Overview

Dataset statistics

Number of variables14
Number of observations305
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.7 KiB
Average record size in memory116.4 B

Variable types

Numeric3
Categorical7
Text4

Dataset

Description전북특별자치도 전주시 내 그늘막을 제공하며, 관리번호, 설치장소명, 설치위치, 설치년도, 그늘막 유형 등을 제공합니다.항목 : 연번, 시도, 시군구, 행정동, 관리번호, 설치장소명, 설치위치 도로명주소, 설치위치 지번주소, 설치년도, 그늘막 유형, 전체높이, 펼침지름, 위도, 경도제공부서 : 안전정책과
Author전북특별자치도 전주시
URLhttps://www.data.go.kr/data/15085193/fileData.do

Alerts

시도 has constant value ""Constant
시군구 has constant value ""Constant
그늘막 유형 is highly overall correlated with 전체높이 and 1 other fieldsHigh correlation
전체높이 is highly overall correlated with 그늘막 유형 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 행정동High correlation
행정동 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
펼침지름 is highly overall correlated with 그늘막 유형 and 1 other fieldsHigh correlation
그늘막 유형 is highly imbalanced (77.6%)Imbalance
펼침지름 is highly imbalanced (68.1%)Imbalance
연번 has unique valuesUnique
관리번호 has unique valuesUnique

Reproduction

Analysis started2024-03-14 09:43:41.241159
Analysis finished2024-03-14 09:43:45.643759
Duration4.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct305
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153
Minimum1
Maximum305
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-14T18:43:45.834739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16.2
Q177
median153
Q3229
95-th percentile289.8
Maximum305
Range304
Interquartile range (IQR)152

Descriptive statistics

Standard deviation88.190136
Coefficient of variation (CV)0.57640611
Kurtosis-1.2
Mean153
Median Absolute Deviation (MAD)76
Skewness0
Sum46665
Variance7777.5
MonotonicityStrictly increasing
2024-03-14T18:43:46.278682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
202 1
 
0.3%
209 1
 
0.3%
208 1
 
0.3%
207 1
 
0.3%
206 1
 
0.3%
205 1
 
0.3%
204 1
 
0.3%
203 1
 
0.3%
201 1
 
0.3%
Other values (295) 295
96.7%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
305 1
0.3%
304 1
0.3%
303 1
0.3%
302 1
0.3%
301 1
0.3%
300 1
0.3%
299 1
0.3%
298 1
0.3%
297 1
0.3%
296 1
0.3%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
전북특별자치도
305 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전북특별자치도 305
100.0%

Length

2024-03-14T18:43:46.701628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:43:47.014114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북특별자치도 305
100.0%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
전주시
305 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 305
100.0%

Length

2024-03-14T18:43:47.333863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:43:47.636831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전주시 305
100.0%

행정동
Categorical

HIGH CORRELATION 

Distinct39
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
혁신동
27 
효자5동
25 
송천1동
23 
효자4동
21 
여의동
 
18
Other values (34)
191 

Length

Max length5
Median length4
Mean length3.642623
Min length3

Unique

Unique6 ?
Unique (%)2.0%

Sample

1st row진북동
2nd row진북동
3rd row인후1동
4th row인후1동
5th row인후1동

Common Values

ValueCountFrequency (%)
혁신동 27
 
8.9%
효자5동 25
 
8.2%
송천1동 23
 
7.5%
효자4동 21
 
6.9%
여의동 18
 
5.9%
평화2동 16
 
5.2%
덕진동 16
 
5.2%
금암1동 15
 
4.9%
서신동 15
 
4.9%
서서학동 12
 
3.9%
Other values (29) 117
38.4%

Length

2024-03-14T18:43:48.007585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
혁신동 27
 
