Overview

Dataset statistics

Number of variables17
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 KiB
Average record size in memory148.1 B

Variable types

Numeric5
Categorical7
Text5

Dataset

Description수도법에 따라 2년마다 소규모수도시설에서 해수를 원수로 사용하는 원수 수질 검사 결과 데이터로, 수질검사결과, 수질검사기관 등을 포함 * 상세자료조회는 아래 URL을 참고 해주시기 바랍니다. https://www.waternow.go.kr/web/lawData2/?pMENUID=96&ATTR_1=3106
URLhttps://www.data.go.kr/data/15093998/fileData.do

Alerts

수원 has constant value ""Constant
수은 has constant value ""Constant
납 has constant value ""Constant
연번 is highly overall correlated with 검사년도 and 3 other fieldsHigh correlation
검사년도 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
채수일자 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
비소 is highly overall correlated with 수도규모 and 1 other fieldsHigh correlation
붕소 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
지역 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
수도규모 is highly overall correlated with 검사년도 and 3 other fieldsHigh correlation
카드뮴 is highly overall correlated with 검사년도 and 2 other fieldsHigh correlation
크롬 is highly overall correlated with 비소High correlation
카드뮴 is highly imbalanced (84.1%)Imbalance
연번 has unique valuesUnique
비소 has 25 (58.1%) zerosZeros
붕소 has 1 (2.3%) zerosZeros

Reproduction

Analysis started2023-12-12 19:15:12.158643
Analysis finished2023-12-12 19:15:16.243077
Duration4.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T04:15:16.343820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.1
Q111.5
median22
Q332.5
95-th percentile40.9
Maximum43
Range42
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.556539
Coefficient of variation (CV)0.57075176
Kurtosis-1.2
Mean22
Median Absolute Deviation (MAD)11
Skewness0
Sum946
Variance157.66667
MonotonicityStrictly increasing
2023-12-13T04:15:16.531297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 1
 
2.3%
2 1
 
2.3%
25 1
 
2.3%
26 1
 
2.3%
27 1
 
2.3%
28 1
 
2.3%
29 1
 
2.3%
30 1
 
2.3%
31 1
 
2.3%
32 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
1 1
2.3%
2 1
2.3%
3 1
2.3%
4 1
2.3%
5 1
2.3%
6 1
2.3%
7 1
2.3%
8 1
2.3%
9 1
2.3%
10 1
2.3%
ValueCountFrequency (%)
43 1
2.3%
42 1
2.3%
41 1
2.3%
40 1
2.3%
39 1
2.3%
38 1
2.3%
37 1
2.3%
36 1
2.3%
35 1
2.3%
34 1
2.3%

검사년도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.1395
Minimum2015
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T04:15:16.658640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12017.5
median2018
Q32020
95-th percentile2020
Maximum2021
Range6
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.9588796
Coefficient of variation (CV)0.00097063636
Kurtosis-0.98866857
Mean2018.1395
Median Absolute Deviation (MAD)2
Skewness-0.50356423
Sum86780
Variance3.8372093
MonotonicityIncreasing
2023-12-13T04:15:16.786411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2018 14
32.6%
2020 14
32.6%
2015 9
20.9%
2019 2
 
4.7%
2021 2
 
4.7%
2016 1
 
2.3%
2017 1
 
2.3%
ValueCountFrequency (%)
2015 9
20.9%
2016 1
 
2.3%
2017 1
 
2.3%
2018 14
32.6%
2019 2
 
4.7%
2020 14
32.6%
2021 2
 
4.7%
ValueCountFrequency (%)
2021 2
 
4.7%
2020 14
32.6%
2019 2
 
4.7%
2018 14
32.6%
2017 1
 
2.3%
2016 1
 
2.3%
2015 9
20.9%

지역
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
전라남도 완도군
27 
전라남도 신안군
9 
인천광역시
6 
제주특별자치도
 
1

Length

Max length8
Median length8
Mean length7.5581395
Min length5

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row전라남도 신안군
2nd row전라남도 신안군
3rd row전라남도 신안군
4th row전라남도 신안군
5th row전라남도 신안군

Common Values

ValueCountFrequency (%)
전라남도 완도군 27
62.8%
전라남도 신안군 9
 
20.9%
인천광역시 6
 
14.0%
제주특별자치도 1
 
2.3%

Length

2023-12-13T04:15:16.937264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:15:17.071782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 36
45.6%
완도군 27
34.2%
신안군 9
 
