Overview

Dataset statistics

Number of variables13
Number of observations104
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.4 KiB
Average record size in memory112.3 B

Variable types

Numeric7
Categorical6

Dataset

Description광역/지방 분기별 해수 수질검사 결과입니다. 제공항목 : 연번,검사년도,검사분기,지역,취수장명,측정지점주소,채수지점,수원,수소이온농도,화학적 산소요구량,총대장균군,대장균_분원성대장균군,노말헥산추출물질(동물성유지류)함유량 * 상세자료조회는 아래 URL을 참고 해주시기 바랍니다. https://www.waternow.go.kr/web/lawData2/?pMENUID=96&ATTR_1=3106
URLhttps://www.data.go.kr/data/15093993/fileData.do

Alerts

수원 has constant value ""Constant
취수장명 is highly overall correlated with 수소이온농도 and 3 other fieldsHigh correlation
측정지점주소 is highly overall correlated with 수소이온농도 and 3 other fieldsHigh correlation
지역 is highly overall correlated with 수소이온농도 and 3 other fieldsHigh correlation
채수지점 is highly overall correlated with 수소이온농도 and 3 other fieldsHigh correlation
연번 is highly overall correlated with 검사년도High correlation
검사년도 is highly overall correlated with 연번High correlation
수소이온농도 is highly overall correlated with 지역 and 3 other fieldsHigh correlation
연번 has unique valuesUnique
총대장균군 has 45 (43.3%) zerosZeros
대장균_분원성대장균군 has 75 (72.1%) zerosZeros
노말헥산추출물질(동물성유지류)함유량 has 80 (76.9%) zerosZeros

Reproduction

Analysis started2023-12-13 00:27:45.474537
Analysis finished2023-12-13 00:27:49.813824
Duration4.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct104
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.5
Minimum1
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T09:27:49.881066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.15
Q126.75
median52.5
Q378.25
95-th percentile98.85
Maximum104
Range103
Interquartile range (IQR)51.5

Descriptive statistics

Standard deviation30.166206
Coefficient of variation (CV)0.5745944
Kurtosis-1.2
Mean52.5
Median Absolute Deviation (MAD)26
Skewness0
Sum5460
Variance910
MonotonicityStrictly increasing
2023-12-13T09:27:49.995902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
54 1
 
1.0%
78 1
 
1.0%
77 1
 
1.0%
76 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
Other values (94) 94
90.4%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
104 1
1.0%
103 1
1.0%
102 1
1.0%
101 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%

검사년도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.1923
Minimum2015
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T09:27:50.077716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12017
median2018
Q32020
95-th percentile2021
Maximum2021
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9512352
Coefficient of variation (CV)0.00096682324
Kurtosis-1.1528442
Mean2018.1923
Median Absolute Deviation (MAD)2
Skewness-0.1159038
Sum209892
Variance3.8073189
MonotonicityIncreasing
2023-12-13T09:27:50.156549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2017 16
15.4%
2018 16
15.4%
2019 16
15.4%
2020 16
15.4%
2021 16
15.4%
2015 12
11.5%
2016 12
11.5%
ValueCountFrequency (%)
2015 12
11.5%
2016 12
11.5%
2017 16
15.4%
2018 16
15.4%
2019 16
15.4%
2020 16
15.4%
2021 16
15.4%
ValueCountFrequency (%)
2021 16
15.4%
2020 16
15.4%
2019 16
15.4%
2018 16
15.4%
2017 16
15.4%
2016 12
11.5%
2015 12
11.5%

검사분기
Categorical

Distinct4
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size964.0 B
1분기
26 
2분기
26 
3분기
26 
4분기
26 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1분기
2nd row1분기
3rd row1분기
4th row2분기
5th row2분기

Common Values

ValueCountFrequency (%)
1분기 26
25.0%
2분기 26
25.0%
3분기 26
25.0%
4분기 26
25.0%

Length

2023-12-13T09:27:50.245955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:27:50.317059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1분기 26
25.0%
2분기 26
25.0%
3분기 26
25.0%
4분기 26
25.0%

지역
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size964.0 B
전라남도 진도군
56 
전라남도 여수시
28 
제주특별자치도
20 

Length

Max length8
Median length8
Mean length7.8076923
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라남도 여수시
2nd row전라남도 진도군
3rd row전라남도 진도군
4th row전라남도 여수시
5th row전라남도 진도군

