Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells14726
Missing cells (%)16.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory810.5 KiB
Average record size in memory83.0 B

Variable types

Numeric3
Categorical1
Text3
DateTime2

Dataset

Description우리나라의 법정 구역으로 법률로 지정된 일정한 명칭과 영역을 지닌 구역으로 토지행정시스템에서 사용하는 법정동 데이터임.
Author국토교통부
URLhttps://www.data.go.kr/data/15063424/fileData.do

Alerts

법정동코드 is highly overall correlated with 시도명High correlation
과거법정동코드 is highly overall correlated with 시도명High correlation
시도명 is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
리명 has 2686 (26.9%) missing valuesMissing
생성일자 has 226 (2.3%) missing valuesMissing
삭제일자 has 4161 (41.6%) missing valuesMissing
과거법정동코드 has 7544 (75.4%) missing valuesMissing
법정동코드 has unique valuesUnique
순위 has 5106 (51.1%) zerosZeros
과거법정동코드 has 255 (2.5%) zerosZeros

Reproduction

Analysis started2024-03-14 14:00:50.761922
Analysis finished2024-03-14 14:00:55.438202
Duration4.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

법정동코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3966033 × 109
Minimum1.1110103 × 109
Maximum5.280042 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T23:00:55.669572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110103 × 109
5-th percentile2.5200139 × 109
Q14.2760368 × 109
median4.5810445 × 109
Q34.783037 × 109
95-th percentile5.176025 × 109
Maximum5.280042 × 109
Range4.1690317 × 109
Interquartile range (IQR)5.0700025 × 108

Descriptive statistics

Standard deviation7.4989283 × 108
Coefficient of variation (CV)0.17056186
Kurtosis7.3694285
Mean4.3966033 × 109
Median Absolute Deviation (MAD)2.12984 × 108
Skewness-2.605004
Sum4.3966033 × 1013
Variance5.6233926 × 1017
MonotonicityNot monotonic
2024-03-14T23:00:56.322857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4801201400 1
 
< 0.1%
4676031021 1
 
< 0.1%
4159025923 1
 
< 0.1%
4415011300 1
 
< 0.1%
4671036028 1
 
< 0.1%
4186031041 1
 
< 0.1%
4483025638 1
 
< 0.1%
4405006001 1
 
< 0.1%
4680035029 1
 
< 0.1%
4280003005 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1111010300 1
< 0.1%
1111011300 1
< 0.1%
1111011900 1
< 0.1%
1111012700 1
< 0.1%
1111013900 1
< 0.1%
1111014600 1
< 0.1%
1111014800 1
< 0.1%
1111015500 1
< 0.1%
1111016200 1
< 0.1%
1111016600 1
< 0.1%
ValueCountFrequency (%)
5280042022 1
< 0.1%
5280041024 1
< 0.1%
5280041023 1
< 0.1%
5280041022 1
< 0.1%
5280039027 1
< 0.1%
5280038029 1
< 0.1%
5280038024 1
< 0.1%
5280037000 1
< 0.1%
5280036024 1
< 0.1%
5280036022 1
< 0.1%

시도명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경상북도
1896 
경기도
1290 
경상남도
1266 
충청남도
1035 
전라남도
991 
Other values (20)
3522 

Length

Max length7
Median length4
Mean length4.1156
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상남도
2nd row충청북도
3rd row충청남도
4th row경상남도
5th row전라북도

Common Values

ValueCountFrequency (%)
경상북도 1896
19.0%
경기도 1290
12.9%
경상남도 1266
12.7%
충청남도 1035
10.3%
전라남도 991
9.9%
전라북도 650
 
6.5%
충청북도 638
 
6.4%
강원도 561
 
5.6%
전북특별자치도 361
 
3.6%
강원특별자치도 301
 
3.0%
Other values (15) 1011
10.1%

Length

2024-03-14T23:00:56.774621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 1896
19.0%
경기도 1290
12.9%
경상남도 1266
12.7%
충청남도 1035
10.3%
전라남도 991
9.9%
전라북도 650
 
