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 댐이름High correlation
유입량(ms) is highly overall correlated with 방류량(ms) and 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 유입량(ms) 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 유입량(ms) and 3 other fieldsHigh correlation
댐이름 is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
강우량(mm) has 68 (68.0%) zerosZeros
유입량(ms) has 30 (30.0%) zerosZeros
방류량(ms) has 30 (30.0%) zerosZeros
저수량(백만m3) has 30 (30.0%) zerosZeros
저수율 has 30 (30.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:52:06.193384
Analysis finished2023-12-10 10:52:14.268345
Duration8.07 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
강천보
30 
공주보
30 
구담보
30 
구미보
10 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강천보 30
30.0%
공주보 30
30.0%
구담보 30
30.0%
구미보 10
 
10.0%

Length

2023-12-10T19:52:14.384700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:52:14.553762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강천보 30
30.0%
공주보 30
30.0%
구담보 30
30.0%
구미보 10
 
10.0%

일자/시간(t)
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200414
Minimum20200401
Maximum20200430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:52:14.731094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200401
5-th percentile20200402
Q120200407
median20200414
Q320200422
95-th percentile20200429
Maximum20200430
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8334763
Coefficient of variation (CV)4.3729183 × 10-7
Kurtosis-1.2329291
Mean20200414
Median Absolute Deviation (MAD)8
Skewness0.16149815
Sum2.0200414 × 109
Variance78.030303
MonotonicityNot monotonic
2023-12-10T19:52:14.938895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20200401 4
 
4.0%
20200403 4
 
4.0%
20200404 4
 
4.0%
20200405 4
 
4.0%
20200406 4
 
4.0%
20200407 4
 
4.0%
20200408 4
 
4.0%
20200409 4
 
4.0%
20200410 4
 
4.0%
20200402 4
 
4.0%
Other values (20) 60
60.0%
ValueCountFrequency (%)
20200401 4
4.0%
20200402 4
4.0%
20200403 4
4.0%
20200404 4
4.0%
20200405 4
4.0%
20200406 4
4.0%
20200407 4
4.0%
20200408 4
4.0%
20200409 4
4.0%
20200410 4
4.0%
ValueCountFrequency (%)
20200430 3
3.0%
20200429 3
3.0%
20200428 3
3.0%
20200427 3
3.0%
20200426 3
3.0%
20200425 3
3.0%
20200424 3
3.0%
20200423 3
3.0%
20200422 3
3.0%
20200421 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.7363
Minimum4.3
Maximum62.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:52:15.147185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.3
5-th percentile4.32
Q14.36
median38.15
Q362.4525
95-th percentile62.47
Maximum62.47
Range58.17
Interquartile range (IQR)58.0925

Descriptive statistics

Standard deviation22.73816
Coefficient of variation (CV)0.6545936
Kurtosis-1.3525503
Mean34.7363
Median Absolute Deviation (MAD)24.31
Skewness-0.1772345
Sum3473.63
Variance517.0239
MonotonicityNot monotonic
2023-12-10T19:52:15.355539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
38.15 15
15.0%
62.47 15
15.0%
62.46 10
10.0%
4.33 8
 
8.0%
38.16 6
 
6.0%
38.14 6
 
6.0%
4.34 6
 
6.0%
4.36 5
 
5.0%
62.45 5
 
5.0%
4.32 4
 
4.0%
Other values (12) 20
20.0%
ValueCountFrequency (%)
4.3 2
 
2.0%
4.32 4
4.0%
4.33 8
8.0%
4.34 6
6.0%
4.35 4
4.0%
4.36 5
5.0%
4.37 1
 
1.0%
32.47 1
 
1.0%
32.48 1
 
1.0%
32.49 1
 
1.0%
ValueCountFrequency (%)
62.47 15
15.0%
62.46 10
10.0%
62.45 5
 
5.0%
38.17 2
 
2.0%
38.16 6
 
6.0%
38.15 15
15.0%
38.14 6
 
6.0%
38.13 1
 
1.0%
32.53 3
 
3.0%
32.52 2
 
2.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.343378
Minimum0
Maximum11.368
Zeros68
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:52:15.586289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0119
95-th percentile0.60628
Maximum11.368
Range11.368
Interquartile range (IQR)0.0119

Descriptive statistics

Standard deviation1.6896473
Coefficient of variation (CV)4.9206626
Kurtosis35.471486
Mean0.343378
Median Absolute Deviation (MAD)0
Skewness5.884177
Sum34.3378
Variance2.8549079
MonotonicityNot monotonic
2023-12-10T19:52:15.795448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 68
68.0%
0.0119 3
 
