Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells44
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory163.0 B

Variable types

Numeric11
Categorical3
Text4

Dataset

Description분기별 먹는물 수질검사 결과 제공항목 : 년도,검사분기,지역,공급정수장,검사지점,수질검사기관,일반세균,총대장균군,암모니아성질소,총트리할로메탄,동,수소이온농도,아연,철,탁도,잔류염소,대장균_분원성대장균군 등 * 상세자료조회는 아래 URL을 참고 해주시기 바랍니다. https://www.waternow.go.kr/web/lawData/?pMENUID=4&ATTR_1=2008
URLhttps://www.data.go.kr/data/15093986/fileData.do

Alerts

총대장균군 is highly overall correlated with 연번 and 12 other fieldsHigh correlation
검사분기 is highly overall correlated with 총대장균군 and 1 other fieldsHigh correlation
대장균_분원성대장균군 is highly overall correlated with 연번 and 12 other fieldsHigh correlation
연번 is highly overall correlated with 검사년도 and 2 other fieldsHigh correlation
검사년도 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
일반세균 is highly overall correlated with 총대장균군 and 1 other fieldsHigh correlation
암모니아성질소 is highly overall correlated with 총대장균군 and 1 other fieldsHigh correlation
총트리할로메탄 is highly overall correlated with 총대장균군 and 1 other fieldsHigh correlation
동 is highly overall correlated with 총대장균군 and 1 other fieldsHigh correlation
수소이온농도 is highly overall correlated with 총대장균군 and 1 other fieldsHigh correlation
아연 is highly overall correlated with 총대장균군 and 1 other fieldsHigh correlation
철 is highly overall correlated with 총대장균군 and 1 other fieldsHigh correlation
탁도 is highly overall correlated with 총대장균군 and 1 other fieldsHigh correlation
잔류염소 is highly overall correlated with 총대장균군 and 1 other fieldsHigh correlation
총대장균군 is highly imbalanced (96.2%)Imbalance
대장균_분원성대장균군 is highly imbalanced (96.2%)Imbalance
일반세균 is highly skewed (γ1 = 27.75343092)Skewed
동 is highly skewed (γ1 = 20.30039231)Skewed
아연 is highly skewed (γ1 = 23.39430635)Skewed
연번 has unique valuesUnique
일반세균 has 9898 (99.0%) zerosZeros
암모니아성질소 has 9818 (98.2%) zerosZeros
총트리할로메탄 has 450 (4.5%) zerosZeros
동 has 7927 (79.3%) zerosZeros
아연 has 2829 (28.3%) zerosZeros
철 has 9436 (94.4%) zerosZeros

Reproduction

Analysis started2023-12-12 22:21:28.234710
Analysis finished2023-12-12 22:21:45.169841
Duration16.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31589.336
Minimum2
Maximum62971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:21:45.238751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3415.65
Q115770.5
median31722.5
Q347402.5
95-th percentile59640.05
Maximum62971
Range62969
Interquartile range (IQR)31632

Descriptive statistics

Standard deviation18093.512
Coefficient of variation (CV)0.5727728
Kurtosis-1.206225
Mean31589.336
Median Absolute Deviation (MAD)15811.5
Skewness-0.011220184
Sum3.1589336 × 108
Variance3.2737519 × 108
MonotonicityNot monotonic
2023-12-13T07:21:45.361106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2861 1
 
< 0.1%
10471 1
 
< 0.1%
18430 1
 
< 0.1%
45148 1
 
< 0.1%
55526 1
 
< 0.1%
9000 1
 
< 0.1%
50467 1
 
< 0.1%
59222 1
 
< 0.1%
61752 1
 
< 0.1%
46715 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
11 1
< 0.1%
20 1
< 0.1%
31 1
< 0.1%
49 1
< 0.1%
50 1
< 0.1%
63 1
< 0.1%
80 1
< 0.1%
84 1
< 0.1%
87 1
< 0.1%
ValueCountFrequency (%)
62971 1
< 0.1%
62967 1
< 0.1%
62946 1
< 0.1%
62931 1
< 0.1%
62930 1
< 0.1%
62929 1
< 0.1%
62921 1
< 0.1%
62915 1
< 0.1%
62911 1
< 0.1%
62907 1
< 0.1%

검사년도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.9563
Minimum2015
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:21:45.476847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016
median2018
Q32020
95-th percentile2021
Maximum2021
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0653854
Coefficient of variation (CV)0.0010235035
Kurtosis-1.3316445
Mean2017.9563
Median Absolute Deviation (MAD)2
Skewness0.067868078
Sum20179563
Variance4.2658169
MonotonicityNot monotonic
2023-12-13T07:21:45.583503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2020 1631
16.3%
2021 1548
15.5%
2015 1544
15.4%
2016 1510
15.1%
2018 1486
14.9%
2017 1486
14.9%
2019 795
8.0%
ValueCountFrequency (%)
2015 1544
15.4%
2016 1510
15.1%
2017 1486
14.9%
2018 1486
14.9%
2019 795
8.0%
2020 1631
16.3%
2021 1548
15.5%
ValueCountFrequency (%)
2021 1548
15.5%
2020 1631
16.3%
2019 795
8.0%
2018 1486
14.9%
2017 1486
14.9%
2016 1510
15.1%
2015 1544
15.4%

