Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells1010
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory849.6 KiB
Average record size in memory87.0 B

Variable types

Text2
Categorical2
Numeric5

Dataset

Description관리_대지_위치_PK,관리_주택대장_PK,대표_여부,시군구_코드,법정동_코드,대지_구분_코드,번,지,작업_일자
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15407/S/1/datasetView.do

Alerts

시군구_코드 is highly overall correlated with 대표_여부High correlation
대표_여부 is highly overall correlated with 시군구_코드High correlation
대지_구분_코드 is highly imbalanced (71.2%)Imbalance
시군구_코드 has 259 (2.6%) missing valuesMissing
법정동_코드 has 474 (4.7%) missing valuesMissing
지 has 274 (2.7%) missing valuesMissing
관리_대지_위치_PK has unique valuesUnique
번 has 1574 (15.7%) zerosZeros
지 has 5583 (55.8%) zerosZeros

Reproduction

Analysis started2024-05-17 23:03:51.547283
Analysis finished2024-05-17 23:04:03.427727
Duration11.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리_대지_위치_PK
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T08:04:03.731659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length14.2011
Min length7

Characters and Unicode

Total characters142011
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

Unique10000 ?
Unique (%)100.0%

Sample

1st row11440-1252
2nd row11350-1522
3rd row11590-3541
4th row11305-100005599
5th row11590-846
ValueCountFrequency (%)
11440-1252 1
 
< 0.1%
11110-100006564 1
 
< 0.1%
11380-100006179 1
 
< 0.1%
11215-100011505 1
 
< 0.1%
11380-36 1
 
< 0.1%
11230-729 1
 
< 0.1%
11560-100007276 1
 
< 0.1%
11590-3489 1
 
< 0.1%
11470-82 1
 
< 0.1%
11290-713 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-18T08:04:04.524439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 46179
32.5%
1 35734
25.2%
- 10000
 
7.0%
5 7871
 
5.5%
2 7349
 
5.2%
7 6687
 
4.7%
4 6488
 
4.6%
6 6282
 
4.4%
3 6216
 
4.4%
9 4837
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 132011
93.0%
Dash Punctuation 10000
 
7.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46179
35.0%
1 35734
27.1%
5 7871
 
6.0%
2 7349
 
5.6%
7 6687
 
5.1%
4 6488
 
4.9%
6 6282
 
4.8%
3 6216
 
4.7%
9 4837
 
3.7%
8 4368
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 142011
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46179
32.5%
1 35734
25.2%
- 10000
 
7.0%
5 7871
 
5.5%
2 7349
 
5.2%
7 6687
 
4.7%
4 6488
 
4.6%
6 6282
 
4.4%
3 6216
 
4.4%
9 4837
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 142011
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46179
32.5%
1 35734
25.2%
- 10000
 
7.0%
5 7871
 
5.5%
2 7349
 
5.2%
7 6687
 
4.7%
4 6488
 
4.6%
6 6282
 
4.4%
3 6216
 
4.4%
9 4837
 
3.4%

관리_주택대장_PK
Text

Distinct6105
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T08:04:05.192988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length13.8299
Min length7

Characters and Unicode

Total characters138299
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

Unique5706 ?
Unique (%)57.1%

Sample

1st row11440-44
2nd row11350-307
3rd row11590-34
4th row11305-100004121
5th row11590-422
ValueCountFrequency (%)
11590-34 234
 
2.3%
11290-93 141
 
1.4%
11305-100004121 139
 
1.4%
11230-76 127
 
1.3%
11305-1000000000000000140142 70
 
0.7%
11410-100008124 68
 
0.7%
11410-100007082 60
 
0.6%
11650-287 57
 
0.6%
11650-100035602 55
 
0.5%
11560-100008826 50
 
0.5%
Other values (6095) 8999
90.0%
2024-05-18T08:04:06.342926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 45171
32.7%
1 35721
25.8%
- 10000
 
7.2%
5 7393
 
5.3%
2 7124
 
5.2%
4 6698
 
4.8%
7 6049
 
4.4%
6 5971
 
4.3%
3 5800
 
4.2%
8 4346
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128299
92.8%
Dash Punctuation 10000
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45171
35.2%
1 35721
27.8%
5 7393
 
