Overview

Dataset statistics

Number of variables11
Number of observations328
Missing cells150
Missing cells (%)4.2%
Duplicate rows1
Duplicate rows (%)0.3%
Total size in memory29.3 KiB
Average record size in memory91.4 B

Variable types

Categorical4
Text4
Numeric3

Dataset

Description건축물미술작품현황201511
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202588

Alerts

Dataset has 1 (0.3%) duplicate rowsDuplicates
작품심의일자 is highly overall correlated with 작품설치일자 and 1 other fieldsHigh correlation
작품설치일자 is highly overall correlated with 작품심의일자High correlation
건축주성격 is highly overall correlated with 작품심의일자 and 1 other fieldsHigh correlation
건축물용도 is highly overall correlated with 건축주성격High correlation
분류 is highly imbalanced (72.4%)Imbalance
연면적(㎡) has 46 (14.0%) missing valuesMissing
작품심의일자 has 104 (31.7%) missing valuesMissing
연면적(㎡) has 65 (19.8%) zerosZeros
작품설치일자 has 6 (1.8%) zerosZeros

Reproduction

Analysis started2024-03-14 01:01:28.376347
Analysis finished2024-03-14 01:01:29.982391
Duration1.61 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류
Categorical

IMBALANCE 

Distinct8
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
조각
280 
회화
34 
기타
 
4
공예
 
4
상징탑
 
3
Other values (3)
 
3

Length

Max length3
Median length2
Mean length2.0091463
Min length2

Unique

Unique3 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
조각 280
85.4%
회화 34
 
10.4%
기타 4
 
1.2%
공예 4
 
1.2%
상징탑 3
 
0.9%
사진 1
 
0.3%
서예 1
 
0.3%
벽화 1
 
0.3%

Length

2024-03-14T10:01:30.032167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:01:30.121159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
조각 280
85.4%
회화 34
 
10.4%
기타 4
 
1.2%
공예 4
 
1.2%
상징탑 3
 
0.9%
사진 1
 
0.3%
서예 1
 
0.3%
벽화 1
 
0.3%

지역
Categorical

Distinct13
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
전북/전주시
171 
전북/군산시
53 
전북/익산시
51 
전북/완주군
18 
전북/김제시
 
9
Other values (8)
26 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row전북/군산시
2nd row전북/전주시
3rd row전북/전주시
4th row전북/군산시
5th row전북/전주시

Common Values

ValueCountFrequency (%)
전북/전주시 171
52.1%
전북/군산시 53
 
16.2%
전북/익산시 51
 
15.5%
전북/완주군 18
 
5.5%
전북/김제시 9
 
2.7%
전북/정읍시 8
 
2.4%
전북/고창군 7
 
2.1%
전북/남원시 3
 
0.9%
전북/부안군 2
 
0.6%
전북/순창군 2
 
0.6%
Other values (3) 4
 
1.2%

Length

2024-03-14T10:01:30.236521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전북/전주시 171
52.1%
전북/군산시 53
 
16.2%
전북/익산시 51
 
15.5%
전북/완주군 18
 
5.5%
전북/김제시 9
 
2.7%
전북/정읍시 8
 
2.4%
전북/고창군 7
 
2.1%
전북/남원시 3
 
0.9%
전북/부안군 2
 
0.6%
전북/순창군 2
 
0.6%
Other values (3) 4
 
1.2%

작품명
Text

Distinct291
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-14T10:01:30.554008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length5.5884146
Min length1

Characters and Unicode

Total characters1833
Distinct characters353
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique273 ?
Unique (%)83.2%

Sample

1st row오후여섯시반
2nd row하모니
3rd row평화의 소리
4th row온고지신-용오름
5th row천지인+자연
ValueCountFrequency (%)
이야기 10
 
2.0%
비상 8
 
1.6%
가족 7
 
1.4%
꿈 7
 
1.4%
7
 
1.4%
풍경 6
 
1.2%
풍요 6
 
1.2%
소리 5
 
1.0%
자연 5
 
1.0%
생명의 4
 
0.8%
Other values (369) 423
86.7%
2024-03-14T10:01:30.971891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
160
 