6.5%
충청북도 638
 
6.4%
강원도 561
 
5.6%
전북특별자치도 361
 
3.6%
강원특별자치도 301
 
3.0%
Other values (15) 1011
10.1%

시군구명
Text

Distinct363
Distinct (%)3.6%
Missing5
Missing (%)< 0.1%
Memory size156.2 KiB
2024-03-14T23:00:58.635946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.1677839
Min length2

Characters and Unicode

Total characters31662
Distinct characters149
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

Unique17 ?
Unique (%)0.2%

Sample

1st row부산시서구
2nd row충주시
3rd row금산군
4th row창원군
5th row익산시
ValueCountFrequency (%)
중구 110
 
1.1%
달성군 102
 
1.0%
영일군 97
 
1.0%
창원군 94
 
0.9%
영천군 92
 
0.9%
안동군 91
 
0.9%
경산군 87
 
0.9%
예천군 87
 
0.9%
청원군 82
 
0.8%
고성군 82
 
0.8%
Other values (353) 9071
90.8%
2024-03-14T23:01:00.718107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 6188
19.5%
시 3252
 
10.3%
구 1578
 
5.0%
주 1477
 
4.7%
산 1297
 
4.1%
천 1209
 
3.8%
성 909
 
2.9%
양 726
 
2.3%
원 719
 
2.3%
안 645
 
2.0%
Other values (139) 13662
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31662
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 6188
19.5%
시 3252
 
10.3%
구 1578
 
5.0%
주 1477
 
4.7%
산 1297
 
4.1%
천 1209
 
3.8%
성 909
 
2.9%
양 726
 
2.3%
원 719
 
2.3%
안 645
 
2.0%
Other values (139) 13662
43.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31662
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 6188
19.5%
시 3252
 
10.3%
구 1578
 
5.0%
주 1477
 
4.7%
산 1297
 
4.1%
천 1209
 
3.8%
성 909
 
2.9%
양 726
 
2.3%
원 719
 
2.3%
안 645
 
2.0%
Other values (139) 13662
43.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31662
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 6188
19.5%
시 3252
 
10.3%
구 1578
 
5.0%
주 1477
 
4.7%
산 1297
 
4.1%
천 1209
 
3.8%
성 909
 
2.9%
양 726
 
2.3%
원 719
 
2.3%
안 645
 
2.0%
Other values (139) 13662
43.1%

읍면동명
Text

Distinct2987
Distinct (%)30.1%
Missing88
Missing (%)0.9%
Memory size156.2 KiB
2024-03-14T23:01:02.046335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0493341
Min length2

Characters and Unicode

Total characters30225
Distinct characters339
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1285 ?
Unique (%)13.0%

Sample

1st row토성동3가
2nd row살미면
3rd row남일면
4th row대산면
5th row함라면
ValueCountFrequency (%)
동면 72
 
0.7%
북면 61
 
0.6%
남면 53
 
0.5%
서면 43
 
0.4%
대산면 29
 
0.3%
금성면 28
 
0.3%
산내면 27
 
0.3%
강동면 26
 
0.3%
진전면 25
 
0.3%
옥산면 22
 
0.2%
Other values (2977) 9526
96.1%
2024-03-14T23:01:03.593914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 6554
21.7%
동 2456
 
8.1%
읍 1393
 
4.6%
산 921
 
3.0%
성 556
 
1.8%
남 473
 
1.6%
천 453
 
1.5%
양 412
 
1.4%
북 392
 
1.3%
가 389
 
1.3%
Other values (329) 16226
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29784
98.5%
Decimal Number 439
 
1.5%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 6554
22.0%
동 2456
 
8.2%
읍 1393
 
4.7%
산 921
 
3.1%
성 556
 
1.9%
남 473
 
1.6%
천 453
 
1.5%
양 412
 
1.4%
북 392
 
1.3%
가 389
 
1.3%
Other values (318) 15785
53.0%
Decimal Number
ValueCountFrequency (%)
1 144
32.8%
2 144
32.8%
3 78
17.8%
4 41
 
