Overview

Dataset statistics

Number of variables15
Number of observations124
Missing cells168
Missing cells (%)9.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.4 KiB
Average record size in memory127.1 B

Variable types

Categorical3
Text3
Numeric6
DateTime3

Dataset

Description용산구 건축물사용승인 현황에 대한 데이터로 건축구분, 건물명, 허가번호, 대지위치, 대지면적(제곱미터), 건축면적(제곱미터), 연면적,(제곱미터), 증축연면적(제곱미터), 건폐율(퍼센트), 용적률(퍼센트), 구조, 허가일, 착공처리일, 사용승인일, 주용도에 대한 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15105883/fileData.do

Alerts

대지면적(제곱미터) is highly overall correlated with 건축면적(제곱미터) and 3 other fieldsHigh correlation
건축면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 3 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 4 other fieldsHigh correlation
증축연면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 4 other fieldsHigh correlation
용적률(퍼센트) is highly overall correlated with 연면적(제곱미터)High correlation
건축구분 is highly overall correlated with 증축연면적(제곱미터)High correlation
주용도 is highly overall correlated with 대지면적(제곱미터) and 3 other fieldsHigh correlation
건물명 has 40 (32.3%) missing valuesMissing
증축연면적(제곱미터) has 116 (93.5%) missing valuesMissing
착공처리일 has 12 (9.7%) missing valuesMissing
허가번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:15:09.254605
Analysis finished2023-12-12 09:15:15.576229
Duration6.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

건축구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
신축
67 
대수선
44 
증축
8 
용도변경
 
4
재축
 
1

Length

Max length4
Median length2
Mean length2.4193548
Min length2

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row용도변경
2nd row대수선
3rd row대수선
4th row대수선
5th row대수선

Common Values

ValueCountFrequency (%)
신축 67
54.0%
대수선 44
35.5%
증축 8
 
6.5%
용도변경 4
 
3.2%
재축 1
 
0.8%

Length

2023-12-12T18:15:15.674310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:15:15.863273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신축 67
54.0%
대수선 44
35.5%
증축 8
 
6.5%
용도변경 4
 
3.2%
재축 1
 
0.8%

건물명
Text

MISSING 

Distinct83
Distinct (%)98.8%
Missing40
Missing (%)32.3%
Memory size1.1 KiB
2023-12-12T18:15:16.161732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length26
Mean length12.904762
Min length1

Characters and Unicode

Total characters1084
Distinct characters212
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)97.6%

Sample

1st rowkt 용산 데이터센터
2nd row민생빌딩
3rd row한남동 657-196
4th row이태원동 251-39 제2종근린생활시설 (민앤진주식회사)
5th row서울드래곤시티
ValueCountFrequency (%)
제2종근린생활시설 8
 
4.1%
이태원동 8
 
4.1%
단독주택 7
 
3.6%
한남동 5
 
2.6%
카이벨로체 4
 
2.1%
후암동 4
 
2.1%
용산 3
 
1.5%
제1종근린생활시설 3
 
1.5%
공동주택 2
 
1.0%
용산동2가 2
 
1.0%
Other values (138) 149
76.4%
2023-12-12T18:15:16.720502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
127
 
11.7%
1 43
 
4.0%
2 40
 
3.7%
동 37
 
3.4%
- 33
 
3.0%
이 27
 
2.5%
( 26
 
2.4%
) 26
 
2.4%
3 21
 
1.9%
주 20
 
1.8%
Other values (202) 684
63.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 620
57.2%
Decimal Number 182
 
16.8%
Space Separator 127
 
11.7%
Lowercase Letter 39
 
3.6%
Dash Punctuation 33
 
3.0%
Uppercase Letter 28
 
2.6%
Open Punctuation 26
 
2.4%
Close Punctuation 26
 
2.4%
Other Punctuation 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 37
 
6.0%
이 27
 
4.4%
주 20
 
3.2%
시 18
 
2.9%
설 16
 
2.6%
생 16
 
2.6%
활 14
 
2.3%
근 14
 
2.3%
한 13
 
2.1%
린 13
 
2.1%
Other values (154) 432
69.7%
Lowercase Letter
ValueCountFrequency (%)
o 5
12.8%
e 5
12.8%
l 4
10.3%
i 3
7.7%
n 3
7.7%
c 3
7.7%
a 3
7.7%
s 2
 
