Overview

Dataset statistics

Number of variables24
Number of observations10000
Missing cells27320
Missing cells (%)11.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory209.0 B

Variable types

Text8
Categorical7
Numeric9

Dataset

Description관리_건축물대장_PK,관리_상위_건축물대장_PK,대장_구분_코드,대장_종류_코드,시군구_코드,법정동_코드,대지_구분_코드,번,지,특수지_명,블록,로트,건물_명,위반_건축물_여부,대장_일련번호,총괄표제부_일련번호,표제부_일련번호,전유부_일련번호,새주소_도로_코드,새주소_법정동_코드,새주소_지상지하_코드,새주소_본_번,새주소_부_번,변동_일자
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15387/S/1/datasetView.do

Alerts

대장_구분_코드 is highly imbalanced (80.9%)Imbalance
대장_종류_코드 is highly imbalanced (83.8%)Imbalance
대지_구분_코드 is highly imbalanced (99.5%)Imbalance
블록 is highly imbalanced (99.5%)Imbalance
위반_건축물_여부 is highly imbalanced (90.4%)Imbalance
새주소_지상지하_코드 is highly imbalanced (99.0%)Imbalance
관리_상위_건축물대장_PK has 304 (3.0%) missing valuesMissing
특수지_명 has 9995 (> 99.9%) missing valuesMissing
로트 has 9996 (> 99.9%) missing valuesMissing
건물_명 has 699 (7.0%) missing valuesMissing
새주소_부_번 has 6219 (62.2%) missing valuesMissing
지 is highly skewed (γ1 = 22.24380138)Skewed
표제부_일련번호 is highly skewed (γ1 = 50.42207266)Skewed
관리_건축물대장_PK has unique valuesUnique
지 has 6472 (64.7%) zerosZeros
전유부_일련번호 has 470 (4.7%) zerosZeros
새주소_부_번 has 2719 (27.2%) zerosZeros

Reproduction

Analysis started2024-04-20 20:55:54.852199
Analysis finished2024-04-20 20:55:55.979036
Duration1.13 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리_건축물대장_PK
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T05:55:56.123986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length15.3597
Min length8

Characters and Unicode

Total characters153597
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 row11740-100219604
2nd row11590-101344
3rd row11440-1000000000000002992925
4th row11680-100227773
5th row11650-100231979
ValueCountFrequency (%)
11740-100219604 1
 
< 0.1%
11650-100289459 1
 
< 0.1%
11500-100348758 1
 
< 0.1%
11380-27108 1
 
< 0.1%
11530-1000000000000002438062 1
 
< 0.1%
11650-127728 1
 
< 0.1%
11740-100248628 1
 
< 0.1%
11500-100244257 1
 
< 0.1%
11710-100512505 1
 
< 0.1%
11500-100253978 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-04-21T05:55:56.444028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 44431
28.9%
1 35584
23.2%
2 12117
 
7.9%
5 10849
 
7.1%
- 10000
 
6.5%
4 8228
 
5.4%
3 7909
 
5.1%
6 7427
 
4.8%
7 6540
 
4.3%
8 5518
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 143597
93.5%
Dash Punctuation 10000
 
6.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44431
30.9%
1 35584
24.8%
2 12117
 
8.4%
5 10849
 
7.6%
4 8228
 
5.7%
3 7909
 
5.5%
6 7427
 
5.2%
7 6540
 
4.6%
8 5518
 
3.8%
9 4994
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 153597
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44431
28.9%
1 35584
23.2%
2 12117
 
7.9%
5 10849
 
7.1%
- 10000
 
6.5%
4 8228
 
5.4%
3 7909
 
5.1%
6 7427
 
4.8%
7 6540
 
4.3%
8 5518
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 153597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44431
28.9%
1 35584
23.2%
2 12117
 
7.9%
5 10849
 
7.1%
- 10000
 
6.5%
4 8228
 
5.4%
3 7909
 
5.1%
6 7427
 
4.8%
7 6540
 
4.3%
8 5518
 
3.6%

관리_상위_건축물대장_PK
Text

MISSING 

Distinct1552
Distinct (%)16.0%
Missing304
Missing (%)3.0%
Memory size156.2 KiB
2024-04-21T05:55:56.644268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length15.185644
Min length7

Characters and Unicode

Total characters147240
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

Unique746 ?
Unique (%)7.7%

Sample

1st row11740-100218881
2nd row11590-3995
3rd row11440-1000000000000002992792
4th row11680-100227746
5th row11650-100231970
ValueCountFrequency (%)
11710-100206053 243
 
2.5%
11710-100512412 176
 
1.8%
11500-100341833 115
 
1.2%
11530-2775 100
 
1.0%
11500-100323653 89
 
0.9%
11500-100282992 88
 
0.9%
11500-100331092 83
 
0.9%
11230-1000000000000002550560 75
 
0.8%
11500-100243739 71
 
0.7%
11590-2414 65
 
0.7%
Other values (1542) 8591
88.6%
2024-04-21T05:55:56.932922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 42792
29.1%
1 34203
23.2%
2 12069
 
8.2%
5 10773
 
7.3%
- 9696
 
6.6%
3 7695
 
5.2%
4 7339
 
5.0%
7 6685
 
4.5%
6 6410
 
4.4%
9 4926
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 137544
93.4%
Dash Punctuation 9696
 
6.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42792
31.1%
1 34203
24.9%
2 12069
 
8.8%
5 10773
 
7.8%
3 7695
 
5.6%
4 7339
 
5.3%
7 6685
 
4.9%
6 6410
 
4.7%
9 4926
 
3.6%
8 4652
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 9696
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42792
29.1%
1 34203
23.2%
2 12069
 