8.7%
의 64
 
3.5%
- 51
 
2.8%
기 33
 
1.8%
이 28
 
1.5%
가 27
 
1.5%
는 23
 
1.3%
생 23
 
1.3%
) 23
 
1.3%
풍 23
 
1.3%
Other values (343) 1378
75.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1313
71.6%
Space Separator 160
 
8.7%
Lowercase Letter 140
 
7.6%
Decimal Number 56
 
3.1%
Dash Punctuation 51
 
2.8%
Uppercase Letter 31
 
1.7%
Close Punctuation 23
 
1.3%
Open Punctuation 23
 
1.3%
Other Punctuation 18
 
1.0%
Letter Number 12
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 64
 
4.9%
기 33
 
2.5%
이 28
 
2.1%
가 27
 
2.1%
는 23
 
1.8%
생 23
 
1.8%
풍 23
 
1.8%
리 21
 
1.6%
화 21
 
1.6%
연 19
 
1.4%
Other values (291) 1031
78.5%
Lowercase Letter
ValueCountFrequency (%)
e 21
15.0%
r 16
11.4%
a 13
 
9.3%
n 12
 
8.6%
o 10
 
7.1%
i 7
 
5.0%
y 6
 
4.3%
t 6
 
4.3%
m 6
 
4.3%
s 5
 
3.6%
Other values (10) 38
27.1%
Uppercase Letter
ValueCountFrequency (%)
H 5
16.1%
S 5
16.1%
E 3
9.7%
N 3
9.7%
T 3
9.7%
I 2
 
6.5%
F 2
 
6.5%
R 2
 
6.5%
G 1
 
3.2%
C 1
 
3.2%
Other values (4) 4
12.9%
Decimal Number
ValueCountFrequency (%)
0 18
32.1%
2 17
30.4%
1 14
25.0%
7 3
 
5.4%
8 2
 
3.6%
4 1
 
1.8%
6 1
 
1.8%
Other Punctuation
ValueCountFrequency (%)
, 11
61.1%
. 4
 
22.2%
" 2
 
11.1%
& 1
 
5.6%
Letter Number
ValueCountFrequency (%)
Ⅱ 7
58.3%
Ⅰ 5
41.7%
Space Separator
ValueCountFrequency (%)
160
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 51
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Math Symbol
ValueCountFrequency (%)
+ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1289
70.3%
Common 337
 
18.4%
Latin 183
 
10.0%
Han 24
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 64
 
5.0%
기 33
 
2.6%
이 28
 
2.2%
가 27
 
2.1%
는 23
 
1.8%
생 23
 
1.8%
풍 23
 
1.8%
리 21
 
1.6%
화 21
 
1.6%
연 19
 
1.5%
Other values (268) 1007
78.1%
Latin
ValueCountFrequency (%)
e 21
 
11.5%
r 16
 
8.7%
a 13
 
7.1%
n 12
 
6.6%
o 10
 
5.5%
Ⅱ 7
 
3.8%
i 7
 
3.8%
y 6
 
3.3%
t 6
 
3.3%
m 6
 
3.3%
Other values (26) 79
43.2%
Han
ValueCountFrequency (%)
合 2
 
8.3%
母 1
 
4.2%
思 1
 
4.2%
生 1
 
4.2%
飛 1
 
4.2%
家 1
 
4.2%
族 1
 
4.2%
氣 1
 
4.2%
天 1
 
4.2%
地 1
 
4.2%
Other values (13) 13
54.2%
Common
ValueCountFrequency (%)
160
47.5%
- 51
 
15.1%
) 23
 
6.8%
( 23
 
6.8%
0 18
 
5.3%
2 17
 
5.0%
1 14
 
4.2%
, 11
 
3.3%
+ 6
 
1.8%
. 4
 
1.2%
Other values (6) 10
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1289
70.3%
ASCII 508
 
27.7%
CJK 24
 
1.3%
Number Forms 12
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
160
31.5%
- 51
 