9.3%
5 16
 
3.6%
6 11
 
2.5%
8 3
 
0.7%
7 1
 
0.2%
9 1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29784
98.5%
Common 441
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 6554
22.0%
동 2456
 
8.2%
읍 1393
 
4.7%
산 921
 
3.1%
성 556
 
1.9%
남 473
 
1.6%
천 453
 
1.5%
양 412
 
1.4%
북 392
 
1.3%
가 389
 
1.3%
Other values (318) 15785
53.0%
Common
ValueCountFrequency (%)
1 144
32.7%
2 144
32.7%
3 78
17.7%
4 41
 
9.3%
5 16
 
3.6%
6 11
 
2.5%
8 3
 
0.7%
7 1
 
0.2%
( 1
 
0.2%
) 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29784
98.5%
ASCII 441
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 6554
22.0%
동 2456
 
8.2%
읍 1393
 
4.7%
산 921
 
3.1%
성 556
 
1.9%
남 473
 
1.6%
천 453
 
1.5%
양 412
 
1.4%
북 392
 
1.3%
가 389
 
1.3%
Other values (318) 15785
53.0%
ASCII
ValueCountFrequency (%)
1 144
32.7%
2 144
32.7%
3 78
17.7%
4 41
 
9.3%
5 16
 
3.6%
6 11
 
2.5%
8 3
 
0.7%
7 1
 
0.2%
( 1
 
0.2%
) 1
 
0.2%

리명
Text

MISSING 

Distinct4202
Distinct (%)57.5%
Missing2686
Missing (%)26.9%
Memory size156.2 KiB
2024-03-14T23:01:04.867146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.9898824
Min length2

Characters and Unicode

Total characters21868
Distinct characters359
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2790 ?
Unique (%)38.1%

Sample

1st row문화리
2nd row신정리
3rd row갈전리
4th row금성리
5th row예초리
ValueCountFrequency (%)
금곡리 21
 
0.3%
용산리 20
 
0.3%
행정리 17
 
0.2%
신촌리 16
 
0.2%
사곡리 16
 
0.2%
송정리 16
 
0.2%
오산리 15
 
0.2%
신월리 15
 
0.2%
신대리 15
 
0.2%
내리 14
 
0.2%
Other values (4192) 7149
97.7%
2024-03-14T23:01:06.433940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 6736
30.8%
동 925
 
4.2%
산 600
 
2.7%
곡 462
 
2.1%
신 380
 
1.7%
천 323
 
1.5%
대 290
 
1.3%
정 268
 
1.2%
성 247
 
1.1%
평 229
 
1.0%
Other values (349) 11408
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21851
99.9%
Decimal Number 7
 
< 0.1%
Close Punctuation 5
 
< 0.1%
Open Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 6736
30.8%
동 925
 
4.2%
산 600
 
2.7%
곡 462
 
2.1%
신 380
 
1.7%
천 323
 
1.5%
대 290
 
1.3%
정 268
 
1.2%
성 247
 
1.1%
평 229
 
1.0%
Other values (344) 11391
52.1%
Decimal Number
ValueCountFrequency (%)
2 3
42.9%
1 3
42.9%
3 1
 
14.3%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21841
99.9%
Common 17
 
0.1%
Han 10
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 6736
30.8%
동 925
 
4.2%
산 600
 
2.7%
곡 462
 
2.1%
신 380
 
1.7%
천 323
 
1.5%
대 290
 
1.3%
정 268
 
1.2%
성 247
 
1.1%
평 229
 
1.0%
Other values (337) 11381
52.1%
Han
ValueCountFrequency (%)
山 3
30.0%
華 2
20.0%
岩 1
 
