Overview

Dataset statistics

Number of variables12
Number of observations113
Missing cells26
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.3 KiB
Average record size in memory102.1 B

Variable types

Categorical3
Text3
Numeric5
DateTime1

Dataset

Description전북특별자치도 건축물 미술작품 현황(분류,지역,작품명, 규격 등) 제공우리기관에서는 더 이상 생성 불가 데이터입니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/15055838/fileData.do

Alerts

규격(가로) is highly overall correlated with 규격(세로) and 1 other fieldsHigh correlation
규격(세로) is highly overall correlated with 규격(가로) and 1 other fieldsHigh correlation
규격(높이) is highly overall correlated with 규격(가로) and 1 other fieldsHigh correlation
분류 is highly imbalanced (61.3%)Imbalance
재질 has 4 (3.5%) missing valuesMissing
규격(가로) has 2 (1.8%) missing valuesMissing
규격(세로) has 4 (3.5%) missing valuesMissing
규격(높이) has 16 (14.2%) missing valuesMissing
연면적 has 3 (2.7%) zerosZeros

Reproduction

Analysis started2024-03-15 01:11:59.906170
Analysis finished2024-03-15 01:12:10.069515
Duration10.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류
Categorical

IMBALANCE 

Distinct6
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
조각
94 
회화
 
8
기타
 
4
공예
 
3
상징탑
 
3

Length

Max length3
Median length2
Mean length2.0265487
Min length2

Unique

Unique1 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
조각 94
83.2%
회화 8
 
7.1%
기타 4
 
3.5%
공예 3
 
2.7%
상징탑 3
 
2.7%
사진 1
 
0.9%

Length

2024-03-15T10:12:10.305007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:12:10.652851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
조각 94
83.2%
회화 8
 
7.1%
기타 4
 
3.5%
공예 3
 
2.7%
상징탑 3
 
2.7%
사진 1
 
0.9%

지역
Categorical

Distinct11
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
전북/전주시
62 
전북/익산시
14 
전북/군산시
13 
전북/완주군
11 
전북/김제시
 
4
Other values (6)
9 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique3 ?
Unique (%)2.7%

Sample

1st row전북/김제시
2nd row전북/완주군
3rd row전북/남원시
4th row전북/전주시
5th row전북/전주시

Common Values

ValueCountFrequency (%)
전북/전주시 62
54.9%
전북/익산시 14
 
12.4%
전북/군산시 13
 
11.5%
전북/완주군 11
 
9.7%
전북/김제시 4
 
3.5%
전북/남원시 2
 
1.8%
전북/정읍시 2
 
1.8%
전북/고창군 2
 
1.8%
전북/부안군 1
 
0.9%
전북/순창군 1
 
0.9%

Length

2024-03-15T10:12:11.124307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전북/전주시 62
54.9%
전북/익산시 14
 
12.4%
전북/군산시 13
 
11.5%
전북/완주군 11
 
9.7%
전북/김제시 4
 
3.5%
전북/남원시 2
 
1.8%
전북/정읍시 2
 
1.8%
전북/고창군 2
 
1.8%
전북/부안군 1
 
0.9%
전북/순창군 1
 
0.9%

작품명
Text

Distinct107
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-03-15T10:12:12.358184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length6.4070796
Min length1

Characters and Unicode

Total characters724
Distinct characters218
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)91.2%

Sample

1st row사랑-2014
2nd row이야기-2015
3rd row가족-愛
4th row풍경이 있는 집
5th row해피데이
ValueCountFrequency (%)
6
 
3.2%
비상 5
 
2.7%
하모니 3
 
1.6%
풍경 3
 
1.6%
소리 3
 
1.6%
사람 2
 
1.1%
dream 2
 
1.1%
happy 2
 
1.1%
풍요 2
 
1.1%
꿈 2
 
1.1%
Other values (150) 157
84.0%
2024-03-15T10:12:13.879764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
74
 
