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

일자/시간(t) is highly overall correlated with 유입량(ms)High correlation
저수위(m) is highly overall correlated with 저수율 and 1 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 유입량(ms) and 2 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 방류량(ms) and 1 other fieldsHigh correlation
저수율 is highly overall correlated with 저수위(m) and 1 other fieldsHigh correlation
댐이름 is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
강우량(mm) has 90 (90.0%) zerosZeros
유입량(ms) has 7 (7.0%) zerosZeros
방류량(ms) has 7 (7.0%) zerosZeros
저수율 has 30 (30.0%) zerosZeros

Reproduction

Analysis started2024-04-19 05:57:41.838586
Analysis finished2024-04-19 05:57:46.742728
Duration4.9 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

2024-04-19T14:57:46.832382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T14:57:46.952692image/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)

HIGH CORRELATION 

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190516
Minimum20190501
Maximum20190531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-19T14:57:47.061321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190501
5-th percentile20190502
Q120190508
median20190516
Q320190524
95-th percentile20190530
Maximum20190531
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9555355
Coefficient of variation (CV)4.4355159 × 10-7
Kurtosis-1.2055892
Mean20190516
Median Absolute Deviation (MAD)8
Skewness-0.024533469
Sum2.0190516 × 109
Variance80.201616
MonotonicityNot monotonic
2024-04-19T14:57:47.184430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20190517 4
 
4.0%
20190513 4
 
4.0%
20190523 4
 
4.0%
20190506 4
 
4.0%
20190525 4
 
4.0%
20190524 4
 
4.0%
20190502 4
 
4.0%
20190511 3
 
3.0%
20190531 3
 
3.0%
20190529 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
20190501 3
3.0%
20190502 4
4.0%
20190503 3
3.0%
20190504 3
3.0%
20190505 3
3.0%
20190506 4
4.0%
20190507 3
3.0%
20190508 3
3.0%
20190509 3
3.0%
20190510 3
3.0%
ValueCountFrequency (%)
20190531 3
3.0%
20190530 3
3.0%
20190529 3
3.0%
20190528 3
3.0%
20190527 3
3.0%
20190526 3
3.0%
20190525 4
4.0%
20190524 4
4.0%
20190523 4
4.0%
20190522 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.6712
Minimum23.16
Maximum163.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-19T14:57:47.316253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.16
5-th percentile23.17
Q126.805
median46.825
Q3162.77
95-th percentile163.132
Maximum163.29
Range140.13
Interquartile range (IQR)135.965

Descriptive statistics

Standard deviation60.139684
Coefficient of variation (CV)0.80539329
Kurtosis-1.3482823
Mean74.6712
Median Absolute Deviation (MAD)22.565
Skewness0.75965694
Sum7467.12
Variance3616.7815
MonotonicityNot monotonic
2024-04-19T14:57:47.449016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.17 11
 
11.0%
47.49 3
 
3.0%
23.18 3
 
3.0%
28.47 3
 
3.0%
163.13 2
 
2.0%
162.77 2
 
2.0%
162.82 2
 
2.0%
23.16 2
 
2.0%
47.43 2
 
2.0%
28.51 2
 
2.0%
Other values (63) 68
68.0%
ValueCountFrequency (%)
23.16 2
 
2.0%
23.17 11
11.0%
23.18 3
 
3.0%
23.2 1
 
1.0%
23.58 1
 
1.0%
24.15 1
 
1.0%
24.37 1
 
1.0%
24.7 1
 
1.0%
25.21 1
 
1.0%
25.98 1
 
1.0%
ValueCountFrequency (%)
163.29 1
1.0%
163.22 2
2.0%
163.2 1
1.0%
163.17 1
1.0%
163.13 2
2.0%
163.04 1
1.0%
162.96 1
1.0%
162.94 1
1.0%
162.93 1
1.0%
162.9 1
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.060723
Minimum0
Maximum26
Zeros90
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-19T14:57:47.573426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8.52145
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.9796299
Coefficient of variation (CV)3.7518088
Kurtosis20.904504
Mean1.060723
Median Absolute Deviation (MAD)0
Skewness4.4358283
Sum106.0723
Variance15.837454
MonotonicityNot monotonic
2024-04-19T14:57:47.675386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 90
90.0%
8.5 1
 
