Overview

Dataset statistics

Number of variables8
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows7
Duplicate rows (%)22.6%
Total size in memory2.2 KiB
Average record size in memory73.3 B

Variable types

Text2
Categorical6

Alerts

하부굴착구경(mm) has constant value ""Constant
수온값 has constant value ""Constant
수위 has constant value ""Constant
Dataset has 7 (22.6%) duplicate rowsDuplicates
설치일자 is highly overall correlated with 관리기관명 and 1 other fieldsHigh correlation
관리기관명 is highly overall correlated with 설치일자 and 1 other fieldsHigh correlation
상부굴착구경(mm) is highly overall correlated with 설치일자 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 11:42:33.060314
Analysis finished2023-12-10 11:42:33.679370
Duration0.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관측소명
Text

Distinct24
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-10T20:42:33.832626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length5.6451613
Min length4

Characters and Unicode

Total characters175
Distinct characters68
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)54.8%

Sample

1st row남원산내
2nd row남원산내
3rd row안동고란
4th row안동고란
5th row영양대천
ValueCountFrequency (%)
남원산내 2
 
6.5%
청도운문 2
 
6.5%
함양휴천 2
 
6.5%
홍천어유포 2
 
6.5%
안동고란 2
 
6.5%
영양대천 2
 
6.5%
영주아지 2
 
6.5%
장군-0013 1
 
3.2%
쌍류-0010 1
 
3.2%
상도동마을마당(1 1
 
3.2%
Other values (14) 14
45.2%
2023-12-10T20:42:34.311255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 24
 
13.7%
- 10
 
5.7%
1 9
 
5.1%
영 7
 
4.0%
도 6
 
3.4%
천 6
 
3.4%
양 5
 
2.9%
마 5
 
2.9%
원 5
 
2.9%
동 4
 
2.3%
Other values (58) 94
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 119
68.0%
Decimal Number 42
 
24.0%
Dash Punctuation 10
 
5.7%
Open Punctuation 2
 
1.1%
Close Punctuation 2
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 7
 
5.9%
도 6
 
5.0%
천 6
 
5.0%
양 5
 
4.2%
마 5
 
4.2%
원 5
 
4.2%
동 4
 
3.4%
을 3
 
2.5%
상 3
 
2.5%
어 3
 
2.5%
Other values (46) 72
60.5%
Decimal Number
ValueCountFrequency (%)
0 24
57.1%
1 9
 
21.4%
5 2
 
4.8%
2 2
 
4.8%
4 1
 
2.4%
6 1
 
2.4%
3 1
 
2.4%
9 1
 
2.4%
8 1
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 119
68.0%
Common 56
32.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 7
 
5.9%
도 6
 
5.0%
천 6
 
5.0%
양 5
 
4.2%
마 5
 
4.2%
원 5
 
4.2%
동 4
 
3.4%
을 3
 
2.5%
상 3
 
2.5%
어 3
 
2.5%
Other values (46) 72
60.5%
Common
ValueCountFrequency (%)
0 24
42.9%
- 10
17.9%
1 9
 
16.1%
5 2
 
3.6%
2 2
 
3.6%
( 2
 
3.6%
) 2
 
3.6%
4 1
 
1.8%
6 1
 
1.8%
3 1
 
1.8%
Other values (2) 2
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 119
68.0%
ASCII 56
32.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24
42.9%
- 10
17.9%
1 9
 
16.1%
5 2
 
3.6%
2 2
 
3.6%
( 2
 
3.6%
) 2
 
3.6%
4 1
 
1.8%
6 1
 
1.8%
3 1
 
1.8%
Other values (2) 2
 
3.6%
Hangul
ValueCountFrequency (%)
영 7
 
5.9%
도 6
 
5.0%
천 6
 
5.0%
양 5
 
4.2%
마 5
 
4.2%
원 5
 
4.2%
동 4
 
3.4%
을 3
 
2.5%
상 3
 
2.5%
어 3
 
2.5%
Other values (46) 72
60.5%

설치일자
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size380.0 B
20191126
13 
20191205
8 
20190411
4 
20191203
4 
20191204
2 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20191205
2nd row20191205
3rd row20191126
4th row20191126
5th row20191126

Common Values

ValueCountFrequency (%)
20191126 13
41.9%
20191205 8
25.8%
20190411 4
 
12.9%
20191203 4
 
12.9%
20191204 2
 
6.5%

Length

2023-12-10T20:42:34.492421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:42:34.640839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20191126 13
41.9%
20191205 8
25.8%
20190411 4
 
