Overview

Dataset statistics

Number of variables20
Number of observations118
Missing cells475
Missing cells (%)20.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.1 KiB
Average record size in memory166.1 B

Variable types

Categorical2
Text10
Numeric5
DateTime3

Dataset

Description부산광역시연제구_착공신고허가현황_20220916
Author부산광역시 연제구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15030074

Alerts

최대지상층수 is highly overall correlated with 최고높이(m)High correlation
최고높이(m) is highly overall correlated with 최대지상층수High correlation
착공처리일 has 89 (75.4%) missing valuesMissing
실제착공일 has 111 (94.1%) missing valuesMissing
최대지하층수 has 48 (40.7%) missing valuesMissing
부속용도 has 42 (35.6%) missing valuesMissing
감리사무소명 has 88 (74.6%) missing valuesMissing
시공자사무소명 has 97 (82.2%) missing valuesMissing
허가번호 has unique valuesUnique
최대지하층수 has 27 (22.9%) zerosZeros
최고높이(m) has 22 (18.6%) zerosZeros
동수 has 3 (2.5%) zerosZeros

Reproduction

Analysis started2023-12-10 17:13:14.484150
Analysis finished2023-12-10 17:13:23.747133
Duration9.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

건축구분
Categorical

Distinct4
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
용도변경
66 
신축
40 
증축
7 
대수선
 
5

Length

Max length4
Median length4
Mean length3.1610169
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row용도변경
2nd row증축
3rd row증축
4th row신축
5th row신축

Common Values

ValueCountFrequency (%)
용도변경 66
55.9%
신축 40
33.9%
증축 7
 
5.9%
대수선 5
 
4.2%

Length

2023-12-11T02:13:23.916437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:13:24.165342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
용도변경 66
55.9%
신축 40
33.9%
증축 7
 
5.9%
대수선 5
 
4.2%

허가번호
Text

UNIQUE 

Distinct118
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T02:13:24.533441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length16.90678
Min length15

Characters and Unicode

Total characters1995
Distinct characters27
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

Unique118 ?
Unique (%)100.0%

Sample

1st row2022-건축과-용도변경허가-66
2nd row2022-건축과-증축허가-7
3rd row2022-건축과-증축허가-6
4th row2022-건축과-신축허가-39
5th row2022-건축과-신축허가-38
ValueCountFrequency (%)
2022-건축과-용도변경허가-66 1
 
0.8%
2022-건축과-용도변경허가-30 1
 
0.8%
2022-건축과-용도변경허가-25 1
 
0.8%
2022-건축과-용도변경허가-24 1
 
0.8%
2022-건축과-신축허가-9 1
 
0.8%
2022-건축과-신축허가-10 1
 
0.8%
2022-건축과-용도변경허가-26 1
 
0.8%
2022-건축과-용도변경허가-27 1
 
0.8%
2022-건축과-대수선허가-2 1
 
0.8%
2022-건축과-신축허가-12 1
 
0.8%
Other values (108) 108
91.5%
2023-12-11T02:13:25.274860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 387
19.4%
- 354
17.7%
축 165
8.3%
0 127
 
6.4%
건 119
 
6.0%
과 118
 
5.9%
허 117
 
5.9%
가 117
 
5.9%
용 67
 
3.4%
도 66
 
3.3%
Other values (17) 358
17.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 964
48.3%
Decimal Number 677
33.9%
Dash Punctuation 354
 
17.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
축 165
17.1%
건 119
12.3%
과 118
12.2%
허 117
12.1%
가 117
12.1%
용 67
7.0%
도 66
 
6.8%
변 66
 
6.8%
경 66
 
6.8%
신 39
 
4.0%
Other values (6) 24
 
2.5%
Decimal Number
ValueCountFrequency (%)
2 387
57.2%
0 127
 
18.8%
3 33
 
4.9%
1 33
 
4.9%
4 24
 
3.5%
5 23
 
3.4%
6 19
 
2.8%
7 11
 
1.6%
9 10
 
1.5%
8 10
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1031
51.7%
Hangul 964
48.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
축 165
17.1%
건 119
12.3%
과 118
12.2%
허 117
12.1%
가 117
12.1%
용 67
7.0%
도 66
 
6.8%
변 66
 
6.8%
경 66
 
6.8%
신 39
 
4.0%
Other values (6) 24
 
2.5%
Common
ValueCountFrequency (%)
2 387
37.5%
- 354
34.3%
0 127
 
12.3%
3 33
 
3.2%
1 33
 
3.2%
4 24
 
2.3%
5 23
 
2.2%
6 19
 
1.8%
7 11
 
1.1%
9 10
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1031
51.7%
Hangul 964
48.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 387
37.5%
- 354
34.3%
0 127
 
12.3%
3 33
 
3.2%
1 33
 
3.2%
4 24
 
2.3%
5 23
 
2.2%
6 19
 
1.8%
7 11
 
1.1%
9 10
 
1.0%
Hangul
ValueCountFrequency (%)
축 165
17.1%
건 119
12.3%
과 118
12.2%
허 117
12.1%
가 117
12.1%
용 67
7.0%
도 66
 
6.8%
변 66
 
6.8%
경 66
 
6.8%
신 39
 
4.0%
Other values (6) 24
 
2.5%

대지위치
Text

Distinct114
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T02:13:25.992643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length20.559322
Min length17

Characters and Unicode

Total characters2426
Distinct characters25
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

Unique110 ?
Unique (%)93.2%

Sample

1st row부산광역시 연제구 연산동 380-6
2nd row부산광역시 연제구 연산동 405-5 외1필지
3rd row부산광역시 연제구 연산동 2015
4th row부산광역시 연제구 연산동 620-7
5th row부산광역시 연제구 거제동 676-41
ValueCountFrequency (%)
부산광역시 118
24.0%
연제구 118
24.0%
연산동 87
17.7%
거제동 31
 