10.0%
) 23
 
4.5%
( 23
 
4.5%
e 21
 
4.1%
0 18
 
3.5%
2 17
 
3.3%
r 16
 
3.1%
1 14
 
2.8%
a 13
 
2.6%
Other values (40) 152
29.9%
Hangul
ValueCountFrequency (%)
의 64
 
5.0%
기 33
 
2.6%
이 28
 
2.2%
가 27
 
2.1%
는 23
 
1.8%
생 23
 
1.8%
풍 23
 
1.8%
리 21
 
1.6%
화 21
 
1.6%
연 19
 
1.5%
Other values (268) 1007
78.1%
Number Forms
ValueCountFrequency (%)
Ⅱ 7
58.3%
Ⅰ 5
41.7%
CJK
ValueCountFrequency (%)
合 2
 
8.3%
母 1
 
4.2%
思 1
 
4.2%
生 1
 
4.2%
飛 1
 
4.2%
家 1
 
4.2%
族 1
 
4.2%
氣 1
 
4.2%
天 1
 
4.2%
地 1
 
4.2%
Other values (13) 13
54.2%

작가명
Text

Distinct155
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-14T10:01:31.234866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.0182927
Min length2

Characters and Unicode

Total characters990
Distinct characters125
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

Unique108 ?
Unique (%)32.9%

Sample

1st row김강석
2nd row소찬섭
3rd row국경오
4th row강용면
5th row우희석
ValueCountFrequency (%)
김동헌 28
 
8.5%
국경오 19
 
5.8%
강용면 16
 
4.8%
엄혁용 16
 
4.8%
이한우 13
 
3.9%
선기현 10
 
3.0%
이효문 6
 
1.8%
김귀복 6
 
1.8%
김오성 6
 
1.8%
유휴열 6
 
1.8%
Other values (145) 204
61.8%
2024-03-14T10:01:31.615127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 77
 
7.8%
이 51
 
5.2%
용 39
 
3.9%
동 34
 
3.4%
경 32
 
3.2%
강 30
 
3.0%
현 30
 
3.0%
헌 28
 
2.8%
오 27
 
2.7%
정 25
 
2.5%
Other values (115) 617
62.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 987
99.7%
Space Separator 2
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 77
 
7.8%
이 51
 
5.2%
용 39
 
4.0%
동 34
 
3.4%
경 32
 
3.2%
강 30
 
3.0%
현 30
 
3.0%
헌 28
 
2.8%
오 27
 
2.7%
정 25
 
2.5%
Other values (113) 614
62.2%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 987
99.7%
Common 3
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 77
 
7.8%
이 51
 
5.2%
용 39
 
4.0%
동 34
 
3.4%
경 32
 
3.2%
강 30
 
3.0%
현 30
 
3.0%
헌 28
 
2.8%
오 27
 
2.7%
정 25
 
2.5%
Other values (113) 614
62.2%
Common
ValueCountFrequency (%)
2
66.7%
, 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 987
99.7%
ASCII 3
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 77
 
7.8%
이 51
 
5.2%
용 39
 
4.0%
동 34
 
3.4%
경 32
 
3.2%
강 30
 
3.0%
현 30
 
3.0%
헌 28
 
2.8%
오 27
 
2.7%
정 25
 
2.5%
Other values (113) 614
62.2%
ASCII
ValueCountFrequency (%)
2
66.7%
, 1
33.3%

건축물명
Text

Distinct235
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-14T10:01:31.843987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length8.9420732
Min length3

Characters and Unicode

Total characters2933
Distinct characters290
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique181 ?
Unique (%)55.2%

Sample

1st row지곡동 서희스타힐
2nd row스카이타워
3rd row동산 골드클래스
4th row군산2차 현대엠코아파트
5th row웰가주상복합
ValueCountFrequency (%)
아파트 44
 
7.8%
전북대학교 13
 
2.3%
어린이병원 13
 
2.3%
익산 11
 
2.0%
전주점 8
 
1.4%
롯데마트 8
 
1.4%
현대 7
 
1.2%
kbs전주방송총국 7
 
1.2%
휴먼시아 7
 
1.2%
주공아파트 6
 
1.1%
Other values (295) 439
78.0%
2024-03-14T10:01:32.195134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
235
 
