Overview

Dataset statistics

Number of variables39
Number of observations500
Missing cells1796
Missing cells (%)9.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory161.8 KiB
Average record size in memory331.3 B

Variable types

Text12
Categorical15
Numeric12

Dataset

Description샘플 데이터
Author국토교통부(open.eais.go.kr)
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=28

Alerts

대장_구분_코드 has constant value ""Constant
대장_구분_코드_명 has constant value ""Constant
대장_종류_코드 has constant value ""Constant
대장_종류_코드_명 has constant value ""Constant
새주소_지상지하_코드 has constant value ""Constant
대지_구분_코드 is highly imbalanced (91.9%)Imbalance
층_구분_코드 is highly imbalanced (90.6%)Imbalance
층_구분_코드_명 is highly imbalanced (90.6%)Imbalance
구조_코드_명 is highly imbalanced (77.4%)Imbalance
주_용도_코드 is highly imbalanced (54.8%)Imbalance
주_용도_코드_명 is highly imbalanced (52.2%)Imbalance
도로명_대지_위치 has 35 (7.0%) missing valuesMissing
건물_명 has 20 (4.0%) missing valuesMissing
특수지_명 has 490 (98.0%) missing valuesMissing
블록 has 497 (99.4%) missing valuesMissing
로트 has 497 (99.4%) missing valuesMissing
새주소_도로_코드 has 30 (6.0%) missing valuesMissing
새주소_법정동_코드 has 33 (6.6%) missing valuesMissing
새주소_본_번 has 21 (4.2%) missing valuesMissing
새주소_부_번 has 25 (5.0%) missing valuesMissing
동_명칭 has 75 (15.0%) missing valuesMissing
층_번호_명 has 51 (10.2%) missing valuesMissing
구조_코드 has 8 (1.6%) missing valuesMissing
기타_용도 has 8 (1.6%) missing valuesMissing
관리_건축물대장_PK has unique valuesUnique
has 283 (56.6%) zerosZeros
새주소_본_번 has 7 (1.4%) zerosZeros
새주소_부_번 has 426 (85.2%) zerosZeros
층_번호 has 115 (23.0%) zerosZeros

Reproduction

Analysis started2023-12-10 15:02:14.518971
Analysis finished2023-12-10 15:02:16.261061
Duration1.74 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:02:16.598920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length12.304
Min length10

Characters and Unicode

Total characters6152
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

Unique500 ?
Unique (%)100.0%

Sample

1st row28237-65287
2nd row41150-89928
3rd row41360-69376
4th row11545-45232
5th row41285-141266
ValueCountFrequency (%)
28237-65287 1
 
0.2%
41273-179244 1
 
0.2%
28170-86535 1
 
0.2%
41465-193175 1
 
0.2%
11500-100261550 1
 
0.2%
26260-77139 1
 
0.2%
41171-100195625 1
 
0.2%
11620-118791 1
 
0.2%
29170-72390 1
 
0.2%
41630-53764 1
 
0.2%
Other values (490) 490
98.0%
2023-12-11T00:02:17.356753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1197
19.5%
0 899
14.6%
4 603
9.8%
2 599
9.7%
- 500
8.1%
3 453
 
7.4%
7 410
 
6.7%
5 392
 
6.4%
8 385
 
6.3%
6 385
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5652
91.9%
Dash Punctuation 500
 
8.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1197
21.2%
0 899
15.9%
4 603
10.7%
2 599
10.6%
3 453
 
8.0%
7 410
 
7.3%
5 392
 
6.9%
8 385
 
6.8%
6 385
 
6.8%
9 329
 
5.8%
Dash Punctuation
ValueCountFrequency (%)
- 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6152
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1197
19.5%
0 899
14.6%
4 603
9.8%
2 599
9.7%
- 500
8.1%
3 453
 
7.4%
7 410
 
6.7%
5 392
 
6.4%
8 385
 
6.3%
6 385
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1197
19.5%
0 899
14.6%
4 603
9.8%
2 599
9.7%
- 500
8.1%
3 453
 
7.4%
7 410
 
6.7%
5 392
 
6.4%
8 385
 
6.3%
6 385
 
6.3%

대장_구분_코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 500
100.0%

Length

2023-12-11T00:02:17.661783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:02:17.906713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 500
100.0%

대장_구분_코드_명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
집합
500 

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 500
100.0%

Length

2023-12-11T00:02:18.180483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:02:18.369502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
집합 500
100.0%

대장_종류_코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
4
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 500
100.0%

Length

2023-12-11T00:02:18.590756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:02:18.808527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 500
100.0%

대장_종류_코드_명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
전유부
500 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전유부 500
100.0%

Length

2023-12-11T00:02:19.018022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:02:19.202535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전유부 500
100.0%
Distinct490
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:02:19.733252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length27
Mean length20.5
Min length16

Characters and Unicode

Total characters10250
Distinct characters251
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique480 ?
Unique (%)96.0%

Sample

1st row경기도 안양시 동안구 관양동 1669번지
2nd row강원도 인제군 기린면 현리 848번지
3rd row전라남도 순천시 서면 동산리 74-1번지
4th row부산광역시 금정구 구서동 775-13번지
5th row부산광역시 해운대구 중동 1516-5번지
ValueCountFrequency (%)
경기도 145
 
6.7%
서울특별시 94
 
4.3%
부산광역시 38
 
1.8%
인천광역시 32
 
1.5%
경상남도 31
 
1.4%
대구광역시 22
 
1.0%
대전광역시 21
 
1.0%
용인시 19
 
0.9%
남구 18
 
0.8%
충청북도 18
 
0.8%
Other values (1040) 1723
79.7%
2023-12-11T00:02:20.611037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1663
 
16.2%
521
 
5.1%
497
 
4.8%
494
 
4.8%
493
 
4.8%
382
 
3.7%
1 378
 
3.7%
285
 
2.8%
- 206
 
2.0%
3 201
 
2.0%
Other values (241) 5130
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6561
64.0%
Decimal Number 1818
 
17.7%
Space Separator 1663
 
16.2%
Dash Punctuation 206
 
2.0%
Other Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
521
 
7.9%
497
 
7.6%
494
 
7.5%
493
 
7.5%
382
 
5.8%
285
 
4.3%
197
 
3.0%
173
 
2.6%
166
 
2.5%
159
 
2.4%
Other values (227) 3194
48.7%
Decimal Number
ValueCountFrequency (%)
1 378
20.8%
3 201
11.1%
2 192
10.6%
5 171
9.4%
4 168
9.2%
6 153
8.4%
7 151
 
8.3%
0 149
 
8.2%
8 138
 
7.6%
9 117
 
6.4%
Space Separator
ValueCountFrequency (%)
1663
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 206
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6561
64.0%
Common 3688
36.0%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
521
 
7.9%
497
 
7.6%
494
 
7.5%
493
 
7.5%
382
 
5.8%
285
 
4.3%
197
 
3.0%
173
 
2.6%
166
 
2.5%
159
 
2.4%
Other values (227) 3194
48.7%
Common
ValueCountFrequency (%)
1663
45.1%
1 378
 
10.2%
- 206
 
5.6%
3 201
 
5.5%
2 192
 
5.2%
5 171
 
4.6%
4 168
 
4.6%
6 153
 
4.1%
7 151
 
4.1%
0 149
 
4.0%
Other values (3) 256
 
6.9%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6561
64.0%
ASCII 3689
36.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1663
45.1%
1 378
 
10.2%
- 206
 
5.6%
3 201
 
5.4%
2 192
 
5.2%
5 171
 
4.6%
4 168
 
4.6%
6 153
 
4.1%
7 151
 
4.1%
0 149
 
4.0%
Other values (4) 257
 
7.0%
Hangul
ValueCountFrequency (%)
521
 
7.9%
497
 
7.6%
494
 
7.5%
493
 
7.5%
382
 
5.8%
285
 
4.3%
197
 
3.0%
173
 
2.6%
166
 
2.5%
159
 
2.4%
Other values (227) 3194
48.7%
Distinct458
Distinct (%)98.5%
Missing35
Missing (%)7.0%
Memory size4.0 KiB
2023-12-11T00:02:21.208763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length25
Mean length18.623656
Min length12

Characters and Unicode

Total characters8660
Distinct characters278
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

Unique451 ?
Unique (%)97.0%

Sample

1st row서울특별시 관악구 남부순환로 1820
2nd row 세종특별자치시 보듬4로 111
3rd row서울특별시 강남구 언주로 316
4th row서울특별시 서초구 마방로10길 15
5th row대전광역시 유성구 대정로28번안길 80
ValueCountFrequency (%)
경기도 131
 