8.9%
효자5동 25
 
8.2%
송천1동 23
 
7.5%
효자4동 21
 
6.9%
여의동 18
 
5.9%
평화2동 16
 
5.2%
덕진동 16
 
5.2%
금암1동 15
 
4.9%
서신동 15
 
4.9%
서서학동 12
 
3.9%
Other values (29) 117
38.4%

관리번호
Text

UNIQUE 

Distinct305
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-03-14T18:43:49.295419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.9442623
Min length3

Characters and Unicode

Total characters1508
Distinct characters47
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

Unique305 ?
Unique (%)100.0%

Sample

1st row진북-1
2nd row진북-2
3rd row인후1-1
4th row인후1-2
5th row인후1-3
ValueCountFrequency (%)
진북-1 1
 
0.3%
서학-1 1
 
0.3%
중화산1-1 1
 
0.3%
중앙-1 1
 
0.3%
중노송-1 1
 
0.3%
완산-1 1
 
0.3%
서학-5 1
 
0.3%
서학-4 1
 
0.3%
서학-3 1
 
0.3%
혁신-9 1
 
0.3%
Other values (295) 295
96.7%
2024-03-14T18:43:50.996129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 304
20.2%
1 193
 
12.8%
2 107
 
7.1%
효 63
 
4.2%
3 63
 
4.2%
자 63
 
4.2%
4 58
 
3.8%
5 58
 
3.8%
서 47
 
3.1%
신 42
 
2.8%
Other values (37) 510
33.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 633
42.0%
Decimal Number 571
37.9%
Dash Punctuation 304
20.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
효 63
 
10.0%
자 63
 
10.0%
서 47
 
7.4%
신 42
 
6.6%
천 38
 
6.0%
송 37
 
5.8%
화 28
 
4.4%
혁 27
 
4.3%
평 22
 
3.5%
후 20
 
3.2%
Other values (26) 246
38.9%
Decimal Number
ValueCountFrequency (%)
1 193
33.8%
2 107
18.7%
3 63
 
11.0%
4 58
 
10.2%
5 58
 
10.2%
6 23
 
4.0%
7 20
 
3.5%
8 19
 
3.3%
9 16
 
2.8%
0 14
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 304
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 875
58.0%
Hangul 633
42.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
효 63
 
10.0%
자 63
 
10.0%
서 47
 
7.4%
신 42
 
6.6%
천 38
 
6.0%
송 37
 
5.8%
화 28
 
4.4%
혁 27
 
4.3%
평 22
 
3.5%
후 20
 
3.2%
Other values (26) 246
38.9%
Common
ValueCountFrequency (%)
- 304
34.7%
1 193
22.1%
2 107
 
12.2%
3 63
 
7.2%
4 58
 
6.6%
5 58
 
6.6%
6 23
 
2.6%
7 20
 
2.3%
8 19
 
2.2%
9 16
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 875
58.0%
Hangul 633
42.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 304
34.7%
1 193
22.1%
2 107
 
12.2%
3 63
 
7.2%
4 58
 
6.6%
5 58
 
6.6%
6 23
 
2.6%
7 20
 
2.3%
8 19
 
2.2%
9 16
 
1.8%
Hangul
ValueCountFrequency (%)
효 63
 
10.0%
자 63
 
10.0%
서 47
 
7.4%
신 42
 
6.6%
천 38
 
6.0%
송 37
 
5.8%
화 28
 
4.4%
혁 27
 
4.3%
평 22
 
3.5%
후 20
 
3.2%
Other values (26) 246
38.9%

설치장소명
Text

Distinct302
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-03-14T18:43:51.976194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length10.229508
Min length3

Characters and Unicode

Total characters3120
Distinct characters268
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

Unique299 ?
Unique (%)98.0%

Sample

1st row전일초등학교 앞
2nd row전주교육지원청 앞
3rd row북일초등학교
4th row전라초등학교
5th row안골네거리-1
ValueCountFrequency (%)
앞 59
 