12.9%
20191203 4
 
12.9%
20191204 2
 
6.5%

주소
Text

Distinct23
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-10T20:42:34.936934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length20.870968
Min length17

Characters and Unicode

Total characters647
Distinct characters94
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

Unique15 ?
Unique (%)48.4%

Sample

1st row전라북도 남원시 산내면 장항리 185-1
2nd row전라북도 남원시 산내면 장항리 185-1
3rd row경상북도 안동시 길안면 고란리 108
4th row경상북도 안동시 길안면 고란리 108
5th row경상북도 영양군 입암면 대천리 194-3
ValueCountFrequency (%)
경상북도 9
 
6.5%
세종특별자치시 9
 
6.5%
강원도 4
 
2.9%
동작구 4
 
2.9%
서울특별시 4
 
2.9%
영양군 3
 
2.2%
상도동 3
 
2.2%
연서면 2
 
1.4%
남원시 2
 
1.4%
148-4 2
 
1.4%
Other values (68) 96
69.6%
2023-12-10T20:42:35.433259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
124
 
19.2%
도 25
 
3.9%
- 24
 
3.7%
1 23
 
3.6%
리 22
 
3.4%
면 21
 
3.2%
시 19
 
2.9%
4 17
 
2.6%
5 14
 
2.2%
상 14
 
2.2%
Other values (84) 344
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 381
58.9%
Space Separator 124
 
19.2%
Decimal Number 118
 
18.2%
Dash Punctuation 24
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 25
 
6.6%
리 22
 
5.8%
면 21
 
5.5%
시 19
 
5.0%
상 14
 
3.7%
동 14
 
3.7%
별 13
 
3.4%
군 13
 
3.4%
특 13
 
3.4%
북 12
 
3.1%
Other values (72) 215
56.4%
Decimal Number
ValueCountFrequency (%)
1 23
19.5%
4 17
14.4%
5 14
11.9%
7 13
11.0%
8 13
11.0%
2 10
8.5%
6 9
 
7.6%
0 8
 
6.8%
3 6
 
5.1%
9 5
 
4.2%
Space Separator
ValueCountFrequency (%)
124
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 381
58.9%
Common 266
41.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 25
 
6.6%
리 22
 
5.8%
면 21
 
5.5%
시 19
 
5.0%
상 14
 
3.7%
동 14
 
3.7%
별 13
 
3.4%
군 13
 
3.4%
특 13
 
3.4%
북 12
 
3.1%
Other values (72) 215
56.4%
Common
ValueCountFrequency (%)
124
46.6%
- 24
 
9.0%
1 23
 
8.6%
4 17
 
6.4%
5 14
 
5.3%
7 13
 
4.9%
8 13
 
4.9%
2 10
 
3.8%
6 9
 
3.4%
0 8
 
3.0%
Other values (2) 11
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 381
58.9%
ASCII 266
41.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
124
46.6%
- 24
 
9.0%
1 23
 
8.6%
4 17
 
6.4%
5 14
 
5.3%
7 13
 
4.9%
8 13
 
4.9%
2 10
 
3.8%
6 9
 
3.4%
0 8
 
3.0%
Other values (2) 11
 
4.1%
Hangul
ValueCountFrequency (%)
도 25
 
6.6%
리 22
 
5.8%
면 21
 
5.5%
시 19
 
5.0%
상 14
 
3.7%
동 14
 
3.7%
별 13
 
3.4%
군 13
 
3.4%
특 13
 
3.4%
북 12
 
3.1%
Other values (72) 215
56.4%

관리기관명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size380.0 B
환경부 한국수자원공사
17 
-
10 
동작구청
4 

Length

Max length11
Median length11
Mean length6.8709677
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row환경부 한국수자원공사
2nd row환경부 한국수자원공사
3rd row환경부 한국수자원공사
4th row환경부 한국수자원공사
5th row환경부 한국수자원공사

Common Values

ValueCountFrequency (%)
환경부 한국수자원공사 17
54.8%
- 10
32.3%
동작구청 4
 
12.9%

Length

2023-12-10T20:42:35.614863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:42:35.739638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
환경부 17
35.4%
한국수자원공사 17
35.4%
10
20.8%
동작구청 4
 
8.3%

상부굴착구경(mm)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size380.0 B
0
19 
200
11 
150
 
