Overview

Dataset statistics

Number of variables9
Number of observations96
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 KiB
Average record size in memory76.4 B

Variable types

Categorical3
Text4
Numeric2

Dataset

Description119안전센터 주소 및 관할구역 정보제공
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15065326

Alerts

집계년도 has constant value ""Constant
소방서별 is highly overall correlated with WSG84경도 and 2 other fieldsHigh correlation
시군명 is highly overall correlated with WSG84경도 and 2 other fieldsHigh correlation
WSG84경도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
WSG84위도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 22:44:02.285542
Analysis finished2023-12-10 22:44:03.510134
Duration1.22 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
2020
96 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2020 96
100.0%

Length

2023-12-11T07:44:03.559434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:44:03.629518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 96
100.0%

시군명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Memory size900.0 B
김해시
15 
양산시
8 
거제시
8 
진주시
7 
밀양시
7 
Other values (12)
51 

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 (%)
김해시 15
15.6%
양산시 8
 
8.3%
거제시 8
 
8.3%
진주시 7
 
7.3%
밀양시 7
 
7.3%
통영시 6
 
6.2%
사천시 5
 
5.2%
창녕군 5
 
5.2%
함안군 4
 
4.2%
합천군 4
 
4.2%
Other values (7) 27
28.1%

Length

2023-12-11T07:44:03.700181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
김해시 15
15.6%
거제시 8
 
8.3%
양산시 8
 
8.3%
진주시 7
 
7.3%
밀양시 7
 
7.3%
통영시 6
 
6.2%
사천시 5
 
5.2%
창녕군 5
 
5.2%
하동군 4
 
4.2%
거창군 4
 
4.2%
Other values (7) 27
28.1%

소방서별
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size900.0 B
김해동부소방서
8 
양산소방서
8 
거제소방서
8 
진주소방서
7 
김해서부소방서
7 
Other values (13)
58 

Length

Max length7
Median length5
Mean length5.3125
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
김해동부소방서 8
 
8.3%
양산소방서 8
 
8.3%
거제소방서 8
 
8.3%
진주소방서 7
 
7.3%
김해서부소방서 7
 
7.3%
밀양소방서 7
 
7.3%
통영소방서 6
 
6.2%
사천소방서 5
 
5.2%
창녕소방서 5
 
5.2%
함안소방서 4
 
4.2%
Other values (8) 31
32.3%

Length

2023-12-11T07:44:04.009886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
김해동부소방서 8
 
8.3%
거제소방서 8
 
8.3%
양산소방서 8
 
8.3%
진주소방서 7
 
7.3%
김해서부소방서 7
 
7.3%
밀양소방서 7
 
7.3%
통영소방서 6
 
6.2%
사천소방서 5
 
5.2%
창녕소방서 5
 
5.2%
하동소방서 4
 
4.2%
Other values (8) 31
32.3%

명 칭
Text

Distinct76
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023-12-11T07:44:04.182592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.7604167
Min length3

Characters and Unicode

Total characters745
Distinct characters86
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

Unique72 ?
Unique (%)75.0%

Sample

1st row구조대
2nd row상대119안전센터
3rd row중앙119안전센터
4th row천전119안전센터
5th row평거119안전센터
ValueCountFrequency (%)
구조대 18
 
18.8%
산악구조대 2
 
2.1%
북부119안전센터 2
 
2.1%
중앙119안전센터 2
 
2.1%
합천119안전센터 1
 
1.0%
영산119안전센터 1
 
1.0%
남지119안전센터 1
 
1.0%
창녕119안전센터 1
 
1.0%
군북119안전센터 1
 
1.0%
칠원119안전센터 1
 
1.0%
Other values (66) 66
68.8%
2023-12-11T07:44:04.482752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 150
20.1%
전 77
10.3%
안 77
10.3%
센 75
10.1%
터 75
10.1%
9 75
10.1%
대 24
 
3.2%
조 22
 
3.0%
구 20
 
2.7%
동 9
 
1.2%
Other values (76) 141
18.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 520
69.8%
Decimal Number 225
30.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 77
14.8%
안 77
14.8%
센 75
14.4%
터 75
14.4%
대 24
 
