Overview

Dataset statistics

Number of variables19
Number of observations10000
Missing cells37179
Missing cells (%)19.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory166.0 B

Variable types

Text6
Categorical8
Unsupported1
Numeric4

Dataset

Description관리_폐쇄말소대장_PK,관리_상위_폐쇄말소대장_PK,폐쇄말소_구분_코드,폐쇄말소_일,폐쇄말소_일련번호,대장_구분_코드,대장_종류_코드,시군구_코드,법정동_코드,대지_구분_코드,건물_명,위반_건축물_여부,대장_일련번호,총괄표제부_일련번호,표제부_일련번호,전유부_일련번호,새주소_도로_코드,새주소_법정동_코드,새주소_지상지하_코드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15392/S/1/datasetView.do

Alerts

대장_종류_코드 is highly overall correlated with 대장_구분_코드 and 2 other fieldsHigh correlation
총괄표제부_일련번호 is highly overall correlated with 위반_건축물_여부 and 1 other fieldsHigh correlation
시군구_코드 is highly overall correlated with 대장_구분_코드 and 2 other fieldsHigh correlation
대지_구분_코드 is highly overall correlated with 위반_건축물_여부 and 1 other fieldsHigh correlation
위반_건축물_여부 is highly overall correlated with 폐쇄말소_일련번호 and 10 other fieldsHigh correlation
폐쇄말소_구분_코드 is highly overall correlated with 위반_건축물_여부 and 1 other fieldsHigh correlation
새주소_지상지하_코드 is highly overall correlated with 폐쇄말소_일련번호 and 10 other fieldsHigh correlation
대장_구분_코드 is highly overall correlated with 대장_종류_코드 and 3 other fieldsHigh correlation
폐쇄말소_일련번호 is highly overall correlated with 위반_건축물_여부 and 1 other fieldsHigh correlation
대장_일련번호 is highly overall correlated with 표제부_일련번호 and 3 other fieldsHigh correlation
표제부_일련번호 is highly overall correlated with 대장_일련번호 and 3 other fieldsHigh correlation
전유부_일련번호 is highly overall correlated with 대장_일련번호 and 3 other fieldsHigh correlation
대지_구분_코드 is highly imbalanced (86.6%)Imbalance
위반_건축물_여부 is highly imbalanced (93.1%)Imbalance
총괄표제부_일련번호 is highly imbalanced (50.3%)Imbalance
새주소_지상지하_코드 is highly imbalanced (71.9%)Imbalance
관리_상위_폐쇄말소대장_PK has 4746 (47.5%) missing valuesMissing
폐쇄말소_일 has 8019 (80.2%) missing valuesMissing
폐쇄말소_일련번호 has 1975 (19.8%) missing valuesMissing
건물_명 has 6139 (61.4%) missing valuesMissing
새주소_도로_코드 has 8150 (81.5%) missing valuesMissing
새주소_법정동_코드 has 8150 (81.5%) missing valuesMissing
표제부_일련번호 is highly skewed (γ1 = 24.14122077)Skewed
관리_폐쇄말소대장_PK has unique valuesUnique
폐쇄말소_일 is an unsupported type, check if it needs cleaning or further analysisUnsupported
표제부_일련번호 has 4925 (49.2%) zerosZeros
전유부_일련번호 has 6013 (60.1%) zerosZeros

Reproduction

Analysis started2024-05-10 23:10:55.026948
Analysis finished2024-05-10 23:11:07.467374
Duration12.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리_폐쇄말소대장_PK
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-10T23:11:07.751570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length12.9663
Min length7

Characters and Unicode

Total characters129663
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 row11500-25138
2nd row11740-8744
3rd row11215-4128
4th row11230-13340
5th row11500-7539
ValueCountFrequency (%)
11500-25138 1
 
< 0.1%
11380-2297 1
 
< 0.1%
11215-98 1
 
< 0.1%
11215-3107 1
 
< 0.1%
11650-1000000000000003499052 1
 
< 0.1%
11500-100371044 1
 
< 0.1%
11380-3191 1
 
< 0.1%
11740-422 1
 
< 0.1%
11590-1000000000000002989318 1
 
< 0.1%
11470-6816 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-10T23:11:08.936470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 35279
27.2%
1 31141
24.0%
- 10000
 
7.7%
2 9223
 
7.1%
3 7978
 
6.2%
5 7599
 
5.9%
4 6786
 
5.2%
7 6469
 
5.0%
6 5832
 
4.5%
9 4703
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119663
92.3%
Dash Punctuation 10000
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35279
29.5%
1 31141
26.0%
2 9223
 
7.7%
3 7978
 
6.7%
5 7599
 
6.4%
4 6786
 
5.7%
7 6469
 
5.4%
6 5832
 
4.9%
9 4703
 
3.9%
8 4653
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 129663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35279
27.2%
1 31141
24.0%
- 10000
 
7.7%
2 9223
 
7.1%
3 7978
 
6.2%
5 7599
 
5.9%
4 6786
 
5.2%
7 6469
 
5.0%
6 5832
 
4.5%
9 4703
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35279
27.2%
1 31141
24.0%
- 10000
 