5.1%
r 2
 
5.1%
m 2
 
5.1%
Other values (6) 7
17.9%
Uppercase Letter
ValueCountFrequency (%)
H 4
14.3%
T 3
10.7%
E 3
10.7%
L 3
10.7%
Y 3
10.7%
P 2
 
7.1%
A 1
 
3.6%
V 1
 
3.6%
B 1
 
3.6%
S 1
 
3.6%
Other values (6) 6
21.4%
Decimal Number
ValueCountFrequency (%)
1 43
23.6%
2 40
22.0%
3 21
11.5%
9 15
 
8.2%
5 14
 
7.7%
4 13
 
7.1%
8 12
 
6.6%
0 9
 
4.9%
6 8
 
4.4%
7 7
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
. 1
33.3%
Space Separator
ValueCountFrequency (%)
127
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 620
57.2%
Common 397
36.6%
Latin 67
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 37
 
6.0%
이 27
 
4.4%
주 20
 
3.2%
시 18
 
2.9%
설 16
 
2.6%
생 16
 
2.6%
활 14
 
2.3%
근 14
 
2.3%
한 13
 
2.1%
린 13
 
2.1%
Other values (154) 432
69.7%
Latin
ValueCountFrequency (%)
o 5
 
7.5%
e 5
 
7.5%
H 4
 
6.0%
l 4
 
6.0%
T 3
 
4.5%
E 3
 
4.5%
L 3
 
4.5%
Y 3
 
4.5%
i 3
 
4.5%
n 3
 
4.5%
Other values (22) 31
46.3%
Common
ValueCountFrequency (%)
127
32.0%
1 43
 
10.8%
2 40
 
10.1%
- 33
 
8.3%
( 26
 
6.5%
) 26
 
6.5%
3 21
 
5.3%
9 15
 
3.8%
5 14
 
3.5%
4 13
 
3.3%
Other values (6) 39
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 620
57.2%
ASCII 464
42.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
127
27.4%
1 43
 
9.3%
2 40
 
8.6%
- 33
 
7.1%
( 26
 
5.6%
) 26
 
5.6%
3 21
 
4.5%
9 15
 
3.2%
5 14
 
3.0%
4 13
 
2.8%
Other values (38) 106
22.8%
Hangul
ValueCountFrequency (%)
동 37
 
6.0%
이 27
 
4.4%
주 20
 
3.2%
시 18
 
2.9%
설 16
 
2.6%
생 16
 
2.6%
활 14
 
2.3%
근 14
 
2.3%
한 13
 
2.1%
린 13
 
2.1%
Other values (154) 432
69.7%

허가번호
Text

UNIQUE 

Distinct124
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T18:15:17.016515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length16.483871
Min length15

Characters and Unicode

Total characters2044
Distinct characters33
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

Unique124 ?
Unique (%)100.0%

Sample

1st row2023-건축과-용도변경허가-18
2nd row2023-건축과-대수선허가-10
3rd row2023-건축과-대수선허가-3
4th row2022-건축과-대수선허가-42
5th row2022-건축과-대수선허가-37
ValueCountFrequency (%)
2023-건축과-용도변경허가-18 1
 
0.8%
2021-건축과-신축허가-96 1
 
0.8%
2021-건축과-신축허가-19 1
 
0.8%
2021-건축과-신축허가-24 1
 
0.8%
2021-건축과-신축허가-25 1
 
0.8%
2021-건축과-신축허가-40 1
 
0.8%
2021-건축과-신축허가-47 1
 
0.8%
2021-건축과-신축허가-51 1
 
0.8%
2021-건축과-대수선허가-10 1
 
0.8%
2021-건축과-신축허가-55 1
 
0.8%
Other values (114) 114
91.9%
2023-12-12T18:15:17.480975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 372
18.2%
2 323
15.8%
축 195
9.5%
0 145
 
7.1%
1 135
 
6.6%
과 124
 
6.1%
건 120
 
5.9%
허 99
 
4.8%
가 99
 
4.8%
신 90
 
4.4%
Other values (23) 342
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 931
45.5%
Decimal Number 741
36.3%
Dash Punctuation 372
 
18.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
축 195
20.9%
과 124
13.3%
건 120
12.9%
허 99
10.6%
가 99
10.6%
신 90
9.7%
대 44
 
