Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells13341
Missing cells (%)10.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory120.0 B

Variable types

Categorical3
DateTime2
Numeric8

Dataset

Description현재 운영하고 있는 국가 비점오염물질 측정망의 측정소 별 미확정 측정자료(수온, pH, 전기전도도, 용존산소, 탁도, T-N, T-P, TOC)를 제공합니다. (한시간단위 자료 제공)
URLhttps://www.data.go.kr/data/15070145/fileData.do

Alerts

측정소 명 is highly overall correlated with 수소이온지수(pH) and 2 other fieldsHigh correlation
측정소 번호 is highly overall correlated with 수소이온지수(pH) and 2 other fieldsHigh correlation
수소이온지수(pH) is highly overall correlated with 전기전도도 and 5 other fieldsHigh correlation
전기전도도 is highly overall correlated with 수소이온지수(pH) and 5 other fieldsHigh correlation
용존산소 is highly overall correlated with 수소이온지수(pH) and 2 other fieldsHigh correlation
탁도 is highly overall correlated with 수소이온지수(pH) and 3 other fieldsHigh correlation
총유기탄소(TOC) is highly overall correlated with 수소이온지수(pH) and 2 other fieldsHigh correlation
총질소(T-N) has 4447 (44.5%) missing valuesMissing
총인(T-P) has 4447 (44.5%) missing valuesMissing
총유기탄소(TOC) has 4447 (44.5%) missing valuesMissing
탁도 is highly skewed (γ1 = 26.07802089)Skewed
수온 has 1952 (19.5%) zerosZeros
수소이온지수(pH) has 2054 (20.5%) zerosZeros
전기전도도 has 1991 (19.9%) zerosZeros
용존산소 has 1957 (19.6%) zerosZeros
탁도 has 2436 (24.4%) zerosZeros
총질소(T-N) has 165 (1.7%) zerosZeros
총인(T-P) has 159 (1.6%) zerosZeros
총유기탄소(TOC) has 1715 (17.2%) zerosZeros

Reproduction

Analysis started2023-12-12 00:37:52.397501
Analysis finished2023-12-12 00:38:01.309222
Duration8.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정소 번호
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
10116054
 
476
1001N40
 
474
1012N10
 
468
2022N20
 
464
1001N20
 
464
Other values (18)
7654 

Length

Max length8
Median length8
Mean length7.5198
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2003N10
2nd row1001N10
3rd row10016043
4th row10186057
5th row10186057

Common Values

ValueCountFrequency (%)
10116054 476
 
4.8%
1001N40 474
 
4.7%
1012N10 468
 
4.7%
2022N20 464
 
4.6%
1001N20 464
 
4.6%
20116050 458
 
4.6%
10116048 453
 
4.5%
1101N20 453
 
4.5%
10186052 452
 
4.5%
20226046 443
 
4.4%
Other values (13) 5395
53.9%

Length

2023-12-12T09:38:01.385010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10116054 476
 
4.8%
1001n40 474
 
4.7%
1012n10 468
 
4.7%
2022n20 464
 
4.6%
1001n20 464
 
4.6%
20116050 458
 
4.6%
10116048 453
 
4.5%
1101n20 453
 
4.5%
10186052 452
 
4.5%
20226046 443
 
4.4%
Other values (13) 5395
53.9%

측정소 명
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
N물골
 
476
N반천
 
474
N자운
 
468
N호포
 
464
N송계
 
464
Other values (18)
7654 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN옥수
2nd rowN삼옥
3rd rowN용산
4th rowN오리
5th rowN오리

Common Values

ValueCountFrequency (%)
N물골 476
 
4.8%
N반천 474
 
4.7%
N자운 468
 
4.7%
N호포 464
 
4.6%
N송계 464
 
4.6%
N동암 458
 
4.6%
N솔정 453
 
4.5%
N황계 453
 
4.5%
N시흥 452
 
4.5%
N화목 443
 
4.4%
Other values (13) 5395
53.9%

Length

2023-12-12T09:38:01.533446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n물골 476
 
4.8%
n반천 474
 
4.7%
n자운 468
 
4.7%
n호포 464
 
4.6%
n송계 464
 
4.6%
n동암 458
 
4.6%
n솔정 453
 
4.5%
n황계 453
 
4.5%
n시흥 452
 
4.5%
n화목 443
 
4.4%
Other values (13) 5395
53.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1H
5077 
1HA
4923 