Common Values

ValueCountFrequency (%)
전라남도 진도군 56
53.8%
전라남도 여수시 28
26.9%
제주특별자치도 20
 
19.2%

Length

2023-12-13T09:27:50.399824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:27:50.475836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 84
44.7%
진도군 56
29.8%
여수시 28
 
14.9%
제주특별자치도 20
 
10.6%

취수장명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size964.0 B
거문도서도
28 
관사
28 
성남
28 
추자담수장
16 
추자지구
4 

Length

Max length5
Median length2
Mean length3.3461538
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row거문도서도
2nd row관사
3rd row성남
4th row거문도서도
5th row관사

Common Values

ValueCountFrequency (%)
거문도서도 28
26.9%
관사 28
26.9%
성남 28
26.9%
추자담수장 16
15.4%
추자지구 4
 
3.8%

Length

2023-12-13T09:27:50.557826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:27:50.638587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거문도서도 28
26.9%
관사 28
26.9%
성남 28
26.9%
추자담수장 16
15.4%
추자지구 4
 
3.8%

측정지점주소
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size964.0 B
전라남도 여수시 삼산면 덕촌리 3621
28 
전라남도 진도군 조도면 관사도리 175
28 
전라남도 진도군 조도면 성남도리 346
28 
제주특별자치도 제주시 추자면 묵리 619
16 
제주특별자치도 제주시 추자면 묵리
4 

Length

Max length22
Median length21
Mean length21.038462
Min length18

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라남도 여수시 삼산면 덕촌리 3621
2nd row전라남도 진도군 조도면 관사도리 175
3rd row전라남도 진도군 조도면 성남도리 346
4th row전라남도 여수시 삼산면 덕촌리 3621
5th row전라남도 진도군 조도면 관사도리 175

Common Values

ValueCountFrequency (%)
전라남도 여수시 삼산면 덕촌리 3621 28
26.9%
전라남도 진도군 조도면 관사도리 175 28
26.9%
전라남도 진도군 조도면 성남도리 346 28
26.9%
제주특별자치도 제주시 추자면 묵리 619 16
15.4%
제주특별자치도 제주시 추자면 묵리 4
 
3.8%

Length

2023-12-13T09:27:50.729390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:27:50.813526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 84
16.3%
진도군 56
10.9%
조도면 56
10.9%
여수시 28
 
5.4%
삼산면 28
 
5.4%
덕촌리 28
 
5.4%
3621 28
 
5.4%
관사도리 28
 
5.4%
175 28
 
5.4%
성남도리 28
 
5.4%
Other values (6) 124
24.0%

채수지점
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size964.0 B
착수정
56 
취수구
48 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row취수구
2nd row착수정
3rd row착수정
4th row취수구
5th row착수정

Common Values

ValueCountFrequency (%)
착수정 56
53.8%
취수구 48
46.2%

Length

2023-12-13T09:27:50.901885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:27:50.968965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
착수정 56
53.8%
취수구 48
46.2%

수원
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size964.0 B
해수
104 

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 104
100.0%

Length

2023-12-13T09:27:51.042383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:27:51.112859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해수 104
100.0%

수소이온농도
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0691346
Minimum6.2
Maximum8.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T09:27:51.179120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile6.315
Q16.6
median6.8
Q37.4
95-th percentile8.3
Maximum8.5
Range2.3
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.65129462
Coefficient of variation (CV)0.092132157
Kurtosis-0.36506495
Mean7.0691346
Median Absolute Deviation (MAD)0.2
Skewness0.98145818
Sum735.19
Variance0.42418468
MonotonicityNot monotonic
2023-12-13T09:27:51.264000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
6.7 16
15.4%
6.6 15
14.4%
6.8 12
11.5%
8.2 8
 
7.7%
7.0 6
 
5.8%
6.9 5
 
4.8%
6.4 4
 
3.8%
7.2 4
 
3.8%
6.5 4
 
3.8%
8.3 4
 
3.8%
Other values (12) 26
25.0%
ValueCountFrequency (%)
6.2 2
 
1.9%
6.3 4
 
3.8%
6.4 4
 
3.8%
6.5 4
 
3.8%
6.59 1
 
1.0%
6.6 15
14.4%
6.7 16
15.4%
6.8 12
11.5%
6.9 5
 
4.8%
7.0 6
 
5.8%
ValueCountFrequency (%)
8.5 2
 
1.9%
8.4 3
 
2.9%
8.3 4
3.8%
8.2 8
7.7%
8.0 3
 
2.9%
7.7 1
 
1.0%
7.6 2
 
1.9%
7.5 2
 
1.9%
7.4 2
 
1.9%
7.3 1
 
1.0%

화학적 산소요구량
Real number (ℝ)