4.7%
수 44
 
4.7%
선 44
 
4.7%
고 24
 
2.6%
Other values (12) 48
 
5.2%
Decimal Number
ValueCountFrequency (%)
2 323
43.6%
0 145
19.6%
1 135
18.2%
3 37
 
5.0%
4 24
 
3.2%
9 19
 
2.6%
5 18
 
2.4%
6 16
 
2.2%
8 13
 
1.8%
7 11
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1113
54.5%
Hangul 931
45.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
축 195
20.9%
과 124
13.3%
건 120
12.9%
허 99
10.6%
가 99
10.6%
신 90
9.7%
대 44
 
4.7%
수 44
 
4.7%
선 44
 
4.7%
고 24
 
2.6%
Other values (12) 48
 
5.2%
Common
ValueCountFrequency (%)
- 372
33.4%
2 323
29.0%
0 145
 
13.0%
1 135
 
12.1%
3 37
 
3.3%
4 24
 
2.2%
9 19
 
1.7%
5 18
 
1.6%
6 16
 
1.4%
8 13
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1113
54.5%
Hangul 931
45.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 372
33.4%
2 323
29.0%
0 145
 
13.0%
1 135
 
12.1%
3 37
 
3.3%
4 24
 
2.2%
9 19
 
1.7%
5 18
 
1.6%
6 16
 
1.4%
8 13
 
1.2%
Hangul
ValueCountFrequency (%)
축 195
20.9%
과 124
13.3%
건 120
12.9%
허 99
10.6%
가 99
10.6%
신 90
9.7%
대 44
 
4.7%
수 44
 
4.7%
선 44
 
4.7%
고 24
 
2.6%
Other values (12) 48
 
5.2%

대지위치
Text

Distinct122
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T18:15:17.877914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length20.701613
Min length16

Characters and Unicode

Total characters2567
Distinct characters46
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

Unique120 ?
Unique (%)96.8%

Sample

1st row서울특별시 용산구 이태원동 258-274
2nd row서울특별시 용산구 이태원동 74-67
3rd row서울특별시 용산구 신창동 31-12
4th row서울특별시 용산구 원효로3가 1-2
5th row서울특별시 용산구 이태원동 128-13 외1필지
ValueCountFrequency (%)
서울특별시 124
24.0%
용산구 124
24.0%
이태원동 35
 
6.8%
외1필지 14
 
2.7%
한남동 13
 
2.5%
한강로2가 7
 
1.4%
후암동 7
 
1.4%
용산동2가 6
 
1.2%
효창동 6
 
1.2%
신창동 5
 
1.0%
Other values (138) 175
33.9%
2023-12-12T18:15:18.431443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
392
 
15.3%
용 132
 
5.1%
산 130
 
5.1%
서 129
 
5.0%
울 124
 
4.8%
구 124
 
4.8%
시 124
 
4.8%
별 124
 
4.8%
특 124
 
4.8%
- 117
 
4.6%
Other values (36) 1047
40.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1498
58.4%
Decimal Number 560
 
21.8%
Space Separator 392
 
15.3%
Dash Punctuation 117
 
4.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 132
 
8.8%
산 130
 
8.7%
서 129
 
8.6%
울 124
 
8.3%
구 124
 
8.3%
시 124
 
8.3%
별 124
 
8.3%
특 124
 
8.3%
동 102
 
6.8%
원 46
 
3.1%
Other values (24) 339
22.6%
Decimal Number
ValueCountFrequency (%)
1 116
20.7%
2 94
16.8%
3 74
13.2%
5 54
9.6%
4 51
9.1%
9 39
 
7.0%
6 36
 
6.4%
7 36
 
6.4%
8 33
 
5.9%
0 27
 
4.8%
Space Separator
ValueCountFrequency (%)
392
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1498
58.4%
Common 1069
41.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 132
 
8.8%
산 130
 
8.7%
서 129
 
8.6%
울 124
 
8.3%
구 124
 
8.3%
시 124
 
8.3%
별 124
 
8.3%
특 124
 
8.3%
동 102
 
6.8%
원 46
 
3.1%
Other values (24) 339
22.6%
Common
ValueCountFrequency (%)
392
36.7%
- 117
 
10.9%
1 116
 
10.9%
2 94
 
8.8%
3 74
 
6.9%
5 54
 
5.1%
4 51
 
4.8%
9 39
 
3.6%
6 36
 
3.4%
7 36
 
3.4%
Other values (2) 60
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1498
58.4%
ASCII 1069
41.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
392
36.7%
- 117
 