8.2%
5 10773
 
7.3%
- 9696
 
6.6%
3 7695
 
5.2%
4 7339
 
5.0%
7 6685
 
4.5%
6 6410
 
4.4%
9 4926
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42792
29.1%
1 34203
23.2%
2 12069
 
8.2%
5 10773
 
7.3%
- 9696
 
6.6%
3 7695
 
5.2%
4 7339
 
5.0%
7 6685
 
4.5%
6 6410
 
4.4%
9 4926
 
3.3%

대장_구분_코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
집합
9706 
일반
 
294

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
집합 9706
97.1%
일반 294
 
2.9%

Length

2024-04-21T05:55:57.043281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T05:55:57.125702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
집합 9706
97.1%
일반 294
 
2.9%

대장_종류_코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
전유부
9530 
일반건축물
 
292
표제부
 
169
총괄표제부
 
9

Length

Max length5
Median length3
Mean length3.0602
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전유부 9530
95.3%
일반건축물 292
 
2.9%
표제부 169
 
1.7%
총괄표제부 9
 
0.1%

Length

2024-04-21T05:55:57.212697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T05:55:57.311405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전유부 9530
95.3%
일반건축물 292
 
2.9%
표제부 169
 
1.7%
총괄표제부 9
 
0.1%

시군구_코드
Categorical

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강서구
2879 
서초구
1166 
송파구
996 
강남구
990 
강동구
826 
Other values (20)
3143 

Length

Max length4
Median length3
Mean length3.0592
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강동구
2nd row동작구
3rd row마포구
4th row강남구
5th row서초구

Common Values

ValueCountFrequency (%)
강서구 2879
28.8%
서초구 1166
11.7%
송파구 996
 
10.0%
강남구 990
 
9.9%
강동구 826
 
8.3%
동작구 441
 
4.4%
영등포구 428
 
4.3%
구로구 365
 
3.6%
성동구 275
 
2.8%
마포구 262
 
2.6%
Other values (15) 1372
13.7%

Length

2024-04-21T05:55:57.403282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구 2879
28.8%
서초구 1166
11.7%
송파구 996
 
10.0%
강남구 990
 
9.9%
강동구 826
 
8.3%
동작구 441
 
4.4%
영등포구 428
 
4.3%
구로구 365
 
3.6%
성동구 275
 
2.8%
마포구 262
 
2.6%
Other values (15) 1372
13.7%

법정동_코드
Text

Distinct259
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T05:55:57.633654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.102
Min length2

Characters and Unicode

Total characters31020
Distinct characters175
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

Unique51 ?
Unique (%)0.5%

Sample

1st row천호동
2nd row대방동
3rd row성산동
4th row자곡동
5th row내곡동
ValueCountFrequency (%)
마곡동 2215
22.1%
상일동 524
 
5.2%
문정동 497
 
5.0%
서초동 375
 
3.8%
신원동 374
 
3.7%
세곡동 315
 
3.1%
내곡동 281
 
2.8%
염창동 227
 
2.3%
마장동 227
 
2.3%
거여동 184
 
1.8%
Other values (249) 4781
47.8%
2024-04-21T05:55:57.997166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 9973
32.2%
곡 3298
 
10.6%
마 2457
 
7.9%
상 826
 
2.7%
신 789
 
2.5%
일 686
 
2.2%
정 578
 
1.9%
원 556
 
1.8%
문 550
 
1.8%
도 443
 
1.4%
Other values (165) 10864
35.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30684
98.9%
Decimal Number 336
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9973
32.5%
곡 3298
 
10.7%
마 2457
 
8.0%
상 826
 
2.7%
신 789
 
2.6%
일 686
 
2.2%
정 578
 
1.9%
원 556
 
1.8%
문 550
 
1.8%
도 443
 
1.4%
Other values (157) 10528
34.3%
Decimal Number
ValueCountFrequency (%)
5 78
23.2%
4 68
20.2%
1 58
17.3%
2 42
12.5%
6 40
11.9%
3 25
 
7.4%
8 13
 
3.9%
7 12
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30684
98.9%
Common 336
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9973
32.5%
곡 3298
 
10.7%
마 2457
 
8.0%
상 826
 
2.7%
신 789
 
2.6%
일 686
 
2.2%
정 578
 
1.9%
원 556
 
1.8%
문 550
 
1.8%
도 443
 
1.4%
Other values (157) 10528
34.3%
Common
ValueCountFrequency (%)
5 78
23.2%
4 68
20.2%
1 58
17.3%
2 42
12.5%
6 40
11.9%
3 25
 
7.4%
8 13
 
3.9%
7 12
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30684
98.9%
ASCII 336
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 9973
32.5%
곡 3298
 
10.7%
마 2457
 
8.0%
상 826
 
2.7%
신 789
 
2.6%
일 686
 
2.2%
정 578
 
1.9%
원 556
 
1.8%
문 550
 
1.8%
도 443
 
1.4%
Other values (157) 10528
34.3%
ASCII
ValueCountFrequency (%)
5 78
23.2%
4 68
20.2%
1 58
17.3%
2 42
12.5%
6 40
11.9%
3 25
 
7.4%
8 13
 
3.9%
7 12
 
3.6%

대지_구분_코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대지
9994 
블록
 
5
산
 
1

Length

Max length2
Median length2
Mean length1.9999
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
대지 9994
99.9%
블록 5
 
0.1%
산 1
 
< 0.1%

Length

2024-04-21T05:55:58.106869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T05:55:58.186190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대지 9994
99.9%
블록 5
 
