Overview

Dataset statistics

Number of variables12
Number of observations611
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.2 KiB
Average record size in memory99.2 B

Variable types

Numeric3
Categorical6
Text3

Dataset

Description김해동부소방서 관내 소방용수 현황입니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15075656

Alerts

관서명 has constant value ""Constant
유지관리 주체 has constant value ""Constant
읍면동 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
센터명 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 센터명 and 1 other fieldsHigh correlation
화재없는 안심마을지정 여부 is highly imbalanced (93.2%)Imbalance
수리형식 is highly imbalanced (75.1%)Imbalance
설치년도 is highly skewed (γ1 = 24.71292224)Skewed
연번 has unique valuesUnique
자체관리번호 has unique valuesUnique
센터와 용수와의 거리(킬로미터) has 9 (1.5%) zerosZeros

Reproduction

Analysis started2023-12-10 23:04:22.479159
Analysis finished2023-12-10 23:04:24.630003
Duration2.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct611
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean306
Minimum1
Maximum611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-11T08:04:24.729346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31.5
Q1153.5
median306
Q3458.5
95-th percentile580.5
Maximum611
Range610
Interquartile range (IQR)305

Descriptive statistics

Standard deviation176.52479
Coefficient of variation (CV)0.57687838
Kurtosis-1.2
Mean306
Median Absolute Deviation (MAD)153
Skewness0
Sum186966
Variance31161
MonotonicityStrictly increasing
2023-12-11T08:04:24.936753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
412 1
 
0.2%
405 1
 
0.2%
406 1
 
0.2%
407 1
 
0.2%
408 1
 
0.2%
409 1
 
0.2%
410 1
 
0.2%
411 1
 
0.2%
413 1
 
0.2%
Other values (601) 601
98.4%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
611 1
0.2%
610 1
0.2%
609 1
0.2%
608 1
0.2%
607 1
0.2%
606 1
0.2%
605 1
0.2%
604 1
0.2%
603 1
0.2%
602 1
0.2%

관서명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
김해동부
611 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
김해동부 611
100.0%

Length

2023-12-11T08:04:25.410242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:04:25.515384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
김해동부 611
100.0%

센터명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
내외
160 
삼정
141 
동상
92 
북부
92 
상동
46 
Other values (2)
80 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
내외 160
26.2%
삼정 141
23.1%
동상 92
15.1%
북부 92
15.1%
상동 46
 
7.5%
생림 41
 
6.7%
대동 39
 
6.4%

Length

2023-12-11T08:04:25.639870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:04:25.770123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
내외 160
26.2%
삼정 141
23.1%
동상 92
15.1%
북부 92
15.1%
상동 46
 
7.5%
생림 41
 
6.7%
대동 39
 
6.4%

자체관리번호
Text

UNIQUE 

Distinct611
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
2023-12-11T08:04:26.152070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.2618658
Min length5

Characters and Unicode

Total characters3826
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique611 ?
Unique (%)100.0%

Sample

1st row1-A-1
2nd row1-A-2
3rd row1-A-3
4th row1-A-4
5th row1-A-5
ValueCountFrequency (%)
1-a-1 1
 
0.2%
8-a-17 1
 
0.2%
8-a-26 1
 
0.2%
8-a-11 1
 
0.2%
8-a-12 1
 
0.2%
8-a-13 1
 
0.2%
8-a-14 1
 
0.2%
8-a-15 1
 
0.2%
8-a-16 1
 
0.2%
8-a-19 1
 
0.2%
Other values (601) 601
98.4%
2023-12-11T08:04:26.708938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1222
31.9%
A 558
14.6%
1 519
13.6%
5 265
 
6.9%
2 248
 
6.5%
3 238
 
6.2%
0 153
 
4.0%
8 136
 
3.6%
9 121
 
3.2%
4 117
 
3.1%
Other values (6) 249
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1990
52.0%
Dash Punctuation 1222
31.9%
Uppercase Letter 611
 