Distinct50
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6438462
Minimum0.1
Maximum40.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T09:27:51.360749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.8
median1.49
Q33.325
95-th percentile16.895
Maximum40.8
Range40.7
Interquartile range (IQR)2.525

Descriptive statistics

Standard deviation6.7439887
Coefficient of variation (CV)1.8507885
Kurtosis14.959537
Mean3.6438462
Median Absolute Deviation (MAD)0.89
Skewness3.7733198
Sum378.96
Variance45.481383
MonotonicityNot monotonic
2023-12-13T09:27:51.470515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 13
 
12.5%
0.6 7
 
6.7%
0.3 6
 
5.8%
2.0 5
 
4.8%
1.4 5
 
4.8%
3.4 4
 
3.8%
1.0 4
 
3.8%
1.6 4
 
3.8%
0.4 3
 
2.9%
0.9 3
 
2.9%
Other values (40) 50
48.1%
ValueCountFrequency (%)
0.1 1
 
1.0%
0.2 2
 
1.9%
0.3 6
5.8%
0.4 3
 
2.9%
0.6 7
6.7%
0.8 13
12.5%
0.9 3
 
2.9%
0.92 1
 
1.0%
1.0 4
 
3.8%
1.1 2
 
1.9%
ValueCountFrequency (%)
40.8 1
1.0%
33.0 1
1.0%
30.7 1
1.0%
27.6 1
1.0%
18.7 1
1.0%
17.0 1
1.0%
16.3 1
1.0%
8.0 2
1.9%
7.4 1
1.0%
7.2 1
1.0%

총대장균군
Real number (ℝ)

ZEROS 

Distinct43
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.70192
Minimum0
Maximum1700
Zeros45
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T09:27:51.573298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q348.25
95-th percentile732.6
Maximum1700
Range1700
Interquartile range (IQR)48.25

Descriptive statistics

Standard deviation275.84074
Coefficient of variation (CV)2.7391805
Kurtosis16.274657
Mean100.70192
Median Absolute Deviation (MAD)2
Skewness3.8768198
Sum10473
Variance76088.114
MonotonicityNot monotonic
2023-12-13T09:27:51.673444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 45
43.3%
2 12
 
11.5%
8 4
 
3.8%
23 2
 
1.9%
5 2
 
1.9%
6 2
 
1.9%
1700 1
 
1.0%
170 1
 
1.0%
76 1
 
1.0%
16 1
 
1.0%
Other values (33) 33
31.7%
ValueCountFrequency (%)
0 45
43.3%
2 12
 
11.5%
5 2
 
1.9%
6 2
 
1.9%
7 1
 
1.0%
8 4
 
3.8%
9 1
 
1.0%
10 1
 
1.0%
11 1
 
1.0%
15 1
 
1.0%
ValueCountFrequency (%)
1700 1
1.0%
1400 1
1.0%
920 1
1.0%
910 1
1.0%
870 1
1.0%
756 1
1.0%
600 1
1.0%
420 1
1.0%
350 1
1.0%
345 1
1.0%

대장균_분원성대장균군
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2692308
Minimum0
Maximum49
Zeros75
Zeros (%)72.1%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T09:27:51.776077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.25
95-th percentile8.7
Maximum49
Range49
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation7.617342
Coefficient of variation (CV)3.3567948
Kurtosis27.347356
Mean2.2692308
Median Absolute Deviation (MAD)0
Skewness5.0409962
Sum236
Variance58.023898
MonotonicityNot monotonic
2023-12-13T09:27:51.855322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 75
72.1%
2 13
 