< 0.1%
산 1
 
< 0.1%

번
Real number (ℝ)

Distinct757
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean647.1388
Minimum0
Maximum4972
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T05:55:58.285773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39
Q1413
median634
Q3750
95-th percentile1454
Maximum4972
Range4972
Interquartile range (IQR)337

Descriptive statistics

Standard deviation487.08341
Coefficient of variation (CV)0.75267224
Kurtosis31.448832
Mean647.1388
Median Absolute Deviation (MAD)139
Skewness4.1123575
Sum6471388
Variance237250.25
MonotonicityNot monotonic
2024-04-21T05:55:58.422263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
743 654
 
6.5%
519 507
 
5.1%
750 279
 
2.8%
751 255
 
2.5%
634 243
 
2.4%
818 218
 
2.2%
639 177
 
1.8%
799 176
 
1.8%
411 151
 
1.5%
747 150
 
1.5%
Other values (747) 7190
71.9%
ValueCountFrequency (%)
0 5
 
0.1%
1 11
0.1%
2 10
0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 24
0.2%
6 10
0.1%
7 3
 
< 0.1%
8 10
0.1%
9 24
0.2%
ValueCountFrequency (%)
4972 1
 
< 0.1%
4969 2
 
< 0.1%
4958 33
0.3%
4950 1
 
< 0.1%
4942 12
 
0.1%
4780 1
 
< 0.1%
4518 1
 
< 0.1%
4234 3
 
< 0.1%
3483 1
 
< 0.1%
3282 8
 
0.1%

지
Real number (ℝ)

SKEWED  ZEROS 

Distinct147
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5215
Minimum0
Maximum2003
Zeros6472
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T05:55:58.530080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile35
Maximum2003
Range2003
Interquartile range (IQR)3

Descriptive statistics

Standard deviation50.900081
Coefficient of variation (CV)5.9731363
Kurtosis688.16909
Mean8.5215
Median Absolute Deviation (MAD)0
Skewness22.243801
Sum85215
Variance2590.8182
MonotonicityNot monotonic
2024-04-21T05:55:58.645871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6472
64.7%
1 581
 
5.8%
4 452
 
4.5%
2 359
 
3.6%
5 276
 
2.8%
3 181
 
1.8%
6 170
 
1.7%
11 107
 
1.1%
41 106
 
1.1%
15 92
 
0.9%
Other values (137) 1204
 
12.0%
ValueCountFrequency (%)
0 6472
64.7%
1 581
 
5.8%
2 359
 
3.6%
3 181
 
1.8%
4 452
 
4.5%
5 276
 
2.8%
6 170
 
1.7%
7 69
 
0.7%
8 29
 
0.3%
9 68
 
0.7%
ValueCountFrequency (%)
2003 1
 
< 0.1%
1843 1
 
< 0.1%
1661 1
 
< 0.1%
1629 1
 
< 0.1%
1119 1
 
< 0.1%
1007 2
< 0.1%
704 1
 
< 0.1%
599 1
 
< 0.1%
570 1
 
< 0.1%
561 3
< 0.1%

특수지_명
Text

MISSING 

Distinct3
Distinct (%)60.0%
Missing9995
Missing (%)> 99.9%
Memory size156.2 KiB
2024-04-21T05:55:58.807588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length8.4
Min length6

Characters and Unicode

Total characters42
Distinct characters16
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

Unique1 ?
Unique (%)20.0%

Sample

1st row고덕강일공공주택지구
2nd row공공주택지구
3rd row고덕강일공공주택지구
4th row공공주택지구
5th row1구역1블럭13롯트
ValueCountFrequency (%)
고덕강일공공주택지구 2
40.0%
공공주택지구 2
40.0%
1구역1블럭13롯트 1
20.0%
2024-04-21T05:55:59.036476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 8
19.0%
구 5
11.9%
주 4
9.5%
택 4
9.5%
지 4
9.5%
1 3
 
7.1%
고 2
 
4.8%
덕 2
 
4.8%
강 2
 
4.8%
일 2
 
4.8%
Other values (6) 6
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 38
90.5%
Decimal Number 4
 
9.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 8
21.1%
구 5
13.2%
주 4
10.5%
택 4
10.5%
지 4
10.5%
고 2
 
5.3%
덕 2
 
5.3%
강 2
 
5.3%
일 2
 
5.3%
역 1
 
2.6%
Other values (4) 4
10.5%
Decimal Number
ValueCountFrequency (%)
1 3
75.0%
3 1
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 38
90.5%
Common 4
 
9.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 8
21.1%
구 5
13.2%
주 4
10.5%
택 4
10.5%
지 4
10.5%
고 2
 
5.3%
덕 2
 
5.3%
강 2
 
5.3%
일 2
 
5.3%
역 1
 
2.6%
Other values (4) 4
10.5%
Common
ValueCountFrequency (%)
1 3
75.0%
3 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 38
90.5%
ASCII 4
 
9.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 8
21.1%
구 5
13.2%
주 4
10.5%
택 4
10.5%
지 4
10.5%
고 2
 
5.3%
덕 2
 
5.3%
강 2
 
5.3%
일 2
 
5.3%
역 1
 
2.6%
Other values (4) 4
10.5%
ASCII
ValueCountFrequency (%)
1 3
75.0%
3 1
 
25.0%

블록
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9996 
근린생활용지
 
4

Length

Max length6
Median length4
Mean length4.0008
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> 9996
> 99.9%
근린생활용지 4
 