10.2%
의 27
 
3.7%
- 21
 
2.9%
e 13
 
1.8%
1 13
 
1.8%
이 12
 
1.7%
기 11
 
1.5%
a 11
 
1.5%
하 11
 
1.5%
풍 11
 
1.5%
Other values (208) 520
71.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 462
63.8%
Lowercase Letter 95
 
13.1%
Space Separator 74
 
10.2%
Decimal Number 39
 
5.4%
Uppercase Letter 24
 
3.3%
Dash Punctuation 21
 
2.9%
Other Punctuation 7
 
1.0%
Math Symbol 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 27
 
5.8%
이 12
 
2.6%
기 11
 
2.4%
하 11
 
2.4%
풍 11
 
2.4%
는 9
 
1.9%
지 9
 
1.9%
리 9
 
1.9%
고 8
 
1.7%
상 8
 
1.7%
Other values (165) 347
75.1%
Lowercase Letter
ValueCountFrequency (%)
e 13
13.7%
a 11
11.6%
r 10
10.5%
n 8
 
8.4%
o 5
 
5.3%
u 5
 
5.3%
i 5
 
5.3%
s 5
 
5.3%
p 5
 
5.3%
m 5
 
5.3%
Other values (9) 23
24.2%
Uppercase Letter
ValueCountFrequency (%)
S 4
16.7%
H 4
16.7%
E 3
12.5%
F 2
8.3%
R 2
8.3%
N 2
8.3%
C 1
 
4.2%
O 1
 
4.2%
A 1
 
4.2%
P 1
 
4.2%
Other values (3) 3
12.5%
Decimal Number
ValueCountFrequency (%)
1 13
33.3%
2 10
25.6%
0 10
25.6%
4 4
 
10.3%
7 1
 
2.6%
5 1
 
2.6%
Other Punctuation
ValueCountFrequency (%)
, 6
85.7%
& 1
 
14.3%
Space Separator
ValueCountFrequency (%)
74
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 460
63.5%
Common 143
 
19.8%
Latin 119
 
16.4%
Han 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 27
 
5.9%
이 12
 
2.6%
기 11
 
2.4%
하 11
 
2.4%
풍 11
 
2.4%
는 9
 
2.0%
지 9
 
2.0%
리 9
 
2.0%
고 8
 
1.7%
상 8
 
1.7%
Other values (163) 345
75.0%
Latin
ValueCountFrequency (%)
e 13
 
10.9%
a 11
 
9.2%
r 10
 
8.4%
n 8
 
6.7%
o 5
 
4.2%
u 5
 
4.2%
i 5
 
4.2%
s 5
 
4.2%
p 5
 
4.2%
m 5
 
4.2%
Other values (22) 47
39.5%
Common
ValueCountFrequency (%)
74
51.7%
- 21
 
14.7%
1 13
 
9.1%
2 10
 
7.0%
0 10
 
7.0%
, 6
 
4.2%
4 4
 
2.8%
+ 2
 
1.4%
& 1
 
0.7%
7 1
 
0.7%
Han
ValueCountFrequency (%)
線 1
50.0%
愛 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 460
63.5%
ASCII 262
36.2%
CJK 2
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
74
28.2%
- 21
 
8.0%
e 13
 
5.0%
1 13
 
5.0%
a 11
 
4.2%
2 10
 
3.8%
r 10
 
3.8%
0 10
 
3.8%
n 8
 
3.1%
, 6
 
2.3%
Other values (33) 86
32.8%
Hangul
ValueCountFrequency (%)
의 27
 
5.9%
이 12
 
2.6%
기 11
 
2.4%
하 11
 
2.4%
풍 11
 
2.4%
는 9
 
2.0%
지 9
 
2.0%
리 9
 
2.0%
고 8
 
1.7%
상 8
 
1.7%
Other values (163) 345
75.0%
CJK
ValueCountFrequency (%)
線 1
50.0%
愛 1
50.0%

