Overview

Dataset statistics

Number of variables11
Number of observations303
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.3%
Total size in memory26.5 KiB
Average record size in memory89.4 B

Variable types

Categorical4
Text6
Numeric1

Alerts

Dataset has 1 (0.3%) duplicate rowsDuplicates
건축주성격 is highly overall correlated with 건축물용도High correlation
건축물용도 is highly overall correlated with 건축주성격High correlation
분류 is highly imbalanced (71.5%)Imbalance
건축물용도 is highly imbalanced (51.2%)Imbalance
작품설치일자 has 6 (2.0%) zerosZeros

Reproduction

Analysis started2024-03-14 00:12:01.851414
Analysis finished2024-03-14 00:12:02.803111
Duration0.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류
Categorical

IMBALANCE 

Distinct8
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
조각
256 
회화
34 
공예
 
4
상징탑
 
3
기타
 
3
Other values (3)
 
3

Length

Max length3
Median length2
Mean length2.009901
Min length2

Unique

Unique3 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
조각 256
84.5%
회화 34
 
11.2%
공예 4
 
1.3%
상징탑 3
 
1.0%
기타 3
 
1.0%
사진 1
 
0.3%
서예 1
 
0.3%
벽화 1
 
0.3%

Length

2024-03-14T09:12:03.106039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:12:03.197025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
조각 256
84.5%
회화 34
 
11.2%
공예 4
 
1.3%
상징탑 3
 
1.0%
기타 3
 
1.0%
사진 1
 
0.3%
서예 1
 
0.3%
벽화 1
 
0.3%

지역
Categorical

Distinct13
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
전북/전주시
159 
전북/익산시
49 
전북/군산시
45 
전북/완주군
18 
전북/정읍시
 
8
Other values (8)
24 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique3 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
전북/전주시 159
52.5%
전북/익산시 49
 
16.2%
전북/군산시 45
 
14.9%
전북/완주군 18
 
5.9%
전북/정읍시 8
 
2.6%
전북/김제시 8
 
2.6%
전북/고창군 7
 
2.3%
전북/순창군 2
 
0.7%
전북/무주군 2
 
0.7%
전북/남원시 2
 
0.7%
Other values (3) 3
 
1.0%

Length

2024-03-14T09:12:03.314902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전북/전주시 159
52.5%
전북/익산시 49
 
16.2%
전북/군산시 45
 
14.9%
전북/완주군 18
 
5.9%
전북/정읍시 8
 
2.6%
전북/김제시 8
 
2.6%
전북/고창군 7
 
2.3%
전북/순창군 2
 
0.7%
전북/무주군 2
 
0.7%
전북/남원시 2
 
0.7%
Other values (3) 3
 
1.0%

작품명
Text

Distinct269
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-03-14T09:12:03.595880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length5.5907591
Min length1

Characters and Unicode

Total characters1694
Distinct characters340
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

Unique253 ?
Unique (%)83.5%

Sample

1st row비상
2nd row향기터
3rd row무형유산의시원
4th row천년의사랑
5th row생성공간-흔적
ValueCountFrequency (%)
이야기 10
 
2.2%
비상 8
 
1.8%
꿈 7
 
1.5%
가족 7
 
1.5%
7
 
1.5%
풍요 6
 
1.3%
풍경 5
 
1.1%
자연 5
 
1.1%
천년의 4
 
0.9%
평화 4
 
0.9%
Other values (347) 391
86.1%
2024-03-14T09:12:04.047730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
151
 
8.9%
의 60
 
3.5%
- 46
 
2.7%
기 33
 
1.9%
가 27
 
1.6%
이 26
 
1.5%
) 23
 
1.4%
( 23
 
1.4%
생 22
 
1.3%
풍 21
 
1.2%
Other values (330) 1262
74.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1202
71.0%
Space Separator 151
 
8.9%
Lowercase Letter 134
 
7.9%
Decimal Number 51
 
3.0%
Dash Punctuation 46
 
2.7%
Uppercase Letter 30
 
1.8%
Close Punctuation 23
 
1.4%
Open Punctuation 23
 
1.4%
Other Punctuation 18
 
1.1%
Letter Number 12
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 60
 
5.0%
기 33
 
2.7%
가 27
 
2.2%
이 26
 
2.2%
생 22
 
1.8%
풍 21
 
1.7%
는 20
 
1.7%
화 20
 
1.7%
상 19
 
1.6%
연 17
 
1.4%
Other values (279) 937
78.0%
Lowercase Letter
ValueCountFrequency (%)
e 20
14.9%
r 15
11.2%
a 13
 