< 0.1%

Length

2024-04-21T05:55:59.147434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T05:55:59.229603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9996
> 99.9%
근린생활용지 4
 
< 0.1%

로트
Text

MISSING 

Distinct2
Distinct (%)50.0%
Missing9996
Missing (%)> 99.9%
Memory size156.2 KiB
2024-04-21T05:55:59.313068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters20
Distinct characters6
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

Unique0 ?
Unique (%)0.0%

Sample

1st row2-1블럭
2nd row3-3블럭
3rd row2-1블럭
4th row3-3블럭
ValueCountFrequency (%)
2-1블럭 2
50.0%
3-3블럭 2
50.0%
2024-04-21T05:55:59.522146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 4
20.0%
블 4
20.0%
럭 4
20.0%
3 4
20.0%
2 2
10.0%
1 2
10.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8
40.0%
Decimal Number 8
40.0%
Dash Punctuation 4
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 4
50.0%
2 2
25.0%
1 2
25.0%
Other Letter
ValueCountFrequency (%)
블 4
50.0%
럭 4
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
60.0%
Hangul 8
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 4
33.3%
3 4
33.3%
2 2
16.7%
1 2
16.7%
Hangul
ValueCountFrequency (%)
블 4
50.0%
럭 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
60.0%
Hangul 8
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 4
33.3%
3 4
33.3%
2 2
16.7%
1 2
16.7%
Hangul
ValueCountFrequency (%)
블 4
50.0%
럭 4
50.0%

건물_명
Text

MISSING 

Distinct847
Distinct (%)9.1%
Missing699
Missing (%)7.0%
Memory size156.2 KiB
2024-04-21T05:55:59.710981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length21
Mean length7.9528008
Min length1

Characters and Unicode

Total characters73969
Distinct characters471
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique483 ?
Unique (%)5.2%

Sample

1st row천호역 한강 푸르지오시티
2nd row대림아파트
3rd row헤리티지 삼영
4th row래미안포레
5th row서초더샵포레
ValueCountFrequency (%)
고덕아르테온 506
 
4.1%
힐스테이트 339
 
2.7%
마곡엠밸리6단지 312
 
2.5%
마곡엠밸리14단지 273
 
2.2%
마곡엠밸리15단지 254
 
2.0%
가든파이브라이프 243
 
2.0%
마곡엠밸리7단지 226
 
1.8%
청계현대아파트 217
 
1.7%
래미안 183
 
1.5%
힐스테이트에코송파 176
 
1.4%
Other values (1006) 9705
78.1%
2024-04-21T05:56:00.019466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3133
 
4.2%
지 2957
 
4.0%
리 2860
 
3.9%
파 2467
 
3.3%
스 2434
 
3.3%
단 2335
 
3.2%
마 2216
 
3.0%
트 2191
 
3.0%
곡 2157
 
2.9%
이 2129
 
2.9%
Other values (461) 49090
66.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65314
88.3%
Decimal Number 4018
 
5.4%
Space Separator 3133
 
4.2%
Uppercase Letter 824
 
1.1%
Lowercase Letter 246
 
0.3%
Other Punctuation 209
 
0.3%
Letter Number 135
 
0.2%
Open Punctuation 42
 
0.1%
Close Punctuation 42
 
0.1%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 2957
 
4.5%
리 2860
 
4.4%
파 2467
 
3.8%
스 2434
 
3.7%
단 2335
 
3.6%
마 2216
 
3.4%
트 2191
 
3.4%
곡 2157
 
3.3%
이 2129
 
3.3%
아 2115
 
3.2%
Other values (402) 41453
63.5%
Uppercase Letter
ValueCountFrequency (%)
S 136
16.5%
H 86
10.4%
L 84
10.2%
E 69
8.4%
K 67
8.1%
C 66
8.0%
I 66
8.0%
M 59
7.2%
W 35
 
4.2%
V 33
 
4.0%
Other values (14) 123
14.9%
Lowercase Letter
ValueCountFrequency (%)
e 40
16.3%
o 31
12.6%
u 28
11.4%
s 27
11.0%
t 26
10.6%
a 22
8.9%
y 21
8.5%
n 13
 
5.3%
d 13
 
5.3%
k 12
 
4.9%
Other values (5) 13
 
5.3%
Decimal Number
ValueCountFrequency (%)
1 921
22.9%
2 562
14.0%
4 527
13.1%
3 463
11.5%
5 462
11.5%
6 378
9.4%
0 367
 
9.1%
7 290
 
7.2%
8 39
 
1.0%
9 9
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 204
97.6%
& 2
 
1.0%
, 2
 
1.0%
/ 1
 
0.5%
Letter Number
ValueCountFrequency (%)
Ⅰ 68
50.4%
Ⅱ 67
49.6%
Space Separator
ValueCountFrequency (%)
3133
100.0%
Open Punctuation
ValueCountFrequency (%)
( 42
100.0%
Close Punctuation
ValueCountFrequency (%)
) 42
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65311
88.3%
Common 7450
 
