Overview

Dataset statistics

Number of variables5
Number of observations291
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.8 KiB
Average record size in memory41.5 B

Variable types

Categorical1
Text3
Numeric1

Dataset

Description의용소방대명, 설치일, 사무실 위치, 대원수 정보 제공
Author강원도
URLhttps://www.data.go.kr/data/15056068/fileData.do

Reproduction

Analysis started2023-12-12 22:21:46.786108
Analysis finished2023-12-12 22:21:47.381238
Duration0.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct18
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
홍천군
25 
춘천시
24 
강릉시
23 
영월군
22 
삼척시
22 
Other values (13)
175 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
홍천군 25
 
8.6%
춘천시 24
 
8.2%
강릉시 23
 
7.9%
영월군 22
 
7.6%
삼척시 22
 
7.6%
원주시 21
 
7.2%
정선군 19
 
6.5%
횡성군 19
 
6.5%
철원군 18
 
6.2%
인제군 16
 
5.5%
Other values (8) 82
28.2%

Length

2023-12-13T07:21:47.457995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
홍천군 25
 
8.6%
춘천시 24
 
8.2%
강릉시 23
 
7.9%
영월군 22
 
7.6%
삼척시 22
 
7.6%
원주시 21
 
7.2%
정선군 19
 
6.5%
횡성군 19
 
6.5%
철원군 18
 
6.2%
인제군 16
 
5.5%
Other values (8) 82
28.2%

의용소방대명
Text

Distinct273
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-12-13T07:21:47.763381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length6.9381443
Min length4

Characters and Unicode

Total characters2019
Distinct characters144
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

Unique266 ?
Unique (%)91.4%

Sample

1st row강릉시의소대
2nd row강릉시여성의소대
3rd row강릉시경포지대
4th row강릉여성경포지대
5th row주문진읍의소대
ValueCountFrequency (%)
남면의소대 6
 
2.0%
서면여성의소대 4
 
1.4%
남면여성의소대 4
 
1.4%
서면의소대 4
 
1.4%
동면의소대 3
 
1.0%
북면의소대 2
 
0.7%
동면여성의소대 2
 
0.7%
신림의용소방대 2
 
0.7%
동내면여성의소대 1
 
0.3%
동내면의소대 1
 
0.3%
Other values (265) 265
90.1%
2023-12-13T07:21:48.214509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 299
14.8%
소 242
 
12.0%
의 240
 
11.9%
면 159
 
7.9%
성 138
 
6.8%
여 127
 
6.3%
읍 50
 
2.5%
지 48
 
2.4%
방 43
 
2.1%
용 41
 
2.0%
Other values (134) 632
31.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2015
99.8%
Space Separator 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 299
14.8%
소 242
 
12.0%
의 240
 
11.9%
면 159
 
7.9%
성 138
 
6.8%
여 127
 
6.3%
읍 50
 
2.5%
지 48
 
2.4%
방 43
 
2.1%
용 41
 
2.0%
Other values (133) 628
31.2%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2015
99.8%
Common 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 299
14.8%
소 242
 
12.0%
의 240
 
11.9%
면 159
 
7.9%
성 138
 
6.8%
여 127
 
6.3%
읍 50
 
2.5%
지 48
 
2.4%
방 43
 
2.1%
용 41
 
2.0%
Other values (133) 628
31.2%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2015
99.8%
ASCII 4
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 299
14.8%
소 242
 
12.0%
의 240
 
11.9%
면 159
 
7.9%
성 138
 
6.8%
여 127
 
6.3%
읍 50
 
2.5%
지 48
 
2.4%
방 43
 
2.1%
용 41
 
2.0%
Other values (133) 628
31.2%
ASCII
ValueCountFrequency (%)
4
100.0%

설치일
Text

Distinct182
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-12-13T07:21:48.573075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.003436
Min length10

Characters and Unicode

Total characters2911
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique152 ?
Unique (%)52.2%

Sample

1st row1931-04-01
2nd row1977-09-17
3rd row1989-06-21
4th row1989-06-21
5th row1943-02-15
ValueCountFrequency (%)
2009-01-01 24
 
