Overview

Dataset statistics

Number of variables5
Number of observations301
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.5 KiB
Average record size in memory42.4 B

Variable types

Numeric2
Text3

Dataset

Description서울특별시 용산구 공공건축물의 현황정보(연번, 건물명, 지번주소, 도로명주소, 연면적)에 대한 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15112830/fileData.do

Alerts

연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 10:58:37.523989
Analysis finished2023-12-12 10:58:38.884170
Duration1.36 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct301
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151
Minimum1
Maximum301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T19:58:39.024237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q176
median151
Q3226
95-th percentile286
Maximum301
Range300
Interquartile range (IQR)150

Descriptive statistics

Standard deviation87.035433
Coefficient of variation (CV)0.5763936
Kurtosis-1.2
Mean151
Median Absolute Deviation (MAD)75
Skewness0
Sum45451
Variance7575.1667
MonotonicityStrictly increasing
2023-12-12T19:58:39.271997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
208 1
 
0.3%
206 1
 
0.3%
205 1
 
0.3%
204 1
 
0.3%
203 1
 
0.3%
202 1
 
0.3%
201 1
 
0.3%
200 1
 
0.3%
199 1
 
0.3%
Other values (291) 291
96.7%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
301 1
0.3%
300 1
0.3%
299 1
0.3%
298 1
0.3%
297 1
0.3%
296 1
0.3%
295 1
0.3%
294 1
0.3%
293 1
0.3%
292 1
0.3%

건물명
Text

Distinct129
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-12T19:58:39.748210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length7
Mean length7.7774086
Min length4

Characters and Unicode

Total characters2341
Distinct characters175
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique127 ?
Unique (%)42.2%

Sample

1st row후암4 공중화장실
2nd row후암어린이집
3rd row양짓말경로당
4th row미래어린이집
5th row후암동사무소
ValueCountFrequency (%)
용산전자상상가 172
51.3%
공영주차장 4
 
1.2%
청소년공부방 3
 
0.9%
용산구 2
 
0.6%
한남빗물펌프장 2
 
0.6%
효창종합사회복지관 2
 
0.6%
구립 2
 
0.6%
용문동 2
 
0.6%
공중화장실 2
 
0.6%
장애인 1
 
0.3%
Other values (143) 143
42.7%
2023-12-12T19:58:40.642132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
상 344
 
14.7%
용 196
 
8.4%
산 191
 
8.2%
자 176
 
7.5%
가 176
 
7.5%
전 174
 
7.4%
동 42
 
1.8%
로 39
 
1.7%
35
 
1.5%
이 35
 
1.5%
Other values (165) 933
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2240
95.7%
Space Separator 35
 
1.5%
Decimal Number 31
 
1.3%
Close Punctuation 18
 
0.8%
Open Punctuation 15
 
0.6%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 344
15.4%
용 196
 
8.8%
산 191
 
8.5%
자 176
 
7.9%
가 176
 
7.9%
전 174
 
7.8%
동 42
 
1.9%
로 39
 
1.7%
이 35
 
1.6%
당 35
 
1.6%
Other values (156) 832
37.1%
Decimal Number
ValueCountFrequency (%)
2 16
51.6%
1 10
32.3%
3 3
 
9.7%
4 2
 
6.5%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
/ 1
50.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2239
95.6%
Common 101
 
4.3%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 344
15.4%
용 196
 
8.8%
산 191
 
8.5%
자 176
 
7.9%
가 176
 
7.9%
전 174
 
7.8%
동 42
 
1.9%
로 39
 
1.7%
이 35
 
1.6%
당 35
 
1.6%
Other values (155) 831
37.1%
Common
ValueCountFrequency (%)
35
34.7%
) 18
17.8%
2 16
15.8%
( 15
14.9%
1 10
 
9.9%
3 3
 
3.0%
4 2
 
2.0%
. 1
 
1.0%
/ 1
 
1.0%
Han
ValueCountFrequency (%)
舊 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2239
95.6%
ASCII 101
 
