Overview

Dataset statistics

Number of variables19
Number of observations108
Missing cells360
Missing cells (%)17.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.8 KiB
Average record size in memory159.2 B

Variable types

Categorical3
Text9
Numeric5
DateTime2

Dataset

Description서울특별시 동대문구 건설현장시공정보에 대한 데이터로건축구분,대지위치,허가일자,착공일자 등 등의 항목을 제공합니다.
Author서울특별시 동대문구
URLhttps://www.data.go.kr/data/15004949/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연면적(m2)_증축연면적(m2) is highly overall correlated with 최대지상층수 and 4 other fieldsHigh correlation
최대지상층수 is highly overall correlated with 연면적(m2)_증축연면적(m2) and 3 other fieldsHigh correlation
최대지하층수 is highly overall correlated with 연면적(m2)_증축연면적(m2) and 3 other fieldsHigh correlation
세대수 is highly overall correlated with 연면적(m2)_증축연면적(m2) and 4 other fieldsHigh correlation
호수 is highly overall correlated with 연면적(m2)_증축연면적(m2) and 2 other fieldsHigh correlation
건축구분 is highly overall correlated with 세대수High correlation
가구수 is highly overall correlated with 연면적(m2)_증축연면적(m2) and 1 other fieldsHigh correlation
가구수 is highly imbalanced (70.0%)Imbalance
착공일자 has 3 (2.8%) missing valuesMissing
부속용도 has 31 (28.7%) missing valuesMissing
세대수 has 67 (62.0%) missing valuesMissing
호수 has 68 (63.0%) missing valuesMissing
시공업체전화번호 has 41 (38.0%) missing valuesMissing
시공업체명 has 5 (4.6%) missing valuesMissing
설계사무소전화번호 has 52 (48.1%) missing valuesMissing
설계사무소명 has 21 (19.4%) missing valuesMissing
감리사무소전화번호 has 50 (46.3%) missing valuesMissing
감리사무소명 has 21 (19.4%) missing valuesMissing
대지위치 has unique valuesUnique
최대지하층수 has 38 (35.2%) zerosZeros

Reproduction

Analysis started2024-04-21 00:59:06.515536
Analysis finished2024-04-21 00:59:12.387207
Duration5.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

건축구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size996.0 B
신축
82 
해체신고
13 
해체허가
 
7
주택사업승인(신축)
 
4
증축
 
2

Length

Max length10
Median length2
Mean length2.6666667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신축 82
75.9%
해체신고 13
 
12.0%
해체허가 7
 
6.5%
주택사업승인(신축) 4
 
3.7%
증축 2
 
1.9%

Length

2024-04-21T09:59:12.451489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T09:59:12.546569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신축 82
75.9%
해체신고 13
 
12.0%
해체허가 7
 
6.5%
주택사업승인(신축 4
 
3.7%
증축 2
 
1.9%

대지위치
Text

UNIQUE 

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size996.0 B
2024-04-21T09:59:12.821613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length22.203704
Min length19

Characters and Unicode

Total characters2398
Distinct characters43
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

Unique108 ?
Unique (%)100.0%

Sample

1st row서울특별시 동대문구 답십리동 21-78
2nd row서울특별시 동대문구 답십리동 22-3 외2필지
3rd row서울특별시 동대문구 답십리동 252-13
4th row서울특별시 동대문구 답십리동 266-1
5th row서울특별시 동대문구 답십리동 467-12 외1필지
ValueCountFrequency (%)
서울특별시 108
23.3%
동대문구 108
23.3%
장안동 22
 
4.8%
외1필지 20
 
4.3%
답십리동 19
 
4.1%
전농동 16
 
3.5%
용두동 13
 
2.8%
휘경동 11
 
2.4%
신설동 8
 
1.7%
외2필지 6
 
1.3%
Other values (115) 132
28.5%
2024-04-21T09:59:13.386300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
355
 
14.8%
동 216
 
9.0%
문 114
 
4.8%
서 108
 
4.5%
특 108
 
4.5%
별 108
 
4.5%
시 108
 
4.5%
대 108
 
4.5%
구 108
 
4.5%
울 108
 
4.5%
Other values (33) 957
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1413
58.9%
Decimal Number 523
 
21.8%
Space Separator 355
 
14.8%
Dash Punctuation 107
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 216
15.3%
문 114
 
8.1%
서 108
 
7.6%
특 108
 
7.6%
별 108
 
7.6%
시 108
 
7.6%
대 108
 
7.6%
구 108
 
7.6%
울 108
 
7.6%
외 32
 
2.3%
Other values (21) 295
20.9%
Decimal Number
ValueCountFrequency (%)
1 93
17.8%
2 84
16.1%
3 65
12.4%
4 59
11.3%
6 49
9.4%
9 43
8.2%
8 39
7.5%
5 38
7.3%
7 31
 
5.9%
0 22
 
4.2%
Space Separator
ValueCountFrequency (%)
355
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 107
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1413
58.9%
Common 985
41.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 216
15.3%
문 114
 
8.1%
서 108
 
7.6%
특 108
 
7.6%
별 108
 
7.6%
시 108
 
7.6%
대 108
 
7.6%
구 108
 
7.6%
울 108
 
7.6%
외 32
 
2.3%
Other values (21) 295
20.9%
Common
ValueCountFrequency (%)
355
36.0%
- 107
 
10.9%
1 93
 
9.4%
2 84
 
8.5%
3 65
 
6.6%
4 59
 
6.0%
6 49
 
5.0%
9 43
 
4.4%
8 39
 
4.0%
5 38
 
3.9%
Other values (2) 53
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1413
58.9%
ASCII 985
41.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
355
36.0%
- 107
 
