Overview

Dataset statistics

Number of variables10
Number of observations71
Missing cells35
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory84.9 B

Variable types

Numeric3
Categorical3
Text4

Dataset

Description서울특별시 강북구에 소재한 기계설비 성능점검 대상이 되는 기준을 갖춘 건축물 세부목록 및 주소지 정보입니다.(20230118 기준)
Author서울특별시 강북구
URLhttps://www.data.go.kr/data/15112033/fileData.do

Alerts

데이터기준일 has constant value ""Constant
연번 is highly overall correlated with 건물구분High correlation
지상층수 is highly overall correlated with 건물구분High correlation
건물구분 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
주용도 is highly overall correlated with 건물구분High correlation
동명칭 has 31 (43.7%) missing valuesMissing
지상층수 has 2 (2.8%) missing valuesMissing
지하층수 has 2 (2.8%) missing valuesMissing
연번 has unique valuesUnique
대지위치 has unique valuesUnique
지상층수 has 1 (1.4%) zerosZeros
지하층수 has 18 (25.4%) zerosZeros

Reproduction

Analysis started2023-12-12 08:59:25.202237
Analysis finished2023-12-12 08:59:27.457815
Duration2.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36
Minimum1
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-12T17:59:27.589800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.5
Q118.5
median36
Q353.5
95-th percentile67.5
Maximum71
Range70
Interquartile range (IQR)35

Descriptive statistics

Standard deviation20.639767
Coefficient of variation (CV)0.57332687
Kurtosis-1.2
Mean36
Median Absolute Deviation (MAD)18
Skewness0
Sum2556
Variance426
MonotonicityStrictly increasing
2023-12-12T17:59:28.155978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
2 1
 
1.4%
53 1
 
1.4%
52 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
46 1
 
1.4%
Other values (61) 61
85.9%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
71 1
1.4%
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%
64 1
1.4%
63 1
1.4%
62 1
1.4%

건물구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size700.0 B
일반건축물
44 
집합건축물
27 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반건축물 44
62.0%
집합건축물 27
38.0%

Length

2023-12-12T17:59:28.348189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:59:28.507170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반건축물 44
62.0%
집합건축물 27
38.0%

대지위치
Text

UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size700.0 B
2023-12-12T17:59:28.804872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length18.267606
Min length15

Characters and Unicode

Total characters1297
Distinct characters29
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

Unique71 ?
Unique (%)100.0%

Sample

1st row서울특별시 강북구 미아동 1353
2nd row서울특별시 강북구 미아동 1354
3rd row서울특별시 강북구 미아동 1357
4th row서울특별시 강북구 미아동 811
5th row서울특별시 강북구 미아동 812
ValueCountFrequency (%)
서울특별시 71
24.9%
강북구 71
24.9%
미아동 33
11.6%
수유동 19
 
6.7%
번동 17
 
6.0%
우이동 2
 
0.7%
229-49 1
 
0.4%
194 1
 
0.4%
1357-11 1
 
0.4%
1353-10 1
 
0.4%
Other values (68) 68
23.9%
2023-12-12T17:59:29.378819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
214
16.5%
서 71
 
5.5%
강 71
 
5.5%
울 71
 
5.5%
동 71
 
5.5%
북 71
 
5.5%
구 71
 
5.5%
시 71
 
5.5%
별 71
 
5.5%
특 71
 
5.5%
Other values (19) 444
34.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 765
59.0%
Decimal Number 276
 
21.3%
Space Separator 214
 
16.5%
Dash Punctuation 42
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 71
9.3%
강 71
9.3%
울 71
9.3%
동 71
9.3%
북 71
9.3%
구 71
9.3%
시 71
9.3%
별 71
9.3%
특 71
9.3%
미 33
 
4.3%
Other values (7) 93
12.2%
Decimal Number
ValueCountFrequency (%)
1 54
19.6%
3 36
13.0%
4 34
12.3%
2 29
10.5%
5 29
10.5%
9 24
8.7%
6 23
8.3%
7 18
 
6.5%
8 16
 
5.8%
0 13
 
4.7%
Space Separator
ValueCountFrequency (%)
214
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 765
59.0%
Common 532
41.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 71
9.3%
강 71
9.3%
울 71
9.3%
동 71
9.3%
북 71
9.3%
구 71
9.3%
시 71
9.3%
별 71
9.3%
특 71
9.3%
미 33
 