11.4%
인천광역시 6
 
7.6%
제주특별자치도 1
 
1.3%

소규모수도시설번호
Text

Distinct28
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-13T04:15:17.308783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters430
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)30.2%

Sample

1st rowSS00013201
2nd rowSS00013226
3rd rowSS00013265
4th rowSS00013267
5th rowSS00013296
ValueCountFrequency (%)
ss00147635 2
 
4.7%
ss00012942 2
 
4.7%
ss00023360 2
 
4.7%
ss00000461 2
 
4.7%
ss00000421 2
 
4.7%
ss00037035 2
 
4.7%
ss00147643 2
 
4.7%
ss00147640 2
 
4.7%
ss00147641 2
 
4.7%
ss00147639 2
 
4.7%
Other values (18) 23
53.5%
2023-12-13T04:15:17.718471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 124
28.8%
S 86
20.0%
1 44
 
10.2%
4 39
 
9.1%
3 39
 
9.1%
6 31
 
7.2%
7 28
 
6.5%
2 19
 
4.4%
5 9
 
2.1%
8 6
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 344
80.0%
Uppercase Letter 86
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124
36.0%
1 44
 
12.8%
4 39
 
11.3%
3 39
 
11.3%
6 31
 
9.0%
7 28
 
8.1%
2 19
 
5.5%
5 9
 
2.6%
8 6
 
1.7%
9 5
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
S 86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 344
80.0%
Latin 86
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 124
36.0%
1 44
 
12.8%
4 39
 
11.3%
3 39
 
11.3%
6 31
 
9.0%
7 28
 
8.1%
2 19
 
5.5%
5 9
 
2.6%
8 6
 
1.7%
9 5
 
1.5%
Latin
ValueCountFrequency (%)
S 86
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124
28.8%
S 86
20.0%
1 44
 
10.2%
4 39
 
9.1%
3 39
 
9.1%
6 31
 
7.2%
7 28
 
6.5%
2 19
 
4.4%
5 9
 
2.1%
8 6
 
1.4%

소규모수도시설명
Text

Distinct28
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-13T04:15:17.963813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.7674419
Min length2

Characters and Unicode

Total characters119
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)30.2%

Sample

1st row고이도
2nd row다물
3rd row마진도
4th row만재
5th row상태
ValueCountFrequency (%)
다랑도 2
 
4.7%
덕우 2
 
4.7%
떼무리 2
 
4.7%
동네기미 2
 
4.7%
당산 2
 
4.7%
흑일 2
 
4.7%
황제도 2
 
4.7%
서화도 2
 
4.7%
어룡도 2
 
4.7%
서넙도 2
 
4.7%
Other values (18) 23
53.5%
2023-12-13T04:15:18.339593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 27
22.7%
마 6
 
5.0%
서 5
 
4.2%
넙 4
 
3.4%
일 4
 
3.4%
동 3
 
2.5%
화 3
 
2.5%
우 3
 
2.5%
고 3
 
2.5%
다 3
 
2.5%
Other values (35) 58
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 119
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 27
22.7%
마 6
 
5.0%
서 5
 
4.2%
넙 4
 
3.4%
일 4
 
3.4%
동 3
 
2.5%
화 3
 
2.5%
우 3
 
2.5%
고 3
 
2.5%
다 3
 
2.5%
Other values (35) 58
48.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 119
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 27
22.7%
마 6
 
5.0%
서 5
 
4.2%
넙 4
 
3.4%
일 4
 
3.4%
동 3
 
2.5%
화 3
 
2.5%
우 3
 
2.5%
고 3
 
2.5%
다 3
 
2.5%
Other values (35) 58
48.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 119
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 27
22.7%
마 6
 
5.0%
서 5
 
4.2%
넙 4
 
3.4%
일 4
 
3.4%
동 3
 
2.5%
화 3
 
2.5%
우 3
 
2.5%
고 3
 
2.5%
다 3
 
2.5%
Other values (35) 58
48.7%

측정지점코드
Text

Distinct28
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-13T04:15:18.592778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters344
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)30.2%

Sample

1st rowS1007676
2nd rowS1025841
3rd rowS1027801
4th rowS1025842
5th rowG1028114
ValueCountFrequency (%)
g1029398 2
 