4.3%
CJK 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
상 344
15.4%
용 196
 
8.8%
산 191
 
8.5%
자 176
 
7.9%
가 176
 
7.9%
전 174
 
7.8%
동 42
 
1.9%
로 39
 
1.7%
이 35
 
1.6%
당 35
 
1.6%
Other values (155) 831
37.1%
ASCII
ValueCountFrequency (%)
35
34.7%
) 18
17.8%
2 16
15.8%
( 15
14.9%
1 10
 
9.9%
3 3
 
3.0%
4 2
 
2.0%
. 1
 
1.0%
/ 1
 
1.0%
CJK
ValueCountFrequency (%)
舊 1
100.0%

지번주소
Text

Distinct289
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-12T19:58:41.000640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length38
Mean length30.272425
Min length18

Characters and Unicode

Total characters9112
Distinct characters75
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

Unique281 ?
Unique (%)93.4%

Sample

1st row서울특별시 용산구 후암동 103-9
2nd row서울특별시 용산구 후암동 160-1
3rd row서울특별시 용산구 후암동 239-1
4th row서울특별시 용산구 후암동 244-104
5th row서울특별시 용산구 후암동 244-109
ValueCountFrequency (%)
서울특별시 301
17.3%
용산구 301
17.3%
한강로3가 180
10.4%
16-20 172
9.9%
상가동 172
9.9%
외12 152
 
8.7%
외12필지 20
 
1.2%
한남동 14
 
0.8%
효창동 13
 
0.7%
이태원동 13
 
0.7%
Other values (328) 400
23.0%
2023-12-12T19:58:41.562543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1568
 
17.2%
2 533
 
5.8%
1 491
 
5.4%
3 383
 
4.2%
가 381
 
4.2%
제 344
 
3.8%
서 313
 
3.4%
용 313
 
3.4%
산 311
 
3.4%
별 301
 
3.3%
Other values (65) 4174
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5003
54.9%
Decimal Number 2263
24.8%
Space Separator 1568
 
17.2%
Dash Punctuation 277
 
3.0%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 381
 
7.6%
제 344
 
6.9%
서 313
 
6.3%
용 313
 
6.3%
산 311
 
6.2%
별 301
 
6.0%
울 301
 
6.0%
특 301
 
6.0%
구 301
 
6.0%
시 301
 
6.0%
Other values (52) 1836
36.7%
Decimal Number
ValueCountFrequency (%)
2 533
23.6%
1 491
21.7%
3 383
16.9%
6 250
11.0%
0 248
11.0%
5 93
 
4.1%
4 69
 
3.0%
9 68
 
3.0%
8 67
 
3.0%
7 61
 
2.7%
Space Separator
ValueCountFrequency (%)
1568
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 277
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5003
54.9%
Common 4108
45.1%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 381
 
7.6%
제 344
 
6.9%
서 313
 
6.3%
용 313
 
6.3%
산 311
 
6.2%
별 301
 
6.0%
울 301
 
6.0%
특 301
 
6.0%
구 301
 
6.0%
시 301
 
6.0%
Other values (52) 1836
36.7%
Common
ValueCountFrequency (%)
1568
38.2%
2 533
 
13.0%
1 491
 
12.0%
3 383
 
9.3%
- 277
 
6.7%
6 250
 
6.1%
0 248
 
6.0%
5 93
 
2.3%
4 69
 
1.7%
9 68
 
1.7%
Other values (2) 128
 
3.1%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5003
54.9%
ASCII 4109
45.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1568
38.2%
2 533
 
13.0%
1 491
 
11.9%
3 383
 
9.3%
- 277
 
6.7%
6 250
 
6.1%
0 248
 
6.0%
5 93
 
2.3%
4 69
 
1.7%
9 68
 
1.7%
Other values (3) 129
 
3.1%
Hangul
ValueCountFrequency (%)
가 381
 
7.6%
제 344
 
6.9%
서 313
 
6.3%
용 313
 
6.3%
산 311
 
6.2%
별 301
 
6.0%
울 301
 
6.0%
특 301
 
6.0%
구 301
 
6.0%
시 301
 
6.0%
Other values (52) 1836
36.7%

도로명주소
Text

Distinct101
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-12T19:58:42.030523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length1
Mean length10.531561
Min length1

Characters and Unicode

Total characters3170
Distinct characters120
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