10.9%
1 116
 
10.9%
2 94
 
8.8%
3 74
 
6.9%
5 54
 
5.1%
4 51
 
4.8%
9 39
 
3.6%
6 36
 
3.4%
7 36
 
3.4%
Other values (2) 60
 
5.6%
Hangul
ValueCountFrequency (%)
용 132
 
8.8%
산 130
 
8.7%
서 129
 
8.6%
울 124
 
8.3%
구 124
 
8.3%
시 124
 
8.3%
별 124
 
8.3%
특 124
 
8.3%
동 102
 
6.8%
원 46
 
3.1%
Other values (24) 339
22.6%

대지면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean882.46895
Minimum36.4
Maximum30364.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T18:15:18.598294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.4
5-th percentile78.226
Q1109.025
median165.15
Q3319.8
95-th percentile1682.78
Maximum30364.4
Range30328
Interquartile range (IQR)210.775

Descriptive statistics

Standard deviation3409.6222
Coefficient of variation (CV)3.8637305
Kurtosis49.054141
Mean882.46895
Median Absolute Deviation (MAD)69.05
Skewness6.555331
Sum109426.15
Variance11625524
MonotonicityNot monotonic
2023-12-12T18:15:18.743023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10702.2 2
 
1.6%
109.1 2
 
1.6%
66.0 1
 
0.8%
211.74 1
 
0.8%
264.4 1
 
0.8%
171.2 1
 
0.8%
664.5 1
 
0.8%
317.2 1
 
0.8%
141.3 1
 
0.8%
94.51 1
 
0.8%
Other values (112) 112
90.3%
ValueCountFrequency (%)
36.4 1
0.8%
66.0 1
0.8%
69.0 1
0.8%
74.7 1
0.8%
75.7 1
0.8%
76.4 1
0.8%
77.56 1
0.8%
82.0 1
0.8%
82.6 1
0.8%
85.21 1
0.8%
ValueCountFrequency (%)
30364.4 1
0.8%
14797.7 1
0.8%
11703.4 1
0.8%
10702.2 2
1.6%
2378.8 1
0.8%
1788.8 1
0.8%
1082.0 1
0.8%
984.0 1
0.8%
745.4 1
0.8%
721.0 1
0.8%

건축면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean474.30256
Minimum31.68
Maximum18215.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T18:15:18.908395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31.68
5-th percentile40.333
Q161.0725
median94.665
Q3176.595
95-th percentile828.652
Maximum18215.12
Range18183.44
Interquartile range (IQR)115.5225

Descriptive statistics

Standard deviation1948.4604
Coefficient of variation (CV)4.1080538
Kurtosis59.43969
Mean474.30256
Median Absolute Deviation (MAD)42.225
Skewness7.2449538
Sum58813.517
Variance3796498.1
MonotonicityNot monotonic
2023-12-12T18:15:19.057213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5745.94 2
 
1.6%
40.5 2
 
1.6%
38.25 1
 
0.8%
126.65 1
 
0.8%
136.69 1
 
0.8%
95.14 1
 
0.8%
398.13 1
 
0.8%
179.31 1
 
0.8%
81.94 1
 
0.8%
48.95 1
 
0.8%
Other values (112) 112
90.3%
ValueCountFrequency (%)
31.68 1
0.8%
34.08 1
0.8%
34.56 1
0.8%
38.25 1
0.8%
38.35 1
0.8%
38.69 1
0.8%
40.33 1
0.8%
40.35 1
0.8%
40.5 2
1.6%
41.92 1
0.8%
ValueCountFrequency (%)
18215.12 1
0.8%
8811.4 1
0.8%
5745.94 2
1.6%
3738.11 1
0.8%
1352.12 1
0.8%
884.89 1
0.8%
509.97 1
0.8%
446.08 1
0.8%
398.13 1
0.8%
388.64 1
0.8%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6785.4084
Minimum31.68
Maximum364262.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T18:15:19.214162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31.68
5-th percentile43.9245
Q1171.6975
median325.86
Q3654.195
95-th percentile8793.2245
Maximum364262.86
Range364231.18
Interquartile range (IQR)482.4975