9.8%
사거리2 28
 
4.6%
사거리1 27
 
4.5%
사거리 16
 
2.7%
사거리3 14
 
2.3%
정문 8
 
1.3%
교통섬 7
 
1.2%
효자점 5
 
0.8%
2 5
 
0.8%
사거리4 5
 
0.8%
Other values (287) 429
71.1%
2024-03-14T18:43:53.368717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
 
9.6%
리 194
 
6.2%
거 185
 
5.9%
사 167
 
5.4%
2 97
 
3.1%
교 96
 
3.1%
1 91
 
2.9%
앞 76
 
2.4%
전 55
 
1.8%
정 53
 
1.7%
Other values (258) 1808
57.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2487
79.7%
Space Separator 298
 
9.6%
Decimal Number 257
 
8.2%
Uppercase Letter 26
 
0.8%
Dash Punctuation 17
 
0.5%
Open Punctuation 12
 
0.4%
Close Punctuation 12
 
0.4%
Other Punctuation 9
 
0.3%
Lowercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 194
 
7.8%
거 185
 
7.4%
사 167
 
6.7%
교 96
 
3.9%
앞 76
 
3.1%
전 55
 
2.2%
정 53
 
2.1%
학 53
 
2.1%
초 48
 
1.9%
장 37
 
1.5%
Other values (234) 1523
61.2%
Uppercase Letter
ValueCountFrequency (%)
C 5
19.2%
L 5
19.2%
G 4
15.4%
H 3
11.5%
V 3
11.5%
B 2
 
7.7%
Y 2
 
7.7%
X 1
 
3.8%
K 1
 
3.8%
Decimal Number
ValueCountFrequency (%)
2 97
37.7%
1 91
35.4%
3 34
 
13.2%
4 19
 
7.4%
5 7
 
2.7%
7 4
 
1.6%
0 3
 
1.2%
8 2
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
t 1
50.0%
Space Separator
ValueCountFrequency (%)
298
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2487
79.7%
Common 605
 
19.4%
Latin 28
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 194
 
7.8%
거 185
 
7.4%
사 167
 
6.7%
교 96
 
3.9%
앞 76
 
3.1%
전 55
 
2.2%
정 53
 
2.1%
학 53
 
2.1%
초 48
 
1.9%
장 37
 
1.5%
Other values (234) 1523
61.2%
Common
ValueCountFrequency (%)
298
49.3%
2 97
 
16.0%
1 91
 
15.0%
3 34
 
5.6%
4 19
 
3.1%
- 17
 
2.8%
( 12
 
2.0%
) 12
 
2.0%
, 9
 
1.5%
5 7
 
1.2%
Other values (3) 9
 
1.5%
Latin
ValueCountFrequency (%)
C 5
17.9%
L 5
17.9%
G 4
14.3%
H 3
10.7%
V 3
10.7%
B 2
 
7.1%
Y 2
 
7.1%
k 1
 
3.6%
X 1
 
3.6%
t 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2487
79.7%
ASCII 633
 
20.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
47.1%
2 97
 
15.3%
1 91
 
14.4%
3 34
 
5.4%
4 19
 
3.0%
- 17
 
2.7%
( 12
 
1.9%
) 12
 
1.9%
, 9
 
1.4%
5 7
 
1.1%
Other values (14) 37
 
5.8%
Hangul
ValueCountFrequency (%)
리 194
 
7.8%
거 185
 
7.4%
사 167
 
6.7%
교 96
 
3.9%
앞 76
 
3.1%
전 55
 
2.2%
정 53
 
2.1%
학 53
 
2.1%
초 48
 
1.9%
장 37
 
1.5%
Other values (234) 1523
61.2%

설치위치 도로명주소
Text

Distinct194
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-03-14T18:43:54.480008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length44
Mean length22.908197
Min length1

Characters and Unicode

Total characters6987
Distinct characters174
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)58.4%

Sample

1st row전북특별자치도 전주시 덕진구 태진로 98, (진북동)
2nd row전북특별자치도 전주시 덕진구 태진로 100, (진북동)
3rd row전북특별자치도 전주시 덕진구 견훤로 336, (인후동1가)
4th row전북특별자치도 전주시 덕진구 심방죽로 24, (인후동1가)
5th row
ValueCountFrequency (%)
전북특별자치도 210
 