7.7%
2 9223
 
7.1%
3 7978
 
6.2%
5 7599
 
5.9%
4 6786
 
5.2%
7 6469
 
5.0%
6 5832
 
4.5%
9 4703
 
3.6%

관리_상위_폐쇄말소대장_PK
Text

MISSING 

Distinct1836
Distinct (%)34.9%
Missing4746
Missing (%)47.5%
Memory size156.2 KiB
2024-05-10T23:11:09.852249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length10
Mean length13.972402
Min length7

Characters and Unicode

Total characters73411
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

Unique935 ?
Unique (%)17.8%

Sample

1st row11500-8131
2nd row11740-1419
3rd row11230-5350
4th row11500-1393
5th row11110-2959
ValueCountFrequency (%)
11740-1000000000000002325557 92
 
1.8%
11560-1000000000000002312389 80
 
1.5%
11440-1000000000000002613542 80
 
1.5%
11740-1738 67
 
1.3%
11260-14109 37
 
0.7%
11500-859 31
 
0.6%
11500-1000000000000003392519 29
 
0.6%
11260-14108 29
 
0.6%
11260-14110 28
 
0.5%
11500-1000000000000003193683 27
 
0.5%
Other values (1826) 4754
90.5%
2024-05-10T23:11:11.134691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 24263
33.1%
1 15901
21.7%
5 5323
 
7.3%
- 5254
 
7.2%
3 4743
 
6.5%
2 4479
 
6.1%
4 3637
 
5.0%
7 3621
 
4.9%
6 2612
 
3.6%
9 1886
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68157
92.8%
Dash Punctuation 5254
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24263
35.6%
1 15901
23.3%
5 5323
 
7.8%
3 4743
 
7.0%
2 4479
 
6.6%
4 3637
 
5.3%
7 3621
 
5.3%
6 2612
 
3.8%
9 1886
 
2.8%
8 1692
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 5254
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73411
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24263
33.1%
1 15901
21.7%
5 5323
 
7.3%
- 5254
 
7.2%
3 4743
 
6.5%
2 4479
 
6.1%
4 3637
 
5.0%
7 3621
 
4.9%
6 2612
 
3.6%
9 1886
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73411
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24263
33.1%
1 15901
21.7%
5 5323
 
7.3%
- 5254
 
7.2%
3 4743
 
6.5%
2 4479
 
6.1%
4 3637
 
5.0%
7 3621
 
4.9%
6 2612
 
3.6%
9 1886
 
2.6%

폐쇄말소_구분_코드
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐쇄
4151 
말소
3735 
지번변경에의한폐쇄
667 
일부말소
 
344
패쇄/말소대장수정
 
319
Other values (5)
784 

Length

Max length10
Median length2
Mean length3.2067
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐쇄
2nd row말소
3rd row말소
4th row말소
5th row폐쇄

Common Values

ValueCountFrequency (%)
폐쇄 4151
41.5%
말소 3735
37.4%
지번변경에의한폐쇄 667
 
6.7%
일부말소 344
 
3.4%
패쇄/말소대장수정 319
 
3.2%
패쇄/말소대장삭제 274
 
2.7%
합병에의한폐쇄 150
 
1.5%
전환에의한폐쇄 125
 
1.2%
호분할합병에의한폐쇄 119
 
1.2%
일부폐쇄 116
 
1.2%

Length

2024-05-10T23:11:11.583135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:11:11.938414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐쇄 4151
41.5%
말소 3735
37.4%
지번변경에의한폐쇄 667
 
6.7%
일부말소 344
 
3.4%
패쇄/말소대장수정 319
 
3.2%
패쇄/말소대장삭제 274
 
2.7%
합병에의한폐쇄 150
 
1.5%
전환에의한폐쇄 125
 
1.2%
호분할합병에의한폐쇄 119
 
1.2%
일부폐쇄 116
 
1.2%

폐쇄말소_일
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8019
Missing (%)80.2%
Memory size156.2 KiB

폐쇄말소_일련번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct283
Distinct (%)3.5%
Missing1975
Missing (%)19.8%
Infinite0
Infinite (%)0.0%
Mean20.146293
Minimum1
Maximum1378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T23:11:12.617100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile35
Maximum1378
Range1377
Interquartile range (IQR)0

Descriptive statistics

Standard deviation118.92553
Coefficient of variation (CV)5.9030976
Kurtosis68.580793
Mean20.146293
Median Absolute Deviation (MAD)0
Skewness8.0208975
Sum161674
Variance14143.282
MonotonicityNot monotonic
2024-05-10T23:11:13.228292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7013
70.1%
2 72
 