4.6%
조 22
 
4.2%
구 20
 
3.8%
동 9
 
1.7%
산 8
 
1.5%
부 6
 
1.2%
Other values (74) 127
24.4%
Decimal Number
ValueCountFrequency (%)
1 150
66.7%
9 75
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 520
69.8%
Common 225
30.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 77
14.8%
안 77
14.8%
센 75
14.4%
터 75
14.4%
대 24
 
4.6%
조 22
 
4.2%
구 20
 
3.8%
동 9
 
1.7%
산 8
 
1.5%
부 6
 
1.2%
Other values (74) 127
24.4%
Common
ValueCountFrequency (%)
1 150
66.7%
9 75
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 520
69.8%
ASCII 225
30.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 150
66.7%
9 75
33.3%
Hangul
ValueCountFrequency (%)
전 77
14.8%
안 77
14.8%
센 75
14.4%
터 75
14.4%
대 24
 
4.6%
조 22
 
4.2%
구 20
 
3.8%
동 9
 
1.7%
산 8
 
1.5%
부 6
 
1.2%
Other values (74) 127
24.4%

소재지도로명주소
Text

Distinct79
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023-12-11T07:44:04.786032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length14.604167
Min length9

Characters and Unicode

Total characters1402
Distinct characters132
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

Unique62 ?
Unique (%)64.6%

Sample

1st row진주시 동진로 249
2nd row진주시 동진로 249
3rd row진주시 북장대로64번길 14
4th row진주시 강남로167번길 16
5th row진주시 순환로 565
ValueCountFrequency (%)
김해시 15
 
4.2%
거제시 8
 
2.3%
양산시 8
 
2.3%
진주시 7
 
2.0%
밀양시 7
 
2.0%
통영시 6
 
1.7%
사천시 5
 
1.4%
창녕군 5
 
1.4%
남해군 4
 
1.1%
산청군 4
 
1.1%
Other values (211) 286
80.6%
2023-12-11T07:44:05.199760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
259
 
18.5%
로 79
 
5.6%
시 58
 
4.1%
1 53
 
3.8%
면 42
 
3.0%
군 41
 
2.9%
2 38
 
2.7%
7 36
 
2.6%
6 32
 
2.3%
5 28
 
2.0%
Other values (122) 736
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 834
59.5%
Decimal Number 293
 
20.9%
Space Separator 259
 
18.5%
Dash Punctuation 16
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 79
 
9.5%
시 58
 
7.0%
면 42
 
5.0%
군 41
 
4.9%
양 28
 
3.4%
해 27
 
3.2%
산 26
 
3.1%
읍 24
 
2.9%
거 21
 
2.5%
남 20
 
2.4%
Other values (110) 468
56.1%
Decimal Number
ValueCountFrequency (%)
1 53
18.1%
2 38
13.0%
7 36
12.3%
6 32
10.9%
5 28
9.6%
4 26
8.9%
3 24
8.2%
9 24
8.2%
0 17
 
5.8%
8 15
 
5.1%
Space Separator
ValueCountFrequency (%)
259
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 834
59.5%
Common 568
40.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 79
 
9.5%
시 58
 
7.0%
면 42
 
5.0%
군 41
 
4.9%
양 28
 
3.4%
해 27
 
3.2%
산 26
 
3.1%
읍 24
 
2.9%
거 21
 
2.5%
남 20
 
2.4%
Other values (110) 468
56.1%
Common
ValueCountFrequency (%)
259
45.6%
1 53
 
9.3%
2 38
 
6.7%
7 36
 
6.3%
6 32
 
5.6%
5 28
 
4.9%
4 26
 
4.6%
3 24
 
4.2%
9 24
 
4.2%
0 17
 
3.0%
Other values (2) 31
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 834
59.5%
ASCII 568
40.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
259
45.6%
1 53
 