6.3%
외1필지 7
 
1.4%
외2필지 5
 
1.0%
외3필지 3
 
0.6%
590-39 2
 
0.4%
702-9 2
 
0.4%
603-7 2
 
0.4%
Other values (114) 116
23.6%
2023-12-11T02:13:26.935752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
373
15.4%
산 205
 
8.5%
연 205
 
8.5%
제 149
 
6.1%
부 118
 
4.9%
광 118
 
4.9%
역 118
 
4.9%
시 118
 
4.9%
구 118
 
4.9%
동 118
 
4.9%
Other values (15) 786
32.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1355
55.9%
Decimal Number 585
24.1%
Space Separator 373
 
15.4%
Dash Punctuation 113
 
4.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 205
15.1%
연 205
15.1%
제 149
11.0%
부 118
8.7%
광 118
8.7%
역 118
8.7%
시 118
8.7%
구 118
8.7%
동 118
8.7%
거 31
 
2.3%
Other values (3) 57
 
4.2%
Decimal Number
ValueCountFrequency (%)
1 112
19.1%
2 79
13.5%
3 63
10.8%
4 62
10.6%
6 54
9.2%
7 51
8.7%
0 42
 
7.2%
8 42
 
7.2%
5 41
 
7.0%
9 39
 
6.7%
Space Separator
ValueCountFrequency (%)
373
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1355
55.9%
Common 1071
44.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 205
15.1%
연 205
15.1%
제 149
11.0%
부 118
8.7%
광 118
8.7%
역 118
8.7%
시 118
8.7%
구 118
8.7%
동 118
8.7%
거 31
 
2.3%
Other values (3) 57
 
4.2%
Common
ValueCountFrequency (%)
373
34.8%
- 113
 
10.6%
1 112
 
10.5%
2 79
 
7.4%
3 63
 
5.9%
4 62
 
5.8%
6 54
 
5.0%
7 51
 
4.8%
0 42
 
3.9%
8 42
 
3.9%
Other values (2) 80
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1355
55.9%
ASCII 1071
44.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
373
34.8%
- 113
 
10.6%
1 112
 
10.5%
2 79
 
7.4%
3 63
 
5.9%
4 62
 
5.8%
6 54
 
5.0%
7 51
 
4.8%
0 42
 
3.9%
8 42
 
3.9%
Other values (2) 80
 
7.5%
Hangul
ValueCountFrequency (%)
산 205
15.1%
연 205
15.1%
제 149
11.0%
부 118
8.7%
광 118
8.7%
역 118
8.7%
시 118
8.7%
구 118
8.7%
동 118
8.7%
거 31
 
2.3%
Other values (3) 57
 
4.2%

대지면적(㎡)
Text

Distinct112
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T02:13:27.616271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.559322
Min length2

Characters and Unicode

Total characters538
Distinct characters12
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

Unique106 ?
Unique (%)89.8%

Sample

1st row135.4
2nd row1,512
3rd row83
4th row201
5th row453
ValueCountFrequency (%)
4,560 2
 
1.7%
138.2 2
 
1.7%
1,487.6 2
 
1.7%
156 2
 
1.7%
83 2
 
1.7%
592 2
 
1.7%
266.8 1
 
0.8%
207.9 1
 
0.8%
1,280 1
 
0.8%
40 1
 
0.8%
Other values (102) 102
86.4%
2023-12-11T02:13:28.485824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 86
16.0%
. 76
14.1%
2 58
10.8%
6 53
9.9%
4 48
8.9%
3 44
8.2%
5 39
7.2%
7 31
 
5.8%
9 30
 
5.6%
8 30
 
5.6%
Other values (2) 43
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 444
82.5%
Other Punctuation 94
 
17.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 86
19.4%
2 58
13.1%
6 53
11.9%
4 48
10.8%
3 44
9.9%
5 39
8.8%
7 31
 
7.0%
9 30
 
6.8%
8 30
 
6.8%
0 25
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 76
80.9%
, 18
 
19.1%

Most occurring scripts

ValueCountFrequency (%)
Common 538
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 86
16.0%
. 76
14.1%
2 58
10.8%
6 53
9.9%
4 48
8.9%
3 44
8.2%
5 39
7.2%
7 31
 
5.8%
9 30
 
5.6%
8 30
 
5.6%
Other values (2) 43
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 86
16.0%
. 76
14.1%
2 58
10.8%
6 53
9.9%
4 48
8.9%
3 44
8.2%
5 39
7.2%
7 31
 
5.8%
9 30
 
5.6%
8 30
 
5.6%
Other values (2) 43
8.0%

건축면적(㎡)
Text

Distinct114
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T02:13:29.051026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7.5
Mean length5.559322
Min length2

Characters and Unicode

Total characters656
Distinct characters12
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

Unique110 ?
Unique (%)93.2%

Sample

1st row80.64
2nd row887.6
3rd row48.59
4th row120.34
5th row177.07
ValueCountFrequency (%)
1,188.88 2
 
1.7%
3,202.58 2
 
1.7%
321 2
 
1.7%
99.17 2
 
1.7%
49.29 1
 
0.8%
94.79 1
 
0.8%
116.35 1
 
0.8%
829.49 1
 
0.8%
21.95 1
 
0.8%
259.9 1
 
0.8%
Other values (104) 104
88.1%
2023-12-11T02:13:29.777692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 115
17.5%
2 74
11.3%
1 72
11.0%
8 61
9.3%
9 58
8.8%
7 53
8.1%
5 50
7.6%
4 47
7.2%
3 46
 
7.0%
6 40
 
6.1%
Other values (2) 40
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 532
81.1%
Other Punctuation 124
 
