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 유입량(ms) and 2 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 저수위(m) and 2 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 유입량(ms) and 2 other fieldsHigh correlation
저수율 is highly overall correlated with 저수량(백만m3) and 1 other fieldsHigh correlation
댐이름 is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
강우량(mm) has 78 (78.0%) zerosZeros
유입량(ms) has 10 (10.0%) zerosZeros
방류량(ms) has 10 (10.0%) zerosZeros
저수율 has 10 (10.0%) zerosZeros

Reproduction

Analysis started2024-04-19 05:57:50.244564
Analysis finished2024-04-19 05:57:55.556801
Duration5.31 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
30 
한탄강
30 
평화의댐
30 
여주저류지
10 

Length

Max length5
Median length4
Mean length3.2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군남 30
30.0%
한탄강 30
30.0%
평화의댐 30
30.0%
여주저류지 10
 
10.0%

Length

2024-04-19T14:57:55.625670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T14:57:55.729635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군남 30
30.0%
한탄강 30
30.0%
평화의댐 30
30.0%
여주저류지 10
 
10.0%

일자/시간(t)
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190415
Minimum20190401
Maximum20190430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-19T14:57:55.840851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190401
5-th percentile20190402
Q120190408
median20190416
Q320190423
95-th percentile20190429
Maximum20190430
Range29
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation8.7190202
Coefficient of variation (CV)4.3183957 × 10-7
Kurtosis-1.2340963
Mean20190415
Median Absolute Deviation (MAD)7.5
Skewness-0.0096013487
Sum2.0190415 × 109
Variance76.021313
MonotonicityNot monotonic
2024-04-19T14:57:56.343382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20190406 4
 
4.0%
20190417 4
 
4.0%
20190424 4
 
4.0%
20190423 4
 
4.0%
20190420 4
 
4.0%
20190425 4
 
4.0%
20190405 4
 
4.0%
20190402 4
 
4.0%
20190413 4
 
4.0%
20190403 4
 
4.0%
Other values (20) 60
60.0%
ValueCountFrequency (%)
20190401 3
3.0%
20190402 4
4.0%
20190403 4
4.0%
20190404 3
3.0%
20190405 4
4.0%
20190406 4
4.0%
20190407 3
3.0%
20190408 3
3.0%
20190409 3
3.0%
20190410 3
3.0%
ValueCountFrequency (%)
20190430 3
3.0%
20190429 3
3.0%
20190428 3
3.0%
20190427 3
3.0%
20190426 3
3.0%
20190425 4
4.0%
20190424 4
4.0%
20190423 4
4.0%
20190422 3
3.0%
20190421 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.2165
Minimum28.44
Maximum165.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-19T14:57:56.472568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28.44
5-th percentile28.5
Q129.77
median47.28
Q3163.5125
95-th percentile164.7915
Maximum165.1
Range136.66
Interquartile range (IQR)133.7425

Descriptive statistics

Standard deviation59.014984
Coefficient of variation (CV)0.78460158
Kurtosis-1.2571282
Mean75.2165
Median Absolute Deviation (MAD)17.89
Skewness0.83428066
Sum7521.65
Variance3482.7684
MonotonicityNot monotonic
2024-04-19T14:57:56.634871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.5 5
 
5.0%
47.41 4
 
4.0%
47.28 3
 
3.0%
47.37 3
 
3.0%
47.4 3
 
3.0%
29.77 2
 
2.0%
28.49 2
 
2.0%
47.32 2
 
2.0%
47.23 2
 
2.0%
163.75 1
 
1.0%
Other values (73) 73
73.0%
ValueCountFrequency (%)
28.44 1
 
1.0%
28.48 1
 
1.0%
28.49 2
 
2.0%
28.5 5
5.0%
28.51 1
 
1.0%
28.53 1
 
1.0%
28.59 1
 
1.0%
28.73 1
 
1.0%
28.78 1
 
1.0%
28.88 1
 
1.0%
ValueCountFrequency (%)
165.1 1
1.0%
164.98 1
1.0%
164.88 1
1.0%
164.84 1
1.0%
164.82 1
1.0%
164.79 1
1.0%
164.77 1
1.0%
164.75 1
1.0%
164.73 1
1.0%
164.59 1
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.885033
Minimum0
Maximum17
Zeros78
Zeros (%)78.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-19T14:57:56.774302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.7663
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.5148392
Coefficient of variation (CV)2.8415203
Kurtosis20.265902
Mean0.885033
Median Absolute Deviation (MAD)0
Skewness4.1390058
Sum88.5033
Variance6.3244163
MonotonicityNot monotonic
2024-04-19T14:57:56.881321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.0 78
78.0%
0.5 3
 
