Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory72.3 B

Variable types

Categorical1
Numeric7

Alerts

저수위(m) is highly overall correlated with 방류량(ms) and 2 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 방류량(ms) and 2 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 저수위(m) and 2 other fieldsHigh correlation
댐이름 is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
강우량(mm) has 86 (86.0%) zerosZeros
유입량(ms) has 7 (7.0%) zerosZeros
방류량(ms) has 7 (7.0%) zerosZeros
저수율 has 7 (7.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:22:12.546733
Analysis finished2023-12-10 13:22:20.675812
Duration8.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
군남
31 
한탄강
31 
평화의댐
31 
여주저류지
7 

Length

Max length5
Median length4
Mean length3.14
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군남 31
31.0%
한탄강 31
31.0%
평화의댐 31
31.0%
여주저류지 7
 
7.0%

Length

2023-12-10T22:22:20.797226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:20.958066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군남 31
31.0%
한탄강 31
31.0%
평화의댐 31
31.0%
여주저류지 7
 
7.0%

일자/시간(t)
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190316
Minimum20190301
Maximum20190331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:21.150788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190301
5-th percentile20190302
Q120190308
median20190316
Q320190324
95-th percentile20190330
Maximum20190331
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9555355
Coefficient of variation (CV)4.4355599 × 10-7
Kurtosis-1.2055892
Mean20190316
Median Absolute Deviation (MAD)8
Skewness-0.024533469
Sum2.0190316 × 109
Variance80.201616
MonotonicityNot monotonic
2023-12-10T22:22:21.348054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20190317 4
 
4.0%
20190313 4
 
4.0%
20190323 4
 
4.0%
20190306 4
 
4.0%
20190325 4
 
4.0%
20190324 4
 
4.0%
20190302 4
 
4.0%
20190311 3
 
3.0%
20190331 3
 
3.0%
20190329 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
20190301 3
3.0%
20190302 4
4.0%
20190303 3
3.0%
20190304 3
3.0%
20190305 3
3.0%
20190306 4
4.0%
20190307 3
3.0%
20190308 3
3.0%
20190309 3
3.0%
20190310 3
3.0%
ValueCountFrequency (%)
20190331 3
3.0%
20190330 3
3.0%
20190329 3
3.0%
20190328 3
3.0%
20190327 3
3.0%
20190326 3
3.0%
20190325 4
4.0%
20190324 4
4.0%
20190323 4
4.0%
20190322 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.3939
Minimum25.6
Maximum167.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:21.550881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.6
5-th percentile25.68
Q127.615
median47.255
Q3165.62
95-th percentile166.9545
Maximum167.25
Range141.65
Interquartile range (IQR)138.005

Descriptive statistics

Standard deviation61.121108
Coefficient of variation (CV)0.80007839
Kurtosis-1.3455727
Mean76.3939
Median Absolute Deviation (MAD)20.755
Skewness0.77158425
Sum7639.39
Variance3735.7899
MonotonicityNot monotonic
2023-12-10T22:22:21.781806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.36 3
 
3.0%
47.39 3
 
3.0%
25.68 3
 
3.0%
25.71 2
 
2.0%
47.28 2
 
2.0%
47.21 2
 
2.0%
47.38 2
 
2.0%
25.73 2
 
2.0%
47.26 2
 
2.0%
165.68 2
 
2.0%
Other values (72) 77
77.0%
ValueCountFrequency (%)
25.6 1
 
1.0%
25.65 1
 
1.0%
25.66 1
 
1.0%
25.67 1
 
1.0%
25.68 3
3.0%
25.69 1
 
1.0%
25.7 1
 
1.0%
25.71 2
2.0%
25.72 1
 
1.0%
25.73 2
2.0%
ValueCountFrequency (%)
167.25 1
1.0%
167.22 1
1.0%
167.19 1
1.0%
167.12 1
1.0%
167.04 1
1.0%
166.95 1
1.0%
166.93 1
1.0%
166.84 1
1.0%
166.81 1
1.0%
166.79 1
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.691409
Minimum0
Maximum14
Zeros86
Zeros (%)86.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:21.998585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.024995
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.520058
Coefficient of variation (CV)3.6448151
Kurtosis17.493064
Mean0.691409
Median Absolute Deviation (MAD)0
Skewness4.1854399
Sum69.1409
Variance6.3506922
MonotonicityNot monotonic
2023-12-10T22:22:22.188747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 86
86.0%
5.0 2
 
