Overview

Dataset statistics

Number of variables19
Number of observations145
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.2 KiB
Average record size in memory156.9 B

Variable types

Categorical10
Text3
Numeric2
DateTime4

Dataset

Description폭염에 대비하여 폭염대응 종합대책에 의거하여 폭염저감시설로 설치된 그늘막(고정형 대평파라솔)에 대한 설치현황입니다.
Author경상남도 진주시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15118038

Alerts

시도 has constant value ""Constant
시군 has constant value ""Constant
전체높이 has constant value ""Constant
당해년도 운영시작 일자 has constant value ""Constant
당해년도 운영종료 일자 has constant value ""Constant
데이터기준일자 has constant value ""Constant
읍면동 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
전화번호 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
위도 is highly overall correlated with 읍면동 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 읍면동 and 1 other fieldsHigh correlation
설치위치_도로명주소 is highly overall correlated with 읍면동 and 1 other fieldsHigh correlation
펼침지름 is highly overall correlated with 원단 and 1 other fieldsHigh correlation
원단 is highly overall correlated with 펼침지름 and 2 other fieldsHigh correlation
영조물 보험가입유무 is highly overall correlated with 읍면동 and 3 other fieldsHigh correlation
원단 is highly imbalanced (66.4%)Imbalance
영조물 보험가입유무 is highly imbalanced (66.4%)Imbalance
관리부서 is highly imbalanced (81.8%)Imbalance
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:11:26.361829
Analysis finished2023-12-10 23:11:28.290679
Duration1.93 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
경상남도
145 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 145
100.0%

Length

2023-12-11T08:11:28.353304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:11:28.456183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 145
100.0%

시군
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
진주시
145 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
진주시 145
100.0%

Length

2023-12-11T08:11:28.577134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:11:28.692342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
진주시 145
100.0%

읍면동
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
충무공동
27 
가호동
26 
초장동
15 
신안동
10 
중앙동
10 
Other values (11)
57 

Length

Max length4
Median length3
Mean length3.1862069
Min length3

Unique

Unique3 ?
Unique (%)2.1%

Sample

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

Common Values

ValueCountFrequency (%)
충무공동 27
18.6%
가호동 26
17.9%
초장동 15
10.3%
신안동 10
 
6.9%
중앙동 10
 
6.9%
천전동 10
 
6.9%
평거동 10
 
6.9%
상대동 9
 
6.2%
판문동 8
 
5.5%
이현동 6
 
4.1%
Other values (6) 14
9.7%

Length

2023-12-11T08:11:28.786314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충무공동 27
18.6%
가호동 26
17.9%
초장동 15
10.3%
신안동 10
 
6.9%
중앙동 10
 
6.9%
천전동 10
 
6.9%
평거동 10
 
6.9%
상대동 9
 
6.2%
판문동 8
 
5.5%
이현동 6
 
4.1%
Other values (6) 14
9.7%

관리번호
Text

UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T08:11:29.089726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.4965517
Min length4

Characters and Unicode

Total characters652
Distinct characters42
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique145 ?
Unique (%)100.0%

Sample

1st row가호동1
2nd row가호동10
3rd row가호동11
4th row가호동12
5th row가호동13
ValueCountFrequency (%)
가호동1 1
 
0.7%
천전동10 1
 
0.7%
충무공동11 1
 
0.7%
초장동6 1
 
0.7%
초장동7 1
 
0.7%
초장동8 1
 
0.7%
초장동9 1
 
0.7%
충무공동1 1
 
0.7%
충무공동10 1
 
0.7%
충무공동12 1
 
0.7%
Other values (135) 135
93.1%
2023-12-11T08:11:29.547767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 140
21.5%
1 51
 
7.8%
2 33
 
5.1%
공 27
 
4.1%
충 27
 
4.1%
무 27
 
4.1%
가 26
 
4.0%
호 26
 
4.0%
3 17
 
2.6%
4 16
 
2.5%
Other values (32) 262
40.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 462
70.9%
Decimal Number 190
29.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 140
30.3%
공 27
 