4.7%
s1009550 2
 
4.7%
g1014718 2
 
4.7%
s1029485 2
 
4.7%
s1029484 2
 
4.7%
g1016463 2
 
4.7%
g1029403 2
 
4.7%
g1029400 2
 
4.7%
g1029401 2
 
4.7%
s1029407 2
 
4.7%
Other values (18) 23
53.5%
2023-12-13T04:15:18.960615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 71
20.6%
1 60
17.4%
9 42
12.2%
2 37
10.8%
4 28
 
8.1%
G 23
 
6.7%
S 20
 
5.8%
8 18
 
5.2%
3 12
 
3.5%
5 12
 
3.5%
Other values (2) 21
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 301
87.5%
Uppercase Letter 43
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 71
23.6%
1 60
19.9%
9 42
14.0%
2 37
12.3%
4 28
 
9.3%
8 18
 
6.0%
3 12
 
4.0%
5 12
 
4.0%
7 11
 
3.7%
6 10
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
G 23
53.5%
S 20
46.5%

Most occurring scripts

ValueCountFrequency (%)
Common 301
87.5%
Latin 43
 
12.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 71
23.6%
1 60
19.9%
9 42
14.0%
2 37
12.3%
4 28
 
9.3%
8 18
 
6.0%
3 12
 
4.0%
5 12
 
4.0%
7 11
 
3.7%
6 10
 
3.3%
Latin
ValueCountFrequency (%)
G 23
53.5%
S 20
46.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 71
20.6%
1 60
17.4%
9 42
12.2%
2 37
10.8%
4 28
 
8.1%
G 23
 
6.7%
S 20
 
5.8%
8 18
 
5.2%
3 12
 
3.5%
5 12
 
3.5%
Other values (2) 21
 
6.1%

측정지점명
Text

Distinct28
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-13T04:15:19.197382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.8139535
Min length2

Characters and Unicode

Total characters121
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)30.2%

Sample

1st row고이도
2nd row다물
3rd row마진도
4th row만재
5th row상태
ValueCountFrequency (%)
다랑도 2
 
4.7%
덕우도 2
 
4.7%
떼무리 2
 
4.7%
동네기미 2
 
4.7%
당산 2
 
4.7%
흑일 2
 
4.7%
황제도 2
 
4.7%
서화도 2
 
4.7%
어룡도 2
 
4.7%
서넙도 2
 
4.7%
Other values (18) 23
53.5%
2023-12-13T04:15:19.616311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 29
24.0%
마 6
 
5.0%
서 5
 
4.1%
넙 4
 
3.3%
일 4
 
3.3%
동 3
 
2.5%
화 3
 
2.5%
우 3
 
2.5%
고 3
 
2.5%
다 3
 
2.5%
Other values (35) 58
47.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 121
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 29
24.0%
마 6
 
5.0%
서 5
 
4.1%
넙 4
 
3.3%
일 4
 
3.3%
동 3
 
2.5%
화 3
 
2.5%
우 3
 
2.5%
고 3
 
2.5%
다 3
 
2.5%
Other values (35) 58
47.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 121
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 29
24.0%
마 6
 
5.0%
서 5
 
4.1%
넙 4
 
3.3%
일 4
 
3.3%
동 3
 
2.5%
화 3
 
2.5%
우 3
 
2.5%
고 3
 
2.5%
다 3
 
2.5%
Other values (35) 58
47.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 121
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 29
24.0%
마 6
 
5.0%
서 5
 
4.1%
넙 4
 
3.3%
일 4
 
3.3%
동 3
 
2.5%
화 3
 
2.5%
우 3
 
2.5%
고 3
 
2.5%
다 3
 
2.5%
Other values (35) 58
47.9%

측정지점주소
Text

Distinct28
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-13T04:15:19.956728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length19.744186
Min length5

Characters and Unicode

Total characters849
Distinct characters97
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

Unique13 ?
Unique (%)30.2%

Sample

1st row전라남도 신안군 압해면 고이도
2nd row전라남도 신안군 흑산면 다물도리
3rd row전라남도 신안군 장산면 마진도
4th row전라남도 신안군 흑산면 만재도리
5th row전라남도 신안군 흑산면 상태도리
ValueCountFrequency (%)
전라남도 35
 
16.4%
완도군 26
 
12.1%
산 13
 
6.1%
군외면 10
 
4.7%
신안군 9
 
4.2%
노화읍 7
 
3.3%
인천광역시 6
 
2.8%
당인리 6
 
2.8%
금일읍 4
 
1.9%
흑산면 4
 
1.9%
Other values (58) 94
43.9%
2023-12-13T04:15:20.502901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
171
20.1%
도 73
 