Descriptive statistics

Standard deviation38016.034
Coefficient of variation (CV)5.6026155
Kurtosis68.246785
Mean6785.4084
Median Absolute Deviation (MAD)182.83
Skewness7.8894901
Sum841390.64
Variance1.4452189 × 109
MonotonicityNot monotonic
2023-12-12T18:15:19.358630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46798.27 2
 
1.6%
40.5 2
 
1.6%
91.63 1
 
0.8%
540.16 1
 
0.8%
1247.84 1
 
0.8%
354.59 1
 
0.8%
6201.6 1
 
0.8%
801.79 1
 
0.8%
312.01 1
 
0.8%
163.05 1
 
0.8%
Other values (112) 112
90.3%
ValueCountFrequency (%)
31.68 1
0.8%
38.35 1
0.8%
40.33 1
0.8%
40.35 1
0.8%
40.5 2
1.6%
42.15 1
0.8%
53.98 1
0.8%
63.16 1
0.8%
74.4 1
0.8%
74.78 1
0.8%
ValueCountFrequency (%)
364262.86 1
0.8%
185482.49 1
0.8%
105491.61 1
0.8%
46798.27 2
1.6%
26471.68 1
0.8%
9250.57 1
0.8%
6201.6 1
0.8%
2965.11 1
0.8%
1998.39 1
0.8%
1954.3 1
0.8%

증축연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing116
Missing (%)93.5%
Infinite0
Infinite (%)0.0%
Mean507.66
Minimum35.57
Maximum1341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T18:15:19.485130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.57
5-th percentile36.0985
Q144.5575
median57.79
Q31195.25
95-th percentile1339.6
Maximum1341
Range1305.43
Interquartile range (IQR)1150.6925

Descriptive statistics

Standard deviation638.49483
Coefficient of variation (CV)1.2577214
Kurtosis-2.1164015
Mean507.66
Median Absolute Deviation (MAD)21.465
Skewness0.67483407
Sum4061.28
Variance407675.64
MonotonicityNot monotonic
2023-12-12T18:15:19.601903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
37.08 1
 
0.8%
47.05 1
 
0.8%
1341.0 1
 
0.8%
1337.0 1
 
0.8%
1148.0 1
 
0.8%
48.0 1
 
0.8%
67.58 1
 
0.8%
35.57 1
 
0.8%
(Missing) 116
93.5%
ValueCountFrequency (%)
35.57 1
0.8%
37.08 1
0.8%
47.05 1
0.8%
48.0 1
0.8%
67.58 1
0.8%
1148.0 1
0.8%
1337.0 1
0.8%
1341.0 1
0.8%
ValueCountFrequency (%)
1341.0 1
0.8%
1337.0 1
0.8%
1148.0 1
0.8%
67.58 1
0.8%
48.0 1
0.8%
47.05 1
0.8%
37.08 1
0.8%
35.57 1
0.8%

건폐율(퍼센트)
Real number (ℝ)

Distinct117
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.17154
Minimum20.153
Maximum97.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T18:15:19.727935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20.153
5-th percentile37.12
Q151.663475
median57.25165
Q359.561775
95-th percentile67.281375
Maximum97.38
Range77.227
Interquartile range (IQR)7.8983

Descriptive statistics

Standard deviation10.786142
Coefficient of variation (CV)0.19550192
Kurtosis4.0048447
Mean55.17154
Median Absolute Deviation (MAD)2.61835
Skewness0.12053029
Sum6841.2709
Variance116.34085
MonotonicityNot monotonic
2023-12-12T18:15:19.888277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.87 3
 
2.4%
37.12 2
 
1.6%
53.6893 2
 
1.6%
57.93 2
 
1.6%
59.12 2
 
1.6%
59.94 2
 
1.6%
57.9545 1
 
0.8%
51.79 1
 
0.8%
56.63 1
 
0.8%
59.9394 1
 
0.8%
Other values (107) 107
86.3%
ValueCountFrequency (%)
20.153 1
0.8%
23.92 1
0.8%
25.09 1
0.8%
29.91 1
0.8%
31.94 1
0.8%
33.31 1
0.8%
37.12 2
1.6%
37.22 1
0.8%
39.99 1
0.8%
43.3852 1
0.8%
ValueCountFrequency (%)
97.38 1
0.8%
87.03 1
0.8%
84.97 1
0.8%
84.61 1
0.8%
83.75 1
0.8%
70.08 1
0.8%
67.65 1
0.8%
65.1925 1
0.8%
63.8415 1
0.8%
59.99 1
0.8%