0.7%
3 35
 
0.4%
4 27
 
0.3%
6 22
 
0.2%
8 22
 
0.2%
12 21
 
0.2%
13 20
 
0.2%
9 20
 
0.2%
22 19
 
0.2%
Other values (273) 754
 
7.5%
(Missing) 1975
 
19.8%
ValueCountFrequency (%)
1 7013
70.1%
2 72
 
0.7%
3 35
 
0.4%
4 27
 
0.3%
5 14
 
0.1%
6 22
 
0.2%
7 14
 
0.1%
8 22
 
0.2%
9 20
 
0.2%
10 17
 
0.2%
ValueCountFrequency (%)
1378 1
< 0.1%
1371 1
< 0.1%
1344 1
< 0.1%
1325 1
< 0.1%
1314 1
< 0.1%
1304 1
< 0.1%
1301 1
< 0.1%
1292 1
< 0.1%
1290 1
< 0.1%
1287 1
< 0.1%

대장_구분_코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
집합
5678 
일반
4322 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
집합 5678
56.8%
일반 4322
43.2%

Length

2024-05-10T23:11:13.755365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:11:14.103197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
집합 5678
56.8%
일반 4322
43.2%

대장_종류_코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
전유부
5265 
일반건축물
3051 
총괄표제부
1389 
표제부
 
295

Length

Max length5
Median length3
Mean length3.888
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전유부
2nd row전유부
3rd row일반건축물
4th row전유부
5th row일반건축물

Common Values

ValueCountFrequency (%)
전유부 5265
52.6%
일반건축물 3051
30.5%
총괄표제부 1389
 
13.9%
표제부 295
 
2.9%

Length

2024-05-10T23:11:14.706688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:11:15.104835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전유부 5265
52.6%
일반건축물 3051
30.5%
총괄표제부 1389
 
13.9%
표제부 295
 
2.9%

시군구_코드
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강서구
1687 
동대문구
1194 
강동구
844 
성북구
810 
송파구
720 
Other values (20)
4745 

Length

Max length4
Median length3
Mean length3.1718
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강서구
2nd row강동구
3rd row광진구
4th row동대문구
5th row강서구

Common Values

ValueCountFrequency (%)
강서구 1687
16.9%
동대문구 1194
11.9%
강동구 844
 
8.4%
성북구 810
 
8.1%
송파구 720
 
7.2%
은평구 495
 
5.0%
종로구 426
 
4.3%
광진구 420
 
4.2%
용산구 417
 
4.2%
영등포구 403
 
4.0%
Other values (15) 2584
25.8%

Length

2024-05-10T23:11:16.224067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구 1687
16.9%
동대문구 1194
11.9%
강동구 844
 
8.4%
성북구 810
 
8.1%
송파구 720
 
7.2%
은평구 495
 
5.0%
종로구 426
 
4.3%
광진구 420
 
4.2%
용산구 417
 
4.2%
영등포구 403
 
4.0%
Other values (15) 2584
25.8%

법정동_코드
Text

Distinct332
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-10T23:11:17.133535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1509
Min length2

Characters and Unicode

Total characters31509
Distinct characters202
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

Unique57 ?
Unique (%)0.6%

Sample

1st row오쇠동
2nd row길동
3rd row자양동
4th row장안동
5th row방화동
ValueCountFrequency (%)
화곡동 639
 
6.4%
장안동 636
 
6.4%
방화동 466
 
4.7%
길음동 264
 
2.6%
하월곡동 238
 
2.4%
문정동 211
 
2.1%
천호동 198
 
2.0%
면목동 186
 
1.9%
암사동 181
 
1.8%
신림동 173
 
1.7%
Other values (322) 6808
68.1%
2024-05-10T23:11:18.521003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 10057
31.9%
화 1142
 
3.6%
곡 1009
 
3.2%
장 701
 
2.2%
가 677
 
2.1%
방 674
 
2.1%
안 653
 
2.1%
신 647
 
2.1%
문 482
 
1.5%
길 453
 
1.4%
Other values (192) 15014
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31021
98.5%
Decimal Number 488
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 10057
32.4%
화 1142
 
3.7%
곡 1009
 
3.3%
장 701
 
2.3%
가 677
 
2.2%
방 674
 
2.2%
안 653
 
2.1%
신 647
 
2.1%
문 482
 
1.6%
길 453
 
1.5%
Other values (184) 14526
46.8%
Decimal Number
ValueCountFrequency (%)
2 132
27.0%
3 117
24.0%
4 104
21.3%
1 84
17.2%
5 22
 
4.5%
6 20
 
4.1%
7 7
 
1.4%
8 2
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31021
98.5%
Common 488
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 10057
32.4%
화 1142
 
3.7%
곡 1009
 
3.3%
장 701
 
2.3%
가 677
 
2.2%
방 674
 
2.2%
안 653
 
2.1%
신 647
 
2.1%
문 482
 
1.6%
길 453
 
1.5%
Other values (184) 14526
46.8%
Common
ValueCountFrequency (%)
2 132
27.0%
3 117
24.0%
4 104
21.3%
1 84
17.2%
5 22
 
4.5%
6 20
 
4.1%
7 7
 
1.4%
8 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31021
98.5%
ASCII 488
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 10057
32.4%
화 1142
 
3.7%
곡 1009
 
3.3%
장 701
 
2.3%
가 677
 
2.2%
방 674
 
2.2%
안 653
 
2.1%
신 647
 
2.1%
문 482
 
1.6%
길 453
 
1.5%
Other values (184) 14526
46.8%
ASCII
ValueCountFrequency (%)
2 132
27.0%
3 117
24.0%
4 104
21.3%
1 84
17.2%
5 22
 