6.7%
서울특별시 96
 
4.9%
인천광역시 30
 
1.5%
부산광역시 29
 
1.5%
경상남도 25
 
1.3%
대구광역시 19
 
1.0%
용인시 19
 
1.0%
충청남도 18
 
0.9%
남구 17
 
0.9%
경상북도 17
 
0.9%
Other values (857) 1558
79.5%
2023-12-11T00:02:22.075874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1501
 
17.3%
455
 
5.3%
426
 
4.9%
353
 
4.1%
1 308
 
3.6%
255
 
2.9%
2 244
 
2.8%
194
 
2.2%
3 186
 
2.1%
186
 
2.1%
Other values (268) 4552
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5509
63.6%
Decimal Number 1579
 
18.2%
Space Separator 1501
 
17.3%
Dash Punctuation 71
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
455
 
8.3%
426
 
7.7%
353
 
6.4%
255
 
4.6%
194
 
3.5%
186
 
3.4%
161
 
2.9%
151
 
2.7%
143
 
2.6%
122
 
2.2%
Other values (256) 3063
55.6%
Decimal Number
ValueCountFrequency (%)
1 308
19.5%
2 244
15.5%
3 186
11.8%
5 147
9.3%
4 136
8.6%
0 133
8.4%
7 121
 
7.7%
6 113
 
7.2%
8 106
 
6.7%
9 85
 
5.4%
Space Separator
ValueCountFrequency (%)
1501
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5509
63.6%
Common 3151
36.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
455
 
8.3%
426
 
7.7%
353
 
6.4%
255
 
4.6%
194
 
3.5%
186
 
3.4%
161
 
2.9%
151
 
2.7%
143
 
2.6%
122
 
2.2%
Other values (256) 3063
55.6%
Common
ValueCountFrequency (%)
1501
47.6%
1 308
 
9.8%
2 244
 
7.7%
3 186
 
5.9%
5 147
 
4.7%
4 136
 
4.3%
0 133
 
4.2%
7 121
 
3.8%
6 113
 
3.6%
8 106
 
3.4%
Other values (2) 156
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5509
63.6%
ASCII 3151
36.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1501
47.6%
1 308
 
9.8%
2 244
 
7.7%
3 186
 
5.9%
5 147
 
4.7%
4 136
 
4.3%
0 133
 
4.2%
7 121
 
3.8%
6 113
 
3.6%
8 106
 
3.4%
Other values (2) 156
 
5.0%
Hangul
ValueCountFrequency (%)
455
 
8.3%
426
 
7.7%
353
 
6.4%
255
 
4.6%
194
 
3.5%
186
 
3.4%
161
 
2.9%
151
 
2.7%
143
 
2.6%
122
 
2.2%
Other values (256) 3063
55.6%

건물_명
Text

MISSING 

Distinct455
Distinct (%)94.8%
Missing20
Missing (%)4.0%
Memory size4.0 KiB
2023-12-11T00:02:22.489990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length7.9875
Min length1

Characters and Unicode

Total characters3834
Distinct characters369
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

Unique436 ?
Unique (%)90.8%

Sample

1st row신일아르디세
2nd row행신1차에스케이뷰아파트
3rd row다솔아파트
4th row동문굿모닝타워1
5th row주공그린빌
ValueCountFrequency (%)
아파트 15
 
2.3%
현대아파트 8
 
1.2%
푸르지오 8
 
1.2%
2단지 6
 
0.9%
휴먼시아 4
 
0.6%
힐스테이트 4
 
0.6%
강변 3
 
0.5%
목동신시가지아파트 3
 
0.5%
롯데캐슬 3
 
0.5%
주공아파트 3
 
0.5%
Other values (547) 593
91.2%
2023-12-11T00:02:23.175094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
275
 
7.2%
253
 
6.6%
252
 
6.6%
170
 
4.4%
91
 
2.4%
73
 
1.9%
70
 
1.8%
66
 
1.7%
63
 
1.6%
63
 
1.6%
Other values (359) 2458
64.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3465
90.4%
Space Separator 170
 
4.4%
Decimal Number 146
 
3.8%
Uppercase Letter 24
 
0.6%
Dash Punctuation 9
 
0.2%
Close Punctuation 5
 
0.1%
Open Punctuation 5
 
0.1%
Lowercase Letter 5
 
0.1%
Other Punctuation 4
 
0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
275
 
7.9%
253
 
7.3%
252
 
7.3%
91
 
2.6%
73
 
2.1%
70
 
2.0%
66
 
1.9%
63
 
1.8%
63
 
1.8%
61
 
1.8%
Other values (326) 2198
63.4%
Uppercase Letter
ValueCountFrequency (%)
C 3
12.5%
E 2
8.3%
O 2
8.3%
T 2
8.3%
L 2
8.3%
A 2
8.3%
S 2
8.3%
G 2
8.3%
I 2
8.3%
B 1
 
4.2%
Other values (4) 4
16.7%
Decimal Number
ValueCountFrequency (%)
1 50
34.2%
2 42
28.8%
3 16
 
11.0%
0 11
 
7.5%
6 9
 
6.2%
5 5
 
3.4%
4 5
 
3.4%
8 4
 
2.7%
7 4
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
& 1
25.0%
: 1
25.0%
Lowercase Letter
ValueCountFrequency (%)
e 4
80.0%
s 1
 
20.0%
Space Separator
ValueCountFrequency (%)
170
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3464
90.3%
Common 339
 
8.8%
Latin 30
 
0.8%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
275
 
7.9%
253
 
7.3%
252
 
7.3%
91
 
2.6%
73
 
2.1%
70
 
2.0%
66
 
1.9%
63
 
1.8%
63
 
1.8%
61
 
1.8%
Other values (325) 2197
63.4%
Latin
ValueCountFrequency (%)
e 4
13.3%
C 3
10.0%
E 2
 
6.7%
O 2
 
6.7%
T 2
 
6.7%
L 2
 
6.7%
A 2
 
6.7%
S 2
 
6.7%
G 2
 
6.7%
I 2
 
6.7%
Other values (7) 7
23.3%
Common
ValueCountFrequency (%)
170
50.1%
1 50
 
14.7%
2 42
 
12.4%
3 16
 
4.7%
0 11
 
3.2%
6 9
 
2.7%
- 9
 
2.7%
) 5
 
1.5%
5 5
 
1.5%
( 5
 
1.5%
Other values (6) 17
 
5.0%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3464
90.3%
ASCII 368
 
9.6%
CJK 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
275
 
7.9%
253
 
7.3%
252
 
7.3%
91
 
2.6%
73
 
2.1%
70
 
2.0%
66
 
1.9%
63
 
1.8%
63
 
1.8%
61
 
1.8%
Other values (325) 2197
63.4%
ASCII
ValueCountFrequency (%)
170
46.2%
1 50
 
13.6%
2 42
 
11.4%
3 16
 
4.3%
0 11
 
3.0%
6 9
 
2.4%
- 9
 
2.4%
) 5
 
1.4%
5 5
 
1.4%
( 5
 
1.4%
Other values (22) 46
 
12.5%
CJK
ValueCountFrequency (%)
1
100.0%
Number Forms
ValueCountFrequency (%)
1
100.0%

시군구_코드
Real number (ℝ)

Distinct157
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32562.792
Minimum11140
Maximum50110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:02:23.415575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11140
5-th percentile11350
Q126350
median41133
Q342115
95-th percentile48125
Maximum50110
Range38970
Interquartile range (IQR)15765

Descriptive statistics

Standard deviation13014.926
Coefficient of variation (CV)0.39968703
Kurtosis-1.1188632
Mean32562.792
Median Absolute Deviation (MAD)7072
Skewness-0.57256288
Sum16281396
Variance1.6938829 × 108
MonotonicityNot monotonic
2023-12-11T00:02:23.702555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41360 11
 
2.2%
11710 11
 
2.2%
41135 10
 
2.0%
11380 10
 
2.0%
28237 9
 
1.8%
47190 8
 
1.6%
11560 8
 
1.6%
11680 7
 
1.4%
28245 7
 
1.4%
44200 7
 
1.4%
Other values (147) 412
82.4%
ValueCountFrequency (%)
11140 3
0.6%
11170 1
 
0.2%
11200 6
1.2%
11215 3
0.6%
11230 2
 
0.4%
11260 1
 
0.2%
11290 3
0.6%
11305 1
 
0.2%
11320 3
0.6%
11350 5
1.0%
ValueCountFrequency (%)
50110 2
 
0.4%
48870 1
 
0.2%
48730 1
 
0.2%
48310 1
 
0.2%
48270 1
 
0.2%
48250 7
1.4%
48240 2
 
0.4%
48170 2
 
0.4%
48129 4
0.8%
48127 2
 
0.4%

법정동_코드
Real number (ℝ)

