Overview

Dataset statistics

Number of variables18
Number of observations113
Missing cells413
Missing cells (%)20.3%
Duplicate rows1
Duplicate rows (%)0.9%
Total size in memory16.8 KiB
Average record size in memory152.2 B

Variable types

Categorical4
Text6
Numeric6
DateTime2

Dataset

Description서울특별시 성동구 공사장 현황 정보입니다. 구분값, 대지위치, 연면적, 허가신고일, 착공처리일, 주용도, 김리사무소 등의 정보를 포함합니다.
Author서울특별시 성동구
URLhttps://www.data.go.kr/data/15071923/fileData.do

Alerts

Dataset has 1 (0.9%) duplicate rowsDuplicates
세대수 is highly overall correlated with 연면적(제곱미터) and 3 other fieldsHigh correlation
구분2 is highly overall correlated with 증축연면적(제곱미터) and 1 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 증축연면적(제곱미터) and 4 other fieldsHigh correlation
증축연면적(제곱미터) is highly overall correlated with 연면적(제곱미터) and 3 other fieldsHigh correlation
최대지상층수 is highly overall correlated with 연면적(제곱미터) and 3 other fieldsHigh correlation
최대지하층수 is highly overall correlated with 연면적(제곱미터) and 3 other fieldsHigh correlation
호수 is highly overall correlated with 연면적(제곱미터) and 3 other fieldsHigh correlation
구분1 is highly overall correlated with 최대지상층수 and 2 other fieldsHigh correlation
주용도 is highly overall correlated with 증축연면적(제곱미터) and 1 other fieldsHigh correlation
세대수 is highly imbalanced (72.1%)Imbalance
증축연면적(제곱미터) has 99 (87.6%) missing valuesMissing
최대지하층수 has 9 (8.0%) missing valuesMissing
부속용도 has 24 (21.2%) missing valuesMissing
호수 has 83 (73.5%) missing valuesMissing
가구수 has 95 (84.1%) missing valuesMissing
감리자 전화번호 has 37 (32.7%) missing valuesMissing
감리사무소명 has 14 (12.4%) missing valuesMissing
시공자 전화번호 has 26 (23.0%) missing valuesMissing
시공자사무소명 has 26 (23.0%) missing valuesMissing
최대지하층수 has 24 (21.2%) zerosZeros

Reproduction

Analysis started2023-12-12 05:42:48.461131
Analysis finished2023-12-12 05:42:54.605915
Duration6.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
허가
95 
신고
18 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
허가 95
84.1%
신고 18
 
15.9%

Length

2023-12-12T14:42:54.683088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:42:54.781937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
허가 95
84.1%
신고 18
 
15.9%

구분2
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
신축
83 
증축
15 
대수선
14 
용도변경
 
1

Length

Max length4
Median length2
Mean length2.1415929
Min length2

Unique

Unique1 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
신축 83
73.5%
증축 15
 
13.3%
대수선 14
 
12.4%
용도변경 1
 
0.9%

Length

2023-12-12T14:42:54.929076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:42:55.052143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신축 83
73.5%
증축 15
 
13.3%
대수선 14
 
12.4%
용도변경 1
 
0.9%

대지위치
Text

Distinct111
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-12T14:42:55.396738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length21.734513
Min length17

Characters and Unicode

Total characters2456
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

Unique109 ?
Unique (%)96.5%

Sample

1st row서울특별시 성동구 성수동2가 331-15 외2필지
2nd row서울특별시 성동구 성수동2가 315-47
3rd row서울특별시 성동구 성수동2가 275-52
4th row서울특별시 성동구 송정동 66-91
5th row서울특별시 성동구 금호동4가 639-1
ValueCountFrequency (%)
서울특별시 113
23.6%
성동구 113
23.6%
성수동1가 26
 
5.4%
성수동2가 24
 
5.0%
마장동 9
 
1.9%
용답동 9
 
1.9%
외1필지 9
 
1.9%
도선동 8
 
1.7%
행당동 8
 
1.7%
외2필지 7
 
1.5%
Other values (125) 153
31.9%
2023-12-12T14:42:55.901885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
366
14.9%
동 226
 
9.2%
성 163
 
6.6%
1 119
 
4.8%
2 117
 
4.8%
서 113
 
4.6%
특 113
 
4.6%
별 113
 
4.6%
시 113
 
4.6%
구 113
 
4.6%
Other values (36) 900
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1390
56.6%
Decimal Number 602
24.5%
Space Separator 366
 
14.9%
Dash Punctuation 98
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 226
16.3%
성 163
11.7%
서 113
8.1%
특 113
8.1%
별 113
8.1%
시 113
8.1%
구 113
8.1%
울 113
8.1%
가 60
 
4.3%
수 55
 
4.0%
Other values (24) 208
15.0%
Decimal Number
ValueCountFrequency (%)
1 119
19.8%
2 117
19.4%
6 77
12.8%
3 59
9.8%
5 56
9.3%
9 39
 
6.5%
8 38
 
6.3%
4 37
 
6.1%
7 36
 
6.0%
0 24
 
4.0%
Space Separator
ValueCountFrequency (%)
366
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1390
56.6%
Common 1066
43.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 226
16.3%
성 163
11.7%
서 113
8.1%
특 113
8.1%
별 113
8.1%
시 113
8.1%
구 113
8.1%
울 113
8.1%
가 60
 