4.5%
6 20
 
4.1%
7 7
 
1.4%
8 2
 
0.4%

대지_구분_코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대지
9719 
산
 
171
블록
 
110

Length

Max length2
Median length2
Mean length1.9829
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대지 9719
97.2%
산 171
 
1.7%
블록 110
 
1.1%

Length

2024-05-10T23:11:19.031429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:11:19.384626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대지 9719
97.2%
산 171
 
1.7%
블록 110
 
1.1%

건물_명
Text

MISSING 

Distinct411
Distinct (%)10.6%
Missing6139
Missing (%)61.4%
Memory size156.2 KiB
2024-05-10T23:11:20.042285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length6.5314685
Min length1

Characters and Unicode

Total characters25218
Distinct characters336
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique157 ?
Unique (%)4.1%

Sample

1st row장안시영아파트
2nd row청우아파트
3rd row동숭지구아파트
4th row면목임대아파트
5th row장안시영아파트
ValueCountFrequency (%)
장안시영아파트 579
 
12.3%
주공시범아파트 201
 
4.3%
동숭지구아파트 136
 
2.9%
푸르지오 132
 
2.8%
면목임대아파트 125
 
2.7%
브라이튼 122
 
2.6%
여의도 122
 
2.6%
동서울아파트 97
 
2.1%
더 94
 
2.0%
강동u1center 92
 
2.0%
Other values (441) 3010
63.9%
2024-05-10T23:11:21.086001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2392
 
9.5%
트 2255
 
8.9%
파 2146
 
8.5%
동 1170
 
4.6%
시 906
 
3.6%
849
 
3.4%
안 647
 
2.6%
장 626
 
2.5%
영 624
 
2.5%
1 393
 
1.6%
Other values (326) 13210
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22236
88.2%
Decimal Number 997
 
4.0%
Uppercase Letter 855
 
3.4%
Space Separator 849
 
3.4%
Lowercase Letter 152
 
0.6%
Other Punctuation 69
 
0.3%
Open Punctuation 22
 
0.1%
Close Punctuation 22
 
0.1%
Dash Punctuation 12
 
< 0.1%
Letter Number 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2392
 
10.8%
트 2255
 
10.1%
파 2146
 
9.7%
동 1170
 
5.3%
시 906
 
4.1%
안 647
 
2.9%
장 626
 
2.8%
영 624
 
2.8%
지 377
 
1.7%
마 326
 
1.5%
Other values (273) 10767
48.4%
Uppercase Letter
ValueCountFrequency (%)
E 187
21.9%
T 140
16.4%
C 108
12.6%
R 94
11.0%
N 93
10.9%
U 92
10.8%
I 27
 
3.2%
K 25
 
2.9%
S 22
 
2.6%
B 20
 
2.3%
Other values (9) 47
 
5.5%
Lowercase Letter
ValueCountFrequency (%)
o 35
23.0%
r 23
15.1%
e 21
13.8%
i 12
 
7.9%
w 12
 
7.9%
s 9
 
5.9%
l 8
 
5.3%
m 6
 
3.9%
f 6
 
3.9%
h 5
 
3.3%
Other values (6) 15
9.9%
Decimal Number
ValueCountFrequency (%)
1 393
39.4%
2 197
19.8%
0 80
 
8.0%
3 72
 
7.2%
4 65
 
6.5%
8 43
 
4.3%
5 43
 
4.3%
6 40
 
4.0%
7 35
 
3.5%
9 29
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 67
97.1%
, 1
 
1.4%
/ 1
 
1.4%
Space Separator
ValueCountFrequency (%)
849
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22236
88.2%
Common 1971
 
7.8%
Latin 1011
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2392
 
10.8%
트 2255
 
10.1%
파 2146
 
9.7%
동 1170
 
5.3%
시 906
 
4.1%
안 647
 
2.9%
장 626
 
2.8%
영 624
 
2.8%
지 377
 
1.7%
마 326
 
1.5%
Other values (273) 10767
48.4%
Latin
ValueCountFrequency (%)
E 187
18.5%
T 140
13.8%
C 108
10.7%
R 94
9.3%
N 93
9.2%
U 92
9.1%
o 35
 
3.5%
I 27
 
2.7%
K 25
 
2.5%
r 23
 
2.3%
Other values (26) 187
18.5%
Common
ValueCountFrequency (%)
849
43.1%
1 393
19.9%
2 197
 
10.0%
0 80
 
4.1%
3 72
 
3.7%
. 67
 
3.4%
4 65
 
3.3%
8 43
 
2.2%
5 43
 
2.2%
6 40
 
2.0%
Other values (7) 122
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22236
88.2%
ASCII 2978
 
11.8%
Number Forms 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2392
 
10.8%
트 2255
 
10.1%
파 2146
 
9.7%
동 1170
 
5.3%
시 906
 
4.1%
안 647
 
2.9%
장 626
 
2.8%
영 624
 
2.8%
지 377
 
1.7%
마 326
 
1.5%
Other values (273) 10767
48.4%
ASCII
ValueCountFrequency (%)
849
28.5%
1 393
13.2%
2 197
 