Length

Max length3
Median length2
Mean length2.4923
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1HA
2nd row1HA
3rd row1HA
4th row1HA
5th row1HA

Common Values

ValueCountFrequency (%)
1H 5077
50.8%
1HA 4923
49.2%

Length

2023-12-12T09:38:01.681873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:38:01.858804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1h 5077
50.8%
1ha 4923
49.2%
Distinct90
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-01-01 00:00:00
Maximum2022-03-31 00:00:00
2023-12-12T09:38:02.051872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:02.288568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 23:00:00
2023-12-12T09:38:02.465279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:02.626824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)

수온
Real number (ℝ)

ZEROS 

Distinct2468
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.331174
Minimum-3.79
Maximum29.31
Zeros1952
Zeros (%)19.5%
Negative7
Negative (%)0.1%
Memory size166.0 KiB
2023-12-12T09:38:02.785214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.79
5-th percentile0
Q13.49
median8.8
Q315.99
95-th percentile25.02
Maximum29.31
Range33.1
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation8.4282609
Coefficient of variation (CV)0.81580863
Kurtosis-0.89331836
Mean10.331174
Median Absolute Deviation (MAD)6.13
Skewness0.48627491
Sum103311.74
Variance71.035582
MonotonicityNot monotonic
2023-12-12T09:38:02.990629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1952
 
19.5%
0.47 54
 
0.5%
14.38 37
 
0.4%
14.76 22
 
0.2%
9.04 14
 
0.1%
9.1 13
 
0.1%
24.96 12
 
0.1%
7.46 12
 
0.1%
7.47 12
 
0.1%
8.6 12
 
0.1%
Other values (2458) 7860
78.6%
ValueCountFrequency (%)
-3.79 1
 
< 0.1%
-0.56 1
 
< 0.1%
-0.38 1
 
< 0.1%
-0.27 1
 
< 0.1%
-0.24 1
 
< 0.1%
-0.13 1
 
< 0.1%
-0.08 1
 
< 0.1%
0.0 1952
19.5%
0.12 1
 
< 0.1%
0.2 1
 
< 0.1%
ValueCountFrequency (%)
29.31 2
< 0.1%
29.28 1
 
< 0.1%
29.24 1
 
< 0.1%
29.23 2
< 0.1%
29.22 2
< 0.1%
29.21 3
< 0.1%
29.19 1
 
< 0.1%
29.17 1
 
< 0.1%
29.14 1
 
< 0.1%
29.12 1
 
< 0.1%

수소이온지수(pH)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct450
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.307475
Minimum0
Maximum9.38
Zeros2054
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:38:03.125960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.86
median6.6
Q37.2525
95-th percentile7.88
Maximum9.38
Range9.38
Interquartile range (IQR)2.3925

Descriptive statistics

Standard deviation2.8335582
Coefficient of variation (CV)0.53388065
Kurtosis-0.29639909
Mean5.307475
Median Absolute Deviation (MAD)0.96
Skewness-1.1411668
Sum53074.75
Variance8.0290521
MonotonicityNot monotonic
2023-12-12T09:38:03.279121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2054
 
20.5%
7.52 83
 
0.8%
7.28 74
 
0.7%
6.97 62
 
0.6%
6.81 61
 
0.6%
6.64 60
 
0.6%
6.89 60
 
0.6%
6.96 60
 
0.6%
6.69 58
 
0.6%
6.7 57
 
0.6%
Other values (440) 7371
73.7%
ValueCountFrequency (%)
0.0 2054
20.5%
0.48 1
 
< 0.1%
1.05 1
 
< 0.1%
2.88 1
 
< 0.1%
3.6 1
 
< 0.1%
3.91 1
 
< 0.1%
4.46 1
 
< 0.1%
4.51 1
 
< 0.1%
4.55 1
 
< 0.1%
4.56 4
 
< 0.1%
ValueCountFrequency (%)
9.38 1
 
< 0.1%
9.35 3
< 0.1%
9.34 1
 
< 0.1%
9.33 1
 
< 0.1%
9.31 1
 
< 0.1%
9.3 1
 
< 0.1%
9.28 2
< 0.1%
9.21 1
 
< 0.1%
9.18 1
 
< 0.1%
9.1 1
 
< 0.1%

전기전도도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1158
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.2272
Minimum0
Maximum2107
Zeros1991
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:38:03.744811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q133
median231
Q3461
95-th percentile956
Maximum2107
Range2107
Interquartile range (IQR)428

