Overview

Dataset statistics

Number of variables6
Number of observations161
Missing cells12
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 KiB
Average record size in memory49.8 B

Variable types

Categorical2
Text3
Numeric1

Dataset

Description인천광역시 부평구 기계설비 성능점검 일반건축물 대상 현황 데이터는 대장종류, 건물명, 주소, 면적, 용도 등의 데이터를 포함하고 있습니다.
Author인천광역시 부평구
URLhttps://www.data.go.kr/data/15124657/fileData.do

Alerts

대장종류 is highly overall correlated with 주용도High correlation
주용도 is highly overall correlated with 대장종류High correlation
건물명 has 11 (6.8%) missing valuesMissing

Reproduction

Analysis started2023-12-13 00:30:25.934719
Analysis finished2023-12-13 00:30:26.740033
Duration0.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대장종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
일반건축물
83 
표제부
78 

Length

Max length5
Median length5
Mean length4.0310559
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반건축물 83
51.6%
표제부 78
48.4%

Length

2023-12-13T09:30:26.793499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:30:26.871749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반건축물 83
51.6%
표제부 78
48.4%

도로명주소
Text

Distinct137
Distinct (%)85.6%
Missing1
Missing (%)0.6%
Memory size1.4 KiB
2023-12-13T09:30:27.131613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length18.20625
Min length15

Characters and Unicode

Total characters2913
Distinct characters72
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

Unique133 ?
Unique (%)83.1%

Sample

1st row인천광역시 부평구 부평대로 233
2nd row인천광역시 부평구 광장로 10
3rd row인천광역시 부평구 체육관로 179
4th row인천광역시 부평구 평천로199번길 34
5th row인천광역시 부평구 동수로 56
ValueCountFrequency (%)
인천광역시 160
25.0%
부평구 160
25.0%
부평대로 36
 
5.6%
233 18
 
2.8%
경원대로 9
 
1.4%
동수로 7
 
1.1%
56 6
 
0.9%
장제로 6
 
0.9%
주부토로 6
 
0.9%
4 6
 
0.9%
Other values (157) 226
35.3%
2023-12-13T09:30:27.518359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
480
16.5%
부 230
 
7.9%
평 219
 
7.5%
천 169
 
5.8%
인 162
 
5.6%
광 162
 
5.6%
시 162
 
5.6%
로 160
 
5.5%
구 160
 
5.5%
역 160
 
5.5%
Other values (62) 849
29.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1932
66.3%
Decimal Number 500
 
17.2%
Space Separator 480
 
16.5%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 230
11.9%
평 219
11.3%
천 169
8.7%
인 162
8.4%
광 162
8.4%
시 162
8.4%
로 160
8.3%
구 160
8.3%
역 160
8.3%
대 57
 
3.0%
Other values (50) 291
15.1%
Decimal Number
ValueCountFrequency (%)
3 101
20.2%
1 68
13.6%
2 61
12.2%
6 57
11.4%
4 52
10.4%
5 49
9.8%
9 31
 
6.2%
0 28
 
5.6%
7 27
 
5.4%
8 26
 
5.2%
Space Separator
ValueCountFrequency (%)
480
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1932
66.3%
Common 981
33.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 230
11.9%
평 219
11.3%
천 169
8.7%
인 162
8.4%
광 162
8.4%
시 162
8.4%
로 160
8.3%
구 160
8.3%
역 160
8.3%
대 57
 
3.0%
Other values (50) 291
15.1%
Common
ValueCountFrequency (%)
480
48.9%
3 101
 
10.3%
1 68
 
6.9%
2 61
 
6.2%
6 57
 
5.8%
4 52
 
5.3%
5 49
 
5.0%
9 31
 
3.2%
0 28
 
2.9%
7 27
 
2.8%
Other values (2) 27
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1932
66.3%
ASCII 981
33.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
480
48.9%
3 101
 
10.3%
1 68
 
6.9%
2 61
 
6.2%
6 57
 
5.8%
4 52
 
5.3%
5 49
 
5.0%
9 31
 
3.2%
0 28
 
2.9%
7 27
 
2.8%
Other values (2) 27
 
2.8%
Hangul
ValueCountFrequency (%)
부 230
11.9%
평 219
11.3%
천 169
8.7%
인 162
8.4%
광 162
8.4%
시 162
8.4%
로 160
8.3%
구 160
8.3%
역 160
8.3%
대 57
 
3.0%
Other values (50) 291
15.1%

건물명
Text

MISSING 

Distinct129
Distinct (%)86.0%
Missing11
Missing (%)6.8%
Memory size1.4 KiB
2023-12-13T09:30:27.711531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length7.12
Min length3