3.0%
0.0654 2
 
2.0%
0.0035 2
 
2.0%
0.1501 1
 
1.0%
11.2081 1
 
1.0%
0.1426 1
 
1.0%
0.0799 1
 
1.0%
0.008 1
 
1.0%
0.122 1
 
1.0%
Other values (19) 19
 
19.0%
ValueCountFrequency (%)
0.0 68
68.0%
0.0014 1
 
1.0%
0.003 1
 
1.0%
0.0035 2
 
2.0%
0.0049 1
 
1.0%
0.008 1
 
1.0%
0.0119 3
 
3.0%
0.012 1
 
1.0%
0.0153 1
 
1.0%
0.0161 1
 
1.0%
ValueCountFrequency (%)
11.368 1
1.0%
11.2081 1
1.0%
5.0226 1
1.0%
3.505 1
1.0%
1.2519 1
1.0%
0.5723 1
1.0%
0.242 1
1.0%
0.1501 1
1.0%
0.1426 1
1.0%
0.1227 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.49625
Minimum0
Maximum104.964
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:52:16.031910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median52.9115
Q398.9585
95-th percentile102.6593
Maximum104.964
Range104.964
Interquartile range (IQR)98.9585

Descriptive statistics

Standard deviation41.041799
Coefficient of variation (CV)0.82919008
Kurtosis-1.5426581
Mean49.49625
Median Absolute Deviation (MAD)47.4185
Skewness0.021516643
Sum4949.625
Variance1684.4293
MonotonicityNot monotonic
2023-12-10T19:52:16.288494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
98.487 1
 
1.0%
50.557 1
 
1.0%
60.823 1
 
1.0%
53.402 1
 
1.0%
71.874 1
 
1.0%
64.19 1
 
1.0%
53.521 1
 
1.0%
58.513 1
 
1.0%
48.563 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 30
30.0%
4.159 1
 
1.0%
4.295 1
 
1.0%
6.674 1
 
1.0%
9.851 1
 
1.0%
19.657 1
 
1.0%
26.844 1
 
1.0%
26.885 1
 
1.0%
29.169 1
 
1.0%
31.448 1
 
1.0%
ValueCountFrequency (%)
104.964 1
1.0%
104.776 1
1.0%
103.754 1
1.0%
103.517 1
1.0%
103.178 1
1.0%
102.632 1
1.0%
102.58 1
1.0%
102.231 1
1.0%
101.852 1
1.0%
101.733 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.46673
Minimum0
Maximum104.226
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:52:16.536344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median52.951
Q399.0325
95-th percentile103.109
Maximum104.226
Range104.226
Interquartile range (IQR)99.0325

Descriptive statistics

Standard deviation41.011188
Coefficient of variation (CV)0.82906608
Kurtosis-1.5436018
Mean49.46673
Median Absolute Deviation (MAD)47.534
Skewness0.020714885
Sum4946.673
Variance1681.9175
MonotonicityNot monotonic
2023-12-10T19:52:17.062461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
94.82 1
 
1.0%
50.557 1
 
1.0%
60.665 1
 
1.0%
53.56 1
 
1.0%
71.48 1
 
1.0%
64.426 1
 
1.0%
53.6 1
 
1.0%
58.198 1
 
1.0%
48.799 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 30
30.0%
2.424 1
 
1.0%
5.738 1
 
1.0%
6.03 1
 
1.0%
14.919 1
 
1.0%
15.525 1
 
1.0%
26.131 1
 
1.0%
27.598 1
 
1.0%
27.662 1
 
1.0%
31.448 1
 
1.0%
ValueCountFrequency (%)
104.226 1
1.0%
103.517 1
1.0%
103.392 1
1.0%
103.23 1
1.0%
103.204 1
1.0%
103.104 1
1.0%
102.231 1
1.0%
102.108 1
1.0%
101.781 1
1.0%
101.764 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.83704
Minimum0
Maximum52.928
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:52:17.276796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.46
Q39.407
95-th percentile52.80125
Maximum52.928
Range52.928
Interquartile range (IQR)9.407

Descriptive statistics

Standard deviation15.202545
Coefficient of variation (CV)1.7203209
Kurtosis4.4427506
Mean8.83704
Median Absolute Deviation (MAD)2.46
Skewness2.4049006
Sum883.704
Variance231.11737
MonotonicityNot monotonic
2023-12-10T19:52:17.489999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 30
30.0%
9.407 15
15.0%
2.45 8
 
8.0%
9.362 6
 
6.0%
9.452 6
 
6.0%
2.457 6
 
6.0%
2.47 5
 
5.0%
2.463 4
 
4.0%
2.443 4
 
4.0%
52.928 3
 
3.0%
Other values (10) 13
13.0%
ValueCountFrequency (%)
0.0 30
30.0%
2.429 2
 
2.0%
2.443 4
 
4.0%
2.45 8
 
8.0%
2.457 6
 
6.0%
2.463 4
 
4.0%
2.47 5
 
5.0%
2.477 1
 
1.0%
9.316 1
 
1.0%
9.362 6
 
6.0%
ValueCountFrequency (%)
52.928 3
 
3.0%
52.863 2
 
2.0%
52.798 1
 
1.0%
52.733 1
 
1.0%
52.652 1
 
1.0%
52.571 1
 
1.0%
52.49 1
 
1.0%
9.497 2
 
2.0%
9.452 6
 
6.0%
9.407 15
15.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.101
Minimum0
Maximum108.8
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:52:17.686305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.85
Q3107.3
95-th percentile108.3
Maximum108.8
Range108.8
Interquartile range (IQR)107.3