5.8%
충 27
 
5.8%
무 27
 
5.8%
가 26
 
5.6%
호 26
 
5.6%
장 15
 
3.2%
초 15
 
3.2%
대 12
 
2.6%
중 10
 
2.2%
Other values (22) 137
29.7%
Decimal Number
ValueCountFrequency (%)
1 51
26.8%
2 33
17.4%
3 17
 
8.9%
4 16
 
8.4%
5 16
 
8.4%
6 14
 
7.4%
7 13
 
6.8%
8 11
 
5.8%
9 10
 
5.3%
0 9
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 462
70.9%
Common 190
29.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 140
30.3%
공 27
 
5.8%
충 27
 
5.8%
무 27
 
5.8%
가 26
 
5.6%
호 26
 
5.6%
장 15
 
3.2%
초 15
 
3.2%
대 12
 
2.6%
중 10
 
2.2%
Other values (22) 137
29.7%
Common
ValueCountFrequency (%)
1 51
26.8%
2 33
17.4%
3 17
 
8.9%
4 16
 
8.4%
5 16
 
8.4%
6 14
 
7.4%
7 13
 
6.8%
8 11
 
5.8%
9 10
 
5.3%
0 9
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 462
70.9%
ASCII 190
29.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 140
30.3%
공 27
 
5.8%
충 27
 
5.8%
무 27
 
5.8%
가 26
 
5.6%
호 26
 
5.6%
장 15
 
3.2%
초 15
 
3.2%
대 12
 
2.6%
중 10
 
2.2%
Other values (22) 137
29.7%
ASCII
ValueCountFrequency (%)
1 51
26.8%
2 33
17.4%
3 17
 
8.9%
4 16
 
8.4%
5 16
 
8.4%
6 14
 
7.4%
7 13
 
6.8%
8 11
 
5.8%
9 10
 
5.3%
0 9
 
4.7%

설치장소명
Text

Distinct81
Distinct (%)56.2%
Missing1
Missing (%)0.7%
Memory size1.3 KiB
2023-12-11T08:11:29.754080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length26
Mean length8.6111111
Min length3

Characters and Unicode

Total characters1240
Distinct characters181
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

Unique48 ?
Unique (%)33.3%

Sample

1st row경상대 정문
2nd row관동교 사거리
3rd row관동교 사거리
4th row센트럴웰가
5th row센트럴웰가
ValueCountFrequency (%)
사거리 33
 
12.6%
앞 12
 
4.6%
맞은편 6
 
2.3%
건널목 6
 
2.3%
정문 6
 
2.3%
개양오거리 5
 
1.9%
시티프라디움 5
 
1.9%
10호광장교차로 5
 
1.9%
교차로 5
 
1.9%
101동 5
 
1.9%
Other values (95) 174
66.4%
2023-12-11T08:11:30.119211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
121
 
9.8%
리 65
 
5.2%
거 60
 
4.8%
교 54
 
4.4%
사 54
 
4.4%
학 26
 
2.1%
로 26
 
2.1%
장 23
 
1.9%
주 21
 
1.7%
차 21
 
1.7%
Other values (171) 769
62.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1040
83.9%
Space Separator 121
 
9.8%
Decimal Number 39
 
3.1%
Uppercase Letter 22
 
1.8%
Dash Punctuation 5
 
0.4%
Open Punctuation 4
 
0.3%
Close Punctuation 4
 
0.3%
Other Punctuation 3
 
0.2%
Lowercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 65
 
6.2%
거 60
 
5.8%
교 54
 
5.2%
사 54
 
5.2%
학 26
 
2.5%
로 26
 
2.5%
장 23
 
2.2%
주 21
 
2.0%
차 21
 
2.0%
대 18
 
1.7%
Other values (151) 672
64.6%
Decimal Number
ValueCountFrequency (%)
1 16
41.0%
0 10
25.6%
4 4
 