Characters and Unicode

Total characters1068
Distinct characters261
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)81.3%

Sample

1st row대우자동차
2nd row토요코인 호텔 인천 부평점
3rd row인천영선고등학교
4th row(주)우성코러스
5th row가톨릭대학교 인천성모병원
ValueCountFrequency (%)
대우자동차 13
 
6.4%
부평 8
 
3.9%
인천성모병원 5
 
2.5%
가톨릭대학교 5
 
2.5%
부평점 3
 
1.5%
더 3
 
1.5%
부평사옥 2
 
1.0%
오피스텔 2
 
1.0%
m-tower 2
 
1.0%
인천 2
 
1.0%
Other values (153) 159
77.9%
2023-12-13T09:30:28.011602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
54
 
5.1%
부 36
 
3.4%
대 33
 
3.1%
평 33
 
3.1%
우 29
 
2.7%
스 22
 
2.1%
자 21
 
2.0%
교 19
 
1.8%
타 18
 
1.7%
동 18
 
1.7%
Other values (251) 785
73.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 923
86.4%
Space Separator 54
 
5.1%
Uppercase Letter 50
 
4.7%
Decimal Number 11
 
1.0%
Close Punctuation 10
 
0.9%
Open Punctuation 10
 
0.9%
Other Punctuation 4
 
0.4%
Lowercase Letter 4
 
0.4%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 36
 
3.9%
대 33
 
3.6%
평 33
 
3.6%
우 29
 
3.1%
스 22
 
2.4%
자 21
 
2.3%
교 19
 
2.1%
타 18
 
2.0%
동 18
 
2.0%
차 18
 
2.0%
Other values (217) 676
73.2%
Uppercase Letter
ValueCountFrequency (%)
E 6
12.0%
T 6
12.0%
R 5
10.0%
M 5
10.0%
O 4
 
8.0%
C 3
 
6.0%
W 3
 
6.0%
H 3
 
6.0%
I 2
 
4.0%
N 2
 
4.0%
Other values (8) 11
22.0%
Decimal Number
ValueCountFrequency (%)
1 3
27.3%
2 2
18.2%
9 2
18.2%
7 1
 
9.1%
3 1
 
9.1%
0 1
 
9.1%
6 1
 
9.1%
Lowercase Letter
ValueCountFrequency (%)
a 2
50.0%
z 1
25.0%
l 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 3
75.0%
, 1
 
25.0%
Space Separator
ValueCountFrequency (%)
54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 923
86.4%
Common 91
 
8.5%
Latin 54
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 36
 
3.9%
대 33
 
3.6%
평 33
 
3.6%
우 29
 
3.1%
스 22
 
2.4%
자 21
 
2.3%
교 19
 
2.1%
타 18
 
2.0%
동 18
 
2.0%
차 18
 
2.0%
Other values (217) 676
73.2%
Latin
ValueCountFrequency (%)
E 6
 
11.1%
T 6
 
11.1%
R 5
 
9.3%
M 5
 
9.3%
O 4
 
7.4%
C 3
 
5.6%
W 3
 
5.6%
H 3
 
5.6%
I 2
 
3.7%
N 2
 
3.7%
Other values (11) 15
27.8%
Common
ValueCountFrequency (%)
54
59.3%
) 10
 
11.0%
( 10
 
11.0%
1 3
 
3.3%
. 3
 
3.3%
2 2
 
2.2%
9 2
 
2.2%
- 2
 
2.2%
7 1
 
1.1%
3 1
 
1.1%
Other values (3) 3
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 923
86.4%
ASCII 145
 
13.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
54
37.2%
) 10
 
6.9%
( 10
 
6.9%
E 6
 
4.1%
T 6
 
4.1%
R 5
 
3.4%
M 5
 
3.4%
O 4
 
2.8%
1 3
 
2.1%
C 3
 
2.1%
Other values (24) 39
26.9%
Hangul
ValueCountFrequency (%)
부 36
 
3.9%
대 33
 
3.6%
평 33
 
3.6%
우 29
 
3.1%
스 22
 
2.4%
자 21
 
2.3%
교 19
 
2.1%
타 18
 
2.0%
동 18
 
2.0%
차 18
 
2.0%
Other values (217) 676
73.2%

연면적
Real number (ℝ)

Distinct160
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23499.745
Minimum10039.24
Maximum281713.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T09:30:28.126885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10039.24
5-th percentile10387.97
Q111490.71
median15294.755
Q323166.61
95-th percentile53079.41
Maximum281713.42
Range271674.18
Interquartile range (IQR)11675.9