검사분기
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1분기
2680 
2분기
2616 
3분기
2386 
4분기
2318 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2분기
2nd row2분기
3rd row3분기
4th row2분기
5th row4분기

Common Values

ValueCountFrequency (%)
1분기 2680
26.8%
2분기 2616
26.2%
3분기 2386
23.9%
4분기 2318
23.2%

Length

2023-12-13T07:21:45.727566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:21:45.838096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1분기 2680
26.8%
2분기 2616
26.2%
3분기 2386
23.9%
4분기 2318
23.2%

지역
Text

Distinct160
Distinct (%)1.6%
Missing2
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-13T07:21:46.176133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.3234647
Min length5

Characters and Unicode

Total characters73220
Distinct characters122
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row경기도 안산시
2nd row제주특별자치도
3rd row경상북도 경주시
4th row경기도 광명시
5th row제주특별자치도
ValueCountFrequency (%)
경상북도 1548
 
8.5%
경기도 1360
 
7.4%
강원도 1101
 
6.0%
경상남도 1080
 
5.9%
전라남도 1027
 
5.6%
충청북도 859
 
4.7%
전라북도 696
 
3.8%
충청남도 599
 
3.3%
서울특별시 354
 
1.9%
제주특별자치도 321
 
1.8%
Other values (157) 9323
51.0%
2023-12-13T07:21:46.967895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 8682
 
11.9%
8270
 
11.3%
시 5531
 
7.6%
군 4342
 
5.9%
경 4306
 
5.9%
북 3103
 
4.2%
남 3032
 
4.1%
상 2695
 
3.7%
전 2014
 
2.8%
청 1746
 
2.4%
Other values (112) 29499
40.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64950
88.7%
Space Separator 8270
 
11.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 8682
 
13.4%
시 5531
 
8.5%
군 4342
 
6.7%
경 4306
 
6.6%
북 3103
 
4.8%
남 3032
 
4.7%
상 2695
 
4.1%
전 2014
 
3.1%
청 1746
 
2.7%
라 1723
 
2.7%
Other values (111) 27776
42.8%
Space Separator
ValueCountFrequency (%)
8270
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64950
88.7%
Common 8270
 
11.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 8682
 
13.4%
시 5531
 
8.5%
군 4342
 
6.7%
경 4306
 
6.6%
북 3103
 
4.8%
남 3032
 
4.7%
상 2695
 
4.1%
전 2014
 
3.1%
청 1746
 
2.7%
라 1723
 
2.7%
Other values (111) 27776
42.8%
Common
ValueCountFrequency (%)
8270
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64950
88.7%
ASCII 8270
 
11.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 8682
 
13.4%
시 5531
 
8.5%
군 4342
 
6.7%
경 4306
 
6.6%
북 3103
 
4.8%
남 3032
 
4.7%
상 2695
 
4.1%
전 2014
 
3.1%
청 1746
 
2.7%
라 1723
 
2.7%
Other values (111) 27776
42.8%
ASCII
ValueCountFrequency (%)
8270
100.0%

공급정수장
Text

Distinct457
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:21:47.352545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length2
Mean length2.3185
Min length2

Characters and Unicode

Total characters23185
Distinct characters223
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.1%

Sample

1st row안산
2nd row별도봉
3rd row탑동
4th row노온
5th row토평
ValueCountFrequency (%)
충주 367
 
3.6%
고산 269
 
2.7%
보령 209
 
2.1%
월평 153
 
1.5%
청주(생활 146
 
1.4%
동화 145
 
1.4%
강북 121
 
1.2%
홍제3 109
 
1.1%
덕소 107
 
1.1%
석성 96
 
1.0%
Other values (447) 8371
82.9%
2023-12-13T07:21:47.899157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 1003
 
4.3%
주 766
 
3.3%
동 680
 
2.9%
천 573
 
2.5%
평 520
 
2.2%
성 488
 
2.1%
양 483
 
2.1%
고 440
 
1.9%
덕 432
 
1.9%
화 393
 
1.7%
Other values (213) 17407
75.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22101
95.3%
Decimal Number 527
 
2.3%
Close Punctuation 220
 
0.9%
Open Punctuation 220
 
0.9%
Space Separator 93
 
0.4%
Math Symbol 12
 
0.1%
Other Punctuation 12
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 1003
 
4.5%
주 766
 
3.5%
동 680
 
3.1%
천 573
 
2.6%
평 520
 
2.4%
성 488
 
2.2%
양 483
 
2.2%
고 440
 
2.0%
덕 432
 
2.0%
화 393
 
1.8%
Other values (204) 16323
73.9%
Decimal Number
ValueCountFrequency (%)
2 267
50.7%
3 137
26.0%
1 123
23.3%
Other Punctuation
ValueCountFrequency (%)
, 10
83.3%
· 2
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 220
100.0%
Open Punctuation
ValueCountFrequency (%)
( 220
100.0%
Space Separator
ValueCountFrequency (%)
93
100.0%
Math Symbol
ValueCountFrequency (%)
+ 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22101
95.3%
Common 1084
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 1003
 
4.5%
주 766
 
3.5%
동 680
 
3.1%
천 573
 
2.6%
평 520
 
2.4%
성 488
 
2.2%
양 483
 
2.2%
고 440
 
2.0%
덕 432
 
2.0%
화 393
 
1.8%
Other values (204) 16323
73.9%
Common
ValueCountFrequency (%)
2 267
24.6%
) 220
20.3%
( 220
20.3%
3 137
12.6%
1 123
11.3%
93
 