2.0%
1.0 2
 
2.0%
13.5 1
 
1.0%
2.0 1
 
1.0%
0.1412 1
 
1.0%
0.2824 1
 
1.0%
5.4999 1
 
1.0%
11.3823 1
 
1.0%
0.1794 1
 
1.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
0.0 86
86.0%
0.1412 1
 
1.0%
0.1794 1
 
1.0%
0.2824 1
 
1.0%
1.0 2
 
2.0%
1.1557 1
 
1.0%
2.0 1
 
1.0%
5.0 2
 
2.0%
5.4999 1
 
1.0%
9.0 1
 
1.0%
ValueCountFrequency (%)
14.0 1
1.0%
13.5 1
1.0%
11.3823 1
1.0%
9.0 1
1.0%
5.4999 1
1.0%
5.0 2
2.0%
2.0 1
1.0%
1.1557 1
1.0%
1.0 2
2.0%
0.2824 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.45198
Minimum0
Maximum21.031
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:22.424208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.463
median9.3405
Q311.818
95-th percentile19.16115
Maximum21.031
Range21.031
Interquartile range (IQR)5.355

Descriptive statistics

Standard deviation4.918221
Coefficient of variation (CV)0.52033764
Kurtosis0.046182759
Mean9.45198
Median Absolute Deviation (MAD)2.841
Skewness0.30362854
Sum945.198
Variance24.188898
MonotonicityNot monotonic
2023-12-10T22:22:22.619393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
7.0%
4.635 2
 
2.0%
12.807 1
 
1.0%
6.586 1
 
1.0%
16.599 1
 
1.0%
10.457 1
 
1.0%
11.496 1
 
1.0%
15.621 1
 
1.0%
19.119 1
 
1.0%
20.269 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
0.0 7
7.0%
4.18 1
 
1.0%
4.402 1
 
1.0%
4.419 1
 
1.0%
4.497 1
 
1.0%
4.548 1
 
1.0%
4.568 1
 
1.0%
4.583 1
 
1.0%
4.595 1
 
1.0%
4.635 2
 
2.0%
ValueCountFrequency (%)
21.031 1
1.0%
20.719 1
1.0%
20.269 1
1.0%
20.153 1
1.0%
19.962 1
1.0%
19.119 1
1.0%
17.747 1
1.0%
17.429 1
1.0%
17.115 1
1.0%
16.779 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.27128
Minimum0
Maximum21.889
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:22.879737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.76225
median6.676
Q312.835
95-th percentile19.26435
Maximum21.889
Range21.889
Interquartile range (IQR)7.07275

Descriptive statistics

Standard deviation5.4304933
Coefficient of variation (CV)0.58573286
Kurtosis-0.45166801
Mean9.27128
Median Absolute Deviation (MAD)3.295
Skewness0.4931567
Sum927.128
Variance29.490258
MonotonicityNot monotonic
2023-12-10T22:22:23.123367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
7.0%
4.404 2
 
2.0%
12.367 1
 
1.0%
6.685 1
 
1.0%
17.479 1
 
1.0%
10.017 1
 
1.0%
10.94 1
 
1.0%
15.181 1
 
1.0%
18.969 1
 
1.0%
21.889 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
0.0 7
7.0%
4.394 1
 
1.0%
4.404 2
 
2.0%
4.435 1
 
1.0%
4.438 1
 
1.0%
4.479 1
 
1.0%
4.506 1
 
1.0%
4.593 1
 
1.0%
4.606 1
 
1.0%
4.607 1
 
1.0%
ValueCountFrequency (%)
21.889 1
1.0%
21.078 1
1.0%
20.475 1
1.0%
19.825 1
1.0%
19.632 1
1.0%
19.245 1
1.0%
19.221 1
1.0%
19.205 1
1.0%
18.969 1
1.0%
18.16 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.04479
Minimum0.012
Maximum7.796
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:23.342780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.0189
Q10.50575
median3.5635
Q34.668
95-th percentile6.62155
Maximum7.796
Range7.784
Interquartile range (IQR)4.16225

Descriptive statistics

Standard deviation2.289943
Coefficient of variation (CV)0.7520857
Kurtosis-1.2339035
Mean3.04479
Median Absolute Deviation (MAD)2.2395
Skewness0.088102705
Sum304.479
Variance5.2438391
MonotonicityNot monotonic
2023-12-10T22:22:23.581138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.516 3
 
3.0%
0.52 3
 
3.0%
3.557 3
 
3.0%
3.595 2
 
2.0%
0.503 2
 
2.0%
0.492 2
 
2.0%
0.519 2
 
2.0%
3.621 2
 
2.0%
0.5 2
 
2.0%
3.758 2
 
2.0%
Other values (72) 77
77.0%
ValueCountFrequency (%)
0.012 2
2.0%
0.014 1
1.0%
0.016 1
1.0%
0.017 1
1.0%
0.019 2
2.0%
0.469 1
1.0%
0.477 1
1.0%
0.48 1
1.0%
0.484 1
1.0%
0.486 1
1.0%
ValueCountFrequency (%)
7.796 1
1.0%
7.6 1
1.0%
7.426 1
1.0%
7.235 1
1.0%
6.917 1
1.0%
6.606 1
1.0%
6.286 1
1.0%
6.044 1
1.0%
6.007 1
1.0%
5.959 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.184
Minimum0
Maximum10.9
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:24.186158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median0.2
Q35
95-th percentile9.225
Maximum10.9
Range10.9
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation3.2343085
Coefficient of variation (CV)1.4809105
Kurtosis0.29013354
Mean2.184
Median Absolute Deviation (MAD)0
Skewness1.2796166
Sum218.4
Variance10.460752
MonotonicityNot monotonic
2023-12-10T22:22:24.446329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.2 53
53.0%
5.0 9
 