1.0%
3.5 1
 
1.0%
26.0 1
 
1.0%
17.5536 1
 
1.0%
4.5897 1
 
1.0%
8.929 1
 
1.0%
14.0 1
 
1.0%
17.0 1
 
1.0%
5.0 1
 
1.0%
ValueCountFrequency (%)
0.0 90
90.0%
1.0 1
 
1.0%
3.5 1
 
1.0%
4.5897 1
 
1.0%
5.0 1
 
1.0%
8.5 1
 
1.0%
8.929 1
 
1.0%
14.0 1
 
1.0%
17.0 1
 
1.0%
17.5536 1
 
1.0%
ValueCountFrequency (%)
26.0 1
1.0%
17.5536 1
1.0%
17.0 1
1.0%
14.0 1
1.0%
8.929 1
1.0%
8.5 1
1.0%
5.0 1
1.0%
4.5897 1
1.0%
3.5 1
1.0%
1.0 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.29723
Minimum0
Maximum21.351
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-19T14:57:47.813483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.9235
median8.2345
Q310.84225
95-th percentile18.5719
Maximum21.351
Range21.351
Interquartile range (IQR)6.91875

Descriptive statistics

Standard deviation5.3745652
Coefficient of variation (CV)0.64775415
Kurtosis-0.34932333
Mean8.29723
Median Absolute Deviation (MAD)3.846
Skewness0.58244875
Sum829.723
Variance28.885951
MonotonicityNot monotonic
2024-04-19T14:57:47.954672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
7.0%
4.247 1
 
1.0%
7.869 1
 
1.0%
18.165 1
 
1.0%
18.741 1
 
1.0%
8.542 1
 
1.0%
18.454 1
 
1.0%
14.405 1
 
1.0%
8.671 1
 
1.0%
3.631 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.0 7
7.0%
2.5 1
 
1.0%
2.552 1
 
1.0%
2.629 1
 
1.0%
2.792 1
 
1.0%
2.891 1
 
1.0%
2.897 1
 
1.0%
2.926 1
 
1.0%
2.955 1
 
1.0%
2.985 1
 
1.0%
ValueCountFrequency (%)
21.351 1
1.0%
20.646 1
1.0%
19.22 1
1.0%
19.035 1
1.0%
18.741 1
1.0%
18.563 1
1.0%
18.454 1
1.0%
18.165 1
1.0%
17.79 1
1.0%
17.589 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.23687
Minimum0
Maximum21.56
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-19T14:57:48.110884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.553
median9.0555
Q312.5945
95-th percentile18.53795
Maximum21.56
Range21.56
Interquartile range (IQR)8.0415

Descriptive statistics

Standard deviation5.4394105
Coefficient of variation (CV)0.58888027
Kurtosis-0.68139172
Mean9.23687
Median Absolute Deviation (MAD)3.701
Skewness0.22618436
Sum923.687
Variance29.587186
MonotonicityNot monotonic
2024-04-19T14:57:48.239527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
7.0%
15.547 2
 
2.0%
2.874 2
 
2.0%
7.569 1
 
1.0%
8.879 1
 
1.0%
18.234 1
 
1.0%
18.822 1
 
1.0%
9.132 1
 
1.0%
18.523 1
 
1.0%
14.486 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
0.0 7
7.0%
2.85 1
 
1.0%
2.856 1
 
1.0%
2.874 2
 
2.0%
2.891 1
 
1.0%
2.922 1
 
1.0%
3.092 1
 
1.0%
3.235 1
 
1.0%
3.379 1
 
1.0%
3.42 1
 
1.0%
ValueCountFrequency (%)
21.56 1
1.0%
20.205 1
1.0%
19.37 1
1.0%
19.035 1
1.0%
18.822 1
1.0%
18.523 1
1.0%
18.482 1
1.0%
18.234 1
1.0%
17.871 1
1.0%
17.67 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.31566
Minimum0.01
Maximum8.237
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-19T14:57:48.370862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.51325
median1.035
Q31.11625
95-th percentile5.2754
Maximum8.237
Range8.227
Interquartile range (IQR)0.603

Descriptive statistics

Standard deviation1.5913251
Coefficient of variation (CV)1.2095261
Kurtosis7.6359107
Mean1.31566
Median Absolute Deviation (MAD)0.491
Skewness2.7911747
Sum131.566
Variance2.5323156
MonotonicityNot monotonic
2024-04-19T14:57:48.511739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.096 11
 
11.0%
0.01 6
 
6.0%
1.103 3
 
3.0%
0.537 3
 
3.0%
1.045 2
 
2.0%
1.013 2
 
2.0%
1.025 2
 
2.0%
0.369 2
 
2.0%
1.032 2
 
2.0%
1.006 2
 
2.0%
Other values (61) 65
65.0%
ValueCountFrequency (%)
0.01 6
6.0%
0.012 1
 
1.0%
0.361 1
 
1.0%
0.362 1
 
1.0%
0.364 1
 
1.0%
0.366 1
 
1.0%
0.367 1
 
1.0%
0.369 2
 
2.0%
0.393 1
 
1.0%
0.407 1
 
1.0%
ValueCountFrequency (%)
8.237 1
1.0%
7.6 1
1.0%
6.88 1
1.0%
6.251 1
1.0%
5.739 1
1.0%
5.251 1
1.0%
4.908 1
1.0%
4.621 1
1.0%
3.954 1
1.0%
2.98 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.216
Minimum0
Maximum11.5
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-19T14:57:48.622612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2
Q31.5
95-th percentile7.335
Maximum11.5
Range11.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation2.4412995
Coefficient of variation (CV)2.0076476
Kurtosis6.8456675
Mean1.216
Median Absolute Deviation (MAD)0.2
Skewness2.7025739
Sum121.6
Variance5.9599434
MonotonicityNot monotonic
2024-04-19T14:57:48.735391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 30
30.0%
0.2 24
24.0%
1.5 16
16.0%
0.1 15
15.0%
6.5 1
 