10.3%
6 4
 
10.3%
2 3
 
7.7%
5 1
 
2.6%
3 1
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
S 6
27.3%
C 5
22.7%
K 4
18.2%
H 4
18.2%
L 2
 
9.1%
U 1
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
c 1
50.0%
u 1
50.0%
Space Separator
ValueCountFrequency (%)
121
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1040
83.9%
Common 176
 
14.2%
Latin 24
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 65
 
6.2%
거 60
 
5.8%
교 54
 
5.2%
사 54
 
5.2%
학 26
 
2.5%
로 26
 
2.5%
장 23
 
2.2%
주 21
 
2.0%
차 21
 
2.0%
대 18
 
1.7%
Other values (151) 672
64.6%
Common
ValueCountFrequency (%)
121
68.8%
1 16
 
9.1%
0 10
 
5.7%
- 5
 
2.8%
4 4
 
2.3%
( 4
 
2.3%
) 4
 
2.3%
6 4
 
2.3%
, 3
 
1.7%
2 3
 
1.7%
Other values (2) 2
 
1.1%
Latin
ValueCountFrequency (%)
S 6
25.0%
C 5
20.8%
K 4
16.7%
H 4
16.7%
L 2
 
8.3%
c 1
 
4.2%
u 1
 
4.2%
U 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1040
83.9%
ASCII 200
 
16.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
121
60.5%
1 16
 
8.0%
0 10
 
5.0%
S 6
 
3.0%
C 5
 
2.5%
- 5
 
2.5%
4 4
 
2.0%
( 4
 
2.0%
) 4
 
2.0%
K 4
 
2.0%
Other values (10) 21
 
10.5%
Hangul
ValueCountFrequency (%)
리 65
 
6.2%
거 60
 
5.8%
교 54
 
5.2%
사 54
 
5.2%
학 26
 
2.5%
로 26
 
2.5%
장 23
 
2.2%
주 21
 
2.0%
차 21
 
2.0%
대 18
 
1.7%
Other values (151) 672
64.6%

설치위치_도로명주소
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
78 
경상남도 진주시 진양호로 206 (평거동)
 
4
경상남도 진주시 새평거로 55 (평거동)
 
4
경상남도 진주시 진양호로 541 (대안동)
 
4
경상남도 진주시 개양로 100 (가좌동, 스카이 시티프라디움)
 
4
Other values (28)
51 

Length

Max length49
Median length1
Mean length11.813793
Min length1

Unique

Unique16 ?
Unique (%)11.0%

Sample

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

Common Values

ValueCountFrequency (%)
78
53.8%
경상남도 진주시 진양호로 206 (평거동) 4
 
2.8%
경상남도 진주시 새평거로 55 (평거동) 4
 
2.8%
경상남도 진주시 진양호로 541 (대안동) 4
 
2.8%
경상남도 진주시 개양로 100 (가좌동, 스카이 시티프라디움) 4
 
2.8%
경상남도 진주시 신안로 104-1(신안동) 4
 
2.8%
경상남도 진주시 진주대로 1095 (평안동) 4
 
2.8%
경상남도 진주시 대신로 224 (상대동) 4
 
2.8%
경상남도 진주시 진양호로 484 (남성동) 4
 
2.8%
경상남도 진주시 대신로 362 (하대동) 3
 
2.1%
Other values (23) 32
22.1%

Length

2023-12-11T08:11:30.264321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상남도 67
19.4%
진주시 67
19.4%
진양호로 13
 
3.8%
평거동 12
 
3.5%
동진로 10
 
2.9%
대신로 9
 
2.6%
상대동 8
 
2.3%
가좌동 8
 
2.3%
새평거로 7
 
2.0%
진주대로 6
 
1.7%
Other values (60) 138
40.0%

설치위치_지번주소
Text

Distinct76
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T08:11:30.511586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length18.365517
Min length16

Characters and Unicode

Total characters2663
Distinct characters55
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