Descriptive statistics

Standard deviation312.76617
Coefficient of variation (CV)1.0180289
Kurtosis0.48088667
Mean307.2272
Median Absolute Deviation (MAD)211
Skewness1.0535927
Sum3072272
Variance97822.68
MonotonicityNot monotonic
2023-12-12T09:38:03.895993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1991
 
19.9%
152 63
 
0.6%
31 60
 
0.6%
39 52
 
0.5%
56 50
 
0.5%
54 41
 
0.4%
53 40
 
0.4%
59 38
 
0.4%
40 38
 
0.4%
57 37
 
0.4%
Other values (1148) 7590
75.9%
ValueCountFrequency (%)
0 1991
19.9%
1 3
 
< 0.1%
2 3
 
< 0.1%
3 6
 
0.1%
4 5
 
0.1%
5 5
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 5
 
0.1%
9 11
 
0.1%
ValueCountFrequency (%)
2107 1
< 0.1%
1997 1
< 0.1%
1907 1
< 0.1%
1798 2
< 0.1%
1608 1
< 0.1%
1468 1
< 0.1%
1461 1
< 0.1%
1435 1
< 0.1%
1404 1
< 0.1%
1387 1
< 0.1%

용존산소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1772
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.277631
Minimum0
Maximum153.67
Zeros1957
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:38:04.031528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8
median5.33
Q39.16
95-th percentile14.3605
Maximum153.67
Range153.67
Interquartile range (IQR)8.36

Descriptive statistics

Standard deviation13.2279
Coefficient of variation (CV)1.8176107
Kurtosis43.958988
Mean7.277631
Median Absolute Deviation (MAD)4.115
Skewness6.1719311
Sum72776.31
Variance174.97734
MonotonicityNot monotonic
2023-12-12T09:38:04.197164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1957
 
19.6%
0.01 109
 
1.1%
6.28 59
 
0.6%
5.72 45
 
0.4%
0.81 27
 
0.3%
6.34 23
 
0.2%
4.83 19
 
0.2%
4.66 18
 
0.2%
0.8 18
 
0.2%
4.81 18
 
0.2%
Other values (1762) 7707
77.1%
ValueCountFrequency (%)
0.0 1957
19.6%
0.01 109
 
1.1%
0.02 11
 
0.1%
0.03 6
 
0.1%
0.04 5
 
0.1%
0.05 9
 
0.1%
0.06 6
 
0.1%
0.07 5
 
0.1%
0.08 3
 
< 0.1%
0.09 2
 
< 0.1%
ValueCountFrequency (%)
153.67 2
< 0.1%
141.12 1
< 0.1%
137.6 1
< 0.1%
137.46 1
< 0.1%
137.02 1
< 0.1%
135.73 1
< 0.1%
134.4 2
< 0.1%
134.11 1
< 0.1%
132.35 1
< 0.1%
127.15 1
< 0.1%

탁도
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2859
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.430585
Minimum0
Maximum7726.77
Zeros2436
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:38:04.342378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02
median2.71
Q310.5025
95-th percentile76.413
Maximum7726.77
Range7726.77
Interquartile range (IQR)10.4825

Descriptive statistics

Standard deviation187.67029
Coefficient of variation (CV)7.6817762
Kurtosis840.80319
Mean24.430585
Median Absolute Deviation (MAD)2.71
Skewness26.078021
Sum244305.85
Variance35220.137
MonotonicityNot monotonic
2023-12-12T09:38:04.503320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2436
 
24.4%
0.38 64
 
0.6%
0.01 59
 
0.6%
0.52 42
 
0.4%
0.53 37
 
0.4%
0.54 34
 
0.3%
13.92 33
 
0.3%
0.86 31
 
0.3%
0.51 31
 
0.3%
0.5 29
 
0.3%
Other values (2849) 7204
72.0%
ValueCountFrequency (%)
0.0 2436
24.4%
0.01 59
 