4.3%
수 55
 
4.0%
Other values (24) 208
15.0%
Common
ValueCountFrequency (%)
366
34.3%
1 119
 
11.2%
2 117
 
11.0%
- 98
 
9.2%
6 77
 
7.2%
3 59
 
5.5%
5 56
 
5.3%
9 39
 
3.7%
8 38
 
3.6%
4 37
 
3.5%
Other values (2) 60
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1390
56.6%
ASCII 1066
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
366
34.3%
1 119
 
11.2%
2 117
 
11.0%
- 98
 
9.2%
6 77
 
7.2%
3 59
 
5.5%
5 56
 
5.3%
9 39
 
3.7%
8 38
 
3.6%
4 37
 
3.5%
Other values (2) 60
 
5.6%
Hangul
ValueCountFrequency (%)
동 226
16.3%
성 163
11.7%
서 113
8.1%
특 113
8.1%
별 113
8.1%
시 113
8.1%
구 113
8.1%
울 113
8.1%
가 60
 
4.3%
수 55
 
4.0%
Other values (24) 208
15.0%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10884.14
Minimum48.75
Maximum792584.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T14:42:56.364811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48.75
5-th percentile74.208
Q1290.08
median845.47
Q32968.89
95-th percentile23023.525
Maximum792584.63
Range792535.88
Interquartile range (IQR)2678.81

Descriptive statistics

Standard deviation74583.531
Coefficient of variation (CV)6.8524964
Kurtosis110.58708
Mean10884.14
Median Absolute Deviation (MAD)725.21
Skewness10.464263
Sum1229907.8
Variance5.5627031 × 109
MonotonicityNot monotonic
2023-12-12T14:42:56.564232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.88 2
 
1.8%
120.26 2
 
1.8%
61.92 2
 
1.8%
5828.67 1
 
0.9%
940.04 1
 
0.9%
792584.63 1
 
0.9%
685.33 1
 
0.9%
2851.41 1
 
0.9%
579.11 1
 
0.9%
9211.08 1
 
0.9%
Other values (100) 100
88.5%
ValueCountFrequency (%)
48.75 1
0.9%
48.88 2
1.8%
49.7 1
0.9%
61.92 2
1.8%
82.4 1
0.9%
82.98 1
0.9%
90.43 1
0.9%
92.62 1
0.9%
98.28 1
0.9%
101.35 1
0.9%
ValueCountFrequency (%)
792584.63 1
0.9%
43780.8 1
0.9%
36501.48 1
0.9%
30088.28 1
0.9%
25104.36 1
0.9%
24228.6027 1
0.9%
22220.14 1
0.9%
19071.56 1
0.9%
18229.77 1
0.9%
18004.89 1
0.9%

증축연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)100.0%
Missing99
Missing (%)87.6%
Infinite0
Infinite (%)0.0%
Mean496.73214
Minimum0.76
Maximum2901.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T14:42:56.726030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.76
5-th percentile3.4055
Q124.535
median55.59
Q379.48
95-th percentile2387.091
Maximum2901.15
Range2900.39
Interquartile range (IQR)54.945

Descriptive statistics

Standard deviation947.61693
Coefficient of variation (CV)1.9077021
Kurtosis2.4839386
Mean496.73214
Median Absolute Deviation (MAD)30.99
Skewness1.9026495
Sum6954.25
Variance897977.86
MonotonicityNot monotonic
2023-12-12T14:42:56.851121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
66.37 1
 
0.9%
2901.15 1
 
0.9%
65.44 1
 
0.9%
17.77 1
 
0.9%
62.3 1
 
0.9%
2110.29 1
 
0.9%
0.76 1
 
0.9%
21.87 1
 
0.9%
83.85 1
 
0.9%
4.83 1
 
0.9%
Other values (4) 4
 
3.5%
(Missing) 99
87.6%
ValueCountFrequency (%)
0.76 1
0.9%
4.83 1
0.9%
17.77 1
0.9%
21.87 1
0.9%
32.53 1
0.9%
44.12 1
0.9%
48.88 1
0.9%
62.3 1
0.9%
65.44 1
0.9%
66.37 1
0.9%
ValueCountFrequency (%)
2901.15 1
0.9%
2110.29 1
0.9%
1494.09 1
0.9%
83.85 1
0.9%
66.37 1
0.9%
65.44 1
0.9%
62.3 1
0.9%
48.88 1
0.9%
44.12 1
0.9%
32.53 1
0.9%

허가신고일
Date

Distinct101
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Minimum2015-10-16 00:00:00
Maximum2020-07-23 00:00:00
2023-12-12T14:42:57.007412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:57.188106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

착공처리일
Date

Distinct97
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Minimum2016-03-31 00:00:00
Maximum2020-08-31 00:00:00
2023-12-12T14:42:57.372862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:57.514104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

최대지상층수
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8230088
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T14:42:57.657335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q39
95-th percentile15
Maximum19
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.355272
Coefficient of variation (CV)0.63832132
Kurtosis-0.099670542
Mean6.8230088
Median Absolute Deviation (MAD)2
Skewness0.91151998
Sum771
Variance18.968394
MonotonicityNot monotonic
2023-12-12T14:42:57.801669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
5 18
15.9%
4 16
14.2%
3 15
13.3%
2 8
 