Unique40 ?
Unique (%)27.6%

Sample

1st row경상남도 진주시 가좌동 911-3
2nd row경상남도 진주시 가좌동 996-1
3rd row경상남도 진주시 가좌동 996-1
4th row경상남도 진주시 가좌동 1980
5th row경상남도 진주시 가좌동 1980
ValueCountFrequency (%)
경상남도 145
24.8%
진주시 145
24.8%
충무공동 27
 
4.6%
가좌동 26
 
4.5%
평거동 18
 
3.1%
초전동 15
 
2.6%
신안동 10
 
1.7%
상대동 9
 
1.5%
이현동 6
 
1.0%
631-5 5
 
0.9%
Other values (91) 178
30.5%
2023-12-11T08:11:30.878675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
584
21.9%
상 155
 
5.8%
1 150
 
5.6%
주 149
 
5.6%
남 149
 
5.6%
경 146
 
5.5%
진 145
 
5.4%
도 145
 
5.4%
시 145
 
5.4%
동 141
 
5.3%
Other values (45) 754
28.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1489
55.9%
Space Separator 584
 
21.9%
Decimal Number 523
 
19.6%
Dash Punctuation 67
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 155
10.4%
주 149
10.0%
남 149
10.0%
경 146
9.8%
진 145
9.7%
도 145
9.7%
시 145
9.7%
동 141
9.5%
공 27
 
1.8%
무 27
 
1.8%
Other values (33) 260
17.5%
Decimal Number
ValueCountFrequency (%)
1 150
28.7%
9 69
13.2%
2 55
 
10.5%
3 49
 
9.4%
0 45
 
8.6%
4 36
 
6.9%
6 35
 
6.7%
5 34
 
6.5%
8 26
 
5.0%
7 24
 
4.6%
Space Separator
ValueCountFrequency (%)
584
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1489
55.9%
Common 1174
44.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 155
10.4%
주 149
10.0%
남 149
10.0%
경 146
9.8%
진 145
9.7%
도 145
9.7%
시 145
9.7%
동 141
9.5%
공 27
 
1.8%
무 27
 
1.8%
Other values (33) 260
17.5%
Common
ValueCountFrequency (%)
584
49.7%
1 150
 
12.8%
9 69
 
5.9%
- 67
 
5.7%
2 55
 
4.7%
3 49
 
4.2%
0 45
 
3.8%
4 36
 
3.1%
6 35
 
3.0%
5 34
 
2.9%
Other values (2) 50
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1489
55.9%
ASCII 1174
44.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
584
49.7%
1 150
 
12.8%
9 69
 
5.9%
- 67
 
5.7%
2 55
 
4.7%
3 49
 
4.2%
0 45
 
3.8%
4 36
 
3.1%
6 35
 
3.0%
5 34
 
2.9%
Other values (2) 50
 
4.3%
Hangul
ValueCountFrequency (%)
상 155
10.4%
주 149
10.0%
남 149
10.0%
경 146
9.8%
진 145
9.7%
도 145
9.7%
시 145
9.7%
동 141
9.5%
공 27
 
1.8%
무 27
 
1.8%
Other values (33) 260
17.5%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct142
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.10228
Minimum128.04939
Maximum128.1647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T08:11:31.011300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.04939
5-th percentile128.05616
Q1128.07887
median128.1076
Q3128.11734
95-th percentile128.14856
Maximum128.1647
Range0.1153123
Interquartile range (IQR)0.0384666

Descriptive statistics

Standard deviation0.029152382
Coefficient of variation (CV)0.00022757114
Kurtosis-0.99196826
Mean128.10228
Median Absolute Deviation (MAD)0.0251581
Skewness-0.021600104
Sum18574.83
Variance0.00084986137
MonotonicityNot monotonic
2023-12-11T08:11:31.140066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.116534 2
 
