Overview

Dataset statistics

Number of variables4
Number of observations245
Missing cells16
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 KiB
Average record size in memory32.5 B

Variable types

Categorical1
Text3

Dataset

Description지역별 지하수 담당 부서명 및 전화번호에 대한 데이터로 지역명, 지하수를 담당하는 부서명, 전화번호 등의 항목을 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15118907/fileData.do

Alerts

시군구 has 16 (6.5%) missing valuesMissing
전화번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:53:25.251336
Analysis finished2023-12-12 19:53:25.658376
Duration0.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct16
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
경기도
35 
서울특별시
26 
경상북도
25 
전라남도
23 
강원도
19 
Other values (11)
117 

Length

Max length7
Median length5
Mean length4.122449
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 35
14.3%
서울특별시 26
10.6%
경상북도 25
10.2%
전라남도 23
9.4%
강원도 19
7.8%
경상남도 19
7.8%
부산광역시 17
6.9%
전라북도 15
 
6.1%
충청남도 15
 
6.1%
충청북도 12
 
4.9%
Other values (6) 39
15.9%

Length

2023-12-13T04:53:25.752420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 35
14.3%
서울특별시 26
10.6%
경상북도 25
10.2%
전라남도 23
9.4%
강원도 19
7.8%
경상남도 19
7.8%
부산광역시 17
6.9%
전라북도 15
 
6.1%
충청남도 15
 
6.1%
충청북도 12
 
4.9%
Other values (6) 39
15.9%

시군구
Text

MISSING 

Distinct207
Distinct (%)90.4%
Missing16
Missing (%)6.5%
Memory size2.0 KiB
2023-12-13T04:53:26.105600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0611354
Min length2

Characters and Unicode

Total characters701
Distinct characters135
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

Unique200 ?
Unique (%)87.3%

Sample

1st row강릉시
2nd row고성군
3rd row동해시
4th row삼척시
5th row속초시
ValueCountFrequency (%)
중구 6
 
2.5%
동구 6
 
2.5%
서구 5
 
2.1%
남구 5
 
2.1%
북구 5
 
2.1%
용인시 3
 
1.3%
성남시 3
 
1.3%
고성군 2
 
0.8%
포항시 2
 
0.8%
강서구 2
 
0.8%
Other values (198) 198
83.5%
2023-12-13T04:53:26.584477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 85
 
12.1%
구 82
 
11.7%
시 79
 
11.3%
천 22
 
3.1%
성 20
 
2.9%
주 18
 
2.6%
양 18
 
2.6%
동 17
 
2.4%
산 16
 
2.3%
남 15
 
2.1%
Other values (125) 329
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 693
98.9%
Space Separator 8
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 85
 
12.3%
구 82
 
11.8%
시 79
 
11.4%
천 22
 
3.2%
성 20
 
2.9%
주 18
 
2.6%
양 18
 
2.6%
동 17
 
2.5%
산 16
 
2.3%
남 15
 
2.2%
Other values (124) 321
46.3%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 693
98.9%
Common 8
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 85
 
12.3%
구 82
 
11.8%
시 79
 
11.4%
천 22
 
3.2%
성 20
 
2.9%
주 18
 
2.6%
양 18
 
2.6%
동 17
 
2.5%
산 16
 
2.3%
남 15
 
2.2%
Other values (124) 321
46.3%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 693
98.9%
ASCII 8
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 85
 
12.3%
구 82
 
11.8%
시 79
 
11.4%
천 22
 
3.2%
성 20
 
2.9%
주 18
 
2.6%
양 18
 
2.6%
동 17
 
2.5%
산 16
 
2.3%
남 15
 
2.2%
Other values (124) 321
46.3%
ASCII
ValueCountFrequency (%)
8
100.0%

부서명
Text

Distinct82
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-13T04:53:26.889157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.7387755
Min length3

Characters and Unicode

Total characters1161
Distinct characters77
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

Unique55 ?
Unique (%)22.4%

Sample

1st row환경과
2nd row상하수도사업소
3rd row상하수도사업소
4th row상수도사업소
5th row하수도사업소
ValueCountFrequency (%)
건설과 37
 
15.0%
상하수도사업소 25
 
10.2%
환경과 17
 
6.9%
환경위생과 17
 
6.9%
치수과 12
 
4.9%
하수과 8
 
3.3%
환경보호과 7
 
2.8%
상하수도과 6
 
2.4%
안전건설과 6
 
2.4%
맑은물사업소 5
 
2.0%
Other values (73) 106
43.1%
2023-12-13T04:53:27.361342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 195
16.8%
수 94
 