작가명
Categorical

Distinct30
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
김**
23 
이**
18 
강**
10 
박**
7 
유**
6 
Other values (25)
49 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique14 ?
Unique (%)12.4%

Sample

1st row김**
2nd row김**
3rd row김**
4th row이**
5th row소**

Common Values

ValueCountFrequency (%)
김** 23
20.4%
이** 18
15.9%
강** 10
 
8.8%
박** 7
 
6.2%
유** 6
 
5.3%
엄** 6
 
5.3%
국** 5
 
4.4%
소** 4
 
3.5%
임** 3
 
2.7%
권** 3
 
2.7%
Other values (20) 28
24.8%

Length

2024-03-15T10:12:14.152033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
김 23
20.4%
이 18
15.9%
강 10
 
8.8%
박 7
 
6.2%
유 6
 
5.3%
엄 6
 
5.3%
국 5
 
4.4%
소 4
 
3.5%
임 3
 
2.7%
권 3
 
2.7%
Other values (20) 28
24.8%

재질
Text

MISSING 

Distinct83
Distinct (%)76.1%
Missing4
Missing (%)3.5%
Memory size1.0 KiB
2024-03-15T10:12:14.759451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length20
Mean length11.59633
Min length2

Characters and Unicode

Total characters1264
Distinct characters109
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)67.0%

Sample

1st row황등석, 대리석, 장흥석
2nd row황등석, 스텔레스스틸, 대리석 외
3rd row청동, 화강석, 유화가리
4th row황등석, 고흥석, 스테인레스스틸
5th row화강석, 고흥석, 청동
ValueCountFrequency (%)
화강석 49
20.9%
스테인레스스틸 32
 
13.7%
브론즈 13
 
5.6%
황등석 13
 
5.6%
스테인레스 11
 
4.7%
스틸 6
 
2.6%
고흥석 6
 
2.6%
청동 6
 
2.6%
대리석 5
 
2.1%
상주석 5
 
2.1%
Other values (64) 88
37.6%
2024-03-15T10:12:15.623063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 171
13.5%
, 129
 
10.2%
125
 
9.9%
석 115
 
9.1%
레 66
 
5.2%
강 59
 
4.7%
화 58
 
4.6%
인 52
 
4.1%
테 51
 
4.0%
틸 51
 
4.0%
Other values (99) 387
30.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 982
77.7%
Other Punctuation 129
 
10.2%
Space Separator 125
 
9.9%
Uppercase Letter 11
 
0.9%
Lowercase Letter 8
 
0.6%
Open Punctuation 4
 
0.3%
Close Punctuation 4
 
0.3%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 171
17.4%
석 115
 
11.7%
레 66
 
6.7%
강 59
 
6.0%
화 58
 
5.9%
인 52
 
5.3%
테 51
 
5.2%
틸 51
 
5.2%
황 21
 
2.1%
동 18
 
1.8%
Other values (85) 320
32.6%
Lowercase Letter
ValueCountFrequency (%)
e 2
25.0%
i 2
25.0%
d 2
25.0%
x 1
12.5%
a 1
12.5%
Uppercase Letter
ValueCountFrequency (%)
D 3
27.3%
E 3
27.3%
L 3
27.3%
M 2
18.2%
Other Punctuation
ValueCountFrequency (%)
, 129
100.0%
Space Separator
ValueCountFrequency (%)
125
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 982
77.7%
Common 263
 
20.8%
Latin 19
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 171
17.4%
석 115
 
11.7%
레 66
 
6.7%
강 59
 
6.0%
화 58
 
5.9%
인 52
 
5.3%
테 51
 
5.2%
틸 51
 
5.2%
황 21
 
2.1%
동 18
 
1.8%
Other values (85) 320
32.6%
Latin
ValueCountFrequency (%)
D 3
15.8%
E 3
15.8%
L 3
15.8%
e 2
10.5%
i 2
10.5%
M 2
10.5%
d 2
10.5%
x 1
 
