Overview

Dataset statistics

Number of variables16
Number of observations78
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.9 KiB
Average record size in memory143.7 B

Variable types

Numeric12
Categorical4

Dataset

Description다목적댐 방류수 수질 조회 서비스
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2787

Alerts

댐이름 is highly overall correlated with 댐코드High correlation
댐코드 is highly overall correlated with 댐이름High correlation
순번 is highly overall correlated with DO(mg/L) and 2 other fieldsHigh correlation
DO(mg/L) is highly overall correlated with 순번 and 1 other fieldsHigh correlation
수온(ºC) is highly overall correlated with 순번 and 1 other fieldsHigh correlation
PO4-P(mg/L) is highly overall correlated with T-N(mg/L)High correlation
T-N(mg/L) is highly overall correlated with PO4-P(mg/L)High correlation
전기전도도 is highly overall correlated with 측정년도High correlation
측정월 is highly overall correlated with 순번High correlation
측정년도 is highly overall correlated with 전기전도도High correlation
PO4-P(mg/L) has 1 (1.3%) missing valuesMissing
SS(mg/L) has 1 (1.3%) zerosZeros
PO4-P(mg/L) has 36 (46.2%) zerosZeros

Reproduction

Analysis started2024-01-09 20:27:08.119498
Analysis finished2024-01-09 20:27:20.681394
Duration12.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2024-01-10T05:27:20.728419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile13
Maximum13
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7658755
Coefficient of variation (CV)0.53798221
Kurtosis-1.2148246
Mean7
Median Absolute Deviation (MAD)3
Skewness0
Sum546
Variance14.181818
MonotonicityNot monotonic
2024-01-10T05:27:20.823158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
13 6
 
7.7%
12 6
 
7.7%
11 6
 
7.7%
10 6
 
7.7%
9 6
 
7.7%
8 6
 
7.7%
7 6
 
7.7%
6 6
 
7.7%
5 6
 
7.7%
4 6
 
7.7%
Other values (3) 18
23.1%
ValueCountFrequency (%)
1 6
7.7%
2 6
7.7%
3 6
7.7%
4 6
7.7%
5 6
7.7%
6 6
7.7%
7 6
7.7%
8 6
7.7%
9 6
7.7%
10 6
7.7%
ValueCountFrequency (%)
13 6
7.7%
12 6
7.7%
11 6
7.7%
10 6
7.7%
9 6
7.7%
8 6
7.7%
7 6
7.7%
6 6
7.7%
5 6
7.7%
4 6
7.7%

SS(mg/L)
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6910256
Minimum0
Maximum9.8
Zeros1
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size834.0 B
2024-01-10T05:27:20.922917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q10.725
median1.2
Q32
95-th percentile5.63
Maximum9.8
Range9.8
Interquartile range (IQR)1.275

Descriptive statistics

Standard deviation1.6911677
Coefficient of variation (CV)1.000084
Kurtosis7.3464149
Mean1.6910256
Median Absolute Deviation (MAD)0.6
Skewness2.4684146
Sum131.9
Variance2.8600483
MonotonicityNot monotonic
2024-01-10T05:27:21.033208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.8 12
15.4%
0.4 11
14.1%
1.2 9
 
11.5%
0.5 4
 
5.1%
2.8 3
 
3.8%
1.6 3
 
3.8%
1.8 3
 
3.8%
2.0 3
 
3.8%
2.4 3
 
3.8%
0.7 2
 
2.6%
Other values (18) 25
32.1%
ValueCountFrequency (%)
0.0 1
 
1.3%
0.4 11
14.1%
0.5 4
 
5.1%
0.6 2
 
2.6%
0.7 2
 
2.6%
0.8 12
15.4%
0.9 1
 
1.3%
1.0 2
 
2.6%
1.1 2
 
2.6%
1.2 9
11.5%
ValueCountFrequency (%)
9.8 1
 
1.3%
6.4 2
2.6%
5.8 1
 
1.3%
5.6 1
 
1.3%
5.2 1
 
1.3%
4.0 1
 
1.3%
3.8 1
 
1.3%
3.0 2
2.6%
2.8 3
3.8%
2.6 1
 
1.3%

DO(mg/L)
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6141026
Minimum4.4
Maximum14.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2024-01-10T05:27:21.150469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.4
5-th percentile6.295
Q18.15
median9.6
Q311.225
95-th percentile13.1
Maximum14.6
Range10.2
Interquartile range (IQR)3.075