16.3%
전주시 210
 
16.3%
완산구 111
 
8.6%
덕진구 99
 
7.7%
효자동2가 17
 
1.3%
효자동3가 16
 
1.2%
효자동1가 16
 
1.2%
금암동 15
 
1.2%
기린대로 13
 
1.0%
송천동2가 13
 
1.0%
Other values (284) 568
44.1%
2024-03-14T18:43:55.995538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1175
 
16.8%
전 432
 
6.2%
자 265
 
3.8%
, 236
 
3.4%
동 229
 
3.3%
시 223
 
3.2%
구 216
 
3.1%
주 216
 
3.1%
북 212
 
3.0%
도 210
 
3.0%
Other values (164) 3573
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4456
63.8%
Space Separator 1175
 
16.8%
Decimal Number 693
 
9.9%
Other Punctuation 236
 
3.4%
Open Punctuation 210
 
3.0%
Close Punctuation 210
 
3.0%
Dash Punctuation 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 432
 
9.7%
자 265
 
5.9%
동 229
 
5.1%
시 223
 
5.0%
구 216
 
4.8%
주 216
 
4.8%
북 212
 
4.8%
도 210
 
4.7%
치 210
 
4.7%
특 210
 
4.7%
Other values (149) 2033
45.6%
Decimal Number
ValueCountFrequency (%)
1 152
21.9%
2 124
17.9%
3 86
12.4%
6 63
9.1%
5 57
 
8.2%
7 54
 
7.8%
0 45
 
6.5%
4 42
 
6.1%
9 39
 
5.6%
8 31
 
4.5%
Space Separator
ValueCountFrequency (%)
1175
100.0%
Other Punctuation
ValueCountFrequency (%)
, 236
100.0%
Open Punctuation
ValueCountFrequency (%)
( 210
100.0%
Close Punctuation
ValueCountFrequency (%)
) 210
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4456
63.8%
Common 2531
36.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 432
 
9.7%
자 265
 
5.9%
동 229
 
5.1%
시 223
 
5.0%
구 216
 
4.8%
주 216
 
4.8%
북 212
 
4.8%
도 210
 
4.7%
치 210
 
4.7%
특 210
 
4.7%
Other values (149) 2033
45.6%
Common
ValueCountFrequency (%)
1175
46.4%
, 236
 
9.3%
( 210
 
8.3%
) 210
 
8.3%
1 152
 
6.0%
2 124
 
4.9%
3 86
 
3.4%
6 63
 
2.5%
5 57
 
2.3%
7 54
 
2.1%
Other values (5) 164
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4456
63.8%
ASCII 2531
36.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1175
46.4%
, 236
 
9.3%
( 210
 
8.3%
) 210
 
8.3%
1 152
 
6.0%
2 124
 
4.9%
3 86
 
3.4%
6 63
 
2.5%
5 57
 
2.3%
7 54
 
2.1%
Other values (5) 164
 
6.5%
Hangul
ValueCountFrequency (%)
전 432
 
9.7%
자 265
 
5.9%
동 229
 
5.1%
시 223
 
5.0%
구 216
 
4.8%
주 216
 
4.8%
북 212
 
4.8%
도 210
 
4.7%
치 210
 
4.7%
특 210
 
4.7%
Other values (149) 2033
45.6%

설치위치 지번주소
Text

Distinct272
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-03-14T18:43:57.248591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length41
Mean length29.878689
Min length24

Characters and Unicode

Total characters9113
Distinct characters111
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

Unique243 ?
Unique (%)79.7%

Sample

1st row전북특별자치도 전주시 덕진구 진북동 382-1번지
2nd row전북특별자치도 전주시 덕진구 진북동 382-7번지
3rd row전북특별자치도 전주시 덕진구 인후동1가 750-1번지
4th row전북특별자치도 전주시 덕진구 인후동1가 575-1번지
5th row전북특별자치도 전주시 덕진구 인후동1가 787번지
ValueCountFrequency (%)
전북특별자치도 305
19.6%
전주시 305
19.6%
덕진구 154
 