1.0%
1.6 1
 
1.0%
2.9 1
 
1.0%
7.3 1
 
1.0%
6.9 1
 
1.0%
3.4 1
 
1.0%
Other values (9) 9
 
9.0%
ValueCountFrequency (%)
0.0 30
30.0%
0.1 15
15.0%
0.2 24
24.0%
1.5 16
16.0%
1.6 1
 
1.0%
2.0 1
 
1.0%
2.6 1
 
1.0%
2.9 1
 
1.0%
3.4 1
 
1.0%
4.2 1
 
1.0%
ValueCountFrequency (%)
11.5 1
1.0%
10.6 1
1.0%
9.6 1
1.0%
8.7 1
1.0%
8.0 1
1.0%
7.3 1
1.0%
6.9 1
1.0%
6.5 1
1.0%
5.5 1
1.0%
4.2 1
1.0%

Interactions

2024-04-19T14:57:45.960605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:42.085355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:42.745580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:43.353001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:43.919087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:44.802031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:45.376300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:46.042208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:42.166145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:42.843927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:43.436281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:44.019089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:44.887883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:45.475834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:46.123848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:42.257210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:42.937481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:43.519965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:44.104726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:44.996845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:45.558186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:46.205378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:42.339672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:43.030850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:43.596127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:44.185051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:45.072184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:45.633851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:46.280256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:42.421519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:43.114451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:43.670888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:44.256555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:45.141856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:45.707216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:46.357470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:42.534611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:43.193384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:43.759681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:44.322627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:45.212942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:45.788566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:46.431313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:42.663030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:43.273200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:43.837699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:44.724182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:45.286451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:57:45.868281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-19T14:57:48.821334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.8330.8610.7850.714
일자/시간(t)0.0001.0000.0000.5240.6940.4730.4870.601
저수위(m)1.0000.0001.0000.0000.6330.7010.7860.695
강우량(mm)0.0000.5240.0001.0000.4190.5390.0000.000
유입량(ms)0.8330.6940.6330.4191.0000.9790.3370.368
방류량(ms)0.8610.4730.7010.5390.9791.0000.6280.615
저수량(백만m3)0.7850.4870.7860.0000.3370.6281.0000.997
저수율0.7140.6010.6950.0000.3680.6150.9971.000
2024-04-19T14:57:48.945492image/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)1.000-0.1250.1810.5070.329-0.241-0.1470.000
저수위(m)-0.1251.0000.0630.2270.091-0.072-0.7100.995
강우량(mm)0.1810.0631.0000.2240.1360.049-0.0560.000
유입량(ms)0.5070.2270.2241.0000.8810.311-0.0560.650
방류량(ms)0.3290.0910.1360.8811.0000.5180.1390.692
저수량(백만m3)-0.241-0.0720.0490.3110.5181.0000.4840.584
저수율-0.147-0.710-0.056-0.0560.1390.4841.0000.500
댐이름0.0000.9950.0000.6500.6920.5840.5001.000

Missing values

2024-04-19T14:57:46.544919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T14:57:46.682449image/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
0군남2019050826.450.04.2477.5694.6216.5
1군남2019052623.180.012.68612.6051.1031.5
2군남2019051324.150.03.4085.8041.8782.6
3군남2019052023.188.511.96612.1161.1031.5
4군남2019050427.460.08.26715.5476.2518.7
5군남2019050527.160.07.87713.8035.7398.0
6군남2019052523.170.010.74210.7421.0961.5
7군남2019051025.210.04.27415.5472.984.2
8군남2019050327.810.08.31716.656.889.6
9군남2019051723.160.09.2549.2541.0891.5
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90평화의댐20190512162.930.03.9463.7151.1170.0
91평화의댐20190510162.880.04.0854.711.0840.0
92평화의댐20190511162.91.03.8563.7061.0970.0
93여주저류지2019051728.450.00.00.00.010.0
94여주저류지2019051328.470.00.00.00.010.0
95여주저류지2019050628.490.00.00.00.010.0
96여주저류지2019050228.510.00.00.00.0120.0
97여주저류지2019052528.470.00.00.00.010.0
98여주저류지2019052428.470.00.00.00.010.0
99여주저류지2019052328.480.00.00.00.010.0