용적률(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct123
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.69167
Minimum20.153
Maximum948.9253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T18:15:20.067127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20.153
5-th percentile46.092
Q1120.0632
median162.31305
Q3196.48
95-th percentile358.524
Maximum948.9253
Range928.7723
Interquartile range (IQR)76.4168

Descriptive statistics

Standard deviation142.08194
Coefficient of variation (CV)0.79069856
Kurtosis14.349633
Mean179.69167
Median Absolute Deviation (MAD)34.99475
Skewness3.5274827
Sum22281.767
Variance20187.279
MonotonicityNot monotonic
2023-12-12T18:15:20.226526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.12 2
 
1.6%
96.9545 1
 
0.8%
158.9 1
 
0.8%
197.59 1
 
0.8%
799.78 1
 
0.8%
147.89 1
 
0.8%
149.73 1
 
0.8%
172.52 1
 
0.8%
221.12 1
 
0.8%
193.0424 1
 
0.8%
Other values (113) 113
91.1%
ValueCountFrequency (%)
20.153 1
0.8%
23.92 1
0.8%
37.12 2
1.6%
39.99 1
0.8%
44.56 1
0.8%
45.87 1
0.8%
47.35 1
0.8%
47.9 1
0.8%
55.63 1
0.8%
56.34 1
0.8%
ValueCountFrequency (%)
948.9253 1
0.8%
805.0642 1
0.8%
799.78 1
0.8%
787.88 1
0.8%
600.0736 1
0.8%
447.24 1
0.8%
358.86 1
0.8%
356.62 1
0.8%
268.63 1
0.8%
267.64 1
0.8%

구조
Categorical

Distinct10
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
철근콘크리트구조
85 
벽돌구조
15 
일반목구조
 
7
철골철근콘크리트구조
 
5
일반철골구조
 
4
Other values (5)
 
8

Length

Max length10
Median length8
Mean length7.1532258
Min length4

Unique

Unique2 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
철근콘크리트구조 85
68.5%
벽돌구조 15
 
12.1%
일반목구조 7
 
5.6%
철골철근콘크리트구조 5
 
4.0%
일반철골구조 4
 
3.2%
<NA> 2
 
1.6%
경량철골구조 2
 
1.6%
블록구조 2
 
1.6%
기타구조 1
 
0.8%
스틸하우스조 1
 
0.8%

Length

2023-12-12T18:15:20.388059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:15:20.517182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
철근콘크리트구조 85
68.5%
벽돌구조 15
 
12.1%
일반목구조 7
 
5.6%
철골철근콘크리트구조 5
 
4.0%
일반철골구조 4
 
3.2%
na 2
 
1.6%
경량철골구조 2
 
1.6%
블록구조 2
 
1.6%
기타구조 1
 
0.8%
스틸하우스조 1
 
0.8%

허가일
Date

Distinct109
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2019-01-18 00:00:00
Maximum2023-04-19 00:00:00
2023-12-12T18:15:20.726680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:20.930630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

착공처리일
Date

MISSING 

Distinct91
Distinct (%)81.2%
Missing12
Missing (%)9.7%
Memory size1.1 KiB
Minimum2019-10-04 00:00:00
Maximum2023-04-20 00:00:00
2023-12-12T18:15:21.097097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:21.273052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사용승인일
Date

Distinct80
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2022-09-26 00:00:00
Maximum2023-05-22 00:00:00
2023-12-12T18:15:21.412098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:21.576178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

주용도
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
제2종근린생활시설
55 
공동주택
33 
단독주택
20 
업무시설
6 
제1종근린생활시설
6 
Other values (3)
 
4

Length

Max length9
Median length6
Mean length6.5080645
Min length4

Unique

Unique2 ?
Unique (%)1.6%

Sample

1st row제2종근린생활시설
2nd row단독주택
3rd row제2종근린생활시설
4th row방송통신시설
5th row업무시설

Common Values

ValueCountFrequency (%)
제2종근린생활시설 55
44.4%
공동주택 33
26.6%
단독주택 20
 
16.1%
업무시설 6
 
4.8%
제1종근린생활시설 6
 
4.8%
방송통신시설 2
 
1.6%
숙박시설 1
 
0.8%
교육연구시설 1
 
0.8%

Length

2023-12-12T18:15:21.729783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:15:21.882733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제2종근린생활시설 55
44.4%
공동주택 33
26.6%
단독주택 20
 