Descriptive statistics

Standard deviation2.1370403
Coefficient of variation (CV)0.22228183
Kurtosis-0.044383733
Mean9.6141026
Median Absolute Deviation (MAD)1.5
Skewness0.11321173
Sum749.9
Variance4.5669414
MonotonicityNot monotonic
2024-01-10T05:27:21.274913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
9.8 6
 
7.7%
11.7 5
 
6.4%
8.9 4
 
5.1%
9.6 3
 
3.8%
10.1 3
 
3.8%
8.1 3
 
3.8%
9.5 3
 
3.8%
11.8 2
 
2.6%
9.4 2
 
2.6%
7.1 2
 
2.6%
Other values (38) 45
57.7%
ValueCountFrequency (%)
4.4 1
1.3%
5.6 1
1.3%
5.7 2
2.6%
6.4 2
2.6%
6.7 1
1.3%
6.8 1
1.3%
7.1 2
2.6%
7.2 1
1.3%
7.3 1
1.3%
7.6 2
2.6%
ValueCountFrequency (%)
14.6 2
 
2.6%
14.3 1
 
1.3%
13.1 2
 
2.6%
13.0 1
 
1.3%
12.8 1
 
1.3%
12.2 1
 
1.3%
12.0 1
 
1.3%
11.8 2
 
2.6%
11.7 5
6.4%
11.6 1
 
1.3%

수온(ºC)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.051282
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2024-01-10T05:27:21.383212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.85
Q18
median13
Q318
95-th percentile24
Maximum27
Range26
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.7128437
Coefficient of variation (CV)0.51434363
Kurtosis-0.86895093
Mean13.051282
Median Absolute Deviation (MAD)5
Skewness0.11280811
Sum1018
Variance45.062271
MonotonicityNot monotonic
2024-01-10T05:27:21.490623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11 7
 
9.0%
8 6
 
7.7%
15 6
 
7.7%
6 5
 
6.4%
13 5
 
6.4%
14 5
 
6.4%
18 4
 
5.1%
22 4
 
5.1%
10 4
 
5.1%
4 4
 
5.1%
Other values (15) 28
35.9%
ValueCountFrequency (%)
1 2
 
2.6%
2 2
 
2.6%
3 2
 
2.6%
4 4
5.1%
5 1
 
1.3%
6 5
6.4%
7 2
 
2.6%
8 6
7.7%
10 4
5.1%
11 7
9.0%
ValueCountFrequency (%)
27 1
 
1.3%
25 2
2.6%
24 3
3.8%
23 2
2.6%
22 4
5.1%
21 1
 
1.3%
20 3
3.8%
19 1
 
1.3%
18 4
5.1%
17 4
5.1%

PO4-P(mg/L)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)11.7%
Missing1
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean0.0020779221
Minimum0
Maximum0.013
Zeros36
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size834.0 B
2024-01-10T05:27:21.871972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.001
Q30.004
95-th percentile0.0052
Maximum0.013
Range0.013
Interquartile range (IQR)0.004

Descriptive statistics

Standard deviation0.0027181952
Coefficient of variation (CV)1.3081314
Kurtosis4.9221551
Mean0.0020779221
Median Absolute Deviation (MAD)0.001
Skewness1.8649889
Sum0.16
Variance7.3885851 × 10-6
MonotonicityNot monotonic
2024-01-10T05:27:21.967951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.0 36
46.2%
0.005 11
 