4.3%
Other values (7) 93
12.2%
Common
ValueCountFrequency (%)
214
40.2%
1 54
 
10.2%
- 42
 
7.9%
3 36
 
6.8%
4 34
 
6.4%
2 29
 
5.5%
5 29
 
5.5%
9 24
 
4.5%
6 23
 
4.3%
7 18
 
3.4%
Other values (2) 29
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 765
59.0%
ASCII 532
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
214
40.2%
1 54
 
10.2%
- 42
 
7.9%
3 36
 
6.8%
4 34
 
6.4%
2 29
 
5.5%
5 29
 
5.5%
9 24
 
4.5%
6 23
 
4.3%
7 18
 
3.4%
Other values (2) 29
 
5.5%
Hangul
ValueCountFrequency (%)
서 71
9.3%
강 71
9.3%
울 71
9.3%
동 71
9.3%
북 71
9.3%
구 71
9.3%
시 71
9.3%
별 71
9.3%
특 71
9.3%
미 33
 
4.3%
Other values (7) 93
12.2%

도로명_주소
Text

Distinct69
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size700.0 B
2023-12-12T17:59:29.786985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length24.901408
Min length20

Characters and Unicode

Total characters1768
Distinct characters55
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

Unique67 ?
Unique (%)94.4%

Sample

1st row 서울특별시 강북구 솔샘로 174 (미아동)
2nd row 서울특별시 강북구 솔샘로 159 (미아동)
3rd row 서울특별시 강북구 삼양로19길 113 (미아동)
4th row 서울특별시 강북구 삼양로27길 80 (미아동)
5th row 서울특별시 강북구 삼양로27길 19 (미아동)
ValueCountFrequency (%)
강북구 71
20.0%
서울특별시 70
19.7%
미아동 34
 
9.6%
수유동 18
 
5.1%
번동 17
 
4.8%
도봉로 14
 
3.9%
삼양로19길 6
 
1.7%
오현로 5
 
1.4%
인수봉로 4
 
1.1%
삼각산로 4
 
1.1%
Other values (92) 112
31.5%
2023-12-12T17:59:30.326612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
353
20.0%
북 71
 
4.0%
) 71
 
4.0%
동 71
 
4.0%
( 71
 
4.0%
로 71
 
4.0%
구 71
 
4.0%
서 71
 
4.0%
강 71
 
4.0%
울 71
 
4.0%
Other values (45) 776
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1019
57.6%
Space Separator 353
 
20.0%
Decimal Number 250
 
14.1%
Close Punctuation 71
 
4.0%
Open Punctuation 71
 
4.0%
Dash Punctuation 3
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
북 71
 
7.0%
동 71
 
7.0%
로 71
 
7.0%
구 71
 
7.0%
서 71
 
7.0%
강 71
 
7.0%
울 71
 
7.0%
별 70
 
6.9%
시 70
 
6.9%
특 70
 
6.9%
Other values (30) 312
30.6%
Decimal Number
ValueCountFrequency (%)
1 49
19.6%
2 37
14.8%
3 28
11.2%
4 25
10.0%
9 25
10.0%
5 23
9.2%
0 19
 
7.6%
7 17
 
6.8%
8 15
 
6.0%
6 12
 
4.8%
Space Separator
ValueCountFrequency (%)
353
100.0%
Close Punctuation
ValueCountFrequency (%)
) 71
100.0%
Open Punctuation
ValueCountFrequency (%)
( 71
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1019
57.6%
Common 749
42.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
북 71
 
7.0%
동 71
 
7.0%
로 71
 
7.0%
구 71
 
7.0%
서 71
 
7.0%
강 71
 
7.0%
울 71
 
7.0%
별 70
 
6.9%
시 70
 
6.9%
특 70
 
6.9%
Other values (30) 312
30.6%
Common
ValueCountFrequency (%)
353
47.1%
) 71
 
9.5%
( 71
 
9.5%
1 49
 
6.5%
2 37
 
4.9%
3 28
 
3.7%
4 25
 
3.3%
9 25
 
3.3%
5 23
 
3.1%
0 19
 
2.5%
Other values (5) 48
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1019
57.6%
ASCII 749
42.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
353
47.1%
) 71
 