5.3%
a 1
 
5.3%
Common
ValueCountFrequency (%)
, 129
49.0%
125
47.5%
( 4
 
1.5%
) 4
 
1.5%
1 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 981
77.6%
ASCII 282
 
22.3%
Compat Jamo 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 171
17.4%
석 115
 
11.7%
레 66
 
6.7%
강 59
 
6.0%
화 58
 
5.9%
인 52
 
5.3%
테 51
 
5.2%
틸 51
 
5.2%
황 21
 
2.1%
동 18
 
1.8%
Other values (84) 319
32.5%
ASCII
ValueCountFrequency (%)
, 129
45.7%
125
44.3%
( 4
 
1.4%
) 4
 
1.4%
D 3
 
1.1%
E 3
 
1.1%
L 3
 
1.1%
e 2
 
0.7%
i 2
 
0.7%
M 2
 
0.7%
Other values (4) 5
 
1.8%
Compat Jamo
ValueCountFrequency (%)
ㅅ 1
100.0%

규격(가로)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct71
Distinct (%)64.0%
Missing2
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean986.22432
Minimum2.3
Maximum11000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-15T10:12:15.966077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile75
Q1217.5
median350
Q3595
95-th percentile5750
Maximum11000
Range10997.7
Interquartile range (IQR)377.5

Descriptive statistics

Standard deviation1928.0744
Coefficient of variation (CV)1.955006
Kurtosis10.953208
Mean986.22432
Median Absolute Deviation (MAD)170
Skewness3.2972873
Sum109470.9
Variance3717471
MonotonicityNot monotonic
2024-03-15T10:12:16.222019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400.0 6
 
5.3%
280.0 5
 
4.4%
220.0 4
 
3.5%
300.0 4
 
3.5%
500.0 4
 
3.5%
600.0 3
 
2.7%
450.0 3
 
2.7%
150.0 3
 
2.7%
250.0 3
 
2.7%
390.0 2
 
1.8%
Other values (61) 74
65.5%
ValueCountFrequency (%)
2.3 1
0.9%
2.6 1
0.9%
45.0 1
0.9%
50.0 2
1.8%
75.0 2
1.8%
82.0 1
0.9%
114.0 1
0.9%
115.0 1
0.9%
130.0 1
0.9%
135.0 2
1.8%
ValueCountFrequency (%)
11000.0 1
0.9%
8755.0 1
0.9%
7600.0 1
0.9%
7400.0 1
0.9%
7200.0 1
0.9%
6300.0 1
0.9%
5200.0 1
0.9%
4200.0 1
0.9%
4000.0 1
0.9%
3800.0 1
0.9%

규격(세로)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct57
Distinct (%)52.3%
Missing4
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean590.65183
Minimum2.4
Maximum5000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-15T10:12:16.601143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.4
5-th percentile80
Q1130
median220
Q3400
95-th percentile2860
Maximum5000
Range4997.6
Interquartile range (IQR)270

Descriptive statistics

Standard deviation1016.1986
Coefficient of variation (CV)1.7204698
Kurtosis7.8132084
Mean590.65183
Median Absolute Deviation (MAD)110
Skewness2.8789705
Sum64381.05
Variance1032659.6
MonotonicityNot monotonic
2024-03-15T10:12:17.064649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300.0 7
 
6.2%
100.0 7
 
6.2%
400.0 6
 
5.3%
130.0 5
 
4.4%
240.0 4
 
3.5%
200.0 4
 
3.5%
600.0 4
 
3.5%
120.0 4
 
3.5%
150.0 3
 
2.7%
80.0 3
 
2.7%
Other values (47) 62
54.9%
(Missing) 4
 
3.5%
ValueCountFrequency (%)
2.4 1
 
0.9%
2.65 1
 
0.9%
70.0 2
 
1.8%
80.0 3
2.7%
90.0 2
 
1.8%
95.0 1
 
0.9%
100.0 7
6.2%
105.0 2
 
1.8%
110.0 1
 
0.9%
115.0 1
 
0.9%
ValueCountFrequency (%)
5000.0 1
0.9%
4500.0 1
0.9%
4400.0 1
0.9%
4000.0 2
1.8%
3100.0 1
0.9%
2500.0 1
0.9%
2400.0 2
1.8%
2300.0 1
0.9%
2200.0 1
0.9%
1600.0 2
1.8%