12.5%
6 3
 
2.9%
1 3
 
2.9%
3 2
 
1.9%
49 2
 
1.9%
7 1
 
1.0%
9 1
 
1.0%
19 1
 
1.0%
22 1
 
1.0%
Other values (2) 2
 
1.9%
ValueCountFrequency (%)
0 75
72.1%
1 3
 
2.9%
2 13
 
12.5%
3 2
 
1.9%
5 1
 
1.0%
6 3
 
2.9%
7 1
 
1.0%
9 1
 
1.0%
19 1
 
1.0%
22 1
 
1.0%
ValueCountFrequency (%)
49 2
 
1.9%
23 1
 
1.0%
22 1
 
1.0%
19 1
 
1.0%
9 1
 
1.0%
7 1
 
1.0%
6 3
 
2.9%
5 1
 
1.0%
3 2
 
1.9%
2 13
12.5%

노말헥산추출물질(동물성유지류)함유량
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.081346154
Minimum0
Maximum1.6
Zeros80
Zeros (%)76.9%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T09:27:51.937504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4
Maximum1.6
Range1.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.22313763
Coefficient of variation (CV)2.7430631
Kurtosis26.072864
Mean0.081346154
Median Absolute Deviation (MAD)0
Skewness4.6440008
Sum8.46
Variance0.049790403
MonotonicityNot monotonic
2023-12-13T09:27:52.041570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.0 80
76.9%
0.2 10
 
9.6%
0.4 6
 
5.8%
0.1 3
 
2.9%
0.6 1
 
1.0%
0.15 1
 
1.0%
0.21 1
 
1.0%
1.6 1
 
1.0%
1.2 1
 
1.0%
ValueCountFrequency (%)
0.0 80
76.9%
0.1 3
 
2.9%
0.15 1
 
1.0%
0.2 10
 
9.6%
0.21 1
 
1.0%
0.4 6
 
5.8%
0.6 1
 
1.0%
1.2 1
 
1.0%
1.6 1
 
1.0%
ValueCountFrequency (%)
1.6 1
 
1.0%
1.2 1
 
1.0%
0.6 1
 
1.0%
0.4 6
 
5.8%
0.21 1
 
1.0%
0.2 10
 
9.6%
0.15 1
 
1.0%
0.1 3
 
2.9%
0.0 80
76.9%

Interactions

2023-12-13T09:27:48.814939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:45.945532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:46.447287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:46.906021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:47.378985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:47.898510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:48.361972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:48.881345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:46.007001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:46.506628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:46.965972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:47.446006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:47.960766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:48.419897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:48.959496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:46.077340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:46.571184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:47.028871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:47.518395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:48.028048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:48.484017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:49.038244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:46.147601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:46.632941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:47.086209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:47.591906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:48.089352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:48.542006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:49.127361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:46.216728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:46.705819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:47.155344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:47.666149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:48.159672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:48.612016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:49.201015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:46.296598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:46.771753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:47.223016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:47.742600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:48.226723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:48.683335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:49.500818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:46.372397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:46.834258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:47.292975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:47.813741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:48.288800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:27:48.747255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:27:52.114207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번검사년도검사분기지역취수장명측정지점주소채수지점수소이온농도화학적 산소요구량총대장균군대장균_분원성대장균군노말헥산추출물질(동물성유지류)함유량
연번1.0000.9690.1670.0000.0000.0000.0000.5410.2190.1120.0000.339
검사년도0.9691.0000.0000.0000.2050.2050.0000.5540.2440.0000.0000.261
검사분기0.1670.0001.0000.0000.0000.0000.0000.0000.0000.1140.1280.114
지역0.0000.0000.0001.0001.0001.0001.0000.8370.0980.3860.2630.740
취수장명0.0000.2050.0001.0001.0001.0001.0000.8800.1350.2790.3040.378
측정지점주소0.0000.2050.0001.0001.0001.0001.0000.8800.1350.2790.3040.378
채수지점0.0000.0000.0001.0001.0001.0001.0000.7630.3720.2450.2050.496
수소이온농도0.5410.5540.0000.8370.8800.8800.7631.0000.0000.3340.0000.338
화학적 산소요구량0.2190.2440.0000.0980.1350.1350.3720.0001.0000.0000.0000.000
총대장균군0.1120.0000.1140.3860.2790.2790.2450.3340.0001.0000.5230.000
대장균_분원성대장균군0.0000.0000.1280.2630.3040.3040.2050.0000.0000.5231.0000.099
노말헥산추출물질(동물성유지류)함유량0.3390.2610.1140.7400.3780.3780.4960.3380.0000.0000.0991.000
2023-12-13T09:27:52.216628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
취수장명측정지점주소지역채수지점검사분기
취수장명1.0001.0000.9900.9850.000
측정지점주소1.0001.0000.9900.9850.000
지역0.9900.9901.0000.9950.000
채수지점0.9850.9850.9951.0000.000
검사분기0.0000.0000.0000.0001.000
2023-12-13T09:27:52.304265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번검사년도수소이온농도화학적 산소요구량총대장균군대장균_분원성대장균군노말헥산추출물질(동물성유지류)함유량검사분기지역취수장명측정지점주소채수지점
연번1.0000.9890.160-0.0110.115-0.153-0.4680.0930.0000.0000.0000.000
검사년도0.9891.0000.143-0.0010.096-0.154-0.4490.0000.0000.1520.1520.000
수소이온농도0.1600.1431.000-0.2950.045-0.171-0.3070.0000.7200.5430.5430.577
화학적 산소요구량-0.011-0.001-0.2951.000-0.1510.0770.0250.0000.0540.0770.0770.270
총대장균군0.1150.0960.045-0.1511.0000.4880.0740.0430.2580.1700.1700.177
대장균_분원성대장균군-0.153-0.154-0.1710.0770.4881.000-0.0490.1030.2030.1160.1160.246
노말헥산추출물질(동물성유지류)함유량-0.468-0.449-0.3070.0250.074-0.0491.0000.0700.4120.2650.2650.351
검사분기0.0930.0000.0000.0000.0430.1030.0701.0000.0000.0000.0000.000
지역0.0000.0000.7200.0540.2580.2030.4120.0001.0000.9900.9900.995
취수장명0.0000.1520.5430.0770.1700.1160.2650.0000.9901.0001.0000.985
측정지점주소0.0000.1520.5430.0770.1700.1160.2650.0000.9901.0001.0000.985
채수지점0.0000.0000.5770.2700.1770.2460.3510.0000.9950.9850.9851.000