Unique94 ?
Unique (%)31.2%

Sample

1st row서울특별시 용산구 한강대로104길 77 (후암동)
2nd row서울특별시 용산구 두텁바위로1길 86 (후암동)
3rd row서울특별시 용산구 후암로13가길 25 (후암동)
4th row서울특별시 용산구 후암로16나길 13 (후암동)
5th row서울특별시 용산구 후암로 32-6 (후암동)
ValueCountFrequency (%)
서울특별시 109
 
19.2%
용산구 109
 
19.2%
한남동 12
 
2.1%
이태원동 10
 
1.8%
효창동 8
 
1.4%
이촌동 7
 
1.2%
후암동 7
 
1.2%
서계동 6
 
1.1%
백범로 6
 
1.1%
13 6
 
1.1%
Other values (196) 289
50.8%
2023-12-12T19:58:42.718848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
662
20.9%
서 131
 
4.1%
로 130
 
4.1%
용 122
 
3.8%
산 119
 
3.8%
구 112
 
3.5%
울 110
 
3.5%
( 109
 
3.4%
별 109
 
3.4%
시 109
 
3.4%
Other values (110) 1457
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1817
57.3%
Space Separator 662
 
20.9%
Decimal Number 448
 
14.1%
Open Punctuation 109
 
3.4%
Close Punctuation 109
 
3.4%
Dash Punctuation 24
 
0.8%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 131
 
7.2%
로 130
 
7.2%
용 122
 
6.7%
산 119
 
6.5%
구 112
 
6.2%
울 110
 
6.1%
별 109
 
6.0%
시 109
 
6.0%
특 109
 
6.0%
동 100
 
5.5%
Other values (95) 666
36.7%
Decimal Number
ValueCountFrequency (%)
1 89
19.9%
2 75
16.7%
3 66
14.7%
4 40
8.9%
5 39
8.7%
7 31
 
6.9%
8 28
 
6.2%
9 27
 
6.0%
6 27
 
6.0%
0 26
 
5.8%
Space Separator
ValueCountFrequency (%)
662
100.0%
Open Punctuation
ValueCountFrequency (%)
( 109
100.0%
Close Punctuation
ValueCountFrequency (%)
) 109
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1817
57.3%
Common 1352
42.6%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 131
 
7.2%
로 130
 
7.2%
용 122
 
6.7%
산 119
 
6.5%
구 112
 
6.2%
울 110
 
6.1%
별 109
 
6.0%
시 109
 
6.0%
특 109
 
6.0%
동 100
 
5.5%
Other values (95) 666
36.7%
Common
ValueCountFrequency (%)
662
49.0%
( 109
 
8.1%
) 109
 
8.1%
1 89
 
6.6%
2 75
 
5.5%
3 66
 
4.9%
4 40
 
3.0%
5 39
 
2.9%
7 31
 
2.3%
8 28
 
2.1%
Other values (4) 104
 
7.7%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1817
57.3%
ASCII 1353
42.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
662
48.9%
( 109
 
8.1%
) 109
 
8.1%
1 89
 
6.6%
2 75
 
5.5%
3 66
 
4.9%
4 40
 
3.0%
5 39
 
2.9%
7 31
 
2.3%
8 28
 
2.1%
Other values (5) 105
 
7.8%
Hangul
ValueCountFrequency (%)
서 131
 
7.2%
로 130
 
7.2%
용 122
 
6.7%
산 119
 
6.5%
구 112
 
6.2%
울 110
 
6.1%
별 109
 
6.0%
시 109
 
6.0%
특 109
 
6.0%
동 100
 
5.5%
Other values (95) 666
36.7%

연면적(제곱미터)
Real number (ℝ)

Distinct136
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean698.87422
Minimum7.8
Maximum59177.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T19:58:42.940838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.8
5-th percentile9
Q113.35
median15
Q3423.18
95-th percentile2987.97
Maximum59177.2
Range59169.4
Interquartile range (IQR)409.83

Descriptive statistics

Standard deviation3598.617
Coefficient of variation (CV)5.1491625
Kurtosis234.41657
Mean698.87422
Median Absolute Deviation (MAD)6
Skewness14.548115
Sum210361.14
Variance12950044
MonotonicityNot monotonic
2023-12-12T19:58:43.189625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.5 72
23.9%
9.0 49
 