Distinct87
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13403.72
Minimum10100
Maximum42000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:02:24.014215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10100
5-th percentile10100
Q110400
median10900
Q312600
95-th percentile25642.8
Maximum42000
Range31900
Interquartile range (IQR)2200

Descriptive statistics

Standard deviation6125.9664
Coefficient of variation (CV)0.45703479
Kurtosis5.5134293
Mean13403.72
Median Absolute Deviation (MAD)700
Skewness2.4971463
Sum6701860
Variance37527465
MonotonicityNot monotonic
2023-12-11T00:02:24.256449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10100 48
 
9.6%
10200 41
 
8.2%
10300 28
 
5.6%
10400 27
 
5.4%
10500 27
 
5.4%
10700 25
 
5.0%
10600 25
 
5.0%
10900 20
 
4.0%
10800 19
 
3.8%
11800 17
 
3.4%
Other values (77) 223
44.6%
ValueCountFrequency (%)
10100 48
9.6%
10200 41
8.2%
10300 28
5.6%
10400 27
5.4%
10500 27
5.4%
10600 25
5.0%
10700 25
5.0%
10800 19
 
3.8%
10900 20
4.0%
11000 12
 
2.4%
ValueCountFrequency (%)
42000 1
0.2%
39031 1
0.2%
39021 1
0.2%
38025 1
0.2%
37027 1
0.2%
37025 1
0.2%
37022 1
0.2%
37021 1
0.2%
36022 1
0.2%
34029 2
0.4%

대지_구분_코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
495 
2
 
5

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 (%)
0 495
99.0%
2 5
 
1.0%

Length

2023-12-11T00:02:24.516880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:02:24.692235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 495
99.0%
2 5
 
1.0%


Real number (ℝ)

Distinct412
Distinct (%)82.6%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean712.31663
Minimum0
Maximum3246
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:02:24.884990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25.8
Q1306
median652
Q31004
95-th percentile1684.9
Maximum3246
Range3246
Interquartile range (IQR)698

Descriptive statistics

Standard deviation523.44951
Coefficient of variation (CV)0.7348551
Kurtosis1.804904
Mean712.31663
Median Absolute Deviation (MAD)350
Skewness0.99528305
Sum355446
Variance273999.39
MonotonicityNot monotonic
2023-12-11T00:02:25.167960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
648 3
 
0.6%
2 3
 
0.6%
1269 3
 
0.6%
369 3
 
0.6%
780 3
 
0.6%
634 3
 
0.6%
1061 3
 
0.6%
1330 3
 
0.6%
641 3
 
0.6%
18 3
 
0.6%
Other values (402) 469
93.8%
ValueCountFrequency (%)
0 2
0.4%
1 2
0.4%
2 3
0.6%
3 1
 
0.2%
4 2
0.4%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
10 2
0.4%
12 1
 
0.2%
ValueCountFrequency (%)
3246 1
0.2%
3126 1
0.2%
2676 1
0.2%
2583 1
0.2%
2315 1
0.2%
2064 1
0.2%
2048 1
0.2%
1954 1
0.2%
1940 1
0.2%
1920 1
0.2%


Real number (ℝ)

ZEROS 

Distinct40
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.284
Minimum0
Maximum999
Zeros283
Zeros (%)56.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:02:25.437606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile22.1
Maximum999
Range999
Interquartile range (IQR)2

Descriptive statistics

Standard deviation50.63492
Coefficient of variation (CV)6.9515266
Kurtosis302.28645
Mean7.284
Median Absolute Deviation (MAD)0
Skewness16.26362
Sum3642
Variance2563.8951
MonotonicityNot monotonic
2023-12-11T00:02:25.733530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 283
56.6%
1 67
 
13.4%
2 26
 
5.2%
3 16
 
3.2%
7 11
 
2.2%
5 10
 
2.0%
4 10
 
2.0%
10 10
 
2.0%
6 8
 
1.6%
9 8
 
1.6%
Other values (30) 51
 
10.2%
ValueCountFrequency (%)
0 283
56.6%
1 67
 
13.4%
2 26
 
5.2%
3 16
 
3.2%
4 10
 
2.0%
5 10
 
2.0%
6 8
 
1.6%
7 11
 
2.2%
8 5
 
1.0%
9 8
 
1.6%
ValueCountFrequency (%)
999 1
0.2%
352 1
0.2%
283 1
0.2%
222 1
0.2%
115 1
0.2%
83 1
0.2%
59 1
0.2%
56 1
0.2%
52 1
0.2%
48 1
0.2%

특수지_명
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing490
Missing (%)98.0%
Memory size4.0 KiB
2023-12-11T00:02:26.026471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length11.1
Min length4

Characters and Unicode

Total characters111
Distinct characters43
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

Unique10 ?
Unique (%)100.0%

Sample

1st row위례 택지개발사업지구
2nd row죽전택지개발지구 26블럭 0노트
3rd row택지개발지구 18블럭
4th row동탄2택지개발지구
5th row15블럭 3롯트
ValueCountFrequency (%)
0노트 2
 
11.8%
위례 1
 
5.9%
택지개발사업지구 1
 
5.9%
죽전택지개발지구 1
 
5.9%
26블럭 1
 
5.9%
택지개발지구 1
 
5.9%
18블럭 1
 
5.9%
동탄2택지개발지구 1
 
5.9%
15블럭 1
 
5.9%
3롯트 1
 
5.9%
Other values (6) 6
35.3%
2023-12-11T00:02:26.771138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16
 
14.4%
9
 
8.1%
7
 
6.3%
7
 
6.3%
6
 
5.4%
6
 
5.4%
5
 
4.5%
5
 
4.5%
4
 
3.6%
1 3
 
2.7%
Other values (33) 43
38.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
80.2%
Decimal Number 15
 
13.5%
Space Separator 7
 
6.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
16
18.0%
9
 
10.1%
7
 
7.9%
6
 
6.7%
6
 
6.7%
5
 
5.6%
5
 
5.6%
4
 
4.5%
2
 
2.2%
2
 
2.2%
Other values (24) 27
30.3%
Decimal Number
ValueCountFrequency (%)
1 3
20.0%
2 3
20.0%
0 2
13.3%
6 2
13.3%
5 2
13.3%
4 1
 
6.7%
3 1
 
6.7%
8 1
 
6.7%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
80.2%
Common 22
 
19.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
16
18.0%
9
 
10.1%
7
 
7.9%
6
 
6.7%
6
 
6.7%
5
 
5.6%
5
 
5.6%
4
 
4.5%
2
 
2.2%
2
 
2.2%
Other values (24) 27
30.3%
Common
ValueCountFrequency (%)
7
31.8%
1 3
13.6%
2 3
13.6%
0 2
 
9.1%
6 2
 
9.1%
5 2
 
9.1%
4 1
 
4.5%
3 1
 
4.5%
8 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
80.2%
ASCII 22
 
19.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
16
18.0%
9
 
10.1%
7
 
7.9%
6
 
6.7%
6
 
6.7%
5
 
5.6%
5
 
5.6%
4
 
4.5%
2
 
2.2%
2
 
2.2%
Other values (24) 27
30.3%
ASCII
ValueCountFrequency (%)
7
31.8%
1 3
13.6%
2 3
13.6%
0 2
 
9.1%
6 2
 
9.1%
5 2
 
9.1%
4 1
 
4.5%
3 1
 
4.5%
8 1
 
4.5%

블록
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing497
Missing (%)99.4%
Memory size4.0 KiB
2023-12-11T00:02:27.013820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.3333333
Min length1

Characters and Unicode

Total characters10
Distinct characters8
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowRM10블록
2nd rowE
3rd row4블록
ValueCountFrequency (%)
rm10블록 1
33.3%
e 1
33.3%
4블록 1
33.3%
2023-12-11T00:02:27.514390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2
20.0%
2
20.0%
R 1
10.0%
M 1
10.0%
1 1
10.0%
0 1
10.0%
E 1
10.0%
4 1
10.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4
40.0%
Uppercase Letter 3
30.0%
Decimal Number 3
30.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 1
33.3%
M 1
33.3%
E 1
33.3%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
0 1
33.3%
4 1
33.3%
Other Letter
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4
40.0%
Latin 3
30.0%
Common 3
30.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1
33.3%
M 1
33.3%
E 1
33.3%
Common
ValueCountFrequency (%)
1 1
33.3%
0 1
33.3%
4 1
33.3%
Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
60.0%
Hangul 4
40.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%
ASCII
ValueCountFrequency (%)
R 1
16.7%
M 1
16.7%
1 1
16.7%
0 1
16.7%
E 1
16.7%
4 1
16.7%

로트
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing497
Missing (%)99.4%
Memory size4.0 KiB
2023-12-11T00:02:27.693608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.6666667
Min length1