6.6%
E 187
 
6.3%
T 140
 
4.7%
C 108
 
3.6%
R 94
 
3.2%
N 93
 
3.1%
U 92
 
3.1%
0 80
 
2.7%
Other values (42) 745
25.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 4
100.0%

위반_건축물_여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9917 
위반건축물
 
83

Length

Max length5
Median length4
Mean length4.0083
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9917
99.2%
위반건축물 83
 
0.8%

Length

2024-05-10T23:11:21.450076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:11:21.771190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9917
99.2%
위반건축물 83
 
0.8%

대장_일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1250
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214.8933
Minimum1
Maximum6980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T23:11:22.102533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median20
Q3170.25
95-th percentile1109.05
Maximum6980
Range6979
Interquartile range (IQR)160.25

Descriptive statistics

Standard deviation503.33785
Coefficient of variation (CV)2.3422687
Kurtosis44.292396
Mean214.8933
Median Absolute Deviation (MAD)19
Skewness5.4544544
Sum2148933
Variance253348.99
MonotonicityNot monotonic
2024-05-10T23:11:22.467087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 2725
27.3%
1 1577
 
15.8%
20 412
 
4.1%
30 215
 
2.1%
40 152
 
1.5%
50 116
 
1.2%
60 95
 
0.9%
70 72
 
0.7%
80 69
 
0.7%
90 66
 
0.7%
Other values (1240) 4501
45.0%
ValueCountFrequency (%)
1 1577
15.8%
2 47
 
0.5%
3 33
 
0.3%
4 36
 
0.4%
5 25
 
0.2%
6 23
 
0.2%
7 27
 
0.3%
8 23
 
0.2%
9 21
 
0.2%
10 2725
27.3%
ValueCountFrequency (%)
6980 1
< 0.1%
6300 1
< 0.1%
6130 1
< 0.1%
6100 1
< 0.1%
6080 1
< 0.1%
6070 1
< 0.1%
6060 1
< 0.1%
6010 1
< 0.1%
5990 1
< 0.1%
5960 1
< 0.1%

총괄표제부_일련번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5653 
0
4341 
2
 
5
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 5653
56.5%
0 4341
43.4%
2 5
 
0.1%
3 1
 
< 0.1%

Length

2024-05-10T23:11:22.868792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:11:23.220776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5653
56.5%
0 4341
43.4%
2 5
 
< 0.1%
3 1
 
< 0.1%

표제부_일련번호
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct107
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.5704
Minimum0
Maximum4267
Zeros4925
Zeros (%)49.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T23:11:23.533665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310
95-th percentile80
Maximum4267
Range4267
Interquartile range (IQR)10

Descriptive statistics

Standard deviation155.11339
Coefficient of variation (CV)7.1910299
Kurtosis630.93134
Mean21.5704
Median Absolute Deviation (MAD)1
Skewness24.141221
Sum215704
Variance24060.164
MonotonicityNot monotonic
2024-05-10T23:11:24.009524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4925
49.2%
10 1313
 
13.1%
1 1139
 
11.4%
20 435
 
4.3%
30 368
 
3.7%
40 294
 
2.9%
50 138
 
1.4%
5 113
 
1.1%
60 88
 
0.9%
70 71
 
0.7%
Other values (97) 1116
 
11.2%
ValueCountFrequency (%)
0 4925
49.2%
1 1139
 
11.4%
2 67
 
0.7%
3 46
 
0.5%
4 22
 
0.2%
5 113
 
1.1%
6 47
 
0.5%
7 21
 
0.2%
8 26
 
0.3%
9 16
 
0.2%
ValueCountFrequency (%)
4267 4
< 0.1%
4266 2
< 0.1%
4221 1
 
< 0.1%
4020 1
 
< 0.1%
4010 2
< 0.1%
3910 2
< 0.1%
3680 1
 
< 0.1%
1550 1
 
< 0.1%
1010 1
 
< 0.1%
1000 1
 
< 0.1%

전유부_일련번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1177
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.5235
Minimum0
Maximum6980
Zeros6013
Zeros (%)60.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T23:11:24.457385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q390
95-th percentile1019.05
Maximum6980
Range6980
Interquartile range (IQR)90

Descriptive statistics

Standard deviation443.53516
Coefficient of variation (CV)2.6318891
Kurtosis38.869244
Mean168.5235
Median Absolute Deviation (MAD)0
Skewness5.1203362
Sum1685235
Variance196723.43
MonotonicityNot monotonic
2024-05-10T23:11:24.997867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6013
60.1%
1 89
 