8.0%
아 191
 
6.5%
트 185
 
6.3%
파 178
 
6.1%
주 67
 
2.3%
전 57
 
1.9%
지 50
 
1.7%
대 50
 
1.7%
동 46
 
1.6%
스 41
 
1.4%
Other values (280) 1833
62.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2538
86.5%
Space Separator 235
 
8.0%
Uppercase Letter 57
 
1.9%
Decimal Number 48
 
1.6%
Lowercase Letter 23
 
0.8%
Dash Punctuation 15
 
0.5%
Other Punctuation 8
 
0.3%
Close Punctuation 4
 
0.1%
Open Punctuation 4
 
0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 191
 
7.5%
트 185
 
7.3%
파 178
 
7.0%
주 67
 
2.6%
전 57
 
2.2%
지 50
 
2.0%
대 50
 
2.0%
동 46
 
1.8%
스 41
 
1.6%
이 41
 
1.6%
Other values (247) 1632
64.3%
Uppercase Letter
ValueCountFrequency (%)
K 12
21.1%
S 9
15.8%
P 9
15.8%
B 8
14.0%
A 5
8.8%
I 4
 
7.0%
R 3
 
5.3%
W 2
 
3.5%
J 2
 
3.5%
T 2
 
3.5%
Decimal Number
ValueCountFrequency (%)
2 19
39.6%
1 12
25.0%
3 6
 
12.5%
5 4
 
8.3%
8 2
 
4.2%
4 2
 
4.2%
7 1
 
2.1%
9 1
 
2.1%
6 1
 
2.1%
Lowercase Letter
ValueCountFrequency (%)
e 10
43.5%
a 4
 
17.4%
k 4
 
17.4%
r 4
 
17.4%
ｉ 1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
' 4
50.0%
, 3
37.5%
？ 1
 
12.5%
Space Separator
ValueCountFrequency (%)
235
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2539
86.6%
Common 314
 
10.7%
Latin 80
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 191
 
7.5%
트 185
 
7.3%
파 178
 
7.0%
주 67
 
2.6%
전 57
 
2.2%
지 50
 
2.0%
대 50
 
2.0%
동 46
 
1.8%
스 41
 
1.6%
이 41
 
1.6%
Other values (248) 1633
64.3%
Common
ValueCountFrequency (%)
235
74.8%
2 19
 
6.1%
- 15
 
4.8%
1 12
 
3.8%
3 6
 
1.9%
5 4
 
1.3%
) 4
 
1.3%
( 4
 
1.3%
' 4
 
1.3%
, 3
 
1.0%
Other values (6) 8
 
2.5%
Latin
ValueCountFrequency (%)
K 12
15.0%
e 10
12.5%
S 9
11.2%
P 9
11.2%
B 8
10.0%
A 5
6.2%
a 4
 
5.0%
k 4
 
5.0%
r 4
 
5.0%
I 4
 
5.0%
Other values (6) 11
13.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2538
86.5%
ASCII 392
 
13.4%
None 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
235
59.9%
2 19
 
4.8%
- 15
 
3.8%
K 12
 
3.1%
1 12
 
3.1%
e 10
 
2.6%
S 9
 
2.3%
P 9
 
2.3%
B 8
 
2.0%
3 6
 
1.5%
Other values (20) 57
 
14.5%
Hangul
ValueCountFrequency (%)
아 191
 
7.5%
트 185
 
7.3%
파 178
 
7.0%
주 67
 
2.6%
전 57
 
2.2%
지 50
 
2.0%
대 50
 
2.0%
동 46
 
1.8%
스 41
 
1.6%
이 41
 
1.6%
Other values (247) 1632
64.3%
None
ValueCountFrequency (%)
㈜ 1
33.3%
？ 1
33.3%
ｉ 1
33.3%

건축물주소
Text

Distinct246
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-14T10:01:32.403044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length27
Mean length21.332317
Min length9

Characters and Unicode

Total characters6997
Distinct characters187
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

