Overview

Dataset statistics

Number of variables19
Number of observations10000
Missing cells6551
Missing cells (%)3.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory169.0 B

Variable types

Categorical5
Text4
Numeric9
DateTime1

Dataset

Description송파구 관내 건축허가현황으로 허가사항, 착공신고, 사용승인 현황으로 건축구분, 대지위치, 용도, 설계자, 주소, 시공자 등 정보제공
URLhttps://www.data.go.kr/data/15029295/fileData.do

Alerts

대지면적(제곱미터) is highly overall correlated with 건축면적(제곱미터) and 2 other fieldsHigh correlation
건축면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 2 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 5 other fieldsHigh correlation
건폐율(퍼센트) is highly overall correlated with 구조High correlation
용적률(퍼센트) is highly overall correlated with 연면적(제곱미터) and 2 other fieldsHigh correlation
최대지상층수 is highly overall correlated with 연면적(제곱미터) and 2 other fieldsHigh correlation
최대지하층수 is highly overall correlated with 대지면적(제곱미터) and 2 other fieldsHigh correlation
최고높이(미터) is highly overall correlated with 연면적(제곱미터) and 2 other fieldsHigh correlation
구조 is highly overall correlated with 건폐율(퍼센트)High correlation
건축구분 is highly imbalanced (54.6%)Imbalance
지목 is highly imbalanced (92.1%)Imbalance
구조 is highly imbalanced (80.9%)Imbalance
주용도 is highly imbalanced (56.6%)Imbalance
허가일 has 227 (2.3%) missing valuesMissing
착공처리일 has 3500 (35.0%) missing valuesMissing
최대지하층수 has 1963 (19.6%) missing valuesMissing
최고높이(미터) has 312 (3.1%) missing valuesMissing
설계사무소명 has 335 (3.4%) missing valuesMissing
건축면적(제곱미터) is highly skewed (γ1 = 28.85120227)Skewed
연면적(제곱미터) is highly skewed (γ1 = 66.33120431)Skewed
건폐율(퍼센트) is highly skewed (γ1 = 99.57575654)Skewed
용적률(퍼센트) is highly skewed (γ1 = 90.73583418)Skewed
최고높이(미터) is highly skewed (γ1 = 58.06062158)Skewed
동수 is highly skewed (γ1 = 99.94083084)Skewed
최대지하층수 has 4614 (46.1%) zerosZeros
최고높이(미터) has 136 (1.4%) zerosZeros
동수 has 277 (2.8%) zerosZeros

Reproduction

Analysis started2023-12-12 07:08:10.237561
Analysis finished2023-12-12 07:08:25.215077
Duration14.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

건축구분
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
신축
7204 
용도변경
1573 
증축
745 
대수선
 
436
가설건축물축조허가
 
33
Other values (2)
 
9

Length

Max length9
Median length2
Mean length2.3848
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신축 7204
72.0%
용도변경 1573
 
15.7%
증축 745
 
7.4%
대수선 436
 
4.4%
가설건축물축조허가 33
 
0.3%
허가/신고사항변경 5
 
0.1%
개축 4
 
< 0.1%

Length

2023-12-12T16:08:25.273209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:08:25.370118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신축 7204
72.0%
용도변경 1573
 
15.7%
증축 745
 
7.4%
대수선 436
 
4.4%
가설건축물축조허가 33
 
0.3%
허가/신고사항변경 5
 
< 0.1%
개축 4
 
< 0.1%
Distinct9888
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T16:08:26.007025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length17.3797
Min length15

Characters and Unicode

Total characters173797
Distinct characters59
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

Unique9778 ?
Unique (%)97.8%

Sample

1st row2008-건축과-신축허가-46
2nd row2019-건축과-신축허가-166
3rd row2016-건축과-용도변경허가-16
4th row2008-건축과-신축허가-170
5th row2016-건축과-신축허가-201
ValueCountFrequency (%)
2007-건축과-특정건축물(신축)-3 3
 
< 0.1%
2006-건축과-특정건축물(신축)-2 3
 
< 0.1%
2006-건축과-신축허가-78 2
 
< 0.1%
2006-건축과-신축허가-5 2
 
< 0.1%
2008-건축과-용도변경허가-8 2
 
< 0.1%
2006-건축과-신축허가-36 2
 
< 0.1%
2006-건축과-신축허가-28 2
 
< 0.1%
2006-건축과-신축허가-13 2
 
< 0.1%
2008-건축과-대수선허가-1 2
 
< 0.1%
2006-건축과-신축허가-64 2
 
< 0.1%
Other values (9878) 9978
99.8%
2023-12-12T16:08:26.592802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 30000
17.3%
18414
10.6%
0 17853
10.3%
2 16657
9.6%
1 11020
 
6.3%
10503
 
6.0%
9996
 
5.8%
9574
 
5.5%
9542
 
5.5%
7221
 
4.2%
Other values (49) 33017
19.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75688
43.5%
Decimal Number 66769
38.4%
Dash Punctuation 30000
 
17.3%
Open Punctuation 670
 
0.4%
Close Punctuation 670
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
18414
24.3%
10503
13.9%
9996
13.2%
9574
12.6%
9542
12.6%
7221
 
9.5%
1605
 
2.1%
1571
 
2.1%
1571
 
2.1%
1570
 
2.1%
Other values (36) 4121
 
5.4%
Decimal Number
ValueCountFrequency (%)
0 17853
26.7%
2 16657
24.9%
1 11020
16.5%
3 4119
 
6.2%
4 3222
 
4.8%
6 2876
 
4.3%
5 2843
 
4.3%
7 2780
 
4.2%
8 2720
 
4.1%
9 2679
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 30000
100.0%
Open Punctuation
ValueCountFrequency (%)
( 670
100.0%
Close Punctuation
ValueCountFrequency (%)
) 670
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 98109
56.5%
Hangul 75688
43.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
18414
24.3%
10503
13.9%
9996
13.2%
9574
12.6%
9542
12.6%
7221
 