18.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 74
13.9%
1 72
13.5%
8 61
11.5%
9 58
10.9%
7 53
10.0%
5 50
9.4%
4 47
8.8%
3 46
8.6%
6 40
7.5%
0 31
5.8%
Other Punctuation
ValueCountFrequency (%)
. 115
92.7%
, 9
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 115
17.5%
2 74
11.3%
1 72
11.0%
8 61
9.3%
9 58
8.8%
7 53
8.1%
5 50
7.6%
4 47
7.2%
3 46
 
7.0%
6 40
 
6.1%
Other values (2) 40
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 115
17.5%
2 74
11.3%
1 72
11.0%
8 61
9.3%
9 58
8.8%
7 53
8.1%
5 50
7.6%
4 47
7.2%
3 46
 
7.0%
6 40
 
6.1%
Other values (2) 40
 
6.1%

연면적(㎡)
Text

Distinct114
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T02:13:30.265262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length6.5762712
Min length5

Characters and Unicode

Total characters776
Distinct characters12
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

Unique110 ?
Unique (%)93.2%

Sample

1st row343.56
2nd row2,043
3rd row200.08
4th row564.71
5th row1,328.179
ValueCountFrequency (%)
15,277.33 2
 
1.7%
37,308.63 2
 
1.7%
621.93 2
 
1.7%
127.74 2
 
1.7%
333.36 1
 
0.8%
262.17 1
 
0.8%
179.65 1
 
0.8%
7,864.69 1
 
0.8%
38.63 1
 
0.8%
998.02 1
 
0.8%
Other values (104) 104
88.1%
2023-12-11T02:13:30.900348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 116
14.9%
1 94
12.1%
3 73
9.4%
4 68
8.8%
6 67
8.6%
9 62
8.0%
7 61
7.9%
2 60
7.7%
8 53
6.8%
5 52
6.7%
Other values (2) 70
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 627
80.8%
Other Punctuation 149
 
19.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 94
15.0%
3 73
11.6%
4 68
10.8%
6 67
10.7%
9 62
9.9%
7 61
9.7%
2 60
9.6%
8 53
8.5%
5 52
8.3%
0 37
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 116
77.9%
, 33
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common 776
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 116
14.9%
1 94
12.1%
3 73
9.4%
4 68
8.8%
6 67
8.6%
9 62
8.0%
7 61
7.9%
2 60
7.7%
8 53
6.8%
5 52
6.7%
Other values (2) 70
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 776
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 116
14.9%
1 94
12.1%
3 73
9.4%
4 68
8.8%
6 67
8.6%
9 62
8.0%
7 61
7.9%
2 60
7.7%
8 53
6.8%
5 52
6.7%
Other values (2) 70
9.0%

건폐율(%)
Real number (ℝ)

Distinct114
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.246589
Minimum25.86
Maximum92.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T02:13:31.129498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.86
5-th percentile42.96
Q153.15
median58.4561
Q359.697675
95-th percentile74.43538
Maximum92.55
Range66.69
Interquartile range (IQR)6.547675

Descriptive statistics

Standard deviation9.5102
Coefficient of variation (CV)0.16612693
Kurtosis3.3556346
Mean57.246589
Median Absolute Deviation (MAD)2.59465
Skewness0.47684548
Sum6755.0975
Variance90.443904
MonotonicityNot monotonic
2023-12-11T02:13:31.392711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.92 2
 
1.7%
57.6 2
 
1.7%
59.65 2
 
1.7%
54.22 2
 
1.7%
59.56 1
 
0.8%
73.84 1
 
0.8%
30.79 1
 
0.8%
59.46 1
 
0.8%
55.96 1
 
0.8%
64.8 1
 
0.8%
Other values (104) 104
88.1%
ValueCountFrequency (%)
25.86 1
0.8%
30.79 1
0.8%
37.17 1
0.8%
39.088 1
0.8%
41.21 1
0.8%
42.28 1
0.8%
43.08 1
0.8%
44.82 1
0.8%
45.1188 1
0.8%
45.47 1
0.8%
ValueCountFrequency (%)
92.55 1
0.8%
89.6377 1
0.8%
79.92 2
1.7%
79.06 1
0.8%
76.2792 1
0.8%
74.11 1
0.8%
73.84 1
0.8%
70.232 1
0.8%
70.23 1
0.8%
69.66 1
0.8%

용적률(%)
Text

Distinct115
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T02:13:31.890770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.0847458
Min length3

Characters and Unicode

Total characters718
Distinct characters12
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

Unique112 ?
Unique (%)94.9%

Sample

1st row178.67
2nd row135.12
3rd row182.5181
4th row280.95
5th row219.78
ValueCountFrequency (%)
1,026.98 2
 
1.7%
105.06 2
 
1.7%
82.23 2
 
1.7%
55.56 1
 
0.8%
104.3133 1
 
0.8%
86.41 1
 
0.8%
563.35 1
 
0.8%
96.58 1
 
0.8%
211.4823 1
 
0.8%
186.75 1
 
0.8%
Other values (105) 105
89.0%
2023-12-11T02:13:32.785122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 115
16.0%
1 91
12.7%
2 75
10.4%
9 71
9.9%
7 63
8.8%
6 54
7.5%
3 54
7.5%
8 53
7.4%
4 52
7.2%
5 50
7.0%
Other values (2) 40
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 601
83.7%
Other Punctuation 117
 