Unique192 ?
Unique (%)58.5%

Sample

1st row전북 군산시 지곡동 226
2nd row전북 전주시 완산구 효자동3가 1526-2
3rd row전북 전주시 덕진구 동산동 671-4
4th row전북 군산시 옥산면 당북리 917
5th row전북 전주시 덕진구 송천동1가 350-4
ValueCountFrequency (%)
전북 228
 
14.0%
전주시 132
 
8.1%
전라북도 100
 
6.1%
완산구 96
 
5.9%
덕진구 75
 
4.6%
익산시 45
 
2.8%
전주 40
 
2.5%
군산시 32
 
2.0%
군산 22
 
1.3%
완주군 18
 
1.1%
Other values (402) 843
51.7%
2024-03-14T10:01:32.945885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1303
18.6%
전 507
 
7.2%
북 333
 
4.8%
1 315
 
4.5%
동 306
 
4.4%
산 242
 
3.5%
시 240
 
3.4%
주 198
 
2.8%
- 188
 
2.7%
구 187
 
2.7%
Other values (177) 3178
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4186
59.8%
Space Separator 1303
 
18.6%
Decimal Number 1293
 
18.5%
Dash Punctuation 188
 
2.7%
Uppercase Letter 12
 
0.2%
Other Punctuation 9
 
0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 507
 
12.1%
북 333
 
8.0%
동 306
 
7.3%
산 242
 
5.8%
시 240
 
5.7%
주 198
 
4.7%
구 187
 
4.5%
지 183
 
4.4%
가 131
 
3.1%
완 115
 
2.7%
Other values (155) 1744
41.7%
Decimal Number
ValueCountFrequency (%)
1 315
24.4%
2 167
12.9%
3 136
10.5%
5 126
 
9.7%
4 118
 
9.1%
6 116
 
9.0%
8 94
 
7.3%
9 86
 
6.7%
7 73
 
5.6%
0 62
 
4.8%
Other Punctuation
ValueCountFrequency (%)
/ 5
55.6%
, 3
33.3%
@ 1
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
A 5
41.7%
B 4
33.3%
L 3
25.0%
Lowercase Letter
ValueCountFrequency (%)
b 1
50.0%
c 1
50.0%
Space Separator
ValueCountFrequency (%)
1303
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 188
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4186
59.8%
Common 2797
40.0%
Latin 14
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 507
 
12.1%
북 333
 
8.0%
동 306
 
7.3%
산 242
 
5.8%
시 240
 
5.7%
주 198
 
4.7%
구 187
 
4.5%
지 183
 
4.4%
가 131
 
3.1%
완 115
 
2.7%
Other values (155) 1744
41.7%
Common
ValueCountFrequency (%)
1303
46.6%
1 315
 
11.3%
- 188
 
6.7%
2 167
 
6.0%
3 136
 
4.9%
5 126
 
4.5%
4 118
 
4.2%
6 116
 
4.1%
8 94
 
3.4%
9 86
 
3.1%
Other values (7) 148
 
5.3%
Latin
ValueCountFrequency (%)
A 5
35.7%
B 4
28.6%
L 3
21.4%
b 1
 
7.1%
c 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4186
59.8%
ASCII 2811
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1303
46.4%
1 315
 
11.2%
- 188
 
6.7%
2 167
 
5.9%
3 136
 
4.8%
5 126
 
4.5%
4 118
 
4.2%
6 116
 
4.1%
8 94
 
3.3%
9 86
 
3.1%
Other values (12) 162
 
5.8%
Hangul
ValueCountFrequency (%)
전 507
 
12.1%
북 333
 
8.0%
동 306
 
7.3%
산 242
 
5.8%
시 240
 
5.7%
주 198
 
4.7%
구 187
 
4.5%
지 183
 
4.4%
가 131
 
3.1%
완 115
 
2.7%
Other values (155) 1744
41.7%

건축주성격
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
-
207 
공공
75 
민간
42 
기타(공공+민간합작)
 
4

Length

Max length11
Median length1
Mean length1.4786585
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
- 207
63.1%
공공 75
 