16.1%
업무시설 6
 
4.8%
제1종근린생활시설 6
 
4.8%
방송통신시설 2
 
1.6%
숙박시설 1
 
0.8%
교육연구시설 1
 
0.8%

Interactions

2023-12-12T18:15:13.948645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:10.277105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:10.982768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:11.761381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:12.460040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:13.180134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:14.063569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:10.407269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:11.103878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:11.874076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:12.571592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:13.304918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:14.186032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:10.530772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:11.247359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:11.983039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:12.712706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:13.438756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:14.314854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:10.639930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:11.404181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:12.092308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:12.813686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:13.582524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:14.421951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:10.744940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:11.528179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:12.225110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:12.911257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:13.699641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:14.543515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:10.869610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:11.642803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:12.343588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:13.060475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:15:13.843756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:15:22.011546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축구분건물명대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)건폐율(퍼센트)용적률(퍼센트)구조착공처리일사용승인일주용도
건축구분1.0001.0000.3210.5130.513NaN0.5230.5110.5160.9670.0000.371
건물명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9971.000
대지면적(제곱미터)0.3211.0001.0001.0001.000NaN0.0000.9570.5061.0000.0000.963
건축면적(제곱미터)0.5131.0001.0001.0001.000NaN0.3150.8560.5901.0000.0000.839
연면적(제곱미터)0.5131.0001.0001.0001.000NaN0.3150.8560.5901.0000.0000.839
증축연면적(제곱미터)NaN1.000NaNNaNNaN1.0000.5780.2350.0001.0000.6460.688
건폐율(퍼센트)0.5231.0000.0000.3150.3150.5781.0000.5250.7990.8980.0000.151
용적률(퍼센트)0.5111.0000.9570.8560.8560.2350.5251.0000.5620.9720.0000.867
구조0.5161.0000.5060.5900.5900.0000.7990.5621.0000.9750.0000.487
착공처리일0.9671.0001.0001.0001.0001.0000.8980.9720.9751.0000.9250.961
사용승인일0.0000.9970.0000.0000.0000.6460.0000.0000.0000.9251.0000.670
주용도0.3711.0000.9630.8390.8390.6880.1510.8670.4870.9610.6701.000
2023-12-12T18:15:22.543102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축구분주용도구조
건축구분1.0000.2340.324
주용도0.2341.0000.262
구조0.3240.2621.000
2023-12-12T18:15:22.674899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)건폐율(퍼센트)용적률(퍼센트)건축구분구조주용도
대지면적(제곱미터)1.0000.9440.8870.881-0.0100.3980.2660.3400.732
건축면적(제곱미터)0.9441.0000.9320.9050.2190.4910.2120.3850.711
연면적(제곱미터)0.8870.9321.0000.8100.1830.6620.2120.3850.711
증축연면적(제곱미터)0.8810.9050.8101.000-0.0480.0481.0000.0000.616
건폐율(퍼센트)-0.0100.2190.183-0.0481.0000.3290.3290.3740.070
용적률(퍼센트)0.3980.4910.6620.0480.3291.0000.3400.3150.468
건축구분0.2660.2120.2121.0000.3290.3401.0000.3240.234
구조0.3400.3850.3850.0000.3740.3150.3241.0000.262
주용도0.7320.7110.7110.6160.0700.4680.2340.2621.000

Missing values

2023-12-12T18:15:14.705128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:15:14.966242image/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.
2023-12-12T18:15:15.510968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