9.7%
n 11
 
8.2%
o 10
 
7.5%
m 6
 
4.5%
y 6
 
4.5%
t 6
 
4.5%
i 6
 
4.5%
p 5
 
3.7%
Other values (10) 36
26.9%
Uppercase Letter
ValueCountFrequency (%)
H 5
16.7%
S 4
13.3%
N 3
10.0%
T 3
10.0%
E 3
10.0%
I 2
 
6.7%
F 2
 
6.7%
R 2
 
6.7%
G 1
 
3.3%
W 1
 
3.3%
Other values (4) 4
13.3%
Decimal Number
ValueCountFrequency (%)
0 17
33.3%
2 15
29.4%
1 13
25.5%
7 3
 
5.9%
8 2
 
3.9%
6 1
 
2.0%
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 (%)
151
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1178
69.5%
Common 316
 
18.7%
Latin 176
 
10.4%
Han 24
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 60
 
5.1%
기 33
 
2.8%
가 27
 
2.3%
이 26
 
2.2%
생 22
 
1.9%
풍 21
 
1.8%
는 20
 
1.7%
화 20
 
1.7%
상 19
 
1.6%
연 17
 
1.4%
Other values (256) 913
77.5%
Latin
ValueCountFrequency (%)
e 20
 
11.4%
r 15
 
8.5%
a 13
 
7.4%
n 11
 
6.2%
o 10
 
5.7%
Ⅱ 7
 
4.0%
m 6
 
3.4%
y 6
 
3.4%
t 6
 
3.4%
i 6
 
3.4%
Other values (26) 76
43.2%
Han
ValueCountFrequency (%)
合 2
 
8.3%
生 1
 
4.2%
天 1
 
4.2%
氣 1
 
4.2%
母 1
 
4.2%
思 1
 
4.2%
地 1
 
4.2%
家 1
 
4.2%
人 1
 
4.2%
飛 1
 
4.2%
Other values (13) 13
54.2%
Common
ValueCountFrequency (%)
151
47.8%
- 46
 
14.6%
) 23
 
7.3%
( 23
 
7.3%
0 17
 
5.4%
2 15
 
4.7%
1 13
 
4.1%
, 11
 
3.5%
. 4
 
1.3%
+ 4
 
1.3%
Other values (5) 9
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1178
69.5%
ASCII 480
28.3%
CJK 24
 
1.4%
Number Forms 12
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
151
31.5%
- 46
 
9.6%
) 23
 
4.8%
( 23
 
4.8%
e 20
 
4.2%
0 17
 
3.5%
2 15
 
3.1%
r 15
 
3.1%
1 13
 
2.7%
a 13
 
2.7%
Other values (39) 144
30.0%
Hangul
ValueCountFrequency (%)
의 60
 
5.1%
기 33
 
2.8%
가 27
 
2.3%
이 26
 
2.2%
생 22
 
1.9%
풍 21
 
1.8%
는 20
 
1.7%
화 20
 
1.7%
상 19
 
1.6%
연 17
 
1.4%
Other values (256) 913
77.5%
Number Forms
ValueCountFrequency (%)
Ⅱ 7
58.3%
Ⅰ 5
41.7%
CJK
ValueCountFrequency (%)
合 2
 
8.3%
生 1
 
4.2%
天 1
 
4.2%
氣 1
 
4.2%
母 1
 
4.2%
思 1
 
4.2%
地 1
 
4.2%
家 1
 
4.2%
人 1
 
4.2%
飛 1
 
4.2%
Other values (13) 13
54.2%

작가명
Text

Distinct146
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-03-14T09:12:04.340756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.019802
Min length2

Characters and Unicode

Total characters915
Distinct characters124
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

Unique105 ?
Unique (%)34.7%

Sample

1st row권치규
2nd row원유진
3rd row이용백
4th row엄혁용
5th row신치현
ValueCountFrequency (%)
김동헌 28
 