5.8%
2 7124
 
5.6%
4 6698
 
5.2%
7 6049
 
4.7%
6 5971
 
4.7%
3 5800
 
4.5%
8 4346
 
3.4%
9 4026
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 138299
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45171
32.7%
1 35721
25.8%
- 10000
 
7.2%
5 7393
 
5.3%
2 7124
 
5.2%
4 6698
 
4.8%
7 6049
 
4.4%
6 5971
 
4.3%
3 5800
 
4.2%
8 4346
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138299
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45171
32.7%
1 35721
25.8%
- 10000
 
7.2%
5 7393
 
5.3%
2 7124
 
5.2%
4 6698
 
4.8%
7 6049
 
4.4%
6 5971
 
4.3%
3 5800
 
4.2%
8 4346
 
3.1%

대표_여부
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5568 
0
4432 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 5568
55.7%
0 4432
44.3%

Length

2024-05-18T08:04:06.953595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T08:04:07.259696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5568
55.7%
0 4432
44.3%

시군구_코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)0.3%
Missing259
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean11492.508
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T08:04:07.589910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11200
Q111320
median11530
Q311650
95-th percentile11710
Maximum11740
Range630
Interquartile range (IQR)330

Descriptive statistics

Standard deviation182.58116
Coefficient of variation (CV)0.015886972
Kurtosis-1.2172792
Mean11492.508
Median Absolute Deviation (MAD)180
Skewness-0.30684403
Sum1.1194852 × 108
Variance33335.881
MonotonicityNot monotonic
2024-05-18T08:04:07.986804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11710 1741
17.4%
11590 793
 
7.9%
11650 724
 
7.2%
11560 559
 
5.6%
11290 556
 
5.6%
11470 518
 
5.2%
11350 513
 
5.1%
11680 421
 
4.2%
11410 380
 
3.8%
11440 362
 
3.6%
Other values (15) 3174
31.7%
ValueCountFrequency (%)
11110 131
 
1.3%
11140 124
 
1.2%
11170 108
 
1.1%
11200 332
3.3%
11215 169
 
1.7%
11230 341
3.4%
11260 196
 
2.0%
11290 556
5.6%
11305 350
3.5%
11320 176
 
1.8%
ValueCountFrequency (%)
11740 261
 
2.6%
11710 1741
17.4%
11680 421
 
4.2%
11650 724
7.2%
11620 191
 
1.9%
11590 793
7.9%
11560 559
 
5.6%
11545 56
 
0.6%
11530 189
 
1.9%
11500 231
 
2.3%

법정동_코드
Real number (ℝ)

MISSING 

Distinct58
Distinct (%)0.6%
Missing474
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean10935.282
Minimum0
Maximum18700
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T08:04:08.458983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10100
Q110200
median10500
Q311000
95-th percentile13300
Maximum18700
Range18700
Interquartile range (IQR)800

Descriptive statistics

Standard deviation1317.3317
Coefficient of variation (CV)0.12046618
Kurtosis12.881913
Mean10935.282
Median Absolute Deviation (MAD)400
Skewness2.6960226
Sum1.041695 × 108
Variance1735362.9
MonotonicityNot monotonic
2024-05-18T08:04:09.080756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10100 2036
20.4%
10200 1263
12.6%
10600 660
 
6.6%
10300 641
 
6.4%
10700 626
 
6.3%
10500 571
 
5.7%
10800 496
 
5.0%
10900 364
 
3.6%
10400 340
 
3.4%
13300 334
 
3.3%
Other values (48) 2195
21.9%
(Missing) 474
 
4.7%
ValueCountFrequency (%)
0 4
 
< 0.1%
10100 2036
20.4%
10200 1263
12.6%
10300 641
 
6.4%
10400 340
 
3.4%
10500 571
 
5.7%
10600 660
 
6.6%
10700 626
 
6.3%
10800 496
 
5.0%
10900 364
 
3.6%
ValueCountFrequency (%)
18700 39
0.4%
18600 5
 
0.1%
18300 2
 
< 0.1%
18100 3
 
< 0.1%
18000 3
 
< 0.1%
17900 16
0.2%
17800 10
 
0.1%
17700 11
 
0.1%
17600 2
 
< 0.1%
17400 10
 
0.1%

대지_구분_코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
8882 
<NA>
976 
1
 
120
2
 
22

Length

Max length4
Median length1
Mean length1.2928
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row0
4th row0
5th row<NA>

