Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory110.0 B

Variable types

Numeric3
Text2
Categorical4
DateTime2
Boolean1

Dataset

Description고창군 개별공시지가 자료
Author전라북도 고창군
URLhttps://www.data.go.kr/data/15004558/fileData.do

Alerts

기준년도 has constant value ""Constant
데이터기준일자 has constant value ""Constant
특수지구분명 is highly overall correlated with 특수지구분코드High correlation
특수지구분코드 is highly overall correlated with 특수지구분명High correlation
고유번호 is highly overall correlated with 법정동코드High correlation
법정동코드 is highly overall correlated with 고유번호High correlation
기준월 is highly imbalanced (93.5%)Imbalance
표준지여부 is highly imbalanced (89.5%)Imbalance
공시지가 is highly skewed (γ1 = 83.95104979)Skewed

Reproduction

Analysis started2023-12-12 03:02:48.361634
Analysis finished2023-12-12 03:02:50.957478
Duration2.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9995
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5790314 × 1018
Minimum4.579025 × 1018
Maximum4.579035 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:02:51.051267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.579025 × 1018
5-th percentile4.579025 × 1018
Q14.579031 × 1018
median4.579032 × 1018
Q34.579034 × 1018
95-th percentile4.579035 × 1018
Maximum4.579035 × 1018
Range1.0006003 × 1013
Interquartile range (IQR)2.9979037 × 1012

Descriptive statistics

Standard deviation3.3522618 × 1012
Coefficient of variation (CV)7.3208972 × 10-7
Kurtosis-0.20806577
Mean4.5790314 × 1018
Median Absolute Deviation (MAD)1.0139986 × 1012
Skewness-1.0988237
Sum5.4953435 × 1018
Variance1.1237659 × 1025
MonotonicityNot monotonic
2023-12-12T12:02:51.538476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4579033021106840003 2
 
< 0.1%
4579032033106950024 2
 
< 0.1%
4579032023106440000 2
 
< 0.1%
4579034028107630000 2
 
< 0.1%
4579033030100230003 2
 
< 0.1%
4579031034200810000 1
 
< 0.1%
4579033025101660000 1
 
< 0.1%
4579035026117270000 1
 
< 0.1%
4579025033100050023 1
 
< 0.1%
4579025036101710000 1
 
< 0.1%
Other values (9985) 9985
99.9%
ValueCountFrequency (%)
4579025021100070002 1
< 0.1%
4579025021100080014 1
< 0.1%
4579025021100080032 1
< 0.1%
4579025021100080041 1
< 0.1%
4579025021100120003 1
< 0.1%
4579025021100180000 1
< 0.1%
4579025021100200001 1
< 0.1%
4579025021100280001 1
< 0.1%
4579025021100310001 1
< 0.1%
4579025021100330000 1
< 0.1%
ValueCountFrequency (%)
4579035027103320000 1
< 0.1%
4579035027103280000 1
< 0.1%
4579035027103080000 1
< 0.1%
4579035027102970001 1
< 0.1%
4579035027102930004 1
< 0.1%
4579035027102820003 1
< 0.1%
4579035027102650001 1
< 0.1%
4579035027101750000 1
< 0.1%
4579035027100770000 1
< 0.1%
4579035027100760000 1
< 0.1%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5790314 × 109
Minimum4.579025 × 109
Maximum4.579035 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:02:51.715718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.579025 × 109
5-th percentile4.579025 × 109
Q14.579031 × 109
median4.579032 × 109
Q34.579034 × 109
95-th percentile4.579035 × 109
Maximum4.579035 × 109
Range10006
Interquartile range (IQR)2998

Descriptive statistics

Standard deviation3352.2595
Coefficient of variation (CV)7.3208921 × 10-7
Kurtosis-0.20806567
Mean4.5790314 × 109
Median Absolute Deviation (MAD)1014
Skewness-1.098824
Sum4.5790314 × 1013
Variance11237644
MonotonicityNot monotonic
2023-12-12T12:02:51.880737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4579025021 316
 