10.9%
1 93
 
9.4%
2 84
 
8.5%
3 65
 
6.6%
4 59
 
6.0%
6 49
 
5.0%
9 43
 
4.4%
8 39
 
4.0%
5 38
 
3.9%
Other values (2) 53
 
5.4%
Hangul
ValueCountFrequency (%)
동 216
15.3%
문 114
 
8.1%
서 108
 
7.6%
특 108
 
7.6%
별 108
 
7.6%
시 108
 
7.6%
대 108
 
7.6%
구 108
 
7.6%
울 108
 
7.6%
외 32
 
2.3%
Other values (21) 295
20.9%

연면적(m2)_증축연면적(m2)
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4977.8035
Minimum31.07
Maximum64523.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-04-21T09:59:13.520215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31.07
5-th percentile64.1895
Q1361.88
median674.645
Q33290.3225
95-th percentile22619.579
Maximum64523.52
Range64492.45
Interquartile range (IQR)2928.4425

Descriptive statistics

Standard deviation10951.467
Coefficient of variation (CV)2.20006
Kurtosis15.718345
Mean4977.8035
Median Absolute Deviation (MAD)609.89
Skewness3.7576161
Sum537602.78
Variance1.1993462 × 108
MonotonicityNot monotonic
2024-04-21T09:59:13.637562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2352.37 2
 
1.9%
221.94 2
 
1.9%
517.82 1
 
0.9%
372.0 1
 
0.9%
21985.79 1
 
0.9%
4137.9 1
 
0.9%
487.88 1
 
0.9%
106.0 1
 
0.9%
7620.12 1
 
0.9%
10789.76 1
 
0.9%
Other values (96) 96
88.9%
ValueCountFrequency (%)
31.07 1
0.9%
31.44 1
0.9%
32.5 1
0.9%
44.07 1
0.9%
58.74 1
0.9%
62.87 1
0.9%
66.64 1
0.9%
106.0 1
0.9%
121.36 1
0.9%
131.1 1
0.9%
ValueCountFrequency (%)
64523.52 1
0.9%
60759.62 1
0.9%
50203.24 1
0.9%
29680.5 1
0.9%
24419.2 1
0.9%
22785.35 1
0.9%
22311.72 1
0.9%
21985.79 1
0.9%
18500.55 1
0.9%
17617.22 1
0.9%

허가(신고)일자
Date

Distinct91
Distinct (%)85.0%
Missing1
Missing (%)0.9%
Memory size996.0 B
Minimum2018-09-21 00:00:00
Maximum2023-03-09 00:00:00
2024-04-21T09:59:13.754453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:13.872712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

착공일자
Text

MISSING 

Distinct89
Distinct (%)84.8%
Missing3
Missing (%)2.8%
Memory size996.0 B
2024-04-21T09:59:14.108810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9238095
Min length2

Characters and Unicode

Total characters1042
Distinct characters13
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

Unique75 ?
Unique (%)71.4%

Sample

1st row2022-10-25
2nd row2023-02-28
3rd row2020-11-20
4th row2022-08-03
5th row2022-06-24
ValueCountFrequency (%)
2023-03-09 3
 
2.9%
2023-02-20 3
 
2.9%
2020-05-11 2
 
1.9%
2022-10-25 2
 
1.9%
2022-10-01 2
 
1.9%
2022-08-16 2
 
1.9%
2022-05-31 2
 
1.9%
2022-09-05 2
 
1.9%
2023-02-28 2
 
1.9%
2023-02-13 2
 
1.9%
Other values (79) 83
79.0%
2024-04-21T09:59:14.487915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 320
30.7%
0 255
24.5%
- 208
20.0%
1 106
 
10.2%
3 49
 
4.7%
5 24
 
2.3%
6 20
 
1.9%
9 16
 
1.5%
8 16
 
1.5%
4 15
 
1.4%
Other values (3) 13
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 832
79.8%
Dash Punctuation 208
 
20.0%
Other Letter 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 320
38.5%
0 255
30.6%
1 106
 
12.7%
3 49
 
5.9%
5 24
 
2.9%
6 20
 
2.4%
9 16
 
1.9%
8 16
 
1.9%
4 15
 
1.8%
7 11
 
1.3%
Other Letter
ValueCountFrequency (%)
중 1
50.0%
지 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1040
99.8%
Hangul 2
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
2 320
30.8%
0 255
24.5%
- 208
20.0%
1 106
 
10.2%
3 49
 
4.7%
5 24
 
2.3%
6 20
 
1.9%
9 16
 
1.5%
8 16
 
1.5%
4 15
 
1.4%
Hangul
ValueCountFrequency (%)
중 1
50.0%
지 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1040
99.8%
Hangul 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 320
30.8%
0 255
24.5%
- 208
20.0%
1 106
 
10.2%
3 49
 
4.7%
5 24
 
2.3%
6 20
 
1.9%
9 16
 
1.5%
8 16
 
1.5%
4 15
 
1.4%
Hangul
ValueCountFrequency (%)
중 1
50.0%
지 1
50.0%

최대지상층수
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.462963
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-04-21T09:59:14.605116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median5.5
Q313.25
95-th percentile20
Maximum43
Range42
Interquartile range (IQR)9.25