Common Values

ValueCountFrequency (%)
0 8882
88.8%
<NA> 976
 
9.8%
1 120
 
1.2%
2 22
 
0.2%

Length

2024-05-18T08:04:09.699563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T08:04:10.119635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 8882
88.8%
na 976
 
9.8%
1 120
 
1.2%
2 22
 
0.2%

번
Real number (ℝ)

ZEROS 

Distinct1244
Distinct (%)12.4%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean386.88136
Minimum0
Maximum4941
Zeros1574
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T08:04:10.860037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median166
Q3577
95-th percentile1353
Maximum4941
Range4941
Interquartile range (IQR)557

Descriptive statistics

Standard deviation533.75522
Coefficient of variation (CV)1.3796354
Kurtosis12.371237
Mean386.88136
Median Absolute Deviation (MAD)166
Skewness2.7614496
Sum3867653
Variance284894.63
MonotonicityNot monotonic
2024-05-18T08:04:11.522028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1574
 
15.7%
44 259
 
2.6%
17 224
 
2.2%
22 186
 
1.9%
19 131
 
1.3%
80 89
 
0.9%
79 77
 
0.8%
1268 77
 
0.8%
1 72
 
0.7%
150 68
 
0.7%
Other values (1234) 7240
72.4%
ValueCountFrequency (%)
0 1574
15.7%
1 72
 
0.7%
2 46
 
0.5%
3 29
 
0.3%
4 54
 
0.5%
5 9
 
0.1%
6 10
 
0.1%
7 38
 
0.4%
8 12
 
0.1%
9 12
 
0.1%
ValueCountFrequency (%)
4941 1
 
< 0.1%
4938 1
 
< 0.1%
4937 1
 
< 0.1%
4934 7
0.1%
4780 4
< 0.1%
4518 1
 
< 0.1%
3884 1
 
< 0.1%
3865 1
 
< 0.1%
3741 1
 
< 0.1%
3690 1
 
< 0.1%

지
Real number (ℝ)

MISSING  ZEROS 

Distinct485
Distinct (%)5.0%
Missing274
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean34.684454
Minimum0
Maximum3575
Zeros5583
Zeros (%)55.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T08:04:12.042342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311
95-th percentile174
Maximum3575
Range3575
Interquartile range (IQR)11

Descriptive statistics

Standard deviation141.27796
Coefficient of variation (CV)4.0732359
Kurtosis169.8093
Mean34.684454
Median Absolute Deviation (MAD)0
Skewness10.606376
Sum337341
Variance19959.463
MonotonicityNot monotonic
2024-05-18T08:04:12.773608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5583
55.8%
1 475
 
4.8%
4 202
 
2.0%
2 200
 
2.0%
3 191
 
1.9%
8 129
 
1.3%
5 121
 
1.2%
6 104
 
1.0%
9 95
 
0.9%
7 86
 
0.9%
Other values (475) 2540
25.4%
(Missing) 274
 
2.7%
ValueCountFrequency (%)
0 5583
55.8%
1 475
 
4.8%
2 200
 
2.0%
3 191
 
1.9%
4 202
 
2.0%
5 121
 
1.2%
6 104
 
1.0%
7 86
 
0.9%
8 129
 
1.3%
9 95
 
0.9%
ValueCountFrequency (%)
3575 1
< 0.1%
3335 1
< 0.1%
3265 1
< 0.1%
2768 1
< 0.1%
2667 1
< 0.1%
2018 1
< 0.1%
1943 1
< 0.1%
1896 1
< 0.1%
1820 1
< 0.1%
1747 1
< 0.1%

작업_일자
Real number (ℝ)

Distinct726
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20153578
Minimum20111227
Maximum20240517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T08:04:13.864314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20111227
5-th percentile20111227
Q120120207
median20120222
Q320191225
95-th percentile20231028
Maximum20240517
Range129290
Interquartile range (IQR)71018

Descriptive statistics

Standard deviation45745.042
Coefficient of variation (CV)0.0022698224
Kurtosis-1.1595273
Mean20153578
Median Absolute Deviation (MAD)8995
Skewness0.68138567
Sum2.0153578 × 1011
Variance2.0926089 × 109
MonotonicityNot monotonic
2024-05-18T08:04:14.817734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20111227 2252
22.5%
20120222 1488
 