규격(높이)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51
Distinct (%)52.6%
Missing16
Missing (%)14.2%
Infinite0
Infinite (%)0.0%
Mean837.3033
Minimum1.12
Maximum8000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-15T10:12:17.328775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.12
5-th percentile190
Q1270
median320
Q3450
95-th percentile3600
Maximum8000
Range7998.88
Interquartile range (IQR)180

Descriptive statistics

Standard deviation1361.5147
Coefficient of variation (CV)1.6260711
Kurtosis10.385171
Mean837.3033
Median Absolute Deviation (MAD)80
Skewness3.0373339
Sum81218.42
Variance1853722.4
MonotonicityNot monotonic
2024-03-15T10:12:17.680853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300.0 11
 
9.7%
350.0 6
 
5.3%
270.0 5
 
4.4%
250.0 4
 
3.5%
200.0 4
 
3.5%
400.0 4
 
3.5%
280.0 3
 
2.7%
210.0 3
 
2.7%
500.0 3
 
2.7%
320.0 3
 
2.7%
Other values (41) 51
45.1%
(Missing) 16
 
14.2%
ValueCountFrequency (%)
1.12 1
 
0.9%
6.3 1
 
0.9%
180.0 1
 
0.9%
185.0 1
 
0.9%
190.0 2
1.8%
193.0 1
 
0.9%
195.0 1
 
0.9%
200.0 4
3.5%
210.0 3
2.7%
211.0 1
 
0.9%
ValueCountFrequency (%)
8000.0 1
0.9%
6500.0 1
0.9%
4200.0 1
0.9%
4000.0 2
1.8%
3500.0 1
0.9%
3200.0 1
0.9%
3000.0 2
1.8%
2800.0 2
1.8%
2700.0 1
0.9%
2500.0 1
0.9%

설치일자
Date

Distinct68
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Minimum2012-02-08 00:00:00
Maximum2016-08-24 00:00:00
2024-03-15T10:12:18.036529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:18.468709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

가격(원)
Real number (ℝ)

Distinct94
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88157513
Minimum4000000
Maximum3.206 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-15T10:12:18.908272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4000000
5-th percentile11600000
Q150000000
median80450000
Q31.15361 × 108
95-th percentile1.796 × 108
Maximum3.206 × 108
Range3.166 × 108
Interquartile range (IQR)65361000

Descriptive statistics

Standard deviation55077370
Coefficient of variation (CV)0.62476093
Kurtosis2.2809772
Mean88157513
Median Absolute Deviation (MAD)33250000
Skewness1.0919535
Sum9.961799 × 109
Variance3.0335167 × 1015
MonotonicityNot monotonic
2024-03-15T10:12:19.364709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000000 3
 
2.7%
50000000 3
 
2.7%
61000000 2
 
1.8%
5000000 2
 
1.8%
20000000 2
 
1.8%
100000000 2
 
1.8%
84354000 2
 
1.8%
90000000 2
 
1.8%
65000000 2
 
1.8%
135000000 2
 
1.8%
Other values (84) 91
80.5%
ValueCountFrequency (%)
4000000 2
1.8%
5000000 2
1.8%
8000000 2
1.8%
14000000 2
1.8%
19360000 1
0.9%
20000000 2
1.8%
20400000 1
0.9%
25000000 1
0.9%
29300000 1
0.9%
31905000 1
0.9%
ValueCountFrequency (%)
320600000 1
0.9%
250000000 1
0.9%
220000000 1
0.9%
212410000 1
0.9%
182670000 1
0.9%
182000000 1
0.9%
178000000 1
0.9%
175000000 1
0.9%
167100000 1
0.9%
165000000 1
0.9%