9.5%
1605
 
2.1%
1571
 
2.1%
1571
 
2.1%
1570
 
2.1%
Other values (36) 4121
 
5.4%
Common
ValueCountFrequency (%)
- 30000
30.6%
0 17853
18.2%
2 16657
17.0%
1 11020
 
11.2%
3 4119
 
4.2%
4 3222
 
3.3%
6 2876
 
2.9%
5 2843
 
2.9%
7 2780
 
2.8%
8 2720
 
2.8%
Other values (3) 4019
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98109
56.5%
Hangul 75688
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 30000
30.6%
0 17853
18.2%
2 16657
17.0%
1 11020
 
11.2%
3 4119
 
4.2%
4 3222
 
3.3%
6 2876
 
2.9%
5 2843
 
2.9%
7 2780
 
2.8%
8 2720
 
2.8%
Other values (3) 4019
 
4.1%
Hangul
ValueCountFrequency (%)
18414
24.3%
10503
13.9%
9996
13.2%
9574
12.6%
9542
12.6%
7221
 
9.5%
1605
 
2.1%
1571
 
2.1%
1571
 
2.1%
1570
 
2.1%
Other values (36) 4121
 
5.4%
Distinct8013
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T16:08:27.030841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length39
Mean length19.4678
Min length1

Characters and Unicode

Total characters194678
Distinct characters110
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6628 ?
Unique (%)66.3%

Sample

1st row서울특별시 송파구 삼전동 15-3
2nd row서울특별시 송파구 송파동 97-22
3rd row서울특별시 송파구 방이동 64-1 외2필지
4th row서울특별시 송파구 방이동 101-21
5th row서울특별시 송파구 석촌동 274-1
ValueCountFrequency (%)
서울특별시 9997
24.2%
송파구 9996
24.2%
방이동 1398
 
3.4%
석촌동 1294
 
3.1%
잠실동 1194
 
2.9%
삼전동 1191
 
2.9%
송파동 1100
 
2.7%
가락동 1027
 
2.5%
문정동 924
 
2.2%
외1필지 855
 
2.1%
Other values (5399) 12275
29.8%
2023-12-12T16:08:27.776783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31258
16.1%
1 11122
 
5.7%
11097
 
5.7%
11097
 
5.7%
10057
 
5.2%
10003
 
5.1%
9998
 
5.1%
9998
 
5.1%
9997
 
5.1%
9997
 
5.1%
Other values (100) 70054
36.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 113990
58.6%
Decimal Number 40145
 
20.6%
Space Separator 31258
 
16.1%
Dash Punctuation 9223
 
4.7%
Uppercase Letter 46
 
< 0.1%
Other Number 5
 
< 0.1%
Lowercase Letter 4
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11097
9.7%
11097
9.7%
10057
8.8%
10003
8.8%
9998
8.8%
9998
8.8%
9997
8.8%
9997
8.8%
9997
8.8%
1398
 
1.2%
Other values (75) 20351
17.9%
Decimal Number
ValueCountFrequency (%)
1 11122
27.7%
2 5697
14.2%
3 4058
 
10.1%
4 3383
 
8.4%
5 3034
 
7.6%
6 2682
 
6.7%
7 2668
 
6.6%
8 2604
 
6.5%
0 2451
 
6.1%
9 2446
 
6.1%
Other Number
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Uppercase Letter
ValueCountFrequency (%)
D 40
87.0%
C 5
 
10.9%
R 1
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
d 3
75.0%
c 1
 
25.0%
Space Separator
ValueCountFrequency (%)
31258
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9223
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 113990
58.6%
Common 80638
41.4%
Latin 50
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11097
9.7%
11097
9.7%
10057
8.8%
10003
8.8%
9998
8.8%
9998
8.8%
9997
8.8%
9997
8.8%
9997
8.8%
1398
 
1.2%
Other values (75) 20351
17.9%
Common
ValueCountFrequency (%)
31258
38.8%
1 11122
 
13.8%
- 9223
 
11.4%
2 5697
 
7.1%
3 4058
 
5.0%
4 3383
 
4.2%
5 3034
 
3.8%
6 2682
 
3.3%
7 2668
 
3.3%
8 2604
 
3.2%
Other values (10) 4909
 
6.1%
Latin
ValueCountFrequency (%)
D 40
80.0%
C 5
 
10.0%
d 3
 
6.0%
R 1
 
2.0%
c 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 113990
58.6%
ASCII 80683
41.4%
Enclosed Alphanum 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31258
38.7%
1 11122
 
13.8%
- 9223
 
11.4%
2 5697
 
7.1%
3 4058
 
5.0%
4 3383
 
4.2%
5 3034
 
3.8%
6 2682
 
3.3%
7 2668
 
3.3%
8 2604
 
3.2%
Other values (10) 4954
 
6.1%
Hangul
ValueCountFrequency (%)
11097
9.7%
11097
9.7%
10057
8.8%
10003
8.8%
9998
8.8%
9998
8.8%
9997
8.8%
9997
8.8%
9997
8.8%
1398
 
1.2%
Other values (75) 20351
17.9%
Enclosed Alphanum
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

지목
Categorical

IMBALANCE 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
9564 
<NA>
 
325
 
14
공원
 
13
주차장
 
12
Other values (14)
 
72

Length

Max length5
Median length1
Mean length1.1173
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
9564
95.6%
<NA> 325
 
3.2%
14
 
0.1%
공원 13
 
0.1%
주차장 12
 
0.1%
주유소용지 12
 
0.1%
학교용지 10
 
0.1%
10
 
0.1%
잡종지 8
 
0.1%
종교용지 6
 
0.1%
Other values (9) 26
 
0.3%

Length

2023-12-12T16:08:27.969717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9564
95.6%
na 325
 
3.2%
14
 
0.1%
공원 13
 
0.1%
주차장 12
 
0.1%
주유소용지 12
 
0.1%
학교용지 10
 
0.1%
10
 
0.1%
잡종지 8
 
0.1%
종교용지 6
 
0.1%
Other values (9) 26
 
0.3%

대지면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct3357
Distinct (%)33.7%
Missing26
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean2572.0193
Minimum6
Maximum656178.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:08:28.110848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile165.3
Q1191.9
median234.05
Q3361.5
95-th percentile1743.6
Maximum656178.8
Range656172.8
Interquartile range (IQR)169.6