9.9%
완산구 151
 
9.7%
송천동2가 25
 
1.6%
효자동2가 25
 
1.6%
효자동3가 21
 
1.4%
효자동1가 19
 
1.2%
금암동 18
 
1.2%
인후동1가 17
 
1.1%
Other values (325) 513
33.0%
2024-03-14T18:43:58.975767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1553
 
17.0%
전 617
 
6.8%
자 372
 
4.1%
1 349
 
3.8%
시 318
 
3.5%
동 314
 
3.4%
지 312
 
3.4%
주 309
 
3.4%
북 307
 
3.4%
도 305
 
3.3%
Other values (101) 4357
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5891
64.6%
Space Separator 1553
 
17.0%
Decimal Number 1456
 
16.0%
Dash Punctuation 213
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 617
 
10.5%
자 372
 
6.3%
시 318
 
5.4%
동 314
 
5.3%
지 312
 
5.3%
주 309
 
5.2%
북 307
 
5.2%
도 305
 
5.2%
번 305
 
5.2%
치 305
 
5.2%
Other values (89) 2427
41.2%
Decimal Number
ValueCountFrequency (%)
1 349
24.0%
2 231
15.9%
3 187
12.8%
7 124
 
8.5%
5 109
 
7.5%
6 100
 
6.9%
8 96
 
6.6%
4 95
 
6.5%
9 86
 
5.9%
0 79
 
5.4%
Space Separator
ValueCountFrequency (%)
1553
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 213
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5891
64.6%
Common 3222
35.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 617
 
10.5%
자 372
 
6.3%
시 318
 
5.4%
동 314
 
5.3%
지 312
 
5.3%
주 309
 
5.2%
북 307
 
5.2%
도 305
 
5.2%
번 305
 
5.2%
치 305
 
5.2%
Other values (89) 2427
41.2%
Common
ValueCountFrequency (%)
1553
48.2%
1 349
 
10.8%
2 231
 
7.2%
- 213
 
6.6%
3 187
 
5.8%
7 124
 
3.8%
5 109
 
3.4%
6 100
 
3.1%
8 96
 
3.0%
4 95
 
2.9%
Other values (2) 165
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5891
64.6%
ASCII 3222
35.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1553
48.2%
1 349
 
10.8%
2 231
 
7.2%
- 213
 
6.6%
3 187
 
5.8%
7 124
 
3.8%
5 109
 
3.4%
6 100
 
3.1%
8 96
 
3.0%
4 95
 
2.9%
Other values (2) 165
 
5.1%
Hangul
ValueCountFrequency (%)
전 617
 
10.5%
자 372
 
6.3%
시 318
 
5.4%
동 314
 
5.3%
지 312
 
5.3%
주 309
 
5.2%
북 307
 
5.2%
도 305
 
5.2%
번 305
 
5.2%
치 305
 
5.2%
Other values (89) 2427
41.2%

설치년도
Categorical

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2020
89 
2019
83 
2021
74 
2022
59 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2020
4th row2020
5th row2021

Common Values

ValueCountFrequency (%)
2020 89
29.2%
2019 83
27.2%
2021 74
24.3%
2022 59
19.3%

Length

2024-03-14T18:43:59.586677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:43:59.908723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 89
29.2%
2019 83
27.2%
2021 74
24.3%
2022 59
19.3%

그늘막 유형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
고정형
294 
스마트
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고정형 294
96.4%
스마트 11
 
3.6%

Length

2024-03-14T18:44:00.278959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:44:00.591552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고정형 294
96.4%
스마트 11
 
3.6%

전체높이
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
3.5 M
227 
3.75 M
59 
5.4 M
 