Descriptive statistics

Standard deviation7.0623735
Coefficient of variation (CV)0.83450366
Kurtosis4.1013782
Mean8.462963
Median Absolute Deviation (MAD)2.5
Skewness1.6680242
Sum914
Variance49.87712
MonotonicityNot monotonic
2024-04-21T09:59:14.698133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
5 21
19.4%
6 12
11.1%
4 11
10.2%
1 8
 
7.4%
3 8
 
7.4%
20 7
 
6.5%
7 6
 
5.6%
17 6
 
5.6%
2 6
 
5.6%
10 3
 
2.8%
Other values (11) 20
18.5%
ValueCountFrequency (%)
1 8
 
7.4%
2 6
 
5.6%
3 8
 
7.4%
4 11
10.2%
5 21
19.4%
6 12
11.1%
7 6
 
5.6%
8 2
 
1.9%
9 1
 
0.9%
10 3
 
2.8%
ValueCountFrequency (%)
43 1
 
0.9%
25 1
 
0.9%
23 1
 
0.9%
20 7
6.5%
19 2
 
1.9%
18 3
2.8%
17 6
5.6%
16 1
 
0.9%
15 2
 
1.9%
14 3
2.8%

최대지하층수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3796296
Minimum0
Maximum7
Zeros38
Zeros (%)35.2%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-04-21T09:59:14.792444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6784371
Coefficient of variation (CV)1.2165853
Kurtosis1.9330669
Mean1.3796296
Median Absolute Deviation (MAD)1
Skewness1.6025711
Sum149
Variance2.8171513
MonotonicityNot monotonic
2024-04-21T09:59:14.896574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 38
35.2%
0 38
35.2%
2 15
 
13.9%
5 5
 
4.6%
6 4
 
3.7%
4 4
 
3.7%
3 3
 
2.8%
7 1
 
0.9%
ValueCountFrequency (%)
0 38
35.2%
1 38
35.2%
2 15
 
13.9%
3 3
 
2.8%
4 4
 
3.7%
5 5
 
4.6%
6 4
 
3.7%
7 1
 
0.9%
ValueCountFrequency (%)
7 1
 
0.9%
6 4
 
3.7%
5 5
 
4.6%
4 4
 
3.7%
3 3
 
2.8%
2 15
 
13.9%
1 38
35.2%
0 38
35.2%

주용도
Categorical

Distinct12
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size996.0 B
공동주택
33 
업무시설
23 
제2종근린생활시설
18 
단독주택
17 
제1종근린생활시설
8 
Other values (7)
9 

Length

Max length16
Median length4
Mean length5.5
Min length4

Unique

Unique6 ?
Unique (%)5.6%

Sample

1st row제2종근린생활시설
2nd row공동주택
3rd row제1종근린생활시설
4th row제1종근린생활시설
5th row공동주택

Common Values

ValueCountFrequency (%)
공동주택 33
30.6%
업무시설 23
21.3%
제2종근린생활시설 18
16.7%
단독주택 17
15.7%
제1종근린생활시설 8
 
7.4%
근린생활시설 3
 
2.8%
공동주택(부속 도시형생활주택) 1
 
0.9%
근린생활시설(다가구주택) 1
 
0.9%
판매시설 1
 
0.9%
숙박시설 1
 
0.9%
Other values (2) 2
 
1.9%

Length

2024-04-21T09:59:15.004002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공동주택 33
30.3%
업무시설 23
21.1%
제2종근린생활시설 18
16.5%
단독주택 17
15.6%
제1종근린생활시설 8
 
7.3%
근린생활시설 3
 
2.8%
공동주택(부속 1
 
0.9%
도시형생활주택 1
 
0.9%
근린생활시설(다가구주택 1
 
0.9%
판매시설 1
 
0.9%
Other values (3) 3
 
2.8%

부속용도
Text

MISSING 

Distinct57
Distinct (%)74.0%
Missing31
Missing (%)28.7%
Memory size996.0 B
2024-04-21T09:59:15.161489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length16
Mean length10.753247
Min length2

Characters and Unicode

Total characters828
Distinct characters67
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

Unique47 ?
Unique (%)61.0%

Sample

1st row사무소
2nd row도시형생활주택(단지형다세대주택)
3rd row소매점,의원,사무소
4th row제2종근린생활시설
5th row도시형생활주택(단지형다세대주택)
ValueCountFrequency (%)
오피스텔 14
 
12.7%
및 9
 
8.2%
근린생활시설 7
 
6.4%
다세대주택 7
 
6.4%
도시형생활주택 6
 
5.5%
생활주택 4
 
3.6%
사무소 3
 
2.7%
도시형 3
 
2.7%
다중주택 3
 
2.7%
다가구주택 2
 
1.8%
Other values (46) 52
47.3%
2024-04-21T09:59:15.456133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 57
 
6.9%
택 54
 
6.5%
시 48
 
5.8%
생 44
 
5.3%
활 43
 
5.2%
33
 
4.0%
형 31
 
3.7%
다 30
 
3.6%
설 28
 
3.4%
근 24
 
2.9%
Other values (57) 436
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 699
84.4%
Space Separator 33
 