8.6%
+ 12
 
1.1%
, 10
 
0.9%
· 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22101
95.3%
ASCII 1082
 
4.7%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 1003
 
4.5%
주 766
 
3.5%
동 680
 
3.1%
천 573
 
2.6%
평 520
 
2.4%
성 488
 
2.2%
양 483
 
2.2%
고 440
 
2.0%
덕 432
 
2.0%
화 393
 
1.8%
Other values (204) 16323
73.9%
ASCII
ValueCountFrequency (%)
2 267
24.7%
) 220
20.3%
( 220
20.3%
3 137
12.7%
1 123
11.4%
93
 
8.6%
+ 12
 
1.1%
, 10
 
0.9%
None
ValueCountFrequency (%)
· 2
100.0%

검사지점
Text

Distinct115
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:21:48.200023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length8.8188
Min length3

Characters and Unicode

Total characters88188
Distinct characters64
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.1%

Sample

1st row관말수도꼭지(직수)-A
2nd row주배수지(후)
3rd row급수구역유입부
4th row가압장(유출부)
5th row주배수지(후)
ValueCountFrequency (%)
관말수도꼭지(직수 1231
 
12.3%
급수구역유입부 1073
 
10.7%
주배수지(후 952
 
9.5%
정수장 766
 
7.7%
주배수지(전 635
 
6.3%
가압장(유출부 531
 
5.3%
관말수도꼭지(직수)-a 402
 
4.0%
관말수도꼭지(직수)-b 312
 
3.1%
급수구역유입부-a 294
 
2.9%
주배수지(후)-a 226
 
2.3%
Other values (105) 3578
35.8%
2023-12-13T07:21:48.659383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 12367
 
14.0%
( 7004
 
7.9%
) 7004
 
7.9%
지 6407
 
7.3%
- 4431
 
5.0%
부 3020
 
3.4%
관 2964
 
3.4%
도 2964
 
3.4%
꼭 2964
 
3.4%
말 2964
 
3.4%
Other values (54) 36099
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65316
74.1%
Open Punctuation 7004
 
7.9%
Close Punctuation 7004
 
7.9%
Uppercase Letter 4433
 
5.0%
Dash Punctuation 4431
 
5.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 12367
18.9%
지 6407
 
9.8%
부 3020
 
4.6%
관 2964
 
4.5%
도 2964
 
4.5%
꼭 2964
 
4.5%
말 2964
 
4.5%
주 2819
 
4.3%
배 2819
 
4.3%
유 2812
 
4.3%
Other values (28) 23216
35.5%
Uppercase Letter
ValueCountFrequency (%)
A 1427
32.2%
B 993
22.4%
C 596
13.4%
D 364
 
8.2%
E 277
 
6.2%
F 170
 
3.8%
G 148
 
3.3%
H 134
 
3.0%
I 84
 
1.9%
J 81
 
1.8%
Other values (13) 159
 
3.6%
Open Punctuation
ValueCountFrequency (%)
( 7004
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7004
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65316
74.1%
Common 18439
 
20.9%
Latin 4433
 
5.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 12367
18.9%
지 6407
 
9.8%
부 3020
 
4.6%
관 2964
 
4.5%
도 2964
 
4.5%
꼭 2964
 
4.5%
말 2964
 
4.5%
주 2819
 
4.3%
배 2819
 
4.3%
유 2812
 
4.3%
Other values (28) 23216
35.5%
Latin
ValueCountFrequency (%)
A 1427
32.2%
B 993
22.4%
C 596
13.4%
D 364
 
8.2%
E 277
 
6.2%
F 170
 
3.8%
G 148
 
3.3%
H 134
 
3.0%
I 84
 
1.9%
J 81
 
1.8%
Other values (13) 159
 
3.6%
Common
ValueCountFrequency (%)
( 7004
38.0%
) 7004
38.0%
- 4431
24.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65316
74.1%
ASCII 22872
 
25.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 12367
18.9%
지 6407
 
9.8%
부 3020
 
4.6%
관 2964
 
4.5%
도 2964
 
4.5%
꼭 2964
 
4.5%
말 2964
 
4.5%
주 2819
 
4.3%
배 2819
 
4.3%
유 2812
 
4.3%
Other values (28) 23216
35.5%
ASCII
ValueCountFrequency (%)
( 7004
30.6%
) 7004
30.6%
- 4431
19.4%
A 1427
 
6.2%
B 993
 
4.3%
C 596
 
2.6%
D 364
 
1.6%
E 277
 
1.2%
F 170
 
0.7%
G 148
 
0.6%
Other values (16) 458
 
2.0%

수질검사기관
Text

Distinct101
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:21:48.914505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length10.406
Min length2

Characters and Unicode

Total characters104060
Distinct characters160
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row안산시상수도사업소
2nd row제주특별자치도 상하수도본부
3rd row자체수질검사
4th row(주)피엘아이환경기술연구원
5th row제주특별자치도 상하수도본부
ValueCountFrequency (%)
자체수질검사 1423
 
10.9%
한국수자원공사 903
 
6.9%
주)기림생명과학원 408
 
3.1%
경북-보건환경연구원 406
 
3.1%
주)동우환경기술연구원 293
 
2.2%
전주대학교 286
 
2.2%
대구시-상수도사업본부 254
 
1.9%
제일랩(대구 243
 
1.9%
대전시-상수도사업본부 240
 
1.8%
제주특별자치도 236
 
1.8%
Other values (116) 8417
64.2%
2023-12-13T07:21:49.269129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 4762
 