8.6%
군 49
 
5.8%
리 36
 
4.2%
라 36
 
4.2%
전 35
 
4.1%
남 35
 
4.1%
면 29
 
3.4%
완 26
 
3.1%
1 25
 
2.9%
Other values (87) 334
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 572
67.4%
Space Separator 171
 
20.1%
Decimal Number 100
 
11.8%
Dash Punctuation 6
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 73
 
12.8%
군 49
 
8.6%
리 36
 
6.3%
라 36
 
6.3%
전 35
 
6.1%
남 35
 
6.1%
면 29
 
5.1%
완 26
 
4.5%
산 18
 
3.1%
인 12
 
2.1%
Other values (75) 223
39.0%
Decimal Number
ValueCountFrequency (%)
1 25
25.0%
7 14
14.0%
2 13
13.0%
6 10
 
10.0%
4 9
 
9.0%
3 8
 
8.0%
5 7
 
7.0%
9 6
 
6.0%
0 6
 
6.0%
8 2
 
2.0%
Space Separator
ValueCountFrequency (%)
171
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 572
67.4%
Common 277
32.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 73
 
12.8%
군 49
 
8.6%
리 36
 
6.3%
라 36
 
6.3%
전 35
 
6.1%
남 35
 
6.1%
면 29
 
5.1%
완 26
 
4.5%
산 18
 
3.1%
인 12
 
2.1%
Other values (75) 223
39.0%
Common
ValueCountFrequency (%)
171
61.7%
1 25
 
9.0%
7 14
 
5.1%
2 13
 
4.7%
6 10
 
3.6%
4 9
 
3.2%
3 8
 
2.9%
5 7
 
2.5%
9 6
 
2.2%
- 6
 
2.2%
Other values (2) 8
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 572
67.4%
ASCII 277
32.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
171
61.7%
1 25
 
9.0%
7 14
 
5.1%
2 13
 
4.7%
6 10
 
3.6%
4 9
 
3.2%
3 8
 
2.9%
5 7
 
2.5%
9 6
 
2.2%
- 6
 
2.2%
Other values (2) 8
 
2.9%
Hangul
ValueCountFrequency (%)
도 73
 
12.8%
군 49
 
8.6%
리 36
 
6.3%
라 36
 
6.3%
전 35
 
6.1%
남 35
 
6.1%
면 29
 
5.1%
완 26
 
4.5%
산 18
 
3.1%
인 12
 
2.1%
Other values (75) 223
39.0%

수도규모
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
마을상수도
22 
소규모급수시설
21 

Length

Max length7
Median length5
Mean length5.9767442
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
마을상수도 22
51.2%
소규모급수시설 21
48.8%

Length

2023-12-13T04:15:20.730675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:15:20.901192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마을상수도 22
51.2%
소규모급수시설 21
48.8%

수원
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
해수
43 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
해수 43
100.0%

Length

2023-12-13T04:15:21.025572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:15:21.163453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해수 43
100.0%

채수일자
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)34.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20181944
Minimum20150317
Maximum20210912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T04:15:21.336849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20150317
5-th percentile20150317
Q120175268
median20180226
Q320200921
95-th percentile20200921
Maximum20210912
Range60595
Interquartile range (IQR)25653.5

Descriptive statistics

Standard deviation19822.347
Coefficient of variation (CV)0.00098218222
Kurtosis-1.0125594
Mean20181944
Median Absolute Deviation (MAD)20695
Skewness-0.48216114
Sum8.6782361 × 108
Variance3.9292544 × 108
MonotonicityNot monotonic
2023-12-13T04:15:21.521867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20180226 13
30.2%
20200921 12
27.9%
20150317 4
 
9.3%
20150320 2
 
4.7%
20200915 2
 
4.7%
20150324 1
 
2.3%
20150427 1
 
2.3%
20150319 1
 
2.3%
20160511 1
 
2.3%
20170309 1
 
2.3%
Other values (5) 5
 
11.6%
ValueCountFrequency (%)
20150317 4
 
9.3%
20150319 1
 
2.3%
20150320 2
 
4.7%
20150324 1
 
2.3%
20150427 1
 
2.3%
20160511 1
 
2.3%
20170309 1
 
2.3%
20180226 13
30.2%
20180530 1
 
2.3%
20190826 1
 
2.3%
ValueCountFrequency (%)
20210912 1
 
2.3%
20210816 1
 
2.3%
20200921 12
27.9%
20200915 2
 
4.7%
20190905 1
 
2.3%
20190826 1
 
2.3%
20180530 1
 
2.3%
20180226 13
30.2%
20170309 1
 
2.3%
20160511 1
 
2.3%

카드뮴
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
0.0
42 
0.073
 
1

Length

Max length5
Median length3
Mean length3.0465116
Min length3

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 42
97.7%
0.073 1
 
2.3%

Length

2023-12-13T04:15:21.667302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:15:21.803848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 42
97.7%
0.073 1
 