9.5%
( 71
 
9.5%
1 49
 
6.5%
2 37
 
4.9%
3 28
 
3.7%
4 25
 
3.3%
9 25
 
3.3%
5 23
 
3.1%
0 19
 
2.5%
Other values (5) 48
 
6.4%
Hangul
ValueCountFrequency (%)
북 71
 
7.0%
동 71
 
7.0%
로 71
 
7.0%
구 71
 
7.0%
서 71
 
7.0%
강 71
 
7.0%
울 71
 
7.0%
별 70
 
6.9%
시 70
 
6.9%
특 70
 
6.9%
Other values (30) 312
30.6%

건물명
Text

Distinct68
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size700.0 B
2023-12-12T17:59:30.608402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length12
Mean length8.1408451
Min length4

Characters and Unicode

Total characters578
Distinct characters166
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

Unique65 ?
Unique (%)91.5%

Sample

1st row에스케이북한산시티아파트
2nd row벽산라이브파크
3rd row삼각산아이원아파트
4th row두산위브트레지움아파트
5th row삼성래미안트리베라아파트
ValueCountFrequency (%)
삼각산아이원아파트 2
 
2.3%
번동주공아파트 2
 
2.3%
경남아너스빌 2
 
2.3%
꿈의숲 2
 
2.3%
삼양초등학교 1
 
1.1%
효성 1
 
1.1%
번동중학교 1
 
1.1%
kt강북지사 1
 
1.1%
서울화계초등학교 1
 
1.1%
삼각산중학교 1
 
1.1%
Other values (73) 73
83.9%
2023-12-12T17:59:31.075508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 29
 
5.0%
학 26
 
4.5%
아 21
 
3.6%
트 19
 
3.3%
파 17
 
2.9%
16
 
2.8%
등 14
 
2.4%
서 13
 
2.2%
수 12
 
2.1%
울 12
 
2.1%
Other values (156) 399
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 544
94.1%
Space Separator 16
 
2.8%
Decimal Number 8
 
1.4%
Uppercase Letter 4
 
0.7%
Open Punctuation 2
 
0.3%
Close Punctuation 2
 
0.3%
Other Punctuation 1
 
0.2%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 29
 
5.3%
학 26
 
4.8%
아 21
 
3.9%
트 19
 
3.5%
파 17
 
3.1%
등 14
 
2.6%
서 13
 
2.4%
수 12
 
2.2%
울 12
 
2.2%
스 12
 
2.2%
Other values (143) 369
67.8%
Decimal Number
ValueCountFrequency (%)
1 3
37.5%
4 2
25.0%
2 1
 
12.5%
5 1
 
12.5%
9 1
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
K 2
50.0%
S 1
25.0%
T 1
25.0%
Space Separator
ValueCountFrequency (%)
16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 544
94.1%
Common 30
 
5.2%
Latin 4
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 29
 
5.3%
학 26
 
4.8%
아 21
 
3.9%
트 19
 
3.5%
파 17
 
3.1%
등 14
 
2.6%
서 13
 
2.4%
수 12
 
2.2%
울 12
 
2.2%
스 12
 
2.2%
Other values (143) 369
67.8%
Common
ValueCountFrequency (%)
16
53.3%
1 3
 
10.0%
( 2
 
6.7%
4 2
 
6.7%
) 2
 
6.7%
. 1
 
3.3%
2 1
 
3.3%
5 1
 
3.3%
- 1
 
3.3%
9 1
 
3.3%
Latin
ValueCountFrequency (%)
K 2
50.0%
S 1
25.0%
T 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 544
94.1%
ASCII 34
 
5.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 29
 
5.3%
학 26
 
4.8%
아 21
 
3.9%
트 19
 
3.5%
파 17
 
3.1%
등 14
 
2.6%
서 13
 
2.4%
수 12
 
2.2%
울 12
 
2.2%
스 12
 
2.2%
Other values (143) 369
67.8%
ASCII
ValueCountFrequency (%)
16
47.1%
1 3
 
8.8%
( 2
 
5.9%
K 2
 
5.9%
4 2
 
5.9%
) 2
 
5.9%
S 1
 
2.9%
. 1
 
2.9%
T 1
 
2.9%
2 1
 
2.9%
Other values (3) 3
 
8.8%

동명칭
Text

MISSING 

Distinct37
Distinct (%)92.5%
Missing31
Missing (%)43.7%
Memory size700.0 B
2023-12-12T17:59:31.326673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length8
Mean length4.55
Min length2