9.2%
국경오 18
 
5.9%
엄혁용 16
 
5.2%
이한우 12
 
3.9%
강용면 12
 
3.9%
선기현 10
 
3.3%
김귀복 6
 
2.0%
이효문 6
 
2.0%
유휴열 6
 
2.0%
김상호 5
 
1.6%
Other values (136) 186
61.0%
2024-03-14T09:12:04.733632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 68
 
7.4%
이 47
 
5.1%
용 35
 
3.8%
동 34
 
3.7%
경 30
 
3.3%
현 29
 
3.2%
헌 28
 
3.1%
오 24
 
2.6%
정 24
 
2.6%
강 23
 
2.5%
Other values (114) 573
62.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 912
99.7%
Space Separator 2
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 68
 
7.5%
이 47
 
5.2%
용 35
 
3.8%
동 34
 
3.7%
경 30
 
3.3%
현 29
 
3.2%
헌 28
 
3.1%
오 24
 
2.6%
정 24
 
2.6%
강 23
 
2.5%
Other values (112) 570
62.5%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 912
99.7%
Common 3
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 68
 
7.5%
이 47
 
5.2%
용 35
 
3.8%
동 34
 
3.7%
경 30
 
3.3%
현 29
 
3.2%
헌 28
 
3.1%
오 24
 
2.6%
정 24
 
2.6%
강 23
 
2.5%
Other values (112) 570
62.5%
Common
ValueCountFrequency (%)
2
66.7%
, 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 912
99.7%
ASCII 3
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 68
 
7.5%
이 47
 
5.2%
용 35
 
3.8%
동 34
 
3.7%
경 30
 
3.3%
현 29
 
3.2%
헌 28
 
3.1%
오 24
 
2.6%
정 24
 
2.6%
강 23
 
2.5%
Other values (112) 570
62.5%
ASCII
ValueCountFrequency (%)
2
66.7%
, 1
33.3%

건축물명
Text

Distinct214
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-03-14T09:12:04.993974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length9.0066007
Min length3

Characters and Unicode

Total characters2729
Distinct characters286
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

Unique164 ?
Unique (%)54.1%

Sample

1st row농촌진흥청
2nd row대광 로제비앙
3rd row국립무형유산원
4th row익산금호어울림
5th row익산금호어울림
ValueCountFrequency (%)
아파트 41
 
7.8%
전북대학교 13
 
2.5%
어린이병원 13
 
2.5%
익산 9
 
1.7%
전주점 8
 
1.5%
롯데마트 8
 
1.5%
휴먼시아 7
 
1.3%
kbs전주방송총국 7
 
1.3%
현대 7
 
1.3%
주공아파트 6
 
1.1%
Other values (270) 404
77.2%
2024-03-14T09:12:05.317949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
220
 
8.1%
아 177
 
6.5%
트 174
 
6.4%
파 165
 
6.0%
주 65
 
2.4%
전 57
 
2.1%
대 48
 
1.8%
지 47
 
1.7%
동 40
 
1.5%
이 34
 
1.2%
Other values (276) 1702
62.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2354
86.3%
Space Separator 220
 
8.1%
Uppercase Letter 57
 
2.1%
Decimal Number 45
 
1.6%
Lowercase Letter 21
 
0.8%
Dash Punctuation 15
 
0.5%
Other Punctuation 8
 
0.3%
Open Punctuation 4
 
0.1%
Close Punctuation 4
 
0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 177
 
7.5%
트 174
 
7.4%
파 165
 
7.0%
주 65
 
2.8%
전 57
 
2.4%
대 48
 
2.0%
지 47
 
2.0%
동 40
 
1.7%
이 34
 
1.4%
스 34
 
1.4%
Other values (243) 1513
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 18
40.0%
1 11
24.4%
3 5
 
11.1%
5 4
 
8.9%
8 2
 
4.4%
4 2
 
4.4%
9 1
 
2.2%
7 1
 
2.2%
6 1
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
e 8
38.1%
r 4
19.0%
a 4
19.0%
k 4
19.0%
ｉ 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
' 4
50.0%
, 3
37.5%
？ 1
 
12.5%
Space Separator
ValueCountFrequency (%)
220
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2355
86.3%
Common 296
 