2.3%

비소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0065813953
Minimum0
Maximum0.036
Zeros25
Zeros (%)58.1%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T04:15:21.952992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.012
95-th percentile0.024
Maximum0.036
Range0.036
Interquartile range (IQR)0.012

Descriptive statistics

Standard deviation0.0094698332
Coefficient of variation (CV)1.4388792
Kurtosis1.3832927
Mean0.0065813953
Median Absolute Deviation (MAD)0
Skewness1.4186061
Sum0.283
Variance8.9677741 × 10-5
MonotonicityNot monotonic
2023-12-13T04:15:22.111418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0 25
58.1%
0.013 2
 
4.7%
0.009 2
 
4.7%
0.024 2
 
4.7%
0.016 2
 
4.7%
0.007 2
 
4.7%
0.017 1
 
2.3%
0.01 1
 
2.3%
0.036 1
 
2.3%
0.03 1
 
2.3%
Other values (4) 4
 
9.3%
ValueCountFrequency (%)
0.0 25
58.1%
0.006 1
 
2.3%
0.007 2
 
4.7%
0.009 2
 
4.7%
0.01 1
 
2.3%
0.011 1
 
2.3%
0.013 2
 
4.7%
0.015 1
 
2.3%
0.016 2
 
4.7%
0.017 1
 
2.3%
ValueCountFrequency (%)
0.036 1
2.3%
0.03 1
2.3%
0.024 2
4.7%
0.02 1
2.3%
0.017 1
2.3%
0.016 2
4.7%
0.015 1
2.3%
0.013 2
4.7%
0.011 1
2.3%
0.01 1
2.3%

수은
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
0
43 

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 43
100.0%

Length

2023-12-13T04:15:22.272803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:15:22.704090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 43
100.0%

납
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
0
43 

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 43
100.0%

Length

2023-12-13T04:15:22.811376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:15:22.914324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 43
100.0%

크롬
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
0.0
38 
0.02
5 

Length

Max length4
Median length3
Mean length3.1162791
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 38
88.4%
0.02 5
 
11.6%

Length

2023-12-13T04:15:23.026335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:15:23.141241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 38
88.4%
0.02 5
 
11.6%

붕소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63693023
Minimum0
Maximum4.9
Zeros1
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T04:15:23.238405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.01
median0.02
Q30.155
95-th percentile3.835
Maximum4.9
Range4.9
Interquartile range (IQR)0.145

Descriptive statistics

Standard deviation1.3745685
Coefficient of variation (CV)2.1581147
Kurtosis2.8865155
Mean0.63693023
Median Absolute Deviation (MAD)0.012
Skewness2.0910465
Sum27.388
Variance1.8894385
MonotonicityNot monotonic
2023-12-13T04:15:23.378709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.01 18
41.9%
0.02 2
 
4.7%
0.08 2
 
4.7%
0.04 2
 
4.7%
0.0 1
 
2.3%
0.18 1
 
2.3%
3.52 1
 
2.3%
3.87 1
 
2.3%
0.15 1
 
2.3%
0.12 1
 
2.3%
Other values (13) 13
30.2%
ValueCountFrequency (%)
0.0 1
 
2.3%
0.008 1
 
2.3%
0.01 18
41.9%
0.02 2
 
4.7%
0.03 1
 
2.3%
0.04 2
 
4.7%
0.07 1
 
2.3%
0.08 2
 
4.7%
0.12 1
 
2.3%
0.13 1
 
2.3%
ValueCountFrequency (%)
4.9 1
2.3%
4.29 1
2.3%
3.87 1
2.3%
3.52 1
2.3%
3.5 1
2.3%
2.8 1
2.3%
2.58 1
2.3%
0.27 1
2.3%
0.21 1
2.3%
0.18 1
2.3%