3.0%
3.0 3
 
3.0%
4.0 3
 
3.0%
0.7211 1
 
1.0%
11.0 1
 
1.0%
17.0 1
 
1.0%
5.0 1
 
1.0%
1.0 1
 
1.0%
10.1788 1
 
1.0%
Other values (7) 7
 
7.0%
ValueCountFrequency (%)
0.0 78
78.0%
0.1464 1
 
1.0%
0.5 3
 
3.0%
0.7211 1
 
1.0%
1.0 1
 
1.0%
1.5 1
 
1.0%
1.6645 1
 
1.0%
3.0 3
 
3.0%
3.241 1
 
1.0%
3.7975 1
 
1.0%
ValueCountFrequency (%)
17.0 1
 
1.0%
11.0 1
 
1.0%
10.1788 1
 
1.0%
6.0 1
 
1.0%
5.0 1
 
1.0%
4.754 1
 
1.0%
4.0 3
3.0%
3.7975 1
 
1.0%
3.241 1
 
1.0%
3.0 3
3.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.63847
Minimum0
Maximum24.564
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-19T14:57:57.009970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.871
median8.074
Q311.31675
95-th percentile17.57555
Maximum24.564
Range24.564
Interquartile range (IQR)5.44575

Descriptive statistics

Standard deviation5.0542258
Coefficient of variation (CV)0.58508345
Kurtosis1.0442889
Mean8.63847
Median Absolute Deviation (MAD)2.37
Skewness0.64342618
Sum863.847
Variance25.545199
MonotonicityNot monotonic
2024-04-19T14:57:57.141444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
7.24 2
 
2.0%
6.168 1
 
1.0%
13.209 1
 
1.0%
8.266 1
 
1.0%
8.758 1
 
1.0%
13.234 1
 
1.0%
19.801 1
 
1.0%
10.808 1
 
1.0%
13.084 1
 
1.0%
Other values (80) 80
80.0%
ValueCountFrequency (%)
0.0 10
10.0%
3.637 1
 
1.0%
3.764 1
 
1.0%
3.885 1
 
1.0%
3.998 1
 
1.0%
5.15 1
 
1.0%
5.51 1
 
1.0%
5.547 1
 
1.0%
5.665 1
 
1.0%
5.703 1
 
1.0%
ValueCountFrequency (%)
24.564 1
1.0%
23.006 1
1.0%
22.784 1
1.0%
19.801 1
1.0%
17.909 1
1.0%
17.558 1
1.0%
16.061 1
1.0%
15.356 1
1.0%
15.103 1
1.0%
15.042 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.73024
Minimum0
Maximum24.707
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-19T14:57:57.272995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.71425
median7.2765
Q312.0615
95-th percentile17.8828
Maximum24.707
Range24.707
Interquartile range (IQR)6.34725

Descriptive statistics

Standard deviation5.3543652
Coefficient of variation (CV)0.61331248
Kurtosis0.66166124
Mean8.73024
Median Absolute Deviation (MAD)3.194
Skewness0.67705605
Sum873.024
Variance28.669226
MonotonicityNot monotonic
2024-04-19T14:57:57.428674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
10.214 1
 
1.0%
6.33 1
 
1.0%
14.494 1
 
1.0%
7.965 1
 
1.0%
10.309 1
 
1.0%
14.507 1
 
1.0%
19.465 1
 
1.0%
10.438 1
 
1.0%
14.23 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
0.0 10
10.0%
3.741 1
 
1.0%
3.79 1
 
1.0%
3.845 1
 
1.0%
4.024 1
 
1.0%
5.1 1
 
1.0%
5.173 1
 
1.0%
5.198 1
 
1.0%
5.28 1
 
1.0%
5.324 1
 
1.0%
ValueCountFrequency (%)
24.707 1
1.0%
24.101 1
1.0%
23.259 1
1.0%
19.465 1
1.0%
19.437 1
1.0%
17.801 1
1.0%
17.693 1
1.0%
16.781 1
1.0%
16.292 1
1.0%
15.055 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1077
Minimum0.01
Maximum13.877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-19T14:57:57.613280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.509
median1.8095
Q39.08975
95-th percentile12.94995
Maximum13.877
Range13.867
Interquartile range (IQR)8.58075

Descriptive statistics

Standard deviation4.710478
Coefficient of variation (CV)1.1467434
Kurtosis-0.8717912
Mean4.1077
Median Absolute Deviation (MAD)1.3065
Skewness0.91840121
Sum410.77
Variance22.188603
MonotonicityNot monotonic
2024-04-19T14:57:57.740541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 8
 