16.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 91
15.1%
2 75
12.5%
9 71
11.8%
7 63
10.5%
6 54
9.0%
3 54
9.0%
8 53
8.8%
4 52
8.7%
5 50
8.3%
0 38
6.3%
Other Punctuation
ValueCountFrequency (%)
. 115
98.3%
, 2
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 718
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 115
16.0%
1 91
12.7%
2 75
10.4%
9 71
9.9%
7 63
8.8%
6 54
7.5%
3 54
7.5%
8 53
7.4%
4 52
7.2%
5 50
7.0%
Other values (2) 40
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 115
16.0%
1 91
12.7%
2 75
10.4%
9 71
9.9%
7 63
8.8%
6 54
7.5%
3 54
7.5%
8 53
7.4%
4 52
7.2%
5 50
7.0%
Other values (2) 40
 
5.6%

허가일
Date

Distinct79
Distinct (%)66.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2022-01-05 00:00:00
Maximum2022-08-30 00:00:00
2023-12-11T02:13:33.123875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:33.423491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

착공처리일
Date

MISSING 

Distinct24
Distinct (%)82.8%
Missing89
Missing (%)75.4%
Memory size1.1 KiB
Minimum2022-02-09 00:00:00
Maximum2022-09-23 00:00:00
2023-12-11T02:13:33.714777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:33.968610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)

실제착공일
Date

MISSING 

Distinct7
Distinct (%)100.0%
Missing111
Missing (%)94.1%
Memory size1.1 KiB
Minimum2022-03-23 00:00:00
Maximum2022-06-15 00:00:00
2023-12-11T02:13:34.214339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:34.508144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)

최대지상층수
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2881356
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T02:13:34.819718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile17
Maximum29
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.8178941
Coefficient of variation (CV)1.1001787
Kurtosis6.8674829
Mean5.2881356
Median Absolute Deviation (MAD)1
Skewness2.5859229
Sum624
Variance33.847892
MonotonicityNot monotonic
2023-12-11T02:13:35.094798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 41
34.7%
5 16
 
13.6%
3 14
 
11.9%
4 13
 
11.0%
1 8
 
6.8%
9 3
 
2.5%
11 3
 
2.5%
6 3
 
2.5%
7 3
 
2.5%
29 2
 
1.7%
Other values (8) 12
 
10.2%
ValueCountFrequency (%)
1 8
 
6.8%
2 41
34.7%
3 14
 
11.9%
4 13
 
11.0%
5 16
 
13.6%
6 3
 
2.5%
7 3
 
2.5%
8 1
 
0.8%
9 3
 
2.5%
11 3
 
2.5%
ValueCountFrequency (%)
29 2
1.7%
28 1
 
0.8%
26 1
 
0.8%
23 1
 
0.8%
17 2
1.7%
15 2
1.7%
14 2
1.7%
13 2
1.7%
11 3
2.5%
9 3
2.5%

최대지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)8.6%
Missing48
Missing (%)40.7%
Infinite0
Infinite (%)0.0%
Mean0.94285714
Minimum0
Maximum5
Zeros27
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T02:13:35.551784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0886198
Coefficient of variation (CV)1.1545968
Kurtosis2.9984608
Mean0.94285714
Median Absolute Deviation (MAD)1
Skewness1.6424589
Sum66
Variance1.1850932
MonotonicityNot monotonic
2023-12-11T02:13:36.074374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 31
26.3%
0 27
22.9%
2 5
 
4.2%
3 4
 
3.4%
4 2
 
1.7%
5 1
 
0.8%
(Missing) 48
40.7%
ValueCountFrequency (%)
0 27
22.9%
1 31
26.3%
2 5
 
4.2%
3 4
 
3.4%
4 2
 
1.7%
5 1
 
0.8%
ValueCountFrequency (%)
5 1
 
0.8%
4 2
 
1.7%
3 4
 
3.4%
2 5
 
4.2%
1 31
26.3%
0 27
22.9%

최고높이(m)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.063432
Minimum0
Maximum91
Zeros22
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T02:13:37.232882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.175
median10.45
Q318.77
95-th percentile60.99
Maximum91
Range91
Interquartile range (IQR)12.595

Descriptive statistics

Standard deviation20.073479
Coefficient of variation (CV)1.1764033
Kurtosis4.1325842
Mean17.063432
Median Absolute Deviation (MAD)6.15
Skewness2.0652478
Sum2013.485
Variance402.94454
MonotonicityNot monotonic
2023-12-11T02:13:37.600952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 22
 
18.6%
6.6 3
 
2.5%
60.9 2
 
1.7%
91.0 2
 
1.7%
7.8 2
 
1.7%
8.2 2
 
1.7%
6.4 2
 
1.7%
8.0 2
 
1.7%
7.3 2
 
1.7%
10.3 2
 
1.7%
Other values (75) 77
65.3%
ValueCountFrequency (%)
0.0 22
18.6%
3.6 1
 
0.8%
3.9 1
 
0.8%
4.2 1
 
0.8%
4.5 1
 
0.8%
4.85 1
 
0.8%
5.5 1
 
0.8%
6.0 1
 
0.8%
6.1 1
 
0.8%
6.4 2
 
1.7%
ValueCountFrequency (%)
91.0 2
1.7%
88.55 1
0.8%
78.0 1
0.8%
68.8 1
0.8%
61.5 1
0.8%
60.9 2
1.7%
59.85 1
0.8%
59.3 1
0.8%
51.45 1
0.8%
45.45 1
0.8%

동수
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1016949
Minimum0
Maximum5
Zeros3
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T02:13:37.847686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.56067466
Coefficient of variation (CV)0.50892008
Kurtosis26.312591
Mean1.1016949
Median Absolute Deviation (MAD)0
Skewness4.4719512
Sum130
Variance0.31435608
MonotonicityNot monotonic
2023-12-11T02:13:38.068763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 106
89.8%
2 6
 