8.1%
경 74
 
6.4%
환 72
 
6.2%
하 63
 
5.4%
설 60
 
5.2%
도 59
 
5.1%
건 57
 
4.9%
상 43
 
3.7%
업 42
 
3.6%
Other values (67) 402
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1158
99.7%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 195
16.8%
수 94
 
8.1%
경 74
 
6.4%
환 72
 
6.2%
하 63
 
5.4%
설 60
 
5.2%
도 59
 
5.1%
건 57
 
4.9%
상 43
 
3.7%
업 42
 
3.6%
Other values (64) 399
34.5%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1158
99.7%
Common 3
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 195
16.8%
수 94
 
8.1%
경 74
 
6.4%
환 72
 
6.2%
하 63
 
5.4%
설 60
 
5.2%
도 59
 
5.1%
건 57
 
4.9%
상 43
 
3.7%
업 42
 
3.6%
Other values (64) 399
34.5%
Common
ValueCountFrequency (%)
) 1
33.3%
( 1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1158
99.7%
ASCII 3
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 195
16.8%
수 94
 
8.1%
경 74
 
6.4%
환 72
 
6.2%
하 63
 
5.4%
설 60
 
5.2%
도 59
 
5.1%
건 57
 
4.9%
상 43
 
3.7%
업 42
 
3.6%
Other values (64) 399
34.5%
ASCII
ValueCountFrequency (%)
) 1
33.3%
( 1
33.3%
1
33.3%

전화번호
Text

UNIQUE 

Distinct245
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-13T04:53:27.609873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.995918
Min length11

Characters and Unicode

Total characters2939
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

Unique245 ?
Unique (%)100.0%

Sample

1st row033-640-5354
2nd row033-680-3763
3rd row033-539-8607
4th row033-570-3829
5th row033-639-2933
ValueCountFrequency (%)
033-640-5354 1
 
0.4%
051-550-4391 1
 
0.4%
02-2199-7663 1
 
0.4%
02-351-7974 1
 
0.4%
02-2148-3242 1
 
0.4%
02-3396-6164 1
 
0.4%
02-2094-2434 1
 
0.4%
02-2133-3778 1
 
0.4%
044-300-4542 1
 
0.4%
052-226-5834 1
 
0.4%
Other values (235) 235
95.9%
2023-12-13T04:53:28.015649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 490
16.7%
0 444
15.1%
3 340
11.6%
2 274
9.3%
5 271
9.2%
6 261
8.9%
4 259
8.8%
1 179
 
6.1%
7 149
 
5.1%
8 149
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2449
83.3%
Dash Punctuation 490
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 444
18.1%
3 340
13.9%
2 274
11.2%
5 271
11.1%
6 261
10.7%
4 259
10.6%
1 179
7.3%
7 149
 
6.1%
8 149
 
6.1%
9 123
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 490
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2939
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 490
16.7%
0 444
15.1%
3 340
11.6%
2 274
9.3%
5 271
9.2%
6 261
8.9%
4 259
8.8%
1 179
 
6.1%
7 149
 
5.1%
8 149
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 490
16.7%
0 444
15.1%
3 340
11.6%
2 274
9.3%
5 271
9.2%
6 261
8.9%
4 259
8.8%
1 179
 
6.1%
7 149
 
5.1%
8 149
 
5.1%

Correlations

2023-12-13T04:53:28.113447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도부서명
시도1.0000.823
부서명0.8231.000

Missing values

2023-12-13T04:53:25.515996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:53:25.611003image/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
0강원도강릉시환경과033-640-5354
1강원도고성군상하수도사업소033-680-3763
2강원도동해시상하수도사업소033-539-8607
3강원도삼척시상수도사업소033-570-3829
4강원도속초시하수도사업소033-639-2933
5강원도양구군상하수도사업소033-480-7850
6강원도양양군상하수도사업소033-670-2519
7강원도영월군상하수도사업소033-370-2721
8강원도원주시수도운영과033-737-4292
9강원도인제군환경보호과033-460-2062
시도시군구부서명전화번호
235충청북도보은군상하수도사업소043-540-4412
236충청북도영동군환경과043-740-3422
237충청북도옥천군상하수도사업소043-730-4825
238충청북도음성군수도사업소043-871-2446
239충청북도제천시자연환경과043-641-6373
240충청북도증평군상하수도사업소043-835-4094
241충청북도진천군상하수도사업소043-539-7643
242충청북도청주시환경관리본부043-201-4723
243충청북도충주시환경수자원과043-850-3642
244충청북도<NA>수자원관리과043-220-4064