3.2%
4579032029 272
 
2.7%
4579035023 236
 
2.4%
4579033029 225
 
2.2%
4579033026 213
 
2.1%
4579034028 208
 
2.1%
4579034026 206
 
2.1%
4579035024 203
 
2.0%
4579035026 195
 
1.9%
4579033027 184
 
1.8%
Other values (73) 7742
77.4%
ValueCountFrequency (%)
4579025021 316
3.2%
4579025022 110
 
1.1%
4579025023 121
 
1.2%
4579025024 69
 
0.7%
4579025025 89
 
0.9%
4579025026 129
1.3%
4579025027 99
 
1.0%
4579025028 72
 
0.7%
4579025029 106
 
1.1%
4579025030 133
1.3%
ValueCountFrequency (%)
4579035027 18
 
0.2%
4579035026 195
1.9%
4579035025 183
1.8%
4579035024 203
2.0%
4579035023 236
2.4%
4579035022 128
1.3%
4579035021 132
1.3%
4579034032 176
1.8%
4579034031 163
1.6%
4579034030 100
1.0%

법정동명
Text

Distinct83
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T12:02:52.166070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70000
Distinct characters79
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고수면 봉산리
2nd row무장면 고라리
3rd row공음면 장곡리
4th row고수면 부곡리
5th row고창읍 월곡리
ValueCountFrequency (%)
무장면 1966
 
9.8%
고창읍 1948
 
9.7%
공음면 1859
 
9.3%
아산면 1657
 
8.3%
고수면 1475
 
7.4%
상하면 1095
 
5.5%
읍내리 316
 
1.6%
성산리 272
 
1.4%
구암리 259
 
1.3%
남산리 250
 
1.2%
Other values (76) 8903
44.5%
2023-12-12T12:02:52.547997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10000
14.3%
리 10000
14.3%
면 8052
 
11.5%
고 3519
 
5.0%
산 3254
 
4.6%
장 2809
 
4.0%
읍 2264
 
3.2%
무 2090
 
3.0%
창 1948
 
2.8%
음 1859
 
2.7%
Other values (69) 24205
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 60000
85.7%
Space Separator 10000
 
14.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 10000
16.7%
면 8052
 
13.4%
고 3519
 
5.9%
산 3254
 
5.4%
장 2809
 
4.7%
읍 2264
 
3.8%
무 2090
 
3.5%
창 1948
 
3.2%
음 1859
 
3.1%
공 1859
 
3.1%
Other values (68) 22346
37.2%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 60000
85.7%
Common 10000
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 10000
16.7%
면 8052
 
13.4%
고 3519
 
5.9%
산 3254
 
5.4%
장 2809
 
4.7%
읍 2264
 
3.8%
무 2090
 
3.5%
창 1948
 
3.2%
음 1859
 
3.1%
공 1859
 
3.1%
Other values (68) 22346
37.2%
Common
ValueCountFrequency (%)
10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 60000
85.7%
ASCII 10000
 
14.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10000
100.0%
Hangul
ValueCountFrequency (%)
리 10000
16.7%
면 8052
 
13.4%
고 3519
 
5.9%
산 3254
 
5.4%
장 2809
 
4.7%
읍 2264
 
3.8%
무 2090
 
3.5%
창 1948
 
3.2%
음 1859
 
3.1%
공 1859
 
3.1%
Other values (68) 22346
37.2%

특수지구분코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8719 
2
1281 

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 8719
87.2%
2 1281
 
12.8%

Length

2023-12-12T12:02:52.668960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:02:52.761516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8719
87.2%
2 1281
 
12.8%

특수지구분명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
8719 
산
1281 

Length

Max length2
Median length2
Mean length1.8719
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 8719
87.2%
산 1281
 
12.8%

Length

2023-12-12T12:02:52.877832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:02:52.968480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 8719
87.2%
산 1281
 
12.8%

지번
Text

Distinct5258
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T12:02:53.254687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters90000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3292 ?
Unique (%)32.9%

Sample

1st row1420-0000
2nd row0422-0000
3rd row0306-0001
4th row0659-0000
5th row0028-0000
ValueCountFrequency (%)
0101-0000 14
 