건축물주소
Text

Distinct84
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-03-15T10:12:20.548720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length29
Mean length20.654867
Min length10

Characters and Unicode

Total characters2334
Distinct characters149
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

Unique68 ?
Unique (%)60.2%

Sample

1st row전북 김제시 검산동 1080-1
2nd row전북 완주군 이서면 갈산리 663-1
3rd row전북 남원시 낙현길 5 (월락동, 양우내안애아파트)
4th row전북 전주시 덕진구 장동 1064
5th row전북 전주시 덕진구 백제대로 721
ValueCountFrequency (%)
전북 113
20.3%
전주시 62
 
11.1%
덕진구 38
 
6.8%
완산구 24
 
4.3%
익산시 14
 
2.5%
금암동 13
 
2.3%
634-8 13
 
2.3%
군산시 13
 
2.3%
완주군 11
 
2.0%
이서면 8
 
1.4%
Other values (166) 249
44.6%
2024-03-15T10:12:22.150982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
461
19.8%
전 180
 
7.7%
북 118
 
5.1%
시 103
 
4.4%
동 102
 
4.4%
1 91
 
3.9%
주 76
 
3.3%
- 73
 
3.1%
구 68
 
2.9%
산 68
 
2.9%
Other values (139) 994
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1382
59.2%
Space Separator 461
 
19.8%
Decimal Number 409
 
17.5%
Dash Punctuation 73
 
3.1%
Lowercase Letter 3
 
0.1%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%
Uppercase Letter 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 180
 
13.0%
북 118
 
8.5%
시 103
 
7.5%
동 102
 
7.4%
주 76
 
5.5%
구 68
 
4.9%
산 68
 
4.9%
덕 44
 
3.2%
진 44
 
3.2%
완 36
 
2.6%
Other values (121) 543
39.3%
Decimal Number
ValueCountFrequency (%)
1 91
22.2%
3 49
12.0%
6 45
11.0%
5 44
10.8%
4 44
10.8%
2 39
9.5%
8 39
9.5%
7 24
 
5.9%
0 18
 
4.4%
9 16
 
3.9%
Lowercase Letter
ValueCountFrequency (%)
c 2
66.7%
b 1
33.3%
Space Separator
ValueCountFrequency (%)
461
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 73
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1382
59.2%
Common 948
40.6%
Latin 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 180
 
13.0%
북 118
 
8.5%
시 103
 
7.5%
동 102
 
7.4%
주 76
 
5.5%
구 68
 
4.9%
산 68
 
4.9%
덕 44
 
3.2%
진 44
 
3.2%
완 36
 
2.6%
Other values (121) 543
39.3%
Common
ValueCountFrequency (%)
461
48.6%
1 91
 
9.6%
- 73
 
7.7%
3 49
 
5.2%
6 45
 
4.7%
5 44
 
4.6%
4 44
 
4.6%
2 39
 
4.1%
8 39
 
4.1%
7 24
 
2.5%
Other values (5) 39
 
4.1%
Latin
ValueCountFrequency (%)
c 2
50.0%
b 1
25.0%
B 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1382
59.2%
ASCII 952
40.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
461
48.4%
1 91
 
9.6%
- 73
 
7.7%
3 49
 
5.1%
6 45
 
4.7%
5 44
 
4.6%
4 44
 
4.6%
2 39
 
4.1%
8 39
 
4.1%
7 24
 
2.5%
Other values (8) 43
 
4.5%
Hangul
ValueCountFrequency (%)
전 180
 
13.0%
북 118
 
8.5%
시 103
 
7.5%
동 102
 
7.4%
주 76
 
5.5%
구 68
 
4.9%
산 68
 
4.9%
덕 44
 
3.2%
진 44
 
3.2%
완 36
 
2.6%
Other values (121) 543
39.3%

연면적
Real number (ℝ)