1.4%
128.1119 2
 
1.4%
128.111458 2
 
1.4%
128.1488576 1
 
0.7%
128.1161977 1
 
0.7%
128.1385909 1
 
0.7%
128.1348337 1
 
0.7%
128.1347236 1
 
0.7%
128.134984 1
 
0.7%
128.1351266 1
 
0.7%
Other values (132) 132
91.0%
ValueCountFrequency (%)
128.0493877 1
0.7%
128.0494353 1
0.7%
128.0534152 1
0.7%
128.0545506 1
0.7%
128.0545633 1
0.7%
128.0545635 1
0.7%
128.0546376 1
0.7%
128.0555671 1
0.7%
128.0585176 1
0.7%
128.0586281 1
0.7%
ValueCountFrequency (%)
128.1647 1
0.7%
128.149428 1
0.7%
128.1493616 1
0.7%
128.1489961 1
0.7%
128.148897 1
0.7%
128.1488576 1
0.7%
128.1487614 1
0.7%
128.1485956 1
0.7%
128.1484087 1
0.7%
128.1483394 1
0.7%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.180124
Minimum35.1509
Maximum35.238544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T08:11:31.273921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.1509
5-th percentile35.153569
Q135.168959
median35.17858
Q335.19285
95-th percentile35.213959
Maximum35.238544
Range0.087644
Interquartile range (IQR)0.0238915

Descriptive statistics

Standard deviation0.017722808
Coefficient of variation (CV)0.00050377333
Kurtosis-0.066377333
Mean35.180124
Median Absolute Deviation (MAD)0.0125671
Skewness0.42072079
Sum5101.118
Variance0.00031409793
MonotonicityNot monotonic
2023-12-11T08:11:31.394265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1509 3
 
2.1%
35.154494 2
 
1.4%
35.2141 2
 
1.4%
35.155579 2
 
1.4%
35.1770742 1
 
0.7%
35.1703095 1
 
0.7%
35.1704984 1
 
0.7%
35.1705072 1
 
0.7%
35.1703749 1
 
0.7%
35.1805356 1
 
0.7%
Other values (130) 130
89.7%
ValueCountFrequency (%)
35.1509 3
2.1%
35.151 1
 
0.7%
35.1524234 1
 
0.7%
35.152433 1
 
0.7%
35.152827 1
 
0.7%
35.1533374 1
 
0.7%
35.154494 2
1.4%
35.1545724 1
 
0.7%
35.1546481 1
 
0.7%
35.1548 1
 
0.7%
ValueCountFrequency (%)
35.238544 1
0.7%
35.2144583 1
0.7%
35.2144466 1
0.7%
35.2143855 1
0.7%
35.2143818 1
0.7%
35.2143173 1
0.7%
35.2141 2
1.4%
35.2133962 1
0.7%
35.2132288 1
0.7%
35.2119071 1
0.7%

설치일자
Date

Distinct12
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2017-08-20 00:00:00
Maximum2023-06-05 00:00:00
2023-12-11T08:11:31.494488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:31.595295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

전체높이
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
3.5
145 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3.5 145
100.0%

Length

2023-12-11T08:11:31.934927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:11:32.025434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3.5 145
100.0%

펼침지름
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
4.0
78 
5.0
39 
3.0
22 
3.5
 
5
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row5.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 78
53.8%
5.0 39
26.9%
3.0 22
 
15.2%
3.5 5
 
3.4%
2.0 1
 
0.7%

Length

2023-12-11T08:11:32.143755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:11:32.255974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0 78
53.8%
5.0 39
26.9%
3.0 22
 
15.2%
3.5 5
 
3.4%
2.0 1
 
0.7%

원단
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
매쉬
136 
메쉬
 
9

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
매쉬 136
93.8%
메쉬 9
 
6.2%

Length

2023-12-11T08:11:32.371655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:11:32.479630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
매쉬 136
93.8%
메쉬 9
 
6.2%

영조물 보험가입유무
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
O
136 
X
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
O 136
93.8%
X 9
 
6.2%

Length

2023-12-11T08:11:32.585029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:11:32.699037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
o 136
93.8%
x 9
 
6.2%

당해년도 운영시작 일자
Date

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-05-20 00:00:00
Maximum2023-05-20 00:00:00
2023-12-11T08:11:32.767705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:32.852800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

