Overview

Dataset statistics

Number of variables10
Number of observations55
Missing cells39
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory84.4 B

Variable types

Categorical2
Text5
DateTime1
Numeric2

Dataset

Description캠코부동산 임대 물건 지역구분, 물건명, 물건주소, 준공년도, 연면적, 빌딩규모, 전용률, 상세설명, 평형정보내용, 물건 키워드를 제공합니다.
URLhttps://www.data.go.kr/data/15120260/fileData.do

Alerts

전용률 is highly overall correlated with 물건키워드High correlation
물건키워드 is highly overall correlated with 전용률High correlation
물건주소 has 1 (1.8%) missing valuesMissing
준공년도 has 1 (1.8%) missing valuesMissing
연면적 has 1 (1.8%) missing valuesMissing
빌딩규모 has 1 (1.8%) missing valuesMissing
전용률 has 1 (1.8%) missing valuesMissing
상세설명 has 2 (3.6%) missing valuesMissing
평형정보내용 has 32 (58.2%) missing valuesMissing
물건명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:39:32.588942
Analysis finished2023-12-12 12:39:34.321813
Duration1.73 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역구분
Categorical

Distinct5
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size572.0 B
수도권
34 
경상도
8 
충청도
6 
전라도
6 
강원도
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.8%

Sample

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

Common Values

ValueCountFrequency (%)
수도권 34
61.8%
경상도 8
 
14.5%
충청도 6
 
10.9%
전라도 6
 
10.9%
강원도 1
 
1.8%

Length

2023-12-12T21:39:34.393027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:39:34.527359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수도권 34
61.8%
경상도 8
 
14.5%
충청도 6
 
10.9%
전라도 6
 
10.9%
강원도 1
 
1.8%

물건명
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-12-12T21:39:34.827839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length7.8909091
Min length4

Characters and Unicode

Total characters434
Distinct characters135
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

Unique55 ?
Unique (%)100.0%

Sample

1st row정왕동 상가
2nd row수진동 상가
3rd row저동빌딩
4th row임대형주택
5th row삼성동A빌딩
ValueCountFrequency (%)
공공복합청사 5
 
6.4%
창원시 4
 
5.1%
공영주차빌딩 4
 
5.1%
구리시 2
 
2.6%
상가 2
 
2.6%
대전센터 1
 
1.3%
원주통합청사 1
 
1.3%
용사의집(현.로카우스 1
 
1.3%
세종국책연구단지 1
 
1.3%
복합청사 1
 
1.3%
Other values (56) 56
71.8%
2023-12-12T21:39:35.352281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23
 
5.3%
사 21
 
4.8%
동 18
 
4.1%
합 17
 
3.9%
복 16
 
3.7%
청 16
 
3.7%
공 15
 
3.5%
주 13
 
3.0%
딩 12
 
2.8%
빌 12
 
2.8%
Other values (125) 271
62.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 394
90.8%
Space Separator 23
 
5.3%
Open Punctuation 4
 
0.9%
Close Punctuation 4
 
0.9%
Uppercase Letter 4
 
0.9%
Decimal Number 4
 
0.9%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 21
 
5.3%
동 18
 
4.6%
합 17
 
4.3%
복 16
 
4.1%
청 16
 
4.1%
공 15
 
3.8%
주 13
 
3.3%
딩 12
 
3.0%
빌 12
 
3.0%
원 9
 
2.3%
Other values (116) 245
62.2%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
3 1
25.0%
1 1
25.0%
Uppercase Letter
ValueCountFrequency (%)
A 2
50.0%
B 2
50.0%
Space Separator
ValueCountFrequency (%)
23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 394
90.8%
Common 36
 
8.3%
Latin 4
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 21
 
5.3%
동 18
 
4.6%
합 17
 
4.3%
복 16
 
4.1%
청 16
 
4.1%
공 15
 
3.8%
주 13
 
3.3%
딩 12
 
3.0%
빌 12
 
3.0%
원 9
 
2.3%
Other values (116) 245
62.2%
Common
ValueCountFrequency (%)
23
63.9%
( 4
 
11.1%
) 4
 
11.1%
2 2
 
5.6%
3 1
 
2.8%
1 1
 
2.8%
. 1
 
2.8%
Latin
ValueCountFrequency (%)
A 2
50.0%
B 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 394
90.8%
ASCII 40
 