7.1%
7 8
 
7.1%
9 7
 
6.2%
6 6
 
5.3%
10 5
 
4.4%
13 4
 
3.5%
15 4
 
3.5%
Other values (9) 22
19.5%
ValueCountFrequency (%)
1 4
 
3.5%
2 8
7.1%
3 15
13.3%
4 16
14.2%
5 18
15.9%
6 6
 
5.3%
7 8
7.1%
8 3
 
2.7%
9 7
 
6.2%
10 5
 
4.4%
ValueCountFrequency (%)
19 1
 
0.9%
18 1
 
0.9%
17 1
 
0.9%
16 2
 
1.8%
15 4
3.5%
14 4
3.5%
13 4
3.5%
12 3
2.7%
11 3
2.7%
10 5
4.4%

최대지하층수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)6.7%
Missing9
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean1.5096154
Minimum0
Maximum7
Zeros24
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T14:42:57.939696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4547892
Coefficient of variation (CV)0.96368199
Kurtosis1.8497777
Mean1.5096154
Median Absolute Deviation (MAD)1
Skewness1.3888043
Sum157
Variance2.1164115
MonotonicityNot monotonic
2023-12-12T14:42:58.060183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 42
37.2%
0 24
21.2%
2 20
17.7%
5 6
 
5.3%
3 6
 
5.3%
4 5
 
4.4%
7 1
 
0.9%
(Missing) 9
 
8.0%
ValueCountFrequency (%)
0 24
21.2%
1 42
37.2%
2 20
17.7%
3 6
 
5.3%
4 5
 
4.4%
5 6
 
5.3%
7 1
 
0.9%
ValueCountFrequency (%)
7 1
 
0.9%
5 6
 
5.3%
4 5
 
4.4%
3 6
 
5.3%
2 20
17.7%
1 42
37.2%
0 24
21.2%

주용도
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
제2종근린생활시설
30 
업무시설
27 
제1종근린생활시설
15 
단독주택
14 
공장
10 
Other values (6)
17 

Length

Max length9
Median length8
Mean length5.9557522
Min length2

Unique

Unique4 ?
Unique (%)3.5%

Sample

1st row공장
2nd row제1종근린생활시설
3rd row제1종근린생활시설
4th row공동주택
5th row단독주택

Common Values

ValueCountFrequency (%)
제2종근린생활시설 30
26.5%
업무시설 27
23.9%
제1종근린생활시설 15
13.3%
단독주택 14
12.4%
공장 10
 
8.8%
공동주택 9
 
8.0%
교육연구시설 4
 
3.5%
자원순환관련시설 1
 
0.9%
노유자시설 1
 
0.9%
운동시설 1
 
0.9%

Length

2023-12-12T14:42:58.203416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제2종근린생활시설 30
26.5%
업무시설 27
23.9%
제1종근린생활시설 15
13.3%
단독주택 14
12.4%
공장 10
 
8.8%
공동주택 9
 
8.0%
교육연구시설 4
 
3.5%
자원순환관련시설 1
 
0.9%
노유자시설 1
 
0.9%
운동시설 1
 
0.9%

부속용도
Text

MISSING 

Distinct67
Distinct (%)75.3%
Missing24
Missing (%)21.2%
Memory size1.0 KiB
2023-12-12T14:42:58.469690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length26
Mean length11.134831
Min length2

Characters and Unicode

Total characters991
Distinct characters95
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)60.7%

Sample

1st row제조업소
2nd row휴게음식점
3rd row다세대주택
4th row단독주택,근린생활시설
5th row도시형생활주택(단지형다세대)
ValueCountFrequency (%)
사무소 11
 
8.5%
일반음식점 7
 
5.4%
오피스텔 6
 
4.7%
단독주택 6
 
4.7%
근린생활시설 6
 
4.7%
휴게음식점 5
 
3.9%
지식산업센터 5
 
3.9%
및 5
 
3.9%
다중주택 4
 
3.1%
소매점 4
 
3.1%
Other values (61) 70
54.3%
2023-12-12T14:42:58.850640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 46
 
4.6%
42
 
4.2%
생 40
 
4.0%
설 40
 
4.0%
, 40
 
4.0%
활 39
 
3.9%
근 33
 
3.3%
린 32
 
3.2%
) 31
 
3.1%
택 31
 
3.1%
Other values (85) 617
62.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 789
79.6%
Other Punctuation 46
 
4.6%
Decimal Number 46
 
4.6%
Space Separator 42
 
4.2%
Close Punctuation 31
 
3.1%
Open Punctuation 30
 
3.0%
Dash Punctuation 7
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 46
 
5.8%
생 40
 
5.1%
설 40
 
5.1%
활 39
 
4.9%
근 33
 
4.2%
린 32
 
4.1%
택 31
 
3.9%
주 31
 
3.9%
점 30
 
3.8%
소 30
 
3.8%
Other values (69) 437
55.4%
Decimal Number
ValueCountFrequency (%)
1 20
43.5%
2 13
28.3%
7 3
 