10.8%
Latin 78
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 177
 
7.5%
트 174
 
7.4%
파 165
 
7.0%
주 65
 
2.8%
전 57
 
2.4%
대 48
 
2.0%
지 47
 
2.0%
동 40
 
1.7%
이 34
 
1.4%
스 34
 
1.4%
Other values (244) 1514
64.3%
Common
ValueCountFrequency (%)
220
74.3%
2 18
 
6.1%
- 15
 
5.1%
1 11
 
3.7%
3 5
 
1.7%
( 4
 
1.4%
) 4
 
1.4%
5 4
 
1.4%
' 4
 
1.4%
, 3
 
1.0%
Other values (6) 8
 
2.7%
Latin
ValueCountFrequency (%)
K 12
15.4%
S 9
11.5%
P 9
11.5%
e 8
10.3%
B 8
10.3%
A 5
6.4%
r 4
 
5.1%
I 4
 
5.1%
a 4
 
5.1%
k 4
 
5.1%
Other values (6) 11
14.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2354
86.3%
ASCII 372
 
13.6%
None 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
220
59.1%
2 18
 
4.8%
- 15
 
4.0%
K 12
 
3.2%
1 11
 
3.0%
S 9
 
2.4%
P 9
 
2.4%
e 8
 
2.2%
B 8
 
2.2%
A 5
 
1.3%
Other values (20) 57
 
15.3%
Hangul
ValueCountFrequency (%)
아 177
 
7.5%
트 174
 
7.4%
파 165
 
7.0%
주 65
 
2.8%
전 57
 
2.4%
대 48
 
2.0%
지 47
 
2.0%
동 40
 
1.7%
이 34
 
1.4%
스 34
 
1.4%
Other values (243) 1513
64.3%
None
ValueCountFrequency (%)
？ 1
33.3%
㈜ 1
33.3%
ｉ 1
33.3%

건축물주소
Text

Distinct225
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-03-14T09:12:05.596302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length28
Mean length21.567657
Min length9

Characters and Unicode

Total characters6535
Distinct characters177
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

Unique175 ?
Unique (%)57.8%

Sample

1st row전북 전주시 완산구 중동 혁신도시개발지구 농생16블럭 1노트
2nd row전북 정읍시 상동 89-4번지
3rd row전북 전주시 완산구 동서학동 896-1
4th row전북 익산시 신동 816-1번지외 5필지
5th row전북 익산시 신동 816-1번지외 5필지
ValueCountFrequency (%)
전북 203
 
13.4%
전주시 120
 
7.9%
전라북도 100
 
6.6%
완산구 90
 
5.9%
덕진구 69
 
4.5%
익산시 43
 
2.8%
전주 40
 
2.6%
군산시 24
 
1.6%
군산 22
 
1.4%
완주군 18
 
1.2%
Other values (376) 789
52.0%
2024-03-14T09:12:05.975884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1215
18.6%
전 470
 
7.2%
북 307
 
4.7%
1 293
 
4.5%
동 282
 
4.3%
산 222
 
3.4%
시 216
 
3.3%
주 186
 
2.8%
지 179
 
2.7%
구 175
 
2.7%
Other values (167) 2990
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3912
59.9%
Space Separator 1215
 
18.6%
Decimal Number 1207
 
18.5%
Dash Punctuation 174
 
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 470
 
12.0%
북 307
 
7.8%
동 282
 
7.2%
산 222
 
5.7%
시 216
 
5.5%
주 186
 
4.8%
지 179
 
4.6%
구 175
 
4.5%
가 123
 
3.1%
완 109
 
2.8%
Other values (145) 1643
42.0%
Decimal Number
ValueCountFrequency (%)
1 293
24.3%
2 157
13.0%
3 130
10.8%
5 114
 
9.4%
4 109
 
9.0%
6 106
 
8.8%
8 89
 
7.4%
9 85
 
7.0%
7 68
 
5.6%
0 56
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
A 5
41.7%
B 4
33.3%
L 3
25.0%
Other Punctuation
ValueCountFrequency (%)
/ 5
55.6%
, 3
33.3%
@ 1
 
11.1%
Lowercase Letter
ValueCountFrequency (%)
b 1
50.0%
c 1
50.0%
Space Separator
ValueCountFrequency (%)
1215
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 174
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3912
59.9%
Common 2609
39.9%
Latin 14
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 470
 