4.0%
Decimal Number 24
 
2.9%
Other Punctuation 23
 
2.8%
Open Punctuation 22
 
2.7%
Close Punctuation 22
 
2.7%
Dash Punctuation 5
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 57
 
8.2%
택 54
 
7.7%
시 48
 
6.9%
생 44
 
6.3%
활 43
 
6.2%
형 31
 
4.4%
다 30
 
4.3%
설 28
 
4.0%
근 24
 
3.4%
린 23
 
3.3%
Other values (45) 317
45.4%
Decimal Number
ValueCountFrequency (%)
2 15
62.5%
1 6
 
25.0%
0 1
 
4.2%
4 1
 
4.2%
8 1
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 17
73.9%
/ 5
 
21.7%
. 1
 
4.3%
Space Separator
ValueCountFrequency (%)
33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 699
84.4%
Common 129
 
15.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 57
 
8.2%
택 54
 
7.7%
시 48
 
6.9%
생 44
 
6.3%
활 43
 
6.2%
형 31
 
4.4%
다 30
 
4.3%
설 28
 
4.0%
근 24
 
3.4%
린 23
 
3.3%
Other values (45) 317
45.4%
Common
ValueCountFrequency (%)
33
25.6%
( 22
17.1%
) 22
17.1%
, 17
13.2%
2 15
11.6%
1 6
 
4.7%
/ 5
 
3.9%
- 5
 
3.9%
0 1
 
0.8%
. 1
 
0.8%
Other values (2) 2
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 699
84.4%
ASCII 129
 
15.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 57
 
8.2%
택 54
 
7.7%
시 48
 
6.9%
생 44
 
6.3%
활 43
 
6.2%
형 31
 
4.4%
다 30
 
4.3%
설 28
 
4.0%
근 24
 
3.4%
린 23
 
3.3%
Other values (45) 317
45.4%
ASCII
ValueCountFrequency (%)
33
25.6%
( 22
17.1%
) 22
17.1%
, 17
13.2%
2 15
11.6%
1 6
 
4.7%
/ 5
 
3.9%
- 5
 
3.9%
0 1
 
0.8%
. 1
 
0.8%
Other values (2) 2
 
1.6%

세대수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)65.9%
Missing67
Missing (%)62.0%
Infinite0
Infinite (%)0.0%
Mean48.682927
Minimum1
Maximum349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-04-21T09:59:15.566565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q112
median19
Q340
95-th percentile284
Maximum349
Range348
Interquartile range (IQR)28

Descriptive statistics

Standard deviation79.502654
Coefficient of variation (CV)1.6330705
Kurtosis7.8537962
Mean48.682927
Median Absolute Deviation (MAD)9
Skewness2.9082194
Sum1996
Variance6320.672
MonotonicityNot monotonic
2024-04-21T09:59:15.692161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
10 5
 
4.6%
12 3
 
2.8%
8 3
 
2.8%
65 2
 
1.9%
38 2
 
1.9%
15 2
 
1.9%
42 2
 
1.9%
16 2
 
1.9%
19 2
 
1.9%
40 1
 
0.9%
Other values (17) 17
 
15.7%
(Missing) 67
62.0%
ValueCountFrequency (%)
1 1
 
0.9%
8 3
2.8%
9 1
 
0.9%
10 5
4.6%
12 3
2.8%
14 1
 
0.9%
15 2
 
1.9%
16 2
 
1.9%
17 1
 
0.9%
19 2
 
1.9%
ValueCountFrequency (%)
349 1
0.9%
299 1
0.9%
284 1
0.9%
143 1
0.9%
99 1
0.9%
65 2
1.9%
53 1
0.9%
42 2
1.9%
40 1
0.9%
38 2
1.9%

호수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)67.5%
Missing68
Missing (%)63.0%
Infinite0
Infinite (%)0.0%
Mean72.675
Minimum1
Maximum409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-04-21T09:59:15.814692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.5
median42
Q395.75
95-th percentile300.75
Maximum409
Range408
Interquartile range (IQR)92.25

Descriptive statistics

Standard deviation97.621033
Coefficient of variation (CV)1.3432547
Kurtosis3.4616225
Mean72.675
Median Absolute Deviation (MAD)40
Skewness1.9117412
Sum2907
Variance9529.866
MonotonicityNot monotonic
2024-04-21T09:59:15.921840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 5
 
4.6%
2 5
 
4.6%
6 3
 
2.8%
4 2
 
1.9%
48 2
 
1.9%
42 2
 
1.9%
180 1
 
0.9%
105 1
 
0.9%
65 1
 
0.9%
98 1
 
0.9%
Other values (17) 17
 
15.7%
(Missing) 68
63.0%
ValueCountFrequency (%)
1 5
4.6%
2 5
4.6%
4 2
 
1.9%
6 3
2.8%
9 1
 
0.9%
18 1
 
0.9%
35 1
 
0.9%
40 1
 
0.9%
42 2
 
1.9%
48 2
 
1.9%
ValueCountFrequency (%)
409 1
0.9%
315 1
0.9%
300 1
0.9%
240 1
0.9%
180 1
0.9%
171 1
0.9%
144 1
0.9%
142 1
0.9%
105 1
0.9%
98 1
0.9%