4.6%
사 4664
 
4.5%
원 4441
 
4.3%
구 3954
 
3.8%
경 3747
 
3.6%
연 3673
 
3.5%
3290
 
3.2%
주 3162
 
3.0%
환 2894
 
2.8%
자 2562
 
2.5%
Other values (150) 66911
64.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 92352
88.7%
Space Separator 3290
 
3.2%
Close Punctuation 2504
 
2.4%
Open Punctuation 2504
 
2.4%
Dash Punctuation 2011
 
1.9%
Uppercase Letter 1370
 
1.3%
Other Punctuation 25
 
< 0.1%
Lowercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 4762
 
5.2%
사 4664
 
5.1%
원 4441
 
4.8%
구 3954
 
4.3%
경 3747
 
4.1%
연 3673
 
4.0%
주 3162
 
3.4%
환 2894
 
3.1%
자 2562
 
2.8%
부 2451
 
2.7%
Other values (136) 56042
60.7%
Uppercase Letter
ValueCountFrequency (%)
I 300
21.9%
T 300
21.9%
M 235
17.2%
E 235
17.2%
K 150
10.9%
O 150
10.9%
Lowercase Letter
ValueCountFrequency (%)
i 2
50.0%
f 1
25.0%
t 1
25.0%
Space Separator
ValueCountFrequency (%)
3290
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2504
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2504
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2011
100.0%
Other Punctuation
ValueCountFrequency (%)
. 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 92352
88.7%
Common 10334
 
9.9%
Latin 1374
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 4762
 
5.2%
사 4664
 
5.1%
원 4441
 
4.8%
구 3954
 
4.3%
경 3747
 
4.1%
연 3673
 
4.0%
주 3162
 
3.4%
환 2894
 
3.1%
자 2562
 
2.8%
부 2451
 
2.7%
Other values (136) 56042
60.7%
Latin
ValueCountFrequency (%)
I 300
21.8%
T 300
21.8%
M 235
17.1%
E 235
17.1%
K 150
10.9%
O 150
10.9%
i 2
 
0.1%
f 1
 
0.1%
t 1
 
0.1%
Common
ValueCountFrequency (%)
3290
31.8%
) 2504
24.2%
( 2504
24.2%
- 2011
19.5%
. 25
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 92352
88.7%
ASCII 11708
 
11.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 4762
 
5.2%
사 4664
 
5.1%
원 4441
 
4.8%
구 3954
 
4.3%
경 3747
 
4.1%
연 3673
 
4.0%
주 3162
 
3.4%
환 2894
 
3.1%
자 2562
 
2.8%
부 2451
 
2.7%
Other values (136) 56042
60.7%
ASCII
ValueCountFrequency (%)
3290
28.1%
) 2504
21.4%
( 2504
21.4%
- 2011
17.2%
I 300
 
2.6%
T 300
 
2.6%
M 235
 
2.0%
E 235
 
2.0%
K 150
 
1.3%
O 150
 
1.3%
Other values (4) 29
 
0.2%

일반세균
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct32
Distinct (%)0.3%
Missing8
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.11339071
Minimum0
Maximum89
Zeros9898
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:21:49.398359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.94367
Coefficient of variation (CV)17.141351
Kurtosis962.90489
Mean0.11339071
Median Absolute Deviation (MAD)0
Skewness27.753431
Sum1133
Variance3.7778529
MonotonicityNot monotonic
2023-12-13T07:21:49.501687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 9898
99.0%
2 15
 
0.1%
1 14
 
0.1%
5 10
 
0.1%
4 7
 
0.1%
3 5
 
0.1%
14 4
 
< 0.1%
20 4
 
< 0.1%
16 4
 
< 0.1%
9 3
 
< 0.1%
Other values (22) 28
 
0.3%
(Missing) 8
 
0.1%
ValueCountFrequency (%)
0 9898
99.0%
1 14
 
0.1%
2 15
 
0.1%
3 5
 
0.1%
4 7
 
0.1%
5 10
 
0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
89 1
< 0.1%
77 1
< 0.1%
67 1
< 0.1%
47 1
< 0.1%
45 1
< 0.1%
41 1
< 0.1%
36 1
< 0.1%
34 1
< 0.1%
33 1
< 0.1%
30 1
< 0.1%

총대장균군
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
불검출
9959 
<NA>
 
41

Length

Max length4
Median length3
Mean length3.0041
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
불검출 9959
99.6%
<NA> 41
 
0.4%

Length

2023-12-13T07:21:49.608899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:21:49.706716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불검출 9959
99.6%
na 41
 
0.4%

암모니아성질소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)0.2%
Missing8
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.00054463571
Minimum0
Maximum0.24
Zeros9818
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:21:49.785203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.24
Range0.24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0058766564
Coefficient of variation (CV)10.790068
Kurtosis484.2641
Mean0.00054463571
Median Absolute Deviation (MAD)0
Skewness18.599651
Sum5.442
Variance3.4535091 × 10-5
MonotonicityNot monotonic
2023-12-13T07:21:49.884858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0 9818
98.2%
0.02 59
 