9.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23
57.5%
( 4
 
10.0%
) 4
 
10.0%
A 2
 
5.0%
2 2
 
5.0%
B 2
 
5.0%
3 1
 
2.5%
1 1
 
2.5%
. 1
 
2.5%
Hangul
ValueCountFrequency (%)
사 21
 
5.3%
동 18
 
4.6%
합 17
 
4.3%
복 16
 
4.1%
청 16
 
4.1%
공 15
 
3.8%
주 13
 
3.3%
딩 12
 
3.0%
빌 12
 
3.0%
원 9
 
2.3%
Other values (116) 245
62.2%

물건주소
Text

MISSING 

Distinct52
Distinct (%)96.3%
Missing1
Missing (%)1.8%
Memory size572.0 B
2023-12-12T21:39:35.741666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length18.981481
Min length15

Characters and Unicode

Total characters1025
Distinct characters113
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

Unique50 ?
Unique (%)92.6%

Sample

1st row경기도 시흥시 정왕동 1853-2
2nd row경기도 성남시 수정구 수진동 16
3rd row서울특별시 중구 저동1가 1-2
4th row서울특별시 강남구 논현동 18-1
5th row서울특별시 강남구 삼성동 154-1
ValueCountFrequency (%)
서울특별시 26
 
11.7%
경기도 8
 
3.6%
강남구 8
 
3.6%
경상남도 4
 
1.8%
성동구 4
 
1.8%
영등포구 4
 
1.8%
창원시 4
 
1.8%
여의도동 3
 
1.4%
서구 3
 
1.4%
광주광역시 3
 
1.4%
Other values (134) 155
69.8%
2023-12-12T21:39:36.361746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
170
 
16.6%
동 60
 
5.9%
시 54
 
5.3%
1 50
 
4.9%
구 48
 
4.7%
- 39
 
3.8%
서 31
 
3.0%
별 30
 
2.9%
특 30
 
2.9%
울 26
 
2.5%
Other values (103) 487
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 605
59.0%
Decimal Number 211
 
20.6%
Space Separator 170
 
16.6%
Dash Punctuation 39
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 60
 
9.9%
시 54
 
8.9%
구 48
 
7.9%
서 31
 
5.1%
별 30
 
5.0%
특 30
 
5.0%
울 26
 
4.3%
도 24
 
4.0%
남 20
 
3.3%
광 13
 
2.1%
Other values (91) 269
44.5%
Decimal Number
ValueCountFrequency (%)
1 50
23.7%
2 26
12.3%
5 25
11.8%
3 24
11.4%
0 19
 
9.0%
9 17
 
8.1%
8 16
 
7.6%
7 12
 
5.7%
4 12
 
5.7%
6 10
 
4.7%
Space Separator
ValueCountFrequency (%)
170
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 605
59.0%
Common 420
41.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 60
 
9.9%
시 54
 
8.9%
구 48
 
7.9%
서 31
 
5.1%
별 30
 
5.0%
특 30
 
5.0%
울 26
 
4.3%
도 24
 
4.0%
남 20
 
3.3%
광 13
 
2.1%
Other values (91) 269
44.5%
Common
ValueCountFrequency (%)
170
40.5%
1 50
 
11.9%
- 39
 
9.3%
2 26
 
6.2%
5 25
 
6.0%
3 24
 
5.7%
0 19
 
4.5%
9 17
 
4.0%
8 16
 
3.8%
7 12
 
2.9%
Other values (2) 22
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 605
59.0%
ASCII 420
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
170
40.5%
1 50
 
11.9%
- 39
 
9.3%
2 26
 
6.2%
5 25
 
6.0%
3 24
 
5.7%
0 19
 
4.5%
9 17
 
4.0%
8 16
 
3.8%
7 12
 
2.9%
Other values (2) 22
 
5.2%
Hangul
ValueCountFrequency (%)
동 60
 
9.9%
시 54
 
8.9%
구 48
 
7.9%
서 31
 
5.1%
별 30
 
5.0%
특 30
 
5.0%
울 26
 
4.3%
도 24
 
4.0%
남 20
 
3.3%
광 13
 
2.1%
Other values (91) 269
44.5%

준공년도
Date

MISSING 

Distinct48
Distinct (%)88.9%
Missing1
Missing (%)1.8%
Memory size572.0 B
Minimum1995-09-01 00:00:00
Maximum2023-03-21 00:00:00
2023-12-12T21:39:36.514856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:36.659124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)