0.1%
0052-0000 14
 
0.1%
0027-0000 13
 
0.1%
0001-0000 13
 
0.1%
0026-0000 13
 
0.1%
0046-0000 12
 
0.1%
0021-0000 12
 
0.1%
0085-0000 12
 
0.1%
0088-0000 12
 
0.1%
0036-0000 12
 
0.1%
Other values (5248) 9873
98.7%
2023-12-12T12:02:53.728655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 47114
52.3%
- 10000
 
11.1%
1 7187
 
8.0%
2 4290
 
4.8%
3 3864
 
4.3%
4 3503
 
3.9%
5 3168
 
3.5%
6 3037
 
3.4%
7 2772
 
3.1%
8 2612
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80000
88.9%
Dash Punctuation 10000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47114
58.9%
1 7187
 
9.0%
2 4290
 
5.4%
3 3864
 
4.8%
4 3503
 
4.4%
5 3168
 
4.0%
6 3037
 
3.8%
7 2772
 
3.5%
8 2612
 
3.3%
9 2453
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 47114
52.3%
- 10000
 
11.1%
1 7187
 
8.0%
2 4290
 
4.8%
3 3864
 
4.3%
4 3503
 
3.9%
5 3168
 
3.5%
6 3037
 
3.4%
7 2772
 
3.1%
8 2612
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 47114
52.3%
- 10000
 
11.1%
1 7187
 
8.0%
2 4290
 
4.8%
3 3864
 
4.3%
4 3503
 
3.9%
5 3168
 
3.5%
6 3037
 
3.4%
7 2772
 
3.1%
8 2612
 
2.9%

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2018
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 10000
100.0%

Length

2023-12-12T12:02:53.887711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:02:53.994534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 10000
100.0%

기준월
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9923 
7
 
77

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 9923
99.2%
7 77
 
0.8%

Length

2023-12-12T12:02:54.122770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:02:54.231508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9923
99.2%
7 77
 
0.8%

공시지가
Real number (ℝ)

SKEWED 

Distinct1676
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20523.089
Minimum171
Maximum16700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:02:54.350978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum171
5-th percentile1350
Q15820
median7100
Q38830
95-th percentile67500
Maximum16700000
Range16699829
Interquartile range (IQR)3010

Descriptive statistics

Standard deviation177050.02
Coefficient of variation (CV)8.6268696
Kurtosis7880.4566
Mean20523.089
Median Absolute Deviation (MAD)1490
Skewness83.95105
Sum2.0523089 × 108
Variance3.1346709 × 1010
MonotonicityNot monotonic
2023-12-12T12:02:54.553804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 253
 
2.5%
7300 175
 
1.8%
7100 163
 
1.6%
6800 77
 
0.8%
6400 68
 
0.7%
6700 67
 
0.7%
6200 67
 
0.7%
7700 61
 
0.6%
7600 56
 
0.6%
7000 54
 
0.5%
Other values (1666) 8959
89.6%
ValueCountFrequency (%)
171 1
 
< 0.1%
242 4
< 0.1%
264 1
 
< 0.1%
300 1
 
< 0.1%
303 1
 
< 0.1%
309 2
< 0.1%
316 2
< 0.1%
322 2
< 0.1%
392 1
 
< 0.1%
399 1
 
< 0.1%
ValueCountFrequency (%)
16700000 1
 
< 0.1%
1060000 2
< 0.1%
901000 1
 
< 0.1%
899100 1
 
< 0.1%
895400 1
 
< 0.1%
875100 3
< 0.1%
860000 1
 
< 0.1%
808400 1
 
< 0.1%
770500 1
 
< 0.1%
760000 1
 
< 0.1%

공시일자
Date

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-05-31 00:00:00
Maximum2018-10-31 00:00:00
2023-12-12T12:02:54.721884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:02:54.880044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