Characters and Unicode

Total characters182
Distinct characters74
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

Unique35 ?
Unique (%)87.5%

Sample

1st row101동
2nd row101동
3rd row105동
4th row304동
5th row221동
ValueCountFrequency (%)
101동 3
 
7.1%
b동 2
 
4.8%
105동 2
 
4.8%
k동(강린동 1
 
2.4%
1동 1
 
2.4%
생활관 1
 
2.4%
북한산스카이 1
 
2.4%
4동 1
 
2.4%
강당동 1
 
2.4%
바동(교사 1
 
2.4%
Other values (28) 28
66.7%
2023-12-12T17:59:31.717858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 30
 
16.5%
1 17
 
9.3%
0 11
 
6.0%
관 8
 
4.4%
) 7
 
3.8%
2 7
 
3.8%
( 7
 
3.8%
5 3
 
1.6%
실 3
 
1.6%
본 3
 
1.6%
Other values (64) 86
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 114
62.6%
Decimal Number 49
26.9%
Close Punctuation 7
 
3.8%
Open Punctuation 7
 
3.8%
Uppercase Letter 3
 
1.6%
Space Separator 2
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 30
26.3%
관 8
 
7.0%
실 3
 
2.6%
본 3
 
2.6%
강 3
 
2.6%
산 2
 
1.8%
스 2
 
1.8%
사 2
 
1.8%
교 2
 
1.8%
한 2
 
1.8%
Other values (49) 57
50.0%
Decimal Number
ValueCountFrequency (%)
1 17
34.7%
0 11
22.4%
2 7
14.3%
5 3
 
6.1%
4 3
 
6.1%
9 2
 
4.1%
8 2
 
4.1%
3 2
 
4.1%
7 1
 
2.0%
6 1
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
66.7%
K 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 114
62.6%
Common 65
35.7%
Latin 3
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 30
26.3%
관 8
 
7.0%
실 3
 
2.6%
본 3
 
2.6%
강 3
 
2.6%
산 2
 
1.8%
스 2
 
1.8%
사 2
 
1.8%
교 2
 
1.8%
한 2
 
1.8%
Other values (49) 57
50.0%
Common
ValueCountFrequency (%)
1 17
26.2%
0 11
16.9%
) 7
10.8%
2 7
10.8%
( 7
10.8%
5 3
 
4.6%
4 3
 
4.6%
9 2
 
3.1%
8 2
 
3.1%
3 2
 
3.1%
Other values (3) 4
 
6.2%
Latin
ValueCountFrequency (%)
B 2
66.7%
K 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 114
62.6%
ASCII 68
37.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 30
26.3%
관 8
 
7.0%
실 3
 
2.6%
본 3
 
2.6%
강 3
 
2.6%
산 2
 
1.8%
스 2
 
1.8%
사 2
 
1.8%
교 2
 
1.8%
한 2
 
1.8%
Other values (49) 57
50.0%
ASCII
ValueCountFrequency (%)
1 17
25.0%
0 11
16.2%
) 7
10.3%
2 7
10.3%
( 7
10.3%
5 3
 
4.4%
4 3
 
4.4%
B 2
 
2.9%
9 2
 
2.9%
8 2
 
2.9%
Other values (5) 7
10.3%

주용도
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Memory size700.0 B
교육연구시설
25 
공동주택
18 
업무시설
10 
판매시설
4 
제2종근린생활시설
 
2
Other values (9)
12 

Length

Max length10
Median length4
Mean length5.2816901
Min length4

Unique

Unique6 ?
Unique (%)8.5%

Sample

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

Common Values

ValueCountFrequency (%)
교육연구시설 25
35.2%
공동주택 18
25.4%
업무시설 10
 
14.1%
판매시설 4
 
5.6%
제2종근린생활시설 2
 
2.8%
문화및집회시설 2
 
2.8%
제1종근린생활시설 2
 
2.8%
방송통신시설 2
 
2.8%
의료시설 1
 
1.4%
<NA> 1
 
1.4%
Other values (4) 4
 
5.6%

Length

2023-12-12T17:59:31.910700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
교육연구시설 25
35.2%
공동주택 18
25.4%
업무시설 10
 