가구수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size996.0 B
<NA>
95 
1
 
6
2
 
2
42
 
2
7
 
2

Length

Max length4
Median length4
Mean length3.6574074
Min length1

Unique

Unique1 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 95
88.0%
1 6
 
5.6%
2 2
 
1.9%
42 2
 
1.9%
7 2
 
1.9%
3 1
 
0.9%

Length

2024-04-21T09:59:16.036813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T09:59:16.142319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 95
88.0%
1 6
 
5.6%
2 2
 
1.9%
42 2
 
1.9%
7 2
 
1.9%
3 1
 
0.9%

시공업체전화번호
Text

MISSING 

Distinct60
Distinct (%)89.6%
Missing41
Missing (%)38.0%
Memory size996.0 B
2024-04-21T09:59:16.339236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.686567
Min length11

Characters and Unicode

Total characters783
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)79.1%

Sample

1st row031-5177-7300
2nd row02-471-7708
3rd row032-425-7679
4th row02-980-8000
5th row02-355-4458
ValueCountFrequency (%)
02-2213-0691 3
 
4.5%
02-2134-1956 2
 
3.0%
031-406-4354 2
 
3.0%
02-746-4401 2
 
3.0%
02-831-3680 2
 
3.0%
02-967-3001 2
 
3.0%
02-888-8885 2
 
3.0%
031-942-1665 1
 
1.5%
062-380-2693 1
 
1.5%
070-7436-7931 1
 
1.5%
Other values (49) 49
73.1%
2024-04-21T09:59:16.682891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 134
17.1%
0 130
16.6%
2 94
12.0%
1 86
11.0%
4 59
7.5%
3 56
7.2%
6 54
6.9%
5 48
 
6.1%
9 41
 
5.2%
8 40
 
5.1%
Other values (2) 41
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 647
82.6%
Dash Punctuation 134
 
17.1%
Space Separator 2
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 130
20.1%
2 94
14.5%
1 86
13.3%
4 59
9.1%
3 56
8.7%
6 54
8.3%
5 48
 
7.4%
9 41
 
6.3%
8 40
 
6.2%
7 39
 
6.0%
Dash Punctuation
ValueCountFrequency (%)
- 134
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 134
17.1%
0 130
16.6%
2 94
12.0%
1 86
11.0%
4 59
7.5%
3 56
7.2%
6 54
6.9%
5 48
 
6.1%
9 41
 
5.2%
8 40
 
5.1%
Other values (2) 41
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 134
17.1%
0 130
16.6%
2 94
12.0%
1 86
11.0%
4 59
7.5%
3 56
7.2%
6 54
6.9%
5 48
 
6.1%
9 41
 
5.2%
8 40
 
5.1%
Other values (2) 41
 
5.2%

시공업체명
Text

MISSING 

Distinct81
Distinct (%)78.6%
Missing5
Missing (%)4.6%
Memory size996.0 B
2024-04-21T09:59:16.904073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length8.5728155
Min length5

Characters and Unicode

Total characters883
Distinct characters135
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)63.1%

Sample

1st row에이치와이종합건설(주)
2nd row주식회사큰대종합건설
3rd row(주)세호종합건설
4th row(주)담을건설
5th row(주)큰대종합건설
ValueCountFrequency (%)
주식회사 8
 
7.1%
주)이도인건설 5
 
4.5%
미래종합중기 3
 
2.7%
현대건설(주 3
 
2.7%
민산건설중기 3
 
2.7%
주식회사큰대종합건설 2
 
1.8%
주)큰대종합건설 2
 
1.8%
신림종합건설(주 2
 
1.8%
주)건희건설 2
 
1.8%
주)블루버드건설 2
 
1.8%
Other values (73) 80
71.4%
2024-04-21T09:59:17.250072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 94
 
10.6%
( 79
 
8.9%
) 79
 
8.9%
건 78
 
8.8%
설 73
 
8.3%
종 34
 
3.9%
합 34
 
3.9%
이 20
 
2.3%
사 15
 
1.7%
회 15
 
1.7%
Other values (125) 362
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 714
80.9%
Open Punctuation 79
 
8.9%
Close Punctuation 79
 
8.9%
Space Separator 9
 
1.0%
Other Symbol 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 94
 
13.2%
건 78
 
10.9%
설 73
 
10.2%
종 34
 
4.8%
합 34
 
4.8%
이 20
 
2.8%
사 15
 
2.1%
회 15
 
2.1%
식 15
 
2.1%
에 13
 
1.8%
Other values (121) 323
45.2%
Open Punctuation
ValueCountFrequency (%)
( 79
100.0%
Close Punctuation
ValueCountFrequency (%)
) 79
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 716
81.1%
Common 167
 
18.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 94
 
13.1%
건 78
 
10.9%
설 73
 
10.2%
종 34
 
4.7%
합 34
 
4.7%
이 20
 
2.8%
사 15
 
2.1%
회 15
 
2.1%
식 15
 
2.1%
에 13
 
1.8%
Other values (122) 325
45.4%
Common
ValueCountFrequency (%)
( 79
47.3%
) 79
47.3%
9
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 714
80.9%
ASCII 167
 
18.9%
None 2
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 94
 
13.2%
건 78
 
10.9%
설 73
 
10.2%
종 34
 
4.8%
합 34
 
4.8%
이 20
 
2.8%
사 15
 
2.1%
회 15
 
2.1%
식 15
 
2.1%
에 13
 
1.8%
Other values (121) 323
45.2%
ASCII
ValueCountFrequency (%)
( 79
47.3%
) 79
47.3%
9
 