표준지여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9862 
True
 
138
ValueCountFrequency (%)
False 9862
98.6%
True 138
 
1.4%
2023-12-12T12:02:55.026019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-03-05 00:00:00
Maximum2019-03-05 00:00:00
2023-12-12T12:02:55.122702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:02:55.237834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T12:02:50.213337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:02:49.471022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:02:49.872863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:02:50.350417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:02:49.614917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:02:49.979878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:02:50.481456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:02:49.740238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:02:50.085070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:02:55.330209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호법정동코드법정동명특수지구분코드특수지구분명기준월공시지가공시일자표준지여부
고유번호1.0001.0001.0000.0880.0880.0330.0000.0330.000
법정동코드1.0001.0001.0000.0740.0740.0330.0000.0330.000
법정동명1.0001.0001.0000.2780.2780.1120.0670.1120.000
특수지구분코드0.0880.0740.2781.0001.0000.0000.0000.0000.006
특수지구분명0.0880.0740.2781.0001.0000.0000.0000.0000.006
기준월0.0330.0330.1120.0000.0001.0000.0001.0000.000
공시지가0.0000.0000.0670.0000.0000.0001.0000.0000.000
공시일자0.0330.0330.1120.0000.0001.0000.0001.0000.000
표준지여부0.0000.0000.0000.0060.0060.0000.0000.0001.000
2023-12-12T12:02:55.506426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지구분명표준지여부기준월특수지구분코드
특수지구분명1.0000.0040.0001.000
표준지여부0.0041.0000.0000.004
기준월0.0000.0001.0000.000
특수지구분코드1.0000.0040.0001.000
2023-12-12T12:02:55.644170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호법정동코드공시지가특수지구분코드특수지구분명기준월표준지여부
고유번호1.0001.000-0.3560.0630.0630.0240.000
법정동코드1.0001.000-0.3540.0630.0630.0240.000
공시지가-0.356-0.3541.0000.0000.0000.0000.000
특수지구분코드0.0630.0630.0001.0001.0000.0000.004
특수지구분명0.0630.0630.0001.0001.0000.0000.004
기준월0.0240.0240.0000.0000.0001.0000.000
표준지여부0.0000.0000.0000.0040.0040.0001.000

Missing values

2023-12-12T12:02:50.669006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:02:50.857374image/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

고유번호법정동코드법정동명특수지구분코드특수지구분명지번기준년도기준월공시지가공시일자표준지여부데이터기준일자
2049845790310231142000004579031023고수면 봉산리1일반1420-00002018187002018-05-31N2019-03-05
6121645790330301042200004579033030무장면 고라리1일반0422-00002018164002018-05-31N2019-03-05
7284645790340251030600014579034025공음면 장곡리1일반0306-00012018161102018-05-31N2019-03-05
2914145790310311065900004579031031고수면 부곡리1일반0659-00002018175302018-05-31N2019-03-05
388545790250231002800004579025023고창읍 월곡리1일반0028-00002018113502018-05-31N2019-03-05
7442945790340261105300454579034026공음면 칠암리1일반1053-00452018173402018-05-31N2019-03-05
298845790250221009900074579025022고창읍 교촌리1일반0099-000720181175002018-05-31N2019-03-05
9074645790350242003500024579035024상하면 자룡리2산0035-00022018158902018-05-31N2019-03-05
4848745790330211017000014579033021무장면 무장리1일반0170-000120181330002018-05-31N2019-03-05
1899145790310211007100004579031021고수면 와촌리1일반0071-000020181131002018-05-31N2019-03-05
고유번호법정동코드법정동명특수지구분코드특수지구분명지번기준년도기준월공시지가공시일자표준지여부데이터기준일자
8767245790350231001000034579035023상하면 용정리1일반0010-00032018117402018-05-31N2019-03-05
4342545790320302018500024579032030아산면 학전리2산0185-00022018161502018-05-31N2019-03-05
6013945790330291131900014579033029무장면 옥산리1일반1319-00012018166002018-05-31N2019-03-05
5831145790330282004200044579033028무장면 덕림리2산0042-00042018115802018-05-31N2019-03-05
8324945790340321012300014579034032공음면 덕암리1일반0123-00012018186202018-05-31N2019-03-05
1346645790250321046800074579025032고창읍 도산리1일반0468-000720181126002018-05-31N2019-03-05
2839445790310311009100014579031031고수면 부곡리1일반0091-00012018180002018-05-31N2019-03-05
4170945790320292014800004579032029아산면 성산리2산0148-00002018119602018-05-31N2019-03-05
2346745790310251078500004579031025고수면 남산리1일반0785-00002018177402018-05-31N2019-03-05
4366045790320321037200014579032032아산면 용계리1일반0372-00012018197602018-05-31N2019-03-05