연면적
Real number (ℝ)

MISSING 

Distinct53
Distinct (%)98.1%
Missing1
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean19531.495
Minimum100
Maximum128727.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T21:39:36.826707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile899.0055
Q14985.825
median10467.69
Q326033.778
95-th percentile58127.622
Maximum128727.77
Range128627.77
Interquartile range (IQR)21047.952

Descriptive statistics

Standard deviation24215.503
Coefficient of variation (CV)1.2398182
Kurtosis7.7108572
Mean19531.495
Median Absolute Deviation (MAD)7143.345
Skewness2.4826078
Sum1054700.8
Variance5.863906 × 108
MonotonicityNot monotonic
2023-12-12T21:39:36.995418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40592.04 2
 
3.6%
639.16 1
 
1.8%
16510.48 1
 
1.8%
1694.0 1
 
1.8%
40265.72 1
 
1.8%
16883.0 1
 
1.8%
41357.71 1
 
1.8%
128727.77 1
 
1.8%
23753.86 1
 
1.8%
42509.82 1
 
1.8%
Other values (43) 43
78.2%
ValueCountFrequency (%)
100.0 1
1.8%
639.16 1
1.8%
754.14 1
1.8%
977.01 1
1.8%
1651.1 1
1.8%
1694.0 1
1.8%
2050.0 1
1.8%
2655.99 1
1.8%
2911.32 1
1.8%
2947.23 1
1.8%
ValueCountFrequency (%)
128727.77 1
1.8%
89441.06 1
1.8%
69037.95 1
1.8%
52252.83 1
1.8%
50132.64 1
1.8%
43472.58 1
1.8%
42509.82 1
1.8%
41357.71 1
1.8%
40592.04 2
3.6%
40265.72 1
1.8%

빌딩규모
Text

MISSING 

Distinct47
Distinct (%)87.0%
Missing1
Missing (%)1.8%
Memory size572.0 B
2023-12-12T21:39:37.204782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length25
Mean length10.648148
Min length1

Characters and Unicode

Total characters575
Distinct characters43
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)74.1%

Sample

1st row지상2층
2nd row지상3층
3rd rowB4-15F
4th row논현A(6F/B1F),논현B(5F),대치(5F),정릉(4F)
5th row지하 2층/지상 6층
ValueCountFrequency (%)
지하 12
 
9.8%
지상 11
 
9.0%
지하1층 9
 
7.4%
6층 6
 
4.9%
5
 
4.1%
지상5층 4
 
3.3%
2층/지상 4
 
3.3%
4층 3
 
2.5%
3층 3
 
2.5%
지상4층 3
 
2.5%
Other values (49) 62
50.8%
2023-12-12T21:39:37.615443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
층 84
14.6%
지 81
14.1%
68
11.8%
상 44
 
7.7%
하 37
 
6.4%
1 32
 
5.6%
/ 26
 
4.5%
, 24
 
4.2%
2 22
 
3.8%
F 20
 
3.5%
Other values (33) 137
23.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 274
47.7%
Decimal Number 129
22.4%
Space Separator 68
 
11.8%
Other Punctuation 52
 
9.0%
Uppercase Letter 33
 
5.7%
Open Punctuation 9
 
1.6%
Close Punctuation 9
 
1.6%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
층 84
30.7%
지 81
29.6%
상 44
16.1%
하 37
13.5%
동 4
 
1.5%
무 3
 
1.1%
업 3
 
1.1%
현 2
 
0.7%
논 2
 
0.7%
개 1
 
0.4%
Other values (13) 13
 
4.7%
Decimal Number
ValueCountFrequency (%)
1 32
24.8%
2 22
17.1%
3 16
12.4%
5 14
10.9%
6 12
 
9.3%
4 12
 
9.3%
0 7
 
5.4%
7 5
 
3.9%
8 5
 
3.9%
9 4
 
3.1%
Other Punctuation
ValueCountFrequency (%)
/ 26
50.0%
, 24
46.2%
: 2
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
F 20
60.6%
B 12
36.4%
A 1
 