14.1%
0.003 10
 
12.8%
0.001 6
 
7.7%
0.004 5
 
6.4%
0.002 5
 
6.4%
0.013 2
 
2.6%
0.007 1
 
1.3%
0.006 1
 
1.3%
(Missing) 1
 
1.3%
ValueCountFrequency (%)
0.0 36
46.2%
0.001 6
 
7.7%
0.002 5
 
6.4%
0.003 10
 
12.8%
0.004 5
 
6.4%
0.005 11
 
14.1%
0.006 1
 
1.3%
0.007 1
 
1.3%
0.013 2
 
2.6%
ValueCountFrequency (%)
0.013 2
 
2.6%
0.007 1
 
1.3%
0.006 1
 
1.3%
0.005 11
 
14.1%
0.004 5
 
6.4%
0.003 10
 
12.8%
0.002 5
 
6.4%
0.001 6
 
7.7%
0.0 36
46.2%

탁도(NTU)
Real number (ℝ)

Distinct34
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5230769
Minimum-1
Maximum5.8
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)3.8%
Memory size834.0 B
2024-01-10T05:27:22.075396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.1
Q10.625
median1.3
Q32.1
95-th percentile4.13
Maximum5.8
Range6.8
Interquartile range (IQR)1.475

Descriptive statistics

Standard deviation1.2668524
Coefficient of variation (CV)0.8317718
Kurtosis1.0848797
Mean1.5230769
Median Absolute Deviation (MAD)0.7
Skewness0.9921476
Sum118.8
Variance1.6049151
MonotonicityNot monotonic
2024-01-10T05:27:22.195481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.4 6
 
7.7%
1.6 6
 
7.7%
0.8 5
 
6.4%
0.7 4
 
5.1%
0.9 4
 
5.1%
0.6 4
 
5.1%
4.3 3
 
3.8%
1.1 3
 
3.8%
2.1 3
 
3.8%
2.2 3
 
3.8%
Other values (24) 37
47.4%
ValueCountFrequency (%)
-1.0 1
 
1.3%
-0.4 2
 
2.6%
0.1 3
3.8%
0.2 2
 
2.6%
0.4 6
7.7%
0.5 2
 
2.6%
0.6 4
5.1%
0.7 4
5.1%
0.8 5
6.4%
0.9 4
5.1%
ValueCountFrequency (%)
5.8 1
 
1.3%
4.3 3
3.8%
4.1 1
 
1.3%
3.9 1
 
1.3%
3.8 2
2.6%
3.2 1
 
1.3%
3.1 1
 
1.3%
3.0 1
 
1.3%
2.6 1
 
1.3%
2.5 1
 
1.3%

BOD(mg/L)
Real number (ℝ)

Distinct19
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4384615
Minimum0.2
Maximum3.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2024-01-10T05:27:22.299963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.7
Q11.2
median1.4
Q31.7
95-th percentile2.1
Maximum3.3
Range3.1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.47788244
Coefficient of variation (CV)0.33221774
Kurtosis2.3956356
Mean1.4384615
Median Absolute Deviation (MAD)0.3
Skewness0.65005228
Sum112.2
Variance0.22837163
MonotonicityNot monotonic
2024-01-10T05:27:22.399508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1.2 10
12.8%
1.4 9
11.5%
1.7 7
9.0%
1.8 6
 
7.7%
1.6 6
 
7.7%
1.5 6
 
7.7%
1.3 5
 
6.4%
0.9 4
 
5.1%
2.1 4
 
5.1%
1.1 4
 
5.1%
Other values (9) 17
21.8%
ValueCountFrequency (%)
0.2 1
 
1.3%
0.6 2
 
2.6%
0.7 2
 
2.6%
0.8 2
 
2.6%
0.9 4
 
5.1%
1.0 3
 
3.8%
1.1 4
 
5.1%
1.2 10
12.8%
1.3 5
6.4%
1.4 9
11.5%
ValueCountFrequency (%)
3.3 1
 
1.3%
2.5 2
 
2.6%
2.1 4
5.1%
2.0 1
 
1.3%
1.9 3
 
3.8%
1.8 6
7.7%
1.7 7
9.0%
1.6 6
7.7%
1.5 6
7.7%
1.4 9
11.5%

pH
Real number (ℝ)