10.0%
岐 1
 
10.0%
花 1
 
10.0%
隅 1
 
10.0%
德 1
 
10.0%
Common
ValueCountFrequency (%)
) 5
29.4%
( 5
29.4%
2 3
17.6%
1 3
17.6%
3 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21841
99.9%
ASCII 17
 
0.1%
CJK 10
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 6736
30.8%
동 925
 
4.2%
산 600
 
2.7%
곡 462
 
2.1%
신 380
 
1.7%
천 323
 
1.5%
대 290
 
1.3%
정 268
 
1.2%
성 247
 
1.1%
평 229
 
1.0%
Other values (337) 11381
52.1%
ASCII
ValueCountFrequency (%)
) 5
29.4%
( 5
29.4%
2 3
17.6%
1 3
17.6%
3 1
 
5.9%
CJK
ValueCountFrequency (%)
山 3
30.0%
華 2
20.0%
岩 1
 
10.0%
岐 1
 
10.0%
花 1
 
10.0%
隅 1
 
10.0%
德 1
 
10.0%

순위
Real number (ℝ)

ZEROS 

Distinct131
Distinct (%)1.3%
Missing16
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5.5046074
Minimum0
Maximum283
Zeros5106
Zeros (%)51.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T23:01:06.670873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile21
Maximum283
Range283
Interquartile range (IQR)7

Descriptive statistics

Standard deviation14.714893
Coefficient of variation (CV)2.6731957
Kurtosis121.76486
Mean5.5046074
Median Absolute Deviation (MAD)0
Skewness9.234347
Sum54958
Variance216.52808
MonotonicityNot monotonic
2024-03-14T23:01:06.915542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5106
51.1%
1 447
 
4.5%
4 385
 
3.9%
2 381
 
3.8%
3 371
 
3.7%
6 365
 
3.6%
5 347
 
3.5%
8 326
 
3.3%
7 325
 
3.2%
10 251
 
2.5%
Other values (121) 1680
 
16.8%
ValueCountFrequency (%)
0 5106
51.1%
1 447
 
4.5%
2 381
 
3.8%
3 371
 
3.7%
4 385
 
3.9%
5 347
 
3.5%
6 365
 
3.6%
7 325
 
3.2%
8 326
 
3.3%
9 245
 
2.5%
ValueCountFrequency (%)
283 1
< 0.1%
279 1
< 0.1%
275 1
< 0.1%
252 1
< 0.1%
248 1
< 0.1%
246 1
< 0.1%
245 1
< 0.1%
236 1
< 0.1%
231 1
< 0.1%
230 1
< 0.1%

생성일자
Date

MISSING 

Distinct227
Distinct (%)2.3%
Missing226
Missing (%)2.3%
Memory size156.2 KiB
Minimum1988-04-23 00:00:00
Maximum2024-02-01 00:00:00
2024-03-14T23:01:07.227812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:01:07.570544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

삭제일자
Date

MISSING 

Distinct167
Distinct (%)2.9%
Missing4161
Missing (%)41.6%
Memory size156.2 KiB
Minimum1988-04-23 00:00:00
Maximum2024-02-01 00:00:00
2024-03-14T23:01:07.912322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:01:08.342067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

과거법정동코드
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct2202
Distinct (%)89.7%
Missing7544
Missing (%)75.4%
Infinite0
Infinite (%)0.0%
Mean1.311011 × 109
Minimum0
Maximum4.972032 × 109
Zeros255
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T23:01:08.764859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114180778
median19180808
Q34.173031 × 109
95-th percentile4.877042 × 109
Maximum4.972032 × 109
Range4.972032 × 109
Interquartile range (IQR)4.1588502 × 109

Descriptive statistics

Standard deviation2.0399489 × 109
Coefficient of variation (CV)1.556012
Kurtosis-1.0146033
Mean1.311011 × 109
Median Absolute Deviation (MAD)5940292
Skewness0.96831215
Sum3.219843 × 1012
Variance4.1613914 × 1018
MonotonicityNot monotonic
2024-03-14T23:01:09.220320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 255
 