12.0%
북 307
 
7.8%
동 282
 
7.2%
산 222
 
5.7%
시 216
 
5.5%
주 186
 
4.8%
지 179
 
4.6%
구 175
 
4.5%
가 123
 
3.1%
완 109
 
2.8%
Other values (145) 1643
42.0%
Common
ValueCountFrequency (%)
1215
46.6%
1 293
 
11.2%
- 174
 
6.7%
2 157
 
6.0%
3 130
 
5.0%
5 114
 
4.4%
4 109
 
4.2%
6 106
 
4.1%
8 89
 
3.4%
9 85
 
3.3%
Other values (7) 137
 
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 3912
59.9%
ASCII 2623
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1215
46.3%
1 293
 
11.2%
- 174
 
6.6%
2 157
 
6.0%
3 130
 
5.0%
5 114
 
4.3%
4 109
 
4.2%
6 106
 
4.0%
8 89
 
3.4%
9 85
 
3.2%
Other values (12) 151
 
5.8%
Hangul
ValueCountFrequency (%)
전 470
 
12.0%
북 307
 
7.8%
동 282
 
7.2%
산 222
 
5.7%
시 216
 
5.5%
주 186
 
4.8%
지 179
 
4.6%
구 175
 
4.5%
가 123
 
3.1%
완 109
 
2.8%
Other values (145) 1643
42.0%

건축주성격
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
-
207 
공공
75 
민간
 
20
기타(공공+민간합작)
 
1

Length

Max length11
Median length1
Mean length1.3465347
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row기타(공공+민간합작)
2nd row민간
3rd row공공
4th row공공
5th row공공

Common Values

ValueCountFrequency (%)
- 207
68.3%
공공 75
 
24.8%
민간 20
 
6.6%
기타(공공+민간합작) 1
 
0.3%

Length

2024-03-14T09:12:06.080701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:12:06.156015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
207
68.3%
공공 75
 
24.8%
민간 20
 
6.6%
기타(공공+민간합작 1
 
0.3%

연면적(㎡)
Text

Distinct151
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-03-14T09:12:06.368323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.5544554
Min length1

Characters and Unicode

Total characters1683
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

Unique122 ?
Unique (%)40.3%

Sample

1st row47196.67
2nd row49864.1809
3rd row0
4th row112135.11
5th row112135.11
ValueCountFrequency (%)
0 58
 
19.1%
46
 
15.2%
30788 13
 
4.3%
14168 7
 
2.3%
134812.825 3
 
1.0%
209648.88 3
 
1.0%
12695.3 3
 
1.0%
104431.27 3
 
1.0%
182718.73 3
 
1.0%
11523.2997 3
 
1.0%
Other values (141) 161
53.1%
2024-03-14T09:12:06.684319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 192
11.4%
8 169
10.0%
0 163
9.7%
. 152
9.0%
9 147
8.7%
2 145
8.6%
7 140
8.3%
5 138
8.2%
4 137
8.1%
3 131
7.8%
Other values (2) 169
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1485
88.2%
Other Punctuation 152
 
9.0%
Dash Punctuation 46
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 192
12.9%
8 169
11.4%
0 163
11.0%
9 147
9.9%
2 145
9.8%
7 140
9.4%
5 138
9.3%
4 137
9.2%
3 131
8.8%
6 123
8.3%
Other Punctuation
ValueCountFrequency (%)
. 152
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1683
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 192
11.4%
8 169
10.0%
0 163
9.7%
. 152
9.0%
9 147
8.7%
2 145
8.6%
7 140
8.3%
5 138
8.2%
4 137
8.1%
3 131
7.8%
Other values (2) 169
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1683
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 192
11.4%
8 169
10.0%
0 163
9.7%
. 152
9.0%
9 147
8.7%
2 145
8.6%
7 140
8.3%
5 138
8.2%
4 137
8.1%
3 131
7.8%
Other values (2) 169
10.0%

건축물용도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
근린생활시설
198 
공동주택
68 
의료시설
 
15
방송 · 통신시설
 
7
업무시설
 
5
Other values (4)
 
10

Length

Max length14
Median length6
Mean length5.5478548
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row업무시설
2nd row공동주택
3rd row공연장, 집회장 및 관람장
4th row공동주택
5th row공동주택