16.0%
Space Separator 3
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 519
26.1%
5 265
13.3%
2 248
12.5%
3 238
12.0%
0 153
 
7.7%
8 136
 
6.8%
9 121
 
6.1%
4 117
 
5.9%
6 98
 
4.9%
7 95
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
A 558
91.3%
B 42
 
6.9%
C 10
 
1.6%
D 1
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 1222
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3215
84.0%
Latin 611
 
16.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1222
38.0%
1 519
16.1%
5 265
 
8.2%
2 248
 
7.7%
3 238
 
7.4%
0 153
 
4.8%
8 136
 
4.2%
9 121
 
3.8%
4 117
 
3.6%
6 98
 
3.0%
Other values (2) 98
 
3.0%
Latin
ValueCountFrequency (%)
A 558
91.3%
B 42
 
6.9%
C 10
 
1.6%
D 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3826
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1222
31.9%
A 558
14.6%
1 519
13.6%
5 265
 
6.9%
2 248
 
6.5%
3 238
 
6.2%
0 153
 
4.0%
8 136
 
3.6%
9 121
 
3.2%
4 117
 
3.1%
Other values (6) 249
 
6.5%

수리위치(도로명주소)
Text

Distinct589
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
2023-12-11T08:04:26.986932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length15.109656
Min length7

Characters and Unicode

Total characters9232
Distinct characters103
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique573 ?
Unique (%)93.8%

Sample

1st row김해시 활천로 36번길 5
2nd row김해시 김해대로 2633
3rd row김해시 삼안로 77번길 28
4th row김해시 분성로 579번길 49
5th row김해시 김해대로 2783
ValueCountFrequency (%)
김해시 601
28.9%
김해대로 63
 
3.0%
상동면 46
 
2.2%
생림면 40
 
1.9%
대동면 34
 
1.6%
분성로 33
 
1.6%
삼안로 28
 
1.3%
인제로 21
 
1.0%
삼계로 21
 
1.0%
가락로 21
 
1.0%
Other values (641) 1170
56.3%
2023-12-11T08:04:27.525481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1650
17.9%
해 701
 
7.6%
김 698
 
7.6%
시 603
 
6.5%
로 574
 
6.2%
1 504
 
5.5%
2 398
 
4.3%
길 324
 
3.5%
번 312
 
3.4%
3 279
 
3.0%
Other values (93) 3189
34.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4853
52.6%
Decimal Number 2576
27.9%
Space Separator 1650
 
17.9%
Dash Punctuation 143
 
1.5%
Open Punctuation 5
 
0.1%
Close Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
해 701
14.4%
김 698
14.4%
시 603
12.4%
로 574
11.8%
길 324
 
6.7%
번 312
 
6.4%
대 185
 
3.8%
동 179
 
3.7%
면 120
 
2.5%
상 65
 
1.3%
Other values (79) 1092
22.5%
Decimal Number
ValueCountFrequency (%)
1 504
19.6%
2 398
15.5%
3 279
10.8%
4 243
9.4%
5 230
8.9%
7 222
8.6%
6 196
 
7.6%
0 186
 
7.2%
9 167
 
6.5%
8 151
 
5.9%
Space Separator
ValueCountFrequency (%)
1650
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 143
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4853
52.6%
Common 4379
47.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
해 701
14.4%
김 698
14.4%
시 603
12.4%
로 574
11.8%
길 324
 
6.7%
번 312
 
6.4%
대 185
 
3.8%
동 179
 
3.7%
면 120
 
2.5%
상 65
 
1.3%
Other values (79) 1092
22.5%
Common
ValueCountFrequency (%)
1650
37.7%
1 504
 
11.5%
2 398
 
9.1%
3 279
 
6.4%
4 243
 
5.5%
5 230
 
5.3%
7 222
 
5.1%
6 196
 
4.5%
0 186
 
4.2%
9 167
 
3.8%
Other values (4) 304
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4853
52.6%
ASCII 4379
47.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1650
37.7%
1 504
 