0.6%
0.02 25
 
0.2%
0.03 21
 
0.2%
0.04 9
 
0.1%
0.05 7
 
0.1%
0.06 10
 
0.1%
0.07 5
 
0.1%
0.08 2
 
< 0.1%
0.09 2
 
< 0.1%
ValueCountFrequency (%)
7726.77 1
< 0.1%
6708.99 1
< 0.1%
6599.64 1
< 0.1%
5804.96 2
< 0.1%
3540.64 1
< 0.1%
3452.1 1
< 0.1%
3429.01 1
< 0.1%
3232.55 1
< 0.1%
3008.94 1
< 0.1%
2888.21 1
< 0.1%

총질소(T-N)
Real number (ℝ)

MISSING  ZEROS 

Distinct2564
Distinct (%)46.2%
Missing4447
Missing (%)44.5%
Infinite0
Infinite (%)0.0%
Mean9.2099092
Minimum0
Maximum91.158
Zeros165
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:38:04.680850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.2348
Q14.27
median6.34
Q38.639
95-th percentile14.5768
Maximum91.158
Range91.158
Interquartile range (IQR)4.369

Descriptive statistics

Standard deviation14.726901
Coefficient of variation (CV)1.5990278
Kurtosis21.802554
Mean9.2099092
Median Absolute Deviation (MAD)2.142
Skewness4.6928512
Sum51142.626
Variance216.88161
MonotonicityNot monotonic
2023-12-12T09:38:04.830793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.27 453
 
4.5%
8.327 418
 
4.2%
5.91 379
 
3.8%
6.34 203
 
2.0%
0.0 165
 
1.7%
2.934 157
 
1.6%
2.556 106
 
1.1%
88.863 87
 
0.9%
88.638 45
 
0.4%
70.55 17
 
0.2%
Other values (2554) 3523
35.2%
(Missing) 4447
44.5%
ValueCountFrequency (%)
0.0 165
1.7%
0.042 1
 
< 0.1%
0.202 1
 
< 0.1%
0.338 1
 
< 0.1%
0.348 1
 
< 0.1%
0.35 2
 
< 0.1%
0.375 1
 
< 0.1%
0.419 1
 
< 0.1%
0.421 1
 
< 0.1%
0.433 1
 
< 0.1%
ValueCountFrequency (%)
91.158 1
 
< 0.1%
90.354 1
 
< 0.1%
90.15 1
 
< 0.1%
89.24 1
 
< 0.1%
88.863 87
0.9%
88.638 45
0.4%
88.093 2
 
< 0.1%
87.192 1
 
< 0.1%
87.181 1
 
< 0.1%
86.872 2
 
< 0.1%

총인(T-P)
Real number (ℝ)

MISSING  ZEROS 

Distinct542
Distinct (%)9.8%
Missing4447
Missing (%)44.5%
Infinite0
Infinite (%)0.0%
Mean0.53522078
Minimum0
Maximum19.549
Zeros159
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:38:04.994598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.019
Q10.041
median0.071
Q30.137
95-th percentile3.163
Maximum19.549
Range19.549
Interquartile range (IQR)0.096

Descriptive statistics

Standard deviation1.7930408
Coefficient of variation (CV)3.3500955
Kurtosis32.230869
Mean0.53522078
Median Absolute Deviation (MAD)0.032
Skewness5.2074823
Sum2972.081
Variance3.2149951
MonotonicityNot monotonic
2023-12-12T09:38:05.201343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.071 474
 
4.7%
0.097 442
 
4.4%
0.041 403
 
4.0%
3.163 238
 
2.4%
0.048 222
 
2.2%
0.022 187
 
1.9%
0.0 159
 
1.6%
8.909 87
 
0.9%
0.027 55
 
0.5%
0.046 47
 
0.5%
Other values (532) 3239
32.4%
(Missing) 4447
44.5%
ValueCountFrequency (%)
0.0 159
1.6%
0.001 13
 
0.1%
0.002 11
 
0.1%
0.003 14
 
0.1%
0.004 11
 
0.1%
0.005 8
 
0.1%
0.006 10
 
0.1%
0.007 5
 
0.1%
0.008 2
 
< 0.1%
0.009 6
 
0.1%
ValueCountFrequency (%)
19.549 1
< 0.1%
19.174 1
< 0.1%
19.116 1
< 0.1%
18.981 1
< 0.1%
18.894 1
< 0.1%
18.693 1
< 0.1%
18.462 2
< 0.1%
18.38 1
< 0.1%
18.037 1
< 0.1%
13.278 1
< 0.1%