3.0%
Space Separator
ValueCountFrequency (%)
68
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 274
47.7%
Common 268
46.6%
Latin 33
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
층 84
30.7%
지 81
29.6%
상 44
16.1%
하 37
13.5%
동 4
 
1.5%
무 3
 
1.1%
업 3
 
1.1%
현 2
 
0.7%
논 2
 
0.7%
개 1
 
0.4%
Other values (13) 13
 
4.7%
Common
ValueCountFrequency (%)
68
25.4%
1 32
11.9%
/ 26
 
9.7%
, 24
 
9.0%
2 22
 
8.2%
3 16
 
6.0%
5 14
 
5.2%
6 12
 
4.5%
4 12
 
4.5%
( 9
 
3.4%
Other values (7) 33
12.3%
Latin
ValueCountFrequency (%)
F 20
60.6%
B 12
36.4%
A 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301
52.3%
Hangul 274
47.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
층 84
30.7%
지 81
29.6%
상 44
16.1%
하 37
13.5%
동 4
 
1.5%
무 3
 
1.1%
업 3
 
1.1%
현 2
 
0.7%
논 2
 
0.7%
개 1
 
0.4%
Other values (13) 13
 
4.7%
ASCII
ValueCountFrequency (%)
68
22.6%
1 32
10.6%
/ 26
 
8.6%
, 24
 
8.0%
2 22
 
7.3%
F 20
 
6.6%
3 16
 
5.3%
5 14
 
4.7%
6 12
 
4.0%
4 12
 
4.0%
Other values (10) 55
18.3%

전용률
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51
Distinct (%)94.4%
Missing1
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean106.42519
Minimum0.07
Maximum1227.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T21:39:37.797472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile13.7725
Q135.3375
median49.7
Q369.585
95-th percentile330.9475
Maximum1227.83
Range1227.76
Interquartile range (IQR)34.2475

Descriptive statistics

Standard deviation243.17506
Coefficient of variation (CV)2.2849391
Kurtosis17.042709
Mean106.42519
Median Absolute Deviation (MAD)17.53
Skewness4.2041829
Sum5746.96
Variance59134.112
MonotonicityNot monotonic
2023-12-12T21:39:38.041033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 3
 
5.5%
1227.83 2
 
3.6%
59.5 1
 
1.8%
65.45 1
 
1.8%
64.48 1
 
1.8%
55.77 1
 
1.8%
53.56 1
 
1.8%
0.07 1
 
1.8%
45.08 1
 
1.8%
24.79 1
 
1.8%
Other values (41) 41
74.5%
ValueCountFrequency (%)
0.07 1
1.8%
11.89 1
1.8%
13.48 1
1.8%
13.93 1
1.8%
14.46 1
1.8%
16.81 1
1.8%
17.07 1
1.8%
23.72 1
1.8%
24.79 1
1.8%
27.09 1
1.8%
ValueCountFrequency (%)
1227.83 2
3.6%
759.85 1
 
1.8%
100.0 3
5.5%
84.3 1
 
1.8%
79.7 1
 
1.8%
78.33 1
 
1.8%
76.65 1
 
1.8%
72.77 1
 
1.8%
70.64 1
 
1.8%
70.19 1
 
1.8%

상세설명
Text

MISSING 

Distinct45
Distinct (%)84.9%
Missing2
Missing (%)3.6%
Memory size572.0 B
2023-12-12T21:39:38.343649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length248
Median length56
Mean length30.264151
Min length1

Characters and Unicode

Total characters1604
Distinct characters246
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)75.5%

Sample

1st row지상 2층 규모의 근생건물
2nd row지상3층 규모의 근생건물
3rd row공공청사와 업무ㆍ근린생활시설 등의 복합 용도 건물
4th row논현A주택 : 서울특별시 강남구 논현동 18-1 지하1층 지상6층 규모의 근생(2호)및 주거용(10호) 건물 논현B주택 : 서울특별시 강남구 논현동 56-7 지상5층 규모의 근생(1호) 및 주거용(7호) 건물 대치동주택 : 서울특별시 강남구 대치동 900-59 지상 5층 규모의 주거용(9호) 건물 정릉동주택 : 서울특별시 성북구 정릉동 772-17 지상 4층 규모의 주거용(9호) 건물
5th row업무시설, 근생시설
ValueCountFrequency (%)
및 14
 