22.9%
민간 42
 
12.8%
기타(공공+민간합작) 4
 
1.2%

Length

2024-03-14T10:01:33.053007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:01:33.435069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
207
63.1%
공공 75
 
22.9%
민간 42
 
12.8%
기타(공공+민간합작 4
 
1.2%

연면적(㎡)
Real number (ℝ)

MISSING  ZEROS 

Distinct165
Distinct (%)58.5%
Missing46
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean47399.583
Minimum0
Maximum567229.74
Zeros65
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-03-14T10:01:33.544151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14083.1298
median30788
Q368464.825
95-th percentile149108.42
Maximum567229.74
Range567229.74
Interquartile range (IQR)64381.695

Descriptive statistics

Standard deviation57101.266
Coefficient of variation (CV)1.2046787
Kurtosis24.07475
Mean47399.583
Median Absolute Deviation (MAD)30788
Skewness3.426769
Sum13366682
Variance3.2605546 × 109
MonotonicityNot monotonic
2024-03-14T10:01:33.650755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 65
 
19.8%
30788.0 13
 
4.0%
14168.0 7
 
2.1%
134812.825 3
 
0.9%
182718.73 3
 
0.9%
75519.35 3
 
0.9%
11523.2997 3
 
0.9%
12695.3 3
 
0.9%
104431.27 3
 
0.9%
209648.88 3
 
0.9%
Other values (155) 176
53.7%
(Missing) 46
 
14.0%
ValueCountFrequency (%)
0.0 65
19.8%
1841.88 1
 
0.3%
3241.368 1
 
0.3%
3267.0 1
 
0.3%
3277.7135 1
 
0.3%
3992.74 1
 
0.3%
4072.6452 1
 
0.3%
4114.5836 1
 
0.3%
4954.95 1
 
0.3%
8568.6419 1
 
0.3%
ValueCountFrequency (%)
567229.7352 1
 
0.3%
209648.88 3
0.9%
192280.0 2
0.6%
182718.73 3
0.9%
172867.7907 2
0.6%
152983.18 2
0.6%
150176.39 1
 
0.3%
149108.42 2
0.6%
143878.8088 2
0.6%
134812.825 3
0.9%

건축물용도
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
근린생활시설
198 
공동주택
89 
의료시설
 
15
방송 · 통신시설
 
7
업무시설
 
6
Other values (4)
 
13

Length

Max length14
Median length6
Mean length5.4115854
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
근린생활시설 198
60.4%
공동주택 89
27.1%
의료시설 15
 
4.6%
방송 · 통신시설 7
 
2.1%
업무시설 6
 
1.8%
기타 4
 
1.2%
공연장, 집회장 및 관람장 4
 
1.2%
숙박시설 3
 
0.9%
판매시설 2
 
0.6%

Length

2024-03-14T10:01:33.754807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:01:33.851080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
근린생활시설 198
55.9%
공동주택 89
25.1%
의료시설 15
 
4.2%
방송 7
 
2.0%
· 7
 
2.0%
통신시설 7
 
2.0%
업무시설 6
 
1.7%
기타 4
 
1.1%
공연장 4
 
1.1%
집회장 4
 
1.1%
Other values (4) 13
 
3.7%

작품심의일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct98
Distinct (%)43.8%
Missing104
Missing (%)31.7%
Infinite0
Infinite (%)0.0%
Mean20092758
Minimum20010000
Maximum20150924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-03-14T10:01:33.962121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010000
5-th percentile20021204
Q120070206
median20095658
Q320121227
95-th percentile20140827
Maximum20150924
Range140924
Interquartile range (IQR)51021

Descriptive statistics

Standard deviation37643.217
Coefficient of variation (CV)0.0018734719
Kurtosis-0.77817191
Mean20092758
Median Absolute Deviation (MAD)25568.5
Skewness-0.44365277
Sum4.5007777 × 109
Variance1.4170118 × 109
MonotonicityNot monotonic
2024-03-14T10:01:34.073823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20111129 11
 