총유기탄소(TOC)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct984
Distinct (%)17.7%
Missing4447
Missing (%)44.5%
Infinite0
Infinite (%)0.0%
Mean3.7114497
Minimum0
Maximum62.38
Zeros1715
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:38:05.387648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.75
Q35.46
95-th percentile9.524
Maximum62.38
Range62.38
Interquartile range (IQR)5.46

Descriptive statistics

Standard deviation4.3152201
Coefficient of variation (CV)1.1626778
Kurtosis32.978843
Mean3.7114497
Median Absolute Deviation (MAD)2.4
Skewness4.0104817
Sum20609.68
Variance18.621125
MonotonicityNot monotonic
2023-12-12T09:38:05.542532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1715
 
17.2%
1.45 375
 
3.8%
4.88 29
 
0.3%
1.44 23
 
0.2%
4.28 18
 
0.2%
4.65 18
 
0.2%
5.05 17
 
0.2%
12.21 16
 
0.2%
4.97 16
 
0.2%
4.78 15
 
0.1%
Other values (974) 3311
33.1%
(Missing) 4447
44.5%
ValueCountFrequency (%)
0.0 1715
17.2%
0.01 2
 
< 0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.05 1
 
< 0.1%
0.06 2
 
< 0.1%
0.07 1
 
< 0.1%
0.08 1
 
< 0.1%
0.09 1
 
< 0.1%
0.11 1
 
< 0.1%
ValueCountFrequency (%)
62.38 1
< 0.1%
54.76 2
< 0.1%
54.42 1
< 0.1%
47.76 1
< 0.1%
47.0 2
< 0.1%
42.03 1
< 0.1%
40.22 2
< 0.1%
37.94 1
< 0.1%
36.81 1
< 0.1%
36.33 1
< 0.1%

Interactions

2023-12-12T09:37:59.899630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:54.052104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:54.789796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:55.509419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:56.207162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:57.062472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:58.158164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:59.020090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:00.018592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:54.149236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:54.890872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:55.591764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:56.321308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:57.452739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:58.269855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:59.133500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:00.125117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:54.224145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:54.977909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:55.670302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:56.421430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:57.552270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:58.379625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:59.229010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:00.210610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:54.302422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:55.072991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:55.763557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:56.538958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:57.642853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:58.489700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:59.340534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:00.332211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:54.414989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:55.165372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:55.850636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:56.633571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:57.750858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:58.598520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:59.486141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:00.468329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:54.515239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:55.257457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:55.936043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:56.739098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:57.860323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:58.696459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:59.604451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:00.588274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:54.589949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:55.339718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:56.024308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:56.831733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:57.950833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:58.785093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:59.700980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:00.712601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:54.697053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:55.426592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:56.113389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:56.968796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:58.049434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:58.909157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:59.802927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:38:05.675544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소 번호측정소 명측정 단위 구분 코드측정 일측정시간수온수소이온지수(pH)전기전도도용존산소탁도총질소(T-N)총인(T-P)총유기탄소(TOC)
측정소 번호1.0001.0000.0260.0000.0000.7980.8450.8380.5700.1460.5730.6320.415
측정소 명1.0001.0000.0260.0000.0000.7980.8450.8380.5700.1460.5730.6320.415
측정 단위 구분 코드0.0260.0261.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.000
측정 일0.0000.0000.0001.0000.0000.5660.4580.3650.3580.2820.4500.4060.399
측정시간0.0000.0000.0000.0001.0000.1150.0530.0370.0790.0410.0490.1150.107
수온0.7980.7980.0000.5660.1151.0000.7180.7090.3280.1270.3550.4260.293
수소이온지수(pH)0.8450.8450.0000.4580.0530.7181.0000.5910.2410.0000.3840.4310.250
전기전도도0.8380.8380.0000.3650.0370.7090.5911.0000.2720.1130.3360.2390.247
용존산소0.5700.5700.0180.3580.0790.3280.2410.2721.0000.0000.0180.0000.000
탁도0.1460.1460.0000.2820.0410.1270.0000.1130.0001.0000.0000.0000.234
총질소(T-N)0.5730.5730.0000.4500.0490.3550.3840.3360.0180.0001.0000.7700.270
총인(T-P)0.6320.6320.0000.4060.1150.4260.4310.2390.0000.0000.7701.0000.144
총유기탄소(TOC)0.4150.4150.0000.3990.1070.2930.2500.2470.0000.2340.2700.1441.000
2023-12-12T09:38:05.858211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정 단위 구분 코드측정소 명측정소 번호
측정 단위 구분 코드1.0000.0230.023
측정소 명0.0231.0001.000
측정소 번호0.0231.0001.000
2023-12-12T09:38:05.978031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수온수소이온지수(pH)전기전도도용존산소탁도총질소(T-N)총인(T-P)총유기탄소(TOC)측정소 번호측정소 명측정 단위 구분 코드
수온1.0000.0770.1660.2600.1580.078-0.073-0.2360.4480.4480.000
수소이온지수(pH)0.0771.0000.7310.7150.7510.017-0.0010.5030.5330.5330.000
전기전도도0.1660.7311.0000.5460.7720.3350.1080.6550.5040.5040.000
용존산소0.2600.7150.5461.0000.531-0.0600.0820.2810.2500.2500.014
탁도0.1580.7510.7720.5311.0000.1090.0420.5900.0590.0590.000
총질소(T-N)0.0780.0170.335-0.0600.1091.0000.2320.2410.2800.2800.000
총인(T-P)-0.073-0.0010.1080.0820.0420.2321.0000.0090.3490.3490.000
총유기탄소(TOC)-0.2360.5030.6550.2810.5900.2410.0091.0000.1850.1850.000
측정소 번호0.4480.5330.5040.2500.0590.2800.3490.1851.0001.0000.023
측정소 명0.4480.5330.5040.2500.0590.2800.3490.1851.0001.0000.023
측정 단위 구분 코드0.0000.0000.0000.0140.0000.0000.0000.0000.0230.0231.000