5.1%
0 3
 
2.5%
5 1
 
0.8%
4 1
 
0.8%
3 1
 
0.8%
ValueCountFrequency (%)
0 3
 
2.5%
1 106
89.8%
2 6
 
5.1%
3 1
 
0.8%
4 1
 
0.8%
5 1
 
0.8%
ValueCountFrequency (%)
5 1
 
0.8%
4 1
 
0.8%
3 1
 
0.8%
2 6
 
5.1%
1 106
89.8%
0 3
 
2.5%

주용도
Categorical

Distinct12
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
제2종근린생활시설
53 
제1종근린생활시설
21 
공동주택
17 
단독주택
9 
업무시설
8 
Other values (7)
10 

Length

Max length9
Median length9
Mean length7.2288136
Min length4

Unique

Unique5 ?
Unique (%)4.2%

Sample

1st row제2종근린생활시설
2nd row자동차관련시설
3rd row제2종근린생활시설
4th row제2종근린생활시설
5th row공동주택

Common Values

ValueCountFrequency (%)
제2종근린생활시설 53
44.9%
제1종근린생활시설 21
 
17.8%
공동주택 17
 
14.4%
단독주택 9
 
7.6%
업무시설 8
 
6.8%
자동차관련시설 3
 
2.5%
창고시설 2
 
1.7%
의료시설 1
 
0.8%
판매시설 1
 
0.8%
교육연구시설 1
 
0.8%
Other values (2) 2
 
1.7%

Length

2023-12-11T02:13:38.326845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제2종근린생활시설 53
44.9%
제1종근린생활시설 21
 
17.8%
공동주택 17
 
14.4%
단독주택 9
 
7.6%
업무시설 8
 
6.8%
자동차관련시설 3
 
2.5%
창고시설 2
 
1.7%
의료시설 1
 
0.8%
판매시설 1
 
0.8%
교육연구시설 1
 
0.8%
Other values (2) 2
 
1.7%

부속용도
Text

MISSING 

Distinct58
Distinct (%)76.3%
Missing42
Missing (%)35.6%
Memory size1.1 KiB
2023-12-11T02:13:38.679945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length28
Mean length12.078947
Min length2

Characters and Unicode

Total characters918
Distinct characters96
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

Unique49 ?
Unique (%)64.5%

Sample

1st row정비공장
2nd row다세대주택
3rd row다세대주택(도시형생활주택) 및 제2종근린생활시설(사무소)
4th row일반음식점
5th row병원
ValueCountFrequency (%)
사무소 12
 
11.5%
및 6
 
5.8%
일반음식점 5
 
4.8%
근린생활시설 4
 
3.8%
소매점 3
 
2.9%
다세대주택 3
 
2.9%
의원 3
 
2.9%
업무시설(오피스텔 3
 
2.9%
도시형생활주택-소형주택 2
 
1.9%
휴게음식점 2
 
1.9%
Other values (53) 61
58.7%
2023-12-11T02:13:39.320179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 51
 
5.6%
설 41
 
4.5%
( 39
 
4.2%
) 39
 
4.2%
소 38
 
4.1%
, 37
 
4.0%
생 35
 
3.8%
활 34
 
3.7%
주 32
 
3.5%
택 31
 
3.4%
Other values (86) 541
58.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 744
81.0%
Open Punctuation 39
 
4.2%
Close Punctuation 39
 
4.2%
Other Punctuation 39
 
4.2%
Space Separator 28
 
3.1%
Decimal Number 20
 
2.2%
Dash Punctuation 9
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 51
 
6.9%
설 41
 
5.5%
소 38
 
5.1%
생 35
 
4.7%
활 34
 
4.6%
주 32
 
4.3%
택 31
 
4.2%
무 28
 
3.8%
린 26
 
3.5%
근 25
 
3.4%
Other values (76) 403
54.2%
Other Punctuation
ValueCountFrequency (%)
, 37
94.9%
/ 1
 
2.6%
: 1
 
2.6%
Decimal Number
ValueCountFrequency (%)
1 10
50.0%
2 8
40.0%
3 2
 
10.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Space Separator
ValueCountFrequency (%)
28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 744
81.0%
Common 174
 
19.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 51
 
6.9%
설 41
 
5.5%
소 38
 
5.1%
생 35
 
4.7%
활 34
 
4.6%
주 32
 
4.3%
택 31
 
4.2%
무 28
 
3.8%
린 26
 
3.5%
근 25
 
3.4%
Other values (76) 403
54.2%
Common
ValueCountFrequency (%)
( 39
22.4%
) 39
22.4%
, 37
21.3%
28
16.1%
1 10
 
5.7%
- 9
 
5.2%
2 8
 
4.6%
3 2
 
1.1%
/ 1
 
0.6%
: 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 744
81.0%
ASCII 174
 
19.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 51
 
6.9%
설 41
 
5.5%
소 38
 
5.1%
생 35
 
4.7%
활 34
 
4.6%
주 32
 
4.3%
택 31
 
4.2%
무 28
 
3.8%
린 26
 
3.5%
근 25
 
3.4%
Other values (76) 403
54.2%
ASCII
ValueCountFrequency (%)
( 39
22.4%
) 39
22.4%
, 37
21.3%
28
16.1%
1 10
 
5.7%
- 9
 
5.2%
2 8
 
4.6%
3 2
 
1.1%
/ 1
 
0.6%
: 1
 
0.6%

설계사무소명
Text

Distinct75
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T02:13:39.793729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length10.508475
Min length8

Characters and Unicode

Total characters1240
Distinct characters124
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

Unique58 ?
Unique (%)49.2%

Sample

1st row현종합건축사사무소
2nd row건축사사무소 케이앤케이
3rd row아키시스 건축사 사무소
4th row건축사사무소포엠
5th row(주)조양종합건축사사무소
ValueCountFrequency (%)
건축사사무소 52
26.5%
일문 19
 