3.4%
20130619 10
 
3.0%
20010000 8
 
2.4%
20130306 8
 
2.4%
20120824 7
 
2.1%
20131126 6
 
1.8%
20070206 6
 
1.8%
20120531 6
 
1.8%
20120105 5
 
1.5%
20071026 5
 
1.5%
Other values (88) 152
46.3%
(Missing) 104
31.7%
ValueCountFrequency (%)
20010000 8
2.4%
20020101 1
 
0.3%
20021022 1
 
0.3%
20021100 1
 
0.3%
20021204 2
 
0.6%
20021207 2
 
0.6%
20030903 2
 
0.6%
20031014 1
 
0.3%
20031024 1
 
0.3%
20031226 1
 
0.3%
ValueCountFrequency (%)
20150924 1
 
0.3%
20150630 3
 
0.9%
20150122 4
 
1.2%
20141124 2
 
0.6%
20140827 3
 
0.9%
20140528 4
 
1.2%
20140320 5
1.5%
20131126 6
1.8%
20131024 4
 
1.2%
20130619 10
3.0%

작품설치일자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct219
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19713881
Minimum0
Maximum20151104
Zeros6
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-03-14T10:01:34.187282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20010535
Q120041229
median20080130
Q320121139
95-th percentile20150115
Maximum20151104
Range20151104
Interquartile range (IQR)79910.25

Descriptive statistics

Standard deviation2695471.6
Coefficient of variation (CV)0.13672963
Kurtosis50.44408
Mean19713881
Median Absolute Deviation (MAD)39606
Skewness-7.219633
Sum6.466153 × 109
Variance7.2655672 × 1012
MonotonicityDecreasing
2024-03-14T10:01:34.313873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20121214 13
 
4.0%
0 6
 
1.8%
20010000 6
 
1.8%
20110725 5
 
1.5%
20140410 4
 
1.2%
20060600 4
 
1.2%
20031114 4
 
1.2%
20090406 3
 
0.9%
20131031 3
 
0.9%
20121115 3
 
0.9%
Other values (209) 277
84.5%
ValueCountFrequency (%)
0 6
1.8%
20000000 1
 
0.3%
20000706 1
 
0.3%
20001125 1
 
0.3%
20010000 6
1.8%
20010214 1
 
0.3%
20010500 1
 
0.3%
20010601 1
 
0.3%
20010721 1
 
0.3%
20010801 1
 
0.3%
ValueCountFrequency (%)
20151104 1
0.3%
20151002 1
0.3%
20150828 1
0.3%
20150819 1
0.3%
20150728 1
0.3%
20150720 1
0.3%
20150702 2
0.6%
20150615 1
0.3%
20150512 1
0.3%
20150428 2
0.6%

Interactions

2024-03-14T10:01:29.475421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:01:29.021849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:01:29.250546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:01:29.556839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:01:29.105897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:01:29.314715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:01:29.630415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:01:29.177282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:01:29.396863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T10:01:34.396941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류지역건축주성격연면적(㎡)건축물용도작품심의일자작품설치일자
분류1.0000.0000.4130.0000.3910.1450.137
지역0.0001.0000.3920.0000.4010.5000.000
건축주성격0.4130.3921.0000.0000.7580.7680.063
연면적(㎡)0.0000.0000.0001.0000.2200.2790.000
건축물용도0.3910.4010.7580.2201.0000.5890.000
작품심의일자0.1450.5000.7680.2790.5891.000NaN
작품설치일자0.1370.0000.0630.0000.000NaN1.000
2024-03-14T10:01:34.487512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류건축주성격건축물용도지역
분류1.0000.1930.2030.000
건축주성격0.1931.0000.6000.235
건축물용도0.2030.6001.0000.183
지역0.0000.2350.1831.000
2024-03-14T10:01:34.567304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연면적(㎡)작품심의일자작품설치일자분류지역건축주성격건축물용도
연면적(㎡)1.0000.091-0.0980.0000.0000.0000.127
작품심의일자0.0911.0000.9710.0730.2610.5760.321
작품설치일자-0.0980.9711.0000.1020.0000.0420.000
분류0.0000.0730.1021.0000.0000.1930.203
지역0.0000.2610.0000.0001.0000.2350.183
건축주성격0.0000.5760.0420.1930.2351.0000.600
건축물용도0.1270.3210.0000.2030.1830.6001.000