6.5%
5 3
 
6.5%
4 2
 
4.3%
9 2
 
4.3%
6 2
 
4.3%
3 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
, 40
87.0%
: 3
 
6.5%
/ 2
 
4.3%
. 1
 
2.2%
Space Separator
ValueCountFrequency (%)
42
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 789
79.6%
Common 202
 
20.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 46
 
5.8%
생 40
 
5.1%
설 40
 
5.1%
활 39
 
4.9%
근 33
 
4.2%
린 32
 
4.1%
택 31
 
3.9%
주 31
 
3.9%
점 30
 
3.8%
소 30
 
3.8%
Other values (69) 437
55.4%
Common
ValueCountFrequency (%)
42
20.8%
, 40
19.8%
) 31
15.3%
( 30
14.9%
1 20
9.9%
2 13
 
6.4%
- 7
 
3.5%
7 3
 
1.5%
: 3
 
1.5%
5 3
 
1.5%
Other values (6) 10
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 789
79.6%
ASCII 202
 
20.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 46
 
5.8%
생 40
 
5.1%
설 40
 
5.1%
활 39
 
4.9%
근 33
 
4.2%
린 32
 
4.1%
택 31
 
3.9%
주 31
 
3.9%
점 30
 
3.8%
소 30
 
3.8%
Other values (69) 437
55.4%
ASCII
ValueCountFrequency (%)
42
20.8%
, 40
19.8%
) 31
15.3%
( 30
14.9%
1 20
9.9%
2 13
 
6.4%
- 7
 
3.5%
7 3
 
1.5%
: 3
 
1.5%
5 3
 
1.5%
Other values (6) 10
 
5.0%

세대수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
<NA>
101 
10
 
4
1
 
3
13
 
2
15
 
2

Length

Max length4
Median length4
Mean length3.7522124
Min length1

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row10
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 101
89.4%
10 4
 
3.5%
1 3
 
2.7%
13 2
 
1.8%
15 2
 
1.8%
8 1
 
0.9%

Length

2023-12-12T14:42:58.978434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:42:59.079128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 101
89.4%
10 4
 
3.5%
1 3
 
2.7%
13 2
 
1.8%
15 2
 
1.8%
8 1
 
0.9%

호수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)86.7%
Missing83
Missing (%)73.5%
Infinite0
Infinite (%)0.0%
Mean67.3
Minimum1
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T14:42:59.176348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18.25
median20.5
Q3110.75
95-th percentile184.75
Maximum400
Range399
Interquartile range (IQR)102.5

Descriptive statistics

Standard deviation86.591849
Coefficient of variation (CV)1.2866545
Kurtosis6.4191393
Mean67.3
Median Absolute Deviation (MAD)18.5
Skewness2.1757538
Sum2019
Variance7498.1483
MonotonicityNot monotonic
2023-12-12T14:42:59.280043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2 2
 
1.8%
9 2
 
1.8%
4 2
 
1.8%
12 2
 
1.8%
111 1
 
0.9%
5 1
 
0.9%
182 1
 
0.9%
400 1
 
0.9%
110 1
 
0.9%
20 1
 
0.9%
Other values (16) 16
 
14.2%
(Missing) 83
73.5%
ValueCountFrequency (%)
1 1
0.9%
2 2
1.8%
4 2
1.8%
5 1
0.9%
7 1
0.9%
8 1
0.9%
9 2
1.8%
12 2
1.8%
15 1
0.9%
17 1
0.9%
ValueCountFrequency (%)
400 1
0.9%
187 1
0.9%
182 1
0.9%
157 1
0.9%
135 1
0.9%
114 1
0.9%
112 1
0.9%
111 1
0.9%
110 1
0.9%
108 1
0.9%

가구수
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)33.3%
Missing95
Missing (%)84.1%
Infinite0
Infinite (%)0.0%
Mean3.2222222
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T14:42:59.369494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34.25
95-th percentile8.8
Maximum19
Range18
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation4.4662862
Coefficient of variation (CV)1.3860888
Kurtosis9.5696304
Mean3.2222222
Median Absolute Deviation (MAD)0
Skewness2.901122
Sum58
Variance19.947712
MonotonicityNot monotonic
2023-12-12T14:42:59.444004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 11
 
9.7%
2 2
 
1.8%
6 2
 
1.8%
7 1
 
0.9%
19 1
 
0.9%
5 1
 
0.9%
(Missing) 95
84.1%
ValueCountFrequency (%)
1 11
9.7%
2 2
 
1.8%
5 1
 
0.9%
6 2
 
1.8%
7 1
 
0.9%
19 1
 
0.9%
ValueCountFrequency (%)
19 1
 
0.9%
7 1
 
0.9%
6 2
 
1.8%
5 1
 
0.9%
2 2
 
1.8%
1 11
9.7%

감리자 전화번호
Text

MISSING 

Distinct69
Distinct (%)90.8%
Missing37
Missing (%)32.7%
Memory size1.0 KiB
2023-12-12T14:42:59.650940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.539474
Min length11

Characters and Unicode

Total characters877
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

Unique62 ?
Unique (%)81.6%

Sample

1st row02-2299-4836
2nd row031-703-9574
3rd row02-6085-3977
4th row02-925-4500
5th row02-956-6951
ValueCountFrequency (%)
02-2297-9633 2
 