8.0%
0.524 4
 
4.0%
0.517 3
 
3.0%
0.522 3
 
3.0%
0.503 3
 
3.0%
0.509 2
 
2.0%
11.014 2
 
2.0%
0.495 2
 
2.0%
9.829 1
 
1.0%
1.797 1
 
1.0%
Other values (71) 71
71.0%
ValueCountFrequency (%)
0.01 8
8.0%
0.012 1
 
1.0%
0.016 1
 
1.0%
0.459 1
 
1.0%
0.468 1
 
1.0%
0.475 1
 
1.0%
0.477 1
 
1.0%
0.487 1
 
1.0%
0.494 1
 
1.0%
0.495 2
 
2.0%
ValueCountFrequency (%)
13.877 1
1.0%
13.76 1
1.0%
13.499 1
1.0%
13.442 1
1.0%
13.215 1
1.0%
12.936 1
1.0%
12.689 1
1.0%
12.392 1
1.0%
12.048 1
1.0%
11.866 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.709
Minimum0
Maximum19.4
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-19T14:57:57.866861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.2
Q312.7
95-th percentile18.12
Maximum19.4
Range19.4
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation7.1502362
Coefficient of variation (CV)1.5184192
Kurtosis-0.88083738
Mean4.709
Median Absolute Deviation (MAD)0.1
Skewness0.99341797
Sum470.9
Variance51.125878
MonotonicityNot monotonic
2024-04-19T14:57:57.991023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.1 30
30.0%
0.2 30
30.0%
0.0 10
 
10.0%
16.0 2
 
2.0%
14.9 2
 
2.0%
15.4 2
 
2.0%
13.7 1
 
1.0%
16.6 1
 
1.0%
19.4 1
 
1.0%
12.6 1
 
1.0%
Other values (20) 20
20.0%
ValueCountFrequency (%)
0.0 10
 
10.0%
0.1 30
30.0%
0.2 30
30.0%
11.3 1
 
1.0%
11.7 1
 
1.0%
12.1 1
 
1.0%
12.3 1
 
1.0%
12.6 1
 
1.0%
13.0 1
 
1.0%
13.1 1
 
1.0%
ValueCountFrequency (%)
19.4 1
1.0%
19.2 1
1.0%
18.9 1
1.0%
18.8 1
1.0%
18.5 1
1.0%
18.1 1
1.0%
17.7 1
1.0%
17.3 1
1.0%
16.8 1
1.0%
16.6 1
1.0%

Interactions

2024-04-19T14:57:54.497044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:50.492726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:51.185100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:51.847319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:52.599728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:53.212987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:53.833944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:54.618939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:50.573232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:51.308908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:51.936129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:52.683447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:53.333587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:53.922277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:54.722392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:50.654519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:51.414554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:52.030005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:52.772421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:53.420154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:54.002295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:54.886671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:50.751674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:51.508083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:52.158699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:52.868554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:53.498246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:54.101907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:54.991991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:50.834480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:51.582933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:52.306612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:52.942398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:53.574932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:54.206155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:55.097667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:50.929461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:51.658349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:52.409601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:53.012707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:53.647206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:54.283880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:55.213374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:51.051019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:51.745977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:52.498529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:53.096613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:53.731277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:54.385432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-19T14:57:58.092172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.8770.8620.9750.714
일자/시간(t)0.0001.0000.0000.3280.3480.4230.4750.597
저수위(m)1.0000.0001.0000.0000.7610.9140.8850.843
강우량(mm)0.0000.3280.0001.0000.6370.5310.0000.000
유입량(ms)0.8770.3480.7610.6371.0000.9230.5180.000
방류량(ms)0.8620.4230.9140.5310.9231.0000.5260.000
저수량(백만m3)0.9750.4750.8850.0000.5180.5261.0001.000
저수율0.7140.5970.8430.0000.0000.0001.0001.000
2024-04-19T14:57:58.201748image/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)1.000-0.0150.006-0.0570.262-0.0180.0630.000
저수위(m)-0.0151.0000.0390.6310.7010.019-0.3690.995
강우량(mm)0.0060.0391.0000.1010.0720.0560.0820.000
유입량(ms)-0.0570.6310.1011.0000.7590.544-0.0400.718
방류량(ms)0.2620.7010.0720.7591.0000.477-0.0700.737
저수량(백만m3)-0.0180.0190.0560.5440.4771.0000.6390.771
저수율0.063-0.3690.082-0.040-0.0700.6391.0000.540
댐이름0.0000.9950.0000.7180.7370.7710.5401.000

Missing values

2024-04-19T14:57:55.354346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T14:57:55.508745image/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
0군남2019042829.260.55.7710.2149.82913.7
1군남2019043028.780.06.39313.2918.79712.3
2군남2019041330.030.09.8277.211.66116.3
3군남2019040829.480.09.0845.65810.32714.4
4군남2019041229.940.010.067.1911.43416.0
5군남2019041430.183.011.7697.2912.04816.8
6군남2019041730.690.09.927.29313.44218.8
7군남2019040929.626.09.5965.810.65514.9
8군남2019042030.710.06.26310.63813.49918.9
9군남2019040529.030.09.0165.3249.32913.0
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90여주저류지2019041728.510.00.00.00.0120.0
91여주저류지2019041328.530.00.00.00.0160.0
92여주저류지2019040628.50.00.00.00.010.0
93여주저류지2019040228.50.00.00.00.010.0
94여주저류지2019040328.50.00.00.00.010.0
95여주저류지2019042528.50.00.00.00.010.0
96여주저류지2019042428.490.00.00.00.010.0
97여주저류지2019042328.480.00.00.00.010.0
98여주저류지2019042028.50.00.00.00.010.0
99여주저류지2019040528.490.00.00.00.010.0