0.9%
10 62
 
0.6%
30 53
 
0.5%
40 44
 
0.4%
20 40
 
0.4%
50 37
 
0.4%
60 31
 
0.3%
90 30
 
0.3%
4 29
 
0.3%
Other values (1167) 3572
35.7%
ValueCountFrequency (%)
0 6013
60.1%
1 89
 
0.9%
2 26
 
0.3%
3 23
 
0.2%
4 29
 
0.3%
5 21
 
0.2%
6 21
 
0.2%
7 19
 
0.2%
8 19
 
0.2%
9 15
 
0.1%
ValueCountFrequency (%)
6980 1
< 0.1%
6130 1
< 0.1%
5111 1
< 0.1%
5100 1
< 0.1%
4880 1
< 0.1%
4840 1
< 0.1%
4800 1
< 0.1%
4790 1
< 0.1%
4720 1
< 0.1%
4570 1
< 0.1%

새주소_도로_코드
Text

MISSING 

Distinct402
Distinct (%)21.7%
Missing8150
Missing (%)81.5%
Memory size156.2 KiB
2024-05-10T23:11:25.912118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length15.491892
Min length12

Characters and Unicode

Total characters28660
Distinct characters216
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique241 ?
Unique (%)13.0%

Sample

1st row서울특별시 중랑구 용마산로
2nd row서울특별시 구로구 경서로9길
3rd row서울특별시 강남구 논현로155길
4th row서울특별시 강남구 테헤란로37길
5th row서울특별시 영등포구 영등포로45길
ValueCountFrequency (%)
서울특별시 1850
33.3%
강서구 342
 
6.2%
영등포구 243
 
4.4%
강남구 182
 
3.3%
중랑구 135
 
2.4%
서초구 133
 
2.4%
마포구 126
 
2.3%
국제금융로 124
 
2.2%
용마산로 122
 
2.2%
방화대로34길 97
 
1.7%
Other values (407) 2196
39.6%
2024-05-10T23:11:27.748335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3700
 
12.9%
서 2470
 
8.6%
구 2008
 
7.0%
로 1879
 
6.6%
울 1860
 
6.5%
시 1853
 
6.5%
별 1850
 
6.5%
특 1850
 
6.5%
길 1023
 
3.6%
강 759
 
2.6%
Other values (206) 9408
32.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23053
80.4%
Space Separator 3700
 
12.9%
Decimal Number 1907
 
6.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 2470
 
10.7%
구 2008
 
8.7%
로 1879
 
8.2%
울 1860
 
8.1%
시 1853
 
8.0%
별 1850
 
8.0%
특 1850
 
8.0%
길 1023
 
4.4%
강 759
 
3.3%
대 538
 
2.3%
Other values (195) 6963
30.2%
Decimal Number
ValueCountFrequency (%)
2 348
18.2%
3 289
15.2%
1 280
14.7%
4 269
14.1%
9 170
8.9%
6 168
8.8%
7 130
 
6.8%
8 127
 
6.7%
5 83
 
4.4%
0 43
 
2.3%
Space Separator
ValueCountFrequency (%)
3700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23053
80.4%
Common 5607
 
19.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 2470
 
10.7%
구 2008
 
8.7%
로 1879
 
8.2%
울 1860
 
8.1%
시 1853
 
8.0%
별 1850
 
8.0%
특 1850
 
8.0%
길 1023
 
4.4%
강 759
 
3.3%
대 538
 
2.3%
Other values (195) 6963
30.2%
Common
ValueCountFrequency (%)
3700
66.0%
2 348
 
6.2%
3 289
 
5.2%
1 280
 
5.0%
4 269
 
4.8%
9 170
 
3.0%
6 168
 
3.0%
7 130
 
2.3%
8 127
 
2.3%
5 83
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23053
80.4%
ASCII 5607
 
19.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3700
66.0%
2 348
 
6.2%
3 289
 
5.2%
1 280
 
5.0%
4 269
 
4.8%
9 170
 
3.0%
6 168
 
3.0%
7 130
 
2.3%
8 127
 
2.3%
5 83
 
1.5%
Hangul
ValueCountFrequency (%)
서 2470
 
10.7%
구 2008
 
8.7%
로 1879
 
8.2%
울 1860
 
8.1%
시 1853
 
8.0%
별 1850
 
8.0%
특 1850
 
8.0%
길 1023
 
4.4%
강 759
 
3.3%
대 538
 
2.3%
Other values (195) 6963
30.2%

새주소_법정동_코드
Text

MISSING 

Distinct170
Distinct (%)9.2%
Missing8150
Missing (%)81.5%
Memory size156.2 KiB
2024-05-10T23:11:28.505979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.2864865
Min length2

Characters and Unicode

Total characters6080
Distinct characters156
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

Unique65 ?
Unique (%)3.5%

Sample

1st row면목동
2nd row고척동
3rd row신사동
4th row역삼동
5th row영등포동2가
ValueCountFrequency (%)
방화동 202
 
10.9%
여의도동 136
 
7.4%
면목동 126
 
6.8%
방배동 109
 
5.9%
아현동 101
 
5.5%
가양동 79
 
4.3%
당산동4가 76
 
4.1%
역삼동 75
 
4.1%
천호동 73
 
3.9%
고척동 38
 
2.1%
Other values (160) 835
45.1%
2024-05-10T23:11:29.797026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1839
30.2%
방 320
 
