Overview

Dataset statistics

Number of variables4
Number of observations255
Missing cells16
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 KiB
Average record size in memory32.5 B

Variable types

Categorical1
Text3

Dataset

Description지역별 지하수담당 부서명 및 전화번호에 대한 데이터로 지역명, 지하수를 담당하는 부서명, 전화번호 등의 항목을 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15087048/fileData.do

Alerts

시군구 has 16 (6.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 09:31:43.810172
Analysis finished2023-12-12 09:31:44.206900
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct17
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
경기도
36 
서울특별시
26 
경상북도
25 
전라남도
23 
경상남도
23 
Other values (12)
122 

Length

Max length7
Median length5
Mean length4.4235294
Min length3

Unique

Unique2 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 36
14.1%
서울특별시 26
10.2%
경상북도 25
9.8%
전라남도 23
9.0%
경상남도 23
9.0%
강원특별자치도 19
7.5%
부산광역시 17
6.7%
충청남도 16
 
6.3%
전라북도 15
 
5.9%
충청북도 15
 
5.9%
Other values (7) 40
15.7%

Length

2023-12-12T18:31:44.320689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 36
14.1%
서울특별시 26
10.2%
경상북도 25
9.8%
전라남도 23
9.0%
경상남도 23
9.0%
강원특별자치도 19
7.5%
부산광역시 17
6.7%
충청남도 16
 
6.3%
충청북도 15
 
5.9%
전라북도 15
 
5.9%
Other values (7) 40
15.7%

시군구
Text

MISSING 

Distinct217
Distinct (%)90.8%
Missing16
Missing (%)6.3%
Memory size2.1 KiB
2023-12-12T18:31:44.797376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.2426778
Min length2

Characters and Unicode

Total characters775
Distinct characters141
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

Unique210 ?
Unique (%)87.9%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
동구 6
 
2.3%
중구 6
 
2.3%
서구 5
 
2.0%
남구 5
 
2.0%
북구 5
 
2.0%
창원시 5
 
2.0%
청주시 4
 
1.6%
용인시 3
 
1.2%
성남시 3
 
1.2%
고성군 2
 
0.8%
Other values (210) 212
82.8%
2023-12-12T18:31:45.351406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 91
 
11.7%
시 89
 
11.5%
군 85
 
11.0%
천 22
 
2.8%
주 22
 
2.8%
성 21
 
2.7%
산 19
 
2.5%
양 18
 
2.3%
17
 
2.2%
동 17
 
2.2%
Other values (131) 374
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 758
97.8%
Space Separator 17
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 91
 
12.0%
시 89
 
11.7%
군 85
 
11.2%
천 22
 
2.9%
주 22
 
2.9%
성 21
 
2.8%
산 19
 
2.5%
양 18
 
2.4%
동 17
 
2.2%
남 15
 
2.0%
Other values (130) 359
47.4%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 758
97.8%
Common 17
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 91
 
12.0%
시 89
 
11.7%
군 85
 
11.2%
천 22
 
2.9%
주 22
 
2.9%
성 21
 
2.8%
산 19
 
2.5%
양 18
 
2.4%
동 17
 
2.2%
남 15
 
2.0%
Other values (130) 359
47.4%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 758
97.8%
ASCII 17
 
2.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 91
 
12.0%
시 89
 
11.7%
군 85
 
11.2%
천 22
 
2.9%
주 22
 
2.9%
성 21
 
2.8%
산 19
 
2.5%
양 18
 
2.4%
동 17
 
2.2%
남 15
 
2.0%
Other values (130) 359
47.4%
ASCII
ValueCountFrequency (%)
17
100.0%

부서명
Text

Distinct71
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-12T18:31:45.661167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.5882353
Min length3

Characters and Unicode

Total characters1170
Distinct characters69
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

Unique41 ?
Unique (%)16.1%

Sample

1st row수변감성도시과
2nd row치수과
3rd row치수과
4th row맑은환경과
5th row치수과
ValueCountFrequency (%)
건설과 44
17.3%
상하수도사업소 25
 
9.8%
환경과 20
 
7.8%
환경위생과 17
 
6.7%
치수과 14
 
5.5%
상하수도과 11
 
4.3%
하수과 8
 
3.1%
건설교통과 7
 
2.7%
생태하천과 7
 
2.7%
맑은물사업소 6
 
2.4%
Other values (60) 96
37.6%
2023-12-12T18:31:46.247239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 205
17.5%
수 101
 