당해년도 운영종료 일자
Date

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-09-30 00:00:00
Maximum2023-09-30 00:00:00
2023-12-11T08:11:32.924551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:32.995233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

관리부서
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
시민안전과
141 
시민안전과
 
4

Length

Max length6
Median length5
Mean length5.0275862
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시민안전과 141
97.2%
시민안전과 4
 
2.8%

Length

2023-12-11T08:11:33.085858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:11:33.166139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시민안전과 145
100.0%

전화번호
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
055-749-3976
25 
055-749-4537
15 
055-749-4408
15 
055-749-4525
11 
055-749-4465
10 
Other values (15)
69 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique5 ?
Unique (%)3.4%

Sample

1st row055-749-4537
2nd row055-749-4537
3rd row055-749-4537
4th row055-749-4537
5th row055-749-4537

Common Values

ValueCountFrequency (%)
055-749-3976 25
17.2%
055-749-4537 15
10.3%
055-749-4408 15
10.3%
055-749-4525 11
7.6%
055-749-4465 10
 
6.9%
055-749-4434 10
 
6.9%
055-749-4113 10
 
6.9%
055-749-4176 9
 
6.2%
055-749-4504 9
 
6.2%
055-749-4025 8
 
5.5%
Other values (10) 23
15.9%

Length

2023-12-11T08:11:33.253502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
055-749-3976 25
17.2%
055-749-4537 15
10.3%
055-749-4408 15
10.3%
055-749-4525 11
7.6%
055-749-4465 10
 
6.9%
055-749-4434 10
 
6.9%
055-749-4113 10
 
6.9%
055-749-4176 9
 
6.2%
055-749-4504 9
 
6.2%
055-749-4025 8
 
5.5%
Other values (10) 23
15.9%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-08-08 00:00:00
Maximum2023-08-08 00:00:00
2023-12-11T08:11:33.332242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:33.402740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T08:11:27.624821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:27.446932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:27.730042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:27.535624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:11:33.471066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동설치장소명설치위치_도로명주소설치위치_지번주소위도경도설치일자펼침지름원단영조물 보험가입유무관리부서전화번호
읍면동1.0001.0000.9441.0000.9490.9540.8500.4200.6020.7600.0001.000
설치장소명1.0001.0000.9980.9991.0001.0000.9980.7770.9710.9710.3921.000
설치위치_도로명주소0.9440.9981.0001.0000.8290.8150.8960.6100.5080.6460.0000.949
설치위치_지번주소1.0000.9991.0001.0000.9970.9980.9970.8670.9340.9250.0001.000
위도0.9491.0000.8290.9971.0000.8730.6120.2670.3720.3760.0000.951
경도0.9541.0000.8150.9980.8731.0000.7620.0000.1720.2850.3710.955
설치일자0.8500.9980.8960.9970.6120.7621.0000.7840.9540.9960.0000.942
펼침지름0.4200.7770.6100.8670.2670.0000.7841.0000.4190.4190.0000.758
원단0.6020.9710.5080.9340.3720.1720.9540.4191.0000.8920.0000.782
영조물 보험가입유무0.7600.9710.6460.9250.3760.2850.9960.4190.8921.0000.0000.923
관리부서0.0000.3920.0000.0000.0000.3710.0000.0000.0000.0001.0000.000
전화번호1.0001.0000.9491.0000.9510.9550.9420.7580.7820.9230.0001.000
2023-12-11T08:11:33.598121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리부서영조물 보험가입유무펼침지름읍면동설치위치_도로명주소원단전화번호
관리부서1.0000.0000.0000.0000.0000.0000.000
영조물 보험가입유무0.0001.0000.5040.5840.4910.7010.741
펼침지름0.0000.5041.0000.2190.3040.5040.410
읍면동0.0000.5840.2191.0000.5870.4530.979
설치위치_도로명주소0.0000.4910.3040.5871.0000.3810.578
원단0.0000.7010.5040.4530.3811.0000.598
전화번호0.0000.7410.4100.9790.5780.5981.000
2023-12-11T08:11:33.696552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도읍면동설치위치_도로명주소펼침지름원단영조물 보험가입유무관리부서전화번호
위도1.000-0.0530.7800.4350.1520.3630.3670.0000.759
경도-0.0531.0000.7960.4220.0000.1660.2770.3620.769
읍면동0.7800.7961.0000.5870.2190.4530.5840.0000.979
설치위치_도로명주소0.4350.4220.5871.0000.3040.3810.4910.0000.578
펼침지름0.1520.0000.2190.3041.0000.5040.5040.0000.410
원단0.3630.1660.4530.3810.5041.0000.7010.0000.598
영조물 보험가입유무0.3670.2770.5840.4910.5040.7011.0000.0000.741
관리부서0.0000.3620.0000.0000.0000.0000.0001.0000.000
전화번호0.7590.7690.9790.5780.4100.5980.7410.0001.000