Characters and Unicode

Total characters5
Distinct characters2
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st row3
2nd row3L
3rd row3L
ValueCountFrequency (%)
3l 2
66.7%
3 1
33.3%
2023-12-11T00:02:28.098755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 3
60.0%
L 2
40.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3
60.0%
Uppercase Letter 2
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
L 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
60.0%
Latin 2
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 3
100.0%
Latin
ValueCountFrequency (%)
L 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 3
60.0%
L 2
40.0%

새주소_도로_코드
Real number (ℝ)

MISSING 

Distinct447
Distinct (%)95.1%
Missing30
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean3.2872049 × 1011
Minimum1.1110301 × 1011
Maximum5.0130335 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:02:28.364226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110301 × 1011
5-th percentile1.1297112 × 1011
Q12.638042 × 1011
median4.1113318 × 1011
Q34.1565321 × 1011
95-th percentile4.8127479 × 1011
Maximum5.0130335 × 1011
Range3.9020035 × 1011
Interquartile range (IQR)1.5184901 × 1011

Descriptive statistics

Standard deviation1.2283976 × 1011
Coefficient of variation (CV)0.37369061
Kurtosis-0.88661211
Mean3.2872049 × 1011
Median Absolute Deviation (MAD)7.1370895 × 1010
Skewness-0.64143105
Sum1.5449863 × 1014
Variance1.5089607 × 1022
MonotonicityNot monotonic
2023-12-11T00:02:29.050113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
361103258103 4
 
0.8%
413603197089 2
 
0.4%
282453155039 2
 
0.4%
411114322040 2
 
0.4%
111404103298 2
 
0.4%
411153175009 2
 
0.4%
113503109006 2
 
0.4%
263202132001 2
 
0.4%
317104319734 2
 
0.4%
411174331141 2
 
0.4%
Other values (437) 448
89.6%
(Missing) 30
 
6.0%
ValueCountFrequency (%)
111103005008 1
0.2%
111403005006 1
0.2%
111403101008 1
0.2%
111404103298 2
0.4%
111703005018 1
0.2%
111703102008 1
0.2%
111704106144 1
0.2%
112003005014 1
0.2%
112302000008 1
0.2%
112303005025 1
0.2%
ValueCountFrequency (%)
501303350369 1
0.2%
501103349035 1
0.2%
483303338061 1
0.2%
483303338048 1
0.2%
483303338005 1
0.2%
483104811737 1
0.2%
483104811134 1
0.2%
483103337044 1
0.2%
483102006004 1
0.2%
482704808327 1
0.2%

새주소_법정동_코드
Real number (ℝ)

MISSING 

Distinct121
Distinct (%)25.9%
Missing33
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean13004.567
Minimum10101
Maximum40002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:02:29.323935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10101
5-th percentile10101
Q110401
median10902
Q312202.5
95-th percentile25301
Maximum40002
Range29901
Interquartile range (IQR)1801.5

Descriptive statistics

Standard deviation5579.3292
Coefficient of variation (CV)0.42902843
Kurtosis7.3326373
Mean13004.567
Median Absolute Deviation (MAD)601
Skewness2.8026944
Sum6073133
Variance31128914
MonotonicityNot monotonic
2023-12-11T00:02:29.609799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10101 32
 
6.4%
10201 31
 
6.2%
10301 27
 
5.4%
10501 23
 
4.6%
25001 16
 
3.2%
10801 16
 
3.2%
11201 13
 
2.6%
10601 13
 
2.6%
10701 12
 
2.4%
10401 11
 
2.2%
Other values (111) 273
54.6%
(Missing) 33
 
6.6%
ValueCountFrequency (%)
10101 32
6.4%
10201 31
6.2%
10202 10
 
2.0%
10301 27
5.4%
10302 8
 
1.6%
10303 3
 
0.6%
10401 11
 
2.2%
10402 5
 
1.0%
10403 4
 
0.8%
10501 23
4.6%
ValueCountFrequency (%)
40002 1
 
0.2%
37001 5
1.0%
36002 1
 
0.2%
35002 3
0.6%
33004 1
 
0.2%
33001 1
 
0.2%
32002 1
 
0.2%
31002 1
 
0.2%
31001 1
 
0.2%
26203 2
 
0.4%

새주소_지상지하_코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
500 

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 (%)
0 500
100.0%

Length

2023-12-11T00:02:29.840385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:02:30.015908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 500
100.0%

새주소_본_번
Real number (ℝ)

MISSING  ZEROS 

Distinct228
Distinct (%)47.6%
Missing21
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean149.43215
Minimum0
Maximum3587
Zeros7
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:02:30.216116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q125
median60
Q3154
95-th percentile546.5
Maximum3587
Range3587
Interquartile range (IQR)129

Descriptive statistics

Standard deviation291.47403
Coefficient of variation (CV)1.9505443
Kurtosis59.526477
Mean149.43215
Median Absolute Deviation (MAD)44
Skewness6.4534022
Sum71578
Variance84957.108
MonotonicityNot monotonic
2023-12-11T00:02:30.475439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 13
 
2.6%
9 9
 
1.8%
12 8
 
1.6%
0 7
 
1.4%
25 7
 
1.4%
11 7
 
1.4%
48 6
 
1.2%
16 6
 
1.2%
22 6
 
1.2%
8 6
 
1.2%
Other values (218) 404
80.8%
(Missing) 21
 
4.2%
ValueCountFrequency (%)
0 7
1.4%
1 1
 
0.2%
2 3
 
0.6%
3 1
 
0.2%
4 3
 
0.6%
5 3
 
0.6%
6 5
1.0%
7 5
1.0%
8 6
1.2%
9 9
1.8%
ValueCountFrequency (%)
3587 1
0.2%
2878 1
0.2%
1864 1
0.2%
1451 1
0.2%
1218 1
0.2%
1110 1
0.2%
1057 1
0.2%
1048 1
0.2%
1003 1
0.2%
947 1
0.2%

새주소_부_번
Real number (ℝ)

MISSING  ZEROS 

Distinct25
Distinct (%)5.3%
Missing25
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean1.2042105
Minimum0
Maximum49
Zeros426
Zeros (%)85.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:02:30.682144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum49
Range49
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.6726585
Coefficient of variation (CV)3.8802671
Kurtosis34.881639
Mean1.2042105
Median Absolute Deviation (MAD)0
Skewness5.2893095
Sum572
Variance21.833738
MonotonicityNot monotonic
2023-12-11T00:02:30.924441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 426
85.2%
6 6
 
1.2%
1 5
 
1.0%
10 4
 
0.8%
7 3
 
0.6%
11 3
 
0.6%
20 2
 
0.4%
3 2
 
0.4%
14 2
 
0.4%
12 2
 
0.4%
Other values (15) 20
 
4.0%
(Missing) 25
 
5.0%
ValueCountFrequency (%)
0 426
85.2%
1 5
 
1.0%
2 2
 
0.4%
3 2
 
0.4%
4 2
 
0.4%
5 1
 
0.2%
6 6
 
1.2%
7 3
 
0.6%
8 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
49 1
0.2%
32 1
0.2%
27 1
0.2%
25 2
0.4%
23 1
0.2%
22 1
0.2%
21 1
0.2%
20 2
0.4%
19 2
0.4%
15 2
0.4%

동_명칭
Text

MISSING 

Distinct179
Distinct (%)42.1%
Missing75
Missing (%)15.0%
Memory size4.0 KiB
2023-12-11T00:02:31.537336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length4
Mean length4.1152941
Min length1

Characters and Unicode

Total characters1749
Distinct characters128
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)28.5%

Sample

1st row101동
2nd row광진 동양파라곤 1단지
3rd row106동
4th row101동
5th row사보이시티디엠씨
ValueCountFrequency (%)
102동 37
 
8.5%
101동 27
 
6.2%
103동 23
 
5.3%
104동 22
 
5.0%
107동 17
 
3.9%
203동 11
 
2.5%
106동 9
 
2.1%
105동 9
 
2.1%
108동 8
 
1.8%
202동 8
 
1.8%
Other values (181) 266
60.9%
2023-12-11T00:02:32.377796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
391
22.4%
1 344
19.7%
0 306
17.5%
2 154
 
8.8%
3 89
 
5.1%
4 59
 
3.4%
5 52
 
3.0%
7 45
 
2.6%
6 40
 
2.3%
8 30
 
1.7%
Other values (118) 239
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1143
65.4%
Other Letter 583
33.3%
Space Separator 12
 
0.7%
Uppercase Letter 7
 
0.4%
Other Punctuation 1
 
0.1%
Letter Number 1
 
0.1%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
391
67.1%
11
 
1.9%
8
 
1.4%
8
 
1.4%
7
 
1.2%
5
 
0.9%
4
 
0.7%
4
 
0.7%
4
 
0.7%
4
 
0.7%
Other values (97) 137
 
23.5%
Decimal Number
ValueCountFrequency (%)
1 344
30.1%
0 306
26.8%
2 154
13.5%
3 89
 