8.6%
하 67
 
5.7%
환 66
 
5.6%
경 66
 
5.6%
도 63
 
5.4%
설 61
 
5.2%
건 58
 
5.0%
소 44
 
3.8%
사 43
 
3.7%
Other values (59) 396
33.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1169
99.9%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 205
17.5%
수 101
 
8.6%
하 67
 
5.7%
환 66
 
5.6%
경 66
 
5.6%
도 63
 
5.4%
설 61
 
5.2%
건 58
 
5.0%
소 44
 
3.8%
사 43
 
3.7%
Other values (58) 395
33.8%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1169
99.9%
Common 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 205
17.5%
수 101
 
8.6%
하 67
 
5.7%
환 66
 
5.6%
경 66
 
5.6%
도 63
 
5.4%
설 61
 
5.2%
건 58
 
5.0%
소 44
 
3.8%
사 43
 
3.7%
Other values (58) 395
33.8%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1169
99.9%
ASCII 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 205
17.5%
수 101
 
8.6%
하 67
 
5.7%
환 66
 
5.6%
경 66
 
5.6%
도 63
 
5.4%
설 61
 
5.2%
건 58
 
5.0%
소 44
 
3.8%
사 43
 
3.7%
Other values (58) 395
33.8%
ASCII
ValueCountFrequency (%)
1
100.0%

전화번호
Text

Distinct253
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-12T18:31:46.610079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.003922
Min length11

Characters and Unicode

Total characters3061
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique251 ?
Unique (%)98.4%

Sample

1st row02-2133-3776
2nd row02-2148-3242
3rd row02-3396-6163
4th row02-2199-7663
5th row02-2286-5812
ValueCountFrequency (%)
055-225-7137 2
 
0.8%
055-225-7269 2
 
0.8%
055-960-6615 1
 
0.4%
063-280-3565 1
 
0.4%
061-339-7662 1
 
0.4%
061-749-6475 1
 
0.4%
061-659-4998 1
 
0.4%
02-2133-3776 1
 
0.4%
063-539-5845 1
 
0.4%
041-360-6475 1
 
0.4%
Other values (243) 243
95.3%
2023-12-12T18:31:47.141472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 510
16.7%
0 455
14.9%
3 361
11.8%
5 299
9.8%
2 269
8.8%
4 269
8.8%
6 261
8.5%
1 191
 
6.2%
8 161
 
5.3%
7 159
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2551
83.3%
Dash Punctuation 510
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 455
17.8%
3 361
14.2%
5 299
11.7%
2 269
10.5%
4 269
10.5%
6 261
10.2%
1 191
7.5%
8 161
 
6.3%
7 159
 
6.2%
9 126
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 510
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 510
16.7%
0 455
14.9%
3 361
11.8%
5 299
9.8%
2 269
8.8%
4 269
8.8%
6 261
8.5%
1 191
 
6.2%
8 161
 
5.3%
7 159
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 510
16.7%
0 455
14.9%
3 361
11.8%
5 299
9.8%
2 269
8.8%
4 269
8.8%
6 261
8.5%
1 191
 
6.2%
8 161
 
5.3%
7 159
 
5.2%

Correlations

2023-12-12T18:31:47.278370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도부서명
시도1.0000.884
부서명0.8841.000

Missing values

2023-12-12T18:31:44.056415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:31:44.164982image/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

시도시군구부서명전화번호
0서울특별시<NA>수변감성도시과02-2133-3776
1서울특별시종로구치수과02-2148-3242
2서울특별시중구치수과02-3396-6163
3서울특별시용산구맑은환경과02-2199-7663
4서울특별시성동구치수과02-2286-5812
5서울특별시광진구치수과02-450-7906
6서울특별시동대문구기후환경과02-2127-4372
7서울특별시중랑구맑은환경과02-2094-2434
8서울특별시성북구치수과02-2241-3642
9서울특별시강북구안전치수과02-901-5898
시도시군구부서명전화번호
245경상남도함안군건설교통과055-580-2605
246경상남도창녕군건설교통과055-530-1744
247경상남도고성군건설과055-670-2755
248경상남도남해군상하수도과055-860-8829
249경상남도하동군건설교통과055-880-2505
250경상남도산청군상하수도과055-970-7062
251경상남도함양군상하수도사업소055-960-6615
252경상남도거창군환경과055-940-3516
253경상남도합천군환경위생과055-930-3312
254제주특별자치도<NA>물정책과064-710-6476