2.5%
22061700 1
 
< 0.1%
22012900 1
 
< 0.1%
4478037030 1
 
< 0.1%
18200504 1
 
< 0.1%
4879033022 1
 
< 0.1%
19270307 1
 
< 0.1%
4781032065 1
 
< 0.1%
2420019400 1
 
< 0.1%
4879032029 1
 
< 0.1%
Other values (2192) 2192
 
21.9%
(Missing) 7544
75.4%
ValueCountFrequency (%)
0 255
2.5%
11040197 1
 
< 0.1%
11040198 1
 
< 0.1%
11040203 1
 
< 0.1%
12010000 1
 
< 0.1%
12010200 1
 
< 0.1%
12010400 1
 
< 0.1%
12010700 1
 
< 0.1%
12011300 1
 
< 0.1%
12011400 1
 
< 0.1%
ValueCountFrequency (%)
4972032025 1
< 0.1%
4972032021 1
< 0.1%
4972031029 1
< 0.1%
4972031026 1
< 0.1%
4972031023 1
< 0.1%
4972031000 1
< 0.1%
4972025926 1
< 0.1%
4972025921 1
< 0.1%
4972025327 1
< 0.1%
4972025323 1
< 0.1%

Interactions

2024-03-14T23:00:53.439733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:00:51.936960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:00:52.658365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:00:53.728523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:00:52.191001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:00:52.911519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:00:54.010067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:00:52.461255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:00:53.166142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T23:01:09.489921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드시도명순위과거법정동코드
법정동코드1.0000.9950.1470.917
시도명0.9951.0000.2600.917
순위0.1470.2601.0000.224
과거법정동코드0.9170.9170.2241.000
2024-03-14T23:01:09.747919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드순위과거법정동코드시도명
법정동코드1.0000.0700.2720.968
순위0.0701.0000.3210.094
과거법정동코드0.2720.3211.0000.694
시도명0.9680.0940.6941.000

Missing values

2024-03-14T23:00:54.375068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T23:00:54.831896image/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.
2024-03-14T23:00:55.220412image/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

법정동코드시도명시군구명읍면동명리명순위생성일자삭제일자과거법정동코드
366294801201400경상남도부산시서구토성동3가<NA>01988-04-231988-04-23<NA>
43064313031022충청북도충주시살미면문화리21995-01-01<NA><NA>
83114471035026충청남도금산군남일면신정리61988-04-23<NA><NA>
330584805004502경상남도창원군대산면갈전리01988-04-231988-04-23<NA>
482654514033022전라북도익산시함라면금성리21995-05-102024-01-18<NA>
416325011032024제주특별자치도제주시추자면예초리02006-07-01<NA>4971032024
223404677036022전라남도고흥군봉래면외초리11988-04-23<NA><NA>
392074816032029경상남도마산시진동면태봉리92001-01-012010-07-01<NA>
144434121010100경기도광명시광명동<NA>11988-04-23<NA><NA>
94874423038033충청남도논산시벌곡면신양리131996-03-01<NA><NA>
법정동코드시도명시군구명읍면동명리명순위생성일자삭제일자과거법정동코드
384564822025031경상남도통영시산양읍연곡리111995-03-02<NA><NA>
10004276003010강원도평창군평창면지동리01988-04-231988-04-23<NA>
394844873025027경상남도함안군가야읍춘곡리71988-04-23<NA><NA>
80264482500000충청남도태안군<NA><NA>141989-01-01<NA><NA>
136022226011700대구직할시수성구사월동<NA>01988-04-231995-01-0122061700
256004511004300전라북도전주시평화동3가<NA>01988-04-231989-04-30<NA>
279864686036027전라남도함평군나산면수상리71988-04-23<NA><NA>
124584375037024충청북도진천군광혜원면죽현리42000-01-01<NA><NA>
455625177025928강원특별자치도정선군신동읍운치리82023-06-09<NA><NA>
64324371039032충청북도청원군강내면석화리121988-04-231988-04-23<NA>