Descriptive statistics

Standard deviation22815.574
Coefficient of variation (CV)8.8706853
Kurtosis494.17722
Mean2572.0193
Median Absolute Deviation (MAD)54.45
Skewness19.775029
Sum25653321
Variance5.2055042 × 108
MonotonicityNot monotonic
2023-12-12T16:08:28.244179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198.4 81
 
0.8%
198.3 63
 
0.6%
165.3 51
 
0.5%
231.5 42
 
0.4%
191.7 39
 
0.4%
128246.2 39
 
0.4%
231.4 37
 
0.4%
175.2 35
 
0.4%
165.4 34
 
0.3%
181.8 34
 
0.3%
Other values (3347) 9519
95.2%
ValueCountFrequency (%)
6.0 1
< 0.1%
44.0 1
< 0.1%
53.0 1
< 0.1%
55.6 1
< 0.1%
73.0 1
< 0.1%
75.0 1
< 0.1%
76.19 1
< 0.1%
77.0 2
< 0.1%
78.0 1
< 0.1%
80.0 1
< 0.1%
ValueCountFrequency (%)
656178.8 5
 
0.1%
580852.8 3
 
< 0.1%
414136.0 2
 
< 0.1%
346570.4 1
 
< 0.1%
168648.5 3
 
< 0.1%
147112.0 5
 
0.1%
138845.3 9
 
0.1%
135861.2 1
 
< 0.1%
128246.2 39
0.4%
123492.3 17
0.2%

건축면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6058
Distinct (%)60.8%
Missing30
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean929.42161
Minimum0
Maximum482045
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:08:28.413851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile91.5045
Q1112.5
median136.6
Q3195.8675
95-th percentile802.084
Maximum482045
Range482045
Interquartile range (IQR)83.3675

Descriptive statistics

Standard deviation7815.9323
Coefficient of variation (CV)8.4094583
Kurtosis1489.6648
Mean929.42161
Median Absolute Deviation (MAD)30.02
Skewness28.851202
Sum9266333.4
Variance61088797
MonotonicityNot monotonic
2023-12-12T16:08:28.552897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76940.47 32
 
0.3%
99.0 19
 
0.2%
118.56 19
 
0.2%
122.4 18
 
0.2%
118.26 17
 
0.2%
24913.76 17
 
0.2%
118.8 16
 
0.2%
100.8 15
 
0.1%
8606.88 15
 
0.1%
118.32 14
 
0.1%
Other values (6048) 9788
97.9%
(Missing) 30
 
0.3%
ValueCountFrequency (%)
0.0 1
< 0.1%
0.0001 1
< 0.1%
6.25 1
< 0.1%
18.0 1
< 0.1%
27.0 2
< 0.1%
30.02 1
< 0.1%
31.5 1
< 0.1%
32.1 1
< 0.1%
32.12 1
< 0.1%
33.3 1
< 0.1%
ValueCountFrequency (%)
482045.0 1
 
< 0.1%
98054.0 1
 
< 0.1%
84018.92 1
 
< 0.1%
83932.15 4
 
< 0.1%
76940.47 32
0.3%
76940.27 1
 
< 0.1%
76914.0 4
 
< 0.1%
76893.17 1
 
< 0.1%
74770.28 1
 
< 0.1%
74377.93 1
 
< 0.1%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8408
Distinct (%)84.1%
Missing8
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean10584.123
Minimum6.25
Maximum13212302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:08:28.695135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.25
5-th percentile285.3915
Q1392.6175
median495.54
Q3899.205
95-th percentile9510.222
Maximum13212302
Range13212296
Interquartile range (IQR)506.5875

Descriptive statistics

Standard deviation155057.59
Coefficient of variation (CV)14.650018
Kurtosis5391.9197
Mean10584.123
Median Absolute Deviation (MAD)140.53
Skewness66.331204
Sum1.0575655 × 108
Variance2.4042857 × 1010
MonotonicityNot monotonic
2023-12-12T16:08:28.851696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
426635.55 15
 
0.1%
172747.67 15
 
0.1%
143730.11 15
 
0.1%
39849.1 13
 
0.1%
6736.65 11
 
0.1%
6130.31 11
 
0.1%
13195.04 10
 
0.1%
255.27 9
 
0.1%
805872.45 9
 
0.1%
12301.78 8
 
0.1%
Other values (8398) 9876
98.8%
ValueCountFrequency (%)
6.25 1
< 0.1%
18.0 1
< 0.1%
27.0 1
< 0.1%
30.02 1
< 0.1%
32.12 1
< 0.1%
33.84 1
< 0.1%
49.2 1
< 0.1%
50.25 1
< 0.1%
51.0 1
< 0.1%
52.69 1
< 0.1%
ValueCountFrequency (%)
13212302.0 1
 
< 0.1%
5222695.0 1
 
< 0.1%
1563379.68 1
 
< 0.1%
1540068.0 1
 
< 0.1%
1319504.0 1
 
< 0.1%
890015.0 1
 
< 0.1%
806049.78 2
 
< 0.1%
805872.45 9
0.1%
782497.25 1
 
< 0.1%
631566.4 3
 
< 0.1%

건폐율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2501
Distinct (%)25.2%
Missing61
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean61.001881
Minimum0
Maximum49462.268
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:08:29.022357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44.717
Q153.48
median59.33
Q359.79
95-th percentile59.97
Maximum49462.268
Range49462.268
Interquartile range (IQR)6.31

Descriptive statistics

Standard deviation495.7747
Coefficient of variation (CV)8.1272035
Kurtosis9923.0881
Mean61.001881
Median Absolute Deviation (MAD)0.61
Skewness99.575757
Sum606297.69
Variance245792.55
MonotonicityNot monotonic
2023-12-12T16:08:29.196116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.97 128
 