2.6%
02-338-4777 2
 
2.6%
02-414-6061 2
 
2.6%
02-401-6838 2
 
2.6%
02-3438-8000 2
 
2.6%
02-3443-4050 2
 
2.6%
02-912-8304 2
 
2.6%
02-6402-5323 1
 
1.3%
02-929-3816 1
 
1.3%
031-701-2880 1
 
1.3%
Other values (59) 59
77.6%
2023-12-12T14:42:59.984382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 152
17.3%
0 151
17.2%
2 117
13.3%
5 68
7.8%
3 64
7.3%
4 62
7.1%
1 60
 
6.8%
8 58
 
6.6%
7 54
 
6.2%
9 48
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 725
82.7%
Dash Punctuation 152
 
17.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 151
20.8%
2 117
16.1%
5 68
9.4%
3 64
8.8%
4 62
8.6%
1 60
 
8.3%
8 58
 
8.0%
7 54
 
7.4%
9 48
 
6.6%
6 43
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
- 152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 877
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 152
17.3%
0 151
17.2%
2 117
13.3%
5 68
7.8%
3 64
7.3%
4 62
7.1%
1 60
 
6.8%
8 58
 
6.6%
7 54
 
6.2%
9 48
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 877
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 152
17.3%
0 151
17.2%
2 117
13.3%
5 68
7.8%
3 64
7.3%
4 62
7.1%
1 60
 
6.8%
8 58
 
6.6%
7 54
 
6.2%
9 48
 
5.5%

감리사무소명
Text

MISSING 

Distinct89
Distinct (%)89.9%
Missing14
Missing (%)12.4%
Memory size1.0 KiB
2023-12-12T14:43:00.203079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length11.848485
Min length5

Characters and Unicode

Total characters1173
Distinct characters149
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

Unique81 ?
Unique (%)81.8%

Sample

1st row(주)아크건축사사무소
2nd row종합건축사사무소 일오삼
3rd row(주)세익아키텍츠건축사사무소
4th row건축사사무소 신영
5th row건축사사무소 엔담
ValueCountFrequency (%)
건축사사무소 26
 
19.4%
주식회사 6
 
4.5%
상진엔지니어링 3
 
2.2%
주)해안종합건축사사무소 3
 
2.2%
성미건축사사무소 2
 
1.5%
주)서연건축사사무소 2
 
1.5%
예토종합건축사사무소 2
 
1.5%
다움건축 2
 
1.5%
주)국전건축사사무소 2
 
1.5%
아키폴건축사사무소 2
 
1.5%
Other values (84) 84
62.7%
2023-12-12T14:43:00.539528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 202
17.2%
건 105
 
9.0%
축 102
 
8.7%
무 98
 
8.4%
소 97
 
8.3%
주 50
 
4.3%
( 39
 
3.3%
) 39
 
3.3%
35
 
3.0%
이 24
 
2.0%
Other values (139) 382
32.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1042
88.8%
Open Punctuation 39
 
3.3%
Close Punctuation 39
 
3.3%
Space Separator 35
 
3.0%
Uppercase Letter 10
 
0.9%
Lowercase Letter 5
 
0.4%
Other Punctuation 2
 
0.2%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 202
19.4%
건 105
 
10.1%
축 102
 
9.8%
무 98
 
9.4%
소 97
 
9.3%
주 50
 
4.8%
이 24
 
2.3%
합 16
 
1.5%
종 16
 
1.5%
지 13
 
1.2%
Other values (122) 319
30.6%
Uppercase Letter
ValueCountFrequency (%)
I 2
20.0%
D 2
20.0%
A 2
20.0%
E 1
10.0%
S 1
10.0%
C 1
10.0%
J 1
10.0%
Lowercase Letter
ValueCountFrequency (%)
u 1
20.0%
l 1
20.0%
p 1
20.0%
m 1
20.0%
s 1
20.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1043
88.9%
Common 115
 
9.8%
Latin 15
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 202
19.4%
건 105
 
10.1%
축 102
 
9.8%
무 98
 
9.4%
소 97
 
9.3%
주 50
 
4.8%
이 24
 
2.3%
합 16
 
1.5%
종 16
 
1.5%
지 13
 
1.2%
Other values (123) 320
30.7%
Latin
ValueCountFrequency (%)
I 2
13.3%
D 2
13.3%
A 2
13.3%
u 1
6.7%
l 1
6.7%
p 1
6.7%
E 1
6.7%
S 1
6.7%
C 1
6.7%
m 1
6.7%
Other values (2) 2
13.3%
Common
ValueCountFrequency (%)
( 39
33.9%
) 39
33.9%
35
30.4%
. 2
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1042
88.8%
ASCII 130
 
11.1%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 202
19.4%
건 105
 
10.1%
축 102
 
9.8%
무 98
 
9.4%
소 97
 
9.3%
주 50
 
4.8%
이 24
 
2.3%
합 16
 
1.5%
종 16
 
1.5%
지 13
 
1.2%
Other values (122) 319
30.6%
ASCII
ValueCountFrequency (%)
( 39
30.0%
) 39
30.0%
35
26.9%
. 2
 