11.5%
2 398
 
9.1%
3 279
 
6.4%
4 243
 
5.5%
5 230
 
5.3%
7 222
 
5.1%
6 196
 
4.5%
0 186
 
4.2%
9 167
 
3.8%
Other values (4) 304
 
6.9%
Hangul
ValueCountFrequency (%)
해 701
14.4%
김 698
14.4%
시 603
12.4%
로 574
11.8%
길 324
 
6.7%
번 312
 
6.4%
대 185
 
3.8%
동 179
 
3.7%
면 120
 
2.5%
상 65
 
1.3%
Other values (79) 1092
22.5%

읍면동
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
삼계동
55 
상동면
46 
어방동
42 
생림면
41 
대동면
39 
Other values (21)
388 

Length

Max length3
Median length3
Mean length2.797054
Min length2

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row삼정동
2nd row안동
3rd row안동
4th row어방동
5th row불암동

Common Values

ValueCountFrequency (%)
삼계동 55
 
9.0%
상동면 46
 
7.5%
어방동 42
 
6.9%
생림면 41
 
6.7%
대동면 39
 
6.4%
내동 38
 
6.2%
외동 36
 
5.9%
삼방동 34
 
5.6%
동상동 30
 
4.9%
풍유동 29
 
4.7%
Other values (16) 221
36.2%

Length

2023-12-11T08:04:27.664188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
삼계동 55
 
9.0%
상동면 46
 
7.5%
어방동 42
 
6.9%
생림면 41
 
6.7%
대동면 39
 
6.4%
내동 38
 
6.2%
외동 37
 
6.1%
삼방동 34
 
5.6%
동상동 30
 
4.9%
풍유동 29
 
4.7%
Other values (15) 220
36.0%

주변 대상물
Text

Distinct567
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
2023-12-11T08:04:27.922294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length26
Mean length9.3338789
Min length2

Characters and Unicode

Total characters5703
Distinct characters442
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique549 ?
Unique (%)89.9%

Sample

1st row파도횟집
2nd row대현정비 앞
3rd row안동 농협 앞
4th row명성정공 옆 삼거리
5th row불암파출소 앞
ValueCountFrequency (%)
앞 281
 
21.5%
옆 52
 
4.0%
내 33
 
2.5%
맞은편 26
 
2.0%
서김해일반산업단지 20
 
1.5%
입구 16
 
1.2%
삼거리 14
 
1.1%
건물 13
 
1.0%
버스정류장 12
 
0.9%
사거리 11
 
0.8%
Other values (690) 827
63.4%
2023-12-11T08:04:28.370452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
697
 
12.2%
앞 294
 
5.2%
( 134
 
2.3%
) 133
 
2.3%
물 120
 
2.1%
건 120
 
2.1%
동 94
 
1.6%
마 86
 
1.5%
해 75
 
1.3%
원 72
 
1.3%
Other values (432) 3878
68.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4567
80.1%
Space Separator 697
 
12.2%
Open Punctuation 134
 
2.3%
Close Punctuation 133
 
2.3%
Decimal Number 95
 
1.7%
Uppercase Letter 55
 
1.0%
Lowercase Letter 15
 
0.3%
Other Punctuation 5
 
0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 294
 
6.4%
물 120
 
2.6%
건 120
 
2.6%
동 94
 
2.1%
마 86
 
1.9%
해 75
 
1.6%
원 72
 
1.6%
김 67
 
1.5%
지 63
 
1.4%
트 60
 
1.3%
Other values (387) 3516
77.0%
Uppercase Letter
ValueCountFrequency (%)
C 9
16.4%
P 6
 
10.9%
M 6
 
10.9%
G 4
 
7.3%
R 3
 
5.5%
S 3
 
5.5%
U 3
 
5.5%
D 2
 
3.6%
F 2
 
3.6%
T 2
 
3.6%
Other values (10) 15
27.3%
Decimal Number
ValueCountFrequency (%)
1 32
33.7%
0 17
17.9%
5 11
 