1.3%
59.99 124
 
1.2%
59.86 117
 
1.2%
59.88 115
 
1.1%
59.93 114
 
1.1%
59.98 113
 
1.1%
59.96 113
 
1.1%
59.94 110
 
1.1%
59.9 110
 
1.1%
59.92 110
 
1.1%
Other values (2491) 8785
87.8%
ValueCountFrequency (%)
0.0 3
< 0.1%
0.01 2
< 0.1%
0.048 1
 
< 0.1%
0.1087 1
 
< 0.1%
0.12 1
 
< 0.1%
0.162 1
 
< 0.1%
0.23 1
 
< 0.1%
0.24 1
 
< 0.1%
0.28 1
 
< 0.1%
0.3004 1
 
< 0.1%
ValueCountFrequency (%)
49462.268 1
< 0.1%
964.5333 1
< 0.1%
803.39 1
< 0.1%
99.55 1
< 0.1%
98.357 1
< 0.1%
95.13 1
< 0.1%
92.57 1
< 0.1%
85.9 1
< 0.1%
79.97 1
< 0.1%
79.55 1
< 0.1%

용적률(퍼센트)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6296
Distinct (%)63.3%
Missing58
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean240.14064
Minimum0
Maximum84080
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:08:29.373130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile109.3125
Q1192.49
median199.91
Q3239.46
95-th percentile419.53
Maximum84080
Range84080
Interquartile range (IQR)46.97

Descriptive statistics

Standard deviation871.53967
Coefficient of variation (CV)3.6292885
Kurtosis8642.8483
Mean240.14064
Median Absolute Deviation (MAD)21.75
Skewness90.735834
Sum2387478.2
Variance759581.39
MonotonicityNot monotonic
2023-12-12T16:08:29.561540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199.92 38
 
0.4%
199.98 35
 
0.4%
199.88 32
 
0.3%
199.95 32
 
0.3%
199.97 31
 
0.3%
199.87 29
 
0.3%
199.9 27
 
0.3%
199.82 27
 
0.3%
199.75 26
 
0.3%
199.91 25
 
0.2%
Other values (6286) 9640
96.4%
(Missing) 58
 
0.6%
ValueCountFrequency (%)
0.0 3
< 0.1%
0.01 2
< 0.1%
0.0907 1
 
< 0.1%
0.12 1
 
< 0.1%
0.1953 1
 
< 0.1%
0.24 1
 
< 0.1%
0.28 1
 
< 0.1%
0.38 1
 
< 0.1%
0.4011 1
 
< 0.1%
0.42 3
< 0.1%
ValueCountFrequency (%)
84080.0 1
 
< 0.1%
19940.0 1
 
< 0.1%
1086.94 1
 
< 0.1%
1068.09 1
 
< 0.1%
1067.8359 1
 
< 0.1%
1062.65 1
 
< 0.1%
1062.649 1
 
< 0.1%
1030.042 2
 
< 0.1%
1024.66 5
0.1%
1024.48 2
 
< 0.1%

구조
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
철근콘크리트구조
8866 
<NA>
 
358
일반철골구조
 
235
철골철근콘크리트구조
 
183
벽돌구조
 
134
Other values (14)
 
224

Length

Max length12
Median length8
Mean length7.7552
Min length3

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
철근콘크리트구조 8866
88.7%
<NA> 358
 
3.6%
일반철골구조 235
 
2.4%
철골철근콘크리트구조 183
 
1.8%
벽돌구조 134
 
1.3%
경량철골구조 134
 
1.3%
기타조적구조 28
 
0.3%
철골콘크리트구조 20
 
0.2%
프리케스트콘크리트구조 10
 
0.1%
기타강구조 8
 
0.1%
Other values (9) 24
 
0.2%

Length

2023-12-12T16:08:29.720880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
철근콘크리트구조 8866
88.7%
na 358
 
3.6%
일반철골구조 235
 
2.4%
철골철근콘크리트구조 183
 
1.8%
벽돌구조 134
 
1.3%
경량철골구조 134
 
1.3%
기타조적구조 28
 
0.3%
철골콘크리트구조 20
 
0.2%
프리케스트콘크리트구조 10
 
0.1%
기타강구조 8
 
0.1%
Other values (9) 24
 
0.2%

허가일
Text

MISSING 

Distinct4118
Distinct (%)42.1%
Missing227
Missing (%)2.3%
Memory size156.2 KiB
2023-12-12T16:08:30.051759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9987721
Min length5

Characters and Unicode

Total characters97718
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

Unique1909 ?
Unique (%)19.5%

Sample

1st row2008-04-17
2nd row2019-04-29
3rd row2016-04-04
4th row2008-07-18
5th row2016-06-20
ValueCountFrequency (%)
2003-06-27 70
 
0.7%
2003-06-24 66
 
0.7%
2003-06-28 56
 
0.6%
2003-06-25 53
 
0.5%
2003-06-26 43
 
0.4%
2003-06-23 41
 
0.4%
2003-06-20 33
 
0.3%
2003-06-30 30
 
0.3%
2003-06-19 26
 
0.3%
2003-07-01 25
 
0.3%
Other values (4108) 9330
95.5%
2023-12-12T16:08:30.562413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 27512
28.2%
- 19546
20.0%
2 18728
19.2%
1 12740
13.0%
3 3707
 
3.8%
6 3130
 
3.2%
7 2653
 
2.7%
5 2558
 
2.6%
4 2481
 
2.5%
8 2376
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 78172
80.0%
Dash Punctuation 19546
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27512
35.2%
2 18728
24.0%
1 12740
16.3%
3 3707
 
4.7%
6 3130
 
4.0%
7 2653
 
3.4%
5 2558
 
3.3%
4 2481
 
3.2%
8 2376
 
3.0%
9 2287
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 19546
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97718
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27512
28.2%
- 19546
20.0%
2 18728
19.2%
1 12740
13.0%
3 3707
 
3.8%
6 3130
 
3.2%
7 2653
 
2.7%
5 2558
 
2.6%
4 2481
 
2.5%
8 2376
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27512
28.2%
- 19546
20.0%
2 18728
19.2%
1 12740
13.0%
3 3707
 