8.2%
2008-01-01 24
 
8.2%
1945-08-15 14
 
4.8%
2007-01-01 10
 
3.4%
2009-01-05 10
 
3.4%
1985-08-15 4
 
1.4%
1946-04-10 4
 
1.4%
1989-04-22 3
 
1.0%
2007-01-11 3
 
1.0%
2007-01-03 3
 
1.0%
Other values (172) 192
66.0%
2023-12-13T07:21:49.070503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 666
22.9%
- 580
19.9%
1 532
18.3%
9 294
10.1%
2 214
 
7.4%
5 155
 
5.3%
8 148
 
5.1%
4 89
 
3.1%
6 86
 
3.0%
7 83
 
2.9%
Other values (2) 64
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2329
80.0%
Dash Punctuation 580
 
19.9%
Other Punctuation 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 666
28.6%
1 532
22.8%
9 294
12.6%
2 214
 
9.2%
5 155
 
6.7%
8 148
 
6.4%
4 89
 
3.8%
6 86
 
3.7%
7 83
 
3.6%
3 62
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 580
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2911
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 666
22.9%
- 580
19.9%
1 532
18.3%
9 294
10.1%
2 214
 
7.4%
5 155
 
5.3%
8 148
 
5.1%
4 89
 
3.1%
6 86
 
3.0%
7 83
 
2.9%
Other values (2) 64
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2911
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 666
22.9%
- 580
19.9%
1 532
18.3%
9 294
10.1%
2 214
 
7.4%
5 155
 
5.3%
8 148
 
5.1%
4 89
 
3.1%
6 86
 
3.0%
7 83
 
2.9%
Other values (2) 64
 
2.2%

사무실 위치
Text

Distinct187
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-12-13T07:21:49.456224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length15.938144
Min length10

Characters and Unicode

Total characters4638
Distinct characters220
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

Unique85 ?
Unique (%)29.2%

Sample

1st row강릉시 옥천동 323
2nd row강릉시 옥천동 323
3rd row강릉시 안현동 89-1
4th row강릉시 안현동 89-1
5th row강릉시 연곡면 영진리 360- 26
ValueCountFrequency (%)
홍천군 25
 
2.2%
춘천시 24
 
2.1%
강릉시 23
 
2.0%
영월군 22
 
1.9%
삼척시 22
 
1.9%
원주시 21
 
1.9%
정선군 19
 
1.7%
횡성군 19
 
1.7%
철원군 18
 
1.6%
인제군 16
 
1.4%
Other values (435) 920
81.5%
2023-12-13T07:21:49.959440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
839
 
18.1%
면 205
 
4.4%
1 191
 
4.1%
군 187
 
4.0%
2 142
 
3.1%
로 121
 
2.6%
시 114
 
2.5%
- 108
 
2.3%
3 105
 
2.3%
길 95
 
2.0%
Other values (210) 2531
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2737
59.0%
Decimal Number 940
 
20.3%
Space Separator 839
 
18.1%
Dash Punctuation 108
 
2.3%
Open Punctuation 7
 
0.2%
Close Punctuation 7
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 205
 
7.5%
군 187
 
6.8%
로 121
 
4.4%
시 114
 
4.2%
길 95
 
3.5%
천 91
 
3.3%
리 87
 
3.2%
동 68
 
2.5%
읍 67
 
2.4%
원 59
 
2.2%
Other values (196) 1643
60.0%
Decimal Number
ValueCountFrequency (%)
1 191
20.3%
2 142
15.1%
3 105
11.2%
6 87
9.3%
4 79
8.4%
5 73
 
7.8%
7 72
 
7.7%
0 68
 
7.2%
9 64
 
6.8%
8 59
 
6.3%
Space Separator
ValueCountFrequency (%)
839
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 108
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2737
59.0%
Common 1901
41.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 205
 
7.5%
군 187
 
6.8%
로 121
 
4.4%
시 114
 
4.2%
길 95
 
3.5%
천 91
 
3.3%
리 87
 
3.2%
동 68
 
2.5%
읍 67
 
2.4%
원 59
 
2.2%
Other values (196) 1643
60.0%
Common
ValueCountFrequency (%)
839
44.1%
1 191
 
10.0%
2 142
 
7.5%
- 108
 
5.7%
3 105
 
5.5%
6 87
 
4.6%
4 79
 
4.2%
5 73
 
3.8%
7 72
 
3.8%
0 68
 
3.6%
Other values (4) 137
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2737
59.0%
ASCII 1901
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
839
44.1%
1 191
 