Missing values

2023-12-13T09:27:49.618640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:27:49.762441image/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

연번검사년도검사분기지역취수장명측정지점주소채수지점수원수소이온농도화학적 산소요구량총대장균군대장균_분원성대장균군노말헥산추출물질(동물성유지류)함유량
0120151분기전라남도 여수시거문도서도전라남도 여수시 삼산면 덕촌리 3621취수구해수6.590.922300.4
1220151분기전라남도 진도군관사전라남도 진도군 조도면 관사도리 175착수정해수6.74.8520.0
2320151분기전라남도 진도군성남전라남도 진도군 조도면 성남도리 346착수정해수6.53.2220.0
3420152분기전라남도 여수시거문도서도전라남도 여수시 삼산면 덕촌리 3621취수구해수6.61.762770.6
4520152분기전라남도 진도군관사전라남도 진도군 조도면 관사도리 175착수정해수7.20.8220.0
5620152분기전라남도 진도군성남전라남도 진도군 조도면 성남도리 346착수정해수7.10.8220.0
6720153분기전라남도 여수시거문도서도전라남도 여수시 삼산면 덕촌리 3621취수구해수6.61.4810730.4
7820153분기전라남도 진도군관사전라남도 진도군 조도면 관사도리 175착수정해수6.60.8220.0
8920153분기전라남도 진도군성남전라남도 진도군 조도면 성남도리 346착수정해수6.62.0520.0
91020154분기전라남도 여수시거문도서도전라남도 여수시 삼산면 덕촌리 3621취수구해수6.61.22920.4
연번검사년도검사분기지역취수장명측정지점주소채수지점수원수소이온농도화학적 산소요구량총대장균군대장균_분원성대장균군노말헥산추출물질(동물성유지류)함유량
949520212분기전라남도 진도군성남전라남도 진도군 조도면 성남도리 346착수정해수6.80.2000.0
959620212분기제주특별자치도추자담수장제주특별자치도 제주시 추자면 묵리 619취수구해수8.21.64810.0
969720213분기전라남도 여수시거문도서도전라남도 여수시 삼산면 덕촌리 3621취수구해수6.73.887010.0
979820213분기전라남도 진도군관사전라남도 진도군 조도면 관사도리 175착수정해수6.60.8000.0
989920213분기전라남도 진도군성남전라남도 진도군 조도면 성남도리 346착수정해수6.51.2170230.0
9910020213분기제주특별자치도추자담수장제주특별자치도 제주시 추자면 묵리 619취수구해수8.40.8170000.0
10010120214분기전라남도 여수시거문도서도전라남도 여수시 삼산면 덕촌리 3621취수구해수7.02.95210.0
10110220214분기전라남도 진도군관사전라남도 진도군 조도면 관사도리 175착수정해수7.10.8200.0
10210320214분기전라남도 진도군성남전라남도 진도군 조도면 성남도리 346착수정해수7.00.6800.0
10310420214분기제주특별자치도추자담수장제주특별자치도 제주시 추자면 묵리 619취수구해수8.40.97600.0