3.8%
6 3130
 
3.2%
7 2653
 
2.7%
5 2558
 
2.6%
4 2481
 
2.5%
8 2376
 
2.4%

착공처리일
Date

MISSING 

Distinct3233
Distinct (%)49.7%
Missing3500
Missing (%)35.0%
Memory size156.2 KiB
Minimum1990-04-04 00:00:00
Maximum2023-06-21 00:00:00
2023-12-12T16:08:30.720896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:30.865015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

최대지상층수
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)0.4%
Missing31
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean5.5596349
Minimum0
Maximum123
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:08:31.020183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median5
Q35
95-th percentile10
Maximum123
Range123
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.6767293
Coefficient of variation (CV)0.66132568
Kurtosis231.20134
Mean5.5596349
Median Absolute Deviation (MAD)0
Skewness10.10734
Sum55424
Variance13.518338
MonotonicityNot monotonic
2023-12-12T16:08:31.220370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
5 5639
56.4%
4 1240
 
12.4%
6 978
 
9.8%
3 425
 
4.2%
7 370
 
3.7%
2 345
 
3.5%
8 144
 
1.4%
1 123
 
1.2%
10 122
 
1.2%
9 92
 
0.9%
Other values (26) 491
 
4.9%
ValueCountFrequency (%)
0 10
 
0.1%
1 123
 
1.2%
2 345
 
3.5%
3 425
 
4.2%
4 1240
 
12.4%
5 5639
56.4%
6 978
 
9.8%
7 370
 
3.7%
8 144
 
1.4%
9 92
 
0.9%
ValueCountFrequency (%)
123 2
 
< 0.1%
46 4
 
< 0.1%
39 3
 
< 0.1%
37 9
0.1%
36 1
 
< 0.1%
33 12
0.1%
32 3
 
< 0.1%
30 13
0.1%
29 3
 
< 0.1%
28 1
 
< 0.1%

최대지하층수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)0.1%
Missing1963
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean0.7317407
Minimum0
Maximum8
Zeros4614
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:08:31.360478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2298972
Coefficient of variation (CV)1.6807829
Kurtosis7.6850591
Mean0.7317407
Median Absolute Deviation (MAD)0
Skewness2.5892245
Sum5881
Variance1.5126472
MonotonicityNot monotonic
2023-12-12T16:08:31.506022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 4614
46.1%
1 2364
23.6%
2 476
 
4.8%
3 177
 
1.8%
5 162
 
1.6%
4 144
 
1.4%
6 62
 
0.6%
7 28
 
0.3%
8 10
 
0.1%
(Missing) 1963
19.6%
ValueCountFrequency (%)
0 4614
46.1%
1 2364
23.6%
2 476
 
4.8%
3 177
 
1.8%
4 144
 
1.4%
5 162
 
1.6%
6 62
 
0.6%
7 28
 
0.3%
8 10
 
0.1%
ValueCountFrequency (%)
8 10
 
0.1%
7 28
 
0.3%
6 62
 
0.6%
5 162
 
1.6%
4 144
 
1.4%
3 177
 
1.8%
2 476
 
4.8%
1 2364
23.6%
0 4614
46.1%

최고높이(미터)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1048
Distinct (%)10.8%
Missing312
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean21.949506
Minimum0
Maximum14600
Zeros136
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:08:31.633990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.7
Q113.2
median14.3
Q317
95-th percentile43.3
Maximum14600
Range14600
Interquartile range (IQR)3.8

Descriptive statistics

Standard deviation215.10128
Coefficient of variation (CV)9.7998232
Kurtosis3492.6909
Mean21.949506
Median Absolute Deviation (MAD)1.31
Skewness58.060622
Sum212646.81
Variance46268.559
MonotonicityNot monotonic
2023-12-12T16:08:31.800141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.2 607
 
6.1%
14.2 363
 
3.6%
14.4 350
 
3.5%
14.3 334
 
3.3%
13.0 329
 
3.3%
13.1 299
 
3.0%
14.5 200
 
2.0%
14.1 168
 
1.7%
13.7 151
 
1.5%
13.3 147
 
1.5%
Other values (1038) 6740
67.4%
(Missing) 312
 
3.1%
ValueCountFrequency (%)
0.0 136
1.4%
1.0 4
 
< 0.1%
2.0 1
 
< 0.1%
2.5 2
 
< 0.1%
2.54 1
 
< 0.1%
2.6 3
 
< 0.1%
2.64 1
 
< 0.1%
2.7 2
 
< 0.1%
2.8 1
 
< 0.1%
2.9 2
 
< 0.1%
ValueCountFrequency (%)
14600.0 1
 
< 0.1%
11700.0 1
 
< 0.1%
9672.0 1
 
< 0.1%
1901.0 1
 
< 0.1%
555.0 2
 
< 0.1%
149.4 4
< 0.1%
149.32 1
 
< 0.1%
148.35 8
0.1%
143.0 1
 
< 0.1%
133.0 1
 
< 0.1%

동수
Real number (ℝ)

SKEWED  ZEROS 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1706
Minimum0
Maximum10626
Zeros277
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:08:31.967677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum10626
Range10626
Interquartile range (IQR)0

Descriptive statistics

Standard deviation106.27003
Coefficient of variation (CV)48.958827
Kurtosis9992.0545
Mean2.1706
Median Absolute Deviation (MAD)0
Skewness99.940831
Sum21706
Variance11293.319
MonotonicityNot monotonic
2023-12-12T16:08:32.106002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 9414
94.1%
0 277
 
2.8%
2 171
 
1.7%
3 33
 
0.3%
5 24
 
0.2%
4 14
 
0.1%
6 12
 
0.1%
7 10
 
0.1%
8 9
 
0.1%
23 6
 
0.1%
Other values (17) 30
 
0.3%
ValueCountFrequency (%)
0 277
 
2.8%
1 9414
94.1%
2 171
 
1.7%
3 33
 
0.3%
4 14
 
0.1%
5 24
 
0.2%
6 12
 
0.1%
7 10
 
0.1%
8 9
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
10626 1
 
< 0.1%
168 1
 
< 0.1%
63 1
 
< 0.1%
30 1
 
< 0.1%
27 1
 
< 0.1%
25 1
 
< 0.1%
24 4
< 0.1%
23 6
0.1%
22 2
 
< 0.1%
21 1
 
< 0.1%

주용도
Categorical

IMBALANCE 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
공동주택
6180 
제2종근린생활시설
1270 
업무시설
678 
제1종근린생활시설
667 
단독주택
 