1.5%
I 2
 
1.5%
D 2
 
1.5%
A 2
 
1.5%
u 1
 
0.8%
l 1
 
0.8%
p 1
 
0.8%
Other values (6) 6
 
4.6%
None
ValueCountFrequency (%)
㈜ 1
100.0%

시공자 전화번호
Text

MISSING 

Distinct77
Distinct (%)88.5%
Missing26
Missing (%)23.0%
Memory size1.0 KiB
2023-12-12T14:43:00.802749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.712644
Min length11

Characters and Unicode

Total characters1019
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

Unique70 ?
Unique (%)80.5%

Sample

1st row02-525-6505
2nd row031-860-6500
3rd row02-455-4244
4th row031-563-0911
5th row042-522-7471
ValueCountFrequency (%)
070-7793-3966 3
 
3.4%
02-2657-4400 3
 
3.4%
02-2213-0691 3
 
3.4%
02-592-3300 2
 
2.3%
02-2140-5000 2
 
2.3%
02-562-3876 2
 
2.3%
02-413-3116 2
 
2.3%
02-969-9445 1
 
1.1%
02-2018-7700 1
 
1.1%
02-517-1305 1
 
1.1%
Other values (67) 67
77.0%
2023-12-12T14:43:01.210824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 176
17.3%
- 174
17.1%
2 135
13.2%
3 88
8.6%
1 80
7.9%
6 72
7.1%
4 68
 
6.7%
5 67
 
6.6%
7 64
 
6.3%
9 49
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 845
82.9%
Dash Punctuation 174
 
17.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
20.8%
2 135
16.0%
3 88
10.4%
1 80
9.5%
6 72
8.5%
4 68
 
8.0%
5 67
 
7.9%
7 64
 
7.6%
9 49
 
5.8%
8 46
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1019
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
17.3%
- 174
17.1%
2 135
13.2%
3 88
8.6%
1 80
7.9%
6 72
7.1%
4 68
 
6.7%
5 67
 
6.6%
7 64
 
6.3%
9 49
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1019
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
17.3%
- 174
17.1%
2 135
13.2%
3 88
8.6%
1 80
7.9%
6 72
7.1%
4 68
 
6.7%
5 67
 
6.6%
7 64
 
6.3%
9 49
 
4.8%

시공자사무소명
Text

MISSING 

Distinct76
Distinct (%)87.4%
Missing26
Missing (%)23.0%
Memory size1.0 KiB
2023-12-12T14:43:01.433338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length8.908046
Min length5

Characters and Unicode

Total characters775
Distinct characters131
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

Unique67 ?
Unique (%)77.0%

Sample

1st row(주)아리수엔지니어링
2nd rowdesign 다름
3rd row(주)에스엠디자인
4th row에이엘엘종합건설
5th row(주)에이원종합건설
ValueCountFrequency (%)
주식회사 4
 
4.3%
주식회사보미건설 3
 
3.2%
주)이도인건설 3
 
3.2%
엘림토건(주 2
 
2.2%
주)농협네트웍스 2
 
2.2%
주)새재종합건설 2
 
2.2%
소미건설(주 2
 
2.2%
주)에스에이치종합개발 2
 
2.2%
금강종합건설(주 2
 
2.2%
비츠로건설(주 2
 
2.2%
Other values (69) 69
74.2%
2023-12-12T14:43:01.746887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 88
 
11.4%
( 74
 
9.5%
) 74
 
9.5%
건 72
 
9.3%
설 68
 
8.8%
종 36
 
4.6%
합 36
 
4.6%
이 17
 
2.2%
사 15
 
1.9%
회 11
 
1.4%
Other values (121) 284
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 615
79.4%
Open Punctuation 74
 
9.5%
Close Punctuation 74
 
9.5%
Space Separator 6
 
0.8%
Lowercase Letter 6
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 88
 
14.3%
건 72
 
11.7%
설 68
 
11.1%
종 36
 
5.9%
합 36
 
5.9%
이 17
 
2.8%
사 15
 
2.4%
회 11
 
1.8%
식 11
 
1.8%
에 11
 
1.8%
Other values (112) 250
40.7%
Lowercase Letter
ValueCountFrequency (%)
e 1
16.7%
d 1
16.7%
s 1
16.7%
n 1
16.7%
g 1
16.7%
i 1
16.7%
Open Punctuation
ValueCountFrequency (%)
( 74
100.0%
Close Punctuation
ValueCountFrequency (%)
) 74
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 615
79.4%
Common 154
 
19.9%
Latin 6
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 88
 
14.3%
건 72
 
11.7%
설 68
 
11.1%
종 36
 
5.9%
합 36
 
5.9%
이 17
 
2.8%
사 15
 
2.4%
회 11
 
1.8%
식 11
 
1.8%
에 11
 
1.8%
Other values (112) 250
40.7%
Latin
ValueCountFrequency (%)
e 1
16.7%
d 1
16.7%
s 1
16.7%
n 1
16.7%
g 1
16.7%
i 1
16.7%
Common
ValueCountFrequency (%)
( 74
48.1%
) 74
48.1%
6
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 615
79.4%
ASCII 160
 