10.0%
2 142
 
7.5%
- 108
 
5.7%
3 105
 
5.5%
6 87
 
4.6%
4 79
 
4.2%
5 73
 
3.8%
7 72
 
3.8%
0 68
 
3.6%
Other values (4) 137
 
7.2%
Hangul
ValueCountFrequency (%)
면 205
 
7.5%
군 187
 
6.8%
로 121
 
4.4%
시 114
 
4.2%
길 95
 
3.5%
천 91
 
3.3%
리 87
 
3.2%
동 68
 
2.5%
읍 67
 
2.4%
원 59
 
2.2%
Other values (196) 1643
60.0%

대원수
Real number (ℝ)

Distinct34
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.195876
Minimum13
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T07:21:50.081128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile19
Q124
median27
Q330
95-th percentile50
Maximum51
Range38
Interquartile range (IQR)6

Descriptive statistics

Standard deviation8.79299
Coefficient of variation (CV)0.30117233
Kurtosis0.43990296
Mean29.195876
Median Absolute Deviation (MAD)3
Skewness1.0527944
Sum8496
Variance77.316673
MonotonicityNot monotonic
2023-12-13T07:21:50.215597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
25 59
20.3%
30 53
18.2%
20 29
10.0%
50 20
 
6.9%
40 17
 
5.8%
24 16
 
5.5%
29 16
 
5.5%
22 9
 
3.1%
28 9
 
3.1%
27 8
 
2.7%
Other values (24) 55
18.9%
ValueCountFrequency (%)
13 1
 
0.3%
14 1
 
0.3%
15 4
 
1.4%
17 1
 
0.3%
18 2
 
0.7%
19 8
 
2.7%
20 29
10.0%
21 1
 
0.3%
22 9
 
3.1%
23 5
 
1.7%
ValueCountFrequency (%)
51 1
 
0.3%
50 20
6.9%
49 1
 
0.3%
48 2
 
0.7%
47 2
 
0.7%
46 1
 
0.3%
45 1
 
0.3%
44 2
 
0.7%
43 1
 
0.3%
41 3
 
1.0%

Interactions

2023-12-13T07:21:47.083131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:21:50.302289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분대원수
구분1.0000.403
대원수0.4031.000
2023-12-13T07:21:50.386585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대원수구분
대원수1.0000.168
구분0.1681.000

Missing values

2023-12-13T07:21:47.227311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:21:47.337029image/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

구분의용소방대명설치일사무실 위치대원수
0강릉시강릉시의소대1931-04-01강릉시 옥천동 32350
1강릉시강릉시여성의소대1977-09-17강릉시 옥천동 32339
2강릉시강릉시경포지대1989-06-21강릉시 안현동 89-120
3강릉시강릉여성경포지대1989-06-21강릉시 안현동 89-120
4강릉시주문진읍의소대1943-02-15강릉시 연곡면 영진리 360- 2650
5강릉시주문진읍여성의소대1989-05-26강릉시 연곡면 영진리 360- 2639
6강릉시사천면의소대1958-08-23강릉시 사천면 미노리 379-1630
7강릉시사천면여성의소대2008-01-01강릉시 사천면 미노리 379-1625
8강릉시연곡면의소대1970-01-01강릉시 연곡면 방내리 3530
9강릉시연곡여성의용소방대2007-01-01강릉시 연곡면 방내리 35번지25
구분의용소방대명설치일사무실 위치대원수
281횡성군서원면여성의소대2009-01-01횡성군 횡성읍 서원면 창촌1리549-125
282횡성군서원면유현지대1963-01-07횡성군 횡성읍 서원면 유현1리53220
283횡성군둔내면의소대1960-03-01횡성군 둔내면 유용리649-1330
284횡성군둔내여성의용소방대2007-01-11횡성군 둔내면 우용리 649-1325
285횡성군안흥면의소대1962-03-11횡성군 안흥면 안흥1리284-630
286횡성군안흥면여성의소대2009-01-01횡성군 안흥면 안흥1리284-625
287횡성군청일면의소대1961-02-28횡성군 청일면 유동2리1037-630
288횡성군청일면여성의소대2009-01-01횡성군 청일면 유동2리1037-622
289횡성군강림면의소대1953-06-10횡성군 강림면 강림리1539-129
290횡성군강림면여성의소대2009-01-01횡성군 강림면 강림리1539-124