9.3%
2 38
 
6.7%
7 36
 
6.3%
6 32
 
5.6%
5 28
 
4.9%
4 26
 
4.6%
3 24
 
4.2%
9 24
 
4.2%
0 17
 
3.0%
Other values (2) 31
 
5.5%
Hangul
ValueCountFrequency (%)
로 79
 
9.5%
시 58
 
7.0%
면 42
 
5.0%
군 41
 
4.9%
양 28
 
3.4%
해 27
 
3.2%
산 26
 
3.1%
읍 24
 
2.9%
거 21
 
2.5%
남 20
 
2.4%
Other values (110) 468
56.1%

관할구역
Text

Distinct94
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023-12-11T07:44:05.361162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length29
Mean length18.010417
Min length10

Characters and Unicode

Total characters1729
Distinct characters158
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)95.8%

Sample

1st row진주소방서 관할구역 일원
2nd row진주시 상대1·상대2·상평·하대1·하대2동 일원
3rd row진주시 성북동·중앙동·상봉동 일원
4th row진주시 천전동·가호동, 내동,정촌면 일원
5th row진주시 평거·이현·신안·판문동, 명석·수곡·대평면 일원
ValueCountFrequency (%)
일원 92
27.5%
구역 19
 
5.7%
관할 19
 
5.7%
김해시 13
 
3.9%
거제시 7
 
2.1%
양산시 7
 
2.1%
밀양시 6
 
1.8%
진주시 6
 
1.8%
창녕군 4
 
1.2%
통영시 4
 
1.2%
Other values (137) 158
47.2%
2023-12-11T07:44:05.645497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
240
 
13.9%
· 158
 
9.1%
일 100
 
5.8%
원 100
 
5.8%
면 57
 
3.3%
동 57
 
3.3%
시 50
 
2.9%
서 33
 
1.9%
산 31
 
1.8%
, 30
 
1.7%
Other values (148) 873
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1275
73.7%
Space Separator 240
 
13.9%
Other Punctuation 189
 
10.9%
Decimal Number 9
 
0.5%
Close Punctuation 8
 
0.5%
Open Punctuation 8
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
일 100
 
7.8%
원 100
 
7.8%
면 57
 
4.5%
동 57
 
4.5%
시 50
 
3.9%
서 33
 
2.6%
산 31
 
2.4%
군 29
 
2.3%
양 28
 
2.2%
천 24
 
1.9%
Other values (139) 766
60.1%
Other Punctuation
ValueCountFrequency (%)
· 158
83.6%
, 30
 
15.9%
? 1
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 4
44.4%
2 4
44.4%
3 1
 
11.1%
Space Separator
ValueCountFrequency (%)
240
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1275
73.7%
Common 454
 
26.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
일 100
 
7.8%
원 100
 
7.8%
면 57
 
4.5%
동 57
 
4.5%
시 50
 
3.9%
서 33
 
2.6%
산 31
 
2.4%
군 29
 
2.3%
양 28
 
2.2%
천 24
 
1.9%
Other values (139) 766
60.1%
Common
ValueCountFrequency (%)
240
52.9%
· 158
34.8%
, 30
 
6.6%
) 8
 
1.8%
( 8
 
1.8%
1 4
 
0.9%
2 4
 
0.9%
3 1
 
0.2%
? 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1275
73.7%
ASCII 296
 
17.1%
None 158
 
9.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240
81.1%
, 30
 
10.1%
) 8
 
2.7%
( 8
 
2.7%
1 4
 
1.4%
2 4
 
1.4%
3 1
 
0.3%
? 1
 
0.3%
None
ValueCountFrequency (%)
· 158
100.0%
Hangul
ValueCountFrequency (%)
일 100
 
7.8%
원 100
 
7.8%
면 57
 
4.5%
동 57
 
4.5%
시 50
 
3.9%
서 33
 
2.6%
산 31
 
2.4%
군 29
 
2.3%
양 28
 
2.2%
천 24
 
1.9%
Other values (139) 766
60.1%

소재지지번주소
Text

Distinct79
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023-12-11T07:44:05.922755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length18.625
Min length14

Characters and Unicode

Total characters1788
Distinct characters131
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