5.3%
가 253
 
4.2%
도 212
 
3.5%
화 210
 
3.5%
의 159
 
2.6%
현 159
 
2.6%
여 136
 
2.2%
목 135
 
2.2%
면 126
 
2.1%
Other values (146) 2531
41.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5915
97.3%
Decimal Number 165
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1839
31.1%
방 320
 
5.4%
가 253
 
4.3%
도 212
 
3.6%
화 210
 
3.6%
의 159
 
2.7%
현 159
 
2.7%
여 136
 
2.3%
목 135
 
2.3%
면 126
 
2.1%
Other values (139) 2366
40.0%
Decimal Number
ValueCountFrequency (%)
4 82
49.7%
3 35
21.2%
1 22
 
13.3%
2 18
 
10.9%
5 5
 
3.0%
6 2
 
1.2%
7 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5915
97.3%
Common 165
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1839
31.1%
방 320
 
5.4%
가 253
 
4.3%
도 212
 
3.6%
화 210
 
3.6%
의 159
 
2.7%
현 159
 
2.7%
여 136
 
2.3%
목 135
 
2.3%
면 126
 
2.1%
Other values (139) 2366
40.0%
Common
ValueCountFrequency (%)
4 82
49.7%
3 35
21.2%
1 22
 
13.3%
2 18
 
10.9%
5 5
 
3.0%
6 2
 
1.2%
7 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5915
97.3%
ASCII 165
 
2.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1839
31.1%
방 320
 
5.4%
가 253
 
4.3%
도 212
 
3.6%
화 210
 
3.6%
의 159
 
2.7%
현 159
 
2.7%
여 136
 
2.3%
목 135
 
2.3%
면 126
 
2.1%
Other values (139) 2366
40.0%
ASCII
ValueCountFrequency (%)
4 82
49.7%
3 35
21.2%
1 22
 
13.3%
2 18
 
10.9%
5 5
 
3.0%
6 2
 
1.2%
7 1
 
0.6%

새주소_지상지하_코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9512 
<NA>
 
488

Length

Max length4
Median length1
Mean length1.1464
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 9512
95.1%
<NA> 488
 
4.9%

Length

2024-05-10T23:11:30.295743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:11:30.668564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9512
95.1%
na 488
 