Distinct21
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8179487
Minimum6.4
Maximum10.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2024-01-10T05:27:22.499958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.4
5-th percentile7.1
Q17.5
median7.8
Q38
95-th percentile8.715
Maximum10.7
Range4.3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.579239
Coefficient of variation (CV)0.074090918
Kurtosis8.5173949
Mean7.8179487
Median Absolute Deviation (MAD)0.2
Skewness1.9379134
Sum609.8
Variance0.33551782
MonotonicityNot monotonic
2024-01-10T05:27:22.593740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
7.8 19
24.4%
7.5 8
10.3%
8.0 8
10.3%
7.6 7
 
9.0%
7.4 7
 
9.0%
7.9 5
 
6.4%
8.1 4
 
5.1%
7.7 3
 
3.8%
7.1 3
 
3.8%
8.5 2
 
2.6%
Other values (11) 12
15.4%
ValueCountFrequency (%)
6.4 1
 
1.3%
6.8 2
 
2.6%
7.1 3
 
3.8%
7.3 1
 
1.3%
7.4 7
 
9.0%
7.5 8
10.3%
7.6 7
 
9.0%
7.7 3
 
3.8%
7.8 19
24.4%
7.9 5
 
6.4%
ValueCountFrequency (%)
10.7 1
 
1.3%
9.7 1
 
1.3%
9.0 1
 
1.3%
8.8 1
 
1.3%
8.7 1
 
1.3%
8.6 1
 
1.3%
8.5 2
2.6%
8.3 1
 
1.3%
8.2 1
 
1.3%
8.1 4
5.1%

COD(mg/L)
Real number (ℝ)

Distinct24
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8576923
Minimum1.5
Maximum5.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2024-01-10T05:27:22.698273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.085
Q12.5
median2.8
Q33.2
95-th percentile3.745
Maximum5.4
Range3.9
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.63440723
Coefficient of variation (CV)0.22199984
Kurtosis3.9929634
Mean2.8576923
Median Absolute Deviation (MAD)0.35
Skewness1.2437771
Sum222.9
Variance0.40247253
MonotonicityNot monotonic
2024-01-10T05:27:22.833852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2.8 8
 
10.3%
3.2 7
 
9.0%
2.5 7
 
9.0%
3.0 6
 
7.7%
2.6 6
 
7.7%
2.7 5
 
6.4%
2.1 4
 
5.1%
2.9 4
 
5.1%
3.5 4
 
5.1%
2.4 3
 
3.8%
Other values (14) 24
30.8%
ValueCountFrequency (%)
1.5 1
 
1.3%
1.6 1
 
1.3%
1.9 1
 
1.3%
2.0 1
 
1.3%
2.1 4
5.1%
2.2 3
3.8%
2.3 3
3.8%
2.4 3
3.8%
2.5 7
9.0%
2.6 6
7.7%
ValueCountFrequency (%)
5.4 1
 
1.3%
5.1 1
 
1.3%
4.0 2
 
2.6%
3.7 1
 
1.3%
3.6 1
 
1.3%
3.5 4
5.1%
3.4 2
 
2.6%
3.3 3
3.8%
3.2 7
9.0%
3.1 3
3.8%

T-N(mg/L)
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1378462
Minimum1.361
Maximum3.257
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2024-01-10T05:27:22.992769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.361
5-th percentile1.666
Q11.90725
median2.107
Q32.3385
95-th percentile2.738
Maximum3.257
Range1.896
Interquartile range (IQR)0.43125

Descriptive statistics

Standard deviation0.34926338
Coefficient of variation (CV)0.16337162
Kurtosis0.4552959
Mean2.1378462
Median Absolute Deviation (MAD)0.207
Skewness0.63728478
Sum166.752
Variance0.12198491
MonotonicityNot monotonic
2024-01-10T05:27:23.141433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.074 2
 