0.6%
0.01 56
 
0.6%
0.04 13
 
0.1%
0.03 11
 
0.1%
0.05 8
 
0.1%
0.09 5
 
0.1%
0.07 5
 
0.1%
0.06 4
 
< 0.1%
0.08 3
 
< 0.1%
Other values (6) 10
 
0.1%
(Missing) 8
 
0.1%
ValueCountFrequency (%)
0.0 9818
98.2%
0.002 1
 
< 0.1%
0.01 56
 
0.6%
0.02 59
 
0.6%
0.03 11
 
0.1%
0.04 13
 
0.1%
0.05 8
 
0.1%
0.06 4
 
< 0.1%
0.07 5
 
0.1%
0.08 3
 
< 0.1%
ValueCountFrequency (%)
0.24 1
 
< 0.1%
0.17 1
 
< 0.1%
0.12 2
 
< 0.1%
0.11 2
 
< 0.1%
0.1 3
 
< 0.1%
0.09 5
0.1%
0.08 3
 
< 0.1%
0.07 5
0.1%
0.06 4
< 0.1%
0.05 8
0.1%

총트리할로메탄
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct107
Distinct (%)1.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.024881948
Minimum0
Maximum0.151
Zeros450
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:21:49.990315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.001
Q10.013
median0.023
Q30.034
95-th percentile0.055
Maximum0.151
Range0.151
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.016300113
Coefficient of variation (CV)0.65509792
Kurtosis1.1904194
Mean0.024881948
Median Absolute Deviation (MAD)0.011
Skewness0.82212692
Sum248.7946
Variance0.00026569367
MonotonicityNot monotonic
2023-12-13T07:21:50.116184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 450
 
4.5%
0.024 280
 
2.8%
0.025 278
 
2.8%
0.022 274
 
2.7%
0.026 259
 
2.6%
0.021 256
 
2.6%
0.027 252
 
2.5%
0.02 251
 
2.5%
0.017 251
 
2.5%
0.018 247
 
2.5%
Other values (97) 7201
72.0%
ValueCountFrequency (%)
0.0 450
4.5%
0.001 108
 
1.1%
0.002 144
 
1.4%
0.003 140
 
1.4%
0.0039 1
 
< 0.1%
0.004 155
 
1.6%
0.005 150
 
1.5%
0.006 165
 
1.7%
0.007 147
 
1.5%
0.008 169
 
1.7%
ValueCountFrequency (%)
0.151 1
 
< 0.1%
0.1 1
 
< 0.1%
0.099 2
< 0.1%
0.098 1
 
< 0.1%
0.097 1
 
< 0.1%
0.096 1
 
< 0.1%
0.095 1
 
< 0.1%
0.093 3
< 0.1%
0.092 2
< 0.1%
0.091 2
< 0.1%

동
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct142
Distinct (%)1.4%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.0057289374
Minimum0
Maximum1
Zeros7927
Zeros (%)79.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:21:50.253102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.022
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.037742692
Coefficient of variation (CV)6.588079
Kurtosis481.48651
Mean0.0057289374
Median Absolute Deviation (MAD)0
Skewness20.300392
Sum57.255
Variance0.0014245108
MonotonicityNot monotonic
2023-12-13T07:21:50.374402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7927
79.3%
0.008 192
 
1.9%
0.009 188
 
1.9%
0.01 173
 
1.7%
0.011 124
 
1.2%
0.012 105
 
1.1%
0.013 99
 
1.0%
0.014 82
 
0.8%
0.015 68
 
0.7%
0.004 66
 
0.7%
Other values (132) 970
 
9.7%
ValueCountFrequency (%)
0.0 7927
79.3%
0.001 8
 
0.1%
0.002 14
 
0.1%
0.003 12
 
0.1%
0.004 66
 
0.7%
0.005 56
 
0.6%
0.006 48
 
0.5%
0.007 28
 
0.3%
0.008 192
 
1.9%
0.009 188
 
1.9%
ValueCountFrequency (%)
1.0 8
0.1%
0.85 1
 
< 0.1%
0.812 1
 
< 0.1%
0.754 1
 
< 0.1%
0.683 1
 
< 0.1%
0.601 1
 
< 0.1%
0.565 1
 
< 0.1%
0.542 1
 
< 0.1%
0.533 1
 
< 0.1%
0.511 1
 
< 0.1%

수소이온농도
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)0.7%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7.268416
Minimum5.9
Maximum8.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:21:50.501776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile6.7
Q17.1
median7.2
Q37.5
95-th percentile7.9
Maximum8.5
Range2.6
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.35166238
Coefficient of variation (CV)0.048382258
Kurtosis0.40620182
Mean7.268416
Median Absolute Deviation (MAD)0.2
Skewness0.0067260634
Sum72640.55
Variance0.12366643
MonotonicityNot monotonic
2023-12-13T07:21:50.638479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 1381
13.8%
7.1 1150
11.5%
7.3 1083
10.8%
7.4 1035
10.3%
7.0 907
9.1%
7.5 793
7.9%
7.6 652
6.5%
6.9 533
 