11.6%
2 10
 
10.5%
9 8
 
8.4%
3 7
 
7.4%
4 5
 
5.3%
7 2
 
2.1%
8 2
 
2.1%
6 1
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
m 5
33.3%
w 2
 
13.3%
b 2
 
13.3%
s 2
 
13.3%
k 1
 
6.7%
c 1
 
6.7%
o 1
 
6.7%
t 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 2
40.0%
. 2
40.0%
& 1
20.0%
Space Separator
ValueCountFrequency (%)
697
100.0%
Open Punctuation
ValueCountFrequency (%)
( 134
100.0%
Close Punctuation
ValueCountFrequency (%)
) 133
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4569
80.1%
Common 1064
 
18.7%
Latin 70
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 294
 
6.4%
물 120
 
2.6%
건 120
 
2.6%
동 94
 
2.1%
마 86
 
1.9%
해 75
 
1.6%
원 72
 
1.6%
김 67
 
1.5%
지 63
 
1.4%
트 60
 
1.3%
Other values (388) 3518
77.0%
Latin
ValueCountFrequency (%)
C 9
 
12.9%
P 6
 
8.6%
M 6
 
8.6%
m 5
 
7.1%
G 4
 
5.7%
R 3
 
4.3%
S 3
 
4.3%
U 3
 
4.3%
D 2
 
2.9%
F 2
 
2.9%
Other values (18) 27
38.6%
Common
ValueCountFrequency (%)
697
65.5%
( 134
 
12.6%
) 133
 
12.5%
1 32
 
3.0%
0 17
 
1.6%
5 11
 
1.0%
2 10
 
0.9%
9 8
 
0.8%
3 7
 
0.7%
4 5
 
0.5%
Other values (6) 10
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4567
80.1%
ASCII 1134
 
19.9%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
697
61.5%
( 134
 
11.8%
) 133
 
11.7%
1 32
 
2.8%
0 17
 
1.5%
5 11
 
1.0%
2 10
 
0.9%
C 9
 
0.8%
9 8
 
0.7%
3 7
 
0.6%
Other values (34) 76
 
6.7%
Hangul
ValueCountFrequency (%)
앞 294
 
6.4%
물 120
 
2.6%
건 120
 
2.6%
동 94
 
2.1%
마 86
 
1.9%
해 75
 
1.6%
원 72
 
1.6%
김 67
 
1.5%
지 63
 
1.4%
트 60
 
1.3%
Other values (387) 3516
77.0%
None
ValueCountFrequency (%)
㈜ 2
100.0%

화재없는 안심마을지정 여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
미지정
606 
지정
 
5

Length

Max length3
Median length3
Mean length2.9918167
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미지정 606
99.2%
지정 5
 
0.8%

Length

2023-12-11T08:04:28.532601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:04:28.632051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미지정 606
99.2%
지정 5
 
0.8%

수리형식
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
지상
558 
일반지하
 
42
저수조
 
10
급수탑
 
1

Length

Max length4
Median length2
Mean length2.1554828
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
지상 558
91.3%
일반지하 42
 
6.9%
저수조 10
 
1.6%
급수탑 1
 
0.2%

Length

2023-12-11T08:04:28.755933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:04:28.905088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지상 558
91.3%
일반지하 42
 
6.9%
저수조 10
 
1.6%
급수탑 1
 
0.2%

설치년도
Real number (ℝ)

SKEWED 

Distinct27
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2038.5728
Minimum1987
Maximum21019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-11T08:04:29.006249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1987
5-th percentile1995
Q12001
median2007
Q32015
95-th percentile2021
Maximum21019
Range19032
Interquartile range (IQR)14

Descriptive statistics

Standard deviation769.18147
Coefficient of variation (CV)0.37731371
Kurtosis610.81872
Mean2038.5728
Median Absolute Deviation (MAD)8
Skewness24.712922
Sum1245568
Variance591640.13
MonotonicityNot monotonic
2023-12-11T08:04:29.155572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2015 87
14.2%
1995 85
13.9%
2001 55
 