2.6%
1.839 2
 
2.6%
2.738 2
 
2.6%
2.118 2
 
2.6%
2.519 2
 
2.6%
2.459 1
 
1.3%
2.144 1
 
1.3%
2.138 1
 
1.3%
2.358 1
 
1.3%
1.748 1
 
1.3%
Other values (63) 63
80.8%
ValueCountFrequency (%)
1.361 1
1.3%
1.57 1
1.3%
1.578 1
1.3%
1.615 1
1.3%
1.675 1
1.3%
1.726 1
1.3%
1.734 1
1.3%
1.748 1
1.3%
1.764 1
1.3%
1.786 1
1.3%
ValueCountFrequency (%)
3.257 1
1.3%
2.866 1
1.3%
2.796 1
1.3%
2.738 2
2.6%
2.726 1
1.3%
2.712 1
1.3%
2.689 1
1.3%
2.669 1
1.3%
2.595 1
1.3%
2.567 1
1.3%

측정월
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size756.0 B
12월
11월
10월
09월
08월
Other values (8)
48 

Length

Max length3
Median length3
Mean length2.9230769
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12월
2nd row11월
3rd row10월
4th row09월
5th row08월

Common Values

ValueCountFrequency (%)
12월 6
 
7.7%
11월 6
 
7.7%
10월 6
 
7.7%
09월 6
 
7.7%
08월 6
 
7.7%
07월 6
 
7.7%
06월 6
 
7.7%
05월 6
 
7.7%
04월 6
 
7.7%
03월 6
 
7.7%
Other values (3) 18
23.1%

Length

2024-01-10T05:27:23.268444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12월 6
 
7.7%
11월 6
 
7.7%
10월 6
 
7.7%
09월 6
 
7.7%
08월 6
 
7.7%
07월 6
 
7.7%
06월 6
 
7.7%
05월 6
 
7.7%
04월 6
 
7.7%
03월 6
 
7.7%
Other values (3) 18
23.1%

T-P(mg/L)
Real number (ℝ)

Distinct24
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013038462
Minimum0.001
Maximum0.033
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2024-01-10T05:27:23.363970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.005
Q10.009
median0.01
Q30.015
95-th percentile0.028
Maximum0.033
Range0.032
Interquartile range (IQR)0.006

Descriptive statistics

Standard deviation0.0067889182
Coefficient of variation (CV)0.52068399
Kurtosis0.74651177
Mean0.013038462
Median Absolute Deviation (MAD)0.003
Skewness1.1609432
Sum1.017
Variance4.6089411 × 10-5
MonotonicityNot monotonic
2024-01-10T05:27:23.467116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.01 16
20.5%
0.009 7
 
9.0%
0.008 5
 
6.4%
0.012 5
 
6.4%
0.007 5
 
6.4%
0.005 4
 
5.1%
0.013 4
 
5.1%
0.011 4
 
5.1%
0.015 4
 
5.1%
0.021 3
 
3.8%
Other values (14) 21
26.9%
ValueCountFrequency (%)
0.001 1
 
1.3%
0.005 4
 
5.1%
0.006 2
 
2.6%
0.007 5
 
6.4%
0.008 5
 
6.4%
0.009 7
9.0%
0.01 16
20.5%
0.011 4
 
5.1%
0.012 5
 
6.4%
0.013 4
 
5.1%
ValueCountFrequency (%)
0.033 1
 
1.3%
0.031 1
 
1.3%
0.029 1
 
1.3%
0.028 2
2.6%
0.025 2
2.6%
0.024 1
 
1.3%
0.023 3
3.8%
0.021 3
3.8%
0.02 1
 
1.3%
0.019 1
 
1.3%

전기전도도
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.44872
Minimum95
Maximum284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2024-01-10T05:27:23.583857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile103.25
Q1119
median139
Q3156
95-th percentile197.6
Maximum284
Range189
Interquartile range (IQR)37