7.8%
4 59
 
5.2%
5 52
 
4.5%
7 45
 
3.9%
6 40
 
3.5%
8 30
 
2.6%
9 24
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
B 2
28.6%
U 1
14.3%
H 1
14.3%
K 1
14.3%
S 1
14.3%
D 1
14.3%
Space Separator
ValueCountFrequency (%)
12
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1158
66.2%
Hangul 583
33.3%
Latin 8
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
391
67.1%
11
 
1.9%
8
 
1.4%
8
 
1.4%
7
 
1.2%
5
 
0.9%
4
 
0.7%
4
 
0.7%
4
 
0.7%
4
 
0.7%
Other values (97) 137
 
23.5%
Common
ValueCountFrequency (%)
1 344
29.7%
0 306
26.4%
2 154
13.3%
3 89
 
7.7%
4 59
 
5.1%
5 52
 
4.5%
7 45
 
3.9%
6 40
 
3.5%
8 30
 
2.6%
9 24
 
2.1%
Other values (4) 15
 
1.3%
Latin
ValueCountFrequency (%)
B 2
25.0%
U 1
12.5%
H 1
12.5%
K 1
12.5%
S 1
12.5%
D 1
12.5%
1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1165
66.6%
Hangul 583
33.3%
Number Forms 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
391
67.1%
11
 
1.9%
8
 
1.4%
8
 
1.4%
7
 
1.2%
5
 
0.9%
4
 
0.7%
4
 
0.7%
4
 
0.7%
4
 
0.7%
Other values (97) 137
 
23.5%
ASCII
ValueCountFrequency (%)
1 344
29.5%
0 306
26.3%
2 154
13.2%
3 89
 
7.6%
4 59
 
5.1%
5 52
 
4.5%
7 45
 
3.9%
6 40
 
3.4%
8 30
 
2.6%
9 24
 
2.1%
Other values (10) 22
 
1.9%
Number Forms
ValueCountFrequency (%)
1
100.0%
Distinct284
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:02:32.981340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length4.106
Min length2

Characters and Unicode

Total characters2053
Distinct characters29
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique183 ?
Unique (%)36.6%

Sample

1st row1103
2nd row1903
3rd row1503
4th row1-107호
5th row236호
ValueCountFrequency (%)
201 9
 
1.8%
202 7
 
1.4%
704호 7
 
1.4%
402호 7
 
1.4%
802 6
 
1.2%
1002호 6
 
1.2%
203호 6
 
1.2%
901호 6
 
1.2%
302 6
 
1.2%
101 6
 
1.2%
Other values (278) 444
87.1%
2023-12-11T00:02:33.900751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 507
24.7%
1 374
18.2%
273
13.3%
2 232
11.3%
3 145
 
7.1%
4 107
 
5.2%
5 82
 
4.0%
8 71
 
3.5%
6 65
 
3.2%
7 63
 
3.1%
Other values (19) 134
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1708
83.2%
Other Letter 314
 
15.3%
Uppercase Letter 12
 
0.6%
Space Separator 10
 
0.5%
Dash Punctuation 9
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
273
86.9%
20
 
6.4%
6
 
1.9%
5
 
1.6%
2
 
0.6%
2
 
0.6%
1
 
0.3%
1
 
0.3%
1
 
0.3%
1
 
0.3%
Other values (2) 2
 
0.6%
Decimal Number
ValueCountFrequency (%)
0 507
29.7%
1 374
21.9%
2 232
13.6%
3 145
 
8.5%
4 107
 
6.3%
5 82
 
4.8%
8 71
 
4.2%
6 65
 
3.8%
7 63
 
3.7%
9 62
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
B 8
66.7%
O 1
 
8.3%
T 1
 
8.3%
A 1
 
8.3%
P 1
 
8.3%
Space Separator
ValueCountFrequency (%)
10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1727
84.1%
Hangul 314
 
15.3%
Latin 12
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 507
29.4%
1 374
21.7%
2 232
13.4%
3 145
 
8.4%
4 107
 
6.2%
5 82
 
4.7%
8 71
 
4.1%
6 65
 
3.8%
7 63
 
3.6%
9 62
 
3.6%
Other values (2) 19
 
1.1%
Hangul
ValueCountFrequency (%)
273
86.9%
20
 
6.4%
6
 
1.9%
5
 
1.6%
2
 
0.6%
2
 
0.6%
1
 
0.3%
1
 
0.3%
1
 
0.3%
1
 
0.3%
Other values (2) 2
 
0.6%
Latin
ValueCountFrequency (%)
B 8
66.7%
O 1
 
8.3%
T 1
 
8.3%
A 1
 
8.3%
P 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1739
84.7%
Hangul 314
 
15.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 507
29.2%
1 374
21.5%
2 232
13.3%
3 145
 
8.3%
4 107
 
6.2%
5 82
 
4.7%
8 71
 
4.1%
6 65
 
3.7%
7 63
 
3.6%
9 62
 
3.6%
Other values (7) 31
 
1.8%
Hangul
ValueCountFrequency (%)
273
86.9%
20
 
6.4%
6
 
1.9%
5
 
1.6%
2
 
0.6%
2
 
0.6%
1
 
0.3%
1
 
0.3%
1
 
0.3%
1
 
0.3%
Other values (2) 2
 
0.6%

층_구분_코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
20
494 
10
 
6

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row20
3rd row20
4th row20
5th row20

Common Values

ValueCountFrequency (%)
20 494
98.8%
10 6
 
1.2%

Length

2023-12-11T00:02:34.198630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:02:34.377739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 494
98.8%
10 6
 
1.2%

층_구분_코드_명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
지상
494 
지하
 
6

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지상 494
98.8%
지하 6
 
1.2%

Length

2023-12-11T00:02:34.529229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:02:34.687606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지상 494
98.8%
지하 6
 
1.2%

층_번호
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.402
Minimum0
Maximum38
Zeros115
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:02:34.850256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q310
95-th percentile19
Maximum38
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.5186949
Coefficient of variation (CV)1.0182279
Kurtosis1.7100309
Mean6.402
Median Absolute Deviation (MAD)4
Skewness1.2238343
Sum3201
Variance42.493383
MonotonicityNot monotonic
2023-12-11T00:02:35.069113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 115
23.0%
3 40
 
8.0%
1 34
 
6.8%
2 32
 
6.4%
4 32
 
6.4%
5 29
 
5.8%
7 23
 
4.6%
10 23
 
4.6%
6 20
 
4.0%
11 18
 
3.6%
Other values (18) 134
26.8%
ValueCountFrequency (%)
0 115
23.0%
1 34
 
6.8%
2 32
 
6.4%
3 40
 
8.0%
4 32
 
6.4%
5 29
 
5.8%
6 20
 
4.0%
7 23
 
4.6%
8 11
 
2.2%
9 17
 
3.4%
ValueCountFrequency (%)
38 1
 
0.2%
37 1
 
0.2%
28 2
 
0.4%
25 3
 
0.6%
24 1
 
0.2%
22 5
1.0%
21 1
 
0.2%
20 9
1.8%
19 4
0.8%
18 7
1.4%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
399 
1
101 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 399
79.8%
1 101
 
20.2%

Length

2023-12-11T00:02:35.255344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:02:35.413395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 399
79.8%
1 101
 
20.2%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
공용
431 
전유
69 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공용 431
86.2%
전유 69
 
13.8%

Length

2023-12-11T00:02:35.593067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:02:35.731811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공용 431
86.2%
전유 69
 
13.8%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
308 
1
192 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 308
61.6%
1 192
38.4%

Length

2023-12-11T00:02:35.912025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:02:36.058266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 308
61.6%
1 192
38.4%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
주건축물
282 
부속건축물
218 

Length

Max length5
Median length4
Mean length4.436
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부속건축물
2nd row주건축물
3rd row주건축물
4th row주건축물
5th row부속건축물

Common Values

ValueCountFrequency (%)
주건축물 282
56.4%
부속건축물 218
43.6%

Length

2023-12-11T00:02:36.240111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:02:36.402061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주건축물 282
56.4%
부속건축물 218
43.6%

층_번호_명
Text

MISSING 

Distinct75
Distinct (%)16.7%
Missing51
Missing (%)10.2%
Memory size4.0 KiB
2023-12-11T00:02:36.722009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length2
Mean length2.5634744
Min length1

Characters and Unicode

Total characters1151
Distinct characters27
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)7.8%