10.1%
Latin 1205
 
1.6%
Han 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 2957
 
4.5%
리 2860
 
4.4%
파 2467
 
3.8%
스 2434
 
3.7%
단 2335
 
3.6%
마 2216
 
3.4%
트 2191
 
3.4%
곡 2157
 
3.3%
이 2129
 
3.3%
아 2115
 
3.2%
Other values (399) 41450
63.5%
Latin
ValueCountFrequency (%)
S 136
 
11.3%
H 86
 
7.1%
L 84
 
7.0%
E 69
 
5.7%
Ⅰ 68
 
5.6%
K 67
 
5.6%
Ⅱ 67
 
5.6%
C 66
 
5.5%
I 66
 
5.5%
M 59
 
4.9%
Other values (31) 437
36.3%
Common
ValueCountFrequency (%)
3133
42.1%
1 921
 
12.4%
2 562
 
7.5%
4 527
 
7.1%
3 463
 
6.2%
5 462
 
6.2%
6 378
 
5.1%
0 367
 
4.9%
7 290
 
3.9%
. 204
 
2.7%
Other values (8) 143
 
1.9%
Han
ValueCountFrequency (%)
笑 1
33.3%
家 1
33.3%
美 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65311
88.3%
ASCII 8520
 
11.5%
Number Forms 135
 
0.2%
CJK 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3133
36.8%
1 921
 
10.8%
2 562
 
6.6%
4 527
 
6.2%
3 463
 
5.4%
5 462
 
5.4%
6 378
 
4.4%
0 367
 
4.3%
7 290
 
3.4%
. 204
 
2.4%
Other values (47) 1213
 
14.2%
Hangul
ValueCountFrequency (%)
지 2957
 
4.5%
리 2860
 
4.4%
파 2467
 
3.8%
스 2434
 
3.7%
단 2335
 
3.6%
마 2216
 
3.4%
트 2191
 
3.4%
곡 2157
 
3.3%
이 2129
 
3.3%
아 2115
 
3.2%
Other values (399) 41450
63.5%
Number Forms
ValueCountFrequency (%)
Ⅰ 68
50.4%
Ⅱ 67
49.6%
CJK
ValueCountFrequency (%)
笑 1
33.3%
家 1
33.3%
美 1
33.3%

위반_건축물_여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9876 
위반건축물
 
124

Length

Max length5
Median length4
Mean length4.0124
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> 9876
98.8%
위반건축물 124
 
1.2%

Length

2024-04-21T05:56:00.137609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T05:56:00.225346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9876
98.8%
위반건축물 124
 
1.2%

대장_일련번호
Real number (ℝ)

Distinct1117
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean295.1831
Minimum1
Maximum28850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T05:56:00.471011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q118
median51
Q3143.25
95-th percentile1230
Maximum28850
Range28849
Interquartile range (IQR)125.25

Descriptive statistics

Standard deviation1017.6025
Coefficient of variation (CV)3.4473602
Kurtosis156.58114
Mean295.1831
Median Absolute Deviation (MAD)41
Skewness9.4780141
Sum2951831
Variance1035514.8
MonotonicityNot monotonic
2024-04-21T05:56:00.582507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 301
 
3.0%
10 262
 
2.6%
2 153
 
1.5%
3 148
 
1.5%
5 146
 
1.5%
7 141
 
1.4%
8 136
 
1.4%
4 133
 
1.3%
20 132
 
1.3%
6 128
 
1.3%
Other values (1107) 8320
83.2%
ValueCountFrequency (%)
1 301
3.0%
2 153
1.5%
3 148
1.5%
4 133
1.3%
5 146
1.5%
6 128
1.3%
7 141
1.4%
8 136
1.4%
9 124
1.2%
10 262
2.6%
ValueCountFrequency (%)
28850 1
< 0.1%
28190 1
< 0.1%
14251 1
< 0.1%
14000 1
< 0.1%
11690 1
< 0.1%
9900 1
< 0.1%
8860 1
< 0.1%
8853 1
< 0.1%
8850 1
< 0.1%
8847 1
< 0.1%

총괄표제부_일련번호
Real number (ℝ)

Distinct34
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13.454145
Minimum1
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T05:56:00.685789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q323
95-th percentile69
Maximum69
Range68
Interquartile range (IQR)22

Descriptive statistics

Standard deviation18.146998
Coefficient of variation (CV)1.3488035
Kurtosis1.9693821
Mean13.454145
Median Absolute Deviation (MAD)0
Skewness1.5847241
Sum134528
Variance329.31354
MonotonicityNot monotonic
2024-04-21T05:56:00.789265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 5473
54.7%
69 506
 
5.1%
35 466
 
4.7%
40 380
 
3.8%
16 331
 
3.3%
24 258
 
2.6%
39 244
 
2.4%
30 227
 
2.3%
9 216
 
2.2%
11 211
 
2.1%
Other values (24) 1687
 
16.9%
ValueCountFrequency (%)
1 5473
54.7%
2 2
 
< 0.1%
3 30
 
0.3%
4 120
 
1.2%
5 15
 
0.1%
6 50
 
0.5%
7 57
 
0.6%
8 1
 
< 0.1%
9 216
 
2.2%
10 79
 
0.8%
ValueCountFrequency (%)
69 506
5.1%
40 380
3.8%
39 244
2.4%
35 466
4.7%
33 33
 
0.3%
31 34
 
0.3%
30 227
2.3%
29 108
 
1.1%
28 60
 
0.6%
26 45
 
0.4%

표제부_일련번호
Real number (ℝ)

SKEWED 

Distinct102
Distinct (%)1.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean23.883188
Minimum0
Maximum8230
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T05:56:00.902119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median10
Q321
95-th percentile68
Maximum8230
Range8230
Interquartile range (IQR)20

Descriptive statistics

Standard deviation131.59686
Coefficient of variation (CV)5.5100205
Kurtosis3052.0427
Mean23.883188
Median Absolute Deviation (MAD)9
Skewness50.422073
Sum238808
Variance17317.733
MonotonicityNot monotonic
2024-04-21T05:56:01.008791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3468
34.7%
10 765
 