4.2%
근생시설 10
 
3.0%
규모의 8
 
2.4%
업무시설 7
 
2.1%
건물 7
 
2.1%
6
 
1.8%
중 5
 
1.5%
근생 5
 
1.5%
주차시설 4
 
1.2%
후 4
 
1.2%
Other values (222) 261
78.9%
2023-12-12T21:39:38.813218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
282
 
17.6%
시 45
 
2.8%
, 36
 
2.2%
설 32
 
2.0%
주 30
 
1.9%
생 29
 
1.8%
근 25
 
1.6%
( 24
 
1.5%
) 24
 
1.5%
사 23
 
1.4%
Other values (236) 1054
65.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1094
68.2%
Space Separator 282
 
17.6%
Decimal Number 82
 
5.1%
Other Punctuation 53
 
3.3%
Open Punctuation 29
 
1.8%
Close Punctuation 29
 
1.8%
Lowercase Letter 13
 
0.8%
Math Symbol 12
 
0.7%
Dash Punctuation 6
 
0.4%
Uppercase Letter 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 45
 
4.1%
설 32
 
2.9%
주 30
 
2.7%
생 29
 
2.7%
근 25
 
2.3%
사 23
 
2.1%
관 21
 
1.9%
공 21
 
1.9%
무 20
 
1.8%
청 19
 
1.7%
Other values (200) 829
75.8%
Decimal Number
ValueCountFrequency (%)
2 18
22.0%
1 17
20.7%
9 9
11.0%
0 8
9.8%
6 6
 
7.3%
7 6
 
7.3%
3 5
 
6.1%
5 5
 
6.1%
4 4
 
4.9%
8 4
 
4.9%
Other Punctuation
ValueCountFrequency (%)
, 36
67.9%
: 6
 
11.3%
· 4
 
7.5%
; 2
 
3.8%
& 2
 
3.8%
. 2
 
3.8%
/ 1
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
p 5
38.5%
b 3
23.1%
s 2
 
15.4%
n 2
 
15.4%
r 1
 
7.7%
Math Symbol
ValueCountFrequency (%)
> 4
33.3%
< 4
33.3%
+ 2
16.7%
~ 2
16.7%
Open Punctuation
ValueCountFrequency (%)
( 24
82.8%
[ 4
 
13.8%
「 1
 
3.4%
Close Punctuation
ValueCountFrequency (%)
) 24
82.8%
] 4
 
13.8%
」 1
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
B 3
75.0%
A 1
 
25.0%
Space Separator
ValueCountFrequency (%)
282
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1094
68.2%
Common 493
30.7%
Latin 17
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 45
 
4.1%
설 32
 
2.9%
주 30
 
2.7%
생 29
 
2.7%
근 25
 
2.3%
사 23
 
2.1%
관 21
 
1.9%
공 21
 
1.9%
무 20
 
1.8%
청 19
 
1.7%
Other values (200) 829
75.8%
Common
ValueCountFrequency (%)
282
57.2%
, 36
 
7.3%
( 24
 
4.9%
) 24
 
4.9%
2 18
 
3.7%
1 17
 
3.4%
9 9
 
1.8%
0 8
 
1.6%
6 6
 
1.2%
: 6
 
1.2%
Other values (19) 63
 
12.8%
Latin
ValueCountFrequency (%)
p 5
29.4%
b 3
17.6%
B 3
17.6%
s 2
 
11.8%
n 2
 
11.8%
A 1
 
5.9%
r 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1089
67.9%
ASCII 504
31.4%
None 6
 
0.4%
Compat Jamo 5
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
282
56.0%
, 36
 
7.1%
( 24
 
4.8%
) 24
 
4.8%
2 18
 
3.6%
1 17
 
3.4%
9 9
 
1.8%
0 8
 
1.6%
6 6
 
1.2%
: 6
 
1.2%
Other values (23) 74
 
14.7%
Hangul
ValueCountFrequency (%)
시 45
 
4.1%
설 32
 
2.9%
주 30
 
2.8%
생 29
 
2.7%
근 25
 
2.3%
사 23
 
2.1%
관 21
 
1.9%
공 21
 
1.9%
무 20
 
1.8%
청 19
 
1.7%
Other values (198) 824
75.7%
Compat Jamo
ValueCountFrequency (%)
ㅇ 4
80.0%
ㆍ 1
 