11
3.3M
 
8

Length

Max length6
Median length5
Mean length5.1672131
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.5 M
2nd row3.5 M
3rd row3.5 M
4th row3.5 M
5th row3.75 M

Common Values

ValueCountFrequency (%)
3.5 M 227
74.4%
3.75 M 59
 
19.3%
5.4 M 11
 
3.6%
3.3M 8
 
2.6%

Length

2024-03-14T18:44:00.952645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:44:01.298249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 297
49.3%
3.5 227
37.7%
3.75 59
 
9.8%
5.4 11
 
1.8%
3.3m 8
 
1.3%

펼침지름
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
4 M
279 
3 M
 
14
5 M
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5 M
2nd row5 M
3rd row4 M
4th row3 M
5th row4 M

Common Values

ValueCountFrequency (%)
4 M 279
91.5%
3 M 14
 
4.6%
5 M 12
 
3.9%

Length

2024-03-14T18:44:01.660251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:44:01.977780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 305
50.0%
4 279
45.7%
3 14
 
2.3%
5 12
 
2.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct266
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.829664
Minimum35.784919
Maximum35.8831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-14T18:44:02.330197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.784919
5-th percentile35.793042
Q135.810014
median35.83256
Q335.8431
95-th percentile35.8735
Maximum35.8831
Range0.09818058
Interquartile range (IQR)0.03308555

Descriptive statistics

Standard deviation0.023897226
Coefficient of variation (CV)0.00066696764
Kurtosis-0.62365743
Mean35.829664
Median Absolute Deviation (MAD)0.0163921
Skewness0.20474417
Sum10928.047
Variance0.00057107743
MonotonicityNot monotonic
2024-03-14T18:44:02.769927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.8355 3
 
1.0%
35.8429 3
 
1.0%
35.837 3
 
1.0%
35.8749 3
 
1.0%
35.8346 3
 
1.0%
35.8699 2
 
0.7%
35.8381 2
 
0.7%
35.8436 2
 
0.7%
35.8433 2
 
0.7%
35.8766 2
 
0.7%
Other values (256) 280
91.8%
ValueCountFrequency (%)
35.78491942 2
0.7%
35.78510188 1
0.3%
35.78522173 1
0.3%
35.78580425 2
0.7%
35.78583852 1
0.3%
35.78725627 1
0.3%
35.78729454 1
0.3%
35.78745845 1
0.3%
35.78765258 1
0.3%
35.78790101 1
0.3%
ValueCountFrequency (%)
35.8831 1
 
0.3%
35.8795 1
 
0.3%
35.87767591 1
 
0.3%
35.8766 2
0.7%
35.87617789 1
 
0.3%
35.876 1
 
0.3%
35.8759 1
 
0.3%
35.87539412 1
 
0.3%
35.8749 3
1.0%
35.8748 1
 
0.3%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct279
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.11807
Minimum127.0564
Maximum127.1752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-14T18:44:03.179173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.0564
5-th percentile127.06327
Q1127.10426
median127.12261
Q3127.1362
95-th percentile127.1581
Maximum127.1752
Range0.1188
Interquartile range (IQR)0.03194

Descriptive statistics

Standard deviation0.028352078
Coefficient of variation (CV)0.00022303735
Kurtosis-0.49807797
Mean127.11807
Median Absolute Deviation (MAD)0.0172321
Skewness-0.48605683
Sum38771.012
Variance0.00080384032
MonotonicityNot monotonic
2024-03-14T18:44:03.629537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1393 2
 