16.3%
13.35 18
 
6.0%
18.0 15
 
5.0%
27.0 6
 
2.0%
10.4 5
 
1.7%
7.8 5
 
1.7%
563.5 2
 
0.7%
15.0 2
 
0.7%
66.36 1
 
0.3%
Other values (126) 126
41.9%
ValueCountFrequency (%)
7.8 5
 
1.7%
9.0 49
16.3%
10.4 5
 
1.7%
13.35 18
 
6.0%
13.5 72
23.9%
15.0 2
 
0.7%
17.5 1
 
0.3%
18.0 15
 
5.0%
19.83 1
 
0.3%
27.0 6
 
2.0%
ValueCountFrequency (%)
59177.2 1
0.3%
10568.13 1
0.3%
6757.46 1
0.3%
6497.43 1
0.3%
6358.64 1
0.3%
5919.42 1
0.3%
5033.38 1
0.3%
4997.07 1
0.3%
4680.1 1
0.3%
4091.81 1
0.3%

Interactions

2023-12-12T19:58:38.273463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:37.959456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:38.436603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:38.102873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:58:43.326172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번연면적(제곱미터)
연번1.0000.000
연면적(제곱미터)0.0001.000
2023-12-12T19:58:43.478751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번연면적(제곱미터)
연번1.000-0.085
연면적(제곱미터)-0.0851.000

Missing values

2023-12-12T19:58:38.659161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:58:38.813381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

연번건물명지번주소도로명주소연면적(제곱미터)
01후암4 공중화장실서울특별시 용산구 후암동 103-9서울특별시 용산구 한강대로104길 77 (후암동)17.5
12후암어린이집서울특별시 용산구 후암동 160-1서울특별시 용산구 두텁바위로1길 86 (후암동)469.96
23양짓말경로당서울특별시 용산구 후암동 239-1서울특별시 용산구 후암로13가길 25 (후암동)119.25
34미래어린이집서울특별시 용산구 후암동 244-104서울특별시 용산구 후암로16나길 13 (후암동)529.6
45후암동사무소서울특별시 용산구 후암동 244-109서울특별시 용산구 후암로 32-6 (후암동)879.1
56새말경로당서울특별시 용산구 후암동 400-39서울특별시 용산구 두텁바위로58길 48 (후암동)65.4
67활터골경로당서울특별시 용산구 후암동 406-43서울특별시 용산구 신흥로20길 55 (후암동)98.1
78용산2가동사무소서울특별시 용산구 용산동2가 11-13서울특별시 용산구 신흥로 90 (용산동2가)1902.23
89소월어린이집서울특별시 용산구 용산동2가 11-3서울특별시 용산구 신흥로18길 3-5 (용산동2가)321.28
910용산2가소월공영주차장서울특별시 용산구 용산동2가 111728.54
연번건물명지번주소도로명주소연면적(제곱미터)
291292서빙고동사무소서울특별시 용산구 서빙고동 134서울특별시 용산구 서빙고로 279 (서빙고동)3293.34
292293서빙고빗물펌프장서울특별시 용산구 서빙고동 15570.12
293294서빙고할아버지경로당서울특별시 용산구 서빙고동 195-10서울특별시 용산구 서빙고로59길 3-20 (서빙고동)193.05
294295용산구 장애인 커뮤니티센터서울특별시 용산구 서빙고동 199-3서울특별시 용산구 서빙고로 245 (서빙고동 용산구창업지원센터)1496.01
295296신동아아파트경로당서울특별시 용산구 서빙고동 271-2서울특별시 용산구 이촌로 347 (서빙고동)231.7
296297서빙고할머니경로당서울특별시 용산구 서빙고동 5196.0
297298보광제1경로당서울특별시 용산구 보광동 216-6789.88
298299보광동사무소서울특별시 용산구 보광동 217-32서울특별시 용산구 장문로 95 (보광동)2424.6
299300보광제3경로당서울특별시 용산구 보광동 242-4서울특별시 용산구 보광로32길 1 (보광동)139.08
300301보광빗물펌프장서울특별시 용산구 보광동 83-2서울특별시 용산구 서빙고로 375 (보광동)376.73