ZEROS 

Distinct83
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62779.322
Minimum0
Maximum209648.88
Zeros3
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-15T10:12:22.790515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12754.022
Q128622.53
median47196.67
Q386549.49
95-th percentile152983.18
Maximum209648.88
Range209648.88
Interquartile range (IQR)57926.96

Descriptive statistics

Standard deviation49570.558
Coefficient of variation (CV)0.78960009
Kurtosis0.95801995
Mean62779.322
Median Absolute Deviation (MAD)23077.03
Skewness1.235033
Sum7094063.3
Variance2.4572402 × 109
MonotonicityNot monotonic
2024-03-15T10:12:23.060647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30788.0 13
 
11.5%
0.0 3
 
2.7%
209648.88 3
 
2.7%
141229.08 2
 
1.8%
112135.11 2
 
1.8%
152983.18 2
 
1.8%
190724.02 2
 
1.8%
32070.21 2
 
1.8%
143878.81 2
 
1.8%
29615.0 2
 
1.8%
Other values (73) 80
70.8%
ValueCountFrequency (%)
0.0 3
2.7%
10197.18 1
 
0.9%
12601.19 2
1.8%
12855.91 1
 
0.9%
14373.32 1
 
0.9%
14791.96 1
 
0.9%
14847.37 1
 
0.9%
14851.95 1
 
0.9%
15223.72 1
 
0.9%
16118.6 1
 
0.9%
ValueCountFrequency (%)
209648.88 3
2.7%
190724.02 2
1.8%
152983.18 2
1.8%
145259.29 2
1.8%
143878.81 2
1.8%
141229.08 2
1.8%
129875.62 1
 
0.9%
125692.76 2
1.8%
120397.48 2
1.8%
112135.11 2
1.8%

Interactions

2024-03-15T10:12:07.368563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:01.858108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:03.101097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:04.302113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:05.870830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:07.615938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:02.099521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:03.426450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:04.558635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:06.140863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:07.803117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:02.339772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:03.568935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:04.888080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:06.419024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:07.971239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:02.608418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:03.817689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:05.263280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:06.824119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:08.221148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:02.863094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:04.069316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:05.544805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:12:07.111023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:12:23.234489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류지역작가명재질규격(가로)규격(세로)규격(높이)설치일자가격(원)건축물주소연면적
분류1.0000.0000.8030.8620.3700.4470.6080.0000.3680.0000.306
지역0.0001.0000.7320.0000.0000.0000.5450.9310.0571.0000.481
작가명0.8030.7321.0000.9190.0000.7780.6920.0000.0000.0000.168
재질0.8620.0000.9191.0000.9880.9750.9290.9070.5380.8110.741
규격(가로)0.3700.0000.0000.9881.0000.9520.8890.9500.6960.9300.112
규격(세로)0.4470.0000.7780.9750.9521.0000.8950.8760.0000.8820.393
규격(높이)0.6080.5450.6920.9290.8890.8951.0000.9190.6380.9570.000
설치일자0.0000.9310.0000.9070.9500.8760.9191.0000.9071.0000.942
가격(원)0.3680.0570.0000.5380.6960.0000.6380.9071.0000.9630.685
건축물주소0.0001.0000.0000.8110.9300.8820.9571.0000.9631.0000.999
연면적0.3060.4810.1680.7410.1120.3930.0000.9420.6850.9991.000
2024-03-15T10:12:23.588353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역작가명분류
지역1.0000.3130.000
작가명0.3131.0000.411
분류0.0000.4111.000
2024-03-15T10:12:23.874285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
규격(가로)규격(세로)규격(높이)가격(원)연면적분류지역작가명
규격(가로)1.0000.7320.5530.2780.2490.1890.0000.000
규격(세로)0.7321.0000.5450.1200.2550.2350.0000.381
규격(높이)0.5530.5451.0000.2290.1130.3000.2870.320
가격(원)0.2780.1200.2291.0000.2430.1700.0430.000
연면적0.2490.2550.1130.2431.0000.1530.2370.027
분류0.1890.2350.3000.1700.1531.0000.0000.411
지역0.0000.0000.2870.0430.2370.0001.0000.313
작가명0.0000.3810.3200.0000.0270.4110.3131.000