0.7%
127.1053597 2
 
0.7%
127.1241 2
 
0.7%
127.1546179 2
 
0.7%
127.063086 2
 
0.7%
127.1144491 2
 
0.7%
127.1356 2
 
0.7%
127.152 2
 
0.7%
127.1053743 2
 
0.7%
127.1315 2
 
0.7%
Other values (269) 285
93.4%
ValueCountFrequency (%)
127.0564 1
0.3%
127.0571671 1
0.3%
127.0573 1
0.3%
127.0576 1
0.3%
127.0587 1
0.3%
127.0589 2
0.7%
127.059 2
0.7%
127.0590106 1
0.3%
127.0592 1
0.3%
127.0614402 1
0.3%
ValueCountFrequency (%)
127.1752 1
0.3%
127.168 1
0.3%
127.1666 1
0.3%
127.1663 1
0.3%
127.1626 1
0.3%
127.1617 1
0.3%
127.1616771 1
0.3%
127.1612453 1
0.3%
127.1609997 1
0.3%
127.1609029 1
0.3%

Interactions

2024-03-14T18:43:43.936005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:42.429368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:43.192501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:44.195398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:42.687685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:43.445063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:44.443854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:42.932033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:43.683810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:44:03.905645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동설치년도그늘막 유형전체높이펼침지름위도경도
연번1.0000.9910.3230.0000.5130.2760.8840.904
행정동0.9911.0000.6740.0000.7510.5200.9490.956
설치년도0.3230.6741.0000.4270.7770.2850.3470.357
그늘막 유형0.0000.0000.4271.0001.0000.6130.0000.000
전체높이0.5130.7510.7771.0001.0000.6080.4910.405
펼침지름0.2760.5200.2850.6130.6081.0000.1690.284
위도0.8840.9490.3470.0000.4910.1691.0000.738
경도0.9040.9560.3570.0000.4050.2840.7381.000
2024-03-14T18:44:04.204642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치년도그늘막 유형펼침지름전체높이행정동
설치년도1.0000.2860.2730.4150.387
그늘막 유형0.2861.0000.8800.9970.000
펼침지름0.2730.8801.0000.6230.266
전체높이0.4150.9970.6231.0000.460
행정동0.3870.0000.2660.4601.000
2024-03-14T18:44:04.480525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도행정동설치년도그늘막 유형전체높이펼침지름
연번1.000-0.703-0.3110.8700.1960.0000.3290.169
위도-0.7031.000-0.1300.6850.2120.0000.3120.100
경도-0.311-0.1301.0000.7080.2180.0000.2500.175
행정동0.8700.6850.7081.0000.3870.0000.4600.266
설치년도0.1960.2120.2180.3871.0000.2860.4150.273
그늘막 유형0.0000.0000.0000.0000.2861.0000.9970.880
전체높이0.3290.3120.2500.4600.4150.9971.0000.623
펼침지름0.1690.1000.1750.2660.2730.8800.6231.000

Missing values

2024-03-14T18:43:44.829515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:43:45.411289image/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