Descriptive statistics

Standard deviation28316.796
Coefficient of variation (CV)1.2049831
Kurtosis46.08149
Mean23499.745
Median Absolute Deviation (MAD)4329.735
Skewness5.9427409
Sum3783458.9
Variance8.0184092 × 108
MonotonicityNot monotonic
2023-12-13T09:30:28.234707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19305.08 2
 
1.2%
13841.8 1
 
0.6%
18222.17 1
 
0.6%
16483.2 1
 
0.6%
29104.11 1
 
0.6%
17100.5 1
 
0.6%
18100.43 1
 
0.6%
23262.42 1
 
0.6%
20991.13 1
 
0.6%
17397.0 1
 
0.6%
Other values (150) 150
93.2%
ValueCountFrequency (%)
10039.24 1
0.6%
10039.34 1
0.6%
10207.95 1
0.6%
10226.45 1
0.6%
10240.2 1
0.6%
10284.27 1
0.6%
10318.6 1
0.6%
10322.005 1
0.6%
10387.97 1
0.6%
10405.86 1
0.6%
ValueCountFrequency (%)
281713.4185 1
0.6%
141198.54 1
0.6%
130716.53 1
0.6%
114100.275 1
0.6%
86173.99 1
0.6%
74083.95 1
0.6%
69181.787 1
0.6%
55476.65 1
0.6%
53079.41 1
0.6%
51681.62 1
0.6%

주용도
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
업무시설
55 
공장
41 
제1종근린생활시설
15 
교육연구시설
13 
판매시설
10 
Other values (9)
27 

Length

Max length9
Median length7
Mean length4.4968944
Min length2

Unique

Unique2 ?
Unique (%)1.2%

Sample

1st row공장
2nd row숙박시설
3rd row교육연구및복지시설
4th row공장
5th row의료시설

Common Values

ValueCountFrequency (%)
업무시설 55
34.2%
공장 41
25.5%
제1종근린생활시설 15
 
9.3%
교육연구시설 13
 
8.1%
판매시설 10
 
6.2%
의료시설 8
 
5.0%
제2종근린생활시설 6
 
3.7%
문화및집회시설 3
 
1.9%
교육연구및복지시설 2
 
1.2%
자동차관련시설 2
 
1.2%
Other values (4) 6
 
3.7%

Length

2023-12-13T09:30:28.336444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
업무시설 55
34.2%
공장 41
25.5%
제1종근린생활시설 15
 
9.3%
교육연구시설 13
 
8.1%
판매시설 10
 
6.2%
의료시설 8
 
5.0%
제2종근린생활시설 6
 
3.7%
문화및집회시설 3
 
1.9%
교육연구및복지시설 2
 
1.2%
자동차관련시설 2
 
1.2%
Other values (4) 6
 
3.7%

기타용도내용
Text

Distinct115
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-13T09:30:28.503729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length28
Mean length13.906832
Min length2

Characters and Unicode

Total characters2239
Distinct characters123
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)59.6%

Sample

1st row차체조립공장
2nd row숙박시설
3rd row교육연구및복지시설
4th row공장
5th row의료시설
ValueCountFrequency (%)
업무시설 29
 
11.9%
제1종근린생활시설 19
 
7.8%
공장 15
 
6.1%
근린생활시설 10
 
4.1%
교육연구시설 7
 
2.9%
의료시설 6
 
2.5%
판매시설 6
 
2.5%
교육연구및복지시설 6
 
2.5%
문화및집회시설 5
 
2.0%
업무시설,제1종근린생활시설 5
 
2.0%
Other values (104) 136
55.7%
2023-12-13T09:30:28.811555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 251
 
11.2%
설 249
 
11.1%
, 167
 
7.5%
생 90
 
4.0%
활 89
 
4.0%
근 88
 
3.9%
린 87
 
3.9%
83
 
3.7%
업 75
 
3.3%
무 73
 
3.3%
Other values (113) 987
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1823
81.4%
Other Punctuation 176
 
7.9%
Decimal Number 103
 
4.6%
Space Separator 83
 
3.7%
Close Punctuation 27
 
1.2%
Open Punctuation 27
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 251
 
13.8%
설 249
 
13.7%
생 90
 
4.9%
활 89
 
4.9%
근 88
 
4.8%
린 87
 
4.8%
업 75
 
4.1%
무 73
 
4.0%
종 71
 
3.9%
제 69
 
3.8%
Other values (101) 681
37.4%
Decimal Number
ValueCountFrequency (%)
1 57
55.3%
2 36
35.0%
4 6
 
5.8%
6 2
 
1.9%
7 1
 
1.0%
3 1
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 167
94.9%
· 7
 
4.0%
/ 2
 
1.1%
Space Separator
ValueCountFrequency (%)
83
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1823
81.4%
Common 416
 