Unique62 ?
Unique (%)64.6%

Sample

1st row경남 진주시 상대동 314-7
2nd row경남 진주시 상대동 314-7
3rd row경남 진주시 봉곡동 440-28
4th row경남 진주시 칠암동 525-6
5th row경남 진주시 평거동 138-6
ValueCountFrequency (%)
경남 96
 
21.2%
김해시 15
 
3.3%
거제시 8
 
1.8%
양산시 8
 
1.8%
밀양시 7
 
1.5%
진주시 7
 
1.5%
통영시 6
 
1.3%
사천시 5
 
1.1%
창녕군 5
 
1.1%
산청군 4
 
0.9%
Other values (220) 292
64.5%
2023-12-11T07:44:06.327140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
357
20.0%
남 108
 
6.0%
경 96
 
5.4%
1 87
 
4.9%
- 78
 
4.4%
리 67
 
3.7%
시 57
 
3.2%
6 45
 
2.5%
동 43
 
2.4%
면 42
 
2.3%
Other values (121) 808
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 963
53.9%
Decimal Number 390
21.8%
Space Separator 357
 
20.0%
Dash Punctuation 78
 
4.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 108
 
11.2%
경 96
 
10.0%
리 67
 
7.0%
시 57
 
5.9%
동 43
 
4.5%
면 42
 
4.4%
군 42
 
4.4%
읍 25
 
2.6%
산 24
 
2.5%
양 22
 
2.3%
Other values (109) 437
45.4%
Decimal Number
ValueCountFrequency (%)
1 87
22.3%
6 45
11.5%
2 39
10.0%
4 33
 
8.5%
5 33
 
8.5%
8 33
 
8.5%
3 32
 
8.2%
9 32
 
8.2%
7 31
 
7.9%
0 25
 
6.4%
Space Separator
ValueCountFrequency (%)
357
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 963
53.9%
Common 825
46.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 108
 
11.2%
경 96
 
10.0%
리 67
 
7.0%
시 57
 
5.9%
동 43
 
4.5%
면 42
 
4.4%
군 42
 
4.4%
읍 25
 
2.6%
산 24
 
2.5%
양 22
 
2.3%
Other values (109) 437
45.4%
Common
ValueCountFrequency (%)
357
43.3%
1 87
 
10.5%
- 78
 
9.5%
6 45
 
5.5%
2 39
 
4.7%
4 33
 
4.0%
5 33
 
4.0%
8 33
 
4.0%
3 32
 
3.9%
9 32
 
3.9%
Other values (2) 56
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 963
53.9%
ASCII 825
46.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
357
43.3%
1 87
 
10.5%
- 78
 
9.5%
6 45
 
5.5%
2 39
 
4.7%
4 33
 
4.0%
5 33
 
4.0%
8 33
 
4.0%
3 32
 
3.9%
9 32
 
3.9%
Other values (2) 56
 
6.8%
Hangul
ValueCountFrequency (%)
남 108
 
11.2%
경 96
 
10.0%
리 67
 
7.0%
시 57
 
5.9%
동 43
 
4.5%
면 42
 
4.4%
군 42
 
4.4%
읍 25
 
2.6%
산 24
 
2.5%
양 22
 
2.3%
Other values (109) 437
45.4%

WSG84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.41358
Minimum127.66027
Maximum129.16249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-11T07:44:06.470461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.66027
5-th percentile127.79554
Q1128.0727
median128.41911
Q3128.75872
95-th percentile129.03098
Maximum129.16249
Range1.5022203
Interquartile range (IQR)0.68602078

Descriptive statistics

Standard deviation0.40482398
Coefficient of variation (CV)0.0031525013
Kurtosis-1.1625991
Mean128.41358
Median Absolute Deviation (MAD)0.3420765
Skewness-0.015174943
Sum12327.704
Variance0.16388245
MonotonicityNot monotonic
2023-12-11T07:44:06.619742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.1184736 2
 