20.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 88
 
14.3%
건 72
 
11.7%
설 68
 
11.1%
종 36
 
5.9%
합 36
 
5.9%
이 17
 
2.8%
사 15
 
2.4%
회 11
 
1.8%
식 11
 
1.8%
에 11
 
1.8%
Other values (112) 250
40.7%
ASCII
ValueCountFrequency (%)
( 74
46.2%
) 74
46.2%
6
 
3.8%
e 1
 
0.6%
d 1
 
0.6%
s 1
 
0.6%
n 1
 
0.6%
g 1
 
0.6%
i 1
 
0.6%

Interactions

2023-12-12T14:42:53.306817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:50.363658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:51.045983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:51.562906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:52.099974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:52.691721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:53.384058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:50.471050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:51.121396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:51.656650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:52.193147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:52.813498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:53.483936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:50.594204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:51.207085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:51.759421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:52.283590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:52.903109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:53.579301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:50.713704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:51.306351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:51.852104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:52.373186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:53.030972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:53.679615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:50.835973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:51.398909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:51.929030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:52.517005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:53.131373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:53.771912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:50.959841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:51.466939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:52.012728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:52.598697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:53.225449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:43:01.847740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분1구분2연면적(제곱미터)증축연면적(제곱미터)착공처리일최대지상층수최대지하층수주용도부속용도세대수호수가구수감리자 전화번호감리사무소명시공자 전화번호시공자사무소명
구분11.0000.5470.0000.3511.0000.6810.2830.5490.9530.5220.0000.0001.0001.0001.0001.000
구분20.5471.0000.291NaN0.9120.3440.2590.4460.899NaN0.0000.0000.9750.9411.0001.000
연면적(제곱미터)0.0000.2911.000NaN1.0000.8400.3060.4291.000NaNNaNNaN1.0001.0001.0001.000
증축연면적(제곱미터)0.351NaNNaN1.0000.0000.8311.0000.8401.000NaNNaNNaN1.0001.0001.0000.314
착공처리일1.0000.9121.0000.0001.0000.9230.9750.0000.9800.0001.0001.0000.9960.9940.9940.992
최대지상층수0.6810.3440.8400.8310.9231.0000.6280.6400.8420.0000.6880.6100.9620.9400.9690.958
최대지하층수0.2830.2590.3061.0000.9750.6281.0000.6660.9600.0000.9150.5400.9880.9190.9550.953
주용도0.5490.4460.4290.8400.0000.6400.6661.0000.9970.5560.8280.5320.8990.8600.9910.533
부속용도0.9530.8991.0001.0000.9800.8420.9600.9971.0000.6521.0001.0000.8930.9690.9630.962
세대수0.522NaNNaNNaN0.0000.0000.0000.5560.6521.000NaNNaN0.5690.0000.5690.408
호수0.0000.000NaNNaN1.0000.6880.9150.8281.000NaN1.000NaN1.0001.0001.0001.000
가구수0.0000.000NaNNaN1.0000.6100.5400.5321.000NaNNaN1.0001.0001.0001.0001.000
감리자 전화번호1.0000.9751.0001.0000.9960.9620.9880.8990.8930.5691.0001.0001.0001.0000.9990.999
감리사무소명1.0000.9411.0001.0000.9940.9400.9190.8600.9690.0001.0001.0001.0001.0000.9990.999
시공자 전화번호1.0001.0001.0001.0000.9940.9690.9550.9910.9630.5691.0001.0000.9990.9991.0001.000
시공자사무소명1.0001.0001.0000.3140.9920.9580.9530.5330.9620.4081.0001.0000.9990.9991.0001.000
2023-12-12T14:43:02.030907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수주용도구분2구분1
세대수1.0000.4081.0000.510
주용도0.4081.0000.2740.507
구분21.0000.2741.0000.370
구분10.5100.5070.3701.000
2023-12-12T14:43:02.162535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연면적(제곱미터)증축연면적(제곱미터)최대지상층수최대지하층수호수가구수구분1구분2주용도세대수
연면적(제곱미터)1.0000.6440.7700.8450.9070.2730.0000.1920.3941.000
증축연면적(제곱미터)0.6441.0000.1010.725NaN0.2580.1701.0000.6200.000
최대지상층수0.7700.1011.0000.7120.7900.3390.5100.2000.3310.000
최대지하층수0.8450.7250.7121.0000.6750.1910.2940.1750.4100.000
호수0.907NaN0.7900.6751.000NaN0.0000.0000.4361.000
가구수0.2730.2580.3390.191NaN1.0000.0000.0000.2040.000
구분10.0000.1700.5100.2940.0000.0001.0000.3700.5070.510
구분20.1921.0000.2000.1750.0000.0000.3701.0000.2741.000
주용도0.3940.6200.3310.4100.4360.2040.5070.2741.0000.408
세대수1.0000.0000.0000.0001.0000.0000.5101.0000.4081.000

Missing values

2023-12-12T14:42:53.904054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:42:54.182399image/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.
2023-12-12T14:42:54.445554image/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