5.3%
7.7 459
 
4.6%
7.8 400
 
4.0%
Other values (57) 1601
16.0%
ValueCountFrequency (%)
5.9 5
 
0.1%
6.0 7
 
0.1%
6.1 8
 
0.1%
6.2 21
 
0.2%
6.3 29
 
0.3%
6.33 1
 
< 0.1%
6.4 64
0.6%
6.41 1
 
< 0.1%
6.5 107
1.1%
6.6 152
1.5%
ValueCountFrequency (%)
8.5 6
 
0.1%
8.4 8
 
0.1%
8.3 19
 
0.2%
8.2 38
 
0.4%
8.1 77
 
0.8%
8.01 1
 
< 0.1%
8.0 154
 
1.5%
7.9 219
2.2%
7.8 400
4.0%
7.7 459
4.6%

아연
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct221
Distinct (%)2.2%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.013898179
Minimum0
Maximum2.685
Zeros2829
Zeros (%)28.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:21:50.776971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.005
Q30.013
95-th percentile0.045
Maximum2.685
Range2.685
Interquartile range (IQR)0.013

Descriptive statistics

Standard deviation0.054310902
Coefficient of variation (CV)3.907771
Kurtosis827.33065
Mean0.013898179
Median Absolute Deviation (MAD)0.005
Skewness23.394306
Sum138.9401
Variance0.0029496741
MonotonicityNot monotonic
2023-12-13T07:21:50.899461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2829
28.3%
0.003 660
 
6.6%
0.004 660
 
6.6%
0.006 503
 
5.0%
0.005 484
 
4.8%
0.002 482
 
4.8%
0.008 408
 
4.1%
0.007 389
 
3.9%
0.01 302
 
3.0%
0.009 293
 
2.9%
Other values (211) 2987
29.9%
ValueCountFrequency (%)
0.0 2829
28.3%
0.001 4
 
< 0.1%
0.002 482
 
4.8%
0.0027 1
 
< 0.1%
0.003 660
 
6.6%
0.0038 1
 
< 0.1%
0.004 660
 
6.6%
0.005 484
 
4.8%
0.006 503
 
5.0%
0.007 389
 
3.9%
ValueCountFrequency (%)
2.685 1
< 0.1%
1.477 1
< 0.1%
1.379 1
< 0.1%
1.378 1
< 0.1%
1.373 1
< 0.1%
1.246 1
< 0.1%
1.073 1
< 0.1%
1.042 1
< 0.1%
0.793 1
< 0.1%
0.61 1
< 0.1%

철
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)0.5%
Missing8
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.0048115592
Minimum0
Maximum1
Zeros9436
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:21:51.013088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.01835
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.033487271
Coefficient of variation (CV)6.9597545
Kurtosis552.18986
Mean0.0048115592
Median Absolute Deviation (MAD)0
Skewness19.935573
Sum48.0771
Variance0.0011213973
MonotonicityNot monotonic
2023-12-13T07:21:51.130884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 9436
94.4%
0.06 84
 
0.8%
0.05 82
 
0.8%
0.07 71
 
0.7%
0.08 62
 
0.6%
0.01 42
 
0.4%
0.09 29
 
0.3%
0.02 28
 
0.3%
0.1 23
 
0.2%
0.12 21
 
0.2%
Other values (36) 114
 
1.1%
ValueCountFrequency (%)
0.0 9436
94.4%
0.002 2
 
< 0.1%
0.0022 1
 
< 0.1%
0.003 1
 
< 0.1%
0.005 2
 
< 0.1%
0.006 2
 
< 0.1%
0.0077 1
 
< 0.1%
0.0078 1
 
< 0.1%
0.008 1
 
< 0.1%
0.0084 1
 
< 0.1%
ValueCountFrequency (%)
1.0 7
0.1%
0.3 1
 
< 0.1%
0.29 3
 
< 0.1%
0.27 1
 
< 0.1%
0.26 1
 
< 0.1%
0.25 3
 
< 0.1%
0.23 2
 
< 0.1%
0.22 1
 
< 0.1%
0.21 1
 
< 0.1%
0.2 8
0.1%

탁도
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)0.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.11087999
Minimum0
Maximum0.98
Zeros96
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:21:51.242751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q10.06
median0.08
Q30.13
95-th percentile0.29
Maximum0.98
Range0.98
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.084280325
Coefficient of variation (CV)0.76010403
Kurtosis5.4280009
Mean0.11087999
Median Absolute Deviation (MAD)0.03
Skewness2.0621663
Sum1108.689
Variance0.0071031732
MonotonicityNot monotonic
2023-12-13T07:21:51.348469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.06 1129
 
11.3%
0.07 1040
 
10.4%
0.08 928
 
9.3%
0.09 747
 
7.5%
0.05 724
 
7.2%
0.1 585
 
5.9%
0.11 473
 
4.7%
0.04 425
 
4.2%
0.12 417
 
4.2%
0.13 337
 
3.4%
Other values (59) 3194
31.9%
ValueCountFrequency (%)
0.0 96
 
1.0%
0.005 1
 
< 0.1%
0.008 1
 
< 0.1%
0.01 46
 
0.5%
0.013 1
 
< 0.1%
0.02 329
3.3%
0.023 1
 
< 0.1%
0.03 302
3.0%
0.04 425
4.2%
0.041 1
 
< 0.1%
ValueCountFrequency (%)
0.98 1
 
< 0.1%
0.5 5
 
0.1%
0.49 12
0.1%
0.48 24
0.2%
0.47 19
0.2%
0.46 15
0.1%
0.45 18
0.2%
0.44 14
0.1%
0.43 17
0.2%
0.42 15
0.1%

잔류염소
Real number (ℝ)