9.7%
종합건축사사무소 13
 
6.6%
주식회사 8
 
4.1%
두레설계 4
 
2.0%
대영설계건축사사무소 4
 
2.0%
미담 4
 
2.0%
대륙 4
 
2.0%
현종합건축사사무소 3
 
1.5%
아키시스 2
 
1.0%
Other values (71) 83
42.3%
2023-12-11T02:13:40.575616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 244
19.7%
건 122
 
9.8%
축 121
 
9.8%
무 120
 
9.7%
소 118
 
9.5%
78
 
6.3%
종 27
 
2.2%
합 27
 
2.2%
일 23
 
1.9%
주 23
 
1.9%
Other values (114) 337
27.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1105
89.1%
Space Separator 78
 
6.3%
Close Punctuation 16
 
1.3%
Open Punctuation 16
 
1.3%
Uppercase Letter 11
 
0.9%
Decimal Number 6
 
0.5%
Lowercase Letter 4
 
0.3%
Other Punctuation 3
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 244
22.1%
건 122
11.0%
축 121
11.0%
무 120
10.9%
소 118
10.7%
종 27
 
2.4%
합 27
 
2.4%
일 23
 
2.1%
주 23
 
2.1%
문 20
 
1.8%
Other values (94) 260
23.5%
Uppercase Letter
ValueCountFrequency (%)
A 3
27.3%
M 2
18.2%
J 2
18.2%
D 1
 
9.1%
B 1
 
9.1%
C 1
 
9.1%
T 1
 
9.1%
Lowercase Letter
ValueCountFrequency (%)
a 1
25.0%
n 1
25.0%
l 1
25.0%
p 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 1
33.3%
· 1
33.3%
& 1
33.3%
Decimal Number
ValueCountFrequency (%)
1 3
50.0%
2 3
50.0%
Space Separator
ValueCountFrequency (%)
78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1105
89.1%
Common 120
 
9.7%
Latin 15
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 244
22.1%
건 122
11.0%
축 121
11.0%
무 120
10.9%
소 118
10.7%
종 27
 
2.4%
합 27
 
2.4%
일 23
 
2.1%
주 23
 
2.1%
문 20
 
1.8%
Other values (94) 260
23.5%
Latin
ValueCountFrequency (%)
A 3
20.0%
M 2
13.3%
J 2
13.3%
D 1
 
6.7%
a 1
 
6.7%
n 1
 
6.7%
l 1
 
6.7%
p 1
 
6.7%
B 1
 
6.7%
C 1
 
6.7%
Common
ValueCountFrequency (%)
78
65.0%
) 16
 
13.3%
( 16
 
13.3%
1 3
 
2.5%
2 3
 
2.5%
. 1
 
0.8%
· 1
 
0.8%
& 1
 
0.8%
- 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1105
89.1%
ASCII 134
 
10.8%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 244
22.1%
건 122
11.0%
축 121
11.0%
무 120
10.9%
소 118
10.7%
종 27
 
2.4%
합 27
 
2.4%
일 23
 
2.1%
주 23
 
2.1%
문 20
 
1.8%
Other values (94) 260
23.5%
ASCII
ValueCountFrequency (%)
78
58.2%
) 16
 
11.9%
( 16
 
11.9%
A 3
 
2.2%
1 3
 
2.2%
2 3
 
2.2%
M 2
 
1.5%
J 2
 
1.5%
. 1
 
0.7%
D 1
 
0.7%
Other values (9) 9
 
6.7%
None
ValueCountFrequency (%)
· 1
100.0%

감리사무소명
Text

MISSING 

Distinct30
Distinct (%)100.0%
Missing88
Missing (%)74.6%
Memory size1.1 KiB
2023-12-11T02:13:40.876292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length10.366667
Min length7

Characters and Unicode

Total characters311
Distinct characters68
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row섬토건축사사무소
2nd row고도종합건축사사무소
3rd row이영건축사사무소
4th row에이플러스건축사사무소
5th row라온 건축사 사무소
ValueCountFrequency (%)
건축사사무소 5
 
12.2%
종합건축사사무소 3
 
7.3%
주식회사 1
 
2.4%
환건축사사무소 1
 
2.4%
주)무이건축사사무소 1
 
2.4%
지구 1
 
2.4%
경진종합김우승건축사사무소 1
 
2.4%
프라임종합건축사사무소 1
 
2.4%
금성 1
 
2.4%
섬토건축사사무소 1
 
2.4%
Other values (25) 25
61.0%
2023-12-11T02:13:41.480972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 62
19.9%
건 31
 
10.0%
무 31
 
10.0%
축 30
 
9.6%
소 30
 
9.6%
합 11
 
3.5%
종 11
 
3.5%
11
 
3.5%
이 6
 
1.9%
주 5
 
1.6%
Other values (58) 83
26.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 288
92.6%
Space Separator 11
 
3.5%
Close Punctuation 4
 
1.3%
Open Punctuation 4
 
1.3%
Decimal Number 4
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 62
21.5%
건 31
10.8%
무 31
10.8%
축 30
10.4%
소 30
10.4%
합 11
 
3.8%
종 11
 
3.8%
이 6
 
2.1%
주 5
 
1.7%
진 4
 
1.4%
Other values (53) 67
23.3%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 288
92.6%
Common 23
 
7.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 62
21.5%
건 31
10.8%
무 31
10.8%
축 30
10.4%
소 30
10.4%
합 11
 
3.8%
종 11
 
3.8%
이 6
 
2.1%
주 5
 
1.7%
진 4
 
1.4%
Other values (53) 67
23.3%
Common
ValueCountFrequency (%)
11
47.8%
) 4
 
17.4%
( 4
 
17.4%
1 2
 
8.7%
2 2
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 288
92.6%
ASCII 23
 
7.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 62
21.5%
건 31
10.8%
무 31
10.8%
축 30
10.4%
소 30
10.4%
합 11
 