14.1%
판매시설 4
 
5.6%
제2종근린생활시설 2
 
2.8%
문화및집회시설 2
 
2.8%
제1종근린생활시설 2
 
2.8%
방송통신시설 2
 
2.8%
의료시설 1
 
1.4%
na 1
 
1.4%
Other values (4) 4
 
5.6%

지상층수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)27.5%
Missing2
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean9.1014493
Minimum0
Maximum25
Zeros1
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-12T17:59:32.045610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6
Q315
95-th percentile21.2
Maximum25
Range25
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.810772
Coefficient of variation (CV)0.74831731
Kurtosis-0.86841775
Mean9.1014493
Median Absolute Deviation (MAD)4
Skewness0.63339608
Sum628
Variance46.386616
MonotonicityNot monotonic
2023-12-12T17:59:32.169794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
5 13
18.3%
15 7
9.9%
3 7
9.9%
2 5
 
7.0%
1 4
 
5.6%
20 4
 
5.6%
6 4
 
5.6%
4 4
 
5.6%
10 4
 
5.6%
12 3
 
4.2%
Other values (9) 14
19.7%
ValueCountFrequency (%)
0 1
 
1.4%
1 4
 
5.6%
2 5
 
7.0%
3 7
9.9%
4 4
 
5.6%
5 13
18.3%
6 4
 
5.6%
8 1
 
1.4%
10 4
 
5.6%
12 3
 
4.2%
ValueCountFrequency (%)
25 1
 
1.4%
23 1
 
1.4%
22 2
 
2.8%
20 4
5.6%
19 2
 
2.8%
17 2
 
2.8%
15 7
9.9%
14 3
4.2%
13 1
 
1.4%
12 3
4.2%

지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)13.0%
Missing2
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean1.8695652
Minimum0
Maximum9
Zeros18
Zeros (%)25.4%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-12T17:59:32.291211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1207174
Coefficient of variation (CV)1.1343372
Kurtosis1.0884014
Mean1.8695652
Median Absolute Deviation (MAD)1
Skewness1.3668832
Sum129
Variance4.4974425
MonotonicityNot monotonic
2023-12-12T17:59:32.438513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 28
39.4%
0 18
25.4%
5 6
 
8.5%
2 5
 
7.0%
6 4
 
5.6%
3 3
 
4.2%
4 3
 
4.2%
7 1
 
1.4%
9 1
 
1.4%
(Missing) 2
 
2.8%
ValueCountFrequency (%)
0 18
25.4%
1 28
39.4%
2 5
 
7.0%
3 3
 
4.2%
4 3
 
4.2%
5 6
 
8.5%
6 4
 
5.6%
7 1
 
1.4%
9 1
 
1.4%
ValueCountFrequency (%)
9 1
 
1.4%
7 1
 
1.4%
6 4
 
5.6%
5 6
 
8.5%
4 3
 
4.2%
3 3
 
4.2%
2 5
 
7.0%
1 28
39.4%
0 18
25.4%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size700.0 B
2023-01-18
71 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-01-18
2nd row2023-01-18
3rd row2023-01-18
4th row2023-01-18
5th row2023-01-18

Common Values

ValueCountFrequency (%)
2023-01-18 71
100.0%

Length

2023-12-12T17:59:32.597402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:59:32.726474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-01-18 71
100.0%

Interactions

2023-12-12T17:59:26.633701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:25.985306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:26.302268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:26.750657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:26.090252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:26.403299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:26.859684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:26.209931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:26.512960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:59:32.826954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번건물구분대지위치도로명_주소건물명동명칭주용도지상층수지하층수
연번1.0000.8541.0000.9710.7820.9660.6300.6350.267
건물구분0.8541.0001.0001.0001.0001.0000.8480.9050.000
대지위치1.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명_주소0.9711.0001.0001.0000.9970.9901.0000.7740.951
건물명0.7821.0001.0000.9971.0000.9700.9740.6900.941
동명칭0.9661.0001.0000.9900.9701.0001.0000.8810.979
주용도0.6300.8481.0001.0000.9741.0001.0000.6360.650
지상층수0.6350.9051.0000.7740.6900.8810.6361.0000.610
지하층수0.2670.0001.0000.9510.9410.9790.6500.6101.000
2023-12-12T17:59:32.970454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건물구분주용도
건물구분1.0000.758
주용도0.7581.000
2023-12-12T17:59:33.102386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번지상층수지하층수건물구분주용도
연번1.000-0.4640.0270.6720.320
지상층수-0.4641.0000.2480.6930.297
지하층수0.0270.2481.0000.0000.333
건물구분0.6720.6930.0001.0000.758
주용도0.3200.2970.3330.7581.000