5.4%
None
ValueCountFrequency (%)
㈜ 2
100.0%

설계사무소전화번호
Text

MISSING 

Distinct45
Distinct (%)80.4%
Missing52
Missing (%)48.1%
Memory size996.0 B
2024-04-21T09:59:17.441854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.642857
Min length11

Characters and Unicode

Total characters652
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)64.3%

Sample

1st row02-3481-5222
2nd row031-284-3361
3rd row053-813-7920
4th row02-3143-7716
5th row02-971-7087
ValueCountFrequency (%)
02-953-2225 3
 
5.4%
02-3443-4050 3
 
5.4%
02-953-2226 2
 
3.6%
02-928-4484 2
 
3.6%
031-284-3361 2
 
3.6%
02-3295-4600 2
 
3.6%
02-2273-5842 2
 
3.6%
02-6959-4783 2
 
3.6%
02-3461-2595 2
 
3.6%
02-3494-3323 2
 
3.6%
Other values (34) 34
60.7%
2024-04-21T09:59:17.770663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 112
17.2%
2 103
15.8%
0 90
13.8%
3 73
11.2%
4 59
9.0%
5 48
7.4%
6 39
 
6.0%
1 38
 
5.8%
7 32
 
4.9%
9 31
 
4.8%
Other values (2) 27
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538
82.5%
Dash Punctuation 112
 
17.2%
Space Separator 2
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 103
19.1%
0 90
16.7%
3 73
13.6%
4 59
11.0%
5 48
8.9%
6 39
 
7.2%
1 38
 
7.1%
7 32
 
5.9%
9 31
 
5.8%
8 25
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 112
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 652
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 112
17.2%
2 103
15.8%
0 90
13.8%
3 73
11.2%
4 59
9.0%
5 48
7.4%
6 39
 
6.0%
1 38
 
5.8%
7 32
 
4.9%
9 31
 
4.8%
Other values (2) 27
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 112
17.2%
2 103
15.8%
0 90
13.8%
3 73
11.2%
4 59
9.0%
5 48
7.4%
6 39
 
6.0%
1 38
 
5.8%
7 32
 
4.9%
9 31
 
4.8%
Other values (2) 27
 
4.1%

설계사무소명
Text

MISSING 

Distinct72
Distinct (%)82.8%
Missing21
Missing (%)19.4%
Memory size996.0 B
2024-04-21T09:59:17.955586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length11.436782
Min length8

Characters and Unicode

Total characters995
Distinct characters121
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)69.0%

Sample

1st row건축사사무소 루연
2nd row아름다운건축사사무소
3rd row미성건축사사무소
4th row핍스알엔디 건축사사무소
5th row(주)영화건축사사무소
ValueCountFrequency (%)
건축사사무소 18
 
15.1%
주식회사 10
 
8.4%
주)기하건축사사무소 4
 
3.4%
주)국전건축사사무소 3
 
2.5%
주)건축사사무소 2
 
1.7%
상진엔지니어링건축사사무소 2
 
1.7%
주)희성건축사사무소 2
 
1.7%
주)기안건축사사무소 2
 
1.7%
수플러스 2
 
1.7%
건축사사무소한다스 2
 
1.7%
Other values (65) 72
60.5%
2024-04-21T09:59:18.248470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 185
18.6%
소 90
 
9.0%
축 89
 
8.9%
건 89
 
8.9%
무 87
 
8.7%
주 50
 
5.0%
( 39
 
3.9%
) 39
 
3.9%
34
 
3.4%
이 13
 
1.3%
Other values (111) 280
28.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 877
88.1%
Open Punctuation 39
 
3.9%
Close Punctuation 39
 
3.9%
Space Separator 34
 
3.4%
Uppercase Letter 4
 
0.4%
Other Punctuation 1
 
0.1%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 185
21.1%
소 90
 
10.3%
축 89
 
10.1%
건 89
 
10.1%
무 87
 
9.9%
주 50
 
5.7%
이 13
 
1.5%
회 11
 
1.3%
식 11
 
1.3%
종 10
 
1.1%
Other values (102) 242
27.6%
Uppercase Letter
ValueCountFrequency (%)
A 1
25.0%
G 1
25.0%
C 1
25.0%
J 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Space Separator
ValueCountFrequency (%)
34
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 877
88.1%
Common 113
 
11.4%
Latin 5
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 185
21.1%
소 90
 
10.3%
축 89
 
10.1%
건 89
 
10.1%
무 87
 
9.9%
주 50
 
5.7%
이 13
 
1.5%
회 11
 
1.3%
식 11
 
1.3%
종 10
 
1.1%
Other values (102) 242
27.6%
Latin
ValueCountFrequency (%)
A 1
20.0%
G 1
20.0%
C 1
20.0%
m 1
20.0%
J 1
20.0%
Common
ValueCountFrequency (%)
( 39
34.5%
) 39
34.5%
34
30.1%
& 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 877
88.1%
ASCII 118
 
11.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 185
21.1%
소 90
 
10.3%
축 89
 
10.1%
건 89
 
10.1%
무 87
 
9.9%
주 50
 
5.7%
이 13
 
1.5%
회 11
 
1.3%
식 11
 
1.3%
종 10
 
1.1%
Other values (102) 242
27.6%
ASCII
ValueCountFrequency (%)
( 39
33.1%
) 39
33.1%
34
28.8%
A 1
 