Descriptive statistics

Standard deviation48.686981
Coefficient of variation (CV)1.0336719
Kurtosis-1.8257633
Mean47.101
Median Absolute Deviation (MAD)15.85
Skewness0.37106286
Sum4710.1
Variance2370.4221
MonotonicityNot monotonic
2023-12-10T19:52:17.857587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.0 30
30.0%
107.8 15
15.0%
15.8 14
14.0%
15.9 10
 
10.0%
107.3 6
 
6.0%
108.3 6
 
6.0%
15.7 4
 
4.0%
100.4 3
 
3.0%
108.8 2
 
2.0%
15.6 2
 
2.0%
Other values (7) 8
 
8.0%
ValueCountFrequency (%)
0.0 30
30.0%
15.6 2
 
2.0%
15.7 4
 
4.0%
15.8 14
14.0%
15.9 10
 
10.0%
99.5 1
 
1.0%
99.7 1
 
1.0%
99.9 1
 
1.0%
100.0 1
 
1.0%
100.1 1
 
1.0%
ValueCountFrequency (%)
108.8 2
 
2.0%
108.3 6
 
6.0%
107.8 15
15.0%
107.3 6
 
6.0%
106.7 1
 
1.0%
100.4 3
 
3.0%
100.3 2
 
2.0%
100.1 1
 
1.0%
100.0 1
 
1.0%
99.9 1
 
1.0%

Interactions

2023-12-10T19:52:12.981620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:06.539803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:07.451549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:08.340409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:09.369273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:10.766992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:11.832448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:13.131648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:06.661962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:07.592222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:08.493614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:09.516725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:10.934220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:12.016885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:13.271130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:06.791191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:07.701456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:08.641401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:09.638142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:11.110454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:12.225381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:13.409586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:06.923060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:07.804160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:08.777544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:09.757411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:11.280018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:12.393002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:13.529993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:07.056655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:07.930450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:08.976445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:09.959154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:11.433308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:12.529980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:13.679290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:07.186240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:08.060691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:09.109931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:10.109375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:11.577629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:12.677174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:13.826753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:07.313582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:08.200991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:09.242974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:10.546631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:11.709569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:52:12.836622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:52:17.990355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.9450.9551.0001.000
일자/시간(t)0.0001.0000.0000.4400.0000.0000.0000.000
저수위(m)1.0000.0001.0000.0000.9450.9551.0001.000
강우량(mm)0.0000.4400.0001.0000.0000.0000.0000.000
유입량(ms)0.9450.0000.9450.0001.0000.9950.9900.998
방류량(ms)0.9550.0000.9550.0000.9951.0000.9950.999
저수량(백만m3)1.0000.0001.0000.0000.9900.9951.0000.940
저수율1.0000.0001.0000.0000.9980.9990.9401.000
2023-12-10T19:52:18.198872image/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)1.0000.1270.2760.0070.007-0.176-0.0530.000
저수위(m)0.1271.000-0.285-0.403-0.408-0.372-0.3011.000
강우량(mm)0.276-0.2851.0000.4720.4550.3360.4210.000
유입량(ms)0.007-0.4030.4721.0000.9980.7050.8900.887
방류량(ms)0.007-0.4080.4550.9981.0000.7000.8860.907
저수량(백만m3)-0.176-0.3720.3360.7050.7001.0000.9240.995
저수율-0.053-0.3010.4210.8900.8860.9241.0000.995
댐이름0.0001.0000.0000.8870.9070.9950.9951.000

Missing values

2023-12-10T19:52:14.001045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:52:14.192562image/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
0강천보2020040138.150.12298.48794.829.407107.8
1강천보2020040238.160.0103.754103.239.452108.3
2강천보2020040338.160.0103.517103.5179.452108.3
3강천보2020040438.140.099.471100.5199.362107.3
4강천보2020040538.170.0119104.964103.3929.497108.8
5강천보2020040638.150.0103.178104.2269.407107.8
6강천보2020040738.150.0119101.733101.7339.407107.8
7강천보2020040838.140.099.01299.5369.362107.3
8강천보2020040938.160.011998.95297.9049.452108.3
9강천보2020041038.150.0101.24101.7649.407107.8
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구미보2020040132.520.031.44831.44852.863100.3
91구미보2020040232.510.040.06740.82152.798100.1
92구미보2020040332.530.015329.16927.66252.928100.4
93구미보2020040432.470.09.85114.91952.4999.5
94구미보2020040532.490.04.2952.42452.65299.9
95구미보2020040632.50.06.6745.73852.733100.0
96구미보2020040732.480.04.1596.0352.57199.7
97구미보2020040832.530.019.65715.52552.928100.4
98구미보2020040932.520.026.84427.59852.863100.3
99구미보2020041032.530.004926.88526.13152.928100.4