7.6%
9 246
 
2.5%
8 244
 
2.4%
12 237
 
2.4%
3 237
 
2.4%
20 221
 
2.2%
13 196
 
2.0%
11 189
 
1.9%
18 187
 
1.9%
Other values (92) 4009
40.1%
ValueCountFrequency (%)
0 9
 
0.1%
1 3468
34.7%
2 186
 
1.9%
3 237
 
2.4%
4 58
 
0.6%
5 128
 
1.3%
6 171
 
1.7%
7 108
 
1.1%
8 244
 
2.4%
9 246
 
2.5%
ValueCountFrequency (%)
8230 1
 
< 0.1%
8220 1
 
< 0.1%
3100 1
 
< 0.1%
1720 1
 
< 0.1%
1160 5
 
0.1%
680 1
 
< 0.1%
670 1
 
< 0.1%
580 2
 
< 0.1%
450 4
 
< 0.1%
330 27
0.3%

전유부_일련번호
Real number (ℝ)

ZEROS 

Distinct1118
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean294.7276
Minimum0
Maximum28850
Zeros470
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T05:56:01.113629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q118
median50
Q3143
95-th percentile1230
Maximum28850
Range28850
Interquartile range (IQR)125

Descriptive statistics

Standard deviation1017.7203
Coefficient of variation (CV)3.453088
Kurtosis156.52423
Mean294.7276
Median Absolute Deviation (MAD)40
Skewness9.4759702
Sum2947276
Variance1035754.7
MonotonicityNot monotonic
2024-04-21T05:56:01.224130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 470
 
4.7%
5 138
 
1.4%
3 137
 
1.4%
2 136
 
1.4%
7 134
 
1.3%
10 133
 
1.3%
8 128
 
1.3%
4 126
 
1.3%
20 124
 
1.2%
9 122
 
1.2%
Other values (1108) 8352
83.5%
ValueCountFrequency (%)
0 470
4.7%
1 112
 
1.1%
2 136
 
1.4%
3 137
 
1.4%
4 126
 
1.3%
5 138
 
1.4%
6 119
 
1.2%
7 134
 
1.3%
8 128
 
1.3%
9 122
 
1.2%
ValueCountFrequency (%)
28850 1
< 0.1%
28190 1
< 0.1%
14251 1
< 0.1%
14000 1
< 0.1%
11690 1
< 0.1%
9900 1
< 0.1%
8860 1
< 0.1%
8853 1
< 0.1%
8850 1
< 0.1%
8847 1
< 0.1%

새주소_도로_코드
Text

Distinct954
Distinct (%)9.6%
Missing36
Missing (%)0.4%
Memory size156.2 KiB
2024-04-21T05:56:01.490775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length15.006022
Min length12

Characters and Unicode

Total characters149520
Distinct characters256
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

Unique553 ?
Unique (%)5.5%

Sample

1st row서울특별시 강동구 구천면로
2nd row서울특별시 동작구 여의대방로
3rd row서울특별시 마포구 모래내로1길
4th row서울특별시 강남구 밤고개로21길
5th row서울특별시 서초구 헌릉로8길
ValueCountFrequency (%)
서울특별시 9964
33.3%
강서구 2879
 
9.6%
서초구 1166
 
3.9%
송파구 996
 
3.3%
강남구 990
 
3.3%
강동구 826
 
2.8%
마곡서1로 608
 
2.0%
마곡중앙로 531
 
1.8%
고덕로 525
 
1.8%
동작구 440
 
1.5%
Other values (946) 10967
36.7%
2024-04-21T05:56:01.885960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19928
13.3%
서 15455
 
10.3%
구 10446
 
7.0%
로 10027
 
6.7%
시 10022
 
6.7%
울 9979
 
6.7%
특 9964
 
6.7%
별 9964
 
6.7%
강 4890
 
3.3%
길 4112
 
2.8%
Other values (246) 44733
29.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 121660
81.4%
Space Separator 19928
 
13.3%
Decimal Number 7930
 
5.3%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 15455
12.7%
구 10446
 
8.6%
로 10027
 
8.2%
시 10022
 
8.2%
울 9979
 
8.2%
특 9964
 
8.2%
별 9964
 
8.2%
강 4890
 
4.0%
길 4112
 
3.4%
마 2380
 
2.0%
Other values (234) 34421
28.3%
Decimal Number
ValueCountFrequency (%)
1 2123
26.8%
3 937
11.8%
2 779
 
9.8%
5 766
 
9.7%
4 663
 
8.4%
8 637
 
8.0%
7 612
 
7.7%
9 513
 
6.5%
6 458
 
5.8%
0 442
 
5.6%
Space Separator
ValueCountFrequency (%)
19928
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 121660
81.4%
Common 27860
 
18.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 15455
12.7%
구 10446
 
8.6%
로 10027
 
8.2%
시 10022
 
8.2%
울 9979
 
8.2%
특 9964
 
8.2%
별 9964
 
8.2%
강 4890
 
4.0%
길 4112
 
3.4%
마 2380
 
2.0%
Other values (234) 34421
28.3%
Common
ValueCountFrequency (%)
19928
71.5%
1 2123
 
7.6%
3 937
 
3.4%
2 779
 
2.8%
5 766
 
2.7%
4 663
 
2.4%
8 637
 
2.3%
7 612
 
2.2%
9 513
 
1.8%
6 458
 
1.6%
Other values (2) 444
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 121660
81.4%
ASCII 27860
 