3.8%
종 11
 
3.8%
이 6
 
2.1%
주 5
 
1.7%
진 4
 
1.4%
Other values (53) 67
23.3%
ASCII
ValueCountFrequency (%)
11
47.8%
) 4
 
17.4%
( 4
 
17.4%
1 2
 
8.7%
2 2
 
8.7%

시공자사무소명
Text

MISSING 

Distinct20
Distinct (%)95.2%
Missing97
Missing (%)82.2%
Memory size1.1 KiB
2023-12-11T02:13:41.857101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length9.1904762
Min length7

Characters and Unicode

Total characters193
Distinct characters45
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

Unique19 ?
Unique (%)90.5%

Sample

1st row주식회사조아스
2nd row(주)예은종합건설
3rd row더세움종합건설(주)
4th row대신종합건설(주)
5th row더세움종합건설(주)
ValueCountFrequency (%)
더세움종합건설(주 2
 
8.7%
주식회사 2
 
8.7%
유영종합건설(주 1
 
4.3%
주)대풍건설 1
 
4.3%
주)세움주택건설 1
 
4.3%
주)동인종합건설 1
 
4.3%
주)지안종합건설 1
 
4.3%
주식회사에스에스텍 1
 
4.3%
주식회사영인건설 1
 
4.3%
진우종합건설(주 1
 
4.3%
Other values (11) 11
47.8%
2023-12-11T02:13:42.501074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 22
 
11.4%
건 19
 
9.8%
설 19
 
9.8%
종 15
 
7.8%
합 15
 
7.8%
( 15
 
7.8%
) 15
 
7.8%
식 6
 
3.1%
사 6
 
3.1%
회 6
 
3.1%
Other values (35) 55
28.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 161
83.4%
Open Punctuation 15
 
7.8%
Close Punctuation 15
 
7.8%
Space Separator 2
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 22
13.7%
건 19
11.8%
설 19
11.8%
종 15
 
9.3%
합 15
 
9.3%
식 6
 
3.7%
사 6
 
3.7%
회 6
 
3.7%
스 4
 
2.5%
에 3
 
1.9%
Other values (32) 46
28.6%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 161
83.4%
Common 32
 
16.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 22
13.7%
건 19
11.8%
설 19
11.8%
종 15
 
9.3%
합 15
 
9.3%
식 6
 
3.7%
사 6
 
3.7%
회 6
 
3.7%
스 4
 
2.5%
에 3
 
1.9%
Other values (32) 46
28.6%
Common
ValueCountFrequency (%)
( 15
46.9%
) 15
46.9%
2
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 161
83.4%
ASCII 32
 
16.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 22
13.7%
건 19
11.8%
설 19
11.8%
종 15
 
9.3%
합 15
 
9.3%
식 6
 
3.7%
사 6
 
3.7%
회 6
 
3.7%
스 4
 
2.5%
에 3
 
1.9%
Other values (32) 46
28.6%
ASCII
ValueCountFrequency (%)
( 15
46.9%
) 15
46.9%
2
 
6.2%

Interactions

2023-12-11T02:13:21.348346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:16.611467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:17.799733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:18.824374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:20.383321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:21.594995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:16.833914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:18.019697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:19.046699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:20.599363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:21.854177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:17.083723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:18.237851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:19.236515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:20.803096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:22.091986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:17.308563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:18.438950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:20.003736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:20.978453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:22.285535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:17.543991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:18.618847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:20.178914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:21.163607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:13:42.713924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축구분건폐율(%)허가일착공처리일실제착공일최대지상층수최대지하층수최고높이(m)동수주용도부속용도설계사무소명감리사무소명시공자사무소명
건축구분1.0000.3920.0000.9201.0000.3760.6240.4710.2800.6320.0000.9011.0001.000
건폐율(%)0.3921.0000.0000.9331.0000.5770.6340.6690.5050.3630.9490.8871.0001.000
허가일0.0000.0001.0000.7381.0000.0000.0000.0000.0000.0000.8230.7911.0000.932
착공처리일0.9200.9330.7381.0001.0000.8480.0000.8271.0000.3741.0000.8611.0000.985
실제착공일1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.000
최대지상층수0.3760.5770.0000.8481.0001.0000.8790.9550.6800.6580.9750.9401.0001.000
최대지하층수0.6240.6340.0000.0001.0000.8791.0000.8850.3930.4400.9870.8821.0001.000
최고높이(m)0.4710.6690.0000.8271.0000.9550.8851.0000.6530.6820.9710.9181.0001.000
동수0.2800.5050.0001.0001.0000.6800.3930.6531.0000.4370.4970.9171.0001.000
주용도0.6320.3630.0000.3741.0000.6580.4400.6820.4371.0000.9910.9711.0000.918
부속용도0.0000.9490.8231.0001.0000.9750.9870.9710.4970.9911.0000.0001.0001.000
설계사무소명0.9010.8870.7910.8611.0000.9400.8820.9180.9170.9710.0001.0001.0000.982
감리사무소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시공자사무소명1.0001.0000.9320.9850.0001.0001.0001.0001.0000.9181.0000.9821.0001.000
2023-12-11T02:13:42.962814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주용도건축구분
주용도1.0000.326
건축구분0.3261.000
2023-12-11T02:13:43.180203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건폐율(%)최대지상층수최대지하층수최고높이(m)동수건축구분주용도
건폐율(%)1.0000.1500.1630.095-0.2930.2360.157
최대지상층수0.1501.0000.4560.890-0.0630.2400.347
최대지하층수0.1630.4561.0000.4860.2030.4460.229
최고높이(m)0.0950.8900.4861.0000.0130.2910.365
동수-0.293-0.0630.2030.0131.0000.1810.179
건축구분0.2360.2400.4460.2910.1811.0000.326
주용도0.1570.3470.2290.3650.1790.3261.000