Common Values

ValueCountFrequency (%)
근린생활시설 198
65.3%
공동주택 68
 
22.4%
의료시설 15
 
5.0%
방송 · 통신시설 7
 
2.3%
업무시설 5
 
1.7%
공연장, 집회장 및 관람장 4
 
1.3%
숙박시설 3
 
1.0%
판매시설 2
 
0.7%
기타 1
 
0.3%

Length

2024-03-14T09:12:06.817002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:12:06.906157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
근린생활시설 198
60.2%
공동주택 68
 
20.7%
의료시설 15
 
4.6%
방송 7
 
2.1%
· 7
 
2.1%
통신시설 7
 
2.1%
업무시설 5
 
1.5%
공연장 4
 
1.2%
집회장 4
 
1.2%
및 4
 
1.2%
Other values (4) 10
 
3.0%

작품심의일자
Text

Distinct93
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-03-14T09:12:07.145353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length5.5973597
Min length1

Characters and Unicode

Total characters1696
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)17.8%

Sample

1st row20130405
2nd row20130619
3rd row20130619
4th row20131126
5th row20131126
ValueCountFrequency (%)
104
34.3%
20111129 11
 
3.6%
20130619 10
 
3.3%
20010000 8
 
2.6%
20130306 8
 
2.6%
20120824 7
 
2.3%
20070206 6
 
2.0%
20120531 6
 
2.0%
20120105 5
 
1.7%
20071026 5
 
1.7%
Other values (83) 133
43.9%
2024-03-14T09:12:07.459025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 575
33.9%
2 342
20.2%
1 265
15.6%
- 104
 
6.1%
6 83
 
4.9%
3 73
 
4.3%
7 69
 
4.1%
9 51
 
3.0%
8 48
 
2.8%
4 46
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1592
93.9%
Dash Punctuation 104
 
6.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 575
36.1%
2 342
21.5%
1 265
16.6%
6 83
 
5.2%
3 73
 
4.6%
7 69
 
4.3%
9 51
 
3.2%
8 48
 
3.0%
4 46
 
2.9%
5 40
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 104
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 575
33.9%
2 342
20.2%
1 265
15.6%
- 104
 
6.1%
6 83
 
4.9%
3 73
 
4.3%
7 69
 
4.1%
9 51
 
3.0%
8 48
 
2.8%
4 46
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 575
33.9%
2 342
20.2%
1 265
15.6%
- 104
 
6.1%
6 83
 
4.9%
3 73
 
4.3%
7 69
 
4.1%
9 51
 
3.0%
8 48
 
2.8%
4 46
 
2.7%

작품설치일자
Real number (ℝ)

ZEROS 

Distinct202
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19678070
Minimum0
Maximum20140813
Zeros6
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-14T09:12:07.574867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20010243
Q120041000
median20071017
Q320110776
95-th percentile20140410
Maximum20140813
Range20140813
Interquartile range (IQR)69775

Descriptive statistics

Standard deviation2801809.9
Coefficient of variation (CV)0.14238235
Kurtosis46.281887
Mean19678070
Median Absolute Deviation (MAD)30399
Skewness-6.9257945
Sum5.9624553 × 109
Variance7.8501388 × 1012
MonotonicityDecreasing
2024-03-14T09:12:07.717969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20121214 13
 
4.3%
0 6
 
2.0%
20010000 6
 
2.0%
20110725 5
 
1.7%
20060600 4
 
1.3%
20140410 4
 
1.3%
20031114 4
 
1.3%
20090406 3
 
1.0%
20031200 3
 
1.0%
20061000 3
 
1.0%
Other values (192) 252
83.2%
ValueCountFrequency (%)
0 6
2.0%
20000000 1
 
0.3%
20000706 1
 
0.3%
20001125 1
 
0.3%
20010000 6
2.0%
20010214 1
 
0.3%
20010500 1
 
0.3%
20010601 1
 
0.3%
20010721 1
 
0.3%
20010801 1
 
0.3%
ValueCountFrequency (%)
20140813 2
0.7%
20140728 3
1.0%
20140721 2
0.7%
20140711 2
0.7%
20140704 1
 