HIGH CORRELATION 

Distinct157
Distinct (%)1.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.4589752
Minimum0
Maximum3
Zeros64
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:21:51.460343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q10.3
median0.43
Q30.6
95-th percentile0.86
Maximum3
Range3
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.23305783
Coefficient of variation (CV)0.50777869
Kurtosis4.2567943
Mean0.4589752
Median Absolute Deviation (MAD)0.15
Skewness1.0597289
Sum4589.293
Variance0.05431595
MonotonicityNot monotonic
2023-12-13T07:21:51.570152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 416
 
4.2%
0.3 347
 
3.5%
0.2 281
 
2.8%
0.5 269
 
2.7%
0.6 218
 
2.2%
0.45 202
 
2.0%
0.7 202
 
2.0%
0.41 199
 
2.0%
0.35 190
 
1.9%
0.1 179
 
1.8%
Other values (147) 7496
75.0%
ValueCountFrequency (%)
0.0 64
 
0.6%
0.01 1
 
< 0.1%
0.04 2
 
< 0.1%
0.05 9
 
0.1%
0.06 8
 
0.1%
0.07 10
 
0.1%
0.08 9
 
0.1%
0.09 7
 
0.1%
0.1 179
1.8%
0.11 103
1.0%
ValueCountFrequency (%)
3.0 1
< 0.1%
2.8 1
< 0.1%
2.2 1
< 0.1%
2.0 1
< 0.1%
1.98 1
< 0.1%
1.96 1
< 0.1%
1.89 1
< 0.1%
1.86 1
< 0.1%
1.81 1
< 0.1%
1.74 1
< 0.1%

대장균_분원성대장균군
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
불검출
9959 
<NA>
 
41

Length

Max length4
Median length3
Mean length3.0041
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
불검출 9959
99.6%
<NA> 41
 
0.4%

Length

2023-12-13T07:21:51.685372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:21:51.769956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불검출 9959
99.6%
na 41
 