533
Other values (22)
672 

Length

Max length10
Median length4
Mean length5.0206
Min length2

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
공동주택 6180
61.8%
제2종근린생활시설 1270
 
12.7%
업무시설 678
 
6.8%
제1종근린생활시설 667
 
6.7%
단독주택 533
 
5.3%
교육연구시설 108
 
1.1%
숙박시설 80
 
0.8%
판매시설 79
 
0.8%
의료시설 63
 
0.6%
공장 62
 
0.6%
Other values (17) 280
 
2.8%

Length

2023-12-12T16:08:32.286188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공동주택 6180
61.8%
제2종근린생활시설 1270
 
12.7%
업무시설 678
 
6.8%
제1종근린생활시설 667
 
6.7%
단독주택 533
 
5.3%
교육연구시설 108
 
1.1%
숙박시설 80
 
0.8%
판매시설 79
 
0.8%
의료시설 63
 
0.6%
공장 62
 
0.6%
Other values (17) 280
 
2.8%

용도지역
Categorical

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
제2종일반주거지역
3511 
일반주거지역
2554 
도시지역
1695 
제3종일반주거지역
844 
<NA>
443 
Other values (18)
953 

Length

Max length13
Median length10
Mean length6.874
Min length4

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row제2종일반주거지역
2nd row제2종일반주거지역
3rd row<NA>
4th row제2종일반주거지역
5th row제2종일반주거지역

Common Values

ValueCountFrequency (%)
제2종일반주거지역 3511
35.1%
일반주거지역 2554
25.5%
도시지역 1695
17.0%
제3종일반주거지역 844
 
8.4%
<NA> 443
 
4.4%
일반상업지역 382
 
3.8%
준주거지역 361
 
3.6%
자연녹지지역 59
 
0.6%
제1종일반주거지역 38
 
0.4%
제2종전용주거지역 26
 
0.3%
Other values (13) 87
 
0.9%

Length

2023-12-12T16:08:32.459331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제2종일반주거지역 3511
35.1%
일반주거지역 2554
25.5%
도시지역 1695
17.0%
제3종일반주거지역 844
 
8.4%
na 443
 
4.4%
일반상업지역 382
 
3.8%
준주거지역 361
 
3.6%
자연녹지지역 59
 
0.6%
제1종일반주거지역 38
 
0.4%
제2종전용주거지역 26
 
0.3%
Other values (13) 87
 
0.9%

설계사무소명
Text

MISSING 

Distinct2790
Distinct (%)28.9%
Missing335
Missing (%)3.4%
Memory size156.2 KiB
2023-12-12T16:08:32.734980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length22
Mean length10.561924
Min length1

Characters and Unicode

Total characters102081
Distinct characters491
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1807 ?
Unique (%)18.7%

Sample

1st row예일건축사사무소
2nd row예일건축사사무소
3rd row성우A&U 건축사사무소
4th row원일환경건축사사무소
5th row(주)태건건축사사무소
ValueCountFrequency (%)
건축사사무소 1287
 
10.3%
사무소 445
 
3.5%
종합건축사사무소 353
 
2.8%
삼영건축사사무소 312
 
2.5%
다원건축사사무소 244
 
1.9%
건축사 242
 
1.9%
주)에이아이종합건축사사무소 187
 
1.5%
주식회사 172
 
1.4%
주)빌라건축사사무소 169
 
1.3%
종합건축사사무소가람건축 169
 
1.3%
Other values (2670) 8974
71.5%
2023-12-12T16:08:33.194518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18813
18.4%
10390
 
10.2%
10239
 
10.0%
9359
 
9.2%
9342
 
9.2%
3338
 
3.3%
) 3035
 
3.0%
( 3014
 
3.0%
2918
 
2.9%
2364
 
2.3%
Other values (481) 29269
28.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91346
89.5%
Close Punctuation 3036
 
3.0%
Open Punctuation 3014
 
3.0%
Space Separator 2918
 
2.9%
Uppercase Letter 1041
 
1.0%
Other Punctuation 503
 
0.5%
Lowercase Letter 101
 
0.1%
Decimal Number 85
 
0.1%
Modifier Symbol 28
 
< 0.1%
Dash Punctuation 5
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
18813
20.6%
10390
11.4%
10239
11.2%
9359
10.2%
9342
10.2%
3338
 
3.7%
2364
 
2.6%
2361
 
2.6%
1779
 
1.9%
1102
 
1.2%
Other values (419) 22259
24.4%
Uppercase Letter
ValueCountFrequency (%)
A 240
23.1%
C 222
21.3%
I 186
17.9%
M 174
16.7%
G 36
 
3.5%
J 28
 
2.7%
L 26
 
2.5%
N 20
 
1.9%
D 17
 
1.6%
U 15
 
1.4%
Other values (12) 77
 
7.4%
Lowercase Letter
ValueCountFrequency (%)
m 24
23.8%
c 17
16.8%
a 11
10.9%
s 7
 
6.9%
i 5
 
5.0%
u 5
 
5.0%
n 4
 
4.0%
l 4
 
4.0%
p 4
 
4.0%
t 3
 
3.0%
Other values (9) 17
16.8%
Decimal Number
ValueCountFrequency (%)
2 38
44.7%
1 36
42.4%
0 4
 
4.7%
4 2
 
2.4%
7 2
 
2.4%
3 2
 
2.4%
5 1
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 429
85.3%
& 52
 
10.3%
, 17
 
3.4%
? 2
 
0.4%
" 2
 
0.4%
· 1
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 3035
> 99.9%
] 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 3014
100.0%
Space Separator
ValueCountFrequency (%)
2918
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Other Symbol
ValueCountFrequency (%)
3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91349
89.5%
Common 9590
 
9.4%
Latin 1142
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
18813
20.6%
10390
11.4%
10239
11.2%
9359
10.2%
9342
10.2%
3338
 
