.0790.0000.0820.0000.0000.0001.000

Missing values

2023-12-13T01:40:09.892518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:40:10.056815image/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.
2023-12-13T01:40:10.162799image/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

번호기준년월시도코드시도이름시군구코드시군구이름행정동코드행정동이름연령성별인구수전입년도
63776378Jan-223000000000대전광역시3020000000유성구3020055000신성동76남42020
2680026801Jan-223000000000대전광역시3020000000유성구3020052000진잠동39남12008
1876418765Jan-223000000000대전광역시3020000000유성구3020052000진잠동55여72006
3432734328Jan-223000000000대전광역시3020000000유성구3020052000진잠동23여112019
55765577Jan-223000000000대전광역시3020000000유성구3020052600학하동78남32017
1231112312Jan-223000000000대전광역시3020000000유성구3020054700노은2동66여42012
1155811559Jan-223000000000대전광역시3020000000유성구3020054600노은1동67남42003
2554025541Jan-223000000000대전광역시3020000000유성구3020052600학하동42여32013
11081109Jan-223000000000대전광역시3020000000유성구3020054800노은3동90남12018
58415842Jan-223000000000대전광역시3020000000유성구3020058000구즉동77남21968
번호기준년월시도코드시도이름시군구코드시군구이름행정동코드행정동이름연령성별인구수전입년도
1260612607Jan-223000000000대전광역시3020000000유성구3020057000전민동65남52003
8586Jan-223000000000대전광역시3020000000유성구3020053000온천1동98여12005
10381039Jan-223000000000대전광역시3020000000유성구3020053000온천1동90남12013
4021640217Jan-223000000000대전광역시3020000000유성구3020053000온천1동8남22017
2302923030Jan-223000000000대전광역시3020000000유성구3020052700상대동47여72016
1321713218Jan-223000000000대전광역시3020000000유성구3020057000전민동64남31997
3156831569Jan-223000000000대전광역시3020000000유성구3020058000구즉동28여32022
2539825399Jan-223000000000대전광역시3020000000유성구3020052700상대동42여652021
2050020501Jan-223000000000대전광역시3020000000유성구3020052600학하동52남182021
1922919230Jan-223000000000대전광역시3020000000유성구3020055000신성동54여22001