20.0%
None
ValueCountFrequency (%)
· 4
66.7%
「 1
 
16.7%
」 1
 
16.7%

평형정보내용
Text

MISSING 

Distinct22
Distinct (%)95.7%
Missing32
Missing (%)58.2%
Memory size572.0 B
2023-12-12T21:39:39.064626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length16
Mean length10.73913
Min length2

Characters and Unicode

Total characters247
Distinct characters13
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

Unique21 ?
Unique (%)91.3%

Sample

1st row10.5, 193
2nd row228
3rd row20, 27, 28
4th row1286
5th row500.3
ValueCountFrequency (%)
12 4
 
6.9%
7 3
 
5.2%
20 2
 
3.4%
9 2
 
3.4%
27 2
 
3.4%
50 2
 
3.4%
100 1
 
1.7%
88 1
 
1.7%
75.51 1
 
1.7%
2321 1
 
1.7%
Other values (39) 39
67.2%
2023-12-12T21:39:39.506665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 35
14.2%
35
14.2%
1 30
12.1%
2 27
10.9%
5 19
7.7%
. 19
7.7%
7 17
6.9%
3 15
6.1%
0 13
 
5.3%
8 10
 
4.0%
Other values (3) 27
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 158
64.0%
Other Punctuation 54
 
21.9%
Space Separator 35
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30
19.0%
2 27
17.1%
5 19
12.0%
7 17
10.8%
3 15
9.5%
0 13
8.2%
8 10
 
6.3%
6 10
 
6.3%
9 9
 
5.7%
4 8
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 35
64.8%
. 19
35.2%
Space Separator
ValueCountFrequency (%)
35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 247
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 35
14.2%
35
14.2%
1 30
12.1%
2 27
10.9%
5 19
7.7%
. 19
7.7%
7 17
6.9%
3 15
6.1%
0 13
 
5.3%
8 10
 
4.0%
Other values (3) 27
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 35
14.2%
35
14.2%
1 30
12.1%
2 27
10.9%
5 19
7.7%
. 19
7.7%
7 17
6.9%
3 15
6.1%
0 13
 
5.3%
8 10
 
4.0%
Other values (3) 27
10.9%

물건키워드
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Memory size572.0 B
근생,오피스,기타
12 
근생,오피스
6 
기타
5 
근생,기타
4 
공공청사
4 
Other values (18)
24 

Length

Max length12
Median length9
Mean length6.1272727
Min length2

Unique

Unique12 ?
Unique (%)21.8%

Sample

1st row근생
2nd row근생
3rd row오피스기타
4th row근생,주거
5th row근생,오피스

Common Values

ValueCountFrequency (%)
근생,오피스,기타 12
21.8%
근생,오피스 6
10.9%
기타 5
 
9.1%
근생,기타 4
 
7.3%
공공청사 4
 
7.3%
<NA> 2
 
3.6%
근생,주거,공공청사 2
 
3.6%
근생,공공청사 2
 
3.6%
근생,관사,오피스텔 2
 
3.6%
근생 2
 
3.6%
Other values (13) 14
25.5%

Length

2023-12-12T21:39:39.679754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
근생,오피스,기타 12
21.8%
근생,오피스 6
10.9%
기타 5
 