Descriptive statistics

Standard deviation30.91196
Coefficient of variation (CV)0.21853828
Kurtosis4.8732966
Mean141.44872
Median Absolute Deviation (MAD)18
Skewness1.5480383
Sum11033
Variance955.54928
MonotonicityNot monotonic
2024-01-10T05:27:23.707461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147 3
 
3.8%
145 3
 
3.8%
119 3
 
3.8%
156 3
 
3.8%
152 3
 
3.8%
157 3
 
3.8%
154 2
 
2.6%
130 2
 
2.6%
138 2
 
2.6%
125 2
 
2.6%
Other values (46) 52
66.7%
ValueCountFrequency (%)
95 2
2.6%
99 2
2.6%
104 1
1.3%
105 1
1.3%
107 1
1.3%
109 1
1.3%
110 2
2.6%
112 1
1.3%
113 1
1.3%
114 1
1.3%
ValueCountFrequency (%)
284 1
1.3%
213 1
1.3%
202 1
1.3%
201 1
1.3%
197 1
1.3%
192 1
1.3%
188 1
1.3%
173 1
1.3%
172 1
1.3%
171 1
1.3%

댐코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size756.0 B
3203110
26 
3008110
26 
3001110
26 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3203110
2nd row3203110
3rd row3203110
4th row3203110
5th row3203110

Common Values

ValueCountFrequency (%)
3203110 26
33.3%
3008110 26
33.3%
3001110 26
33.3%

Length

2024-01-10T05:27:23.828210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:27:23.915009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3203110 26
33.3%
3008110 26
33.3%
3001110 26
33.3%

댐이름
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size756.0 B
보령댐
26 
대청댐
26 
용담댐
26 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
보령댐 26
33.3%
대청댐 26
33.3%
용담댐 26
33.3%

Length

2024-01-10T05:27:24.004313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:27:24.098174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보령댐 26
33.3%
대청댐 26
33.3%
용담댐 26
33.3%

측정년도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size756.0 B
2021
39 
2022
39 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 39
50.0%
2022 39
50.0%

Length

2024-01-10T05:27:24.190135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:27:24.282302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 39
50.0%
2022 39
50.0%