0.8%
& 1
 
0.8%
G 1
 
0.8%
C 1
 
0.8%
m 1
 
0.8%
J 1
 
0.8%

감리사무소전화번호
Text

MISSING 

Distinct51
Distinct (%)87.9%
Missing50
Missing (%)46.3%
Memory size996.0 B
2024-04-21T09:59:18.450529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.465517
Min length11

Characters and Unicode

Total characters665
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)79.3%

Sample

1st row02-3481-5222
2nd row02-945-9564
3rd row02-3436-3404
4th row02-953-2226
5th row02-549-6693
ValueCountFrequency (%)
02-3443-4050 3
 
5.2%
02-953-2225 3
 
5.2%
02-953-2226 2
 
3.4%
02-6959-4783 2
 
3.4%
02-458-4181 2
 
3.4%
02-2273-5842 1
 
1.7%
02-3481-5222 1
 
1.7%
02-964-9777 1
 
1.7%
070-5214-0030 1
 
1.7%
02-582-0369 1
 
1.7%
Other values (41) 41
70.7%
2024-04-21T09:59:18.784439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 116
17.4%
2 109
16.4%
0 103
15.5%
4 57
8.6%
3 55
8.3%
5 55
8.3%
9 41
 
6.2%
1 36
 
5.4%
6 34
 
5.1%
7 33
 
5.0%
Other values (2) 26
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 548
82.4%
Dash Punctuation 116
 
17.4%
Space Separator 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 109
19.9%
0 103
18.8%
4 57
10.4%
3 55
10.0%
5 55
10.0%
9 41
 
7.5%
1 36
 
6.6%
6 34
 
6.2%
7 33
 
6.0%
8 25
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 116
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 665
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 116
17.4%
2 109
16.4%
0 103
15.5%
4 57
8.6%
3 55
8.3%
5 55
8.3%
9 41
 
6.2%
1 36
 
5.4%
6 34
 
5.1%
7 33
 
5.0%
Other values (2) 26
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 665
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 116
17.4%
2 109
16.4%
0 103
15.5%
4 57
8.6%
3 55
8.3%
5 55
8.3%
9 41
 
6.2%
1 36
 
5.4%
6 34
 
5.1%
7 33
 
5.0%
Other values (2) 26
 
3.9%

감리사무소명
Text

MISSING 

Distinct75
Distinct (%)86.2%
Missing21
Missing (%)19.4%
Memory size996.0 B
2024-04-21T09:59:19.006401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length11.45977
Min length8

Characters and Unicode

Total characters997
Distinct characters122
Distinct categories6 ?
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 row건축사사무소 루연
2nd row예마루종합건축사사무소
3rd row열린건축사사무소
4th row정원건축사사무소
5th row(주)영화건축사사무소
ValueCountFrequency (%)
건축사사무소 22
 
17.7%
주 5
 
4.0%
주식회사 5
 
4.0%
상진엔지니어링건축사사무소 5
 
4.0%
주)기하건축사사무소 4
 
3.2%
종합건축사사무소 3
 
2.4%
주)국전건축사사무소 3
 
2.4%
주)영화건축사사무소 2
 
1.6%
문 2
 
1.6%
아성 2
 
1.6%
Other values (70) 71
57.3%
2024-04-21T09:59:19.328111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 178
17.9%
건 89
 
8.9%
축 88
 
8.8%
소 87
 
8.7%
무 86
 
8.6%
주 48
 
4.8%
) 42
 
4.2%
( 41
 
4.1%
38
 
3.8%
종 12
 
1.2%
Other values (112) 288
28.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 873
87.6%
Close Punctuation 42
 
4.2%
Open Punctuation 41
 
4.1%
Space Separator 38
 
3.8%
Uppercase Letter 2
 
0.2%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 178
20.4%
건 89
 
10.2%
축 88
 
10.1%
소 87
 
10.0%
무 86
 
9.9%
주 48
 
5.5%
종 12
 
1.4%
합 12
 
1.4%
이 11
 
1.3%
니 8
 
0.9%
Other values (106) 254
29.1%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
J 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 42
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Space Separator
ValueCountFrequency (%)
38
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 873
87.6%
Common 121
 
12.1%
Latin 3
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 178
20.4%
건 89
 
10.2%
축 88
 
10.1%
소 87
 
10.0%
무 86
 
9.9%
주 48
 
5.5%
종 12
 
1.4%
합 12
 
1.4%
이 11
 
1.3%
니 8
 
0.9%
Other values (106) 254
29.1%
Common
ValueCountFrequency (%)
) 42
34.7%
( 41
33.9%
38
31.4%
Latin
ValueCountFrequency (%)
C 1
33.3%
m 1
33.3%
J 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 873
87.6%
ASCII 124
 
12.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 178
20.4%
건 89
 
10.2%
축 88
 
10.1%
소 87
 
10.0%
무 86
 
9.9%
주 48
 
5.5%
종 12
 
1.4%
합 12
 
1.4%
이 11
 
1.3%
니 8
 
0.9%
Other values (106) 254
29.1%
ASCII
ValueCountFrequency (%)
) 42
33.9%
( 41
33.1%
38
30.6%
C 1
 