Missing values

2024-03-14T10:01:29.737693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:01:29.860191image/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.
2024-03-14T10:01:29.941014image/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

분류지역작품명작가명건축물명건축물주소건축주성격연면적(㎡)건축물용도작품심의일자작품설치일자
0조각전북/군산시오후여섯시반김강석지곡동 서희스타힐전북 군산시 지곡동 226민간0.0공동주택2015012220151104
1조각전북/전주시하모니소찬섭스카이타워전북 전주시 완산구 효자동3가 1526-2민간0.0공동주택2015092420151002
2조각전북/전주시평화의 소리국경오동산 골드클래스전북 전주시 덕진구 동산동 671-4민간0.0공동주택2015063020150828
3조각전북/군산시온고지신-용오름강용면군산2차 현대엠코아파트전북 군산시 옥산면 당북리 917민간65854.688공동주택2013112620150819
4조각전북/전주시천지인+자연우희석웰가주상복합전북 전주시 덕진구 송천동1가 350-4민간0.0기타2015012220150728
5조각전북/군산시생명의 하모니이한우제일 오투그란데 아파트전북 군산시 경암동 504-4민간0.0공동주택2015063020150720
6조각전북/익산시움직임-여행김건주익산 어양동 e편한세상 아파트전북 익산시 어양동 606-1민간0.0공동주택2015063020150702
7조각전북/익산시천공-머무는자리유영열익산 어양동 e편한세상 아파트전북 익산시 어양동 606-1민간0.0공동주택2015012220150702
8조각전북/군산시해피데이김강석해강솔비앙전북 군산시 소룡동 768민간19368.0업무시설2015012220150615
9조각전북/전주시아낌없이 주는 나무우희석엘르디움아파트전북 전주시 덕진구 덕진동2가 3-1 엘르디움아파트민간14791.96공동주택2014052820150512
분류지역작품명작가명건축물명건축물주소건축주성격연면적(㎡)건축물용도작품심의일자작품설치일자
318회화전북/익산시자연1선기현금강동 쉐르빌전북 익산 금강동-<NA>근린생활시설2001000020010000
319조각전북/김제시가족김동헌건축물기록없음전북 김제시 요촌동 423-2 김제쇼핑 센터-<NA>근린생활시설<NA>20001125
320조각전북/김제시이야기김동헌건축물기록없음전북 김제시 신풍동 63 부영3차아파트-<NA>근린생활시설<NA>20000706
321조각전북/익산시家族(가족)이한우영등동 제일아파트전북 익산 영등동-<NA>근린생활시설<NA>20000000
322조각전북/전주시평화국경오알리앙스 웨딩홀전북 전주시 완산구 효자동1가 410-1-17711.51근린생활시설<NA>0
323조각전북/전주시21c 탄생축제김영숙롯데백화점 전주점전북 전주시 완산구 서신동 971번지-75519.35근린생활시설<NA>0
324조각전북/전주시젊음의 광장황순례롯데백화점 전주점전북 전주시 완산구 서신동 971번지-75519.35근린생활시설<NA>0
325회화전북/전주시조바심김두해롯데백화점 전주점전북 전주 완산구 서신동 971번지-<NA>근린생활시설<NA>0
326회화전북/전주시신포항신세자알리앙스 웨딩홀전북 전주 완산구 효자동1가 410-1-<NA>근린생활시설<NA>0
327회화전북/전주시서해김두해알리앙스 웨딩홀전북 전주 완산구 효자동1가 410-1-<NA>근린생활시설<NA>0

Duplicate rows

Most frequently occurring

분류지역작품명작가명건축물명건축물주소건축주성격연면적(㎡)건축물용도작품심의일자작품설치일자# duplicates
0조각전북/전주시The flower(꽃의 향연)류경미휴먼시아 국민임대아파트전북 전주 완산구 효자동 택지개발지구 A-3블럭-0.0근린생활시설20070807200711062