9.0%
0.1 9
 
9.0%
0.0 7
 
7.0%
5.1 2
 
2.0%
4.9 2
 
2.0%
5.9 1
 
1.0%
6.2 1
 
1.0%
6.7 1
 
1.0%
8.8 1
 
1.0%
Other values (14) 14
 
14.0%
ValueCountFrequency (%)
0.0 7
 
7.0%
0.1 9
 
9.0%
0.2 53
53.0%
4.8 1
 
1.0%
4.9 2
 
2.0%
5.0 9
 
9.0%
5.1 2
 
2.0%
5.2 1
 
1.0%
5.6 1
 
1.0%
5.9 1
 
1.0%
ValueCountFrequency (%)
10.9 1
1.0%
10.6 1
1.0%
10.4 1
1.0%
10.1 1
1.0%
9.7 1
1.0%
9.2 1
1.0%
8.8 1
1.0%
8.4 1
1.0%
8.1 1
1.0%
7.7 1
1.0%

Interactions

2023-12-10T22:22:19.366775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:12.891663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:13.959245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:15.027549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:16.408197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:17.387104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:18.292956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:19.516605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:13.040019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:14.125767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:15.175624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:16.555045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:17.503790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:18.439112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:19.655912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:13.191234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:14.274848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:15.329575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:16.710928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:17.632897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:18.703184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:19.812470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:13.333301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:14.443251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:15.496147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:16.854979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:17.769381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:18.860923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:19.936041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:13.464798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:14.577315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:15.628115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:17.003497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:17.912198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:18.981649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:20.061167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:13.607415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:14.712063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:16.089897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:17.146091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:18.020787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:19.106989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:20.216729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:13.790859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:14.864637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:16.249126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:17.271192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:18.168844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:19.247041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:22:24.588235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.9240.8950.9010.676
일자/시간(t)0.0001.0000.0000.4080.5570.2790.5680.534
저수위(m)1.0000.0001.0000.0000.8410.9490.7560.666
강우량(mm)0.0000.4080.0001.0000.0000.0000.2080.437
유입량(ms)0.9240.5570.8410.0001.0000.8580.6840.515
방류량(ms)0.8950.2790.9490.0000.8581.0000.7060.460
저수량(백만m3)0.9010.5680.7560.2080.6840.7061.0000.881
저수율0.6760.5340.6660.4370.5150.4600.8811.000
2023-12-10T22:22:24.771589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.0000.0620.255-0.095-0.0760.107-0.0250.000
저수위(m)0.0621.0000.1180.3540.5210.179-0.5840.995
강우량(mm)0.2550.1181.0000.0520.0910.0450.0190.000
유입량(ms)-0.0950.3540.0521.0000.9230.7270.1650.800
방류량(ms)-0.0760.5210.0910.9231.0000.512-0.0710.792
저수량(백만m3)0.1070.1790.0450.7270.5121.0000.5540.588
저수율-0.025-0.5840.0190.165-0.0710.5541.0000.530
댐이름0.0000.9950.0000.8000.7920.5880.5301.000

Missing values

2023-12-10T22:22:20.392145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:22:20.595202image/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

댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
0군남2019030825.710.012.80712.3673.5955.0
1군남2019032627.660.09.2985.5946.6069.2
2군남2019031325.690.010.610.453.575.0
3군남2019032026.5813.58.3785.6124.8166.7
4군남2019030425.660.010.21810.0683.5314.9
5군남2019030525.680.011.55811.2573.5575.0
6군남2019032527.480.08.7725.9716.2868.8
7군남2019031025.670.013.37912.3493.5445.0
8군남2019030325.650.011.95312.8443.5184.9
9군남2019031726.160.07.8355.2664.2035.9
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90평화의댐20190312166.590.011.77313.8224.9940.2
91평화의댐20190310166.840.016.77916.2585.3660.2
92평화의댐20190311166.710.013.79716.0545.1710.2
93여주저류지2019031728.510.00.00.00.0120.0
94여주저류지2019031328.510.00.00.00.0120.0
95여주저류지2019030628.520.00.00.00.0140.0
96여주저류지2019030228.530.00.00.00.0160.0
97여주저류지2019032528.540.00.00.00.0170.0
98여주저류지2019032428.550.00.00.00.0190.0
99여주저류지2019032328.550.00.00.00.0190.0