18.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19928
71.5%
1 2123
 
7.6%
3 937
 
3.4%
2 779
 
2.8%
5 766
 
2.7%
4 663
 
2.4%
8 637
 
2.3%
7 612
 
2.2%
9 513
 
1.8%
6 458
 
1.6%
Other values (2) 444
 
1.6%
Hangul
ValueCountFrequency (%)
서 15455
12.7%
구 10446
 
8.6%
로 10027
 
8.2%
시 10022
 
8.2%
울 9979
 
8.2%
특 9964
 
8.2%
별 9964
 
8.2%
강 4890
 
4.0%
길 4112
 
3.4%
마 2380
 
2.0%
Other values (234) 34421
28.3%

새주소_법정동_코드
Text

Distinct259
Distinct (%)2.6%
Missing36
Missing (%)0.4%
Memory size156.2 KiB
2024-04-21T05:56:02.135607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1018667
Min length2

Characters and Unicode

Total characters30907
Distinct characters175
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

Unique53 ?
Unique (%)0.5%

Sample

1st row천호동
2nd row대방동
3rd row성산동
4th row자곡동
5th row내곡동
ValueCountFrequency (%)
마곡동 2214
22.2%
상일동 524
 
5.3%
문정동 497
 
5.0%
서초동 375
 
3.8%
신원동 374
 
3.8%
세곡동 315
 
3.2%
내곡동 281
 
2.8%
마장동 227
 
2.3%
염창동 227
 
2.3%
거여동 184
 
1.8%
Other values (249) 4746
47.6%
2024-04-21T05:56:02.493601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 9938
32.2%
곡 3297
 
10.7%
마 2456
 
7.9%
상 826
 
2.7%
신 771
 
2.5%
일 686
 
2.2%
정 571
 
1.8%
원 556
 
1.8%
문 550
 
1.8%
도 443
 
1.4%
Other values (165) 10813
35.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30574
98.9%
Decimal Number 333
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9938
32.5%
곡 3297
 
10.8%
마 2456
 
8.0%
상 826
 
2.7%
신 771
 
2.5%
일 686
 
2.2%
정 571
 
1.9%
원 556
 
1.8%
문 550
 
1.8%
도 443
 
1.4%
Other values (157) 10480
34.3%
Decimal Number
ValueCountFrequency (%)
5 76
22.8%
4 68
20.4%
1 58
17.4%
2 42
12.6%
6 39
11.7%
3 25
 
7.5%
8 13
 
3.9%
7 12
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30574
98.9%
Common 333
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9938
32.5%
곡 3297
 
10.8%
마 2456
 
8.0%
상 826
 
2.7%
신 771
 
2.5%
일 686
 
2.2%
정 571
 
1.9%
원 556
 
1.8%
문 550
 
1.8%
도 443
 
1.4%
Other values (157) 10480
34.3%
Common
ValueCountFrequency (%)
5 76
22.8%
4 68
20.4%
1 58
17.4%
2 42
12.6%
6 39
11.7%
3 25
 
7.5%
8 13
 
3.9%
7 12
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30574
98.9%
ASCII 333
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 9938
32.5%
곡 3297
 
10.8%
마 2456
 
8.0%
상 826
 
2.7%
신 771
 
2.5%
일 686
 
2.2%
정 571
 
1.9%
원 556
 
1.8%
문 550
 
1.8%
도 443
 
1.4%
Other values (157) 10480
34.3%
ASCII
ValueCountFrequency (%)
5 76
22.8%
4 68
20.4%
1 58
17.4%
2 42
12.6%
6 39
11.7%
3 25
 
7.5%
8 13
 
3.9%
7 12
 
3.6%

새주소_지상지하_코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
지상
9991 
<NA>
 
9

Length

Max length4
Median length2
Mean length2.0018
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지상 9991
99.9%
<NA> 9
 
0.1%

Length

2024-04-21T05:56:02.632309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T05:56:02.717057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지상 9991
99.9%
na 9
 
0.1%

새주소_본_번
Real number (ℝ)

Distinct351
Distinct (%)3.5%
Missing33
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean126.52764
Minimum0
Maximum3318
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T05:56:02.802179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q131
median66
Q3157
95-th percentile427
Maximum3318
Range3318
Interquartile range (IQR)126

Descriptive statistics

Standard deviation161.73109
Coefficient of variation (CV)1.2782273
Kurtosis36.692595
Mean126.52764
Median Absolute Deviation (MAD)46
Skewness3.8618745
Sum1261101
Variance26156.944
MonotonicityNot monotonic
2024-04-21T05:56:02.909803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360 505
 
5.1%
36 394
 
3.9%
100 372
 
3.7%
22 336
 
3.4%
33 290
 
2.9%
13 273
 
2.7%
50 251
 
2.5%
66 246
 
2.5%
133 227
 
2.3%
62 202
 
2.0%
Other values (341) 6871
68.7%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 89
0.9%
2 12
 
0.1%
3 63
0.6%
4 15
 
0.1%
5 105
1.1%
6 27
 
0.3%
7 125
1.2%
8 80
0.8%
9 41
 
0.4%
ValueCountFrequency (%)
3318 1
 
< 0.1%
2803 1
 
< 0.1%
2275 1
 
< 0.1%
1808 1
 
< 0.1%
1666 6
0.1%
1665 2
 
< 0.1%
1496 1
 
< 0.1%
1383 1
 
< 0.1%
1222 1
 
< 0.1%
1197 2
 
< 0.1%

새주소_부_번
Real number (ℝ)