Missing values

2024-03-15T10:12:08.891505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:12:09.516506image/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.
2024-03-15T10:12:09.889122image/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

분류지역작품명작가명재질규격(가로)규격(세로)규격(높이)설치일자가격(원)건축물주소연면적
0조각전북/김제시사랑-2014김**황등석, 대리석, 장흥석280.0130.0190.02016-08-2261000000전북 김제시 검산동 1080-151892.7
1조각전북/완주군이야기-2015김**황등석, 스텔레스스틸, 대리석 외450.0180.0296.02016-08-24122954000전북 완주군 이서면 갈산리 663-120219.8
2조각전북/남원시가족-愛김**청동, 화강석, 유화가리82.0205.0250.02016-08-0263000000전북 남원시 낙현길 5 (월락동, 양우내안애아파트)40247.65
3조각전북/전주시풍경이 있는 집이**황등석, 고흥석, 스테인레스스틸800.0600.0470.02016-06-02115361000전북 전주시 덕진구 장동 106491545.18
4조각전북/전주시해피데이소**화강석, 고흥석, 청동220.0220.0210.02016-07-1561000000전북 전주시 덕진구 백제대로 72146770.1
5조각전북/전주시마법박**화강석, 고흥석, 청동200.090.0180.02016-06-2037500000전북 전주시 덕진구 고랑동 71529746.27
6기타전북/전주시책과 사람엄**황등석, 상주석, 브론즈 등240.0120.0300.02016-01-14167100000전북 전주시 완산구 효자동 2가 1157-476541.92
7조각전북/군산시동행황**하강석130.090.0250.02016-03-1840000000전북 군산시 대명동 385-1145259.29
8조각전북/군산시비상조**브론즈, 화강석400.0350.0450.02016-03-18162500000전북 군산시 대명동 385-1145259.29
9조각전북/전주시즐거운 아침박**스테인레스스틸, 브론즈, 화강석820.0500.0500.02016-03-18141100000전북 전주시 덕진구 출판로 69106849.28
분류지역작품명작가명재질규격(가로)규격(세로)규격(높이)설치일자가격(원)건축물주소연면적
103조각전북/전주시Nature 2011조**화강석, 스텐레스스틸600.0600.0270.02012-10-1272000000전북 전주시 완산구 평화동3가 94-1외58929.3
104조각전북/익산시합창 sing in chorus최**화강석, 스텐레스 스틸 황동160.080.0280.02012-09-1874937000전북 익산시 부송동 67559653.84
105조각전북/익산시하모니2011조**화강석(백색)220.0100.0211.02012-07-1337000000전북 익산시 송학동 138-24번지외 2필지21540.98
106공예전북/익산시Human & Space김**<NA><NA><NA><NA>2012-05-31120000000전북 익산시 모현동1가 60.0
107조각전북/완주군삶의 작은 찬가오**화강석,스텐리스스틸2.32.46.32012-05-17175000000전북 완주군 봉동읍 둔산리 873-261766.64
108조각전북/전주시전통, 현재, 미래엄**화강석, 스테인레스스틸180.0160.0350.02012-05-16152000000전북 전주시 덕진구 덕진동2가 68361176.39
109조각전북/완주군행복하여라,생의 공간,내안의 대화한**<NA>2.62.651.122012-04-26212410000전북 완주군 용진면 운곡리 975-7814847.37
110조각전북/고창군갈망강**<NA>140.0100.0280.02012-02-2499000000전북 고창군 고창읍 석정리 7950.0
111조각전북/고창군사랑강**화강석,고흥석,마강석150.0100.0250.02012-02-24108000000전북 고창군 고창읍 석정리 7950.0
112조각전북/전주시관조-비상소**화강석,고흥석270.0170.0415.02012-02-08135000000전북 전주시 완산구 효자동2가 1154-116118.6