2.1%
128.3999857 2
 
2.1%
127.8926877 2
 
2.1%
128.7587191 2
 
2.1%
128.7551343 2
 
2.1%
127.7955419 2
 
2.1%
127.8834566 2
 
2.1%
128.4791272 2
 
2.1%
128.9013972 2
 
2.1%
128.6861691 2
 
2.1%
Other values (69) 76
79.2%
ValueCountFrequency (%)
127.6602741 1
1.0%
127.7322935 1
1.0%
127.7420039 1
1.0%
127.7743318 1
1.0%
127.7955419 2
2.1%
127.8103654 1
1.0%
127.8326881 1
1.0%
127.8388954 1
1.0%
127.8834566 2
2.1%
127.8926877 2
2.1%
ValueCountFrequency (%)
129.1624944 1
1.0%
129.1468446 1
1.0%
129.080179 1
1.0%
129.0648291 1
1.0%
129.0369963 1
1.0%
129.0289811 1
1.0%
129.0100282 2
2.1%
128.9929025 1
1.0%
128.9378689 1
1.0%
128.9013972 2
2.1%

WSG84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.230143
Minimum34.730498
Maximum35.749241
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-11T07:44:06.747686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.730498
5-th percentile34.829589
Q134.996178
median35.25044
Q335.414976
95-th percentile35.641276
Maximum35.749241
Range1.0187436
Interquartile range (IQR)0.41879878

Descriptive statistics

Standard deviation0.2556048
Coefficient of variation (CV)0.0072552869
Kurtosis-0.87818759
Mean35.230143
Median Absolute Deviation (MAD)0.1932873
Skewness-0.10281085
Sum3382.0937
Variance0.065333812
MonotonicityNot monotonic
2023-12-11T07:44:06.856733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1803702 2
 
2.1%
35.28620172 2
 
2.1%
34.82958896 2
 
2.1%
35.49987961 2
 
2.1%
35.23795294 2
 
2.1%
34.97788233 2
 
2.1%
35.41524328 2
 
2.1%
35.5504487 2
 
2.1%
35.22859737 2
 
2.1%
34.89613984 2
 
2.1%
Other values (69) 76
79.2%
ValueCountFrequency (%)
34.7304976 1
1.0%
34.77537385 1
1.0%
34.82834044 2
2.1%
34.82958896 2
2.1%
34.82965586 1
1.0%
34.84228609 1
1.0%
34.84725867 1
1.0%
34.85927268 1
1.0%
34.87150948 1
1.0%
34.87579135 1
1.0%
ValueCountFrequency (%)
35.74924121 1
1.0%
35.71151852 1
1.0%
35.70659034 1
1.0%
35.67761185 2
2.1%
35.62916465 1
1.0%
35.5728083 2
2.1%
35.55689961 1
1.0%
35.5504487 2
2.1%
35.52385437 1
1.0%
35.51069413 1
1.0%

Interactions

2023-12-11T07:44:03.204326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:44:03.049693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:44:03.277861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:44:03.128101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:44:06.930734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명소방서별명 칭소재지도로명주소관할구역소재지지번주소WSG84경도WSG84위도
시군명1.0001.0000.0001.0001.0001.0000.9410.926
소방서별1.0001.0000.0001.0001.0001.0000.9560.928
명 칭0.0000.0001.0000.9900.9990.9900.0000.000
소재지도로명주소1.0001.0000.9901.0000.9881.0001.0001.000
관할구역1.0001.0000.9990.9881.0000.9881.0000.923
소재지지번주소1.0001.0000.9901.0000.9881.0001.0001.000
WSG84경도0.9410.9560.0001.0001.0001.0001.0000.741
WSG84위도0.9260.9280.0001.0000.9231.0000.7411.000
2023-12-11T07:44:07.033596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소방서별시군명
소방서별1.0000.994
시군명0.9941.000
2023-12-11T07:44:07.116274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WSG84경도WSG84위도시군명소방서별
WSG84경도1.0000.0750.7240.758
WSG84위도0.0751.0000.6800.672
시군명0.7240.6801.0000.994
소방서별0.7580.6720.9941.000

Missing values

2023-12-11T07:44:03.365474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:44:03.469044image/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