18.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 251
 
13.8%
설 249
 
13.7%
생 90
 
4.9%
활 89
 
4.9%
근 88
 
4.8%
린 87
 
4.8%
업 75
 
4.1%
무 73
 
4.0%
종 71
 
3.9%
제 69
 
3.8%
Other values (101) 681
37.4%
Common
ValueCountFrequency (%)
, 167
40.1%
83
20.0%
1 57
 
13.7%
2 36
 
8.7%
) 27
 
6.5%
( 27
 
6.5%
· 7
 
1.7%
4 6
 
1.4%
6 2
 
0.5%
/ 2
 
0.5%
Other values (2) 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1823
81.4%
ASCII 409
 
18.3%
None 7
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 251
 
13.8%
설 249
 
13.7%
생 90
 
4.9%
활 89
 
4.9%
근 88
 
4.8%
린 87
 
4.8%
업 75
 
4.1%
무 73
 
4.0%
종 71
 
3.9%
제 69
 
3.8%
Other values (101) 681
37.4%
ASCII
ValueCountFrequency (%)
, 167
40.8%
83
20.3%
1 57
 
13.9%
2 36
 
8.8%
) 27
 
6.6%
( 27
 
6.6%
4 6
 
1.5%
6 2
 
0.5%
/ 2
 
0.5%
7 1
 
0.2%
None
ValueCountFrequency (%)
· 7
100.0%

Interactions

2023-12-13T09:30:26.264354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:30:28.882137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대장종류연면적주용도
대장종류1.0000.1830.803
연면적0.1831.0000.000
주용도0.8030.0001.000
2023-12-13T09:30:28.943974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주용도대장종류
주용도1.0000.627
대장종류0.6271.000
2023-12-13T09:30:29.006788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연면적대장종류주용도
연면적1.0000.1310.000
대장종류0.1311.0000.627
주용도0.0000.6271.000

Missing values

2023-12-13T09:30:26.564345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:30:26.640358image/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.
2023-12-13T09:30:26.705247image/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

대장종류도로명주소건물명연면적주용도기타용도내용
0일반건축물인천광역시 부평구 부평대로 233대우자동차13841.8공장차체조립공장
1일반건축물인천광역시 부평구 광장로 10토요코인 호텔 인천 부평점12052.36숙박시설숙박시설
2일반건축물인천광역시 부평구 체육관로 179인천영선고등학교13292.47교육연구및복지시설교육연구및복지시설
3일반건축물인천광역시 부평구 평천로199번길 34(주)우성코러스14133.2공장공장
4일반건축물인천광역시 부평구 동수로 56가톨릭대학교 인천성모병원13715.08의료시설의료시설
5일반건축물인천광역시 부평구 부평대로 175의료법인안은의료재단10832.718의료시설의료시설(종합병원)
6일반건축물인천광역시 부평구 충선로 293영선초등학교11350.45교육연구시설교육연구및복지시설
7일반건축물인천광역시 부평구 수변로57번길 61인천부흥고등학교13177.14교육연구시설교육연구시설
8일반건축물인천광역시 부평구 안남로418번길 41동서식품11341.0공장공장,창고
9일반건축물인천광역시 부평구 백범로 593신성실리콘공장11645.14공장공장
대장종류도로명주소건물명연면적주용도기타용도내용
151표제부인천광역시 부평구 부평대로 138부평 신일유스테이션 오피스텔31736.75업무시설업무시설(오피스텔),근린생활시설
152표제부인천광역시 부평구 부평대로 301남광센트렉스86173.99공장공장,업무시설,운동시설,지원시설
153표제부인천광역시 부평구 장제로 45부평 현대 더 로프트69181.787업무시설업무시설(오피스텔)외6종
154표제부인천광역시 부평구 새벌로 29부평테크노타워40050.11공장공장, 제1,2종근린생활시설, 업무시설
155표제부인천광역시 부평구 길주로 623대덕리치아노141198.54판매시설판매시설,업무시설,근린생활시설,운동시설,문화및집회시설
156표제부인천광역시 부평구 부평대로 75한화생명보험(주) 부평사옥36535.31업무시설업무시설
157표제부인천광역시 부평구 부평대로 337부평 제이타워3차 지식산업센터114100.275공장공장(지식산업센터)
158표제부인천광역시 부평구 부평대로 153부평구청역대명벨리온55476.65업무시설업무시설, 판매시설
159표제부인천광역시 부평구 대정로 7부평한라비발디74083.95업무시설업무시설,제1·2종근린생활시설
160표제부인천광역시 부평구 광장로 16부평민자역사46421.93운수시설판매및 영업시설,제2종근린생활시설,문화및집회시설