구분1구분2대지위치연면적(제곱미터)증축연면적(제곱미터)허가신고일착공처리일최대지상층수최대지하층수주용도부속용도세대수호수가구수감리자 전화번호감리사무소명시공자 전화번호시공자사무소명
0허가대수선서울특별시 성동구 성수동2가 331-15 외2필지999.49<NA>2020-06-172020-06-242<NA>공장제조업소<NA><NA><NA>02-2299-4836(주)아크건축사사무소02-525-6505(주)아리수엔지니어링
1허가신축서울특별시 성동구 성수동2가 315-47169.67<NA>2020-05-272020-06-1030제1종근린생활시설휴게음식점<NA><NA><NA><NA>종합건축사사무소 일오삼<NA>design 다름
2허가대수선서울특별시 성동구 성수동2가 275-52719.1<NA>2020-05-202020-06-0341제1종근린생활시설<NA><NA><NA><NA>031-703-9574(주)세익아키텍츠건축사사무소031-860-6500(주)에스엠디자인
3허가신축서울특별시 성동구 송정동 66-91351.11<NA>2020-05-202020-06-0150공동주택다세대주택101<NA>02-6085-3977건축사사무소 신영02-455-4244에이엘엘종합건설
4허가신축서울특별시 성동구 금호동4가 639-1120.26<NA>2020-04-202020-05-1820단독주택단독주택,근린생활시설<NA><NA>102-925-4500건축사사무소 엔담<NA><NA>
5허가신축서울특별시 성동구 홍익동 289-1550.23<NA>2020-04-162020-06-0861공동주택도시형생활주택(단지형다세대)13<NA><NA>02-956-6951건축사사무소 안옥031-563-0911(주)에이원종합건설
6허가증축서울특별시 성동구 마장동 773-5774.0<NA>2020-04-132020-06-0331제2종근린생활시설금융업소<NA><NA><NA>070-8118-7488에이티씨종합건축사사무소042-522-7471대선종합건설(주)
7허가신축서울특별시 성동구 도선동 168301.53<NA>2020-04-012020-04-1440단독주택제1종근린생활시설<NA>12<NA><NA>건축사사무소 예도070-7793-3966주식회사 새재종합건설
8허가신축서울특별시 성동구 금호동1가 511 외1필지199.89<NA>2020-03-202020-05-1930제2종근린생활시설일반음식점<NA><NA><NA>02-6217-1718건축사사무소한아키텍트<NA><NA>
9허가증축서울특별시 성동구 성수동1가 8-28173.166.372020-03-132020-05-2651제1종근린생활시설소매점<NA><NA><NA>02-569-2711(주)팀반파트너스건축사사무소02-323-0482(주)엔에스종합건설
구분1구분2대지위치연면적(제곱미터)증축연면적(제곱미터)허가신고일착공처리일최대지상층수최대지하층수주용도부속용도세대수호수가구수감리자 전화번호감리사무소명시공자 전화번호시공자사무소명
103신고신축서울특별시 성동구 금호동2가 75182.4<NA>2018-05-102018-07-0330단독주택근린생활시설<NA>2<NA><NA><NA><NA><NA>
104신고증축서울특별시 성동구 성수동2가 302-2548.8848.882017-07-142017-09-071<NA>제1종근린생활시설공중화장실<NA><NA><NA><NA><NA>02-834-1012(주)태양씨앤디
105신고증축서울특별시 성동구 용답동 34-11227.7132.532017-04-252017-05-0131단독주택다가구주택(6가구)<NA><NA>6<NA><NA><NA><NA>
106신고대수선서울특별시 성동구 성수동1가 685-210296.57<NA>2016-10-122016-10-2431단독주택다가구주택<NA><NA>5<NA><NA><NA><NA>
107신고신축서울특별시 성동구 하왕십리동 970-14382.98<NA>2016-04-072016-07-0730제2종근린생활시설<NA><NA><NA><NA><NA><NA><NA><NA>
108신고증축서울특별시 성동구 송정동 73-821128.0544.122016-03-142016-03-313<NA>단독주택<NA><NA><NA>1<NA><NA><NA><NA>
109신고증축서울특별시 성동구 마장동 509-51494.091494.092016-02-242016-03-3141제2종근린생활시설창고시설<NA><NA><NA><NA>주식회사건축사사무소이룸<NA><NA>
110신고신축서울특별시 성동구 성수동1가 656-48748.75<NA>2020-07-062020-07-2410제1종근린생활시설<NA><NA><NA><NA>02-897-6675(주)동명루마루건축사사무소<NA><NA>
111신고신축서울특별시 성동구 금호동4가 16161.92<NA>2020-04-292020-06-3040제1종근린생활시설휴게음식점<NA><NA><NA><NA><NA><NA><NA>
112신고신축서울특별시 성동구 도선동 33898.28<NA>2020-06-182020-08-2430단독주택근린생활시설<NA><NA>102-969-2831건축사사무소 아텍 plus<NA><NA>

Duplicate rows

Most frequently occurring

구분1구분2대지위치연면적(제곱미터)증축연면적(제곱미터)허가신고일착공처리일최대지상층수최대지하층수주용도부속용도세대수호수가구수감리자 전화번호감리사무소명시공자 전화번호시공자사무소명# duplicates
0신고신축서울특별시 성동구 금호동4가 16161.92<NA>2020-04-292020-06-3040제1종근린생활시설휴게음식점<NA><NA><NA><NA><NA><NA><NA>2