0.8%
m 1
 
0.8%
J 1
 
0.8%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size996.0 B
Minimum2024-03-25 00:00:00
Maximum2024-03-25 00:00:00
2024-04-21T09:59:19.427986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:19.505743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-21T09:59:11.434169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:09.725199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:10.184564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:10.572421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:11.022945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:11.526196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:09.858163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:10.264950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:10.676635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:11.100565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:11.611717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:09.929437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:10.332669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:10.764157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:11.175318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:11.694126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:10.017527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:10.409442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:10.844330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:11.262359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:11.764355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:10.092379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:10.481375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:10.932828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:59:11.353657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T09:59:19.586637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축구분연면적(m2)_증축연면적(m2)허가(신고)일자착공일자최대지상층수최대지하층수주용도부속용도세대수호수가구수시공업체전화번호시공업체명설계사무소전화번호설계사무소명감리사무소전화번호감리사무소명
건축구분1.0000.0000.9600.9820.2260.1270.5501.0000.8030.0000.3831.0000.974NaN1.0001.0001.000
연면적(m2)_증축연면적(m2)0.0001.0001.0000.0000.8180.7310.0000.0000.9190.841NaN0.0000.0000.0000.0000.0000.000
허가(신고)일자0.9601.0001.0000.9900.9960.9930.9840.9890.9871.0001.0000.9690.9910.9090.9760.9210.986
착공일자0.9820.0000.9901.0000.3440.9160.9520.9711.0000.0001.0000.9970.9950.9920.9900.9300.905
최대지상층수0.2260.8180.9960.3441.0000.6490.5340.5860.8330.6080.3690.0000.0000.0000.0000.9350.926
최대지하층수0.1270.7310.9930.9160.6491.0000.4190.8830.9370.7290.2340.0000.7930.0000.0000.0000.000
주용도0.5500.0000.9840.9520.5340.4191.0000.9500.0000.0000.4420.9880.8260.8780.9380.7660.923
부속용도1.0000.0000.9890.9710.5860.8830.9501.0000.9920.0001.0000.9700.9600.9620.9670.9300.981
세대수0.8030.9190.9871.0000.8330.9370.0000.9921.0000.678NaN1.0000.9910.8160.9580.8970.673
호수0.0000.8411.0000.0000.6080.7290.0000.0000.6781.0000.0000.7140.9310.7460.7901.0001.000
가구수0.383NaN1.0001.0000.3690.2340.4421.000NaN0.0001.0001.0001.0001.0001.0001.0001.000
시공업체전화번호1.0000.0000.9690.9970.0000.0000.9880.9701.0000.7141.0001.0001.0001.0000.9980.9620.943
시공업체명0.9740.0000.9910.9950.0000.7930.8260.9600.9910.9311.0001.0001.0000.9960.9970.9820.991
설계사무소전화번호NaN0.0000.9090.9920.0000.0000.8780.9620.8160.7461.0001.0000.9961.0000.9990.9940.998
설계사무소명1.0000.0000.9760.9900.0000.0000.9380.9670.9580.7901.0000.9980.9970.9991.0000.9960.998
감리사무소전화번호1.0000.0000.9210.9300.9350.0000.7660.9300.8971.0001.0000.9620.9820.9940.9961.0001.000
감리사무소명1.0000.0000.9860.9050.9260.0000.9230.9810.6731.0001.0000.9430.9910.9980.9981.0001.000
2024-04-21T09:59:19.728861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가구수주용도건축구분
가구수1.0000.3090.369
주용도0.3091.0000.331
건축구분0.3690.3311.000
2024-04-21T09:59:19.807492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연면적(m2)_증축연면적(m2)최대지상층수최대지하층수세대수호수건축구분주용도가구수
연면적(m2)_증축연면적(m2)1.0000.8780.7640.8440.7680.0000.0001.000
최대지상층수0.8781.0000.6880.6450.7210.1430.2860.211
최대지하층수0.7640.6881.0000.5920.7010.0720.1850.000
세대수0.8440.6450.5921.0000.4600.5740.0001.000
호수0.7680.7210.7010.4601.0000.0000.0000.000
건축구분0.0000.1430.0720.5740.0001.0000.3310.369
주용도0.0000.2860.1850.0000.0000.3311.0000.309
가구수1.0000.2110.0001.0000.0000.3690.3091.000

Missing values

2024-04-21T09:59:11.888576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T09:59:12.091844image/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.
2024-04-21T09:59:12.263269image/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

건축구분대지위치연면적(m2)_증축연면적(m2)허가(신고)일자착공일자최대지상층수최대지하층수주용도부속용도세대수호수가구수시공업체전화번호시공업체명설계사무소전화번호설계사무소명감리사무소전화번호감리사무소명데이터기준일자
0신축서울특별시 동대문구 답십리동 21-78517.822022-06-152022-10-2541제2종근린생활시설사무소<NA><NA><NA>031-5177-7300에이치와이종합건설(주)02-3481-5222건축사사무소 루연02-3481-5222건축사사무소 루연2024-03-25
1신축서울특별시 동대문구 답십리동 22-3 외2필지625.42022-10-182023-02-2861공동주택도시형생활주택(단지형다세대주택)162<NA><NA>주식회사큰대종합건설031-284-3361아름다운건축사사무소02-945-9564예마루종합건축사사무소2024-03-25
2증축서울특별시 동대문구 답십리동 252-131189.792020-11-092020-11-2051제1종근린생활시설소매점,의원,사무소<NA><NA><NA><NA>(주)세호종합건설<NA>미성건축사사무소02-3436-3404열린건축사사무소2024-03-25
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