9.1%
근생,기타 4
 
7.3%
공공청사 4
 
7.3%
근생 3
 
5.5%
근생,관사,오피스텔 2
 
3.6%
관사 2
 
3.6%
오피스,기타 2
 
3.6%
근생,공공청사 2
 
3.6%
Other values (11) 13
23.6%

Interactions

2023-12-12T21:39:33.605750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:33.391603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:33.708798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:33.493021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:39:39.798476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역구분물건명물건주소준공년도연면적빌딩규모전용률상세설명평형정보내용물건키워드
지역구분1.0001.0000.9340.9620.3480.7880.0000.9871.0000.000
물건명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
물건주소0.9341.0001.0000.9950.9860.9941.0000.9841.0000.000
준공년도0.9621.0000.9951.0001.0000.9621.0000.9731.0000.969
연면적0.3481.0000.9861.0001.0000.9140.1180.7431.0000.000
빌딩규모0.7881.0000.9940.9620.9141.0001.0000.9551.0000.699
전용률0.0001.0001.0001.0000.1181.0001.0001.0001.0001.000
상세설명0.9871.0000.9840.9730.7430.9551.0001.0000.9430.978
평형정보내용1.0001.0001.0001.0001.0001.0001.0000.9431.0000.000
물건키워드0.0001.0000.0000.9690.0000.6991.0000.9780.0001.000
2023-12-12T21:39:39.954873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
물건키워드지역구분
물건키워드1.0000.000
지역구분0.0001.000
2023-12-12T21:39:40.074930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연면적전용률지역구분물건키워드
연면적1.000-0.2370.2090.000
전용률-0.2371.0000.0000.787
지역구분0.2090.0001.0000.000
물건키워드0.0000.7870.0001.000

Missing values

2023-12-12T21:39:33.883212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:39:34.039587image/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.
2023-12-12T21:39:34.193190image/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

지역구분물건명물건주소준공년도연면적빌딩규모전용률상세설명평형정보내용물건키워드
0수도권정왕동 상가경기도 시흥시 정왕동 1853-22007-07-12639.16지상2층59.5지상 2층 규모의 근생건물10.5, 193근생
1수도권수진동 상가경기도 성남시 수정구 수진동 162007-07-12754.14지상3층70.64지상3층 규모의 근생건물228근생
2수도권저동빌딩서울특별시 중구 저동1가 1-22008-06-3026937.81B4-15F50.28공공청사와 업무ㆍ근린생활시설 등의 복합 용도 건물<NA>오피스기타
3수도권임대형주택서울특별시 강남구 논현동 18-12008-11-182947.23논현A(6F/B1F),논현B(5F),대치(5F),정릉(4F)49.88논현A주택 : 서울특별시 강남구 논현동 18-1 지하1층 지상6층 규모의 근생(2호)및 주거용(10호) 건물 논현B주택 : 서울특별시 강남구 논현동 56-7 지상5층 규모의 근생(1호) 및 주거용(7호) 건물 대치동주택 : 서울특별시 강남구 대치동 900-59 지상 5층 규모의 주거용(9호) 건물 정릉동주택 : 서울특별시 성북구 정릉동 772-17 지상 4층 규모의 주거용(9호) 건물20, 27, 28근생,주거
4수도권삼성동A빌딩서울특별시 강남구 삼성동 154-12013-05-314246.3지하 2층/지상 6층48.66업무시설, 근생시설1286근생,오피스
5수도권삼성동B빌딩서울특별시 강남구 삼성동 155-32013-05-311651.1지하 2층/지상 4층47.37업무시설, 근생시설500.3근생,오피스
6수도권대학생주택서울특별시 마포구 성산동 81-152016-02-01977.01B1 / 4F100.0「나라키움 대학생주택」은 주거안정 지원 및 거주환경 개선을 위하여 기획재정부가 주관하고 한국자산관리공사가 건축·관리하는 기숙사형 주택으로, 수도권(서울, 경기, 인천) 소재 대학교(원)(전문대 포함)에 재학 중(예정자 포함)인 대학생·대학원생, 대학교 휴학생, 취업준비생은 입주를 신청할 수 있습니다. (2016.3월부터 1인 1실로 운영 중)3.35, 3.51, 4주거
7수도권지식협력단지서울특별시 동대문구 청량리동 207-412017-03-3112387.63지하 1층/지상 3층14.46지식협력단지<NA>오피스,기타
8수도권인재캠퍼스서울특별시 동대문구 청량리동 206-92016-12-206382.99지하 1층/지상 3층13.48인재캠퍼스<NA>오피스,기타
9수도권여의도빌딩(기금)서울특별시 영등포구 여의도동 55-22018-07-2540592.04지하6층 지상23층1227.83(입주자격) 중앙행정기관 소속 공무원 중 서울 근무자7, 9, 12관사,
지역구분물건명물건주소준공년도연면적빌딩규모전용률상세설명평형정보내용물건키워드
45전라도무안다산마을전라남도 무안군 삼향읍 남악리 25992021-07-3115546.41지하1층 지상8층17.07기획재정부 주관 캠코가 위탁받아 국유재산관리기금으로 개발 후 무안신도시 이전 및 전라남도 소재 국가직 공무원의 통합관사로 운영 중27, 35관사
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