Sample

1st row3층
2nd row1층
3rd row1층
4th row지1층
5th row지층
ValueCountFrequency (%)
각층 106
23.6%
1층 69
15.3%
지1 36
 
8.0%
지1층 30
 
6.7%
지층 21
 
4.7%
3층 18
 
4.0%
4층 13
 
2.9%
2층 11
 
2.4%
5층 9
 
2.0%
13층 7
 
1.6%
Other values (64) 130
28.9%
2023-12-11T00:02:37.312508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
395
34.3%
1 249
21.6%
152
 
13.2%
106
 
9.2%
2 56
 
4.9%
3 34
 
3.0%
, 19
 
1.7%
4 18
 
1.6%
6 15
 
1.3%
5 13
 
1.1%
Other values (17) 94
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 680
59.1%
Decimal Number 417
36.2%
Other Punctuation 24
 
2.1%
Dash Punctuation 13
 
1.1%
Math Symbol 12
 
1.0%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
395
58.1%
152
 
22.4%
106
 
15.6%
11
 
1.6%
8
 
1.2%
3
 
0.4%
2
 
0.3%
1
 
0.1%
1
 
0.1%
1
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 249
59.7%
2 56
 
13.4%
3 34
 
8.2%
4 18
 
4.3%
6 15
 
3.6%
5 13
 
3.1%
7 10
 
2.4%
9 9
 
2.2%
0 9
 
2.2%
8 4
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 19
79.2%
/ 5
 
20.8%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Math Symbol
ValueCountFrequency (%)
~ 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 680
59.1%
Common 471
40.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 249
52.9%
2 56
 
11.9%
3 34
 
7.2%
, 19
 
4.0%
4 18
 
3.8%
6 15
 
3.2%
5 13
 
2.8%
- 13
 
2.8%
~ 12
 
2.5%
7 10
 
2.1%
Other values (7) 32
 
6.8%
Hangul
ValueCountFrequency (%)
395
58.1%
152
 
22.4%
106
 
15.6%
11
 
1.6%
8
 
1.2%
3
 
0.4%
2
 
0.3%
1
 
0.1%
1
 
0.1%
1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 680
59.1%
ASCII 471
40.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
395
58.1%
152
 
22.4%
106
 
15.6%
11
 
1.6%
8
 
1.2%
3
 
0.4%
2
 
0.3%
1
 
0.1%
1
 
0.1%
1
 
0.1%
ASCII
ValueCountFrequency (%)
1 249
52.9%
2 56
 
11.9%
3 34
 
7.2%
, 19
 
4.0%
4 18
 
3.8%
6 15
 
3.2%
5 13
 
2.8%
- 13
 
2.8%
~ 12
 
2.5%
7 10
 
2.1%
Other values (7) 32
 
6.8%

구조_코드
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)2.0%
Missing8
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean21.296748
Minimum11
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:02:37.516601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile19
Q121
median21
Q321
95-th percentile21
Maximum42
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.2168015
Coefficient of variation (CV)0.19800213
Kurtosis15.408092
Mean21.296748
Median Absolute Deviation (MAD)0
Skewness2.9911393
Sum10478
Variance17.781415
MonotonicityNot monotonic
2023-12-11T00:02:37.723561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
21 443
88.6%
11 19
 
3.8%
42 10
 
2.0%
19 9
 
1.8%
41 4
 
0.8%
31 2
 
0.4%
22 2
 
0.4%
40 1
 
0.2%
32 1
 
0.2%
33 1
 
0.2%
(Missing) 8
 
1.6%
ValueCountFrequency (%)
11 19
 
3.8%
19 9
 
1.8%
21 443
88.6%
22 2
 
0.4%
31 2
 
0.4%
32 1
 
0.2%
33 1
 
0.2%
40 1
 
0.2%
41 4
 
0.8%
42 10
 
2.0%
ValueCountFrequency (%)
42 10
 
2.0%
41 4
 
0.8%
40 1
 
0.2%
33 1
 
0.2%
32 1
 
0.2%
31 2
 
0.4%
22 2
 
0.4%
21 443
88.6%
19 9
 
1.8%
11 19
 
3.8%

구조_코드_명
Categorical

IMBALANCE 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
철근콘크리트구조
450 
벽돌구조
 
20
철골철근콘크리트구조
 
8
<NA>
 
7
기타조적구조
 
6
Other values (4)
 
9

Length

Max length11
Median length8
Mean length7.792
Min length4

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
철근콘크리트구조 450
90.0%
벽돌구조 20
 
4.0%
철골철근콘크리트구조 8
 
1.6%
<NA> 7
 
1.4%
기타조적구조 6
 
1.2%
철골콘크리트구조 5
 
1.0%
프리케스트콘크리트구조 2
 
0.4%
조적구조 1
 
0.2%
경량철골구조 1
 
0.2%

Length

2023-12-11T00:02:37.953162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:02:38.173312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
철근콘크리트구조 450
90.0%
벽돌구조 20
 
4.0%
철골철근콘크리트구조 8
 
1.6%
na 7
 
1.4%
기타조적구조 6
 
1.2%
철골콘크리트구조 5
 
1.0%
프리케스트콘크리트구조 2
 
0.4%
조적구조 1
 
0.2%
경량철골구조 1
 
0.2%
Distinct51
Distinct (%)10.3%
Missing5
Missing (%)1.0%
Memory size4.0 KiB
2023-12-11T00:02:38.483760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length7.7313131
Min length2

Characters and Unicode

Total characters3827
Distinct characters48
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)6.7%

Sample

1st row철근콘크리트조
2nd row철근콘크리트조
3rd row조적조
4th row철근콘크리트조
5th row철근콘크리트조
ValueCountFrequency (%)
철근콘크리트구조 236
46.3%
철근콘크리트조 131
25.7%
철근콘크리트 29
 
5.7%
철근콘크리트벽식구조 15
 
2.9%
조적조 11
 
2.2%
철근콘크리트라멘조 9
 
1.8%
철골철근콘크리트구조 7
 
1.4%
벽돌구조 7
 
1.4%
철근콘크리트벽식조 5
 
1.0%
벽식구조 5
 
1.0%
Other values (42) 55
 
10.8%
2023-12-11T00:02:39.090821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
484
12.6%
473
12.4%
465
12.2%
461
12.0%
461
12.0%
461
12.0%
460
12.0%
278
7.3%
52
 
1.4%
34
 
0.9%
Other values (38) 198
5.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3783
98.9%
Other Punctuation 16
 
0.4%
Space Separator 15
 
0.4%
Uppercase Letter 6
 
0.2%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
484
12.8%
473
12.5%
465
12.3%
461
12.2%
461
12.2%
461
12.2%
460
12.2%
278
7.3%
52
 
1.4%
34
 
0.9%
Other values (28) 154
 
4.1%
Other Punctuation
ValueCountFrequency (%)
/ 7
43.8%
, 5
31.2%
. 4
25.0%
Uppercase Letter
ValueCountFrequency (%)
C 3
50.0%
R 2
33.3%
P 1
 
16.7%
Space Separator
ValueCountFrequency (%)
15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3783
98.9%
Common 38
 
1.0%
Latin 6
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
484
12.8%
473
12.5%
465
12.3%
461
12.2%
461
12.2%
461
12.2%
460
12.2%
278
7.3%
52
 
1.4%
34
 
0.9%
Other values (28) 154
 
4.1%
Common
ValueCountFrequency (%)
15
39.5%
/ 7
18.4%
, 5
 
13.2%
. 4
 
10.5%
) 3
 
7.9%
( 3
 
7.9%
+ 1
 
2.6%
Latin
ValueCountFrequency (%)
C 3
50.0%
R 2
33.3%
P 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3783
98.9%
ASCII 44
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
484
12.8%
473
12.5%
465
12.3%
461
12.2%
461
12.2%
461
12.2%
460
12.2%
278
7.3%
52
 
1.4%
34
 
0.9%
Other values (28) 154
 
4.1%
ASCII
ValueCountFrequency (%)
15
34.1%
/ 7
15.9%
, 5
 
11.4%
. 4
 
9.1%
) 3
 
6.8%
( 3
 
6.8%
C 3
 
6.8%
R 2
 
4.5%
P 1
 
2.3%
+ 1
 
2.3%

주_용도_코드
Categorical

IMBALANCE 

Distinct35
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
02001
255 
02005
142 
02003
 
23
14202
 
14
02006
 
11
Other values (30)
55 

Length

Max length5
Median length5
Mean length4.98
Min length4

Unique

Unique22 ?
Unique (%)4.4%

Sample

1st row02001
2nd row02001
3rd row02001
4th row02005
5th row02005

Common Values

ValueCountFrequency (%)
02001 255
51.0%
02005 142
28.4%
02003 23
 
4.6%
14202 14
 
2.8%
02006 11
 
2.2%
<NA> 10
 
2.0%
20001 6
 
1.2%
04001 3
 
0.6%
07999 3
 
0.6%
02002 3
 
0.6%
Other values (25) 30
 
6.0%

Length

2023-12-11T00:02:39.289605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
02001 255
51.0%
02005 142
28.4%
02003 23
 