3.7%
2364
 
2.6%
2361
 
2.6%
1779
 
1.9%
1102
 
1.2%
Other values (420) 22262
24.4%
Latin
ValueCountFrequency (%)
A 240
21.0%
C 222
19.4%
I 186
16.3%
M 174
15.2%
G 36
 
3.2%
J 28
 
2.5%
L 26
 
2.3%
m 24
 
2.1%
N 20
 
1.8%
c 17
 
1.5%
Other values (31) 169
14.8%
Common
ValueCountFrequency (%)
) 3035
31.6%
( 3014
31.4%
2918
30.4%
. 429
 
4.5%
& 52
 
0.5%
2 38
 
0.4%
1 36
 
0.4%
` 28
 
0.3%
, 17
 
0.2%
- 5
 
0.1%
Other values (10) 18
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91346
89.5%
ASCII 10731
 
10.5%
None 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
18813
20.6%
10390
11.4%
10239
11.2%
9359
10.2%
9342
10.2%
3338
 
3.7%
2364
 
2.6%
2361
 
2.6%
1779
 
1.9%
1102
 
1.2%
Other values (419) 22259
24.4%
ASCII
ValueCountFrequency (%)
) 3035
28.3%
( 3014
28.1%
2918
27.2%
. 429
 
4.0%
A 240
 
2.2%
C 222
 
2.1%
I 186
 
1.7%
M 174
 
1.6%
& 52
 
0.5%
2 38
 
0.4%
Other values (50) 423
 
3.9%
None
ValueCountFrequency (%)
3
75.0%
· 1
 
25.0%

Interactions

2023-12-12T16:08:23.769936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:14.966446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:16.008322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:17.122210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:18.281115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T16:08:23.876992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:15.074804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:16.129926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:17.257401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:18.411775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:19.515204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:20.690404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:21.809188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:22.953572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:23.958503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:15.179976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:16.263499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:17.389677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:18.524986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:19.615729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:20.789963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:21.937020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:23.080137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:24.044923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:15.319399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:16.392437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:17.511339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:18.639216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:19.723034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:20.909923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:22.062585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:23.191452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:24.143054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:15.437224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:16.486993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:17.642448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:18.764041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:19.831315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:21.032504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:22.201331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:23.293782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:24.222818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:15.546442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:16.590508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:17.742410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:18.870704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:20.148196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:21.165630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:22.339164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:23.388950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:24.304361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:15.647093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:16.713430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:17.853056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:19.009967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:20.243770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:21.288637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:22.455546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:23.486132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:24.385502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:15.789645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:16.850677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:18.026940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:19.141013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:20.352878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:21.417620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:22.585196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:23.586526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:24.464392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:15.901170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:17.004775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:18.172349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:19.262944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:20.454699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:21.555265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:22.714975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:08:23.679172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:08:33.613938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축구분지목대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)건폐율(퍼센트)용적률(퍼센트)구조최대지상층수최대지하층수최고높이(미터)동수주용도용도지역
건축구분1.0000.3880.5060.2030.0200.0000.0340.5560.1720.4820.0270.0000.7440.491
지목0.3881.0000.6140.1610.0000.0000.0000.6310.0000.0790.3060.0000.7740.407
대지면적(제곱미터)0.5060.6141.0000.5640.5460.0000.0000.7720.2330.2430.0000.0000.6090.326
건축면적(제곱미터)0.2030.1610.5641.0000.1271.0000.0000.5360.1870.2520.0000.0000.4700.233
연면적(제곱미터)0.0200.0000.5460.1271.0000.0000.0000.0000.1130.0960.0000.0000.0000.000
건폐율(퍼센트)0.0000.0000.0001.0000.0001.0000.000NaN0.0000.0000.0000.0000.0000.000
용적률(퍼센트)0.0340.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
구조0.5560.6310.7720.5360.000NaN0.0001.0000.3940.5750.0000.0000.5980.495
최대지상층수0.1720.0000.2330.1870.1130.0000.0000.3941.0000.6560.0000.0410.5960.514
최대지하층수0.4820.0790.2430.2520.0960.0000.0000.5750.6561.0000.0000.0760.7020.626
최고높이(미터)0.0270.3060.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.3540.000
동수0.0000.0000.0000.0000.0000.0000.0000.0000.0410.0760.0001.0000.1260.027
주용도0.7440.7740.6090.4700.0000.0000.0000.5980.5960.7020.3540.1261.0000.760
용도지역0.4910.4070.3260.2330.0000.0000.0000.4950.5140.6260.0000.0270.7601.000
2023-12-12T16:08:33.758121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구조주용도용도지역건축구분지목
구조1.0000.2100.1710.2870.189
주용도0.2101.0000.2990.4310.333
용도지역0.1710.2991.0000.2340.130
건축구분0.2870.4310.2341.0000.184
지목0.1890.3330.1300.1841.000
2023-12-12T16:08:33.887336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)건폐율(퍼센트)용적률(퍼센트)최대지상층수최대지하층수최고높이(미터)동수건축구분지목구조주용도용도지역
대지면적(제곱미터)1.0000.9640.874-0.4110.2480.3410.6000.4680.2080.1970.3300.4260.3110.128
건축면적(제곱미터)0.9641.0000.889-0.2710.3070.3860.5610.4970.2090.1400.0880.2920.2640.127
연면적(제곱미터)0.8740.8891.000-0.2880.5310.5060.6830.5820.1860.0130.0000.0000.0000.000
건폐율(퍼센트)-0.411-0.271-0.2881.0000.1360.010-0.392-0.114-0.1230.0000.0001.0000.0000.000
용적률(퍼센트)0.2480.3070.5310.1361.0000.6950.3630.5390.0050.0230.0000.0000.0000.000
최대지상층수0.3410.3860.5060.0100.6951.0000.3070.7580.0060.1110.0000.2130.3010.281
최대지하층수0.6000.5610.683-0.3920.3630.3071.0000.4480.1290.2790.0260.2270.3570.298
최고높이(미터)0.4680.4970.582-0.1140.5390.7580.4481.0000.0680.0170.1610.0000.1600.000
동수0.2080.2090.186-0.1230.0050.0060.1290.0681.0000.0000.0000.0000.0990.022
건축구분0.1970.1400.0130.0000.0230.1110.2790.0170.0001.0000.1840.2870.4310.234
지목0.3300.0880.0000.0000.0000.0000.0260.1610.0000.1841.0000.1890.3330.130
구조0.4260.2920.0001.0000.0000.2130.2270.0000.0000.2870.1891.0000.2100.171
주용도0.3110.2640.0000.0000.0000.3010.3570.1600.0990.4310.3330.2101.0000.299
용도지역0.1280.1270.0000.0000.0000.2810.2980.0000.0220.2340.1300.1710.2991.000