0.3%
20140425 3
1.0%
20140410 4
1.3%
20140117 1
 
0.3%
20131223 2
0.7%
20131220 2
0.7%

Interactions

2024-03-14T09:12:02.447365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T09:12:07.824091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류지역건축주성격건축물용도작품심의일자작품설치일자
분류1.0000.2130.2660.3650.0000.118
지역0.2131.0000.2780.4240.9560.000
건축주성격0.2660.2781.0000.7240.9040.000
건축물용도0.3650.4240.7241.0000.9630.000
작품심의일자0.0000.9560.9040.9631.0000.000
작품설치일자0.1180.0000.0000.0000.0001.000
2024-03-14T09:12:07.940702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류건축주성격건축물용도지역
분류1.0000.1210.1880.096
건축주성격0.1211.0000.5570.162
건축물용도0.1880.5571.0000.195
지역0.0960.1620.1951.000
2024-03-14T09:12:08.027932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
작품설치일자분류지역건축주성격건축물용도
작품설치일자1.0000.0870.0000.0000.000
분류0.0871.0000.0960.1210.188
지역0.0000.0961.0000.1620.195
건축주성격0.0000.1210.1621.0000.557
건축물용도0.0000.1880.1950.5571.000

Missing values

2024-03-14T09:12:02.575316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:12:02.744859image/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.

Sample

분류지역작품명작가명건축물명건축물주소건축주성격연면적(㎡)건축물용도작품심의일자작품설치일자
0조각전북/전주시비상권치규농촌진흥청전북 전주시 완산구 중동 혁신도시개발지구 농생16블럭 1노트기타(공공+민간합작)47196.67업무시설2013040520140813
1조각전북/정읍시향기터원유진대광 로제비앙전북 정읍시 상동 89-4번지민간49864.1809공동주택2013061920140813
2조각전북/전주시무형유산의시원이용백국립무형유산원전북 전주시 완산구 동서학동 896-1공공0공연장, 집회장 및 관람장2013061920140728
3공예전북/익산시천년의사랑엄혁용익산금호어울림전북 익산시 신동 816-1번지외 5필지공공112135.11공동주택2013112620140728
4조각전북/익산시생성공간-흔적신치현익산금호어울림전북 익산시 신동 816-1번지외 5필지공공112135.11공동주택2013112620140728
5조각전북/전주시평화의소리국경오호반베르디움전북 전주시 완산구 중동 전주 완주 혁신도시 c13블럭공공86549.4898공동주택2014032020140721
6조각전북/전주시공간저너머국경오전주효자복합시설전북 전주시 완산구 효자동1가 434번지 외공공25908.59근린생활시설2014052820140721
7조각전북/김제시내면속의풍경이상현김제 아이지파크전북 김제시 신풍동 300번지외 2필지공공22308.156공동주택2014032020140711
8조각전북/군산시늘봄김오성지곡동 쌍용예가전북 군산시 지곡동 31-7번지외공공129875.6236공동주택2013040520140711
9조각전북/익산시한옥의꿈이한우오투그란테아파트전북 익산시 모현동1가 배산택지지구 4블럭민간111916.1603공동주택2013030620140704
분류지역작품명작가명건축물명건축물주소건축주성격연면적(㎡)건축물용도작품심의일자작품설치일자
293회화전북/익산시자연1선기현금강동 쉐르빌전북 익산 금강동--근린생활시설2001000020010000
294조각전북/김제시가족김동헌건축물기록없음전북 김제시 요촌동 423-2 김제쇼핑 센터--근린생활시설-20001125
295조각전북/김제시이야기김동헌건축물기록없음전북 김제시 신풍동 63 부영3차아파트--근린생활시설-20000706
296조각전북/익산시家族(가족)이한우영등동 제일아파트전북 익산 영등동--근린생활시설-20000000
297조각전북/전주시평화국경오알리앙스 웨딩홀전북 전주시 완산구 효자동1가 410-1-17711.51근린생활시설-0
298조각전북/전주시21c 탄생축제김영숙롯데백화점 전주점전북 전주시 완산구 서신동 971번지-75519.35근린생활시설-0
299조각전북/전주시젊음의 광장황순례롯데백화점 전주점전북 전주시 완산구 서신동 971번지-75519.35근린생활시설-0
300회화전북/전주시조바심김두해롯데백화점 전주점전북 전주 완산구 서신동 971번지--근린생활시설-0
301회화전북/전주시신포항신세자알리앙스 웨딩홀전북 전주 완산구 효자동1가 410-1--근린생활시설-0
302회화전북/전주시서해김두해알리앙스 웨딩홀전북 전주 완산구 효자동1가 410-1--근린생활시설-0

Duplicate rows

Most frequently occurring

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