4.6%
14202 14
 
2.8%
02006 11
 
2.2%
na 10
 
2.0%
20001 6
 
1.2%
02002 3
 
0.6%
14299 3
 
0.6%
07201 3
 
0.6%
Other values (25) 30
 
6.0%

주_용도_코드_명
Categorical

IMBALANCE 

Distinct32
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
아파트
254 
부대시설
136 
다세대주택
 
25
복리시설
 
13
연립주택
 
9
Other values (27)
63 

Length

Max length11
Median length3
Mean length3.678
Min length2

Unique

Unique13 ?
Unique (%)2.6%

Sample

1st row아파트
2nd row부대시설
3rd row아파트
4th row아파트
5th row아파트

Common Values

ValueCountFrequency (%)
아파트 254
50.8%
부대시설 136
27.2%
다세대주택 25
 
5.0%
복리시설 13
 
2.6%
연립주택 9
 
1.8%
오피스텔 9
 
1.8%
<NA> 7
 
1.4%
휴양콘도미니엄 5
 
1.0%
기타제1종근린생활시설 4
 
0.8%
사무소 3
 
0.6%
Other values (22) 35
 
7.0%

Length

2023-12-11T00:02:39.524302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
아파트 254
50.8%
부대시설 136
27.2%
다세대주택 25
 
5.0%
복리시설 13
 
2.6%
연립주택 9
 
1.8%
오피스텔 9
 
1.8%
na 7
 
1.4%
휴양콘도미니엄 5
 
1.0%
기타제1종근린생활시설 4
 
0.8%
사무소 3
 
0.6%
Other values (22) 35
 
7.0%

기타_용도
Text

MISSING 

Distinct244
Distinct (%)49.6%
Missing8
Missing (%)1.6%
Memory size4.0 KiB
2023-12-11T00:02:39.895904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length37
Mean length7.1178862
Min length2

Characters and Unicode

Total characters3502
Distinct characters195
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique196 ?
Unique (%)39.8%

Sample

1st row아파트
2nd row벽체,계단실,승강기,홀
3rd row지하대피소
4th row경비실
5th row경비실,MDF실
ValueCountFrequency (%)
아파트 58
 
11.2%
경비실 39
 
7.5%
지하주차장 27
 
5.2%
계단실 22
 
4.2%
지하대피소 12
 
2.3%
대피소 10
 
1.9%
주차장 9
 
1.7%
계단 9
 
1.7%
주민공동시설 8
 
1.5%
다세대주택 8
 
1.5%
Other values (233) 318
61.2%
2023-12-11T00:02:40.544233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
303
 
8.7%
, 294
 
8.4%
120
 
3.4%
119
 
3.4%
118
 
3.4%
87
 
2.5%
79
 
2.3%
73
 
2.1%
73
 
2.1%
71
 
2.0%
Other values (185) 2165
61.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2942
84.0%
Other Punctuation 325
 
9.3%
Uppercase Letter 85
 
2.4%
Close Punctuation 38
 
1.1%
Open Punctuation 38
 
1.1%
Decimal Number 37
 
1.1%
Space Separator 28
 
0.8%
Math Symbol 5
 
0.1%
Dash Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
303
 
10.3%
120
 
4.1%
119
 
4.0%
118
 
4.0%
87
 
3.0%
79
 
2.7%
73
 
2.5%
73
 
2.5%
71
 
2.4%
71
 
2.4%
Other values (160) 1828
62.1%
Uppercase Letter
ValueCountFrequency (%)
E 23
27.1%
V 13
15.3%
D 12
14.1%
F 12
14.1%
M 11
12.9%
L 11
12.9%
I 1
 
1.2%
P 1
 
1.2%
G 1
 
1.2%
Decimal Number
ValueCountFrequency (%)
2 15
40.5%
1 15
40.5%
6 2
 
5.4%
3 2
 
5.4%
0 1
 
2.7%
4 1
 
2.7%
7 1
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 294
90.5%
. 16
 
4.9%
/ 13
 
4.0%
· 2
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Space Separator
ValueCountFrequency (%)
28
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2942
84.0%
Common 475
 
13.6%
Latin 85
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
303
 
10.3%
120
 
4.1%
119
 
4.0%
118
 
4.0%
87
 
3.0%
79
 
2.7%
73
 
2.5%
73
 
2.5%
71
 
2.4%
71
 
2.4%
Other values (160) 1828
62.1%
Common
ValueCountFrequency (%)
, 294
61.9%
) 38
 
8.0%
( 38
 
8.0%
28
 
5.9%
. 16
 
3.4%
2 15
 
3.2%
1 15
 
3.2%
/ 13
 
2.7%
~ 5
 
1.1%
- 4
 
0.8%
Other values (6) 9
 
1.9%
Latin
ValueCountFrequency (%)
E 23
27.1%
V 13
15.3%
D 12
14.1%
F 12
14.1%
M 11
12.9%
L 11
12.9%
I 1
 
1.2%
P 1
 
1.2%
G 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2942
84.0%
ASCII 558
 
15.9%
None 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
303
 
10.3%
120
 
4.1%
119
 
4.0%
118
 
4.0%
87
 
3.0%
79
 
2.7%
73
 
2.5%
73
 
2.5%
71
 
2.4%
71
 
2.4%
Other values (160) 1828
62.1%
ASCII
ValueCountFrequency (%)
, 294
52.7%
) 38
 
6.8%
( 38
 
6.8%
28
 
5.0%
E 23
 
4.1%
. 16
 
2.9%
2 15
 
2.7%
1 15
 
2.7%
/ 13
 
2.3%
V 13
 
2.3%
Other values (14) 65
 
11.6%
None
ValueCountFrequency (%)
· 2
100.0%

면적(㎡)
Real number (ℝ)

Distinct476
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.693825
Minimum0
Maximum300
Zeros3
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:02:40.828651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.060295
Q10.568125
median5.88
Q322.63125
95-th percentile84.808625
Maximum300
Range300
Interquartile range (IQR)22.063125

Descriptive statistics

Standard deviation31.185816
Coefficient of variation (CV)1.5835327
Kurtosis14.832402
Mean19.693825
Median Absolute Deviation (MAD)5.71015
Skewness2.9091618
Sum9846.9124
Variance972.5551
MonotonicityNot monotonic
2023-12-11T00:02:41.109408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05 4
 
0.8%
49.94 3
 
0.6%
0.11 3
 
0.6%
0.141 3
 
0.6%
0.0 3
 
0.6%
84.94 2
 
0.4%
4.68 2
 
0.4%
0.53 2
 
0.4%
0.39 2
 
0.4%
1.26 2
 
0.4%
Other values (466) 474
94.8%
ValueCountFrequency (%)
0.0 3
0.6%
0.011 1
 
0.2%
0.016 1
 
0.2%
0.0197 1
 
0.2%
0.02 1
 
0.2%
0.025 1
 
0.2%
0.0279 1
 
0.2%
0.0288 1
 
0.2%
0.0289 1
 
0.2%
0.0293 1
 
0.2%
ValueCountFrequency (%)
300.0 1
0.2%
170.09 1
0.2%
148.77 1
0.2%
134.98 1
0.2%
131.81 1
0.2%
115.0 1
0.2%
114.7 1
0.2%
114.63 1
0.2%
108.453 1
0.2%
97.16 1
0.2%

생성_일자
Real number (ℝ)

Distinct312
Distinct (%)62.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20128461
Minimum20090317
Maximum20160601
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:02:41.400643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090317
5-th percentile20090319
Q120110416
median20130927
Q320150628
95-th percentile20160409
Maximum20160601
Range70284
Interquartile range (IQR)40212

Descriptive statistics

Standard deviation24467.757
Coefficient of variation (CV)0.0012155801
Kurtosis-1.2932991
Mean20128461
Median Absolute Deviation (MAD)20004.5
Skewness-0.3172493
Sum1.0064231 × 1010
Variance5.9867112 × 108
MonotonicityNot monotonic
2023-12-11T00:02:41.671812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20090320 31
 