건축구분건물명허가번호대지위치대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)건폐율(퍼센트)용적률(퍼센트)구조허가일착공처리일사용승인일주용도
0용도변경<NA>2023-건축과-용도변경허가-18서울특별시 용산구 이태원동 258-27466.038.2591.63<NA>57.954596.9545벽돌구조2023-04-12<NA>2023-04-21제2종근린생활시설
1대수선<NA>2023-건축과-대수선허가-10서울특별시 용산구 이태원동 74-67222.0132.82522.19<NA>59.83172.21철근콘크리트구조2023-04-10<NA>2023-04-20단독주택
2대수선<NA>2023-건축과-대수선허가-3서울특별시 용산구 신창동 31-1282.052.35100.39<NA>63.8415122.4268벽돌구조2023-02-02<NA>2023-02-09제2종근린생활시설
3대수선kt 용산 데이터센터2022-건축과-대수선허가-42서울특별시 용산구 원효로3가 1-210702.25745.9446798.27<NA>53.6893197.5556철골철근콘크리트구조2022-12-302023-01-182023-04-07방송통신시설
4대수선민생빌딩2022-건축과-대수선허가-37서울특별시 용산구 이태원동 128-13 외1필지638.9369.381998.39<NA>57.82267.64철근콘크리트구조2022-10-142022-10-202022-12-09업무시설
5대수선한남동 657-1962022-건축과-대수선허가-35서울특별시 용산구 한남동 657-196275.0164.67784.77<NA>59.88148.5818철근콘크리트구조2022-09-262022-12-052023-02-28제2종근린생활시설
6대수선<NA>2022-건축과-대수선허가-32서울특별시 용산구 이태원동 258-63721.0215.62443.64<NA>29.9145.87철근콘크리트구조2022-09-222022-11-292023-03-15제2종근린생활시설
7대수선<NA>2022-건축과-대수선허가-33서울특별시 용산구 이태원동 258-69430.0143.24254.13<NA>33.3156.56벽돌구조2022-09-222022-11-292023-04-28제2종근린생활시설
8대수선<NA>2022-건축과-대수선허가-30서울특별시 용산구 한남동 684-94151.888.43310.22<NA>58.25204.36철근콘크리트구조2022-08-252022-11-232023-02-13제2종근린생활시설
9대수선이태원동 251-39 제2종근린생활시설 (민앤진주식회사)2022-건축과-대수선허가-29서울특별시 용산구 이태원동 251-39105.2961.6262.64<NA>58.51175.52<NA>2022-08-232022-09-192023-02-03제2종근린생활시설
건축구분건물명허가번호대지위치대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)건폐율(퍼센트)용적률(퍼센트)구조허가일착공처리일사용승인일주용도
114대수선<NA>2022-도시계획과-대수선신고-2서울특별시 용산구 한강로3가 40-16795.938.3538.35<NA>39.9939.99일반목구조2022-07-14<NA>2022-12-19제2종근린생활시설
115대수선<NA>2022-건축과-대수선신고-4서울특별시 용산구 이태원동 72-17 외1필지92.441.92156.73<NA>45.3790.74벽돌구조2022-06-092022-06-242023-02-06제2종근린생활시설
116대수선<NA>2022-건축과-대수선신고-3서울특별시 용산구 이태원동 34-97101.6260.6174.96<NA>59.6339112.5369벽돌구조2022-05-112022-07-262022-11-14제1종근린생활시설
117신축<NA>2022-건축과-신축신고-3서울특별시 용산구 이태원동 64-20132.7474.7874.78<NA>56.3456.34경량철골구조2022-03-152022-06-242022-10-13제2종근린생활시설
118대수선한남동 267-11 단독주택 (한규리)2022-건축과-대수선신고-1서울특별시 용산구 한남동 267-11264.063.1663.16<NA>23.9223.92벽돌구조2022-02-172022-07-182022-11-21단독주택
119증축<NA>2022-건축과-증축신고-2서울특별시 용산구 이태원동 78-13122.072.3322.1735.5759.2623199.0738철근콘크리트구조2022-02-082022-03-072022-11-22제2종근린생활시설
120신축청파동1가 19 단독주택 (김현식)2021-건축과-신축신고-8서울특별시 용산구 청파동1가 1985.2140.3540.35<NA>47.3547.35벽돌구조2021-12-202022-01-132022-11-18단독주택
121신축웅지파크2020-건축과-신축신고-11서울특별시 용산구 이태원동 79-3969.034.0893.51<NA>49.39135.52철근콘크리트구조2020-11-302022-08-242023-01-25제2종근린생활시설
122재축<NA>2020-건축과-재축신고-1서울특별시 용산구 이태원동 63-11209.1542.1542.15<NA>20.15320.153철근콘크리트구조2020-07-232021-03-162022-10-26단독주택
123대수선원효로2가 57-2 제1종근린생활시설 (엠디텍인터내셔널(주))2019-건축과-대수선신고-5서울특별시 용산구 원효로2가 57-296.256.73113.46<NA>58.9709117.9418벽돌구조2019-09-112022-12-192022-12-30제1종근린생활시설