14.9%
20120207 1249
 
12.5%
20211029 250
 
2.5%
20180927 223
 
2.2%
20240208 147
 
1.5%
20170928 141
 
1.4%
20240102 125
 
1.2%
20231028 98
 
1.0%
20230908 95
 
0.9%
Other values (716) 3932
39.3%
ValueCountFrequency (%)
20111227 2252
22.5%
20120102 6
 
0.1%
20120104 1
 
< 0.1%
20120105 1
 
< 0.1%
20120106 1
 
< 0.1%
20120111 1
 
< 0.1%
20120113 2
 
< 0.1%
20120118 1
 
< 0.1%
20120120 6
 
0.1%
20120125 1
 
< 0.1%
ValueCountFrequency (%)
20240517 15
0.1%
20240514 11
0.1%
20240511 19
0.2%
20240507 15
0.1%
20240425 18
0.2%
20240420 4
 
< 0.1%
20240417 7
 
0.1%
20240411 9
0.1%
20240402 2
 
< 0.1%
20240330 11
0.1%

Interactions

2024-05-18T08:04:00.684186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:53.901137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:55.527794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:57.413757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:59.087391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:04:01.195999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:54.214975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:55.939316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:57.806592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:59.394655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:04:01.472399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:54.532188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:56.246099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:58.118809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:59.774498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:04:01.747286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:54.848778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:56.606389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:58.453248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:04:00.058520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:04:02.032593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:55.146635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:57.012847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:03:58.778171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:04:00.364192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T08:04:15.164839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대표_여부시군구_코드법정동_코드대지_구분_코드번지작업_일자
대표_여부1.0000.6560.3130.0840.1960.1920.424
시군구_코드0.6561.0000.6670.2640.4320.1620.508
법정동_코드0.3130.6671.0000.0540.3360.0770.260
대지_구분_코드0.0840.2640.0541.0000.1260.0000.163
번0.1960.4320.3360.1261.0000.0450.279
지0.1920.1620.0770.0000.0451.0000.116
작업_일자0.4240.5080.2600.1630.2790.1161.000
2024-05-18T08:04:15.615650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대표_여부대지_구분_코드
대표_여부1.0000.139
대지_구분_코드0.1391.000
2024-05-18T08:04:16.002683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구_코드법정동_코드번지작업_일자대표_여부대지_구분_코드
시군구_코드1.000-0.289-0.089-0.145-0.0490.5100.163
법정동_코드-0.2891.000-0.065-0.0160.0570.2250.022
번-0.089-0.0651.0000.0860.2940.1950.055
지-0.145-0.0160.0861.0000.1800.1440.000
작업_일자-0.0490.0570.2940.1801.0000.1500.071
대표_여부0.5100.2250.1950.1440.1501.0000.139
대지_구분_코드0.1630.0220.0550.0000.0710.1391.000

Missing values

2024-05-18T08:04:02.494531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T08:04:02.920427image/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.
2024-05-18T08:04:03.241938image/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

관리_대지_위치_PK관리_주택대장_PK대표_여부시군구_코드법정동_코드대지_구분_코드번지작업_일자
5548311440-125211440-4401147011700<NA>0020111227
7608211350-152211350-30701135010200<NA>0020111227
6401711590-354111590-34011590102000804020111227
1627511305-10000559911305-100004121011305101000126828220120207
6537911590-84611590-42201159010600<NA>0020111227
2096211710-10005240911710-100063677111710101000101120190924
582311305-100000000000000023732911305-1000000000000000140142011305101000322420230908
7622611470-100000000000000007730511470-1000000000000000054874111470101000314020221123
2978611710-10003626311710-10004759111171010100022020170110
2391511710-524711710-5138111710<NA>00020120222
관리_대지_위치_PK관리_주택대장_PK대표_여부시군구_코드법정동_코드대지_구분_코드번지작업_일자
289511680-100000000000000023505511680-1000000000000000139916111680118000527020230908
5226811650-10000942811650-1000095251116501080001682020111227
1477611710-815211710-80431<NA>10100044020120222
2177911710-10000651311710-10000718911171010100017<NA>20120222
832011200-10003963511200-1000366351112001110001331020191225
4472311710-216511710-2056111710<NA>0320020120222
3062611710-795111710-7842111710<NA>00020120222
5912211710-10000573511710-10000620711171010100022<NA>20120222
2796811710-10000723911710-10000800811171010200017<NA>20120222
98711380-10000447611380-100004281011380103000575320111227