Missing values

2023-12-12T09:38:00.869247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:38:01.074845image/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.
2023-12-12T09:38:01.228918image/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

측정소 번호측정소 명측정 단위 구분 코드측정 일측정시간수온수소이온지수(pH)전기전도도용존산소탁도총질소(T-N)총인(T-P)총유기탄소(TOC)
690002003N10N옥수1HA2022-01-2411:0022.855.35504.860.52<NA><NA><NA>
74931001N10N삼옥1HA2022-02-1912:000.00.000.00.00.03.1630.0
289710016043N용산1HA2022-02-0407:0026.835.83590.141.695.910.0410.0
4029410186057N오리1HA2022-01-1913:0012.456.634376.841.783.0790.2714.2
4005110186057N오리1HA2022-01-0815:0013.996.624716.177.356.2890.0325.11
298571012N10N자운1H2022-01-2219:0023.296.3816012.811.34<NA><NA><NA>
306641012N10N자운1H2022-02-2516:0022.386.191859.230.0<NA><NA><NA>
623201101N20N황계1HA2022-03-0112:0010.556.914152.5613.87<NA><NA><NA>
582991101N10N팽성1HA2022-03-1005:0010.87.3479011.736.096.1480.144.75
625551101N20N황계1HA2022-03-1111:000.00.000.00.0<NA><NA><NA>
측정소 번호측정소 명측정 단위 구분 코드측정 일측정시간수온수소이온지수(pH)전기전도도용존산소탁도총질소(T-N)총인(T-P)총유기탄소(TOC)
864952022N10N효충1HA2022-02-1519:009.817.693179.4929.98<NA><NA><NA>
7731220136051N도진1HA2022-01-1700:003.437.722755.022.282.950.02728.33
2785010116054N물골1HA2022-01-2407:0024.225.4880.481.058.3270.0970.0
307561012N10N자운1H2022-03-0113:0023.466.191869.280.0<NA><NA><NA>
7198820116050N동암1H2022-03-0106:008.017.543222.24.92<NA><NA><NA>
6526720036055N송야1HA2022-01-3106:0017.444.85452.070.06.340.0480.0
4963711016053N동연1HA2022-02-2615:009.516.7310504.84.878.0940.1176.75
704112003N10N옥수1HA2022-03-2408:007.337.454110.553.35<NA><NA><NA>
298981012N10N자운1H2022-01-2413:0023.356.3216211.361.14<NA><NA><NA>
4600111016049N수직1HA2022-03-2502:0014.476.84109710.1811.066.8790.1298.47