Interactions

2024-01-10T05:27:19.321392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:08.685320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:09.592877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:10.490025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:11.361258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:12.502462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:13.457660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:14.413761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:15.309915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:16.163650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:17.339165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:18.304192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:19.417112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:08.747255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:09.675676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:10.558080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:11.442460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:12.581689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:13.552602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:14.486604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:15.371193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:16.240376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:17.413704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:18.404320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:19.508486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:08.814656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:09.746998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:10.625239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:11.519122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:12.661478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:13.630296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:14.555860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:15.431768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:16.315166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:17.485948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:18.479595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:19.588980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:08.877341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:09.814496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:10.696832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:11.592465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:12.734633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:13.707108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:14.628615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:15.499861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:16.392701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:17.564182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:18.557911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:19.683981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:08.949969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:09.884787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:10.764706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:11.660275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:12.809503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:13.785266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:14.698982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:15.572042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:16.467010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:17.647373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:18.632195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:19.785976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:09.036631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:09.956379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:10.836037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:11.746918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:12.886495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:13.873233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:14.773199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:15.647924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:16.547605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:17.731843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:18.711498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:19.888269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:09.124656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:10.034402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:10.914619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:11.825865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:12.969042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:13.950822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:14.860276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:15.724247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:16.630794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:17.812151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:18.794645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:19.980116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:09.205864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:10.106323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:10.983204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:11.898077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:13.046793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:14.026468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:14.936465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:15.791192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:16.708296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:17.890185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:18.869575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:20.066906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:09.269016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:10.172796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:11.050541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:12.193826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:13.119895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:14.092502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:15.006649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:15.850883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:16.775916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:17.959244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:18.938626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:20.159993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:09.359033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:10.259869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:11.129439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:12.271278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:13.203271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:14.178015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:15.086106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:15.932155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:17.090938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:18.055814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:19.023228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:20.240614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:09.436311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:10.339489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:11.206267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:12.348761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:13.287433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:14.259168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:15.162957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:16.016627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:17.170898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:18.135615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:19.114229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:20.321864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:09.516255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:10.415076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:11.280346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:12.422622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:13.362650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:14.336737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:15.234054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:16.091979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:17.258629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:18.217557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:27:19.211119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:27:24.353627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번SS(mg/L)DO(mg/L)수온(ºC)PO4-P(mg/L)탁도(NTU)BOD(mg/L)pHCOD(mg/L)T-N(mg/L)측정월T-P(mg/L)전기전도도댐코드댐이름측정년도
순번1.0000.1950.5530.7570.0000.2620.2330.2390.3530.2811.0000.0000.2630.0000.0000.000
SS(mg/L)0.1951.0000.2460.4050.0000.3570.0000.0000.0000.0000.1960.5970.7580.5080.5080.378
DO(mg/L)0.5530.2461.0000.8540.5020.5990.4200.6480.4050.5170.4380.0000.3760.4020.4020.284
수온(ºC)0.7570.4050.8541.0000.3910.0000.4190.4540.0000.0000.7080.5700.4920.2900.2900.169
PO4-P(mg/L)0.0000.0000.5020.3911.0000.4780.7420.2100.4300.7010.0000.4490.0000.4990.4990.172
탁도(NTU)0.2620.3570.5990.0000.4781.0000.5110.3260.2470.4090.2850.1960.1460.6510.6510.217
BOD(mg/L)0.2330.0000.4200.4190.7420.5111.0000.0000.5370.6330.2220.6440.0000.6990.6990.208
pH0.2390.0000.6480.4540.2100.3260.0001.0000.4910.0000.2770.0000.0000.5210.5210.228
COD(mg/L)0.3530.0000.4050.0000.4300.2470.5370.4911.0000.5070.4410.0000.3030.0000.0000.000
T-N(mg/L)0.2810.0000.5170.0000.7010.4090.6330.0000.5071.0000.0000.0000.3160.7020.7020.282
측정월1.0000.1960.4380.7080.0000.2850.2220.2770.4410.0001.0000.0000.0000.0000.0000.000
T-P(mg/L)0.0000.5970.0000.5700.4490.1960.6440.0000.0000.0000.0001.0000.2880.5940.5940.373
전기전도도0.2630.7580.3760.4920.0000.1460.0000.0000.3030.3160.0000.2881.0000.3880.3880.706
댐코드0.0000.5080.4020.2900.4990.6510.6990.5210.0000.7020.0000.5940.3881.0001.0000.000
댐이름0.0000.5080.4020.2900.4990.6510.6990.5210.0000.7020.0000.5940.3881.0001.0000.000
측정년도0.0000.3780.2840.1690.1720.2170.2080.2280.0000.2820.0000.3730.7060.0000.0001.000
2024-01-10T05:27:24.484370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름측정년도댐코드측정월
댐이름1.0000.0001.0000.000
측정년도0.0001.0000.0000.000
댐코드1.0000.0001.0000.000
측정월0.0000.0000.0001.000
2024-01-10T05:27:24.578273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번SS(mg/L)DO(mg/L)수온(ºC)PO4-P(mg/L)탁도(NTU)BOD(mg/L)pHCOD(mg/L)T-N(mg/L)T-P(mg/L)전기전도도측정월댐코드댐이름측정년도
순번1.0000.143-0.6010.5930.0860.1600.317-0.033-0.0030.1770.233-0.1160.9780.0000.0000.000
SS(mg/L)0.1431.000-0.2760.4070.1060.4110.004-0.1730.1390.1730.3850.0110.0730.3610.3610.271
DO(mg/L)-0.601-0.2761.000-0.799-0.131-0.360-0.1390.140-0.185-0.131-0.2730.2150.1720.2770.2770.183
수온(ºC)0.5930.407-0.7991.0000.1270.4380.142-0.0670.0990.1470.262-0.2730.3780.1730.1730.114
PO4-P(mg/L)0.0860.106-0.1310.1271.000-0.0500.306-0.0760.2750.5030.3780.1510.0000.3750.3750.176
탁도(NTU)0.1600.411-0.3600.438-0.0501.000-0.276-0.2550.032-0.0490.0230.0650.1130.3490.3490.204
BOD(mg/L)0.3170.004-0.1390.1420.306-0.2761.0000.2970.1430.4380.3740.2150.0810.3890.3890.195
pH-0.033-0.1730.140-0.067-0.076-0.2550.2971.000-0.0740.0410.2060.0810.1090.2550.2550.214
COD(mg/L)-0.0030.139-0.1850.0990.2750.0320.143-0.0741.0000.2280.1490.1180.2040.0000.0000.000
T-N(mg/L)0.1770.173-0.1310.1470.503-0.0490.4380.0410.2281.0000.1990.4210.0000.3920.3920.267
T-P(mg/L)0.2330.385-0.2730.2620.3780.0230.3740.2060.1490.1991.0000.0910.0000.4160.4160.268
전기전도도-0.1160.0110.215-0.2730.1510.0650.2150.0810.1180.4210.0911.0000.0000.2570.2570.516
측정월0.9780.0730.1720.3780.0000.1130.0810.1090.2040.0000.0000.0001.0000.0000.0000.000
댐코드0.0000.3610.2770.1730.3750.3490.3890.2550.0000.3920.4160.2570.0001.0001.0000.000
댐이름0.0000.3610.2770.1730.3750.3490.3890.2550.0000.3920.4160.2570.0001.0001.0000.000
측정년도0.0000.2710.1830.1140.1760.2040.1950.2140.0000.2670.2680.5160.0000.0000.0001.000