4.9%

Interactions

2024-05-10T23:11:04.143208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:00.166938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:01.740611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:03.060038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:04.714789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:00.560422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:02.160737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:03.349327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:04.998736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:00.961071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:02.511235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:03.624944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:05.263546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:01.375515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:02.779340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:11:03.876222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T23:11:30.878456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폐쇄말소_구분_코드폐쇄말소_일련번호대장_구분_코드대장_종류_코드시군구_코드대지_구분_코드대장_일련번호총괄표제부_일련번호표제부_일련번호전유부_일련번호
폐쇄말소_구분_코드1.0000.0990.4330.5150.8040.4280.5240.3810.1050.531
폐쇄말소_일련번호0.0991.0000.2080.1610.4330.0000.2760.0920.0000.079
대장_구분_코드0.4330.2081.0000.9990.5930.0710.3610.6020.0350.338
대장_종류_코드0.5150.1610.9991.0000.6990.0960.2830.5650.0730.265
시군구_코드0.8040.4330.5930.6991.0000.6430.4260.4440.3220.433
대지_구분_코드0.4280.0000.0710.0960.6431.0000.3210.0890.5050.350
대장_일련번호0.5240.2760.3610.2830.4260.3211.0000.1820.0970.996
총괄표제부_일련번호0.3810.0920.6020.5650.4440.0890.1821.0000.0100.245
표제부_일련번호0.1050.0000.0350.0730.3220.5050.0970.0101.0000.000
전유부_일련번호0.5310.0790.3380.2650.4330.3500.9960.2450.0001.000
2024-05-10T23:11:31.300182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대장_종류_코드총괄표제부_일련번호시군구_코드대지_구분_코드위반_건축물_여부폐쇄말소_구분_코드새주소_지상지하_코드대장_구분_코드
대장_종류_코드1.0000.2490.4500.0901.0000.3331.0000.977
총괄표제부_일련번호0.2491.0000.2500.0841.0000.2371.0000.416
시군구_코드0.4500.2501.0000.4241.0000.4361.0000.517
대지_구분_코드0.0900.0840.4241.0001.0000.2851.0000.119
위반_건축물_여부1.0001.0001.0001.0001.0001.0001.0001.000
폐쇄말소_구분_코드0.3330.2370.4360.2851.0001.0001.0000.332
새주소_지상지하_코드1.0001.0001.0001.0001.0001.0001.0001.000
대장_구분_코드0.9770.4160.5170.1191.0000.3321.0001.000
2024-05-10T23:11:31.699924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폐쇄말소_일련번호대장_일련번호표제부_일련번호전유부_일련번호폐쇄말소_구분_코드대장_구분_코드대장_종류_코드시군구_코드대지_구분_코드위반_건축물_여부총괄표제부_일련번호새주소_지상지하_코드
폐쇄말소_일련번호1.0000.1450.1480.1320.0450.1590.0970.1670.0001.0000.0551.000
대장_일련번호0.1451.0000.5460.7110.1850.2770.1720.1640.2031.0000.1101.000
표제부_일련번호0.1480.5461.0000.7450.0550.0250.0470.1500.2411.0000.0061.000
전유부_일련번호0.1320.7110.7451.0000.1880.2600.1610.1670.2241.0000.1491.000
폐쇄말소_구분_코드0.0450.1850.0550.1881.0000.3320.3330.4360.2851.0000.2371.000
대장_구분_코드0.1590.2770.0250.2600.3321.0000.9770.5170.1191.0000.4161.000
대장_종류_코드0.0970.1720.0470.1610.3330.9771.0000.4500.0901.0000.2491.000
시군구_코드0.1670.1640.1500.1670.4360.5170.4501.0000.4241.0000.2501.000
대지_구분_코드0.0000.2030.2410.2240.2850.1190.0900.4241.0001.0000.0841.000
위반_건축물_여부1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
총괄표제부_일련번호0.0550.1100.0060.1490.2370.4160.2490.2500.0841.0001.0001.000
새주소_지상지하_코드1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-10T23:11:05.857917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T23:11:06.646691image/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.
2024-05-10T23:11:07.155353image/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_폐쇄말소대장_PK관리_상위_폐쇄말소대장_PK폐쇄말소_구분_코드폐쇄말소_일폐쇄말소_일련번호대장_구분_코드대장_종류_코드시군구_코드법정동_코드대지_구분_코드건물_명위반_건축물_여부대장_일련번호총괄표제부_일련번호표제부_일련번호전유부_일련번호새주소_도로_코드새주소_법정동_코드새주소_지상지하_코드
374311500-2513811500-8131폐쇄NaN1집합전유부강서구오쇠동대지<NA><NA>590000<NA><NA>0
2744311740-874411740-1419말소NaN1집합전유부강동구길동대지<NA><NA>8011080<NA><NA>0
5059311215-4128<NA>말소NaN1일반일반건축물광진구자양동대지<NA><NA>10000<NA><NA>0
2980511230-1334011230-5350말소NaN27집합전유부동대문구장안동대지장안시영아파트<NA>4712147<NA><NA><NA>
1740511500-7539<NA>폐쇄NaN1일반일반건축물강서구방화동대지<NA><NA>10000<NA><NA>0
2078111500-1138511500-1393폐쇄NaN1집합전유부강서구등촌동대지청우아파트<NA>12041301204<NA><NA>0
1036711110-873211110-2959폐쇄NaN1집합전유부종로구동숭동산동숭지구아파트<NA>150511701505<NA><NA>0
1442011500-2835<NA>폐쇄NaN1일반일반건축물강서구화곡동대지<NA><NA>10000<NA><NA>0
4959611110-1880<NA>폐쇄NaN1일반일반건축물종로구삼청동대지<NA><NA>10000<NA><NA>0
6705911260-4242711260-14108말소20231229<NA>집합전유부중랑구면목동대지면목임대아파트<NA>9012090서울특별시 중랑구 용마산로면목동0
관리_폐쇄말소대장_PK관리_상위_폐쇄말소대장_PK폐쇄말소_구분_코드폐쇄말소_일폐쇄말소_일련번호대장_구분_코드대장_종류_코드시군구_코드법정동_코드대지_구분_코드건물_명위반_건축물_여부대장_일련번호총괄표제부_일련번호표제부_일련번호전유부_일련번호새주소_도로_코드새주소_법정동_코드새주소_지상지하_코드
4055711710-4675711710-5201폐쇄NaN310집합전유부송파구문정동대지10동<NA>310000<NA><NA>0
5801911290-10516<NA>폐쇄NaN1일반일반건축물성북구길음동대지<NA><NA>10000<NA><NA>0
1685611500-2369611500-7847폐쇄NaN1집합전유부강서구방화동대지<NA><NA>2161100216<NA><NA>0
2089111290-4054<NA>폐쇄NaN1일반일반건축물성북구안암동2가대지<NA><NA>10000<NA><NA>0
1734311500-100371248<NA>패쇄/말소대장수정NaN1집합전유부강서구화곡동대지주공시범아파트<NA>1292111292<NA><NA>0
2075211230-1291411230-5342말소NaN1집합전유부동대문구장안동대지장안시영아파트<NA>3211332<NA><NA>0
6614611410-100000000000000305507411410-1000000000000003054701합병에의한폐쇄20240124<NA>집합전유부서대문구대현동대지이화블로섬하우스<NA>181118서울특별시 서대문구 이화여대1안길대현동0
3538811380-198<NA>말소NaN1일반총괄표제부은평구녹번동대지<NA><NA>1100<NA><NA><NA>
6477311500-100000000000000325349211500-1000000000000003253434지번변경에의한폐쇄20240222<NA>집합전유부강서구방화동대지마곡우림필유아파트<NA>12312123서울특별시 강서구 양천로28길방화동0
6381911500-100000000000000332716211500-1000000000000003327107지번변경에의한폐쇄20240305<NA>집합전유부강서구방화동대지마곡 푸르지오<NA>621762서울특별시 강서구 방화대로34길방화동0