Interactions

2023-12-13T04:15:15.238781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:13.063831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:13.484527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:13.981001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:14.590470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:15.359454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:13.143991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:13.571312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:14.072734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:14.715895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:15.485480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:13.224282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:13.652044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:14.198314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:14.854500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:15.606613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:13.307244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:13.760006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:14.319164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:14.992202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:15.717751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:13.406963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:13.883465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:14.466725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:15.115477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:15:23.490446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번검사년도지역소규모수도시설번호소규모수도시설명측정지점코드측정지점명측정지점주소수도규모채수일자카드뮴비소크롬붕소
연번1.0000.6730.7860.4370.4370.4370.4370.4370.4450.7930.2220.0540.3070.465
검사년도0.6731.0000.9980.0000.0000.0000.0000.0000.5621.0001.0000.0000.4730.889
지역0.7860.9981.0001.0001.0001.0001.0001.0000.7590.9590.4280.0000.1420.826
소규모수도시설번호0.4370.0001.0001.0001.0001.0001.0001.0001.0000.3690.0000.0000.0000.000
소규모수도시설명0.4370.0001.0001.0001.0001.0001.0001.0001.0000.3690.0000.0000.0000.000
측정지점코드0.4370.0001.0001.0001.0001.0001.0001.0001.0000.3690.0000.0000.0000.000
측정지점명0.4370.0001.0001.0001.0001.0001.0001.0001.0000.3690.0000.0000.0000.000
측정지점주소0.4370.0001.0001.0001.0001.0001.0001.0001.0000.3690.0000.0000.0000.000
수도규모0.4450.5620.7591.0001.0001.0001.0001.0001.0000.5130.0000.6280.3970.000
채수일자0.7931.0000.9590.3690.3690.3690.3690.3690.5131.0001.0000.0000.3350.888
카드뮴0.2221.0000.4280.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.545
비소0.0540.0000.0000.0000.0000.0000.0000.0000.6280.0000.0001.0000.7610.000
크롬0.3070.4730.1420.0000.0000.0000.0000.0000.3970.3350.0000.7611.0000.000
붕소0.4650.8890.8260.0000.0000.0000.0000.0000.0000.8880.5450.0000.0001.000
2023-12-13T04:15:23.629827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역수도규모카드뮴크롬
지역1.0000.5360.2780.082
수도규모0.5361.0000.0000.259
카드뮴0.2780.0001.0000.000
크롬0.0820.2590.0001.000
2023-12-13T04:15:23.729018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번검사년도채수일자비소붕소지역수도규모카드뮴크롬
연번1.0000.9600.9410.3100.5430.5560.2750.1350.200
검사년도0.9601.0000.9850.2750.5580.9250.5770.9370.365
채수일자0.9410.9851.0000.2240.5820.9250.5770.9370.365
비소0.3100.2750.2241.000-0.1700.0000.5750.0000.708
붕소0.5430.5580.582-0.1701.0000.7840.0000.6340.000
지역0.5560.9250.9250.0000.7841.0000.5360.2780.082
수도규모0.2750.5770.5770.5750.0000.5361.0000.0000.259
카드뮴0.1350.9370.9370.0000.6340.2780.0001.0000.000
크롬0.2000.3650.3650.7080.0000.0820.2590.0001.000

Missing values

2023-12-13T04:15:15.876557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:15:16.135330image/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

연번검사년도지역소규모수도시설번호소규모수도시설명측정지점코드측정지점명측정지점주소수도규모수원채수일자카드뮴비소수은납크롬붕소
012015전라남도 신안군SS00013201고이도S1007676고이도전라남도 신안군 압해면 고이도마을상수도해수201503240.00.0000.00.0
122015전라남도 신안군SS00013226다물S1025841다물전라남도 신안군 흑산면 다물도리마을상수도해수201503170.00.0000.00.01
232015전라남도 신안군SS00013265마진도S1027801마진도전라남도 신안군 장산면 마진도마을상수도해수201504270.00.0000.00.008
342015전라남도 신안군SS00013267만재S1025842만재전라남도 신안군 흑산면 만재도리마을상수도해수201503170.00.0000.00.01
452015전라남도 신안군SS00013296상태G1028114상태전라남도 신안군 흑산면 상태도리마을상수도해수201503170.00.0000.00.01
562015전라남도 신안군SS00013301서소우이도G1025865서소우이도전라남도 신안군 도초면 우이도리마을상수도해수201503200.00.0000.00.27
672015전라남도 신안군SS00013338옥도S1010110옥도전라남도 신안군 하의면 옥도리마을상수도해수201503190.00.0000.00.01
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