Missing values

2023-12-12T17:59:27.004256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:59:27.212714image/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.
2023-12-12T17:59:27.371388image/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

연번건물구분대지위치도로명_주소건물명동명칭주용도지상층수지하층수데이터기준일
01집합건축물서울특별시 강북구 미아동 1353서울특별시 강북구 솔샘로 174 (미아동)에스케이북한산시티아파트101동공동주택2512023-01-18
12집합건축물서울특별시 강북구 미아동 1354서울특별시 강북구 솔샘로 159 (미아동)벽산라이브파크101동공동주택2202023-01-18
23집합건축물서울특별시 강북구 미아동 1357서울특별시 강북구 삼양로19길 113 (미아동)삼각산아이원아파트105동공동주택2002023-01-18
34집합건축물서울특별시 강북구 미아동 811서울특별시 강북구 삼양로27길 80 (미아동)두산위브트레지움아파트304동공동주택1712023-01-18
45집합건축물서울특별시 강북구 미아동 812서울특별시 강북구 삼양로27길 19 (미아동)삼성래미안트리베라아파트221동공동주택2002023-01-18
56집합건축물서울특별시 강북구 미아동 813서울특별시 강북구 삼양로19길 25 (미아동)래미안 트리베라 1차116동공동주택2002023-01-18
67집합건축물서울특별시 강북구 번동 229서울특별시 강북구 한천로115길 20 (번동)번동주공5단지아파트5단지 관리동공동주택212023-01-18
78집합건축물서울특별시 강북구 번동 237서울특별시 강북구 오현로 208 (번동)번동주공아파트308동공동주택1512023-01-18
89집합건축물서울특별시 강북구 번동 241서울특별시 강북구 한천로105길 24 (번동)번동2단지주공아파트209동공동주택1512023-01-18
910집합건축물서울특별시 강북구 번동 242서울특별시 강북구 한천로105길 23 (번동)번동1단지주공아파트102동공동주택512023-01-18
연번건물구분대지위치도로명_주소건물명동명칭주용도지상층수지하층수데이터기준일
6162일반건축물서울특별시 강북구 수유동 409-6서울특별시 강북구 삼양로99길 36 (수유동)서울우이초등학교(B동)교육연구시설412023-01-18
6263일반건축물서울특별시 강북구 수유동 468-43서울특별시 강북구 인수봉로 127 (수유동)혜화여자고등학교생활관교육연구시설302023-01-18
6364일반건축물서울특별시 강북구 수유동 491-61서울특별시 강북구 인수봉로37길 40 (수유동)수유중학교<NA>교육연구시설512023-01-18
6465일반건축물서울특별시 강북구 수유동 522서울특별시 강북구 삼각산로 43 (수유동)서울영어마을 수유캠프(경비실)교육연구시설102023-01-18
6566일반건축물서울특별시 강북구 수유동 55-5서울특별시 강북구 삼양로74길 39 (수유동)서울수유초등학교<NA>교육연구시설512023-01-18
6667일반건축물서울특별시 강북구 수유동 551-19서울특별시 강북구 인수봉로 269 (수유동)서울인수초등학교<NA>교육연구시설302023-01-18
6768일반건축물서울특별시 강북구 수유동 694서울특별시 강북구 한천로150길 67 (수유동)강북중학교4동교육연구시설302023-01-18
6869일반건축물서울특별시 강북구 우이동 103-9서울특별시 강북구 삼양로155길 37-9 (우이동)서라벌중학교중학교본관교육연구시설502023-01-18
6970집합건축물서울특별시 강북구 수유동 48-1서울특별시 강북구 덕릉로 82 (미아동)제네스타워제네스타워업무시설1942023-01-18
7071집합건축물서울특별시 강북구 수유동 95-7서울특별시 강북구 도봉로77길 6 (수유동)수유동이테크밸리오피스텔<NA>업무시설1752023-01-18