Missing values

2023-12-11T02:13:22.629380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:13:23.226540image/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.
2023-12-11T02:13:23.595582image/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

건축구분허가번호대지위치대지면적(㎡)건축면적(㎡)연면적(㎡)건폐율(%)용적률(%)허가일착공처리일실제착공일최대지상층수최대지하층수최고높이(m)동수주용도부속용도설계사무소명감리사무소명시공자사무소명
0용도변경2022-건축과-용도변경허가-66부산광역시 연제구 연산동 380-6135.480.64343.5659.56178.672022-08-30<NA><NA>319.01제2종근린생활시설<NA>현종합건축사사무소<NA><NA>
1증축2022-건축과-증축허가-7부산광역시 연제구 연산동 405-5 외1필지1,512887.62,04358.7135.122022-08-29<NA><NA>3<NA>15.031자동차관련시설정비공장건축사사무소 케이앤케이<NA><NA>
2증축2022-건축과-증축허가-6부산광역시 연제구 연산동 20158348.59200.0858.5422182.51812022-08-25<NA><NA>4114.571제2종근린생활시설<NA>아키시스 건축사 사무소<NA><NA>
3신축2022-건축과-신축허가-39부산광역시 연제구 연산동 620-7201120.34564.7159.87280.952022-08-25<NA><NA>5019.51제2종근린생활시설<NA>건축사사무소포엠<NA><NA>
4신축2022-건축과-신축허가-38부산광역시 연제구 거제동 676-41453177.071,328.17939.088219.782022-08-23<NA><NA>7122.91공동주택다세대주택(주)조양종합건축사사무소<NA><NA>
5신축2022-건축과-신축허가-37부산광역시 연제구 거제동 833-311356.33145.0149.85128.332022-08-182022-09-23<NA>3010.31공동주택다세대주택(도시형생활주택) 및 제2종근린생활시설(사무소)종합건축사사무소 두레설계섬토건축사사무소주식회사조아스
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7용도변경2022-건축과-용도변경허가-65부산광역시 연제구 연산동 406-19221.1149.73781.9467.72331.372022-08-08<NA><NA>5117.71제2종근린생활시설<NA>미상건축사사무소<NA><NA>
8신축2022-건축과-신축허가-36부산광역시 연제구 거제동 815-33 외1필지288168.1459.4258.37159.522022-08-032022-08-23<NA>308.9451제2종근린생활시설<NA>희성건축사사무소고도종합건축사사무소(주)예은종합건설
9용도변경2022-건축과-용도변경허가-62부산광역시 연제구 연산동 589-91,406.3839.433,081.3159.6907204.62212022-08-03<NA><NA>6123.91의료시설병원주식회사 다음건축사사무소<NA><NA>
건축구분허가번호대지위치대지면적(㎡)건축면적(㎡)연면적(㎡)건폐율(%)용적률(%)허가일착공처리일실제착공일최대지상층수최대지하층수최고높이(m)동수주용도부속용도설계사무소명감리사무소명시공자사무소명
108용도변경2022-건축과-용도변경허가-6부산광역시 연제구 연산동 1287-12 외2필지1,098452.536,710.6941.21532.482022-01-19<NA><NA>15259.32제1종근린생활시설<NA>일문 건축사사무소<NA><NA>
109용도변경2022-건축과-용도변경허가-7부산광역시 연제구 연산동 1500-36125.672.29135.9557.5557108.24042022-01-19<NA><NA>2<NA>7.51제2종근린생활시설일반음식점종합건축사사무소 돔<NA><NA>
110용도변경2022-건축과-용도변경허가-5부산광역시 연제구 연산동 579-32153.191.21187.1659.58122.252022-01-17<NA><NA>2<NA>0.01제2종근린생활시설<NA>일문 건축사사무소<NA><NA>
111신축2022-건축과-신축허가-3부산광역시 연제구 거제동 42-25206102.3364.4649.6602176.92232022-01-13<NA><NA>4011.91제2종근린생활시설사무소공간 더하기 건축사사무소<NA><NA>
112용도변경2022-건축과-용도변경허가-4부산광역시 연제구 연산동 1291-37133.279.36373.0859.5796217.23722022-01-11<NA><NA>4113.851단독주택근린생활시설명보 건축사사무소<NA><NA>
113신축2022-건축과-신축허가-1부산광역시 연제구 거제동 676-142567.7258.161,196.4645.47210.762022-01-072022-04-262022-04-267021.01공동주택(다세대주택) 및 업무시설(오피스텔)(주)다인종합건축사사무소(주)시엔티종합건축사사무소(주)세움주택건설
114신축2022-건축과-신축허가-2부산광역시 연제구 연산동 1123-25 외1필지2,1601,048.764,941.5748.5537162.16532022-01-072022-05-30<NA>4127.471종교시설<NA>(주)종합건축사사무소일감(주)아이엔지니어링건축사사무소엘에스종합건설(주)
115용도변경2022-건축과-용도변경허가-3부산광역시 연제구 연산동 419-4653387.854,23459.4392.432022-01-06<NA><NA>7324.61제2종근린생활시설수리점케이앤에스건축사사무소<NA><NA>
116용도변경2022-건축과-용도변경허가-1부산광역시 연제구 연산동 1253-1712063.48116.8752.997.392022-01-05<NA><NA>2<NA>0.01제1종근린생활시설2종근린생활시설건축사사무소 예천<NA><NA>
117용도변경2022-건축과-용도변경허가-2부산광역시 연제구 연산동 702-91,487.61,188.8815,277.3379.921,026.982022-01-05<NA><NA>17260.91업무시설외 3용도(의료시설(병원), 운동시설(스크린골프장), 근린생활시설)건축사사무소 가전건축<NA><NA>