집계년도시군명소방서별명 칭소재지도로명주소관할구역소재지지번주소WSG84경도WSG84위도
02020진주시진주소방서구조대진주시 동진로 249진주소방서 관할구역 일원경남 진주시 상대동 314-7128.11847435.18037
12020진주시진주소방서상대119안전센터진주시 동진로 249진주시 상대1·상대2·상평·하대1·하대2동 일원경남 진주시 상대동 314-7128.11847435.18037
22020진주시진주소방서중앙119안전센터진주시 북장대로64번길 14진주시 성북동·중앙동·상봉동 일원경남 진주시 봉곡동 440-28128.07456635.195894
32020진주시진주소방서천전119안전센터진주시 강남로167번길 16진주시 천전동·가호동, 내동,정촌면 일원경남 진주시 칠암동 525-6128.09408635.182855
42020진주시진주소방서평거119안전센터진주시 순환로 565진주시 평거·이현·신안·판문동, 명석·수곡·대평면 일원경남 진주시 평거동 138-6128.05879935.174113
52020진주시진주소방서문산119안전센터진주시 문산읍 월아산로 979진주시 충무공동, 문산읍, 금곡·지수·일반성·이반성·사봉·진성면 일원경남 진주시 문산읍 삼곡리 1033-2128.16359935.158779
62020진주시진주소방서금산119안전센터진주시 금산면 중천리 585-2진주시 초장동, 금산·집현·미천·대곡면 일원경남 진주시 금산면 중천리 585-2128.13394135.211645
72020통영시통영소방서구조대통영시 광도면 죽림4로 49통영소방서 관할 구역 일원경남 통영시 광도면 죽림리 1573-1128.41901834.883531
82020통영시통영소방서소방정대통영시 도남로 257한산·사량·욕지면(욕지도 제외), 경상남도 남해안 일원경남 통영시 도남동 199-6128.43225234.82834
92020통영시통영소방서죽림119안전센터통영시 광도면 죽림4로 49통영시 도산·용남·광도면 일원경남 통영시 광도면 죽림리 1573-1128.41901834.883531
집계년도시군명소방서별명 칭소재지도로명주소관할구역소재지지번주소WSG84경도WSG84위도
862020함양군함양소방서안의119안전센터함양군 안의면 금성길 19함양군 안의·서하·서상·지곡·수동면 일원경남 함양군 안의면 당본리 175-5127.81036535.629165
872020함양군함양소방서산악구조대함양군 마천면 마천삼정로 28함양소방서 관할 구역 일원경남 함양군 마천면 군자리 97-2127.66027435.394441
882020거창군거창소방서구조대거창군 거창읍 거함대로 3324거창소방서 관할 구역 일원경남 거창군 거창읍 대평리 1359-1127.92345335.677612
892020거창군거창소방서대평119안전센터거창군 거창읍 거함대로 3324거창군 거창읍, 신원·남상·주상·웅양면 일원경남 거창군 거창읍 대평리 1359-1127.92345335.677612
902020거창군거창소방서위천119안전센터거창군 위천면 원학길 321거창군 위천·마리·북상·고제면 일원경남 거창군 위천면 장기리 509127.83268835.749241
912020거창군거창소방서가조119안전센터거창군 가조면 마상3길 32거창군 남하·가조·가북면 일원경남 거창군 가조면 마상리 467-6128.016535.711519
922020합천군합천소방서구조대합천군 합천읍 인덕로 2453합천소방서 관할 구역 일원경남 합천군 합천읍 서산리 860128.14853835.572808
932020합천군합천소방서합천119안전센터합천군 합천읍 인덕로 2453합천군 합천읍, 율곡·대양·대병·용주·삼가·가회·쌍백면 일원경남 합천군 합천읍 서산리 860128.14853835.572808
942020합천군합천소방서초계119안전센터합천군 초계면 양동로 6-16합천군 초계·적중·청덕·쌍책· 덕곡면 일원경남 합천군 초계면 초계리 409-7128.2616535.5569
952020합천군합천소방서북부119안전센터합천군 야로면 시장길 6-5합천군 묘산·봉산·가야·야로면 일원경남 합천군 야로면 구정리 270-8128.16853835.70659