Missing values

2024-01-10T05:27:20.435897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:27:20.610123image/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

순번SS(mg/L)DO(mg/L)수온(ºC)PO4-P(mg/L)탁도(NTU)BOD(mg/L)pHCOD(mg/L)T-N(mg/L)측정월T-P(mg/L)전기전도도댐코드댐이름측정년도
0132.210.5110.0034.31.17.52.11.96312월0.0051473203110보령댐2021
1122.89.4180.02.61.27.92.32.07411월0.0131363203110보령댐2021
2111.68.3240.0041.41.37.73.51.97610월0.0151193203110보령댐2021
3101.88.4240.04.30.67.63.02.11509월0.0151183203110보령댐2021
491.87.6230.01.60.67.53.02.07708월0.0051173203110보령댐2021
580.87.6230.01.20.87.52.71.83507월0.0011173203110보령댐2021
675.68.0170.05.81.17.42.81.93506월0.0121133203110보령댐2021
762.211.7140.00.91.27.43.22.66905월0.0071103203110보령댐2021
851.411.780.0031.10.87.62.81.5704월0.0131053203110보령댐2021
942.011.740.00.91.27.52.11.79903월0.01953203110보령댐2021
순번SS(mg/L)DO(mg/L)수온(ºC)PO4-P(mg/L)탁도(NTU)BOD(mg/L)pHCOD(mg/L)T-N(mg/L)측정월T-P(mg/L)전기전도도댐코드댐이름측정년도
68101.08.3220.00.61.98.12.42.00409월0.011043001110용담댐2022
6990.88.1250.00.81.88.22.62.10608월0.0081213001110용담댐2022
7080.89.2140.00.51.88.83.52.05107월0.011223001110용담댐2022
7170.89.8150.00.61.87.82.71.90606월0.011263001110용담댐2022
7260.710.1140.00.81.28.12.72.21105월0.0081433001110용담댐2022
7350.59.8100.01.10.97.82.82.10504월0.0071373001110용담댐2022
7440.710.450.00.41.27.82.52.05703월0.0091523001110용담댐2022
7531.511.830.00.81.47.42.12.11902월0.0071713001110용담댐2022
7621.210.040.00.71.57.43.02.11801월0.0091603001110용담댐2022
7710.99.6130.00.71.67.92.72.108평균0.0091383001110용담댐2022