0.4%

Interactions

2023-12-13T07:21:43.285244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:32.192309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:33.518907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:34.567804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:35.529998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:36.497797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:37.634325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:38.742767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:40.179672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:41.246148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:42.262727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:43.397516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:32.278280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:33.604244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:34.643147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:35.612216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:36.588065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:37.731421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:38.850852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:40.293785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:41.335295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:42.354995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:43.495730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:32.366275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:33.702691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:34.730070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:35.699450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:36.679245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:37.822162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:38.974696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:40.397427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:41.423146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:42.453436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:43.579779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:32.468220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:33.795743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:34.818663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:35.776896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:36.767685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:37.906599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:39.066435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:40.501191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:41.503773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:42.549897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:43.690140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:32.563663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:33.896646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:34.905757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:35.864863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:36.871220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:38.000998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:39.182507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:40.592978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:41.588073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:42.643962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:43.808218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:32.663231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:34.002590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:34.994666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:35.949546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:36.975424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:38.108834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:39.280383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:40.690588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:41.673668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:42.739511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:43.934588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:32.748549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:34.095715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:35.071538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:36.025719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:37.089398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:38.199852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:39.390344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:40.788545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:41.755212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:42.834364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:44.035881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:32.841656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:34.180605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:35.154246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:36.103905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:37.187549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:38.304400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:39.513226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:40.876044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:41.840426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:42.914633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:44.149420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:32.957124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:34.283237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:35.259719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:36.186990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:37.302141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:38.416133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:39.621721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:40.959608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:41.968477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:43.008453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:44.244472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:33.045283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:34.379602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:35.345782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:36.271430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:37.403324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:38.513736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:39.715414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:41.063360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:42.058398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:43.093492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:44.376300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:33.134078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:34.478113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:35.428511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:36.403326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:37.524848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:38.631534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:40.087984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:41.153599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:42.172159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:21:43.188454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:21:51.850199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번검사년도검사분기일반세균암모니아성질소총트리할로메탄동수소이온농도아연철탁도잔류염소
연번1.0000.9810.5420.0140.0420.1530.0460.1430.0420.1070.0720.102
검사년도0.9811.0000.2270.0000.0330.1010.0470.0940.0290.0530.1090.116
검사분기0.5420.2271.0000.0290.0150.2350.0440.1140.0080.0020.0320.090
일반세균0.0140.0000.0291.0000.0660.0000.0000.0260.0000.0530.1470.000
암모니아성질소0.0420.0330.0150.0661.0000.0000.0000.0000.0780.0000.0500.000
총트리할로메탄0.1530.1010.2350.0000.0001.0000.0000.1290.0000.0000.0370.079
동0.0460.0470.0440.0000.0000.0001.0000.0610.5540.5990.0390.000
수소이온농도0.1430.0940.1140.0260.0000.1290.0611.0000.0870.0630.0940.066
아연0.0420.0290.0080.0000.0780.0000.5540.0871.0000.0720.1120.000
철0.1070.0530.0020.0530.0000.0000.5990.0630.0721.0000.1100.000
탁도0.0720.1090.0320.1470.0500.0370.0390.0940.1120.1101.0000.052
잔류염소0.1020.1160.0900.0000.0000.0790.0000.0660.0000.0000.0521.000
2023-12-13T07:21:51.967414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총대장균군검사분기대장균_분원성대장균군
총대장균군1.0001.0001.000
검사분기1.0001.0001.000
대장균_분원성대장균군1.0001.0001.000
2023-12-13T07:21:52.055771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번검사년도일반세균암모니아성질소총트리할로메탄동수소이온농도아연철탁도잔류염소검사분기총대장균군대장균_분원성대장균군
연번1.0000.9890.035-0.0770.1340.043-0.0100.027-0.018-0.0300.0910.3551.0001.000
검사년도0.9891.0000.031-0.0720.1280.039-0.0090.023-0.021-0.0340.0920.1391.0001.000
일반세균0.0350.0311.0000.027-0.035-0.0050.0190.0100.0140.048-0.0350.0191.0001.000
암모니아성질소-0.077-0.0720.0271.000-0.0310.054-0.0210.0320.037-0.030-0.0620.0071.0001.000
총트리할로메탄0.1340.128-0.035-0.0311.000-0.036-0.0900.027-0.0200.0280.0240.1071.0001.000
동0.0430.039-0.0050.054-0.0361.000-0.0710.3270.0920.057-0.0570.0281.0001.000
수소이온농도-0.010-0.0090.019-0.021-0.090-0.0711.000-0.144-0.001-0.0150.0120.0621.0001.000
아연0.0270.0230.0100.0320.0270.327-0.1441.0000.0870.098-0.0930.0051.0001.000
철-0.018-0.0210.0140.037-0.0200.092-0.0010.0871.0000.120-0.0410.0031.0001.000
탁도-0.030-0.0340.048-0.0300.0280.057-0.0150.0980.1201.000-0.0770.0241.0001.000
잔류염소0.0910.092-0.035-0.0620.024-0.0570.012-0.093-0.041-0.0771.0000.0551.0001.000
검사분기0.3550.1390.0190.0070.1070.0280.0620.0050.0030.0240.0551.0001.0001.000
총대장균군1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
대장균_분원성대장균군1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-13T07:21:44.558004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:21:44.815115image/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.
2023-12-13T07:21:45.022551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번검사년도검사분기지역공급정수장검사지점수질검사기관일반세균총대장균군암모니아성질소총트리할로메탄동수소이온농도아연철탁도잔류염소대장균_분원성대장균군
2860286120152분기경기도 안산시안산관말수도꼭지(직수)-A안산시상수도사업소0불검출0.010.0170.07.70.0080.00.080.1불검출
334593346020182분기제주특별자치도별도봉주배수지(후)제주특별자치도 상하수도본부0불검출0.00.0060.07.60.0130.00.160.31불검출
6743674420153분기경상북도 경주시탑동급수구역유입부자체수질검사0불검출0.00.0240.0437.10.0070.00.070.75불검출
459544595520202분기경기도 광명시노온가압장(유출부)(주)피엘아이환경기술연구원0불검출0.00.0250.07.50.00.00.190.56불검출
629456294620214분기제주특별자치도토평주배수지(후)제주특별자치도 상하수도본부0불검출0.00.00.07.60.0040.00.120.78불검출
597655976620213분기경상북도 포항시양덕관말수도꼭지(직수)포항시 맑은물사업본부0불검출0.00.0750.07.20.0050.00.080.55불검출
232812328220172분기전라남도 보성군득량주배수지(후)이산 친환경연구원0불검출0.00.0140.07.40.00.00.080.18불검출
209652096620171분기전라남도 강진군병영관말수도꼭지(직수)바른환경연구소0불검출0.020.0250.07.30.0110.00.070.21불검출
459104591120202분기경기도 수원시파장관말수도꼭지(공동저수조)-A수원시상수도사업소0불검출0.00.0590.07.50.0080.00.060.3불검출
281702817120174분기경상북도 경주시탑동급수구역유입부자체수질검사0불검출0.00.0310.0076.80.0320.00.050.5불검출
연번검사년도검사분기지역공급정수장검사지점수질검사기관일반세균총대장균군암모니아성질소총트리할로메탄동수소이온농도아연철탁도잔류염소대장균_분원성대장균군
242752427620173분기대구광역시매곡급수구역유입부-H대구시-상수도사업본부0불검출0.00.0380.06.80.010.00.080.41불검출
235992360020172분기경상북도 의성군다인급수구역유입부경북-보건환경연구원0불검출0.00.0030.07.40.0110.00.140.2불검출
577715777220212분기경상남도 창녕군상월주배수지(후)(주)동진생명연구원0불검출0.00.0350.0077.30.0180.00.080.1불검출
116851168620161분기경상북도 영주시풍기정수장경북-보건환경연구원0불검출0.00.0080.0137.20.0140.00.050.72불검출
116181161920161분기경상북도 경주시안강주배수지(후)자체수질검사0불검출0.00.0140.07.40.00.00.051.1불검출
3963396420152분기전라남도 목포시몽탄관말수도꼭지(직수)전남-보건환경연구원18불검출0.00.0080.0336.60.0080.00.330.0불검출
382993830020191분기서울특별시뚝도관말수도꼭지(직수)-O자체수질검사0불검출0.00.0250.07.00.0040.00.060.18불검출
261822618320173분기경상남도 거제시구천관말수도꼭지(직수)한국수자원공사 부산울산경남지역협력본부0불검출0.00.0260.06.90.0180.00.080.5불검출
247172471820173분기경기도 양평군양평통합주배수지(후)-D(주)수앤수물환경연구소0불검출0.00.0290.07.60.0120.00.060.45불검출
129661296720162분기강원도 동해시이원다른계통과합쳐지는지점-A강원-보건환경연구원0불검출0.00.0060.07.50.0030.00.020.15불검출