MISSING  ZEROS 

Distinct36
Distinct (%)1.0%
Missing6219
Missing (%)62.2%
Infinite0
Infinite (%)0.0%
Mean2.8257075
Minimum0
Maximum83
Zeros2719
Zeros (%)27.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T05:56:03.016053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile13
Maximum83
Range83
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.3912585
Coefficient of variation (CV)2.261826
Kurtosis31.104014
Mean2.8257075
Median Absolute Deviation (MAD)0
Skewness4.1847807
Sum10684
Variance40.848186
MonotonicityNot monotonic
2024-04-21T05:56:03.114851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 2719
27.2%
11 180
 
1.8%
12 175
 
1.8%
2 111
 
1.1%
1 104
 
1.0%
8 72
 
0.7%
3 55
 
0.5%
20 43
 
0.4%
5 42
 
0.4%
10 34
 
0.3%
Other values (26) 246
 
2.5%
(Missing) 6219
62.2%
ValueCountFrequency (%)
0 2719
27.2%
1 104
 
1.0%
2 111
 
1.1%
3 55
 
0.5%
4 14
 
0.1%
5 42
 
0.4%
6 28
 
0.3%
7 15
 
0.1%
8 72
 
0.7%
9 26
 
0.3%
ValueCountFrequency (%)
83 3
 
< 0.1%
59 4
< 0.1%
51 1
 
< 0.1%
42 7
0.1%
38 2
 
< 0.1%
37 3
 
< 0.1%
33 8
0.1%
32 1
 
< 0.1%
30 2
 
< 0.1%
29 1
 
< 0.1%

변동_일자
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20240413
Minimum20240411
Maximum20240419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T05:56:03.204527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20240411
5-th percentile20240411
Q120240411
median20240413
Q320240417
95-th percentile20240419
Maximum20240419
Range8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.5938456
Coefficient of variation (CV)1.2815181 × 10-7
Kurtosis-0.72339092
Mean20240413
Median Absolute Deviation (MAD)2
Skewness0.82530912
Sum2.0240413 × 1011
Variance6.7280351
MonotonicityNot monotonic
2024-04-21T05:56:03.293954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20240411 3976
39.8%
20240413 3400
34.0%
20240417 2084
20.8%
20240419 533
 
5.3%
20240418 5
 
0.1%
20240416 2
 
< 0.1%
ValueCountFrequency (%)
20240411 3976
39.8%
20240413 3400
34.0%
20240416 2
 
< 0.1%
20240417 2084
20.8%
20240418 5
 
0.1%
20240419 533
 
5.3%
ValueCountFrequency (%)
20240419 533
 
5.3%
20240418 5
 
0.1%
20240417 2084
20.8%
20240416 2
 
< 0.1%
20240413 3400
34.0%
20240411 3976
39.8%

Sample

관리_건축물대장_PK관리_상위_건축물대장_PK대장_구분_코드대장_종류_코드시군구_코드법정동_코드대지_구분_코드번지특수지_명블록로트건물_명위반_건축물_여부대장_일련번호총괄표제부_일련번호표제부_일련번호전유부_일련번호새주소_도로_코드새주소_법정동_코드새주소_지상지하_코드새주소_본_번새주소_부_번변동_일자
3018611740-10021960411740-100218881집합전유부강동구천호동대지4253<NA><NA><NA>천호역 한강 푸르지오시티<NA>791179서울특별시 강동구 구천면로천호동지상192<NA>20240411
2684311590-10134411590-3995집합전유부동작구대방동대지5010<NA><NA><NA>대림아파트<NA>3101330310서울특별시 동작구 여의대방로대방동지상250<NA>20240413
1985311440-100000000000000299292511440-1000000000000002992792집합전유부마포구성산동대지5936<NA><NA><NA>헤리티지 삼영<NA>481148서울특별시 마포구 모래내로1길성산동지상8<NA>20240413
3250711680-10022777311680-100227746집합전유부강남구자곡동대지6870<NA><NA><NA>래미안포레<NA>36292236서울특별시 강남구 밤고개로21길자곡동지상25020240411
2989711650-10023197911650-100231970집합전유부서초구내곡동대지4110<NA><NA><NA>서초더샵포레<NA>32353032서울특별시 서초구 헌릉로8길내곡동지상58020240411
278411500-10024098111500-100240961집합전유부강서구마곡동대지7510<NA><NA><NA>마곡엠밸리15단지<NA>39241339서울특별시 강서구 마곡중앙로마곡동지상36<NA>20240417
2595111590-3812611590-2414집합전유부동작구신대방동대지7190<NA><NA><NA>동작상떼빌<NA>14916149서울특별시 동작구 신대방1가길신대방동지상38<NA>20240413
3595211680-10022069111680-100220660집합전유부강남구세곡동대지5790<NA><NA><NA>강남엘에이치1단지<NA>68252068서울특별시 강남구 헌릉로571길세곡동지상20<NA>20240411
1791611410-100000000000000316451911410-1000000000000003063607집합전유부서대문구창천동대지2081<NA><NA><NA>CHIME 20<NA>11911119서울특별시 서대문구 연세로2다길창천동지상20020240413
2415911590-3818611590-2414집합전유부동작구신대방동대지7190<NA><NA><NA>동작상떼빌<NA>22116221서울특별시 동작구 신대방1가길신대방동지상38<NA>20240413
관리_건축물대장_PK관리_상위_건축물대장_PK대장_구분_코드대장_종류_코드시군구_코드법정동_코드대지_구분_코드번지특수지_명블록로트건물_명위반_건축물_여부대장_일련번호총괄표제부_일련번호표제부_일련번호전유부_일련번호새주소_도로_코드새주소_법정동_코드새주소_지상지하_코드새주소_본_번새주소_부_번변동_일자
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