Missing values

2023-12-12T16:08:24.583777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:08:24.823834image/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.
2023-12-12T16:08:25.020445image/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

건축구분허가번호대지위치지목대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)건폐율(퍼센트)용적률(퍼센트)구조허가일착공처리일최대지상층수최대지하층수최고높이(미터)동수주용도용도지역설계사무소명
7249신축2008-건축과-신축허가-46서울특별시 송파구 삼전동 15-3253.4151.65500.459.85197.47철근콘크리트구조2008-04-172008-04-235013.21공동주택제2종일반주거지역예일건축사사무소
1642신축2019-건축과-신축허가-166서울특별시 송파구 송파동 97-22288.2164.83617.6957.19199.48철근콘크리트구조2019-04-292019-07-166119.61공동주택제2종일반주거지역예일건축사사무소
3302용도변경2016-건축과-용도변경허가-16서울특별시 송파구 방이동 64-1 외2필지793.8381.032306.6648.0227.0철근콘크리트구조2016-04-04<NA>510.01제2종근린생활시설<NA>성우A&U 건축사사무소
7067신축2008-건축과-신축허가-170서울특별시 송파구 방이동 101-21241.9144.62467.4659.785193.2451철근콘크리트구조2008-07-18<NA>5014.11공동주택제2종일반주거지역원일환경건축사사무소
3191신축2016-건축과-신축허가-201서울특별시 송파구 석촌동 274-1197.8118.44526.3559.88199.39철근콘크리트구조2016-06-202016-06-225113.91공동주택제2종일반주거지역(주)태건건축사사무소
920신축2021-건축과-신축허가-72서울특별시 송파구 방이동 173-11344.5206.34685.6359.9199.02철근콘크리트구조2021-04-232021-06-185016.91공동주택도시지역(주)토림종합건축사사무소
5511신축2012-건축과-신축허가-20서울특별시 송파구 문정동 63-17245.8146.4485.3759.56197.47철근콘크리트구조2012-02-032012-02-155014.651공동주택제2종일반주거지역터건축사사무소
1637대수선2019-건축과-대수선허가-13서울특별시 송파구 잠실동 191-1558.8332.91708.1259.57237.35철근콘크리트구조2019-05-08<NA>5117.21제2종근린생활시설제3종일반주거지역신태양건축사사무소
6293신축2010-건축과-신축허가-145서울특별시 송파구 풍납동 229-25120.069.09236.7157.575159.2333철근콘크리트구조2010-08-17<NA>4010.91단독주택제2종일반주거지역건축사사무소 네모건축
8521신축2005-건축과-신축허가-111서울특별시 송파구 마천동 320-1155.090.06309.8858.1199.92철근콘크리트구조2005-08-122005-09-085013.11공동주택제2종일반주거지역예일건축사사무소
건축구분허가번호대지위치지목대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)건폐율(퍼센트)용적률(퍼센트)구조허가일착공처리일최대지상층수최대지하층수최고높이(미터)동수주용도용도지역설계사무소명
7550용도변경2007-건축과-용도변경허가-133서울특별시 송파구 잠실동 183-1<NA>908.9523.054000.3157.55280.81철근콘크리트구조2007-08-02<NA>52<NA>0제1종근린생활시설<NA><NA>
10257신축2003-건축과-신축허가-156서울특별시 송파구 잠실동 317-19197.8118.48392.7159.9198.54철근콘크리트구조2003-04-282003-07-225014.41공동주택일반주거지역(주)A.I.종합건축사사무소
13098신축2001-건축과-신축허가-324서울특별시 송파구 석촌동 211-3<NA>187.4111.78466.259.6478248.7727철근콘크리트구조2001-07-07<NA>5<NA>13.01공동주택일반주거지역한숲건축사사무소
703대수선2021-건축과-대수선허가-49서울특별시 송파구 마천동 579645.2364.87886.5456.55137.41일반철골구조2021-10-082021-10-224<NA>17.31종교시설제2종일반주거지역다원건축사사무소
8251신축2006-건축과-신축허가-56서울특별시 송파구 가락동 127-18190.0113.76378.0859.87198.99철근콘크리트구조2006-01-172006-02-065012.851공동주택제2종일반주거지역성민그룹건축사사무소
5128용도변경2012-건축과-용도변경허가-68서울특별시 송파구 석촌동 1337.1202.022046.759.93379.31철근콘크리트구조2012-08-08<NA>7327.11업무시설준주거지역(주)에이아이종합건축사사무소
7731신축2007-건축과-신축허가-46서울특별시 송파구 장지동 841-6600.0359.822420.359.97295.34철근콘크리트구조2007-03-272007-04-135221.81제1종근린생활시설준주거지역건축사사무소토우
14128증축2014-건축과-특정건축물(증축)-1334서울특별시 송파구 풍납동 87-1250.0149.79568.1559.92227.26철근콘크리트구조<NA><NA>5<NA>13.31공동주택제2종일반주거지역amc건축사사무소
13361신축2001-건축과-신축허가-103서울특별시 송파구 석촌동 54-8355.6186.47659.9852.44185.6철근콘크리트구조2001-04-20<NA>5013.951공동주택일반주거지역기성건축
13279신축2001-건축과-신축허가-173서울특별시 송파구 삼전동 116-1165.498.6397.0459.61240.05철근콘크리트구조2001-05-292002-05-095<NA>13.01공동주택일반주거지역(주)빌라건축사사무소