9.0%
1997 42
 
6.9%
2014 33
 
5.4%
2007 32
 
5.2%
2021 32
 
5.2%
2019 25
 
4.1%
2003 22
 
3.6%
2006 19
 
3.1%
Other values (17) 179
29.3%
ValueCountFrequency (%)
1987 13
 
2.1%
1995 85
13.9%
1997 42
6.9%
1998 11
 
1.8%
2001 55
9.0%
2002 17
 
2.8%
2003 22
 
3.6%
2004 14
 
2.3%
2005 4
 
0.7%
2006 19
 
3.1%
ValueCountFrequency (%)
21019 1
 
0.2%
2023 7
 
1.1%
2022 13
 
2.1%
2021 32
 
5.2%
2020 10
 
1.6%
2019 25
 
4.1%
2018 13
 
2.1%
2017 18
 
2.9%
2016 6
 
1.0%
2015 87
14.2%

유지관리 주체
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
시군
611 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시군 611
100.0%

Length

2023-12-11T08:04:29.287631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:04:29.390401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시군 611
100.0%

센터와 용수와의 거리(킬로미터)
Real number (ℝ)

ZEROS 

Distinct82
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5168576
Minimum0
Maximum14
Zeros9
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-11T08:04:29.491631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.9
median1.7
Q33.4
95-th percentile6.8
Maximum14
Range14
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.2070143
Coefficient of variation (CV)0.87689279
Kurtosis3.4322332
Mean2.5168576
Median Absolute Deviation (MAD)1.1
Skewness1.6296908
Sum1537.8
Variance4.8709121
MonotonicityNot monotonic
2023-12-11T08:04:29.625000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 28
 
4.6%
1.0 25
 
4.1%
0.7 25
 
4.1%
4.7 24
 
3.9%
1.7 22
 
3.6%
0.6 21
 
3.4%
0.9 20
 
3.3%
0.5 19
 
3.1%
1.4 18
 
2.9%
0.4 18
 
2.9%
Other values (72) 391
64.0%
ValueCountFrequency (%)
0.0 9
 
1.5%
0.1 8
 
1.3%
0.2 5
 
0.8%
0.3 13
2.1%
0.4 18
2.9%
0.5 19
3.1%
0.6 21
3.4%
0.7 25
4.1%
0.8 28
4.6%
0.9 20
3.3%
ValueCountFrequency (%)
14.0 1
 
0.2%
13.0 1
 
0.2%
12.0 3
0.5%
11.0 1
 
0.2%
10.0 1
 
0.2%
9.4 1
 
0.2%
9.1 1
 
0.2%
9.0 1
 
0.2%
8.9 1
 
0.2%
8.3 1
 
0.2%

Interactions

2023-12-11T08:04:23.952424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:04:23.252824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:04:23.619874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:04:24.065511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:04:23.368948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:04:23.736191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:04:24.190700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:04:23.485849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:04:23.834988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:04:29.729265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번센터명읍면동화재없는 안심마을지정 여부수리형식설치년도센터와 용수와의 거리(킬로미터)
연번1.0000.9300.9380.3210.3740.0000.617
센터명0.9301.0000.9990.3130.1580.0940.523
읍면동0.9380.9991.0000.3640.3250.0000.787
화재없는 안심마을지정 여부0.3210.3130.3641.0000.0000.0000.000
수리형식0.3740.1580.3250.0001.0000.0000.000
설치년도0.0000.0940.0000.0000.0001.0000.051
센터와 용수와의 거리(킬로미터)0.6170.5230.7870.0000.0000.0511.000
2023-12-11T08:04:29.832000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수리형식읍면동센터명화재없는 안심마을지정 여부
수리형식1.0000.1720.1090.000
읍면동0.1721.0000.9830.283
센터명0.1090.9831.0000.334
화재없는 안심마을지정 여부0.0000.2830.3341.000
2023-12-11T08:04:29.922749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치년도센터와 용수와의 거리(킬로미터)센터명읍면동화재없는 안심마을지정 여부수리형식
연번1.000-0.1460.2030.8190.7030.2440.224
설치년도-0.1461.0000.2340.1020.0000.0000.000
센터와 용수와의 거리(킬로미터)0.2030.2341.0000.2950.4170.0000.000
센터명0.8190.1020.2951.0000.9830.3340.109
읍면동0.7030.0000.4170.9831.0000.2830.172
화재없는 안심마을지정 여부0.2440.0000.0000.3340.2831.0000.000
수리형식0.2240.0000.0000.1090.1720.0001.000