연번시도시군구행정동관리번호설치장소명설치위치 도로명주소설치위치 지번주소설치년도그늘막 유형전체높이펼침지름위도경도
01전북특별자치도전주시진북동진북-1전일초등학교 앞전북특별자치도 전주시 덕진구 태진로 98, (진북동)전북특별자치도 전주시 덕진구 진북동 382-1번지2019고정형3.5 M5 M35.8294127.1365
12전북특별자치도전주시진북동진북-2전주교육지원청 앞전북특별자치도 전주시 덕진구 태진로 100, (진북동)전북특별자치도 전주시 덕진구 진북동 382-7번지2019고정형3.5 M5 M35.8303127.1362
23전북특별자치도전주시인후1동인후1-1북일초등학교전북특별자치도 전주시 덕진구 견훤로 336, (인후동1가)전북특별자치도 전주시 덕진구 인후동1가 750-1번지2020고정형3.5 M4 M35.8421127.1556
34전북특별자치도전주시인후1동인후1-2전라초등학교전북특별자치도 전주시 덕진구 심방죽로 24, (인후동1가)전북특별자치도 전주시 덕진구 인후동1가 575-1번지2020고정형3.5 M3 M35.8393127.1523
45전북특별자치도전주시인후1동인후1-3안골네거리-1전북특별자치도 전주시 덕진구 인후동1가 787번지2021고정형3.75 M4 M35.8369127.1564
56전북특별자치도전주시인후1동인후1-4안골네거리-2전북특별자치도 전주시 덕진구 인후동1가 787번지2021고정형3.75 M4 M35.837127.1568
67전북특별자치도전주시인후1동인후1-5안골네거리-3전북특별자치도 전주시 덕진구 인후동1가 787번지2021고정형3.75 M4 M35.8367127.1569
78전북특별자치도전주시인후1동인후1-6안골네거리-4전북특별자치도 전주시 덕진구 인후동1가 787번지2021고정형3.75 M4 M35.8366127.1565
89전북특별자치도전주시인후1동인후1-7삼호아파트전북특별자치도 전주시 덕진구 인후동1가 산7-5번지2022고정형3.75 M4 M35.837809127.160903
910전북특별자치도전주시인후1동인후1-8궁전아파트 옆 교통섬전북특별자치도 전주시 덕진구 안덕원로 217, (인후동1가)전북특별자치도 전주시 덕진구 인후동1가 592-32번지2022고정형3.75 M4 M35.836439127.150645
연번시도시군구행정동관리번호설치장소명설치위치 도로명주소설치위치 지번주소설치년도그늘막 유형전체높이펼침지름위도경도
295296전북특별자치도전주시효자5동효자5-16전주대 신정문 사거리3전북특별자치도 전주시 완산구 농소1길 15-1, (효자동3가)전북특별자치도 전주시 완산구 효자동3가 1716-9번지2020고정형3.5 M4 M35.813933127.094937
296297전북특별자치도전주시효자5동효자5-17효자5동 주민센터 앞전북특별자치도 전주시 완산구 우전로 255, (효자동2가)전북특별자치도 전주시 완산구 효자동2가 1233-2번지2020고정형3.5 M4 M35.817264127.101627
297298전북특별자치도전주시효자5동효자5-18비타민공인중개사 앞전북특별자치도 전주시 완산구 홍산로 371, (효자동3가)전북특별자치도 전주시 완산구 효자동3가 1609-18번지2021고정형3.5 M4 M35.82691127.105614
298299전북특별자치도전주시효자5동효자5-19전북도청 사거리(교통섬)1전북특별자치도 전주시 완산구 효자로 225, (효자동3가)전북특별자치도 전주시 완산구 효자동3가 1번지2019고정형3.5 M4 M35.819444127.106373
299300전북특별자치도전주시효자5동효자5-20전북도청 사거리(교통섬)2전북특별자치도 전주시 완산구 효자로 185, (효자동3가)전북특별자치도 전주시 완산구 효자동3가 1695-4번지2019고정형3.5 M4 M35.819456127.105476
300301전북특별자치도전주시효자5동효자5-21전북도청 사거리(교통섬)3전북특별자치도 전주시 완산구 효자동2가 1232-1번지2019고정형3.5 M4 M35.818635127.105485
301302전북특별자치도전주시효자5동효자5-22전북도, 세관 사거리1전북특별자치도 전주시 완산구 효자로 225, (효자동3가)전북특별자치도 전주시 완산구 효자동3가 1번지2019고정형3.5 M4 M35.819444127.106373
302303전북특별자치도전주시효자5동효자5-23전북도, 세관 사거리2전북특별자치도 전주시 완산구 홍산로 303, (효자동3가)전북특별자치도 전주시 완산구 효자동3가 1695-1번지2020고정형3.5 M4 M35.820798127.105461
303304전북특별자치도전주시효자5동효자5-24효자다리 전북도청 사거리전북특별자치도 전주시 완산구 효자동3가 15-5번지2021스마트5.4 M3 M35.819282127.112428
304305전북특별자치도전주시효자5동효자5-25서곡초등학교 후문전북특별자치도 전주시 완산구 서곡7길 30, (효자동3가)전북특별자치도 전주시 완산구 효자동3가 1474번지2022고정형3.5 M4 M35.833058127.100896