6.2%
20090319 18
 
3.6%
20110420 9
 
1.8%
20090321 8
 
1.6%
20090317 8
 
1.6%
20150725 5
 
1.0%
20110416 4
 
0.8%
20131224 4
 
0.8%
20110422 4
 
0.8%
20110417 4
 
0.8%
Other values (302) 405
81.0%
ValueCountFrequency (%)
20090317 8
 
1.6%
20090318 4
 
0.8%
20090319 18
3.6%
20090320 31
6.2%
20090321 8
 
1.6%
20090323 4
 
0.8%
20090325 2
 
0.4%
20090421 2
 
0.4%
20090430 1
 
0.2%
20090507 2
 
0.4%
ValueCountFrequency (%)
20160601 1
0.2%
20160531 1
0.2%
20160528 1
0.2%
20160527 1
0.2%
20160521 1
0.2%
20160520 1
0.2%
20160517 1
0.2%
20160514 1
0.2%
20160512 2
0.4%
20160511 1
0.2%

Sample

관리_건축물대장_PK대장_구분_코드대장_구분_코드_명대장_종류_코드대장_종류_코드_명대지_위치도로명_대지_위치건물_명시군구_코드법정동_코드대지_구분_코드특수지_명블록로트새주소_도로_코드새주소_법정동_코드새주소_지상지하_코드새주소_본_번새주소_부_번동_명칭호_명칭층_구분_코드층_구분_코드_명층_번호전유_공용_구분_코드전유_공용_구분_코드_명주_부속_구분_코드주_부속_구분_코드_명층_번호_명구조_코드구조_코드_명기타_구조주_용도_코드주_용도_코드_명기타_용도면적(㎡)생성_일자
028237-652872집합4전유부경기도 안양시 동안구 관양동 1669번지서울특별시 관악구 남부순환로 1820신일아르디세282601050001332<NA><NA><NA>264104205138105010670101동110320지상32공용0부속건축물3층21철근콘크리트구조철근콘크리트조02001아파트아파트1.679620110416
141150-899282집합4전유부강원도 인제군 기린면 현리 848번지세종특별자치시 보듬4로 111행신1차에스케이뷰아파트415502562602050<NA><NA><NA>421104454450<NA>08360광진 동양파라곤 1단지190320지상22공용1주건축물1층21철근콘크리트구조철근콘크리트조02001부대시설벽체,계단실,승강기,홀84.9820150115
241360-693762집합4전유부전라남도 순천시 서면 동산리 74-1번지서울특별시 강남구 언주로 316다솔아파트115001170004920<NA><NA><NA>263503133007114010420106동150320지상02공용1주건축물1층21철근콘크리트구조조적조02001아파트지하대피소0.51520150509
311545-452322집합4전유부부산광역시 금정구 구서동 775-13번지서울특별시 서초구 마방로10길 15동문굿모닝타워1411731080001887<NA><NA><NA>413903199071<NA>0550101동1-107호20지상132공용0주건축물지1층21철근콘크리트구조철근콘크리트조02005아파트경비실5.826820130917
441285-1412662집합4전유부부산광역시 해운대구 중동 1516-5번지대전광역시 유성구 대정로28번안길 80주공그린빌4825013400017470<NA><NA><NA>4117331830051050102420사보이시티디엠씨236호20지상162공용0부속건축물지층21철근콘크리트구조철근콘크리트조02005아파트경비실,MDF실0.348120150108
511350-1683362집합4전유부울산광역시 중구 반구동 22-37번지경기도 화성시 동탄대로 591센텀우신골든빌263501020005810위례 택지개발사업지구<NA><NA>2626031300321200101057<NA>30220지상02공용0주건축물<NA>21철근콘크리트구조시멘트벽돌14204부대시설지하대피소10.9920150725
641610-439092집합4전유부경기도 평택시 청북면 옥길리 1110번지인천광역시 동구 화도진로186번길 48선우아파트361102502106281<NA><NA><NA>36110325810310501000102동4층 052호20지상22공용0주건축물1층21철근콘크리트구조철근콘크리트구조02005부대시설아파트11.67420151118
741220-967002집합4전유부경기도 남양주시 화도읍 답내리 1074번지경기도 안양시 만안구 태평로 214형곡삼우타운42110133000300<NA><NA><NA>42230200002310401052014동150420지상02공용0부속건축물각층21철근콘크리트구조철근콘크리트구조,라멘구조02005시장벽체,발코니1.388320150620
848310-1001823762집합4전유부부산광역시 부산진구 부암동 731번지서울특별시 구로구 구일로 154-17부영아파트2818510100013520<NA><NA><NA>4146544151061020206220723층 2143호20지상101공용0주건축물지1/1층21철근콘크리트구조철근콘크리트구조02003부대시설경비실59.98720111014
944180-291982집합4전유부경기도 부천시 원미구 역곡동 204번지광주광역시 서구 상무버들로40번길 14진천신정주공아파트2814010100013306<NA><NA><NA>4111343252711280201601006동2308호20지상182공용0부속건축물1층21철근콘크리트구조철근콘크리트조04402아파트계단실,승강기59.27620090318
관리_건축물대장_PK대장_구분_코드대장_구분_코드_명대장_종류_코드대장_종류_코드_명대지_위치도로명_대지_위치건물_명시군구_코드법정동_코드대지_구분_코드특수지_명블록로트새주소_도로_코드새주소_법정동_코드새주소_지상지하_코드새주소_본_번새주소_부_번동_명칭호_명칭층_구분_코드층_구분_코드_명층_번호전유_공용_구분_코드전유_공용_구분_코드_명주_부속_구분_코드주_부속_구분_코드_명층_번호_명구조_코드구조_코드_명기타_구조주_용도_코드주_용도_코드_명기타_용도면적(㎡)생성_일자
49048220-1002100302집합4전유부경기도 광명시 하안동 863번지대구광역시 달서구 월곡로 320대치아파트103동442701130009050<NA><NA><NA>112303105003110010850<NA>20420지상32공용1부속건축물지111철근콘크리트구조철근콘크리트조,스라브02001아파트지하(물탱크,관리)16.8820130412
49144200-1001819142집합4전유부대구광역시 달서구 본리동 433번지서울특별시 서초구 양재대로2길 11인터빌아파트103동11200118000132428<NA><NA><NA>2729031470112500201540<NA>806호20지상32공용1부속건축물1층21철근콘크리트구조세벽조02005부대시설<NA>22.34320130123
49211545-541752집합4전유부대구광역시 동구 불로동 959번지전라남도 목포시 남악로 22-10호계2차현대홈타운1114010700010614<NA><NA><NA>411353180039102010140105동201호20지상31공용1주건축물각층21철근콘크리트구조철근콘크리트조02001부대시설아파트0.5920130626
49311440-1272912집합4전유부세종특별자치시 도담동 845번지경기도 오산시 여계산로 21대전 학하지구 오투그란데 미학 아파트115901470009112<NA><NA><NA>48270333607410402011<NA>203동501호20지상81공용0주건축물1층21철근콘크리트구조<NA>02005아파트펌프실0.1120140515
49429170-1684952집합4전유부서울특별시 영등포구 여의도동 37-2번지경기도 군포시 고산로677번길 34수색진흥엣세벨451301110003662<NA><NA>3L2824542654251120102480104동440220지상12공용0주건축물6층21철근콘크리트구조철근콘크리트구조02001아파트관리,노인정27.780520150905
49541117-2656992집합4전유부강원도 춘천시 후평동 542-3번지경기도 화성시 동탄반석로 41호수공원아파트428002562806691<NA><NA><NA>482503335068133010<NA>0203동10310지상32공용0주건축물각층21철근콘크리트구조철근콘크리트조02001부대시설지하주차장6.420150321
49641360-1002424162집합4전유부강원도 춘천시 동내면 거두리 1065번지대전광역시 대덕구 신탄진로218번길 62신원마을엘에이치2단지1138010300051235<NA><NA><NA>111403005006<NA>01310337동15층 1503호20지상01공용0주건축물각층21철근콘크리트구조철근콘크리트02001아파트계단실84.577620151003
49744133-1002455362집합4전유부대구광역시 달서구 용산동 416-1번지서울특별시 성북구 종암로24가길 80오양평구아파트114701070003690<NA><NA><NA>41111317400410101060<NA>105동110320지상152공용1부속건축물지121철근콘크리트구조철근콘크리트조02001아파트보육실,경로당,관리실,주민공동시설,문고(1-2층)7.592820150410
49811290-852802집합4전유부충청남도 서산시 성연면 일람리 1128번지대구광역시 북구 중앙대로 591홍성대우아파트41310114000751<NA><NA><NA>411953184006107010790608동208호20지상102공용1주건축물1층21철근콘크리트구조벽돌구조,철근콘크리트구조02001아파트다세대주택1.217920160331
49927260-776772집합4전유부부산광역시 수영구 광안동 50-13번지충청북도 충주시 예성로 353롯데캐슬 캠퍼스타운114401010001380<NA><NA><NA>3017031660161040101870<NA>13층 1303호20지상92공용0주건축물121철근콘크리트구조철근콘크리트구조02001부대시설경비실0.62320140121