Missing values

2023-12-11T08:04:24.341407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:04:24.544982image/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

연번관서명센터명자체관리번호수리위치(도로명주소)읍면동주변 대상물화재없는 안심마을지정 여부수리형식설치년도유지관리 주체센터와 용수와의 거리(킬로미터)
01김해동부삼정1-A-1김해시 활천로 36번길 5삼정동파도횟집미지정지상2016시군1.0
12김해동부삼정1-A-2김해시 김해대로 2633안동대현정비 앞미지정지상2014시군1.7
23김해동부삼정1-A-3김해시 삼안로 77번길 28안동안동 농협 앞미지정지상2014시군2.5
34김해동부삼정1-A-4김해시 분성로 579번길 49어방동명성정공 옆 삼거리미지정지상2015시군1.9
45김해동부삼정1-A-5김해시 김해대로 2783불암동불암파출소 앞미지정지상2015시군3.3
56김해동부삼정1-A-6김해시 삼안로 24번길 7지내동활천초 앞미지정지상2014시군2.3
67김해동부삼정1-A-7김해시 김해대로 2733불암동sk대지주유소 앞미지정지상2015시군3.1
78김해동부삼정1-A-8김해시 분성로 546어방동성경 1급 종합자동차 정비 앞미지정지상2015시군1.1
89김해동부삼정1-A-9김해시 활천로 202번길 58어방동동원카센터 앞미지정지상2015시군1.4
910김해동부삼정1-A-10김해시 활천로208번길 72어방동화목궁전빌라 앞미지정지상2015시군1.3
연번관서명센터명자체관리번호수리위치(도로명주소)읍면동주변 대상물화재없는 안심마을지정 여부수리형식설치년도유지관리 주체센터와 용수와의 거리(킬로미터)
601602김해동부대동9-A-30김해시 대동면 동북로 401대동면고암마을 버스정류장 옆미지정지상2019시군10.0
602603김해동부대동9-A-31김해시 대동면 대동로 984대동면대감초등학교 버스정류장 맞은편 창고건물 앞미지정지상2003시군4.8
603604김해동부대동9-A-32김해시 대동면 대동로962번길 113대동면청송산업기계 뒤편 단독주택 앞미지정지상2003시군4.1
604605김해동부대동9-A-33김해시 대동면 동남로41번길 138대동면일레븐풋살장 옆미지정지상2020시군1.3
605606김해동부대동9-A-34김해시 대동면 동남로41번길 95대동면신정교 버스정류장 옆미지정지상2020시군0.8
606607김해동부대동9-A-35김해시 대동로983번길 54대동면울타리식품 앞지정지상2021시군6.0
607608김해동부대동9-A-36김해시 대동로417번길 130-1대동면시례경로당 앞미지정지상2021시군4.5
608609김해동부대동9-A-37김해시 대동로962번길 63대동면감천교회 주차장미지정지상2021시군5.8
609610김해동부대동9-A-38동북로166번길 26(덕산리 298-5)대동면하나이프(석면해체) 공장옆미지정지상2022시군7.8
610611김해동부대동9-A-39동북로 229(덕산리 160-2)대동면신촌마을 버스정류장 뒤미지정지상2022시군8.3