1

Length

Max length3
Median length1
Mean length1.7741935
Min length1

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
61.3%
200 11
35.5%
150 1
 
3.2%

Length

2023-12-10T20:42:35.914777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:42:36.063410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19
61.3%
200 11
35.5%
150 1
 
3.2%

하부굴착구경(mm)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
0
31 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31
100.0%

Length

2023-12-10T20:42:36.196125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:42:36.325316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
100.0%

수온값
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
0
31 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31
100.0%

Length

2023-12-10T20:42:36.447335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:42:36.634037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
100.0%

수위
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
0
31 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31
100.0%

Length

2023-12-10T20:42:36.813397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:42:36.922771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
100.0%

Correlations

2023-12-10T20:42:37.007810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소명설치일자주소관리기관명상부굴착구경(mm)
관측소명1.0001.0001.0001.0001.000
설치일자1.0001.0001.0000.8620.606
주소1.0001.0001.0001.0001.000
관리기관명1.0000.8621.0001.0000.924
상부굴착구경(mm)1.0000.6061.0000.9241.000
2023-12-10T20:42:37.155864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상부굴착구경(mm)관리기관명설치일자
상부굴착구경(mm)1.0000.6620.537
관리기관명0.6621.0000.871
설치일자0.5370.8711.000
2023-12-10T20:42:37.258253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치일자관리기관명상부굴착구경(mm)
설치일자1.0000.8710.537
관리기관명0.8711.0000.662
상부굴착구경(mm)0.5370.6621.000

Missing values

2023-12-10T20:42:33.434035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:42:33.594549image/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

관측소명설치일자주소관리기관명상부굴착구경(mm)하부굴착구경(mm)수온값수위
0남원산내20191205전라북도 남원시 산내면 장항리 185-1환경부 한국수자원공사0000
1남원산내20191205전라북도 남원시 산내면 장항리 185-1환경부 한국수자원공사0000
2안동고란20191126경상북도 안동시 길안면 고란리 108환경부 한국수자원공사0000
3안동고란20191126경상북도 안동시 길안면 고란리 108환경부 한국수자원공사0000
4영양대천20191126경상북도 영양군 입암면 대천리 194-3환경부 한국수자원공사0000
5영양대천20191126경상북도 영양군 입암면 대천리 194-3환경부 한국수자원공사0000
6영양무창20191126경상북도 영양군 영양읍 무창리 584환경부 한국수자원공사0000
7영월북면20191126강원도 영월군 북면 연덕리 505환경부 한국수자원공사0000
8영주아지20191126경상북도 영주시 아지동 147-4환경부 한국수자원공사0000
9영주아지20191126경상북도 영주시 아지동 147-4환경부 한국수자원공사0000
관측소명설치일자주소관리기관명상부굴착구경(mm)하부굴착구경(mm)수온값수위
21와촌-000920191203세종특별자치시 연서면 와촌리 968-1-200000
22쌍류-001020191203세종특별자치시 연서면 쌍류리 706-72-0000
23소정-001120191203세종특별자치시 소정면 소정리 156-6-200000
24서창-001220191203세종특별자치시 조치원읍 서창리 119-2-200000
25장군-001320191204세종특별자치시 장군면 도계리 356-47-200000
26보람-001420191205세종특별자치시 보람동 626-1-200000
27연기-001520191205세종특별자치시 연기면 세종리 1057-200000
28금남-001620191205세종특별자치시 금남면 용포로 67-200000
29상도동마을마당(1)20190411서울특별시 동작구 상도동 27-44동작구청200000
30상도동마을마당(2)20190411서울특별시 동작구 상도동 27-44동작구청200000

Duplicate rows

Most frequently occurring

관측소명설치일자주소관리기관명상부굴착구경(mm)하부굴착구경(mm)수온값수위# duplicates
0남원산내20191205전라북도 남원시 산내면 장항리 185-1환경부 한국수자원공사00002
1안동고란20191126경상북도 안동시 길안면 고란리 108환경부 한국수자원공사00002
2영양대천20191126경상북도 영양군 입암면 대천리 194-3환경부 한국수자원공사00002
3영주아지20191126경상북도 영주시 아지동 147-4환경부 한국수자원공사00002
4청도운문20191126경상북도 청도군 운문면 봉하리 148-4환경부 한국수자원공사00002
5함양휴천20191205경상남도 함양군 휴천면 목현리 87-5환경부 한국수자원공사00002
6홍천어유포20191126강원도 홍천군 서면 어유포리 205-7환경부 한국수자원공사00002