Missing values

2023-12-11T08:11:27.884119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:11:28.194684image/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

시도시군읍면동관리번호설치장소명설치위치_도로명주소설치위치_지번주소위도경도설치일자전체높이펼침지름원단영조물 보험가입유무당해년도 운영시작 일자당해년도 운영종료 일자관리부서전화번호데이터기준일자
0경상남도진주시가호동가호동1경상대 정문경상남도 진주시 가좌동 911-3128.10499735.1524232017-08-203.55.0매쉬O2023-05-202023-09-30시민안전과055-749-45372023-08-08
1경상남도진주시가호동가호동10관동교 사거리경상남도 진주시 가좌동 996-1128.10723535.1557962020-08-283.54.0매쉬O2023-05-202023-09-30시민안전과055-749-45372023-08-08
2경상남도진주시가호동가호동11관동교 사거리경상남도 진주시 가좌동 996-1128.10667335.1558752020-08-283.54.0매쉬O2023-05-202023-09-30시민안전과055-749-45372023-08-08
3경상남도진주시가호동가호동12센트럴웰가경상남도 진주시 가좌동 1980128.11354235.1546482020-08-283.54.0매쉬O2023-05-202023-09-30시민안전과055-749-45372023-08-08
4경상남도진주시가호동가호동13센트럴웰가경상남도 진주시 가좌동 1980128.11386835.1545722020-08-283.54.0매쉬O2023-05-202023-09-30시민안전과055-749-45372023-08-08
5경상남도진주시가호동가호동14시티프라디움 101동경상남도 진주시 개양로 100 (가좌동, 스카이 시티프라디움)경상남도 진주시 가좌동 1958128.11754735.154862020-08-283.54.0매쉬O2023-05-202023-09-30시민안전과055-749-45372023-08-08
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시도시군읍면동관리번호설치장소명설치위치_도로명주소설치위치_지번주소위도경도설치일자전체높이펼침지름원단영조물 보험가입유무당해년도 운영시작 일자당해년도 운영종료 일자관리부서전화번호데이터기준일자
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136경상남도진주시평거동평거동410호광장교차로경상남도 진주시 진양호로 206 (평거동)경상남도 진주시 평거동 901128.06021735.1725422017-08-203.55.0매쉬O2023-05-202023-09-30시민안전과055-749-44342023-08-08
137경상남도진주시평거동평거동510호광장교차로경상남도 진주시 평거동 302-1128.05964435.1732832019-05-103.55.0매쉬O2023-05-202023-09-30시민안전과055-749-44342023-08-08
138경상남도진주시평거동평거동6서진초등학교경상남도 진주시 평거동 1034128.05851835.1689592020-05-143.54.0매쉬O2023-05-202023-09-30시민안전과055-749-44342023-08-08
139경상남도진주시평거동평거동7서진초등학교경상남도 진주시 평거동 1034128.05862835.1688162020-05-143.54.0매쉬O2023-05-202023-09-30시민안전과055-749-44342023-08-08
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